The use of generalized linear models and generalized estimating equations in bioarchaeological studies.

PubMed

Nikita, Efthymia

2014-03-01

The current article explores whether the application of generalized linear models (GLM) and generalized estimating equations (GEE) can be used in place of conventional statistical analyses in the study of ordinal data that code an underlying continuous variable, like entheseal changes. The analysis of artificial data and ordinal data expressing entheseal changes in archaeological North African populations gave the following results. Parametric and nonparametric tests give convergent results particularly for P values <0.1, irrespective of whether the underlying variable is normally distributed or not under the condition that the samples involved in the tests exhibit approximately equal sizes. If this prerequisite is valid and provided that the samples are of equal variances, analysis of covariance may be adopted. GLM are not subject to constraints and give results that converge to those obtained from all nonparametric tests. Therefore, they can be used instead of traditional tests as they give the same amount of information as them, but with the advantage of allowing the study of the simultaneous impact of multiple predictors and their interactions and the modeling of the experimental data. However, GLM should be replaced by GEE for the study of bilateral asymmetry and in general when paired samples are tested, because GEE are appropriate for correlated data. Copyright © 2013 Wiley Periodicals, Inc.

Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

PubMed

Choi, Jae-Seok; Kim, Munchurl

2017-03-01

Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower computational complexity when compared with a super-resolution method based on convolutional neural nets (SRCNN15). Compared with the previous SI method that is limited with a scale factor of 2, GLM-SI shows superior performance with average 0.79 dB higher in PSNR, and can be used for scale factors of 3 or higher.

Support vector machine learning-based fMRI data group analysis.

PubMed

Wang, Ze; Childress, Anna R; Wang, Jiongjiong; Detre, John A

2007-07-15

To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

Continuous-time discrete-space models for animal movement

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.; Alldredge, Mat W.

2015-01-01

The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.

Optimizing the general linear model for functional near-infrared spectroscopy: an adaptive hemodynamic response function approach

PubMed Central

Uga, Minako; Dan, Ippeita; Sano, Toshifumi; Dan, Haruka; Watanabe, Eiju

2014-01-01

Abstract. An increasing number of functional near-infrared spectroscopy (fNIRS) studies utilize a general linear model (GLM) approach, which serves as a standard statistical method for functional magnetic resonance imaging (fMRI) data analysis. While fMRI solely measures the blood oxygen level dependent (BOLD) signal, fNIRS measures the changes of oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) signals at a temporal resolution severalfold higher. This suggests the necessity of adjusting the temporal parameters of a GLM for fNIRS signals. Thus, we devised a GLM-based method utilizing an adaptive hemodynamic response function (HRF). We sought the optimum temporal parameters to best explain the observed time series data during verbal fluency and naming tasks. The peak delay of the HRF was systematically changed to achieve the best-fit model for the observed oxy- and deoxy-Hb time series data. The optimized peak delay showed different values for each Hb signal and task. When the optimized peak delays were adopted, the deoxy-Hb data yielded comparable activations with similar statistical power and spatial patterns to oxy-Hb data. The adaptive HRF method could suitably explain the behaviors of both Hb parameters during tasks with the different cognitive loads during a time course, and thus would serve as an objective method to fully utilize the temporal structures of all fNIRS data. PMID:26157973

Further advances in predicting species distributions

Treesearch

Gretchen G. Moisen; Thomas C. Edwards; Patrick E. Osborne

2006-01-01

In 2001, a workshop focused on the use of generalized linear models (GLM: McCullagh and Nelder, 1989) and generalized additive models (GAM: Hastie and Tibshirani, 1986, 1990) for predicting species distributions was held in Riederalp, Switzerland. This topic led to the publication of special issues in Ecological Modelling (Guisan et al., 2002) and Biodiversity and...

Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes.

PubMed

Lord, Dominique; Guikema, Seth D; Geedipally, Srinivas Reddy

2008-05-01

This paper documents the application of the Conway-Maxwell-Poisson (COM-Poisson) generalized linear model (GLM) for modeling motor vehicle crashes. The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to over- and under-dispersion. This innovative distribution is an extension of the Poisson distribution. The objectives of this study were to evaluate the application of the COM-Poisson GLM for analyzing motor vehicle crashes and compare the results with the traditional negative binomial (NB) model. The comparison analysis was carried out using the most common functional forms employed by transportation safety analysts, which link crashes to the entering flows at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs were developed and compared using two datasets. The first dataset contained crash data collected at signalized four-legged intersections in Toronto, Ont. The second dataset included data collected for rural four-lane divided and undivided highways in Texas. Several methods were used to assess the statistical fit and predictive performance of the models. The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance. Given the fact the COM-Poisson distribution can also handle under-dispersed data (while the NB distribution cannot or has difficulties converging), which have sometimes been observed in crash databases, the COM-Poisson GLM offers a better alternative over the NB model for modeling motor vehicle crashes, especially given the important limitations recently documented in the safety literature about the latter type of model.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods

NASA Astrophysics Data System (ADS)

Pourghasemi, Hamid Reza; Rossi, Mauro

2017-10-01

Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide susceptibility mapping in the study area. Moreover, results obtained showed that the M-AHP model performed slightly better than the MARS, GLM, and GAM models in prediction. These algorithms can be very useful for landslide susceptibility and hazard mapping and land use planning in regional scale.

Modelling and economic evaluation of forest biome shifts under climate change in Southwest Germany

Treesearch

Marc Hanewinkel; Susan Hummel; Dominik Cullmann

2010-01-01

We evaluated the economic effects of a predicted shift from Norway spruce (Picea abies) to European beech (Fagus sylvatica) for a forest area of 1.3 million ha in southwest Germany. The shift was modelled with a generalized linear model (GLM) by using presence/absence data from the National Forest Inventory in Baden-Wurttemberg...

Effect of a diet enriched with green-lipped mussel on pain behavior and functioning in dogs with clinical osteoarthritis.

PubMed

Rialland, Pascale; Bichot, Sylvain; Lussier, Bertrand; Moreau, Maxim; Beaudry, Francis; del Castillo, Jérôme R E; Gauvin, Dominique; Troncy, Eric

2013-01-01

This study aimed to establish the effect of a diet enriched with green-lipped mussel (GLM) on pain and functional outcomes in osteoarthritic dogs. Twenty-three client-owned dogs with osteoarthritis (OA) were fed a balanced control diet for 30 d and then a GLM-enriched balanced diet for the next 60 d. We assessed peak vertical force (PVF), which is considered to be the gold standard method, at Day (D)0 (start), D30 (end of control diet), and D90 (end of GLM-enriched diet). The owners completed a client-specific outcome measure (CSOM), which is a pain questionnaire, once a week. Motor activity (MA) was continuously recorded in 7 dogs for 12 wk. Concentrations of plasma omega-3 fatty acids were quantified as indicative of diet change. Statistical analyses were linear-mixed models and multinomial logistic regression for repeated measures. The GLM diet (from D30 to D90) resulted in an increase in concentrations of plasma omega-3 fatty acids (P < 0.016) and improvement of PVF (P = 0.003). From D0 to D30, PVF did not significantly change (P = 0.06), which suggests that the GLM diet had a beneficial effect on gait function. Moreover, PVF (P = 0.0004), CSOM (P = 0.006), and MA (P = 0.02) improved significantly from D0 to D90. In general, the balanced control diet could have contributed to reduced OA symptoms, an effect that was subsequently amplified by the GLM diet.

Mapping interictal epileptic discharges using mutual information between concurrent EEG and fMRI.

PubMed

Caballero-Gaudes, César; Van de Ville, Dimitri; Grouiller, Frédéric; Thornton, Rachel; Lemieux, Louis; Seeck, Margitta; Lazeyras, François; Vulliemoz, Serge

2013-03-01

The mapping of haemodynamic changes related to interictal epileptic discharges (IED) in simultaneous electroencephalography (EEG) and functional MRI (fMRI) studies is usually carried out by means of EEG-correlated fMRI analyses where the EEG information specifies the model to test on the fMRI signal. The sensitivity and specificity critically depend on the accuracy of EEG detection and the validity of the haemodynamic model. In this study we investigated whether an information theoretic analysis based on the mutual information (MI) between the presence of epileptic activity on EEG and the fMRI data can provide further insights into the haemodynamic changes related to interictal epileptic activity. The important features of MI are that: 1) both recording modalities are treated symmetrically; 2) no requirement for a-priori models for the haemodynamic response function, or assumption of a linear relationship between the spiking activity and BOLD responses, and 3) no parametric model for the type of noise or its probability distribution is necessary for the computation of MI. Fourteen patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI and intracranial EEG and/or surgical resection with positive postoperative outcome (seizure freedom or considerable reduction in seizure frequency) was available in 7/14 patients. We used nonparametric statistical assessment of the MI maps based on a four-dimensional wavelet packet resampling method. The results of MI were compared to the statistical parametric maps obtained with two conventional General Linear Model (GLM) analyses based on the informed basis set (canonical HRF and its temporal and dispersion derivatives) and the Finite Impulse Response (FIR) models. The MI results were concordant with the electro-clinically or surgically defined epileptogenic area in 8/14 patients and showed the same degree of concordance as the results obtained with the GLM-based methods in 12 patients (7 concordant and 5 discordant). In one patient, the information theoretic analysis improved the delineation of the irritative zone compared with the GLM-based methods. Our findings suggest that an information theoretic analysis can provide clinically relevant information about the BOLD signal changes associated with the generation and propagation of interictal epileptic discharges. The concordance between the MI, GLM and FIR maps support the validity of the assumptions adopted in GLM-based analyses of interictal epileptic activity with EEG-fMRI in such a manner that they do not significantly constrain the localization of the epileptogenic zone. Copyright © 2012 Elsevier Inc. All rights reserved.

Input-output mapping reconstruction of spike trains at dorsal horn evoked by manual acupuncture

NASA Astrophysics Data System (ADS)

Wei, Xile; Shi, Dingtian; Yu, Haitao; Deng, Bin; Lu, Meili; Han, Chunxiao; Wang, Jiang

2016-12-01

In this study, a generalized linear model (GLM) is used to reconstruct mapping from acupuncture stimulation to spike trains driven by action potential data. The electrical signals are recorded in spinal dorsal horn after manual acupuncture (MA) manipulations with different frequencies being taken at the “Zusanli” point of experiment rats. Maximum-likelihood method is adopted to estimate the parameters of GLM and the quantified value of assumed model input. Through validating the accuracy of firings generated from the established GLM, it is found that the input-output mapping of spike trains evoked by acupuncture can be successfully reconstructed for different frequencies. Furthermore, via comparing the performance of several GLMs based on distinct inputs, it suggests that input with the form of half-sine with noise can well describe the generator potential induced by acupuncture mechanical action. Particularly, the comparison of reproducing the experiment spikes for five selected inputs is in accordance with the phenomenon found in Hudgkin-Huxley (H-H) model simulation, which indicates the mapping from half-sine with noise input to experiment spikes meets the real encoding scheme to some extent. These studies provide us a new insight into coding processes and information transfer of acupuncture.

Subject-level reliability analysis of fast fMRI with application to epilepsy.

PubMed

Hao, Yongfu; Khoo, Hui Ming; von Ellenrieder, Nicolas; Gotman, Jean

2017-07-01

Recent studies have applied the new magnetic resonance encephalography (MREG) sequence to the study of interictal epileptic discharges (IEDs) in the electroencephalogram (EEG) of epileptic patients. However, there are no criteria to quantitatively evaluate different processing methods, to properly use the new sequence. We evaluated different processing steps of this new sequence under the common generalized linear model (GLM) framework by assessing the reliability of results. A bootstrap sampling technique was first used to generate multiple replicated data sets; a GLM with different processing steps was then applied to obtain activation maps, and the reliability of these maps was assessed. We applied our analysis in an event-related GLM related to IEDs. A higher reliability was achieved by using a GLM with head motion confound regressor with 24 components rather than the usual 6, with an autoregressive model of order 5 and with a canonical hemodynamic response function (HRF) rather than variable latency or patient-specific HRFs. Comparison of activation with IED field also favored the canonical HRF, consistent with the reliability analysis. The reliability analysis helps to optimize the processing methods for this fast fMRI sequence, in a context in which we do not know the ground truth of activation areas. Magn Reson Med 78:370-382, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

An Evaluation of Nutrition Education Program for Low-Income Youth

ERIC Educational Resources Information Center

Kemirembe, Olive M. K.; Radhakrishna, Rama B.; Gurgevich, Elise; Yoder, Edgar P.; Ingram, Patreese D.

2011-01-01

A quasi-experimental design consisting of pretest, posttest, and delayed posttest comparison control group was used. Nutrition knowledge and behaviors were measured at pretest (time 1) posttest (time 2) and delayed posttest (time 3). General Linear Model (GLM) repeated measure ANCOVA results showed that youth who received nutrition education…

Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

PubMed

Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J

2016-05-01

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.

A predictive modeling approach to increasing the economic effectiveness of disease management programs.

PubMed

Bayerstadler, Andreas; Benstetter, Franz; Heumann, Christian; Winter, Fabian

2014-09-01

Predictive Modeling (PM) techniques are gaining importance in the worldwide health insurance business. Modern PM methods are used for customer relationship management, risk evaluation or medical management. This article illustrates a PM approach that enables the economic potential of (cost-) effective disease management programs (DMPs) to be fully exploited by optimized candidate selection as an example of successful data-driven business management. The approach is based on a Generalized Linear Model (GLM) that is easy to apply for health insurance companies. By means of a small portfolio from an emerging country, we show that our GLM approach is stable compared to more sophisticated regression techniques in spite of the difficult data environment. Additionally, we demonstrate for this example of a setting that our model can compete with the expensive solutions offered by professional PM vendors and outperforms non-predictive standard approaches for DMP selection commonly used in the market.

A comparison of methods to handle skew distributed cost variables in the analysis of the resource consumption in schizophrenia treatment.

PubMed

Kilian, Reinhold; Matschinger, Herbert; Löeffler, Walter; Roick, Christiane; Angermeyer, Matthias C

2002-03-01

Transformation of the dependent cost variable is often used to solve the problems of heteroscedasticity and skewness in linear ordinary least square regression of health service cost data. However, transformation may cause difficulties in the interpretation of regression coefficients and the retransformation of predicted values. The study compares the advantages and disadvantages of different methods to estimate regression based cost functions using data on the annual costs of schizophrenia treatment. Annual costs of psychiatric service use and clinical and socio-demographic characteristics of the patients were assessed for a sample of 254 patients with a diagnosis of schizophrenia (ICD-10 F 20.0) living in Leipzig. The clinical characteristics of the participants were assessed by means of the BPRS 4.0, the GAF, and the CAN for service needs. Quality of life was measured by WHOQOL-BREF. A linear OLS regression model with non-parametric standard errors, a log-transformed OLS model and a generalized linear model with a log-link and a gamma distribution were used to estimate service costs. For the estimation of robust non-parametric standard errors, the variance estimator by White and a bootstrap estimator based on 2000 replications were employed. Models were evaluated by the comparison of the R2 and the root mean squared error (RMSE). RMSE of the log-transformed OLS model was computed with three different methods of bias-correction. The 95% confidence intervals for the differences between the RMSE were computed by means of bootstrapping. A split-sample-cross-validation procedure was used to forecast the costs for the one half of the sample on the basis of a regression equation computed for the other half of the sample. All three methods showed significant positive influences of psychiatric symptoms and met psychiatric service needs on service costs. Only the log- transformed OLS model showed a significant negative impact of age, and only the GLM shows a significant negative influences of employment status and partnership on costs. All three models provided a R2 of about.31. The Residuals of the linear OLS model revealed significant deviances from normality and homoscedasticity. The residuals of the log-transformed model are normally distributed but still heteroscedastic. The linear OLS model provided the lowest prediction error and the best forecast of the dependent cost variable. The log-transformed model provided the lowest RMSE if the heteroscedastic bias correction was used. The RMSE of the GLM with a log link and a gamma distribution was higher than those of the linear OLS model and the log-transformed OLS model. The difference between the RMSE of the linear OLS model and that of the log-transformed OLS model without bias correction was significant at the 95% level. As result of the cross-validation procedure, the linear OLS model provided the lowest RMSE followed by the log-transformed OLS model with a heteroscedastic bias correction. The GLM showed the weakest model fit again. None of the differences between the RMSE resulting form the cross- validation procedure were found to be significant. The comparison of the fit indices of the different regression models revealed that the linear OLS model provided a better fit than the log-transformed model and the GLM, but the differences between the models RMSE were not significant. Due to the small number of cases in the study the lack of significance does not sufficiently proof that the differences between the RSME for the different models are zero and the superiority of the linear OLS model can not be generalized. The lack of significant differences among the alternative estimators may reflect a lack of sample size adequate to detect important differences among the estimators employed. Further studies with larger case number are necessary to confirm the results. Specification of an adequate regression models requires a careful examination of the characteristics of the data. Estimation of standard errors and confidence intervals by nonparametric methods which are robust against deviations from the normal distribution and the homoscedasticity of residuals are suitable alternatives to the transformation of the skew distributed dependent variable. Further studies with more adequate case numbers are needed to confirm the results.

An evaluation of three statistical estimation methods for assessing health policy effects on prescription drug claims.

PubMed

Mittal, Manish; Harrison, Donald L; Thompson, David M; Miller, Michael J; Farmer, Kevin C; Ng, Yu-Tze

2016-01-01

While the choice of analytical approach affects study results and their interpretation, there is no consensus to guide the choice of statistical approaches to evaluate public health policy change. This study compared and contrasted three statistical estimation procedures in the assessment of a U.S. Food and Drug Administration (FDA) suicidality warning, communicated in January 2008 and implemented in May 2009, on antiepileptic drug (AED) prescription claims. Longitudinal designs were utilized to evaluate Oklahoma (U.S. State) Medicaid claim data from January 2006 through December 2009. The study included 9289 continuously eligible individuals with prevalent diagnoses of epilepsy and/or psychiatric disorder. Segmented regression models using three estimation procedures [i.e., generalized linear models (GLM), generalized estimation equations (GEE), and generalized linear mixed models (GLMM)] were used to estimate trends of AED prescription claims across three time periods: before (January 2006-January 2008); during (February 2008-May 2009); and after (June 2009-December 2009) the FDA warning. All three statistical procedures estimated an increasing trend (P < 0.0001) in AED prescription claims before the FDA warning period. No procedures detected a significant change in trend during (GLM: -30.0%, 99% CI: -60.0% to 10.0%; GEE: -20.0%, 99% CI: -70.0% to 30.0%; GLMM: -23.5%, 99% CI: -58.8% to 1.2%) and after (GLM: 50.0%, 99% CI: -70.0% to 160.0%; GEE: 80.0%, 99% CI: -20.0% to 200.0%; GLMM: 47.1%, 99% CI: -41.2% to 135.3%) the FDA warning when compared to pre-warning period. Although the three procedures provided consistent inferences, the GEE and GLMM approaches accounted appropriately for correlation. Further, marginal models estimated using GEE produced more robust and valid population-level estimations. Copyright © 2016 Elsevier Inc. All rights reserved.

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Factor Scores, Structure and Communality Coefficients: A Primer

ERIC Educational Resources Information Center

Odum, Mary

2011-01-01

(Purpose) The purpose of this paper is to present an easy-to-understand primer on three important concepts of factor analysis: Factor scores, structure coefficients, and communality coefficients. Given that statistical analyses are a part of a global general linear model (GLM), and utilize weights as an integral part of analyses (Thompson, 2006;…

Establishing a Spinal Injury Criterion for Military Seats

DTIC Science & Technology

1997-01-01

Table represents 54 Trials (18 [phase I] + 36 [phase II]); "Combined Effects" of Delta V, Gpk & ATD Size illM-l A General Linear Model (GLM) analysis...5thpercentilemale AID would not have compliedwith the tolerance criterion under the higher impulse severity levels (i.e., 20 and 30 Gpk ). Similarly, the

The Use of Structure Coefficients to Address Multicollinearity in Sport and Exercise Science

ERIC Educational Resources Information Center

Yeatts, Paul E.; Barton, Mitch; Henson, Robin K.; Martin, Scott B.

2017-01-01

A common practice in general linear model (GLM) analyses is to interpret regression coefficients (e.g., standardized ß weights) as indicators of variable importance. However, focusing solely on standardized beta weights may provide limited or erroneous information. For example, ß weights become increasingly unreliable when predictor variables are…

A randomized approach to speed up the analysis of large-scale read-count data in the application of CNV detection.

PubMed

Wang, WeiBo; Sun, Wei; Wang, Wei; Szatkiewicz, Jin

2018-03-01

The application of high-throughput sequencing in a broad range of quantitative genomic assays (e.g., DNA-seq, ChIP-seq) has created a high demand for the analysis of large-scale read-count data. Typically, the genome is divided into tiling windows and windowed read-count data is generated for the entire genome from which genomic signals are detected (e.g. copy number changes in DNA-seq, enrichment peaks in ChIP-seq). For accurate analysis of read-count data, many state-of-the-art statistical methods use generalized linear models (GLM) coupled with the negative-binomial (NB) distribution by leveraging its ability for simultaneous bias correction and signal detection. However, although statistically powerful, the GLM+NB method has a quadratic computational complexity and therefore suffers from slow running time when applied to large-scale windowed read-count data. In this study, we aimed to speed up substantially the GLM+NB method by using a randomized algorithm and we demonstrate here the utility of our approach in the application of detecting copy number variants (CNVs) using a real example. We propose an efficient estimator, the randomized GLM+NB coefficients estimator (RGE), for speeding up the GLM+NB method. RGE samples the read-count data and solves the estimation problem on a smaller scale. We first theoretically validated the consistency and the variance properties of RGE. We then applied RGE to GENSENG, a GLM+NB based method for detecting CNVs. We named the resulting method as "R-GENSENG". Based on extensive evaluation using both simulated and empirical data, we concluded that R-GENSENG is ten times faster than the original GENSENG while maintaining GENSENG's accuracy in CNV detection. Our results suggest that RGE strategy developed here could be applied to other GLM+NB based read-count analyses, i.e. ChIP-seq data analysis, to substantially improve their computational efficiency while preserving the analytic power.

A critical issue in model-based inference for studying trait-based community assembly and a solution.

PubMed

Ter Braak, Cajo J F; Peres-Neto, Pedro; Dray, Stéphane

2017-01-01

Statistical testing of trait-environment association from data is a challenge as there is no common unit of observation: the trait is observed on species, the environment on sites and the mediating abundance on species-site combinations. A number of correlation-based methods, such as the community weighted trait means method (CWM), the fourth-corner correlation method and the multivariate method RLQ, have been proposed to estimate such trait-environment associations. In these methods, valid statistical testing proceeds by performing two separate resampling tests, one site-based and the other species-based and by assessing significance by the largest of the two p -values (the p max test). Recently, regression-based methods using generalized linear models (GLM) have been proposed as a promising alternative with statistical inference via site-based resampling. We investigated the performance of this new approach along with approaches that mimicked the p max test using GLM instead of fourth-corner. By simulation using models with additional random variation in the species response to the environment, the site-based resampling tests using GLM are shown to have severely inflated type I error, of up to 90%, when the nominal level is set as 5%. In addition, predictive modelling of such data using site-based cross-validation very often identified trait-environment interactions that had no predictive value. The problem that we identify is not an "omitted variable bias" problem as it occurs even when the additional random variation is independent of the observed trait and environment data. Instead, it is a problem of ignoring a random effect. In the same simulations, the GLM-based p max test controlled the type I error in all models proposed so far in this context, but still gave slightly inflated error in more complex models that included both missing (but important) traits and missing (but important) environmental variables. For screening the importance of single trait-environment combinations, the fourth-corner test is shown to give almost the same results as the GLM-based tests in far less computing time.

Focal activation of primary visual cortex following supra-choroidal electrical stimulation of the retina: Intrinsic signal imaging and linear model analysis.

PubMed

Cloherty, Shaun L; Hietanen, Markus A; Suaning, Gregg J; Ibbotson, Michael R

2010-01-01

We performed optical intrinsic signal imaging of cat primary visual cortex (Area 17 and 18) while delivering bipolar electrical stimulation to the retina by way of a supra-choroidal electrode array. Using a general linear model (GLM) analysis we identified statistically significant (p < 0.01) activation in a localized region of cortex following supra-threshold electrical stimulation at a single retinal locus. (1) demonstrate that intrinsic signal imaging combined with linear model analysis provides a powerful tool for assessing cortical responses to prosthetic stimulation, and (2) confirm that supra-choroidal electrical stimulation can achieve localized activation of the cortex consistent with focal activation of the retina.

Feature-space-based FMRI analysis using the optimal linear transformation.

PubMed

Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

2010-09-01

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

Applications of multivariate modeling to neuroimaging group analysis: a comprehensive alternative to univariate general linear model.

PubMed

Chen, Gang; Adleman, Nancy E; Saad, Ziad S; Leibenluft, Ellen; Cox, Robert W

2014-10-01

All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance-covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within-subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT) with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse-Geisser and Huynh-Feldt) with MVT-WS. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in the field, and the following two issues were raised: a) the improper formulation of test statistics in some univariate GLM implementations when a within-subject factor is involved in a data structure with two or more factors, and b) the unjustified presumption of uniform sphericity violation and the practice of estimating the variance-covariance structure through pooling across brain regions. Published by Elsevier Inc.

Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model

PubMed Central

Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.

2014-01-01

All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281

Longitudinal data analyses using linear mixed models in SPSS: concepts, procedures and illustrations.

PubMed

Shek, Daniel T L; Ma, Cecilia M S

2011-01-05

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

PubMed Central

Shek, Daniel T. L.; Ma, Cecilia M. S.

2011-01-01

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented. PMID:21218263

Permutation inference for the general linear model

PubMed Central

Winkler, Anderson M.; Ridgway, Gerard R.; Webster, Matthew A.; Smith, Stephen M.; Nichols, Thomas E.

2014-01-01

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. PMID:24530839

Stochastic search, optimization and regression with energy applications

NASA Astrophysics Data System (ADS)

Hannah, Lauren A.

Designing clean energy systems will be an important task over the next few decades. One of the major roadblocks is a lack of mathematical tools to economically evaluate those energy systems. However, solutions to these mathematical problems are also of interest to the operations research and statistical communities in general. This thesis studies three problems that are of interest to the energy community itself or provide support for solution methods: R&D portfolio optimization, nonparametric regression and stochastic search with an observable state variable. First, we consider the one stage R&D portfolio optimization problem to avoid the sequential decision process associated with the multi-stage. The one stage problem is still difficult because of a non-convex, combinatorial decision space and a non-convex objective function. We propose a heuristic solution method that uses marginal project values---which depend on the selected portfolio---to create a linear objective function. In conjunction with the 0-1 decision space, this new problem can be solved as a knapsack linear program. This method scales well to large decision spaces. We also propose an alternate, provably convergent algorithm that does not exploit problem structure. These methods are compared on a solid oxide fuel cell R&D portfolio problem. Next, we propose Dirichlet Process mixtures of Generalized Linear Models (DPGLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate. We also give examples for when those conditions hold, including models for compactly supported continuous distributions and a model with continuous covariates and categorical response. We empirically analyze the properties of the DP-GLM and why it provides better results than existing Dirichlet process mixture regression models. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression like CART, Bayesian trees and Gaussian processes. Compared to existing techniques, the DP-GLM provides a single model (and corresponding inference algorithms) that performs well in many regression settings. Finally, we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose algorithm to solve this class of problems. We use nonparametric density estimation to take observations from the joint state-outcome distribution and use them to infer the optimal decision for a given query state. We propose two solution methods that depend on the problem characteristics: function-based and gradient-based optimization. We examine two weighting schemes, kernel-based weights and Dirichlet process-based weights, for use with the solution methods. The weights and solution methods are tested on a synthetic multi-product newsvendor problem and the hour-ahead wind commitment problem. Our results show that in some cases Dirichlet process weights offer substantial benefits over kernel based weights and more generally that nonparametric estimation methods provide good solutions to otherwise intractable problems.

Association between nighttime artificial light pollution and sea turtle nest density along Florida coast: A geospatial study using VIIRS remote sensing data.

PubMed

Hu, Zhiyong; Hu, Hongda; Huang, Yuxia

2018-08-01

Artificial lighting at night has becoming a new type of pollution posing an important anthropogenic environmental pressure on organisms. The objective of this research was to examine the potential association between nighttime artificial light pollution and nest densities of the three main sea turtle species along Florida beaches, including green turtles, loggerheads, and leatherbacks. Sea turtle survey data was obtained from the "Florida Statewide Nesting Beach Survey program". We used the new generation of satellite sensor "Visible Infrared Imaging Radiometer Suite (VIIRS)" (version 1 D/N Band) nighttime annual average radiance composite image data. We defined light pollution as artificial light brightness greater than 10% of the natural sky brightness above 45° of elevation (>1.14 × 10 -11 Wm -2 sr -1 ). We fitted a generalized linear model (GLM), a GLM with eigenvectors spatial filtering (GLM-ESF), and a generalized estimating equations (GEE) approach for each species to examine the potential correlation of nest density with light pollution. Our models are robust and reliable in terms of the ability to deal with data distribution and spatial autocorrelation (SA) issues violating model assumptions. All three models found that nest density is significantly negatively correlated with light pollution for each sea turtle species: the higher light pollution, the lower nest density. The two spatially extended models (GLM-ESF and GEE) show that light pollution influences nest density in a descending order from green turtles, to loggerheads, and then to leatherbacks. The research findings have an implication for sea turtle conservation policy and ordinance making. Near-coastal lights-out ordinances and other approaches to shield lights can protect sea turtles and their nests. The VIIRS DNB light data, having significant improvements over comparable data by its predecessor, the DMSP-OLS, shows promise for continued and improved research about ecological effects of artificial light pollution. Copyright © 2018 Elsevier Ltd. All rights reserved.

TU-C-12A-12: Differentiating Bone Lesions and Degenerative Joint Disease in NaF PET/CT Scans Using Machine Learning

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

Perk, T; Bradshaw, T; Muzahir, S

2014-06-15

Purpose: [F-18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F-18]NaF scans that correspond to DJD. Methods: 10 metastatic prostate cancer patients received whole body [F-18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to trainmore » eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers. Results: The training set consisted of 37 DJD ROIs with 475 non-DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non-DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT. Conclusion: Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone. Research supported by the Prostate Cancer Foundation.« less

Load-dependent brain activation assessed by time-domain functional near-infrared spectroscopy during a working memory task with graded levels of difficulty

NASA Astrophysics Data System (ADS)

Molteni, Erika; Contini, Davide; Caffini, Matteo; Baselli, Giuseppe; Spinelli, Lorenzo; Cubeddu, Rinaldo; Cerutti, Sergio; Bianchi, Anna Maria; Torricelli, Alessandro

2012-05-01

We evaluated frontal brain activation during a mixed attentional/working memory task with graded levels of difficulty in a group of 19 healthy subjects, by means of time-domain functional near-infrared spectroscopy (fNIRS). Brain activation was assessed, and load-related oxy- and deoxy-hemoglobin changes were studied. Generalized linear model (GLM) was applied to the data to explore the metabolic processes occurring during the mental effort and, possibly, their involvement in short-term memorization. GLM was applied to the data twice: for modeling the task as a whole and for specifically investigating brain activation at each cognitive load. This twofold employment of GLM allowed (1) the extraction and isolation of different information from the same signals, obtained through the modeling of different cognitive categories (sustained attention and working memory), and (2) the evaluation of model fitness, by inspection and comparison of residuals (i.e., unmodeled part of the signal) obtained in the two different cases. Results attest to the presence of a persistent attentional-related metabolic activity, superimposed to a task-related mnemonic contribution. Some hemispherical differences have also been highlighted frontally: deoxy-hemoglobin changes manifested a strong right lateralization, whereas modifications in oxy- and total hemoglobin showed a medial localization. The present work successfully explored the capability of fNIRS to detect the two neurophysiological categories under investigation and distinguish their activation patterns.

Modelling the habitat suitability of cetaceans: Example of the sperm whale in the northwestern Mediterranean Sea

NASA Astrophysics Data System (ADS)

Praca, Emilie; Gannier, Alexandre; Das, Krishna; Laran, Sophie

2009-04-01

Cetaceans are mobile and spend long periods underwater. Because of this, modelling their habitat could be subject to a serious problem of false absence. Furthermore, extensive surveys at sea are time and money consuming, and presence-absence data are difficult to apply. This study compares the ability of two presence-absence and two presence-only habitat modelling methods and uses the example of the sperm whale ( Physeter macrocephalus) in the northwestern Mediterranean Sea. The data consist of summer visual and acoustical detections of sperm whales, compiled between 1998 and 2005. Habitat maps were computed using topographical and hydrological eco-geographical variables. Four methods were compared: principal component analysis (PCA), ecological niche factor analysis (ENFA), generalized linear model (GLM) and multivariate adaptive regression splines (MARS). The evaluation of the models was achieved by calculating the receiver operating characteristic (ROC) of the models and their respective area under the curve (AUC). Presence-absence methods (GLM, AUC=0.70, and MARS, AUC=0.79) presented better AUC than presence-only methods (PCA, AUC=0.58, and ENFA, AUC=0.66), but this difference was not statistically significant, except between the MARS and the PCA models. The four models showed an influence of both topographical and hydrological factors, but the resulting habitat suitability maps differed. The core habitat on the continental slope was well highlighted by the four models, while GLM and MARS maps also showed a suitable habitat in the offshore waters. Presence-absence methods are therefore recommended for modelling the habitat suitability of cetaceans, as they seem more accurate to highlight complex habitat. However, the use of presence-only techniques, in particular ENFA, could be very useful for a first model of the habitat range or when important surveys at sea are not possible.

The Effect of Habitual Smoking on VO2max

NASA Technical Reports Server (NTRS)

Wier, Larry T.; Suminski, Richard R.; Poston, Walker S.; Randles, Anthony M.; Arenare, Brian; Jackson, Andrew S.

2008-01-01

VO2max is associated with many factors, including age, gender, physical activity, and body composition. It is popularly believed that habitual smoking lowers aerobic fitness. PURPOSE: to determine the effect of habitual smoking on VO2max after controlling for age, gender, activity and BMI. METHODS: 2374 men and 375 women employed at the NASA/Johnson Space Center were measured for VO2max by indirect calorimetry (RER>=1.1), activity by the 11 point (0-10) NASA Physical Activity Status Scale (PASS), BMI and smoking pack-yrs (packs day*y of smoking). Age was recorded in years and gender was coded as M=1, W=0. Pack.y was made a categorical variable consisting of four levels as follows: Never Smoked (0), Light (1-10), Regular (11-20), Heavy (>20). Group differences were verified by ANOVA. A General Linear Models (GLM) was used to develop two models to examine the relationship of smoking behavior on VO2max. GLM #1(without smoking) determined the combined effects of age, gender, PASS and BMI on VO2max. GLM #2 (with smoking) determined the added effects of smoking (pack.y groupings) on VO2max after controlling for age, gender, PASS and BMI. Constant errors (CE) were calculated to compare the accuracy of the two models for estimating the VO2max of the smoking subgroups. RESULTS: ANOVA affirmed the mean VO2max of each pack.y grouping decreased significantly (p<0.01) as the level of smoking exposure increased. GLM #1 showed that age, gender, PASS and BMI were independently related with VO2max (R2 = 0.642, SEE = 4.90, p<0.001). The added pack.y variables in GLM #2 were statistically significant (R2 change = 0.7%, p<0.01). Post hoc analysis showed that compared to Never Smoked, the effects on VO2max from Light and Regular smoking habits were -0.83 and -0.85 ml.kg- 1.min-1 respectively (p<0.05). The effect of Heavy smoking on VO2max was -2.56 ml.kg- 1.min-1 (p<0.001). The CE s of each smoking group in GLM #2 was smaller than the CE s of the smoking group counterparts in GLM #1. CONCLUSIONS: After accounting for the effects of gender, age, PASS and BMI the effect of habitual smoking on reducing VO2max is minimal, about 0.85 ml/kg/min, until the habit exceeds 20 pack.y at which point an additional decrease of 1.71 ml/kg/min is noted. Adding pack.y data improves the accuracy of predicting the VO2max of smokers.

Real-time fMRI processing with physiological noise correction - Comparison with off-line analysis.

PubMed

Misaki, Masaya; Barzigar, Nafise; Zotev, Vadim; Phillips, Raquel; Cheng, Samuel; Bodurka, Jerzy

2015-12-30

While applications of real-time functional magnetic resonance imaging (rtfMRI) are growing rapidly, there are still limitations in real-time data processing compared to off-line analysis. We developed a proof-of-concept real-time fMRI processing (rtfMRIp) system utilizing a personal computer (PC) with a dedicated graphic processing unit (GPU) to demonstrate that it is now possible to perform intensive whole-brain fMRI data processing in real-time. The rtfMRIp performs slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis with multiple noise regressors including physiological noise modeled with cardiac (RETROICOR) and respiration volume per time (RVT). The whole-brain data analysis with more than 100,000voxels and more than 250volumes is completed in less than 300ms, much faster than the time required to acquire the fMRI volume. Real-time processing implementation cannot be identical to off-line analysis when time-course information is used, such as in slice-timing correction, signal scaling, and GLM. We verified that reduced slice-timing correction for real-time analysis had comparable output with off-line analysis. The real-time GLM analysis, however, showed over-fitting when the number of sampled volumes was small. Our system implemented real-time RETROICOR and RVT physiological noise corrections for the first time and it is capable of processing these steps on all available data at a given time, without need for recursive algorithms. Comprehensive data processing in rtfMRI is possible with a PC, while the number of samples should be considered in real-time GLM. Copyright © 2015 Elsevier B.V. All rights reserved.

Estimating organ doses from tube current modulated CT examinations using a generalized linear model.

PubMed

Bostani, Maryam; McMillan, Kyle; Lu, Peiyun; Kim, Grace Hyun J; Cody, Dianna; Arbique, Gary; Greenberg, S Bruce; DeMarco, John J; Cagnon, Chris H; McNitt-Gray, Michael F

2017-04-01

Currently, available Computed Tomography dose metrics are mostly based on fixed tube current Monte Carlo (MC) simulations and/or physical measurements such as the size specific dose estimate (SSDE). In addition to not being able to account for Tube Current Modulation (TCM), these dose metrics do not represent actual patient dose. The purpose of this study was to generate and evaluate a dose estimation model based on the Generalized Linear Model (GLM), which extends the ability to estimate organ dose from tube current modulated examinations by incorporating regional descriptors of patient size, scanner output, and other scan-specific variables as needed. The collection of a total of 332 patient CT scans at four different institutions was approved by each institution's IRB and used to generate and test organ dose estimation models. The patient population consisted of pediatric and adult patients and included thoracic and abdomen/pelvis scans. The scans were performed on three different CT scanner systems. Manual segmentation of organs, depending on the examined anatomy, was performed on each patient's image series. In addition to the collected images, detailed TCM data were collected for all patients scanned on Siemens CT scanners, while for all GE and Toshiba patients, data representing z-axis-only TCM, extracted from the DICOM header of the images, were used for TCM simulations. A validated MC dosimetry package was used to perform detailed simulation of CT examinations on all 332 patient models to estimate dose to each segmented organ (lungs, breasts, liver, spleen, and kidneys), denoted as reference organ dose values. Approximately 60% of the data were used to train a dose estimation model, while the remaining 40% was used to evaluate performance. Two different methodologies were explored using GLM to generate a dose estimation model: (a) using the conventional exponential relationship between normalized organ dose and size with regional water equivalent diameter (WED) and regional CTDI vol as variables and (b) using the same exponential relationship with the addition of categorical variables such as scanner model and organ to provide a more complete estimate of factors that may affect organ dose. Finally, estimates from generated models were compared to those obtained from SSDE and ImPACT. The Generalized Linear Model yielded organ dose estimates that were significantly closer to the MC reference organ dose values than were organ doses estimated via SSDE or ImPACT. Moreover, the GLM estimates were better than those of SSDE or ImPACT irrespective of whether or not categorical variables were used in the model. While the improvement associated with a categorical variable was substantial in estimating breast dose, the improvement was minor for other organs. The GLM approach extends the current CT dose estimation methods by allowing the use of additional variables to more accurately estimate organ dose from TCM scans. Thus, this approach may be able to overcome the limitations of current CT dose metrics to provide more accurate estimates of patient dose, in particular, dose to organs with considerable variability across the population. © 2017 American Association of Physicists in Medicine.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

PubMed Central

Douglas, Pamela K.; Lau, Edward; Anderson, Ariana; Head, Austin; Kerr, Wesley; Wollner, Margalit; Moyer, Daniel; Li, Wei; Durnhofer, Mike; Bramen, Jennifer; Cohen, Mark S.

2013-01-01

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities. PMID:23914164

Post Launch Calibration and Testing of the Geostationary Lightning Mapper on the GOES-R Satellite

NASA Technical Reports Server (NTRS)

Rafal, Marc D.; Clarke, Jared T.; Cholvibul, Ruth W.

2016-01-01

The Geostationary Operational Environmental Satellite R (GOES-R) series is the planned next generation of operational weather satellites for the United States National Oceanic and Atmospheric Administration (NOAA). The National Aeronautics and Space Administration (NASA) is procuring the GOES-R spacecraft and instruments with the first launch of the GOES-R series planned for October 2016. Included in the GOES-R Instrument suite is the Geostationary Lightning Mapper (GLM). GLM is a single-channel, near-infrared optical detector that can sense extremely brief (800 microseconds) transient changes in the atmosphere, indicating the presence of lightning. GLM will measure total lightning activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. Due to its large CCD (1372x1300 pixels), high frame rate, sensitivity and onboard event filtering, GLM will require extensive post launch characterization and calibration. Daytime and nighttime images will be used to characterize both image quality criteria inherent to GLM as a space-based optic system (focus, stray light, crosstalk, solar glint) and programmable image processing criteria (dark offsets, gain, noise, linearity, dynamic range). In addition ground data filtering will be adjusted based on lightning-specific phenomenology (coherence) to isolate real from false transients with their own characteristics. These parameters will be updated, as needed, on orbit in an iterative process guided by pre-launch testing. This paper discusses the planned tests to be performed on GLM over the six-month Post Launch Test period to optimize and demonstrate GLM performance.

Post launch calibration and testing of the Geostationary Lightning Mapper on GOES-R satellite

NASA Astrophysics Data System (ADS)

Rafal, Marc; Clarke, Jared T.; Cholvibul, Ruth W.

2016-05-01

The Geostationary Operational Environmental Satellite R (GOES-R) series is the planned next generation of operational weather satellites for the United States National Oceanic and Atmospheric Administration (NOAA). The National Aeronautics and Space Administration (NASA) is procuring the GOES-R spacecraft and instruments with the first launch of the GOES-R series planned for October 2016. Included in the GOES-R Instrument suite is the Geostationary Lightning Mapper (GLM). GLM is a single-channel, near-infrared optical detector that can sense extremely brief (800 μs) transient changes in the atmosphere, indicating the presence of lightning. GLM will measure total lightning activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. Due to its large CCD (1372x1300 pixels), high frame rate, sensitivity and onboard event filtering, GLM will require extensive post launch characterization and calibration. Daytime and nighttime images will be used to characterize both image quality criteria inherent to GLM as a space-based optic system (focus, stray light, crosstalk, solar glint) and programmable image processing criteria (dark offsets, gain, noise, linearity, dynamic range). In addition ground data filtering will be adjusted based on lightning-specific phenomenology (coherence) to isolate real from false transients with their own characteristics. These parameters will be updated, as needed, on orbit in an iterative process guided by pre-launch testing. This paper discusses the planned tests to be performed on GLM over the six-month Post Launch Test period to optimize and demonstrate GLM performance.

Post Launch Calibration and Testing of the Geostationary Lightning Mapper on GOES-R Satellite

NASA Technical Reports Server (NTRS)

Rafal, Marc; Cholvibul, Ruth; Clarke, Jared

2016-01-01

The Geostationary Operational Environmental Satellite R (GOES-R) series is the planned next generation of operational weather satellites for the United States National Oceanic and Atmospheric Administration (NOAA). The National Aeronautics and Space Administration (NASA) is procuring the GOES-R spacecraft and instruments with the first launch of the GOES-R series planned for October 2016. Included in the GOES-R Instrument suite is the Geostationary Lightning Mapper (GLM). GLM is a single-channel, near-infrared optical detector that can sense extremely brief (800 s) transient changes in the atmosphere, indicating the presence of lightning. GLM will measure total lightning activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. Due to its large CCD (1372x1300 pixels), high frame rate, sensitivity and onboard event filtering, GLM will require extensive post launch characterization and calibration. Daytime and nighttime images will be used to characterize both image quality criteria inherent to GLM as a space-based optic system (focus, stray light, crosstalk, solar glint) and programmable image processing criteria (dark offsets, gain, noise, linearity, dynamic range). In addition ground data filtering will be adjusted based on lightning-specific phenomenology (coherence) to isolate real from false transients with their own characteristics. These parameters will be updated, as needed, on orbit in an iterative process guided by pre-launch testing. This paper discusses the planned tests to be performed on GLM over the six-month Post Launch Test period to optimize and demonstrate GLM performance.

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data.

PubMed

Liu, Zhenqiu; Sun, Fengzhu; McGovern, Dermot P

2017-01-01

Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1 , SCAD and MC+. However, none of the existing algorithms optimizes L 0 , which penalizes the number of nonzero features directly. In this paper, we develop a novel sparse generalized linear model (GLM) with L 0 approximation for feature selection and prediction with big omics data. The proposed approach approximate the L 0 optimization directly. Even though the original L 0 problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms ( L 0 ADRIDGE) for L 0 penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored. The developed Software L 0 ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.

Structure of a small-molecule inhibitor complexed with GlmU from Haemophilus influenzae reveals an allosteric binding site

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

Mochalkin, Igor; Lightle, Sandra; Narasimhan, Lakshmi

2008-04-02

N-Acetylglucosamine-1-phosphate uridyltransferase (GlmU) is an essential enzyme in aminosugars metabolism and an attractive target for antibiotic drug discovery. GlmU catalyzes the formation of uridine-diphospho-N-acetylglucosamine (UDP-GlcNAc), an important precursor in the peptidoglycan and lipopolisaccharide biosynthesis in both Gram-negative and Gram-positive bacteria. Here we disclose a 1.9 {angstrom} resolution crystal structure of a synthetic small-molecule inhibitor of GlmU from Haemophilus influenzae (hiGlmU). The compound was identified through a high-throughput screening (HTS) configured to detect inhibitors that target the uridyltransferase active site of hiGlmU. The original HTS hit exhibited a modest micromolar potency (IC{sub 50} - 18 {mu}M in a racemic mixture) againstmore » hiGlmU and no activity against Staphylococcus aureus GlmU (saGlmU). The determined crystal structure indicated that the inhibitor occupies an allosteric site adjacent to the GlcNAc-1-P substrate-binding region. Analysis of the mechanistic model of the uridyltransferase reaction suggests that the binding of this allosteric inhibitor prevents structural rearrangements that are required for the enzymatic reaction, thus providing a basis for structure-guided design of a new class of mechanism-based inhibitors of GlmU.« less

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

PubMed Central

Lin, Tiger W.; Das, Anup; Krishnan, Giri P.; Bazhenov, Maxim; Sejnowski, Terrence J.

2017-01-01

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005; Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. PMID:28777719

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

PubMed

Lin, Tiger W; Das, Anup; Krishnan, Giri P; Bazhenov, Maxim; Sejnowski, Terrence J

2017-10-01

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

Reticulo-rumen temperature as a predictor of calving time in primiparous and parous Holstein females.

PubMed

Costa, J B G; Ahola, J K; Weller, Z D; Peel, R K; Whittier, J C; Barcellos, J O J

2016-06-01

The objective of this research was to define and analyze drops in reticulo-rumen temperature (Trr) as an indicator of calving time in Holstein females. Data were collected from 111 primiparous and 150 parous Holstein females between November 2012 and March 2013. Between -15 and -5 d relative to anticipated calving date, each female received an orally administered temperature sensing reticulo-rumen bolus that collected temperatures hourly. Daily mean Trr was calculated from d -5 to 0 relative to using all Trr values (A-Trr) or only Trr values ≥37.7°C (W-Trr) not altered by water intake. To identify a Trr drop, 2 methodologies for computing the baseline temperature were used. Generalized linear models (GLM) were used to estimate the probability of calving within the next 12 or 24 h for primiparous, parous, and all females, based on the size of the Trr drop. For all GLM, a large drop in Trr corresponded with a large estimated probability of calving. The predictive power of the GLM was assessed using receiver-operating characteristic (ROC) curves. The ROC curve analyses showed that all models, regardless of methodology in calculation of the baseline or tested category (primiparous or parous), were able to predict calving; however, area under the ROC curve values, an indication of prediction quality, were greater for methods predicting calving within 24 h. Further comparisons between GLM for primiparous and parous, and using baseline 1 and 2, provide insight on the differences in predictive performance. Based on the GLM, Trr drops of 0.2, 0.3, and 0.4°C were identified as useful indicators of parturition and further analyzed using sensitivity, specificity, and diagnostic odds ratios. Based on sensitivity, specificity, and diagnostic odds ratios, the best indicator of calving was an average Trr drop ≥0.2°C, regardless of methodology used to compute the baseline or category of animal evaluated. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Humpback whale-generated ambient noise levels provide insight into singers' spatial densities.

PubMed

Seger, Kerri D; Thode, Aaron M; Urbán-R, Jorge; Martínez-Loustalot, Pamela; Jiménez-López, M Esther; López-Arzate, Diana

2016-09-01

Baleen whale vocal activity can be the dominant underwater ambient noise source for certain locations and seasons. Previous wind-driven ambient-noise formulations have been adjusted to model ambient noise levels generated by random distributions of singing humpback whales in ocean waveguides and have been combined to a single model. This theoretical model predicts that changes in ambient noise levels with respect to fractional changes in singer population (defined as the noise "sensitivity") are relatively unaffected by the source level distributions and song spectra of individual humpback whales (Megaptera novaeangliae). However, the noise "sensitivity" does depend on frequency and on how the singers' spatial density changes with population size. The theoretical model was tested by comparing visual line transect surveys with bottom-mounted passive acoustic data collected during the 2013 and 2014 humpback whale breeding seasons off Los Cabos, Mexico. A generalized linear model (GLM) estimated the noise "sensitivity" across multiple frequency bands. Comparing the GLM estimates with the theoretical predictions suggests that humpback whales tend to maintain relatively constant spacing between one another while singing, but that individual singers either slightly increase their source levels or song duration, or cluster more tightly as the singing population increases.

UAV Swarm Tactics: An Agent-Based Simulation and Markov Process Analysis

DTIC Science & Technology

2013-06-01

CRN Common Random Numbers CSV Comma Separated Values DoE Design of Experiment GLM Generalized Linear Model HVT High Value Target JAR Java ARchive JMF... Java Media Framework JRE Java runtime environment Mason Multi-Agent Simulator Of Networks MOE Measure Of Effectiveness MOP Measures Of Performance...with every set several times, and to write a CSV file with the results. Rather than scripting the agent behavior deterministically, the agents should

Application of physical scaling towards downscaling climate model precipitation data

NASA Astrophysics Data System (ADS)

Gaur, Abhishek; Simonovic, Slobodan P.

2018-04-01

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2-4 day), and long (more than 5-day) precipitation events is projected.

The glmS Ribozyme Cofactor is a General Acid-Base Catalyst

PubMed Central

Viladoms, Julia; Fedor, Martha J.

2012-01-01

The glmS ribozyme is the first natural self-cleaving ribozyme known to require a cofactor. The D-glucosamine-6-phosphate (GlcN6P) cofactor has been proposed to serve as a general acid, but its role in the catalytic mechanism has not been established conclusively. We surveyed GlcN6P-like molecules for their ability to support self-cleavage of the glmS ribozyme and found a strong correlation between the pH dependence of the cleavage reaction and the intrinsic acidity of the cofactors. For cofactors with low binding affinities the contribution to rate enhancement was proportional to their intrinsic acidity. This linear free-energy relationship between cofactor efficiency and acid dissociation constants is consistent with a mechanism in which the cofactors participate directly in the reaction as general acid-base catalysts. A high value for the Brønsted coefficient (β ~ 0.7) indicates that a significant amount of proton transfer has already occurred in the transition state. The glmS ribozyme is the first self-cleaving RNA to use an exogenous acid-base catalyst. PMID:23113700

The glmS ribozyme cofactor is a general acid-base catalyst.

PubMed

Viladoms, Júlia; Fedor, Martha J

2012-11-21

The glmS ribozyme is the first natural self-cleaving ribozyme known to require a cofactor. The d-glucosamine-6-phosphate (GlcN6P) cofactor has been proposed to serve as a general acid, but its role in the catalytic mechanism has not been established conclusively. We surveyed GlcN6P-like molecules for their ability to support self-cleavage of the glmS ribozyme and found a strong correlation between the pH dependence of the cleavage reaction and the intrinsic acidity of the cofactors. For cofactors with low binding affinities, the contribution to rate enhancement was proportional to their intrinsic acidity. This linear free-energy relationship between cofactor efficiency and acid dissociation constants is consistent with a mechanism in which the cofactors participate directly in the reaction as general acid-base catalysts. A high value for the Brønsted coefficient (β ~ 0.7) indicates that a significant amount of proton transfer has already occurred in the transition state. The glmS ribozyme is the first self-cleaving RNA to use an exogenous acid-base catalyst.

Modelling alpha-diversities of coastal lagoon fish assemblages from the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Riera, R.; Tuset, V. M.; Betancur-R, R.; Lombarte, A.; Marcos, C.; Pérez-Ruzafa, A.

2018-07-01

Coastal lagoons are marine ecosystems spread worldwide with high ecological value; however, they are increasingly becoming deteriorated as a result of anthropogenic activity. Their conservation requires a better understanding of the biodiversity factors that may help identifying priority areas. The present study is focused on 37 Mediterranean coastal lagoons and we use predictive modelling approaches based on Generalized Linear Model (GLM) analysis to investigate variables (geomorphological, environmental, trophic or biogeographic) that may predict variations in alpha-diversity. It included taxonomic diversity, average taxonomic distinctness, and phylogenetic and functional diversity. Two GLM models by index were built depending on available variables for lagoons: in the model 1 all lagoons were used, and in the model 2 only 23. All alpha-diversity indices showed variability between lagoons associated to exogenous factors considered. The biogeographic region strongly conditioned most of models, being the first variable introduced in the models. The salinity and chlorophyll a concentration played a secondary role for the models 1 and 2, respectively. In general, the highest values of alpha-diversities were found in northwestern Mediterranean (Balearic Sea, Alborán Sea and Gulf of Lion), hence they might be considered "hotspots" at the Mediterranean scale and should have a special status for their protection.

A Taxonomic Reduced-Space Pollen Model for Paleoclimate Reconstruction

NASA Astrophysics Data System (ADS)

Wahl, E. R.; Schoelzel, C.

2010-12-01

Paleoenvironmental reconstruction from fossil pollen often attempts to take advantage of the rich taxonomic diversity in such data. Here, a taxonomically "reduced-space" reconstruction model is explored that would be parsimonious in introducing parameters needing to be estimated within a Bayesian Hierarchical Modeling context. This work involves a refinement of the traditional pollen ratio method. This method is useful when one (or a few) dominant pollen type(s) in a region have a strong positive correlation with a climate variable of interest and another (or a few) dominant pollen type(s) have a strong negative correlation. When, e.g., counts of pollen taxa a and b (r >0) are combined with pollen types c and d (r <0) to form ratios of the form (a + b) / (a + b + c + d), an appropriate estimation form is the binomial logistic generalized linear model (GLM). The GLM can readily model this relationship in the forward form, pollen = g(climate), which is more physically realistic than inverse models often used in paleoclimate reconstruction [climate = f(pollen)]. The specification of the model is: rnum Bin(n,p), where E(r|T) = p = exp(η)/[1+exp(η)], and η = α + β(T); r is the pollen ratio formed as above, rnum is the ratio numerator, n is the ratio denominator (i.e., the sum of pollen counts), the denominator-specific count is (n - rnum), and T is the temperature at each site corresponding to a specific value of r. Ecological and empirical screening identified the model (Spruce+Birch) / (Spruce+Birch+Oak+Hickory) for use in temperate eastern N. America. α and β were estimated using both "traditional" and Bayesian GLM algorithms (in R). Although it includes only four pollen types, the ratio model yields more explained variation ( 80%) in the pollen-temperature relationship of the study region than a 64-taxon modern analog technique (MAT). Thus, the new pollen ratio method represents an information-rich, reduced space data model that can be efficiently employed in a BHM framework. The ratio model can directly reconstruct past temperature by solving the GLM equations for T as a function of α, β, and E(r|T): T = {ln[E(r|T)/{1-E(r|T)}]-α}/β. To enable use in paleoreconstruction, the observed r values from fossil pollen data are, by assumption, treated as unbiased estimators of the true r value at each time sampled, which can be substituted for E(r|T). Uncertainty in this reconstruction is systematically evaluated in two parts: 1) the observed r values and their corresponding n values are input as parameters into the binomial distribution, Monte Carlo random pollen count draws are made, and a new ratio value is determined for each iteration; and 2) in the "traditional" GLM the estimated SEs for α and β are used with the α and β EV estimates to yield Monte Carlo random draws for each binomial draw (assuming α and β are Gaussian), in the Bayesian GLM random draws for α and β are taken directly from their estimated posterior distribution. Both methods yield nearly identical reconstructions from varved lakes in Wisconsin where the model has been tested; slightly narrower uncertainty ranges are produced by the Bayesian model. The Little Ice Age is readily identified. Pine:Oak and Fir:Oak versions of the model used in S. California show differences from MAT-based reconstructions.

Forecasting daily emergency department visits using calendar variables and ambient temperature readings.

PubMed

Marcilio, Izabel; Hajat, Shakoor; Gouveia, Nelson

2013-08-01

This study aimed to develop different models to forecast the daily number of patients seeking emergency department (ED) care in a general hospital according to calendar variables and ambient temperature readings and to compare the models in terms of forecasting accuracy. The authors developed and tested six different models of ED patient visits using total daily counts of patient visits to an ED in Sao Paulo, Brazil, from January 1, 2008, to December 31, 2010. The first 33 months of the data set were used to develop the ED patient visits forecasting models (the training set), leaving the last 3 months to measure each model's forecasting accuracy by the mean absolute percentage error (MAPE). Forecasting models were developed using three different time-series analysis methods: generalized linear models (GLM), generalized estimating equations (GEE), and seasonal autoregressive integrated moving average (SARIMA). For each method, models were explored with and without the effect of mean daily temperature as a predictive variable. The daily mean number of ED visits was 389, ranging from 166 to 613. Data showed a weekly seasonal distribution, with highest patient volumes on Mondays and lowest patient volumes on weekends. There was little variation in daily visits by month. GLM and GEE models showed better forecasting accuracy than SARIMA models. For instance, the MAPEs from GLM models and GEE models at the first month of forecasting (October 2012) were 11.5 and 10.8% (models with and without control for the temperature effect, respectively), while the MAPEs from SARIMA models were 12.8 and 11.7%. For all models, controlling for the effect of temperature resulted in worse or similar forecasting ability than models with calendar variables alone, and forecasting accuracy was better for the short-term horizon (7 days in advance) than for the longer term (30 days in advance). This study indicates that time-series models can be developed to provide forecasts of daily ED patient visits, and forecasting ability was dependent on the type of model employed and the length of the time horizon being predicted. In this setting, GLM and GEE models showed better accuracy than SARIMA models. Including information about ambient temperature in the models did not improve forecasting accuracy. Forecasting models based on calendar variables alone did in general detect patterns of daily variability in ED volume and thus could be used for developing an automated system for better planning of personnel resources. © 2013 by the Society for Academic Emergency Medicine.

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts : Neurometric analysis of body schema extension.

PubMed

Suzuki, Satoshi

2017-09-01

This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task. In Task 1, participants traced Japanese Hiragana characters using the right forefinger, requiring no BS expansion. In Task 2, participants performed the tracing task with a long stick, requiring BS expansion. Spatial distribution was analyzed using general linear model (GLM)-based statistical parametric mapping of near-infrared spectroscopy data contaminated with motion artifacts caused by the hand-tracing task. Three methods were utilized in series to counter the artifacts, and optimal conditions and modifications were investigated: a model-free method (Step 1), a convolution matrix method (Step 2), and a boxcar-function-based Gaussian convolution method (Step 3). The results revealed four methodological findings: (1) Deoxyhemoglobin was suitable for the GLM because both Akaike information criterion and the variance against the averaged hemodynamic response function were smaller than for other signals, (2) a high-pass filter with a cutoff frequency of .014 Hz was effective, (3) the hemodynamic response function computed from a Gaussian kernel function and its first- and second-derivative terms should be included in the GLM model, and (4) correction of non-autocorrelation and use of effective degrees of freedom were critical. Investigating z-maps computed according to these guidelines revealed that contiguous areas of BA7-BA40-BA21 in the right hemisphere became significantly activated ([Formula: see text], [Formula: see text], and [Formula: see text], respectively) during BS modification while performing the hand-tracing task.

Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China.

PubMed

Gou, Faxiang; Liu, Xinfeng; He, Jian; Liu, Dongpeng; Cheng, Yao; Liu, Haixia; Yang, Xiaoting; Wei, Kongfu; Zheng, Yunhe; Jiang, Xiaojuan; Meng, Lei; Hu, Wenbiao

2018-01-08

To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission. Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways. GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2.8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 °C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 °C of Jiuquan to 16 °C in Tianshui; and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui. Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan; Relative humidity's effect on HFMD showed a non-linear interacting relationship with average temperature.

The Impact of Climate Change on the Potential Distribution of Agricultural Pests: The Case of the Coffee White Stem Borer (Monochamus leuconotus P.) in Zimbabwe

PubMed Central

Kutywayo, Dumisani; Chemura, Abel; Kusena, Winmore; Chidoko, Pardon; Mahoya, Caleb

2013-01-01

The production of agricultural commodities faces increased risk of pests, diseases and other stresses due to climate change and variability. This study assesses the potential distribution of agricultural pests under projected climatic scenarios using evidence from the African coffee white stem borer (CWB), Monochamus leuconotus (Pascoe) (Coleoptera: Cerambycidae), an important pest of coffee in Zimbabwe. A species distribution modeling approach utilising Boosted Regression Trees (BRT) and Generalized Linear Models (GLM) was applied on current and projected climate data obtained from the WorldClim database and occurrence data (presence and absence) collected through on-farm biological surveys in Chipinge, Chimanimani, Mutare and Mutasa districts in Zimbabwe. Results from both the BRT and GLM indicate that precipitation-related variables are more important in determining species range for the CWB than temperature related variables. The CWB has extensive potential habitats in all coffee areas with Mutasa district having the largest model average area suitable for CWB under current and projected climatic conditions. Habitat ranges for CWB will increase under future climate scenarios for Chipinge, Chimanimani and Mutare districts while it will decrease in Mutasa district. The highest percentage change in area suitable for the CWB was for Chimanimani district with a model average of 49.1% (3 906 ha) increase in CWB range by 2080. The BRT and GLM predictions gave similar predicted ranges for Chipinge, Chimanimani and Mutasa districts compared to the high variation in current and projected habitat area for CWB in Mutare district. The study concludes that suitable area for CWB will increase significantly in Zimbabwe due to climate change and there is need to develop adaptation mechanisms. PMID:24014222

Taking the Good Life to the Institution: Forensic Service Users' Perceptions of the Good Lives Model.

PubMed

Barnao, Mary; Ward, Tony; Casey, Sharon

2016-05-01

Previous literature has highlighted a number of concerns about forensic care and rehabilitation by those who use the services. The Good Lives Model (GLM) is a strength-based, humanistic approach to offender rehabilitation that has been largely overlooked by forensic mental health practitioners. This study explored the impact of a brief GLM program on forensic service users' perceptions of rehabilitation, both within and beyond therapeutic programs, using a thematically linked, multiple-case study research design. Pre-post comparisons of participants' perceptions of rehabilitation suggested three different outcomes: definite change, subtle change, and no change. Possible factors associated with participants' divergent experiences included level of exposure to the GLM, readiness to change, and practitioners' adherence to the GLM and experience with the model. The importance of attending to the wider system for successful implementation of this innovative approach is highlighted. © The Author(s) 2015.

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

Evaluation of Verisyse and Artiflex phakic intraocular lenses during accommodation using Visante optical coherence tomography.

PubMed

Güell, José Luis; Morral, Merce; Gris, Oscar; Gaytan, Javier; Sisquella, Maite; Manero, Felicidad

2007-08-01

To perform a dynamic study of the relationship between Verisyse (AMO) and Artiflex (Ophtec B.V.) phakic intraocular lenses (pIOLs) and anterior chamber structures during accommodation using optical coherence tomography (OCT) (Visante, Carl Zeiss Meditec, Inc.) Institutional practice. Eleven myopic patients were randomly selected to have implantation of a Verisyse pIOL in 1 eye and an Artiflex pIOL in the other. Using a 2-dimensional image, dynamic measurements of the relationship between the anterior surface of the pIOL and the corneal endothelium, the posterior surface of the pIOL and the anterior surface of the crystalline lens, and the pupil diameter were performed using Visante OCT. Physiological accommodation was stimulated by adding lenses in 1.00 diopter (D) steps from +1.00 to -7.00 D. Both groups had a significant decrease in pupil diameter (P<.0001, generalized linear model [GLM]) and in the distance between the anterior surface of the pIOL and the corneal endothelium (P<.0001, GLM) with accommodation. There were no statistically significant changes in the distance between the posterior surface of either pIOL and the anterior surface of the crystalline lens (P = .2845, GLM). There were no statistically significant differences between the 2 pIOLs in any measurement (P>.05, GLM). The results fit with Helmholtz' theory of accommodation as forward movement of the diaphragm iris-crystalline lens was seen. There was a decrease in the distance between the pIOL and corneal endothelium and in the pupil diameter, whereas the distance between both pIOLs and the crystalline lens remained constant throughout the accommodation examination. This suggests that the risk for cataract from intermittent contact between the crystalline lens and IOL from accommodative effort is unlikely.

Sparse Spectro-Temporal Receptive Fields Based on Multi-Unit and High-Gamma Responses in Human Auditory Cortex

PubMed Central

Jenison, Rick L.; Reale, Richard A.; Armstrong, Amanda L.; Oya, Hiroyuki; Kawasaki, Hiroto; Howard, Matthew A.

2015-01-01

Spectro-Temporal Receptive Fields (STRFs) were estimated from both multi-unit sorted clusters and high-gamma power responses in human auditory cortex. Intracranial electrophysiological recordings were used to measure responses to a random chord sequence of Gammatone stimuli. Traditional methods for estimating STRFs from single-unit recordings, such as spike-triggered-averages, tend to be noisy and are less robust to other response signals such as local field potentials. We present an extension to recently advanced methods for estimating STRFs from generalized linear models (GLM). A new variant of regression using regularization that penalizes non-zero coefficients is described, which results in a sparse solution. The frequency-time structure of the STRF tends toward grouping in different areas of frequency-time and we demonstrate that group sparsity-inducing penalties applied to GLM estimates of STRFs reduces the background noise while preserving the complex internal structure. The contribution of local spiking activity to the high-gamma power signal was factored out of the STRF using the GLM method, and this contribution was significant in 85 percent of the cases. Although the GLM methods have been used to estimate STRFs in animals, this study examines the detailed structure directly from auditory cortex in the awake human brain. We used this approach to identify an abrupt change in the best frequency of estimated STRFs along posteromedial-to-anterolateral recording locations along the long axis of Heschl’s gyrus. This change correlates well with a proposed transition from core to non-core auditory fields previously identified using the temporal response properties of Heschl’s gyrus recordings elicited by click-train stimuli. PMID:26367010

Intersubject synchronisation analysis of brain activity associated with the instant effects of acupuncture: an fMRI study.

PubMed

Jin, Lingmin; Sun, Jinbo; Xu, Ziliang; Yang, Xuejuan; Liu, Peng; Qin, Wei

2018-02-01

To use a promising analytical method, namely intersubject synchronisation (ISS), to evaluate the brain activity associated with the instant effects of acupuncture and compare the findings with traditional general linear model (GLM) methods. 30 healthy volunteers were recruited for this study. Block-designed manual acupuncture stimuli were delivered at SP6, and de qi sensations were measured after acupuncture stimulation. All subjects underwent functional MRI (fMRI) scanning during the acupuncture stimuli. The fMRI data were separately analysed by ISS and traditional GLM methods. All subjects experienced de qi sensations. ISS analysis showed that the regions activated during acupuncture stimulation at SP6 were mainly divided into five clusters based on the time courses. The time courses of clusters 1 and 2 were in line with the acupuncture stimulation pattern, and the active regions were mainly involved in the sensorimotor system and salience network. Clusters 3, 4 and 5 displayed an almost contrary time course relative to the stimulation pattern. The brain regions activated included the default mode network, descending pain modulation pathway and visual cortices. GLM analysis indicated that the brain responses associated with the instant effects of acupuncture were largely implicated in sensory and motor processing and sensory integration. The ISS analysis considered the sustained effect of acupuncture and uncovered additional information not shown by GLM analysis. We suggest that ISS may be a suitable approach to investigate the brain responses associated with the instant effects of acupuncture. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Prediction of cardiovascular disease risk among low-income urban dwellers in metropolitan Kuala Lumpur, Malaysia.

PubMed

Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Bulgiba, Awang; Majid, Hazreen Abdul

2015-01-01

We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS model (figures in parentheses are from the modified model) revealed that 20.5% (21.8%) and 38.46% (38.9%) of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers.

Prediction of Cardiovascular Disease Risk among Low-Income Urban Dwellers in Metropolitan Kuala Lumpur, Malaysia

PubMed Central

Su, Tin Tin; Amiri, Mohammadreza; Mohd Hairi, Farizah; Thangiah, Nithiah; Majid, Hazreen Abdul

2015-01-01

We aimed to predict the ten-year cardiovascular disease (CVD) risk among low-income urban dwellers of metropolitan Malaysia. Participants were selected from a cross-sectional survey conducted in Kuala Lumpur. To assess the 10-year CVD risk, we employed the Framingham risk scoring (FRS) models. Significant determinants of the ten-year CVD risk were identified using General Linear Model (GLM). Altogether 882 adults (≥30 years old with no CVD history) were randomly selected. The classic FRS model (figures in parentheses are from the modified model) revealed that 20.5% (21.8%) and 38.46% (38.9%) of respondents were at high and moderate risk of CVD. The GLM models identified the importance of education, occupation, and marital status in predicting the future CVD risk. Our study indicated that one out of five low-income urban dwellers has high chance of having CVD within ten years. Health care expenditure, other illness related costs and loss of productivity due to CVD would worsen the current situation of low-income urban population. As such, the public health professionals and policy makers should establish substantial effort to formulate the public health policy and community-based intervention to minimize the upcoming possible high mortality and morbidity due to CVD among the low-income urban dwellers. PMID:25821810

Height of Northern Jordanian middle-class adults, born 1960-1990 in the response to improving socio-economic conditions.

PubMed

Abu Dalou, Ahmad Yosuf

2016-09-01

The purpose of this study is to document and explain secular trends in stature among Northern Jordanian men and women between the years of birth 1960 and 1990, as they relate to overall per capita socio-economic improvement, the stature of 360 adults from two Northern governorates, those of Jerash and Irbid, was measured. General linear model (GLM) was used to examine the effect of birth-decade, education level of subject, and their interaction on mean stature of each sex separately. GLM results revealed that women who were born during the following three decades pooled together (1951-1980) did not differ significantly in mean stature from those born during (1981-1990). Among men, stature of those born in the two pooled birth-decades together (1951-1970) did not significantly differ of those were born in the two pooled birth-decades (1971-1990). Copyright © 2016. Published by Elsevier B.V.

A new analytical method for characterizing nonlinear visual processes with stimuli of arbitrary distribution: Theory and applications.

PubMed

Hayashi, Ryusuke; Watanabe, Osamu; Yokoyama, Hiroki; Nishida, Shin'ya

2017-06-01

Characterization of the functional relationship between sensory inputs and neuronal or observers' perceptual responses is one of the fundamental goals of systems neuroscience and psychophysics. Conventional methods, such as reverse correlation and spike-triggered data analyses are limited in their ability to resolve complex and inherently nonlinear neuronal/perceptual processes because these methods require input stimuli to be Gaussian with a zero mean. Recent studies have shown that analyses based on a generalized linear model (GLM) do not require such specific input characteristics and have advantages over conventional methods. GLM, however, relies on iterative optimization algorithms and its calculation costs become very expensive when estimating the nonlinear parameters of a large-scale system using large volumes of data. In this paper, we introduce a new analytical method for identifying a nonlinear system without relying on iterative calculations and yet also not requiring any specific stimulus distribution. We demonstrate the results of numerical simulations, showing that our noniterative method is as accurate as GLM in estimating nonlinear parameters in many cases and outperforms conventional, spike-triggered data analyses. As an example of the application of our method to actual psychophysical data, we investigated how different spatiotemporal frequency channels interact in assessments of motion direction. The nonlinear interaction estimated by our method was consistent with findings from previous vision studies and supports the validity of our method for nonlinear system identification.

Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

A note about high blood pressure in childhood

NASA Astrophysics Data System (ADS)

Teodoro, M. Filomena; Simão, Carla

2017-06-01

In medical, behavioral and social sciences it is usual to get a binary outcome. In the present work is collected information where some of the outcomes are binary variables (1='yes'/ 0='no'). In [14] a preliminary study about the caregivers perception of pediatric hypertension was introduced. An experimental questionnaire was designed to be answered by the caregivers of routine pediatric consultation attendees in the Santa Maria's hospital (HSM). The collected data was statistically analyzed, where a descriptive analysis and a predictive model were performed. Significant relations between some socio-demographic variables and the assessed knowledge were obtained. In [14] can be found a statistical data analysis using partial questionnaire's information. The present article completes the statistical approach estimating a model for relevant remaining questions of questionnaire by Generalized Linear Models (GLM). Exploring the binary outcome issue, we intend to extend this approach using Generalized Linear Mixed Models (GLMM), but the process is still ongoing.

Pre-Launch GOES-R Risk Reduction Activities for the Geostationary Lightning Mapper

NASA Technical Reports Server (NTRS)

Goodman, S. J.; Blakeslee, R. J.; Boccippio, D. J.; Christian, H. J.; Koshak, W. J.; Petersen, W. A.

2005-01-01

The GOES-R Geostationary Lightning Mapper (GLM) is a new instrument planned for GOES-R that will greatly improve storm hazard nowcasting and increase warning lead time day and night. Daytime detection of lightning is a particularly significant technological advance given the fact that the solar illuminated cloud-top signal can exceed the intensity of the lightning signal by a factor of one hundred. Our approach is detailed across three broad themes which include: Data Processing Algorithm Readiness, Forecast Applications, and Radiance Data Mining. These themes address how the data will be processed and distributed, and the algorithms and models for developing, producing, and using the data products. These pre-launch risk reduction activities will accelerate the operational and research use of the GLM data once GOES-R begins on-orbit operations. The GLM will provide unprecedented capabilities for tracking thunderstorms and earlier warning of impending severe and hazardous weather threats. By providing direct information on lightning initiation, propagation, extent, and rate, the GLM will also capture the updraft dynamics and life cycle of convective storms, as well as internal ice precipitation processes. The GLM provides information directly from the heart of the thunderstorm as opposed to cloud-top only. Nowcasting applications enabled by the GLM data will expedite the warning and response time of emergency management systems, improve the dispatch of electric power utility repair crews, and improve airline routing around thunderstorms thereby improving safety and efficiency, saving fuel and reducing delays. The use of GLM data will assist the Bureau of Land Management (BLM) and the Forest Service in quickly detecting lightning ground strikes that have a high probability of causing fires. Finally, GLM data will help assess the role of thunderstorms and deep convection in global climate, and will improve regional air quality and global chemistry/climate modeling. The GLM has a robust design that benefits and improves upon its strong heritage of NASA-developed LEO predecessors, the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS). GLM will have a substantially larger number of pixels within the focal plane, two lens systems, and multiple Real-Time Event Processors REPS for on-board event detection and data compression to provide continuous observations of the Americas and adjacent oceans.

Extreme deconstruction supports niche conservatism driving New World bird diversity

NASA Astrophysics Data System (ADS)

Diniz-Filho, José Alexandre Felizola; Rangel, Thiago Fernando; dos Santos, Mariana Rocha

2012-08-01

It is expected that if environment fully establishes the borders of species geographic distribution, then richness patterns will arise simple by changing parameters on how environment affect each of the species. However, if other mechanisms (i.e., non-equilibrium of species' distributions with climate and historical contingency, shifts in adaptive peaks or biotic interactions) are driving species geographic distribution, models for species distribution and richness will not entirely match. Here we used the extreme deconstruction principle to test how niche conservatism keeping species geographic distributions in certain parts of environmental space drives richness patterns in New World birds, under tropical niche conservatism. Eight environmental variables were used to model the geographic distribution of 2790 species within 28 bird families using a GLM. Spatial patterns in richness for each of these families were also modeled as a function of these same variables using a standard OLS regression. Fit of these two types of models (mean MacFadden's ρ2 for GLM and R2 of OLS) across families and the match between GLM and OLS standardized slopes within and among bird families were then compared. We found a positive and significant correlation between GLM and OLS model fit (r = 0.601; P < 0.01), indicating that when environment strongly determine richness of a family, it also explains its species geographic distributions. The match between GLM and OLS slopes is significantly correlated with families' phylogenetic root distance (r = -0.467; P = 0.012), so that more basal families tend to have a better match between environmental drivers of richness and geographic distribution models. This is expected under tropical niche conservatism model and provides an integrated explanation on how processes at a lower hierarchical level (species' geographic distribution) drive diversity patterns.

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Portfolio Decisions and Brain Reactions via the CEAD method.

PubMed

Majer, Piotr; Mohr, Peter N C; Heekeren, Hauke R; Härdle, Wolfgang K

2016-09-01

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

Structural and functional features of enzymes of Mycobacterium tuberculosis peptidoglycan biosynthesis as targets for drug development

PubMed Central

Moraes, Gleiciane Leal; Gomes, Guelber Cardoso; de Sousa, Paulo Robson Monteiro; Alves, Cláudio Nahum; Govender, Thavendran; Kruger, Hendrik G.; Maguire, Glenn E.M.; Lamichhane, Gyanu; Lameira, Jerônimo

2015-01-01

SUMMARY Tuberculosis (TB) is the second leading cause of human mortality from infectious diseases worldwide. The WHO reported 1.3 million deaths and 8.6 million new cases of TB in 2012. Mycobacterium tuberculosis (M. tuberculosis), the infectious bacteria that causes TB, is encapsulated by a thick and robust cell wall. The innermost segment of the cell wall is comprised of peptidoglycan, a layer that is required for survival and growth of the pathogen. Enzymes that catalyse biosynthesis of the peptidoglycan are essential and are therefore attractive targets for discovery of novel antibiotics as humans lack similar enzymes making it possible to selectively target bacteria only. In this paper, we have reviewed the structures and functions of enzymes GlmS, GlmM, GlmU, MurA, MurB, MurC, MurD, MurE and MurF from M. tuberculosis that are involved in peptidoglycan biosynthesis. In addition, we report homology modelled 3D structures of those key enzymes from M. tuberculosis of which the structures are still unknown. We demonstrated that natural substrates can be successfully docked into the active sites of the GlmS and GlmU respectively. It is therefore expected that the models and the data provided herein will facilitate translational research to develop new drugs to treat TB. PMID:25701501

Common and divergent features in transcriptional control of the homologous small RNAs GlmY and GlmZ in Enterobacteriaceae

PubMed Central

Göpel, Yvonne; Lüttmann, Denise; Heroven, Ann Kathrin; Reichenbach, Birte; Dersch, Petra; Görke, Boris

2011-01-01

Small RNAs GlmY and GlmZ compose a cascade that feedback-regulates synthesis of enzyme GlmS in Enterobacteriaceae. Here, we analyzed the transcriptional regulation of glmY/glmZ from Yersinia pseudotuberculosis, Salmonella typhimurium and Escherichia coli, as representatives for other enterobacterial species, which exhibit similar promoter architectures. The GlmY and GlmZ sRNAs of Y. pseudotuberculosis are transcribed from σ54-promoters that require activation by the response regulator GlrR through binding to three conserved sites located upstream of the promoters. This also applies to glmY/glmZ of S. typhimurium and glmY of E. coli, but as a difference additional σ70-promoters overlap the σ54-promoters and initiate transcription at the same site. In contrast, E. coli glmZ is transcribed from a single σ70-promoter. Thus, transcription of glmY and glmZ is controlled by σ54 and the two-component system GlrR/GlrK (QseF/QseE) in Y. pseudotuberculosis and presumably in many other Enterobacteria. However, in a subset of species such as E. coli this relationship is partially lost in favor of σ70-dependent transcription. In addition, we show that activity of the σ54-promoter of E. coli glmY requires binding of the integration host factor to sites upstream of the promoter. Finally, evidence is provided that phosphorylation of GlrR increases its activity and thereby sRNA expression. PMID:20965974

The Geostationary Lightning Mapper: Its Performance and Calibration

NASA Astrophysics Data System (ADS)

Christian, H. J., Jr.

2015-12-01

The Geostationary Lightning Mapper (GLM) has been developed to be an operational instrument on the GOES-R series of spacecraft. The GLM is a unique instrument, unlike other meteorological instruments, both in how it operates and in the information content that it provides. Instrumentally, it is an event detector, rather than an imager. While processing almost a billion pixels per second with 14 bits of resolution, the event detection process reduces the required telemetry bandwidth by almost 105, thus keeping the telemetry requirements modest and enabling efficient ground processing that leads to rapid data distribution to operational users. The GLM was designed to detect about 90 percent of the total lightning flashes within its almost hemispherical field of view. Based on laboratory calibration, we expect the on-orbit detection efficiency to be closer to 85%, making it the highest performing, large area coverage total lightning detector. It has a number of unique design features that will enable it have near uniform special resolution over most of its field of view and to operate with minimal impact on performance during solar eclipses. The GLM has no dedicated on-orbit calibration system, thus the ground-based calibration provides the bases for the predicted radiometric performance. A number of problems were encountered during the calibration of Flight Model 1. The issues arouse from GLM design features including its wide field of view, fast lens, the narrow-band interference filters located in both object and collimated space and the fact that the GLM is inherently a event detector yet the calibration procedures required both calibration of images and events. The GLM calibration techniques were based on those developed for the Lightning Imaging Sensor calibration, but there are enough differences between the sensors that the initial GLM calibration suggested that it is significantly more sensitive than its design parameters. The calibration discrepancies have been resolved and will be discussed. Absolute calibration will be verified on-orbit using vicarious cloud reflections. In addition to details of the GLM calibration, the presentation will address the unique design of the GLM, its features, capabilities and performance.

Effect of climate on incidence of respiratory syncytial virus infections in a refugee camp in Kenya: A non-Gaussian time-series analysis

PubMed Central

Omony, Jimmy; Mwalili, Samuel M.; Achia, Thomas N. O.; Gichangi, Anthony; Mwambi, Henry

2017-01-01

Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infections (ALRTI) in children. Children younger than 1 year are the most susceptible to RSV infection. RSV infections occur seasonally in temperate climate regions. Based on RSV surveillance and climatic data, we developed statistical models that were assessed and compared to predict the relationship between weather and RSV incidence among refugee children younger than 5 years in Dadaab refugee camp in Kenya. Most time-series analyses rely on the assumption of Gaussian-distributed data. However, surveillance data often do not have a Gaussian distribution. We used a generalized linear model (GLM) with a sinusoidal component over time to account for seasonal variation and extended it to a generalized additive model (GAM) with smoothing cubic splines. Climatic factors were included as covariates in the models before and after timescale decompositions, and the results were compared. Models with decomposed covariates fit RSV incidence data better than those without. The Poisson GAM with decomposed covariates of climatic factors fit the data well and had a higher explanatory and predictive power than GLM. The best model predicted the relationship between atmospheric conditions and RSV infection incidence among children younger than 5 years. This knowledge helps public health officials to prepare for, and respond more effectively to increasing RSV incidence in low-resource regions or communities. PMID:28570627

Model-based estimation of breast percent density in raw and processed full-field digital mammography images from image-acquisition physics and patient-image characteristics

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Nathan, Diane L.; Conant, Emily F.; Kontos, Despina

2012-03-01

Breast percent density (PD%), as measured mammographically, is one of the strongest known risk factors for breast cancer. While the majority of studies to date have focused on PD% assessment from digitized film mammograms, digital mammography (DM) is becoming increasingly common, and allows for direct PD% assessment at the time of imaging. This work investigates the accuracy of a generalized linear model-based (GLM) estimation of PD% from raw and postprocessed digital mammograms, utilizing image acquisition physics, patient characteristics and gray-level intensity features of the specific image. The model is trained in a leave-one-woman-out fashion on a series of 81 cases for which bilateral, mediolateral-oblique DM images were available in both raw and post-processed format. Baseline continuous and categorical density estimates were provided by a trained breast-imaging radiologist. Regression analysis is performed and Pearson's correlation, r, and Cohen's kappa, κ, are computed. The GLM PD% estimation model performed well on both processed (r=0.89, p<0.001) and raw (r=0.75, p<0.001) images. Model agreement with radiologist assigned density categories was also high for processed (κ=0.79, p<0.001) and raw (κ=0.76, p<0.001) images. Model-based prediction of breast PD% could allow for a reproducible estimation of breast density, providing a rapid risk assessment tool for clinical practice.

The Development of Web-based Graphical User Interface for Unified Modeling Data with Multi (Correlated) Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian

2018-04-01

Statistical models have been developed rapidly into various directions to accommodate various types of data. Data collected from longitudinal, repeated measured, clustered data (either continuous, binary, count, or ordinal), are more likely to be correlated. Therefore statistical model for independent responses, such as Generalized Linear Model (GLM), Generalized Additive Model (GAM) are not appropriate. There are several models available to apply for correlated responses including GEEs (Generalized Estimating Equations), for marginal model and various mixed effect model such as GLMM (Generalized Linear Mixed Models) and HGLM (Hierarchical Generalized Linear Models) for subject spesific models. These models are available on free open source software R, but they can only be accessed through command line interface (using scrit). On the othe hand, most practical researchers very much rely on menu based or Graphical User Interface (GUI). We develop, using Shiny framework, standard pull down menu Web-GUI that unifies most models for correlated responses. The Web-GUI has accomodated almost all needed features. It enables users to do and compare various modeling for repeated measure data (GEE, GLMM, HGLM, GEE for nominal responses) much more easily trough online menus. This paper discusses the features of the Web-GUI and illustrates the use of them. In General we find that GEE, GLMM, HGLM gave very closed results.

Tidal Energy: The benthic effects of an operational tidal stream turbine.

PubMed

O'Carroll, J P J; Kennedy, R M; Creech, A; Savidge, G

2017-08-01

The effect of modified flow on epifaunal boulder reef communities adjacent to the SeaGen, the world's first grid-compliant tidal stream turbine, were assessed. The wake of the SeaGen was modelled and the outputs were used in conjunction with positional and substrate descriptor variables, to relate variation in epifaunal community structure to the modified physical environment. An Artificial Neural Network (ANN) and Generalised Linear Model (GLM) were used to make predictions on the distribution of Ecological Status (ES) of epifaunal communities in relation to the turbulent wake of the SeaGen. ES was assigned using the High Energy Hard Substrate (HEHS) index. ES was largely High throughout the survey area and it was not possible to make predictions on the spatial distribution of ES using an ANN or GLM. Spatial pattern in epifaunal community structure was detected when the study area was partitioned into three treatment areas: area D1; within one rotor diameter (16 m) of the centre of SeaGen, area D2; between one and three rotor diameters, and area D3; outside of three rotor diameters. Area D1 was found to be significantly more variable than D2 and D3 in terms of epifaunal community structure, bare rock distributions and ES. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structural and functional features of enzymes of Mycobacterium tuberculosis peptidoglycan biosynthesis as targets for drug development.

PubMed

Moraes, Gleiciane Leal; Gomes, Guelber Cardoso; Monteiro de Sousa, Paulo Robson; Alves, Cláudio Nahum; Govender, Thavendran; Kruger, Hendrik G; Maguire, Glenn E M; Lamichhane, Gyanu; Lameira, Jerônimo

2015-03-01

Tuberculosis (TB) is the second leading cause of human mortality from infectious diseases worldwide. The WHO reported 1.3 million deaths and 8.6 million new cases of TB in 2012. Mycobacterium tuberculosis (M. tuberculosis), the infectious bacteria that causes TB, is encapsulated by a thick and robust cell wall. The innermost segment of the cell wall is comprised of peptidoglycan, a layer that is required for survival and growth of the pathogen. Enzymes that catalyse biosynthesis of the peptidoglycan are essential and are therefore attractive targets for discovery of novel antibiotics as humans lack similar enzymes making it possible to selectively target bacteria only. In this paper, we have reviewed the structures and functions of enzymes GlmS, GlmM, GlmU, MurA, MurB, MurC, MurD, MurE and MurF from M. tuberculosis that are involved in peptidoglycan biosynthesis. In addition, we report homology modelled 3D structures of those key enzymes from M. tuberculosis of which the structures are still unknown. We demonstrated that natural substrates can be successfully docked into the active sites of the GlmS and GlmU respectively. It is therefore expected that the models and the data provided herein will facilitate translational research to develop new drugs to treat TB. Copyright © 2015. Published by Elsevier Ltd.

Coupling of Action-Perception Brain Networks during Musical Pulse Processing: Evidence from Region-of-Interest-Based Independent Component Analysis.

PubMed

Burunat, Iballa; Tsatsishvili, Valeri; Brattico, Elvira; Toiviainen, Petri

2017-01-01

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model (GLM) approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach (independent component analysis; ICA). Participants' (18 musicians and 18 controls) functional magnetic resonance imaging (fMRI) responses were acquired while listening to music. A targeted region of interest (ROI) related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICA decomposition was performed under different model orders, i.e., under a varying number of assumed independent sources, to avoid relying on prior model order assumptions. The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified. Their corresponding spatial maps uncovered a network of auditory (perception) and motor (action) areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders. Results revealed (a) a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and (b) group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing.

Generalized structural equations improve sexual-selection analyses

PubMed Central

Santini, Giacomo; Marchetti, Giovanni Maria; Focardi, Stefano

2017-01-01

Sexual selection is an intense evolutionary force, which operates through competition for the access to breeding resources. There are many cases where male copulatory success is highly asymmetric, and few males are able to sire most females. Two main hypotheses were proposed to explain this asymmetry: “female choice” and “male dominance”. The literature reports contrasting results. This variability may reflect actual differences among studied populations, but it may also be generated by methodological differences and statistical shortcomings in data analysis. A review of the statistical methods used so far in lek studies, shows a prevalence of Linear Models (LM) and Generalized Linear Models (GLM) which may be affected by problems in inferring cause-effect relationships; multi-collinearity among explanatory variables and erroneous handling of non-normal and non-continuous distributions of the response variable. In lek breeding, selective pressure is maximal, because large numbers of males and females congregate in small arenas. We used a dataset on lekking fallow deer (Dama dama), to contrast the methods and procedures employed so far, and we propose a novel approach based on Generalized Structural Equations Models (GSEMs). GSEMs combine the power and flexibility of both SEM and GLM in a unified modeling framework. We showed that LMs fail to identify several important predictors of male copulatory success and yields very imprecise parameter estimates. Minor variations in data transformation yield wide changes in results and the method appears unreliable. GLMs improved the analysis, but GSEMs provided better results, because the use of latent variables decreases the impact of measurement errors. Using GSEMs, we were able to test contrasting hypotheses and calculate both direct and indirect effects, and we reached a high precision of the estimates, which implies a high predictive ability. In synthesis, we recommend the use of GSEMs in studies on lekking behaviour, and we provide guidelines to implement these models. PMID:28809923

Targeted decay of a regulatory small RNA by an adaptor protein for RNase E and counteraction by an anti-adaptor RNA

PubMed Central

Göpel, Yvonne; Papenfort, Kai; Reichenbach, Birte; Vogel, Jörg; Görke, Boris

2013-01-01

Bacterial small RNAs (sRNAs) are well established to regulate diverse cellular processes, but how they themselves are regulated is less understood. Recently, we identified a regulatory circuit wherein the GlmY and GlmZ sRNAs of Escherichia coli act hierarchically to activate mRNA glmS, which encodes glucosamine-6-phosphate (GlcN6P) synthase. Although the two sRNAs are highly similar, only GlmZ is a direct activator that base-pairs with the glmS mRNA, aided by protein Hfq. GlmY, however, does not bind Hfq and activates glmS indirectly by protecting GlmZ from RNA cleavage. This complex regulation feedback controls the levels of GlmS protein in response to its product, GlcN6P, a key metabolite in cell wall biosynthesis. Here, we reveal the molecular basis for the regulated turnover of GlmZ, identifying RapZ (RNase adaptor protein for sRNA GlmZ; formerly YhbJ) as a novel type of RNA-binding protein that recruits the major endoribonuclease RNase E to GlmZ. This involves direct interaction of RapZ with the catalytic domain of RNase E. GlmY binds RapZ through a secondary structure shared by both sRNAs and therefore acts by molecular mimicry as a specific decoy for RapZ. Thus, in analogy to regulated proteolysis, RapZ is an adaptor, and GlmY is an anti-adaptor in regulated turnover of a regulatory small RNA. PMID:23475961

LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs

PubMed Central

Äijö, Tarmo; Yue, Xiaojing; Rao, Anjana; Lähdesmäki, Harri

2016-01-01

Motivation: 5-methylcytosine (5mC) is a widely studied epigenetic modification of DNA. The ten-eleven translocation (TET) dioxygenases oxidize 5mC into oxidized methylcytosines (oxi-mCs): 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). DNA methylation modifications have multiple functions. For example, 5mC is shown to be associated with diseases and oxi-mC species are reported to have a role in active DNA demethylation through 5mC oxidation and DNA repair, among others, but the detailed mechanisms are poorly understood. Bisulphite sequencing and its various derivatives can be used to gain information about all methylation modifications at single nucleotide resolution. Analysis of bisulphite based sequencing data is complicated due to the convoluted read-outs and experiment-specific variation in biochemistry. Moreover, statistical analysis is often complicated by various confounding effects. How to analyse 5mC and oxi-mC data sets with arbitrary and complex experimental designs is an open and important problem. Results: We propose the first method to quantify oxi-mC species with arbitrary covariate structures from bisulphite based sequencing data. Our probabilistic modeling framework combines a previously proposed hierarchical generative model for oxi-mC-seq data and a general linear model component to account for confounding effects. We show that our method provides accurate methylation level estimates and accurate detection of differential methylation when compared with existing methods. Analysis of novel and published data gave insights into to the demethylation of the forkhead box P3 (Foxp3) locus during the induced T regulatory cell differentiation. We also demonstrate how our covariate model accurately predicts methylation levels of the Foxp3 locus. Collectively, LuxGLM method improves the analysis of DNA methylation modifications, particularly for oxi-mC species. Availability and Implementation: An implementation of the proposed method is available under MIT license at https://github.org/tare/LuxGLM/ Contact: taijo@simonsfoundation.org or harri.lahdesmaki@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27587669

Regulatory insights into the production of UDP-N-acetylglucosamine by Lactobacillus casei

PubMed Central

Rodríguez-Díaz, Jesús; Rubio-del-Campo, Antonio; Yebra, María J.

2012-01-01

UDP-N-acetylglucosamine (UDP-GlcNAc) is an important sugar nucleotide used as a precursor of cell wall components in bacteria, and as a substrate in the synthesis of oligosaccharides in eukaryotes. In bacteria UDP-GlcNAc is synthesized from the glycolytic intermediate D-fructose-6-phosphate (fructose-6P) by four successive reactions catalyzed by three enzymes: glucosamine-6-phosphate synthase (GlmS), phosphoglucosamine mutase (GlmM) and the bi-functional enzyme glucosamine-1-phosphate acetyltransferase/ N-acetylglucosamine-1-phosphate uridyltransferase (GlmU). We have previously reported a metabolic engineering strategy in Lactobacillus casei directed to increase the intracellular levels of UDP-GlcNAc by homologous overexpression of the genes glmS, glmM and glmU. One of the most remarkable features regarding the production of UDP-GlcNAc in L. casei was to find multiple regulation points on its biosynthetic pathway: (1) regulation by the NagB enzyme, (2) glmS RNA specific degradation through the possible participation of a glmS riboswitch mechanism, (3) regulation of the GlmU activity probably by end product inhibition and (4) transcription of glmU. PMID:22825354

Uncertainty Estimates of Psychoacoustic Thresholds Obtained from Group Tests

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Christian, Andrew

2016-01-01

Adaptive psychoacoustic test methods, in which the next signal level depends on the response to the previous signal, are the most efficient for determining psychoacoustic thresholds of individual subjects. In many tests conducted in the NASA psychoacoustic labs, the goal is to determine thresholds representative of the general population. To do this economically, non-adaptive testing methods are used in which three or four subjects are tested at the same time with predetermined signal levels. This approach requires us to identify techniques for assessing the uncertainty in resulting group-average psychoacoustic thresholds. In this presentation we examine the Delta Method of frequentist statistics, the Generalized Linear Model (GLM), the Nonparametric Bootstrap, a frequentist method, and Markov Chain Monte Carlo Posterior Estimation and a Bayesian approach. Each technique is exercised on a manufactured, theoretical dataset and then on datasets from two psychoacoustics facilities at NASA. The Delta Method is the simplest to implement and accurate for the cases studied. The GLM is found to be the least robust, and the Bootstrap takes the longest to calculate. The Bayesian Posterior Estimate is the most versatile technique examined because it allows the inclusion of prior information.

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

PubMed Central

Wijeakumar, Sobanawartiny; Ambrose, Joseph P.; Spencer, John P.; Curtu, Rodica

2017-01-01

A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus-response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior. PMID:29118459

Investigating the Use of Deep Convective Clouds (DCCT) to Monitor On-orbit Performance of the Geostationary Lightning Mapper (GLM) using Lightning Imaging Sensor (LIS) Measurements

NASA Technical Reports Server (NTRS)

Buechler, Dennis E.; Christian, Hugh J.; Koshak, William J.; Goodman, Steven J.

2013-01-01

There is a need to monitor the on-orbit performance of the Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite R (GOES-R) for changes in instrument calibration that will affect GLM's lightning detection efficiency. GLM has no onboard calibration so GLM background radiance observations (available every 2.5 min) of Deep Convective Clouds (DCCs) are investigated as invariant targets to monitor GLM performance. Observations from the Lightning Imaging Sensor (LIS) and the Visible and Infrared Scanner (VIRS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite are used as proxy datasets for GLM and ABI 11 m measurements.

Predicting Deforestation Patterns in Loreto, Peru from 2000-2010 Using a Nested GLM Approach

NASA Astrophysics Data System (ADS)

Vijay, V.; Jenkins, C.; Finer, M.; Pimm, S.

2013-12-01

Loreto is the largest province in Peru, covering about 370,000 km2. Because of its remote location in the Amazonian rainforest, it is also one of the most sparsely populated. Though a majority of the region remains covered by forest, deforestation is being driven by human encroachment through industrial activities and the spread of colonization and agriculture. The importance of accurate predictive modeling of deforestation has spawned an extensive body of literature on the topic. We present a nested GLM approach based on predictions of deforestation from 2000-2010 and using variables representing the expected drivers of deforestation. Models were constructed using 2000 to 2005 changes and tested against data for 2005 to 2010. The most complex model, which included transportation variables (roads and navigable rivers), spatial contagion processes, population centers and industrial activities, performed better in predicting the 2005 to 2010 changes (75.8% accurate) than did a simpler model using only transportation variables (69.2% accurate). Finally we contrast the GLM approach with a more complex spatially articulated model.

The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Buechler, D.; Carey, L.; Chronis, T.; Mach, D.; Bateman, M.; Peterson, H.; McCaul, E. W., Jr.;

Effects of Gymnema sylvestre extract on the pharmacokinetics and pharmacodynamics of glimepiride in streptozotocin induced diabetic rats.

PubMed

Kamble, Bhagyashree; Gupta, Ankur; Moothedath, Ismail; Khatal, Laxman; Janrao, Shirish; Jadhav, Amol; Duraiswamy, B

2016-02-05

Gymnema sylvestre, important Indian traditional herbal medicine has been used for diabetes from several years and marketed as single or multi-herb formulations globally. People are consuming G. sylvestre along with conventional hypoglycemic drugs. Therefore, there is need of evidence based assessment of risk versus benefits when G. sylvestre co-administered with conventional oral hypoglycemic drugs. In present investigation, pharmacodynamics and pharmacokinetic interactions with oral hypoglycemic drug, glimepiride (GLM) was studied in streptozotocin (STZ) induced diabetic rats. A specific and rapid HPLC-ESI-MS/MS method was established for simultaneous quantification of GLM and gymnemagenin (GMG) in rat plasma. Pharmacokinetic and pharmacodynamic interaction studies were carried out in STZ induced diabetic rats after concomitant administration of 400 mg/kg of G. sylvestre extract and 0.8 mg/kg of GLM for 28 days. The developed HPLC-ESI-MS/MS method was rapid, specific, and precise. Con-comitant oral administration of G. sylvestre extract (400 mg/kg) and GLM (0.8 mg/kg) in diabetic rats for 28 days showed beneficial pharmacodynamic interactions whereas no major alterations in the pharmacokinetics parameters of GLM and GMG were observed. This interaction demonstrated in animal model implies that significant clinical outcome might occur during concomitant administration of G. sylvestre extract and GLM especially in diabetic patients and warrants further studies in the same set up. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Comparative shotgun proteomics using spectral count data and quasi-likelihood modeling.

PubMed

Li, Ming; Gray, William; Zhang, Haixia; Chung, Christine H; Billheimer, Dean; Yarbrough, Wendell G; Liebler, Daniel C; Shyr, Yu; Slebos, Robbert J C

2010-08-06

Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher's Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples.

Comparative Shotgun Proteomics Using Spectral Count Data and Quasi-Likelihood Modeling

PubMed Central

2010-01-01

Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography−tandem mass spectrometry (LC−MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher’s Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography−multiple reaction monitoring mass spectrometry (LC−MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples. PMID:20586475

Stereoscopic processing of crossed and uncrossed disparities in the human visual cortex.

PubMed

Li, Yuan; Zhang, Chuncheng; Hou, Chunping; Yao, Li; Zhang, Jiacai; Long, Zhiying

2017-12-21

Binocular disparity provides a powerful cue for depth perception in a stereoscopic environment. Despite increasing knowledge of the cortical areas that process disparity from neuroimaging studies, the neural mechanism underlying disparity sign processing [crossed disparity (CD)/uncrossed disparity (UD)] is still poorly understood. In the present study, functional magnetic resonance imaging (fMRI) was used to explore different neural features that are relevant to disparity-sign processing. We performed an fMRI experiment on 27 right-handed healthy human volunteers by using both general linear model (GLM) and multi-voxel pattern analysis (MVPA) methods. First, GLM was used to determine the cortical areas that displayed different responses to different disparity signs. Second, MVPA was used to determine how the cortical areas discriminate different disparity signs. The GLM analysis results indicated that shapes with UD induced significantly stronger activity in the sub-region (LO) of the lateral occipital cortex (LOC) than those with CD. The results of MVPA based on region of interest indicated that areas V3d and V3A displayed higher accuracy in the discrimination of crossed and uncrossed disparities than LOC. The results of searchlight-based MVPA indicated that the dorsal visual cortex showed significantly higher prediction accuracy than the ventral visual cortex and the sub-region LO of LOC showed high accuracy in the discrimination of crossed and uncrossed disparities. The results may suggest the dorsal visual areas are more discriminative to the disparity signs than the ventral visual areas although they are not sensitive to the disparity sign processing. Moreover, the LO in the ventral visual cortex is relevant to the recognition of shapes with different disparity signs and discriminative to the disparity sign.

Women Build Long Bones With Less Cortical Mass Relative to Body Size and Bone Size Compared With Men.

PubMed

Jepsen, Karl J; Bigelow, Erin M R; Schlecht, Stephen H

2015-08-01

The twofold greater lifetime risk of fracturing a bone for white women compared with white men and black women has been attributed in part to differences in how the skeletal system accumulates bone mass during growth. On average, women build more slender long bones with less cortical area compared with men. Although slender bones are known to have a naturally lower cortical area compared with wider bones, it remains unclear whether the relatively lower cortical area of women is consistent with their increased slenderness or is reduced beyond that expected for the sex-specific differences in bone size and body size. Whether this sexual dimorphism is consistent with ethnic background and is recapitulated in the widely used mouse model also remains unclear. We asked (1) do black women build bones with reduced cortical area compared with black men; (2) do white women build bones with reduced cortical area compared with white men; and (3) do female mice build bones with reduced cortical area compared with male mice? Bone strength and cross-sectional morphology of adult human and mouse bone were calculated from quantitative CT images of the femoral midshaft. The data were tested for normality and regression analyses were used to test for differences in cortical area between men and women after adjusting for body size and bone size by general linear model (GLM). Linear regression analysis showed that the femurs of black women had 11% lower cortical area compared with those of black men after adjusting for body size and bone size (women: mean=357.7 mm2; 95% confidence interval [CI], 347.9-367.5 mm2; men: mean=400.1 mm2; 95% CI, 391.5-408.7 mm2; effect size=1.2; p<0.001, GLM). Likewise, the femurs of white women had 12% less cortical area compared with those of white men after adjusting for body size and bone size (women: mean=350.1 mm2; 95% CI, 340.4-359.8 mm2; men: mean=394.3 mm2; 95% CI, 386.5-402.1 mm2; effect size=1.3; p<0.001, GLM). In contrast, female and male femora from recombinant inbred mouse strains showed the opposite trend; femurs from female mice had a 4% larger cortical area compared with those of male mice after adjusting for body size and bone size (female: mean=0.73 mm2; 95% CI, 0.71-0.74 mm2; male: mean=0.70 mm2; 95% CI, 0.68-0.71 mm2; effect size=0.74; p=0.04, GLM). Female femurs are not simply a more slender version of male femurs. Women acquire substantially less mass (cortical area) for their body size and bone size compared with men. Our analysis questions whether mouse long bone is a suitable model to study human sexual dimorphism. Identifying differences in the way bones are constructed may be clinically important for developing sex-specific diagnostics and treatment strategies to reduce fragility fractures.

Drug retention and discontinuation reasons between seven biologics in patients with rheumatoid arthritis -The ANSWER cohort study.

PubMed

Ebina, Kosuke; Hashimoto, Motomu; Yamamoto, Wataru; Ohnishi, Akira; Kabata, Daijiro; Hirano, Toru; Hara, Ryota; Katayama, Masaki; Yoshida, Shuzo; Nagai, Koji; Son, Yonsu; Amuro, Hideki; Akashi, Kengo; Fujimura, Takanori; Hirao, Makoto; Yamamoto, Keiichi; Shintani, Ayumi; Kumanogoh, Atsushi; Yoshikawa, Hideki

2018-01-01

The purpose of this study was to evaluate the retention and discontinuation reasons of seven biological disease-modifying antirheumatic drugs (bDMARDs) in a real-world setting of patients with rheumatoid arthritis (RA). 1,037 treatment courses with bDMARDs from 2009 to 2016 [female, 81.8%; baseline age, 59.6 y; disease duration 7.8 y; rheumatoid factor positivity 81.5%; Disease Activity Score in 28 joints using erythrocyte sedimentation rate (DAS28-ESR), 4.4; concomitant prednisolone 43.5% and methotrexate 68.6%; Bio-naïve, 57.1%; abatacept (ABT), 21.3%; tocilizumab (TCZ), 20.7%; golimumab (GLM), 16.9%; etanercept (ETN), 13.6%; adalimumab (ADA), 11.1%; infliximab (IFX), 8.5%; certolizumab pegol (CZP), 7.9%] were included in this multi-center, retrospective study. Drug retention and discontinuation reasons at 36 months were estimated using the Kaplan-Meier method and adjusted by potent confounders using Cox proportional hazards modeling. As a result, 455 treatment courses (43.9%) were stopped, with 217 (20.9%) stopping due to inefficacy, 113 (10.9%) due to non-toxic reasons, 86 (8.3%) due to toxic adverse events, and 39 (3.8%) due to remission. Drug retention rates in the adjusted model were as follows: total retention (ABT, 60.7%; ADA, 32.7%; CZP, 43.3%; ETN, 51.9%; GLM, 45.4%; IFX, 31.1%; and TCZ, 59.2%; P < 0.001); inefficacy (ABT, 81.4%; ADA, 65.7%; CZP, 60.7%; ETN, 71.3%; GLM, 68.5%; IFX, 65.0%; and TCZ, 81.4%; P = 0.015), toxic adverse events (ABT, 89.8%; ADA, 80.5%; CZP, 83.9%; ETN, 89.2%; GLM, 85.5%; IFX, 75.6%; and TCZ, 77.2%; P = 0.50), and remission (ABT, 95.5%; ADA, 88.1%; CZP, 91.1%; ETN, 97.5%; GLM, 94.7%; IFX, 86.4%; and TCZ, 98.4%; P < 0.001). In the treatment of RA, ABT and TCZ showed higher overall retention, and TCZ showed lower inefficacy compared to IFX, while IFX showed higher discontinuation due to remission compared to ABT, ETN, GLM, and TCZ in adjusted modeling.

Quality-by-Design (QbD): An integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and process design space development.

PubMed

Wu, Huiquan; White, Maury; Khan, Mansoor A

2011-02-28

The aim of this work was to develop an integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and design space development. A dynamic co-precipitation process by gradually introducing water to the ternary system of naproxen-Eudragit L100-alcohol was monitored at real-time in situ via Lasentec FBRM and PVM. 3D map of count-time-chord length revealed three distinguishable process stages: incubation, transition, and steady-state. The effects of high risk process variables (slurry temperature, stirring rate, and water addition rate) on both derived co-precipitation process rates and final chord-length-distribution were evaluated systematically using a 3(3) full factorial design. Critical process variables were identified via ANOVA for both transition and steady state. General linear models (GLM) were then used for parameter estimation for each critical variable. Clear trends about effects of each critical variable during transition and steady state were found by GLM and were interpreted using fundamental process principles and Nyvlt's transfer model. Neural network models were able to link process variables with response variables at transition and steady state with R(2) of 0.88-0.98. PVM images evidenced nucleation and crystal growth. Contour plots illustrated design space via critical process variables' ranges. It demonstrated the utility of integrated PAT approach for QbD development. Published by Elsevier B.V.

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

PubMed

Rovniak, Liza S; Sallis, James F; Kraschnewski, Jennifer L; Sciamanna, Christopher N; Kiser, Elizabeth J; Ray, Chester A; Chinchilli, Vernon M; Ding, Ding; Matthews, Stephen A; Bopp, Melissa; George, Daniel R; Hovell, Melbourne F

2013-08-14

High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA. A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants' online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met. Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living. ClinicalTrials.gov NCT01142804.

A conditional Granger causality model approach for group analysis in functional MRI

PubMed Central

Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

2011-01-01

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI. PMID:21232892

Dry spell trend analysis in Kenya and the Murray Darling Basin using daily rainfall

NASA Astrophysics Data System (ADS)

Muita, R. R.; van Ogtrop, F. F.; Vervoort, R. W.

2012-04-01

Important agricultural areas in Kenya and the Murray Darling Basin (MDB) in Australia are largely semi-arid to arid. Persistent dry periods and timing of dry spells directly impact the availability of soil moisture and hence crop production in these regions. Most studies focus on the analysis of dry spell lengths at an annual scale. However, timing and length of dry spells at finer temporal scales is more beneficial for cropping when considering a trade-off between the time scale and the ability to analyse dry spell length. The aim of this study was to analyse the interannual and intra annual variations in dry spell lengths in the regions to inform crop management. This study analysed monthly dry spells based on daily rainfall for 1961-2010 on a limited dataset of 13 locations in Kenya and 17 locations in the MDB. This dataset was the most consistent across both regions and future analysis will incorporate more stations and longer time periods where available. Dry spell lengths were analysed by month and year and trends in monthly and annual dry spell lengths were analysed using Generalised Linear Models (GLM) and the Mann Kendall test (MK). Overall, monthly dryspell lengths are right skewed with higher frequency of shorter dryspells (3-25 days). In Kenya, significant increases in mean dry spell lengths (p≤0.02) are observed in inland arid-to semi humid locations but this temporal trend appears to decrease in highland and the coastal regions. Analysis of the MDB stations suggests changes in seasonality. For example, spatial trends suggest a North-South increase in dry spell length in summer (December - February), but a shortening after February. Generally, the GLM and MK results are similar in the two regions but the MK test tends to give higher values of positive slope coefficients and lower values for negative coefficients compared to GLM. This may limit the ability of finding the best estimates for model coefficients. Previous studies in Australia and Kenya have relied on continuous climatic indices based on global climate models and stochastic processes resulting in limited and mixed results. For agronomical purposes, our results show that direct assessment of dry spells lengths from daily rainfall also indicates changes in dry spells trends in Kenya and the MDB and that such an analysis is easy to use and requires limited assumptions. This initial analysis identifies significant increasing trends in the dry spell lengths in some areas and periods in Kenya and the MDB. This has major implications for crop production in these regions and it is recommended that this information be incorporated in the regions' management decisions. KEY WORDS: monthly dry spell length; Generalized Linear Models; Mann -Kendall test; month; Kenya, Murray Darling Basin (MDB).

nSTAT: Open-Source Neural Spike Train Analysis Toolbox for Matlab

PubMed Central

Cajigas, I.; Malik, W.Q.; Brown, E.N.

2012-01-01

Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - Generalized Linear Model (PPGLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT – an open source neural spike train analysis toolbox for Matlab®. By adopting an Object-Oriented Programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems. PMID:22981419

Comparison of golimumab 100-mg monotherapy to golimumab 50 mg plus methotrexate in patients with rheumatoid arthritis: Results from a multicenter, cohort study.

PubMed

Yonemoto, Yukio; Okamura, Koichi; Takeuchi, Kimihiko; Ayabe, Keio; Kaneko, Tetsuya; Matsushita, Masatoshi; Tamura, Yasuyuki; Iso, Takenobu; Okura, Chisa; Otsuka, Keiko; Inoue, Hiroshi; Takagishi, Kenji

2016-01-01

The aim of this study was to compare the efficacy and safety of golimumab (GLM) 50 mg + methotrexate (MTX) combination therapy and GLM 100 mg monotherapy in patients with rheumatoid arthritis (RA). The subjects were 115 RA patients (92 females and 23 males; median (range) age, 64 (17-87) years; median (range) disease duration, 8 (0.6-48) years) started on GLM. Eighty-three patients received GLM 50 mg/4 weeks + MTX (C group; median (range) MTX dosage 8 (2-16) mg/week), and 32 patients received GLM 100 mg/4 weeks (M group). Serum C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), matrix metalloproteinase-3, disease activity score (DAS) 28-ESR, DAS28-CRP, simplified disease activity index, and clinical disease activity index were evaluated 4, 12, and 24 weeks after starting GLM. There were no significant differences in disease activity, adverse events, and drug continuation rates at 24 weeks between the groups. The DAS28-ESR remission rate was 34% in the C group and 26% in the M group. GLM 100 mg monotherapy improved disease activity as well as GLM 50 mg + MTX combination therapy. GLM 100 mg monotherapy appears to have a sufficient therapeutic effect in RA patients who cannot take MTX.

Modified Regression Correlation Coefficient for Poisson Regression Model

NASA Astrophysics Data System (ADS)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

Marine diet and tobacco exposure affects mercury concentrations in pregnant women (I) from Baja California Sur, Mexico.

PubMed

Gaxiola-Robles, Ramón; Bentzen, Rebecca; Zenteno-Savín, Tania; Labrada-Martagón, Vanessa; Castellini, J Margaret; Celis, Alfredo; O'Hara, Todd; Celina Méndez-Rodríguez, Lía

2014-01-01

Seafood provides essential polyunsaturated fatty acids (PUFA) and other nutrients to pregnant women and their fetus(es) while a diet rich in finfish can be a major pathway of monomethyl mercury (MeHg + ) exposure. We measured total mercury concentration ([THg]) in hair samples provided by 75 women in Baja California Sur (BCS) to assess its relationship with age, parity, tobacco smoke exposure, and diet based on survey methodologies. Generalized linear models (GLM) were used to explain the possible association of the different variables with [THg] in hair. Median [THg] in hair was 1.52 µgg -1 , ranging from 0.12 to 24.19 µgg -1 and varied significantly by segment. Approximately 72% (54/75) of those evaluated exceed 1 µgg -1 [THg] and 8% (6/75) exceed 5 µgg -1 [THg] in hair. Although frequency of fish consumption contributed significantly to explaining hair [THg], fish consumption only explained 43% of [THg] in a GLM incorporating tobacco exposure and body mass index. This study establishes possible relationships among multiple potential sources of exposure and other factors related to [THg] in hair of women in the prenatal period. A more detailed examination of other sources of exposure and factors contributing to [THg] is warranted.

Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach.

PubMed

Hjort, Jan; Hugg, Timo T; Antikainen, Harri; Rusanen, Jarmo; Sofiev, Mikhail; Kukkonen, Jaakko; Jaakkola, Maritta S; Jaakkola, Jouni J K

2016-05-01

Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data. We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM). GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data-based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing-based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales. The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments. Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619-626; http://dx.doi.org/10.1289/ehp.1509761.

EMG prediction from Motor Cortical Recordings via a Non-Negative Point Process Filter

PubMed Central

Nazarpour, Kianoush; Ethier, Christian; Paninski, Liam; Rebesco, James M.; Miall, R. Chris; Miller, Lee E.

2012-01-01

A constrained point process filtering mechanism for prediction of electromyogram (EMG) signals from multi-channel neural spike recordings is proposed here. Filters from the Kalman family are inherently sub-optimal in dealing with non-Gaussian observations, or a state evolution that deviates from the Gaussianity assumption. To address these limitations, we modeled the non-Gaussian neural spike train observations by using a generalized linear model (GLM) that encapsulates covariates of neural activity, including the neurons’ own spiking history, concurrent ensemble activity, and extrinsic covariates (EMG signals). In order to predict the envelopes of EMGs, we reformulated the Kalman filter (KF) in an optimization framework and utilized a non-negativity constraint. This structure characterizes the non-linear correspondence between neural activity and EMG signals reasonably. The EMGs were recorded from twelve forearm and hand muscles of a behaving monkey during a grip-force task. For the case of limited training data, the constrained point process filter improved the prediction accuracy when compared to a conventional Wiener cascade filter (a linear causal filter followed by a static non-linearity) for different bin sizes and delays between input spikes and EMG output. For longer training data sets, results of the proposed filter and that of the Wiener cascade filter were comparable. PMID:21659018

Interannual and seasonal variability of winter-spring cohort of neon flying squid abundance in the Northwest Pacific Ocean during 1995-2011

NASA Astrophysics Data System (ADS)

Yu, Wei; Chen, Xinjun; Yi, Qian

2016-06-01

The neon flying squid, Ommastrephes bartramii, is a species of economically important cephalopod in the Northwest Pacific Ocean. Its short lifespan increases the susceptibility of the distribution and abundance to the direct impact of the environmental conditions. Based on the generalized linear model (GLM) and generalized additive model (GAM), the commercial fishery data from the Chinese squid-jigging fleets during 1995 to 2011 were used to examine the interannual and seasonal variability in the abundance of O. bartramii, and to evaluate the influences of variables on the abundance (catch per unit effort, CPUE). The results from GLM suggested that year, month, latitude, sea surface temperature (SST), mixed layer depth (MLD), and the interaction term ( SST×MLD) were significant factors. The optimal model based on GAM included all the six significant variables and could explain 42.43% of the variance in nominal CPUE. The importance of the six variables was ranked by decreasing magnitude: year, month, latitude, SST, MLD and SST×MLD. The squid was mainly distributed in the waters between 40°N and 44°N in the Northwest Pacific Ocean. The optimal ranges of SST and MLD were from 14 to 20°C and from 10 to 30 m, respectively. The squid abundance greatly fluctuated from 1995 to 2011. The CPUE was low during 1995-2002 and high during 2003-2008. Furthermore, the squid abundance was typically high in August. The interannual and seasonal variabilities in the squid abundance were associated with the variations of marine environmental conditions and the life history characteristics of squid.

A systematic review of methodology: time series regression analysis for environmental factors and infectious diseases.

PubMed

Imai, Chisato; Hashizume, Masahiro

2015-03-01

Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.

Effects of episodic rainfall on a subterranean estuary

NASA Astrophysics Data System (ADS)

Yu, Xiayang; Xin, Pei; Lu, Chunhui; Robinson, Clare; Li, Ling; Barry, D. A.

2017-07-01

Numerical simulations were conducted to examine the effect of episodic rainfall on nearshore groundwater dynamics in a tidally influenced unconfined coastal aquifer, with a focus on both long-term (yearly) and short-term (daily) behavior of submarine groundwater discharge (SGD) and seawater intrusion (SWI). The results showed nonlinear interactions among the processes driven by rainfall, tides, and density gradients. Rainfall-induced infiltration increased the yearly averaged fresh groundwater discharge to the ocean but reduced the extents of the saltwater wedge and upper saline plume as well as the total rate of seawater circulation through both zones. Overall, the net effect of the interactions led to an increase of the SGD. The nearshore groundwater responded to individual rainfall events in a delayed and cumulative fashion, as evident in the variations of daily averaged SGD and salt stored in the saltwater wedge (quantifying the extent of SWI). A generalized linear model (GLM) along with a Gamma distribution function was developed to describe the delayed and prolonged effect of rainfall events on short-term groundwater behavior. This model validated with results of daily averaged SGD and SWI from the simulations of groundwater and solute transport using independent rainfall data sets, performed well in predicting the behavior of the nearshore groundwater system under the combined influence of episodic rainfall, tides, and density gradients. The findings and developed GLM form a basis for evaluating and predicting SGD, SWI, and associated mass fluxes from unconfined coastal aquifers under natural conditions, including episodic rainfall.

Substrate-bound Crystal Structures Reveal Features Unique to Mycobacterium tuberculosis N-Acetyl-glucosamine 1-Phosphate Uridyltransferase and a Catalytic Mechanism for Acetyl Transfer

PubMed Central

Jagtap, Pravin Kumar Ankush; Soni, Vijay; Vithani, Neha; Jhingan, Gagan Deep; Bais, Vaibhav Singh; Nandicoori, Vinay Kumar; Prakash, Balaji

2012-01-01

N-Acetyl-glucosamine-1-phosphate uridyltransferase (GlmU), a bifunctional enzyme involved in bacterial cell wall synthesis is exclusive to prokaryotes. GlmU, now recognized as a promising target to develop new antibacterial drugs, catalyzes two key reactions: acetyl transfer and uridyl transfer at two independent domains. Hitherto, we identified GlmU from Mycobacterium tuberculosis (GlmUMtb) to be unique in possessing a 30-residue extension at the C terminus. Here, we present the crystal structures of GlmUMtb in complex with substrates/products bound at the acetyltransferase active site. Analysis of these and mutational data, allow us to infer a catalytic mechanism operative in GlmUMtb. In this SN2 reaction, His-374 and Asn-397 act as catalytic residues by enhancing the nucleophilicity of the attacking amino group of glucosamine 1-phosphate. Ser-416 and Trp-460 provide important interactions for substrate binding. A short helix at the C-terminal extension uniquely found in mycobacterial GlmU provides the highly conserved Trp-460 for substrate binding. Importantly, the structures reveal an uncommon mode of acetyl-CoA binding in GlmUMtb; we term this the U conformation, which is distinct from the L conformation seen in the available non-mycobacterial GlmU structures. Residues, likely determining U/L conformation, were identified, and their importance was evaluated. In addition, we identified that the primary site for PknB-mediated phosphorylation is Thr-418, near the acetyltransferase active site. Down-regulation of acetyltransferase activity upon Thr-418 phosphorylation is rationalized by the structures presented here. Overall, this work provides an insight into substrate recognition, catalytic mechanism for acetyl transfer, and features unique to GlmUMtb, which may be exploited for the development of inhibitors specific to GlmU. PMID:22969087

The role of meteorological conditions and pollution control strategies in reducing air pollution in Beijing during APEC 2014 and Victory Parade 2015

NASA Astrophysics Data System (ADS)

Liang, Pengfei; Zhu, Tong; Fang, Yanhua; Li, Yingruo; Han, Yiqun; Wu, Yusheng; Hu, Min; Wang, Junxia

2017-11-01

To control severe air pollution in China, comprehensive pollution control strategies have been implemented throughout the country in recent years. To evaluate the effectiveness of these strategies, the influence of meteorological conditions on levels of air pollution needs to be determined. Using the intensive air pollution control strategies implemented during the Asia-Pacific Economic Cooperation Forum in 2014 (APEC 2014) and the 2015 China Victory Day Parade (Victory Parade 2015) as examples, we estimated the role of meteorological conditions and pollution control strategies in reducing air pollution levels in Beijing. Atmospheric particulate matter of aerodynamic diameter ≤ 2.5 µm (PM2.5) samples were collected and gaseous pollutants (SO2, NO, NOx, and O3) were measured online at a site in Peking University (PKU). To determine the influence of meteorological conditions on the levels of air pollution, we first compared the air pollutant concentrations during days with stable meteorological conditions. However, there were few days with stable meteorological conditions during the Victory Parade. As such, we were unable to estimate the level of emission reduction efforts during this period. Finally, a generalized linear regression model (GLM) based only on meteorological parameters was built to predict air pollutant concentrations, which could explain more than 70 % of the variation in air pollutant concentration levels, after incorporating the nonlinear relationships between certain meteorological parameters and the concentrations of air pollutants. Evaluation of the GLM performance revealed that the GLM, even based only on meteorological parameters, could be satisfactory to estimate the contribution of meteorological conditions in reducing air pollution and, hence, the contribution of control strategies in reducing air pollution. Using the GLM, we found that the meteorological conditions and pollution control strategies contributed 30 and 28 % to the reduction of the PM2.5 concentration during APEC and 38 and 25 % during the Victory Parade, respectively, based on the assumption that the concentrations of air pollutants are only determined by meteorological conditions and emission intensities. We also estimated the contribution of meteorological conditions and control strategies in reducing the concentrations of gaseous pollutants and PM2.5 components with the GLMs, revealing the effective control of anthropogenic emissions.

International migration beyond gravity: A statistical model for use in population projections

PubMed Central

Cohen, Joel E.; Roig, Marta; Reuman, Daniel C.; GoGwilt, Cai

2008-01-01

International migration will play an increasing role in the demographic future of most nations if fertility continues to decline globally. We developed an algorithm to project future numbers of international migrants from any country or region to any other. The proposed generalized linear model (GLM) used geographic and demographic independent variables only (the population and area of origins and destinations of migrants, the distance between origin and destination, the calendar year, and indicator variables to quantify nonrandom characteristics of individual countries). The dependent variable, yearly numbers of migrants, was quantified by 43653 reports from 11 countries of migration from 228 origins and to 195 destinations during 1960–2004. The final GLM based on all data was selected by the Bayesian information criterion. The number of migrants per year from origin to destination was proportional to (population of origin)0.86(area of origin)−0.21(population of destination)0.36(distance)−0.97, multiplied by functions of year and country-specific indicator variables. The number of emigrants from an origin depended on both its population and its population density. For a variable initial year and a fixed terminal year 2004, the parameter estimates appeared stable. Multiple R2, the fraction of variation in log numbers of migrants accounted for by the starting model, improved gradually with recentness of the data: R2 = 0.57 for data from 1960 to 2004, R2 = 0.59 for 1985–2004, R2 = 0.61 for 1995–2004, and R2 = 0.64 for 2000–2004. The migration estimates generated by the model may be embedded in deterministic or stochastic population projections. PMID:18824693

The GOES-R Geostationary Lightning Mapper (GLM)

NASA Astrophysics Data System (ADS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas; Bailey, Jeffrey; Buechler, Dennis; Carey, Larry; Schultz, Chris; Bateman, Monte; McCaul, Eugene; Stano, Geoffrey

2013-05-01

The Geostationary Operational Environmental Satellite R-series (GOES-R) is the next block of four satellites to follow the existing GOES constellation currently operating over the Western Hemisphere. Advanced spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved cloud and moisture imagery with the 16-channel Advanced Baseline Imager (ABI). The GLM will map total lightning activity continuously day and night with near-uniform storm-scale spatial resolution of 8 km with a product refresh rate of less than 20 s over the Americas and adjacent oceanic regions in the western hemisphere. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low Earth orbit, and from ground-based lightning networks and intensive prelaunch field campaigns. The GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extend their combined climatology over the western hemisphere into the coming decades. Science and application development along with preoperational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and checkout of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model

PubMed Central

2013-01-01

Background High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA. Methods/Design A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants’ online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met. Discussion Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living. Trial registration ClinicalTrials.gov NCT01142804 PMID:23945138

Preliminary Assessment of Detection Efficiency for the Geostationary Lightning Mapper Using Intercomparisons with Ground-Based Systems

NASA Technical Reports Server (NTRS)

Bateman, Monte; Mach, Douglas; Blakeslee, Richard J.; Koshak, William

2018-01-01

As part of the calibration/validation (cal/val) effort for the Geostationary Lightning Mapper (GLM) on GOES-16, we need to assess instrument performance (detection efficiency and accuracy). One major effort is to calculate the detection efficiency of GLM by comparing to multiple ground-based systems. These comparisons will be done pair-wise between GLM and each other source. A complication in this process is that the ground-based systems sense different properties of the lightning signal than does GLM (e.g., RF vs. optical). Also, each system has a different time and space resolution and accuracy. Preliminary results indicate that GLM is performing at or above its specification.

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).

Enhanced oral bioavailability and sustained delivery of glimepiride via niosomal encapsulation: in-vitro characterization and in-vivo evaluation.

PubMed

Mohsen, Amira Mohamed; AbouSamra, Mona Mahmoud; ElShebiney, Shaimaa Ahmed

2017-08-01

This study was designed to investigate the potency of niosomes, for glimepiride (GLM) encapsulation, aiming at enhancing its oral bioavailability and hypoglycemic efficacy. Niosomes containing nonionic surfactants (NIS) were prepared by thin film hydration technique and characterized. In-vitro release study was performed using a dialysis technique. In-vivo pharmacodynamic studies, as well as pharmacokinetic evaluation were performed on alloxan-induced diabetic rats. GLM niosomes exhibited high-entrapment efficiency percentages (E.E. %) up to 98.70% and a particle size diameter ranging from 186.8 ± 18.69 to 797.7 ± 12.45 nm, with negatively charged zeta potential (ZP). Different GLM niosomal formulation showed retarded in vitro release, compared to free drug. In-vivo studies revealed the superiority of GLM niosomes in lowering blood glucose level (BGL) and in maintaining a therapeutic level of GLM for a longer period of time, as compared to free drug and market product. There was no significant difference between mean plasma AUC 0-48 hr of GLM-loaded niosomes and that of market product. GLM-loaded niosomes exhibited seven-fold enhancement in relative bioavailability in comparison with free drug. These findings reinforce the potential use of niosomes for enhancing the oral bioavailability and prolonged delivery of GLM via oral administration.

Desert Express: An Analysis on Improved Customer Service

DTIC Science & Technology

1991-09-01

Nt MARQ 3,199 Of. DESERT EXPRESS: AN ANALYSIS ON IMPROVED CUSTOMER SERVICE THESIS Thomas C Thaiheim, Majo-,r USAF AFTT/GLM/LSM,/91S-64 ?Z; W...Astq vt.: tyc a l AFIT/GLM/,LSM/91S-64 DESERT EXPRESS: AN ANALYSIS ON IMPROVED CUSTOMER SERVICE THESIS Thomas C. Thalheim, Major, USAF AFIT/GLM/LSM...91S-64 Approved for public release; distribution unlimited AFIT/GLM/LSM/91S-64 DESERT EXPRESS: AN ANALYSIS ON IMPROVED CUSTOMER SERVICE THESIS

Age at menarche and its influencing factors in North Korean female refugees.

PubMed

Ku, Seung-Yup; Kang, Jong Won; Kim, Heon; Kim, Yong Dae; Jee, Byung Chul; Suh, Chang Suk; Choi, Young Min; Kim, Jung Gu; Moon, Shin Yong; Kim, Seok Hyun

2006-03-01

Age at menarche is known to be regulated by genetic and environmental factors. To date, no menarcheal data are available on North Korean women. In this cross-sectional survey, we investigated age at menarche and its possible influencing factors in North Korean refugees. Four hundred and eleven North Korean refugees were surveyed at a North Korean female refugee camp using a structured questionnaire within 3 months of immigration. Menarcheal age was requested and the data obtained were analysed with respect to age at interview, region of residence in North Korea, education, food preference and sleep duration. Mean age at menarche was 16.0+/-2.1 years (mean+/-SD). Univariate analysis demonstrated a significant difference in the menarcheal age among the different food preference groups (P=0.0236). Sleep duration was found to be significantly and negatively correlated with age at menarche (R=-0.23, P<0.0001). However, generalized linear model (GLM) analysis revealed that region of residence at menarche (P=0.0209) and sleep duration (P=0.0007) were significant determinants. Food preference played a role as an effect modifier in the relationship between the region of residence at menarche and age at menarche. Age at menarche seemed to be delayed in North Korean refugees. GLM analysis showed that sleep duration and region of residence at menarche were significant influencing factors of age at menarche in this study population.

Blood Lead Concentrations in Jamaican Children with and without Autism Spectrum Disorder

PubMed Central

Rahbar, Mohammad H.; Samms-Vaughan, Maureen; Dickerson, Aisha S.; Loveland, Katherine A.; Ardjomand-Hessabi, Manouchehr; Bressler, Jan; Shakespeare-Pellington, Sydonnie; Grove, Megan L.; Pearson, Deborah A.; Boerwinkle, Eric

2014-01-01

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder manifesting by early childhood. Lead is a toxic metal shown to cause neurodevelopmental disorders in children. Several studies have investigated the possible association between exposure to lead and ASD, but their findings are conflicting. Using data from 100 ASD cases (2–8 years of age) and their age- and sex-matched typically developing controls, we investigated the association between blood lead concentrations (BLC) and ASD in Jamaican children. We administered a questionnaire to assess demographic and socioeconomic information as well as exposure to potential lead sources. We used General Linear Models (GLM) to assess the association of BLC with ASD status as well as with sources of exposure to lead. In univariable GLM, we found a significant difference between geometric mean blood lead concentrations of ASD cases and controls (2.25 μg/dL cases vs. 2.73 μg/dL controls, p < 0.05). However, after controlling for potential confounders, there were no significant differences between adjusted geometric mean blood lead concentrations of ASD cases and controls (2.55 μg/dL vs. 2.72 μg/dL, p = 0.64). Our results do not support an association between BLC and ASD in Jamaican children. We have identified significant confounders when assessing an association between ASD and BLC. PMID:25546274

Brain activity in the right-frontal pole and lateral occipital cortex predicts successful post-operatory outcome after surgery for anterior glenoumeral instability.

PubMed

Zanchi, Davide; Cunningham, Gregory; Lädermann, Alexandre; Ozturk, Mehmet; Hoffmeyer, Pierre; Haller, Sven

2017-03-29

Shoulder apprehension is more complex than a pure mechanical problem of the shoulder, creating a scar at the brain level that prevents the performance of specific movements. Surgery corrects for shoulder instability at the physical level, but a re-dislocation within the first year is rather common. Predicting which patient will be likely to have re-dislocation is therefore crucial. We hypothesized that the assessment of neural activity at baseline and follow-up is the key factor to predict the post-operatory outcome. 13 patients with shoulder apprehension (30.03 ± 7.64 years) underwent clinical and fMRI examination before and one year after surgery for shoulder dislocation contrasting apprehension cue videos and control videos. Data analyses included task-related general linear model (GLM) and correlations imaging results with clinical scores. Clinical examination showed decreased pain and increased shoulder functions for post-op vs. pre-op. Coherently, GLM results show decreased activation of the left pre-motor cortex for post-surgery vs. pre-surgery. Right-frontal pole and right-occipital cortex activity predicts good recovery of shoulder function measured by STT. Our findings demonstrate that beside physical changes, changes at the brain level also occur one year after surgery. In particular, decreased activity in pre-motor and orbito-frontal cortex is key factor for a successful post-operatory outcome.

Consumer evaluation of 'Veggycation®', a website promoting the health benefits of vegetables.

PubMed

Rekhy, Reetica; Khan, Aila; van Ogtrop, Floris; McConchie, Robyn

2017-03-01

Issue addressed Whether the website Veggycation ® appeals to particular groups of consumers significantly more than other groups. Methods Australian adults aged ≥18 years (n = 1000) completed an online survey. The website evaluation instrument used was tested for validity and reliability. Associations between demographic variables and website evaluation dimensions of attractiveness, content, user-friendliness and loyalty intentions were examined using a general linear model (GLM). The appraisal of the website was further investigated based on the respondents' daily consumption level of vegetables and the importance they attach to vegetable consumption in their diet, using GLM and a Tukey's all-pair comparison. Results Veggycation ® has a high level of acceptance among the Australian community with certain groups evaluating the website more favourably. These include women, people aged≤29 years, higher income respondents, non-metro respondents and those who viewed vegetables as extremely important in their daily diet. Conclusions Customisation of the website for consumer groups with low vegetable consumption is recommended. Designing tailored communication tools will assist in enhancing the knowledge base of vegetable-related health benefits and may promote vegetable consumption among the Australian population. So what? The promotion of higher vegetable consumption is aided by tailored, well-designed web communication. This study adds to the existing body of knowledge for the education of organisations developing e-tools for promoting health education and literacy.

The effects of the smart environment on the information divide experienced by people with disabilities.

PubMed

Nam, Su-Jung; Park, Eun-Young

2017-04-01

Information and Communication Technology (ICT) is connected with every aspect of social, cultural, economic, educational, and commercial activity. Smart devices in particular have changed society and are necessary goods for modern people. Smart device usage is rapidly growing in everyday life, so the ability to use a smart device is increasingly important, yet there is little data supporting increased digital inclusion of people with disabilities in mobile device use. This study investigates the effects of the smart environment on the information divide experienced by people with disabilities. Data from the 2013 Information Divide Index Data of the National Information Society Agency was analyzed regarding three aspects: access, skill, and competence. The accessibility difference was investigated by comparing access to a PC or smart device in two groups. The effects of a smart environment on the information divide were analyzed using General Linear Modeling (GLM). The access rate was higher for the general group than for that of those with disabilities, and this difference appeared to be greater in the smart environment. The results of the GLM showed that disability and device access had statistically significant effects on skill and all aspects of competence. These results provide evidence that the smart environment further creates the information divide for people with disabilities. Strategies should be formed to reduce this divide, particularly within smart environments. Copyright © 2016 Elsevier Inc. All rights reserved.

GOES-R AWG GLM Val Tool Development

NASA Technical Reports Server (NTRS)

Bateman, Monte; Mach, Douglas; Goodman, Steve; Blakeslee, Richard; Koshak, William

2012-01-01

We are developing tools needed to enable the validation of the Geostationary Lightning Mapper (GLM). In order to develop and test these tools, we have need of a robust, high-fidelity set of GLM proxy data. Many steps have been taken to ensure that the proxy data are high quality. LIS is the closest analog that exists for GLM, so it has been used extensively in developing the GLM proxy. We have verified the proxy data both statistically and algorithmically. The proxy data are pixel (event) data, called Level 1B. These data were then clustered into flashes by the Lightning Cluster-Filter Algorithm (LCFA), generating proxy Level 2 data. These were then compared with the data used to generate the proxy, and both the proxy data and the LCFA were validated. We have developed tools to allow us to visualize and compare the GLM proxy data with several other sources of lightning and other meteorological data (the so-called shallow-dive tool). The shallow-dive tool shows storm-level data and can ingest many different ground-based lightning detection networks, including: NLDN, LMA, WWLLN, and ENTLN. These are presented in a way such that it can be seen if the GLM is properly detecting the lightning in location and time comparable to the ground-based networks. Currently in development is the deep-dive tool, which will allow us to dive into the GLM data, down to flash, group and event level. This will allow us to assess performance in comparison with other data sources, and tell us if there are detection, timing, or geolocation problems. These tools will be compatible with the GLM Level-2 data format, so they can be used beginning on Day 0.

Numerical methods for the weakly compressible Generalized Langevin Model in Eulerian reference frame

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

Azarnykh, Dmitrii, E-mail: d.azarnykh@tum.de; Litvinov, Sergey; Adams, Nikolaus A.

2016-06-01

A well established approach for the computation of turbulent flow without resolving all turbulent flow scales is to solve a filtered or averaged set of equations, and to model non-resolved scales by closures derived from transported probability density functions (PDF) for velocity fluctuations. Effective numerical methods for PDF transport employ the equivalence between the Fokker–Planck equation for the PDF and a Generalized Langevin Model (GLM), and compute the PDF by transporting a set of sampling particles by GLM (Pope (1985) [1]). The natural representation of GLM is a system of stochastic differential equations in a Lagrangian reference frame, typically solvedmore » by particle methods. A representation in a Eulerian reference frame, however, has the potential to significantly reduce computational effort and to allow for the seamless integration into a Eulerian-frame numerical flow solver. GLM in a Eulerian frame (GLMEF) formally corresponds to the nonlinear fluctuating hydrodynamic equations derived by Nakamura and Yoshimori (2009) [12]. Unlike the more common Landau–Lifshitz Navier–Stokes (LLNS) equations these equations are derived from the underdamped Langevin equation and are not based on a local equilibrium assumption. Similarly to LLNS equations the numerical solution of GLMEF requires special considerations. In this paper we investigate different numerical approaches to solving GLMEF with respect to the correct representation of stochastic properties of the solution. We find that a discretely conservative staggered finite-difference scheme, adapted from a scheme originally proposed for turbulent incompressible flow, in conjunction with a strongly stable (for non-stochastic PDE) Runge–Kutta method performs better for GLMEF than schemes adopted from those proposed previously for the LLNS. We show that equilibrium stochastic fluctuations are correctly reproduced.« less

Characterizing the GOES-R (GOES-16) Geostationary Lightning Mapper (GLM) On-Orbit Performance

NASA Technical Reports Server (NTRS)

Rudlosky, Scott D.; Goodman, Steven J.; Koshak, William J.; Blakeslee, Richard J.; Buechler, Dennis E.; Mach, Douglas M.; Bateman, Monte

2017-01-01

Two overlapping efforts help to characterize the GLM performance, the Post Launch Test (PLT) phase to validate the predicted pre-launch instrument performance and the Post Launch Product Test (PLPT) phase to validate the lightning detection product used in forecast and warning decision-making. This paper documents the calibration and validation plans and activities for the first 6 months of GLM on-orbit testing and validation commencing with first light on 4 January 2017. The PLT phase addresses image quality, on-orbit calibration, RTEP threshold tuning, image navigation, noise filtering, and solar intrusion assessment, resulting in a GLM calibration parameter file. The PLPT includes four main activities, the Reference Data Comparisons (RDC), Algorithm Testing (AT), Instrument Navigation and Registration Testing (INRT), and Long Term Baseline Testing (LTBT). Field campaigns are also designed to contribute valuable insights into the GLM performance capabilities. The PLPT tests each contribute to the beta, provisional, and fully validated GLM data.

A method for deterministic statistical downscaling of daily precipitation at a monsoonal site in Eastern China

NASA Astrophysics Data System (ADS)

Liu, Yonghe; Feng, Jinming; Liu, Xiu; Zhao, Yadi

2017-12-01

Statistical downscaling (SD) is a method that acquires the local information required for hydrological impact assessment from large-scale atmospheric variables. Very few statistical and deterministic downscaling models for daily precipitation have been conducted for local sites influenced by the East Asian monsoon. In this study, SD models were constructed by selecting the best predictors and using generalized linear models (GLMs) for Feixian, a site in the Yishu River Basin and Shandong Province. By calculating and mapping Spearman rank correlation coefficients between the gridded standardized values of five large-scale variables and daily observed precipitation, different cyclonic circulation patterns were found for monsoonal precipitation in summer (June-September) and winter (November-December and January-March); the values of the gridded boxes with the highest absolute correlations for observed precipitation were selected as predictors. Data for predictors and predictands covered the period 1979-2015, and different calibration and validation periods were divided when fitting and validating the models. Meanwhile, the bootstrap method was also used to fit the GLM. All the above thorough validations indicated that the models were robust and not sensitive to different samples or different periods. Pearson's correlations between downscaled and observed precipitation (logarithmically transformed) on a daily scale reached 0.54-0.57 in summer and 0.56-0.61 in winter, and the Nash-Sutcliffe efficiency between downscaled and observed precipitation reached 0.1 in summer and 0.41 in winter. The downscaled precipitation partially reflected exact variations in winter and main trends in summer for total interannual precipitation. For the number of wet days, both winter and summer models were able to reflect interannual variations. Other comparisons were also made in this study. These results demonstrated that when downscaling, it is appropriate to combine a correlation-based predictor selection across a spatial domain with GLM modeling.

Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia

PubMed Central

Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe

2017-01-01

Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable—the time elapsed since the first flooding of the year—was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10–1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25–3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia. PMID:28704461

Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia.

PubMed

Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe; Tarantola, Arnaud; Cappelle, Julien

2017-01-01

Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable-the time elapsed since the first flooding of the year-was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10-1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25-3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia.

Fatty infiltration of the minor salivary glands is a selective feature of aging but not Sjögren's syndrome.

PubMed

Leehan, Kerry M; Pezant, Nathan P; Rasmussen, Astrid; Grundahl, Kiely; Moore, Jacen S; Radfar, Lida; Lewis, David M; Stone, Donald U; Lessard, Christopher J; Rhodus, Nelson L; Segal, Barbara M; Kaufman, C Erick; Scofield, R Hal; Sivils, Kathy L; Montgomery, Courtney; Farris, A Darise

2017-12-01

Determine the presence and assess the extent of fatty infiltration of the minor salivary glands (SG) of primary SS patients (pSS) as compared to those with non-SS sicca (nSS). Minor SG biopsy samples from 134 subjects with pSS (n = 72) or nSS (n = 62) were imaged. Total area and fatty replacement area for each glandular cross-section (n = 4-6 cross-sections per subject) were measured using Image J (National Institutes of Health, Bethesda, MD). The observer was blinded to subject classification status. The average area of fatty infiltration calculated per subject was evaluated by logistic regression and general linearized models (GLM) to assess relationships between fatty infiltration and clinical exam results, extent of fibrosis and age. The average area of fatty infiltration for subjects with pSS (median% (range) 4.97 (0.05-30.2)) was not significantly different from that of those with nSS (3.75 (0.087-41.9). Infiltration severity varied widely, and subjects with fatty replacement greater than 6% were equivalently distributed between pSS and nSS participants (χ 2 p = .50). Age accounted for all apparent relationships between fatty infiltration and fibrosis or reduced saliva flow. The all-inclusive GLM for prediction of pSS versus non-SS classification including fibrosis, age, fatty replacement, and focus score was not significantly different from any desaturated model. In no iteration of the model did fatty replacement exert a significant effect on the capacity to predict pSS classification. Fatty infiltration is an age-associated phenomenon and not a selective feature of Sjögren's syndrome. Sicca patients who do not fulfil pSS criteria have similar rates of fatty infiltration of the minor SG.

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies

PubMed Central

Bernal-Casas, David; Fang, Zhongnan; Lee, Jin Hyung

2017-01-01

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods’ performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data. PMID:27993672

A Systematic Review of Methodology: Time Series Regression Analysis for Environmental Factors and Infectious Diseases

PubMed Central

Imai, Chisato; Hashizume, Masahiro

2015-01-01

Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases. PMID:25859149

Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

NASA Astrophysics Data System (ADS)

Kim, Dong-Youl; Lee, Jong-Hwan

2014-05-01

A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

"Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments".

PubMed

Collado-Mateo, Daniel; Chen, Gang; Garcia-Gordillo, Miguel A; Iezzi, Angelo; Adsuar, José C; Olivares, Pedro R; Gusi, Narcis

2017-05-30

The revised version of the Fibromyalgia Impact Questionnaire (FIQR) is one of the most widely used specific questionnaires in FM studies. However, this questionnaire does not allow calculation of QALYs as it is not a preference-based measure. The aim of this study was to develop mapping algorithm which enable FIQR scores to be transformed into utility scores that can be used in the cost utility analyses. A cross-sectional survey was conducted. One hundred and 92 Spanish women with Fibromyalgia were asked to complete four general quality of life questionnaires, i.e. EQ-5D-5 L, 15D, AQoL-8D and SF-12, and one specific disease instrument, the FIQR. A direct mapping approach was adopted to derive mapping algorithms between the FIQR and each of the four multi-attribute utility (MAU) instruments. Health state utility was treated as the dependent variable in the regression analysis, whilst the FIQR score and age were predictors. The mean utility scores ranged from 0.47 (AQoL-8D) to 0.69 (15D). All correlations between the FIQR total score and MAU instruments utility scores were highly significant (p < 0.0001) with magnitudes larger than 0.5. Although very slight differences in the mean absolute error were found between ordinary least squares (OLS) estimator and generalized linear model (GLM), models based on GLM were better for EQ-5D-5 L, AQoL-8D and 15D. Mapping algorithms developed in this study enable the estimation of utility values from scores in a fibromyalgia specific questionnaire.

Greater healthcare utilization and costs among Black persons compared to White persons with aphasia in the North Carolina stroke belt.

PubMed

Ellis, Charles; Hardy, Rose Y; Lindrooth, Richard C

2017-05-15

To examine racial differences in healthcare utilization and costs for persons with aphasia (PWA) being treated in acute care hospitals in North Carolina (NC). NC Healthcare Cost and Utilization Project State Inpatient Database (HCUP-SID) data from 2011-2012 were analyzed to examine healthcare utilization and costs of care for stroke patients with aphasia. Analyses emphasized length of stay, charges and cost of general hospital services. Generalized linear models (GLM) were constructed to determine the impact of demographic characteristics, stroke/illness severity, and observed hospital characteristics on utilization and costs. Hospital fixed effects were included to yield within-hospital estimates of disparities. GLM models demonstrated that Blacks with aphasia experienced 1.9days longer lengths of stay compared to Whites with aphasia after controlling for demographic characteristics, 1.4days controlling for stroke/illness severity, 1.2days controlling for observed hospital characteristics, and ~1 extra day controlling for unobserved hospital characteristics. Similarly, Blacks accrued ~$2047 greater total costs compared to Whites after controlling for demographic characteristics, $1659 controlling for stroke/illness severity, $1338 controlling for observed hospital characteristics, and ~$1311 greater total costs after controlling for unobserved hospital characteristics. In the acute hospital setting, Blacks with aphasia utilize greater hospital services during longer hospitalizations and at substantially higher costs in the state of NC. A substantial portion of the adjusted difference was related to the hospital treating the patient. However, even after controlling for the hospital, the differences remained clinically and statistically significant. Copyright © 2017 Elsevier B.V. All rights reserved.

Variational Bayesian Parameter Estimation Techniques for the General Linear Model

PubMed Central

Starke, Ludger; Ostwald, Dirk

2017-01-01

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation. PMID:28966572

The GOES-R Geostationary Lightning Mapper (GLM)

NASA Astrophysics Data System (ADS)

Goodman, S. J.; Blakeslee, R. J.; Koshak, W. J.; Mach, D. M.; Bailey, J. C.; Buechler, D. E.; Carey, L. D.; Schultz, C. J.; Bateman, M. G.; McCaul, E., Jr.; Stano, G. T.

2012-12-01

The Geostationary Operational Environmental Satellite (GOES-R) series provides the continuity for the existing GOES system currently operating over the Western Hemisphere. New and improved instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved temporal, spatial, and spectral resolution for the next generation Advanced Baseline Imager (ABI). The GLM will map total lightning activity (in-cloud and cloud-to-ground lightning flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low earth orbit, and from ground-based lightning networks and intensive pre-launch field campaigns. GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extends their combined climatology over the western hemisphere into the coming decades. Science and application development along with pre-operational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and check-out of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings. Results from recent field campaigns and forecaster evaluations on the utility of the total lightning products will be presented.

A GLM Post-processor to Adjust Ensemble Forecast Traces

NASA Astrophysics Data System (ADS)

Thiemann, M.; Day, G. N.; Schaake, J. C.; Draijer, S.; Wang, L.

2011-12-01

The skill of hydrologic ensemble forecasts has improved in the last years through a better understanding of climate variability, better climate forecasts and new data assimilation techniques. Having been extensively utilized for probabilistic water supply forecasting, interest is developing to utilize these forecasts in operational decision making. Hydrologic ensemble forecast members typically have inherent biases in flow timing and volume caused by (1) structural errors in the models used, (2) systematic errors in the data used to calibrate those models, (3) uncertain initial hydrologic conditions, and (4) uncertainties in the forcing datasets. Furthermore, hydrologic models have often not been developed for operational decision points and ensemble forecasts are thus not always available where needed. A statistical post-processor can be used to address these issues. The post-processor should (1) correct for systematic biases in flow timing and volume, (2) preserve the skill of the available raw forecasts, (3) preserve spatial and temporal correlation as well as the uncertainty in the forecasted flow data, (4) produce adjusted forecast ensembles that represent the variability of the observed hydrograph to be predicted, and (5) preserve individual forecast traces as equally likely. The post-processor should also allow for the translation of available ensemble forecasts to hydrologically similar locations where forecasts are not available. This paper introduces an ensemble post-processor (EPP) developed in support of New York City water supply operations. The EPP employs a general linear model (GLM) to (1) adjust available ensemble forecast traces and (2) create new ensembles for (nearby) locations where only historic flow observations are available. The EPP is calibrated by developing daily and aggregated statistical relationships form historical flow observations and model simulations. These are then used in operation to obtain the conditional probability density function (PDF) of the observations to be predicted, thus jointly adjusting individual ensemble members. These steps are executed in a normalized transformed space ('z'-space) to account for the strong non-linearity in the flow observations involved. A data window centered on each calibration date is used to minimize impacts from sampling errors and data noise. Testing on datasets from California and New York suggests that the EPP can successfully minimize biases in ensemble forecasts, while preserving the raw forecast skill in a 'days to weeks' forecast horizon and reproducing the variability of climatology for 'weeks to years' forecast horizons.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

A real-world, multi-site, observational study of infusion time and treatment satisfaction with rheumatoid arthritis patients treated with intravenous golimumab or infliximab.

PubMed

Daniel, Shoshana R; McDermott, John D; Le, Cathy; Pierce, Christine A; Ziskind, Michael A; Ellis, Lorie A

2018-05-25

To assess real-world infusion times for golimumab (GLM-IV) and infliximab (IFX) for rheumatoid arthritis (RA) patients and factors associated with treatment satisfaction. An observational study assessed infusion time including: clinic visit duration, RA medication preparation and infusion time, and infusion process time. Satisfaction was assessed by a modified Treatment Satisfaction Questionnaire for Medication (patient) and study-specific questionnaires (patient and clinic personnel). Comparative statistical testing for patient data utilized analysis of variance for continuous measures, and Fisher's exact or Chi-square test for categorical measures. Multivariate analysis was performed for the primary time endpoints and patient satisfaction. One hundred and fifty patients were enrolled from six US sites (72 GLM-IV, 78 IFX). The majority of patients were female (80.0%) and Caucasian (88.7%). GLM-IV required fewer vials per infusion (3.7) compared to IFX (4.9; p = .0001). Clinic visit duration (minutes) was shorter for GLM-IV (65.1) compared to IFX (153.1; p < .0001), as was total infusion time for RA medication (32.8 GLM-IV, 119.5 IFX; p < .0001) and infusion process times (45.8 GLM-IV, 134.1 IFX; p < .0001). Patients treated with GLM-IV reported higher satisfaction ratings with infusion time (p < .0001) and total visit time (p = .0003). Clinic personnel reported higher satisfaction with GLM-IV than IFX specific to medication preparation time, ease of mixing RA medication, frequency of patients requiring pre-medication, and infusion time. Findings may not be representative of care delivery for all RA infusion practices or RA patients. Shorter overall clinic visit duration, infusion process, and RA medication infusion times were observed for GLM-IV compared to IFX. A shorter duration in infusion time was associated with higher patient and clinic personnel satisfaction ratings.

Genome-Wide Association Mapping of Acid Soil Resistance in Barley (Hordeum vulgare L.)

PubMed Central

Zhou, Gaofeng; Broughton, Sue; Zhang, Xiao-Qi; Ma, Yanling; Zhou, Meixue; Li, Chengdao

2016-01-01

Genome-wide association studies (GWAS) based on linkage disequilibrium (LD) have been used to detect QTLs underlying complex traits in major crops. In this study, we collected 218 barley (Hordeum vulgare L.) lines including wild barley and cultivated barley from China, Canada, Australia, and Europe. A total of 408 polymorphic markers were used for population structure and LD analysis. GWAS for acid soil resistance were performed on the population using a general linkage model (GLM) and a mixed linkage model (MLM), respectively. A total of 22 QTLs (quantitative trait loci) were detected with the GLM and MLM analyses. Two QTLs, close to markers bPb-1959 (133.1 cM) and bPb-8013 (86.7 cM), localized on chromosome 1H and 4H respectively, were consistently detected in two different trials with both the GLM and MLM analyses. Furthermore, bPb-8013, the closest marker to the major Al3+ resistance gene HvAACT1 in barley, was identified to be QTL5. The QTLs could be used in marker-assisted selection to identify and pyramid different loci for improved acid soil resistance in barley. PMID:27064793

Evaluation of Radar Vegetation Indices for Vegetation Water Content Estimation Using Data from a Ground-Based SMAP Simulator

NASA Technical Reports Server (NTRS)

Srivastava, Prashant K.; O'Neill, Peggy; Cosh, Michael; Lang, Roger; Joseph, Alicia

2015-01-01

Vegetation water content (VWC) is an important component of microwave soil moisture retrieval algorithms. This paper aims to estimate VWC using L band active and passive radar/radiometer datasets obtained from a NASA ground-based Soil Moisture Active Passive (SMAP) simulator known as ComRAD (Combined Radar/Radiometer). Several approaches to derive vegetation information from radar and radiometer data such as HH, HV, VV, Microwave Polarization Difference Index (MPDI), HH/VV ratio, HV/(HH+VV), HV/(HH+HV+VV) and Radar Vegetation Index (RVI) are tested for VWC estimation through a generalized linear model (GLM). The overall analysis indicates that HV radar backscattering could be used for VWC content estimation with highest performance followed by HH, VV, MPDI, RVI, and other ratios.

Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and other Meteorological Measurements

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Larry; Cecil, Dan; Bateman, Monte; Stano, Geoffrey; Goodman, Steve

2012-01-01

Objective of project is to refine, adapt and demonstrate the Lightning Jump Algorithm (LJA) for transition to GOES -R GLM (Geostationary Lightning Mapper) readiness and to establish a path to operations Ongoing work . reducing risk in GLM lightning proxy, cell tracking, LJA algorithm automation, and data fusion (e.g., radar + lightning).

Image navigation and registration for the geostationary lightning mapper (GLM)

NASA Astrophysics Data System (ADS)

van Bezooijen, Roel W. H.; Demroff, Howard; Burton, Gregory; Chu, Donald; Yang, Shu S.

2016-10-01

The Geostationary Lightning Mappers (GLM) for the Geostationary Operational Environmental Satellite (GOES) GOES-R series will, for the first time, provide hemispherical lightning information 24 hours a day from longitudes of 75 and 137 degrees west. The first GLM of a series of four is planned for launch in November, 2016. Observation of lightning patterns by GLM holds promise to improve tornado warning lead times to greater than 20 minutes while halving the present false alarm rates. In addition, GLM will improve airline traffic flow management, and provide climatology data allowing us to understand the Earth's evolving climate. The paper describes the method used for translating the pixel position of a lightning event to its corresponding geodetic longitude and latitude, using the J2000 attitude of the GLM mount frame reported by the spacecraft, the position of the spacecraft, and the alignment of the GLM coordinate frame relative to its mount frame. Because the latter alignment will experience seasonal variation, this alignment is determined daily using GLM background images collected over the previous 7 days. The process involves identification of coastlines in the background images and determination of the alignment change necessary to match the detected coastline with the coastline predicted using the GSHHS database. Registration is achieved using a variation of the Lucas-Kanade algorithm where we added a dither and average technique to improve performance significantly. An innovative water mask technique was conceived to enable self-contained detection of clear coastline sections usable for registration. Extensive simulations using accurate visible images from GOES13 and GOES15 have been used to demonstrate the performance of the coastline registration method, the results of which are presented in the paper.

High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William; Mach, Douglas M.

2011-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. A major advancement over the current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM). The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Science Team and Algorithm Working Group Lightning Applications Team have begun to develop cal/val performance monitoring tools and new applications using the GLM alone, in conjunction with other instruments, and merged or blended integrated observing system products combining satellite, radar, in-situ and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Petersen, W.; Buechler, D. E.; Krehbiel, P. R.; Gatlin, P.; Zubrick, S.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models is expected to be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 ground processing algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area)

Worldwide patterns of fish biodiversity in estuaries: Effect of global vs. local factors

NASA Astrophysics Data System (ADS)

Pasquaud, Stéphanie; Vasconcelos, Rita P.; França, Susana; Henriques, Sofia; Costa, Maria José; Cabral, Henrique

2015-03-01

The main ecological patterns and the functioning of estuarine ecosystems are difficult to evaluate due to natural and human induced complexity and variability. Broad geographical approaches appear particularly useful. This study tested, at a worldwide scale, the influence of global and local variables in fish species richness in estuaries, aiming to determine the latitudinal pattern of species richness, and patterns which could be driven by local features such as estuary area, estuary mouth width, river flow and intertidal area. Seventy one estuarine systems were considered with data obtained from the literature and geographical information system. Correlation tests and generalized linear models (GLM) were used in data analyses. Species richness varied from 23 to 153 fish species. GLM results showed that estuary area was the most important factor explaining species richness, followed by latitude and mouth width. Species richness increased towards the equator, and higher values were found in larger estuaries and with a wide mouth. All these trends showed a high variability. A larger estuary area probably reflects a higher diversity of habitats and/or productivity, which are key features for estuarine ecosystem functioning and biota. The mouth width effect is particularly notorious for marine and diadromous fish species, enhancing connectivity between marine and freshwater realms. The effects of river flow and intertidal area on the fish species richness appear to be less evident. These two factors may have a marked influence in the trophic structure of fish assemblages.

Testing bird response to roads on a rural environment: A case study from Central Italy

NASA Astrophysics Data System (ADS)

Morelli, Federico; Jerzak, Leszek; Pruscini, Fabio; Santolini, Riccardo; Benedetti, Yanina; Tryjanowski, Piotr

2015-11-01

The construction of roads is currently well spread in many parts of our world and impacts strongly on wildlife distribution. Some bird species avoid, while other prefer to be in the vicinity of these human structures. However, studies on roads effects on birds, in terms of strength or direction of these effects, are scarce. Therefore, in a study carried out in Central Italy we tested the responses of different bird species to roads at a local spatial scale, using generalized linear models (GLM). Analysis were conducted on a large dataset (more than 1400 sampled sites, mainly on rural environments). Both positive and negative effects of roads on birds were found for bird species of close or semi-close environments, while the negative effects of roads were negligible for bird species of open and semi-open environments. This fact suggest that roads can be a source of "functional heterogeneity" on semi-open environments, providing marginal habitats, hedgerows and residual vegetation typical of roadsides, offering breeding and feeding habitat for some bird species. The proposed methodology provide a useful explorative tool, in order to develop conservation policies to preserve the biodiversity, mainly in rural landscapes. The outputs of GLM can be used as inputs in ecological planning: direction and strength of the effects of roads on bird species are adequate to estimate the response of bird community, up front to the presence of new structures, or identifying which of them should be mitigated to reduce negative effects on the biodiversity.

Sugar Allocation to Metabolic Pathways is Tightly Regulated and Affects the Virulence of Streptococcus mutans

PubMed Central

Kawada-Matsuo, Miki; Oogai, Yuichi; Komatsuzawa, Hitoshi

2016-01-01

Bacteria take up and metabolize sugar as a carbohydrate source for survival. Most bacteria can utilize many sugars, including glucose, sucrose, and galactose, as well as amino sugars, such as glucosamine and N-acetylglucosamine. After entering the cytoplasm, the sugars are mainly allocated to the glycolysis pathway (energy production) and to various bacterial component biosynthesis pathways, including the cell wall, nucleic acids and amino acids. Sugars are also utilized to produce several virulence factors, such as capsule and lipoteichoic acid. Glutamine-fructose-6-phosphate aminotransferase (GlmS) and glucosamine-6-phosphate deaminase (NagB) have crucial roles in sugar distribution to the glycolysis pathway and to cell wall biosynthesis. In Streptococcus mutans, a cariogenic pathogen, the expression levels of glmS and nagB are coordinately regulated in response to the presence or absence of amino sugars. In addition, the disruption of this regulation affects the virulence of S. mutans. The expression of nagB and glmS is regulated by NagR in S. mutans, but the precise mechanism underlying glmS regulation is not clear. In Staphylococcus aureus and Bacillus subtilis, the mRNA of glmS has ribozyme activity and undergoes self-degradation at the mRNA level. However, there is no ribozyme activity region on glmS mRNA in S. mutans. In this review article, we summarize the sugar distribution, particularly the coordinated regulation of GlmS and NagB expression, and its relationship with the virulence of S. mutans. PMID:28036052

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand Intensive Care Adult Patient Data-Base, 2008-2009.

PubMed

Moran, John L; Solomon, Patricia J

2012-05-16

For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable. Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator. The data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function. For ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.

PubMed

Liu, Jia; Duffy, Ben A; Bernal-Casas, David; Fang, Zhongnan; Lee, Jin Hyung

2017-02-15

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods' performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

Sample size and the detection of a hump-shaped relationship between biomass and species richness in Mediterranean wetlands

USGS Publications Warehouse

Espinar, J.L.

2006-01-01

Questions: What is the observed relationship between biomass and species richness across both spatial and temporal scales in communities of submerged annual macrophytes? Does the number of plots sampled affect detection of hump-shaped pattern? Location: Don??ana National Park, southwestern Spain. Methods: A total of 102 plots were sampled during four hydrological cycles. In each hydrological cycle, the plots were distributed randomly along an environmental flooding gradient in three contrasted microhabitats located in the transition zone just below the upper marsh. In each plot (0.5 m x 0.5 m), plant density and above- and below-ground biomass of submerged vegetation were measured. The hump-shaped model was tested by using a generalized linear model (GLM). A bootstrap procedure was used to test the effect of the number of plots on the ability to detect hump-shaped patterns. Result: The area exhibited low species density with a range of 1 - 9 species and low values of biomass with a range of 0.2 - 87.6 g-DW / 0.25 m2. When data from all years and all microhabitats were combined, the relationships between biomass and species richness showed a hump-shaped pattern. The number of plots was large enough to allow detection of the hump-shaped pattern across microhabitats but it was too small to confirm the hump-shaped pattern within each individual microhabitat. Conclusion: This study provides evidence of hump-shaped patterns across microhabitats when GLM analysis is used. In communities of submerged annual macrophytes in Mediterranean wetlands, the highest species density occurs in intermediate values of biomass. The bootstrap procedure indicates that the number of plots affects the detection of hump-shaped patterns. ?? IAVS; Opulus Press.

YOCAS©® Yoga Reduces Self-reported Memory Difficulty in Cancer Survivors in a Nationwide Randomized Clinical Trial: Investigating Relationships Between Memory and Sleep.

PubMed

Janelsins, Michelle C; Peppone, Luke J; Heckler, Charles E; Kesler, Shelli R; Sprod, Lisa K; Atkins, James; Melnik, Marianne; Kamen, Charles; Giguere, Jeffrey; Messino, Michael J; Mohile, Supriya G; Mustian, Karen M

2016-09-01

Background Interventions are needed to alleviate memory difficulty in cancer survivors. We previously showed in a phase III randomized clinical trial that YOCAS©® yoga-a program that consists of breathing exercises, postures, and meditation-significantly improved sleep quality in cancer survivors. This study assessed the effects of YOCAS©® on memory and identified relationships between memory and sleep. Survivors were randomized to standard care (SC) or SC with YOCAS©® . 328 participants who provided data on the memory difficulty item of the MD Anderson Symptom Inventory are included. Sleep quality was measured using the Pittsburgh Sleep Quality Index. General linear modeling (GLM) determined the group effect of YOCAS©® on memory difficulty compared with SC. GLM also determined moderation of baseline memory difficulty on postintervention sleep and vice versa. Path modeling assessed the mediating effects of changes in memory difficulty on YOCAS©® changes in sleep and vice versa. YOCAS©® significantly reduced memory difficulty at postintervention compared with SC (mean change: yoga=-0.60; SC=-0.16; P<.05). Baseline memory difficulty did not moderate the effects of postintervention sleep quality in YOCAS©® compared with SC. Baseline sleep quality did moderate the effects of postintervention memory difficulty in YOCAS©® compared with SC (P<.05). Changes in sleep quality was a significant mediator of reduced memory difficulty in YOCAS©® compared with SC (P<.05); however, changes in memory difficulty did not significantly mediate improved sleep quality in YOCAS©® compared with SC. In this large nationwide trial, YOCAS©® yoga significantly reduced patient-reported memory difficulty in cancer survivors. © The Author(s) 2015.

Reorganization of large-scale cognitive networks during automation of imagination of a complex sequential movement.

PubMed

Sauvage, C; De Greef, N; Manto, M; Jissendi, P; Nioche, C; Habas, C

2015-04-01

We investigated the functional reconfiguration of the cerebral networks involved in imagination of sequential movements of the left foot, both performed at regular and fast speed after mental imagery training. Thirty-five volunteers were scanned with a 3T MRI while they imagined a sequence of ankle movements (dorsiflexion, plantar flexion, varus and valgus) before and after mental practice. Subjects were distributed in two groups: the first group executed regular movements whereas the second group made fast movements. We applied the general linear model (GLM) and model-free, exploratory tensorial independent component analytic (TICA) approaches to identify plastic post-training effects on brain activation. GLM showed that post-training imagination of movement was accompanied by a dual effect: a specific recruitment of a medial prefronto-cingulo-parietal circuit reminiscent of the default-mode network, with the left putamen, and a decreased activity of a lateral fronto-parietal network. Training-related subcortical changes only consisted in an increased activity in the left striatum. Unexpectedly, no difference was observed in the cerebellum. TICA also revealed involvement of the left executive network, and of the dorsal control executive network but no significant differences were found between pre- and post-training phases. Therefore, repetitive motor mental imagery induced specific putamen (motor rehearsal) recruitment that one previously observed during learning of overt movements, and, simultaneously, a specific shift of activity from the dorsolateral prefrontal cortex (attention, working memory) to the medial posterior parietal and cingulate cortices (mental imagery and memory rehearsal). Our data complement and confirm the notion that differential and coupled recruitment of cognitive networks can constitute a neural marker of training effects. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI

PubMed Central

Maneshi, Mona; Vahdat, Shahabeddin; Gotman, Jean; Grova, Christophe

2016-01-01

Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named “shared and specific independent component analysis” (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered “specific” for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks. PMID:27729843

[Economic impact of nosocomial bacteraemia. A comparison of three calculation methods].

PubMed

Riu, Marta; Chiarello, Pietro; Terradas, Roser; Sala, Maria; Castells, Xavier; Knobel, Hernando; Cots, Francesc

2016-12-01

The excess cost associated with nosocomial bacteraemia (NB) is used as a measurement of the impact of these infections. However, some authors have suggested that traditional methods overestimate the incremental cost due to the presence of various types of bias. The aim of this study was to compare three assessment methods of NB incremental cost to correct biases in previous analyses. Patients who experienced an episode of NB between 2005 and 2007 were compared with patients grouped within the same All Patient Refined-Diagnosis-Related Group (APR-DRG) without NB. The causative organisms were grouped according to the Gram stain, and whether bacteraemia was caused by a single or multiple microorganisms, or by a fungus. Three assessment methods are compared: stratification by disease; econometric multivariate adjustment using a generalised linear model (GLM); and propensity score matching (PSM) was performed to control for biases in the econometric model. The analysis included 640 admissions with NB and 28,459 without NB. The observed mean cost was €24,515 for admissions with NB and €4,851.6 for controls (without NB). Mean incremental cost was estimated at €14,735 in stratified analysis. Gram positive microorganism had the lowest mean incremental cost, €10,051. In the GLM, mean incremental cost was estimated as €20,922, and adjusting with PSM, the mean incremental cost was €11,916. The three estimates showed important differences between groups of microorganisms. Using enhanced methodologies improves the adjustment in this type of study and increases the value of the results. Copyright © 2015 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Applying the health action process approach to bicycle helmet use and evaluating a social marketing campaign.

PubMed

Karl, Florian M; Smith, Jennifer; Piedt, Shannon; Turcotte, Kate; Pike, Ian

2017-08-05

Bicycle injuries are of concern in Canada. Since helmet use was mandated in 1996 in the province of British Columbia, Canada, use has increased and head injuries have decreased. Despite the law, many cyclists do not wear a helmet. Health action process approach (HAPA) model explains intention and behaviour with self-efficacy, risk perception, outcome expectancies and planning constructs. The present study examines the impact of a social marketing campaign on HAPA constructs in the context of bicycle helmet use. A questionnaire was administered to identify factors determining helmet use. Intention to obey the law, and perceived risk of being caught if not obeying the law were included as additional constructs. Path analysis was used to extract the strongest influences on intention and behaviour. The social marketing campaign was evaluated through t-test comparisons after propensity score matching and generalised linear modelling (GLM) were applied to adjust for the same covariates. 400 cyclists aged 25-54 years completed the questionnaire. Self-efficacy and Intention were most predictive of intention to wear a helmet, which, moderated by planning, strongly predicted behaviour. Perceived risk and outcome expectancies had no significant impact on intention. GLM showed that exposure to the campaign was significantly associated with higher values in self-efficacy, intention and bicycle helmet use. Self-efficacy and planning are important points of action for promoting helmet use. Social marketing campaigns that remind people of appropriate preventive action have an impact on behaviour. © 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.

Modulation by EEG features of BOLD responses to interictal epileptiform discharges

PubMed Central

LeVan, Pierre; Tyvaert, Louise; Gotman, Jean

2013-01-01

Introduction EEG-fMRI of interictal epileptiform discharges (IEDs) usually assumes a fixed hemodynamic response function (HRF). This study investigates HRF variability with respect to IED amplitude fluctuations using independent component analysis (ICA), with the goal of improving the specificity of EEG-fMRI analyses. Methods We selected EEG-fMRI data from 10 focal epilepsy patients with a good quality EEG. IED amplitudes were calculated in an average reference montage. The fMRI data were decomposed by ICA and a deconvolution method identified IED-related components by detecting time courses with a significant HRF time-locked to the IEDs (F-test, p<0.05). Individual HRF amplitudes were then calculated for each IED. Components with a significant HRF/IED amplitude correlation (Spearman test, p< 0.05) were compared to the presumed epileptogenic focus and to results of a general linear model (GLM) analysis. Results In 7 patients, at least one IED-related component was concordant with the focus, but many IED-related components were at distant locations. When considering only components with a significant HRF/IED amplitude correlation, distant components could be discarded, significantly increasing the relative proportion of activated voxels in the focus (p=0.02). In the 3 patients without concordant IED-related components, no HRF/IED amplitude correlations were detected inside the brain. Integrating IED-related amplitudes in the GLM significantly improved fMRI signal modeling in the epileptogenic focus in 4 patients (p< 0.05). Conclusion Activations in the epileptogenic focus appear to show significant correlations between HRF and IED amplitudes, unlike distant responses. These correlations could be integrated in the analysis to increase the specificity of EEG-fMRI studies in epilepsy. PMID:20026222

Connections between intraparietal sulcus and a sensorimotor network underpin sustained tactile attention.

PubMed

Goltz, Dominique; Gundlach, Christopher; Nierhaus, Till; Villringer, Arno; Müller, Matthias; Pleger, Burkhard

2015-05-20

Previous studies on sustained tactile attention draw conclusions about underlying cortical networks by averaging over experimental conditions without considering attentional variance in single trials. This may have formed an imprecise picture of brain processes underpinning sustained tactile attention. In the present study, we simultaneously recorded EEG-fMRI and used modulations of steady-state somatosensory evoked potentials (SSSEPs) as a measure of attentional trial-by-trial variability. Therefore, frequency-tagged streams of vibrotactile stimulations were simultaneously presented to both index fingers. Human participants were cued to sustain attention to either the left or right finger stimulation and to press a button whenever they perceived a target pulse embedded in the to-be-attended stream. In-line with previous studies, a classical general linear model (GLM) analysis based on cued attention conditions revealed increased activity mainly in somatosensory and cerebellar regions. Yet, parametric modeling of the BOLD response using simultaneously recorded SSSEPs as a marker of attentional trial-by-trial variability quarried the intraparietal sulcus (IPS). The IPS in turn showed enhanced functional connectivity to a modality-unspecific attention network. However, this was only revealed on the basis of cued attention conditions in the classical GLM. By considering attentional variability as captured by SSSEPs, the IPS showed increased connectivity to a sensorimotor network, underpinning attentional selection processes between competing tactile stimuli and action choices (press a button or not). Thus, the current findings highlight the potential value by considering attentional variations in single trials and extend previous knowledge on the role of the IPS in tactile attention. Copyright © 2015 the authors 0270-6474/15/357938-12$15.00/0.

Prescribing Patterns of Intravenous Golimumab for Rheumatoid Arthritis.

PubMed

Brady, Brenna L; Tkacz, Joseph P; Lofland, Jennifer; Meyer, Roxanne; Bolge, Susan C

2015-09-01

The use of intravenous golimumab (GLM-IV), in combination with methotrexate, was approved by the US Food and Drug Administration in July 2013 for the treatment of moderate to severe, active rheumatoid arthritis (RA). GLM-IV is available in 50-mg vials, and the prescribing information specifies a dosing regimen of 2 mg/kg at 0 and 4 weeks and then every 8 weeks thereafter. The purpose of this study was to examine the patterns of prescribing and administration of GLM-IV, including the demographic, clinical, and utilization characteristics of patients with RA newly treated with GLM-IV. Rheumatology practices across the continental United States were solicited for a chart-review study. Inclusion criteria were: (1) diagnosis of RA; (2) current treatment with GLM-IV; (3) age ≥18 years; and (4) lack of pregnancy (in female patients). Physicians were offered a monetary incentive for each eligible chart provided. An electronic case-report form was developed to aid in the chart data extraction and included fields for demographic characteristics, available comorbid diagnoses, prior RA treatments, and doses and dates of GLM-IV administration. A total of 117 eligible patient charts from 15 rheumatologist practices were reviewed. The patient sample was predominantly female (81.2%), with a mean (SD) age of 55.4 (14.5) years. A total of 55.6% of patients had evidence of biologic treatment before receiving GLM-IV, and 53% had at least 1 comorbid condition. In total, 300 individual GLM-IV infusions from this sample were reviewed. Due to the relatively recent approval of GLM-IV use by the US Food and Drug Administration, the majority of patients in this sample (69.2%) had received only between 2 and 4 infusions at the time of the review. For infusion records with valid dose data, the mean number of administered vials was 3.6 (0.8) (total dose, 180 mg); the majority of patients received a dose consistent with the prescribed dose of 2 mg/kg. Combination therapy with methotrexate was observed in the charts of a minority of patients (27.4%). The mean interval between induction and the first follow-up infusion was 32.9 (11.4) days, with a mean maintenance interval of 56.5 (13.3) days. This analysis provides an early glimpse of the patterns of prescribing GLM-IV. Overall, patients appeared to have been receiving GLM-IV in accordance with Food and Drug Administration labeling; although the rate of prescribing methotrexate was low, dosages and administration intervals were within the expected ranges. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Sensory processing during viewing of cinematographic material: Computational modeling and functional neuroimaging

PubMed Central

Bordier, Cecile; Puja, Francesco; Macaluso, Emiliano

2013-01-01

The investigation of brain activity using naturalistic, ecologically-valid stimuli is becoming an important challenge for neuroscience research. Several approaches have been proposed, primarily relying on data-driven methods (e.g. independent component analysis, ICA). However, data-driven methods often require some post-hoc interpretation of the imaging results to draw inferences about the underlying sensory, motor or cognitive functions. Here, we propose using a biologically-plausible computational model to extract (multi-)sensory stimulus statistics that can be used for standard hypothesis-driven analyses (general linear model, GLM). We ran two separate fMRI experiments, which both involved subjects watching an episode of a TV-series. In Exp 1, we manipulated the presentation by switching on-and-off color, motion and/or sound at variable intervals, whereas in Exp 2, the video was played in the original version, with all the consequent continuous changes of the different sensory features intact. Both for vision and audition, we extracted stimulus statistics corresponding to spatial and temporal discontinuities of low-level features, as well as a combined measure related to the overall stimulus saliency. Results showed that activity in occipital visual cortex and the superior temporal auditory cortex co-varied with changes of low-level features. Visual saliency was found to further boost activity in extra-striate visual cortex plus posterior parietal cortex, while auditory saliency was found to enhance activity in the superior temporal cortex. Data-driven ICA analyses of the same datasets also identified “sensory” networks comprising visual and auditory areas, but without providing specific information about the possible underlying processes, e.g., these processes could relate to modality, stimulus features and/or saliency. We conclude that the combination of computational modeling and GLM enables the tracking of the impact of bottom–up signals on brain activity during viewing of complex and dynamic multisensory stimuli, beyond the capability of purely data-driven approaches. PMID:23202431

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data

PubMed Central

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-01-01

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. PMID:26921716

Granulomatous Lobular Mastitis Associated with Mycobacterium abscessus in South China: A Case Report and Review of the Literature.

PubMed

Wang, Ye-Sheng; Li, Qi-Wei; Zhou, Lin; Guan, Run-Feng; Zhou, Xiang-Ming; Wu, Ji-Hong; Rao, Nan-Yan; Zhu, Shuang

2017-01-01

Mycobacteria, which are known as rapidly growing bacteria, are pathogens that are responsible for cutaneous or subcutaneous infections that especially occur after injection, trauma, or surgery. In this report, we describe a species of Mycobacterium abscessus that was isolated from a breast abscess in a patient who was previously diagnosed with granulomatous lobular mastitis (GLM). This current case is the first ever presented case of GLM associated with M. abscessus documented in South China. The case presentation highlights the role of M. abscessus in GLM. The association of M. abscessus and GLM is discussed and a summary of breast infection due to Mycobacteria is given.

Granulomatous Lobular Mastitis Associated with Mycobacterium abscessus in South China: A Case Report and Review of the Literature

PubMed Central

Li, Qi-wei; Guan, Run-feng; Zhou, Xiang-ming; Wu, Ji-hong

2017-01-01

Mycobacteria, which are known as rapidly growing bacteria, are pathogens that are responsible for cutaneous or subcutaneous infections that especially occur after injection, trauma, or surgery. In this report, we describe a species of Mycobacterium abscessus that was isolated from a breast abscess in a patient who was previously diagnosed with granulomatous lobular mastitis (GLM). This current case is the first ever presented case of GLM associated with M. abscessus documented in South China. The case presentation highlights the role of M. abscessus in GLM. The association of M. abscessus and GLM is discussed and a summary of breast infection due to Mycobacteria is given. PMID:28286681

Behavioral and environmental influences on fishing rewards and the outcomes of alternative management scenarios for large tropical rivers.

PubMed

Hallwass, Gustavo; Lopes, Priscila F M; Juras, Anastácio A; Silvano, Renato A M

2013-10-15

Identifying the factors that influence the amount of fish caught, and thus the fishers' income, is important for proposing or improving management plans. Some of these factors influencing fishing rewards may be related to fishers' behavior, which is driven by economic motivations. Therefore, those management rules that have less of an impact on fishers' income could achieve better acceptance and compliance from fishers. We analyzed the relative influence of environmental and socioeconomic factors on fish catches (biomass) in fishing communities of a large tropical river. We then used the results from this analysis to propose alternative management scenarios in which we predicted potential fishers' compliance (high, moderate and low) based on the extent to which management proposals would affect fish catches and fishers' income. We used a General Linear Model (GLM) to analyze the influence of environmental (fishing community, season and habitat) and socioeconomic factors (number of fishers in the crew, time spent fishing, fishing gear used, type of canoe, distance traveled to fishing grounds) on fish catches (dependent variable) in 572 fishing trips by small-scale fishers in the Lower Tocantins River, Brazilian Amazon. According to the GLM, all factors together accounted for 43% of the variation in the biomass of the fish that were caught. The behaviors of fishers' that are linked to fishing effort, such as time spent fishing (42% of the total explained by GLM), distance traveled to the fishing ground (12%) and number of fishers (10%), were all positively related to the biomass of fish caught and could explain most of the variation on it. The environmental factor of the fishing habitat accounted for 10% of the variation in fish caught. These results, when applied to management scenarios, indicated that some combinations of the management measures, such as selected lakes as no-take areas, restrictions on the use of gillnets (especially during the high-water season) and individual quotas larger than fishers' usual catches, would most likely have less impact on fishers' income. The proposed scenarios help to identify feasible management options, which could promote the conservation of fish, potentially achieving higher fishers' compliance. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Ground Flash Fraction Retrieval Algorithm for GLM

NASA Technical Reports Server (NTRS)

Koshak, William J.

2010-01-01

A Bayesian inversion method is introduced for retrieving the fraction of ground flashes in a set of N lightning observed by a satellite lightning imager (such as the Geostationary Lightning Mapper, GLM). An exponential model is applied as a physically reasonable constraint to describe the measured lightning optical parameter distributions. Population statistics (i.e., the mean and variance) are invoked to add additional constraints to the retrieval process. The Maximum A Posteriori (MAP) solution is employed. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. The approach is feasible for N greater than 2000, and retrieval errors decrease as N is increased.

Cross-Referencing GLM and ISS-LIS with Ground-Based Lightning Networks

NASA Astrophysics Data System (ADS)

Virts, K.; Blakeslee, R. J.; Goodman, S. J.; Koshak, W. J.

2017-12-01

The Geostationary Lightning Mapper (GLM), in geostationary orbit aboard GOES-16 since late 2016, and the Lightning Imaging Sensor (LIS), installed on the International Space Station in February 2017, provide observations of total lightning activity from space. ISS-LIS samples the global tropics and mid-latitudes, while GLM observes the full thunderstorm life-cycle over the Americas and surrounding oceans. The launch of these instruments provides an unprecedented opportunity to compare lightning observations across multiple space-based optical lightning sensors. In this study, months of observations from GLM and ISS-LIS are cross-referenced with each other and with lightning detected by the ground-based Earth Networks Global Lightning Network (ENGLN) and the Vaisala Global Lightning Dataset 360 (GLD360) throughout and beyond the GLM field-of-view. In addition to calibration/validation of the new satellite sensors, this study provides a statistical comparison of the characteristics of lightning observed by the satellite and ground-based instruments, with an emphasis on the lightning flashes uniquely identified by the satellites.

Fly's Eye GLM Simulator Preliminary Validation Analysis

NASA Astrophysics Data System (ADS)

Quick, M. G.; Christian, H. J., Jr.; Blakeslee, R. J.; Stewart, M. F.; Corredor, D.; Podgorny, S.

2017-12-01

As part of the validation effort for the Geostationary Lightning Mapper (GLM) an airborne radiometer array has been fabricated to observe lightning optical emission through the cloud top. The Fly's Eye GLM Simulator (FEGS) is a multi-spectral, photo-electric radiometer array with a nominal spatial resolution of 2 x 2 km and spatial footprint of 10 x 10 km at cloud top. A main 25 pixel array observes the 777.4 nm oxygen emission triplet using an optical passband filter with a 10 nm FWHM, a sampling rate of 100 kHz, and 16 bit resolution. From March to May of 2017 FEGS was flown on the NASA ER-2 high altitude aircraft during the GOES-R Validation Flight Campaign. Optical signatures of lightning were observed during a variety of thunderstorm scenarios while coincident measurements were obtained by GLM and ground based antennae networks. This presentation will describe the preliminary analysis of the FEGS dataset in the context of GLM validation.

Granulomatous Lobular Mastitis.

PubMed

Mahlab-Guri, Keren; Asher, Ilan; Allweis, Tanir; Diment, Judith; Sthoeger, Zev M; Mavor, Eliezer

2015-08-01

Granulomatous lobular mastitis (GLM) is a rare disorder that can clinically mimic breast carcinoma. The recommendation for diagnosis and treatment of GLM has not yet been established. To assess a series of GLM patients, including their clinical presentation, diagnosis, treatment and outcome. We retrospectively analyzed the clinical data and treatment of 17 female patients with biopsy-proven GLM. Breast tissue was obtained by a core needle biopsy (15 patients) or open biopsy (2 patients). Images were reviewed by an experienced radiologist. The mean age of the patients at diagnosis was 44.6 ± 12.6 years. Five patients (29%) presented with bilateral disease, and seven (41%) presented with a mass, suggesting the initial diagnosis of breast carcinoma. Treatment comprised observation alone (23%), antibiotics (58.8%) and/or corticosteroids (with or without methotrexate) (35%). At the end of the study 70.6% of the patients demonstrated complete remission. None of the patients developed any systemic (granulomatous) disease or breast carcinoma during the follow-up period (4.7 ± 3.8 years). Core needle biopsy is mandatory for the diagnosis of GLM and the exclusion of breast carcinoma. The recommended treatment modalities are observation alone or corticosteroids; surgery should be avoided. GLM is a benign disease with a high rate of resolution and complete remission.

Projecting temperature-related years of life lost under different climate change scenarios in one temperate megacity, China.

PubMed

Li, Yixue; Li, Guoxing; Zeng, Qiang; Liang, Fengchao; Pan, Xiaochuan

2018-02-01

Temperature has been associated with population health, but few studies have projected the future temperature-related years of life lost attributable to climate change. To project future temperature-related disease burden in Tianjin, we selected years of life lost (YLL) as the dependent variable to explore YLL attributable to climate change. A generalized linear model (GLM) and distributed lag non-linear model were combined to assess the non-linear and delayed effects of temperature on the YLL of non-accidental mortality. Then, we calculated the YLL changes attributable to future climate scenarios in 2055 and 2090. The relationships of daily mean temperature with the YLL of non-accident mortality were basically U-shaped. Both the daily mean temperature increase on high-temperature days and its drop on low-temperature days caused an increase of YLL and non-accidental deaths. The temperature-related YLL will worsen if future climate change exceeds 2 °C. In addition, the adverse effects of extreme temperature on YLL occurred more quickly than that of the overall temperature. The impact of low temperature was greater than that of high temperature. Men were vulnerable to high temperature compared with women. This analysis highlights that the government should formulate environmental policies to reach the Paris Agreement goal. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inter-Comparison of Lightning Trends from Ground-Based Networks During Severe Weather: Applications Toward GLM

NASA Technical Reports Server (NTRS)

Carey, Lawrence D.; Schultz, Chris J.; Petersen, Walter A.; Rudlosky, Scott D.; Bateman, Monte; Cecil, Daniel J.; Blakeslee, Richard J.; Goodman, Steven J.

2011-01-01

The planned GOES-R Geostationary Lightning Mapper (GLM) will provide total lightning data on the location and intensity of thunderstorms over a hemispheric spatial domain. Ongoing GOES-R research activities are demonstrating the utility of total flash rate trends for enhancing forecasting skill of severe storms. To date, GLM total lightning proxy trends have been well served by ground-based VHF systems such as the Northern Alabama Lightning Mapping Array (NALMA). The NALMA (and other similar networks in Washington DC and Oklahoma) provide high detection efficiency (> 90%) and location accuracy (< 1 km) observations of total lightning within about 150 km from network center. To expand GLM proxy applications for high impact convective weather (e.g., severe, aviation hazards), it is desirable to investigate the utility of additional sources of continuous lightning that can serve as suitable GLM proxy over large spatial scales (order 100 s to 1000 km or more), including typically data denied regions such as the oceans. Potential sources of GLM proxy include ground-based long-range (regional or global) VLF/LF lightning networks such as the relatively new Vaisala Global Lightning Dataset (GLD360) and Weatherbug Total Lightning Network (WTLN). Before using these data in GLM research applications, it is necessary to compare them with LMAs and well-quantified cloud-to-ground (CG) lightning networks, such as Vaisala s National Lightning Detection Network (NLDN), for assessment of total and CG lightning location accuracy, detection efficiency and flash rate trends. Preliminary inter-comparisons from these lightning networks during selected severe weather events will be presented and their implications discussed.

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

A Graphical Representation of Multiple Stressor Effects on River Eutrophication as Simulated by a Physics-Based River Quality Model

NASA Astrophysics Data System (ADS)

Hitt, O.; Hutchins, M.

2016-12-01

UK river waters face considerable future pressures, primarily from population growth and climate change. In understanding controls on river water quality, experimental studies have successfully identified response to single or paired stressors under controlled conditions. Generalised Linear Model (GLM) approaches are commonly used to quantify stressor-response relationships. To explore a wider variety of stressors physics-based models are used. Our objective is to evaluate how five different types of stressor influence the severity of river eutrophication and its impact on Dissolved Oxygen (DO) an integrated measure of river ecological health. This is done by applying a physics-based river quality model for 4 years at daily time step to a 92 km stretch in the 3445 km2 Thames (UK) catchment. To understand the impact of model structural uncertainty we present results from two alternative formulations of the biological response. Sensitivity analysis carried out using the QUESTOR model (QUality Evaluation and Simulation TOol for River systems) considered gradients of various stressors: river flow, water temperature, urbanisation (abstractions and sewage/industrial effluents), phosphate concentrations in effluents and tributaries and riparian tree shading (modifying the light input). Scalar modifiers applied to the 2009-12 time-series inputs define the gradients. The model has been run for each combination of the values of these 5 variables. Results are analysed using graphical methods in order to identify variation in the type of relationship between different pairs of stressors on the system response. The method allows for all outputs from each combination of stressors to be displayed in one graphic and so showing the results of hundreds of model runs simultaneously. This approach can be carried out for all stressor pairs, and many locations/determinands. Supporting statistical analysis (GLM) reinforces the findings from the graphical analysis. Analysis suggests that climate-driven variables (flow and river temperature) give strong explanation of variation in DO content. An indicator of low DO values typically seen in summer is chosen (10th percentile). Increasing temperature clearly has adverse effects lowering DO, and is illustrated in three example graphics.

Electrochemical approach for acute myocardial infarction diagnosis based on direct antibodies-free analysis of human blood plasma.

PubMed

Suprun, Elena V; Saveliev, Anatoly A; Evtugyn, Gennady A; Lisitsa, Alexander V; Bulko, Tatiana V; Shumyantseva, Victoria V; Archakov, Alexander I

2012-03-15

A novel direct antibodies-free electrochemical approach for acute myocardial infarction (AMI) diagnosis has been developed. For this purpose, a combination of the electrochemical assay of plasma samples with chemometrics was proposed. Screen printed carbon electrodes modified with didodecyldimethylammonium bromide were used for plasma charactrerization by cyclic (CV) and square wave voltammetry and square wave (SWV) voltammetry. It was shown that the cathodic peak in voltammograms at about -250 mV vs. Ag/AgCl can be associated with AMI. In parallel tests, cardiac myoglobin and troponin I, the AMI biomarkers, were determined in each sample by RAMP immunoassay. The applicability of the electrochemical testing for AMI diagnostics was confirmed by statistical methods: generalized linear model (GLM), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), artificial neural net (multi-layer perception, MLP), and support vector machine (SVM), all of which were created to obtain the "True-False" distribution prediction where "True" and "False" are, respectively, positive and negative decision about an illness event. Copyright © 2011 Elsevier B.V. All rights reserved.

Effective time closures: quantifying the conservation benefits of input control for the Pacific chub mackerel fishery.

PubMed

Ichinokawa, Momoko; Okamura, Hiroshi; Watanabe, Chikako; Kawabata, Atsushi; Oozeki, Yoshioki

2015-09-01

Restricting human access to a specific wildlife species, community, or ecosystem, i.e., input control, is one of the most popular tools to control human impacts for natural resource management and wildlife conservation. However, quantitative evaluations of input control are generally difficult, because it is unclear how much human impacts can actually be reduced by the control. We present a model framework to quantify the effectiveness of input control using day closures to reduce actual fishing impact by considering the observed fishery dynamics. The model framework was applied to the management of the Pacific stock of the chub mackerel (Scomber japonicus) fishery, in which fishing was suspended for one day following any day when the total mackerel catch exceeded a threshold level. We evaluated the management measure according to the following steps: (1) we fitted the daily observed catch and fishing effort data to a generalized linear model (GLM) or generalized autoregressive state-space model (GASSM), (2) we conducted population dynamics simulations based on annual catches randomly generated from the parameters estimated in the first step, (3) we quantified the effectiveness of day closures by comparing the results of two simulation scenarios with and without day closures, and (4) we conducted additional simulations based on different sets of explanatory variables and statistical models (sensitivity analysis). In the first step, we found that the GASSM explained the observed data far better than the simple GLM. The model parameterized with the estimates from the GASSM demonstrated that the day closures implemented from 2004 to 2009 would have decreased exploitation fractions by ~10% every year and increased the 2009 stock biomass by 37-46% (median), relative to the values without day closures. The sensitivity analysis revealed that the effectiveness of day closures was particularly influenced by autoregressive processes in the fishery data and by positive relationships between fishing effort and total biomass. Those results indicated the importance of human behavioral dynamics under input control in quantifying the conservation benefit of natural resource management and the applicability of our model framework to the evaluation of the input controls that are actually implemented.

GLM Validation Studies in Colorado

NASA Astrophysics Data System (ADS)

Rutledge, S. A.; Reimel, K.; Fuchs, B.; Xu, W.

2017-12-01

On 8 May 2017 the Geostationary Lightning Mapper (GLM) calibration/validation field campaign completed a mission over the domain of the Colorado Lightning Mapping Array (LMA). This "gold mine day" produced a mixture of normal polarity and anomalous storms of varying intensity. A case study analysis has been completed for a portion of three individual storms from this day. By utilizing a cell tracking algorithm and lightning flash attribution program, individual lightning flashes detected by the GLM, LMA, the National Lightning Detection Network (NLDN), and Earth Networks Total Lightning Network (ENTLN) are attributed to individual storm cells. The focus of this analysis is the detection efficiency of GLM. We will discuss how the GLM detection efficiency changes as a result of storm morphology and lightning flash characteristics. Lightning flash size, flash height, and the amount of ice present between the lightning flash altitude and the top of the cloud all appear to play a role in how well GLM detects lightning flashes. Since GLM shares the same concept as its predecessor TRMM LIS (optically-based lightning detection), the evaluation of TRMM LIS against LMA network-detected lightning provides insights into the GLM detection efficiency. We have collected observations by LIS and LMA coincident in time and space during 2008-2014. The sample includes 400 LIS overpasses with both LIS and LMA detecting flashes within 150 km radius of the center of the LMA array during the 120 second LIS observing time period (analysis presently confined to the Alabama LMA network). The overall LIS detection efficiency (DE, defined as the ratio of flash rates between LIS and LMA) is 0.45, with higher DE for lower flash rate cases. LIS showed a DE of nearly 100% for cases with flash rates < 10 fl/min, but had a DE of only 20-30% for high flash rates within intense storms (> 300 fl/min). We further separated the dataset into day and night, and found that the night-time DE (0.6) increased by 20% compared to day-time DE (0.5). LIS DE also increased as a function of LMA-derived flash size, possibly due to stronger radiance from larger flashes. LIS DE was the lowest ( 40%) for flashes with sizes smaller than a single LIS pixel (< 16 km2). These results may be applicable to GLM as well.

GOES-16 Geostationary Lightning Mapper Comparison with the Earth Networks Total Lightning Network

NASA Astrophysics Data System (ADS)

Lapierre, J. L.; Stock, M.; Zhu, Y.

2017-12-01

Lightning location systems have shown to be an integral part of weather research and forecasting. The launch of the GOES-16 Geostationary Lightning Mapper (GLM) will provide a new tool to help improve lightning detection throughout the Americas and ocean regions. However, before this data can be effectively used, there must be a thorough analysis of its performance to validate the data it produces. Here, we compare GLM data to data from the Earth Networks Total Lightning Network (ENTLN). We analyze data during the months of May and June of 2017 to determine the detection efficiency of each system. A successful match occurs when two flashes overlap in time and are less than 0.2 degrees apart. Of the flashes detected by ENTLN, GLM detects about 50% overall. The highest DEs for GLM are over the ocean and South America, and lowest are in Central America and the Northeastern and Western parts of the U.S. Of the flashes detected by GLM, ENTLN detected over 80% in the Central and Eastern parts of the U.S. and 10-20% in Central and South America. Finally, we determined all the unique flashes detected by both systems and determined the DE of both systems from this unique flash dataset. We find that GLM does very well in South America, over the tropical islands in the Caribbean Sea as well as Northern U.S. It detects above 50% of the unique flashes over Central and off the Eastern Coast of the U.S. as well as in Mexico. GLM detects less than 50% of the unique flashes over Florida, the Mid-Atlantic, Mid-West, and Southwestern U.S., areas where ENTLN is expected to perform well.

Molecular Docking Studies of Catechin and Its Derivatives as Anti-bacterial Inhibitor for Glucosamine-6-Phosphate Synthase

NASA Astrophysics Data System (ADS)

Fikrika, H.; Ambarsari, L.; Sumaryada, T.

2016-01-01

Molecular docking simulation of catechin and its derivatives on Glucosamine-6- Phosphate Synthase (GlmS) has been performed in this research. GlmS inhibition by a particular ligand will suppress the production of bacterial cell wall and significantly reduce the population of invading bacteria. In this study, catechin derivatives i.e epicatechin, galloatechin and epigalloatechin were found to have stronger binding affinities as compared to natural ligand of GlmS, Fructose-6-Phosphate (F6P). Those three ligands were docked on the same pocket in GlmS target as F6P, with 70% binding sites similarity. Based on the docking results, gallocatechin turns out to be the most potent ligand for anti-bacterial agent with ΔG= -8.00 kcal/mol. The docking between GlmS and catechin derivatives are characterized by a constant present of a strong hydrogen bond between functional group O3 and Ser-349. This hydrogen bond most likely plays a significant role in the docking mechanism and binding modes selection. The surprising result is catechin itself exhibited a quite strong binding with GlmS (ΔG= -7.80 kcal.mol), but docked on a completely different pocket compared to other ligands. This results suggest that catechin might still have a curing effect but with a completely different pathway and mechanism as compared to its derivatives.

Combining GOES-16 Geostationary Lightning Mapper with the ground based Earth Networks Total Lightning Network

NASA Astrophysics Data System (ADS)

Stock, M.; Lapierre, J. L.; Zhu, Y.

2017-12-01

Recently, the Geostationary Lightning Mapper (GLM) began collecting optical data to locate lightning events and flashes over the North and South American continents. This new instrument promises uniformly high detection efficiency (DE) over its entire field of view, with location accuracy on the order of 10 km. In comparison, Earth Networks Total Lightning Networks (ENTLN) has a less uniform coverage, with higher DE in regions with dense sensor coverage, and lower DE with sparse sensor coverage. ENTLN also offers better location accuracy, lightning classification, and peak current estimation for their lightning locations. It is desirable to produce an integrated dataset, combining the strong points of GLM and ENTLN. The easiest way to achieve this is to simply match located lightning processes from each system using time and distance criteria. This simple method will be limited in scope by the uneven coverage of the ground based network. Instead, we will use GLM group locations to look up the electric field change data recorded by ground sensors near each GLM group, vastly increasing the coverage of the ground network. The ground waveforms can then be used for: improvements to differentiation between glint and lightning for GLM, higher precision lighting location, current estimation, and lightning process classification. Presented is an initial implementation of this type of integration using preliminary GLM data, and waveforms from ENTLN.

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining

PubMed Central

Truccolo, Wilson

2017-01-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics (“order parameters”) inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. PMID:28336305

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.

PubMed

Truccolo, Wilson

2016-11-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.

Recursive approach of EEG-segment-based principal component analysis substantially reduces cryogenic pump artifacts in simultaneous EEG-fMRI data.

PubMed

Kim, Hyun-Chul; Yoo, Seung-Schik; Lee, Jong-Hwan

2015-01-01

Electroencephalography (EEG) data simultaneously acquired with functional magnetic resonance imaging (fMRI) data are preprocessed to remove gradient artifacts (GAs) and ballistocardiographic artifacts (BCAs). Nonetheless, these data, especially in the gamma frequency range, can be contaminated by residual artifacts produced by mechanical vibrations in the MRI system, in particular the cryogenic pump that compresses and transports the helium that chills the magnet (the helium-pump). However, few options are available for the removal of helium-pump artifacts. In this study, we propose a recursive approach of EEG-segment-based principal component analysis (rsPCA) that enables the removal of these helium-pump artifacts. Using the rsPCA method, feature vectors representing helium-pump artifacts were successfully extracted as eigenvectors, and the reconstructed signals of the feature vectors were subsequently removed. A test using simultaneous EEG-fMRI data acquired from left-hand (LH) and right-hand (RH) clenching tasks performed by volunteers found that the proposed rsPCA method substantially reduced helium-pump artifacts in the EEG data and significantly enhanced task-related gamma band activity levels (p=0.0038 and 0.0363 for LH and RH tasks, respectively) in EEG data that have had GAs and BCAs removed. The spatial patterns of the fMRI data were estimated using a hemodynamic response function (HRF) modeled from the estimated gamma band activity in a general linear model (GLM) framework. Active voxel clusters were identified in the post-/pre-central gyri of motor area, only from the rsPCA method (uncorrected p<0.001 for both LH/RH tasks). In addition, the superior temporal pole areas were consistently observed (uncorrected p<0.001 for the LH task and uncorrected p<0.05 for the RH task) in the spatial patterns of the HRF model for gamma band activity when the task paradigm and movement were also included in the GLM. Copyright © 2014 Elsevier Inc. All rights reserved.

Characterizing recent and projecting future potential patterns of mountain pine beetle outbreaks in the Southern Rocky Mountains

USGS Publications Warehouse

Liang, Lu; Hawbaker, Todd J.; Chen, Yanlei; Zhu, Zhi-Liang; Gong, Peng

2014-01-01

The recent widespread mountain pine beetle (MPB) outbreak in the Southern Rocky Mountains presents an opportunity to investigate the relative influence of anthropogenic, biologic, and physical drivers that have shaped the spatiotemporal patterns of the outbreak. The aim of this study was to quantify the landscape-level drivers that explained the dynamic patterns of MPB mortality, and simulate areas with future potential MPB mortality under projected climate-change scenarios in Grand County, Colorado, USA. The outbreak patterns of MPB were characterized by analysis of a decade-long Landsat time-series stack, aided by automatic attribution of change detected by the Landsat-based Detection of Trends in Disturbance and Recovery algorithm (LandTrendr). The annual area of new MPB mortality was then related to a suite of anthropogenic, biologic, and physical predictor variables under a general linear model (GLM) framework. Data from years 2001–2005 were used to train the model and data from years 2006–2011 were retained for validation. After stepwise removal of non-significant predictors, the remaining predictors in the GLM indicated that neighborhood mortality, winter mean temperature anomaly, and residential housing density were positively associated with MPB mortality, whereas summer precipitation was negatively related. The final model had an average area under the curve (AUC) of a receiver operating characteristic plot value of 0.72 in predicting the annual area of new mortality for the independent validation years, and the mean deviation from the base maps in the MPB mortality areal estimates was around 5%. The extent of MPB mortality will likely expand under two climate-change scenarios (RCP 4.5 and 8.5) in Grand County, which implies that the impacts of MPB outbreaks on vegetation composition and structure, and ecosystem functioning are likely to increase in the future.

Inmate responses to prison-based drug treatment: a repeated measures analysis.

PubMed

Welsh, Wayne N

2010-06-01

Using a sample of 347 prison inmates and general linear modeling (GLM) repeated measures analyses, this paper examined during-treatment responses (e.g., changes in psychological and social functioning) to prison-based TC drug treatment. These effects have rarely been examined in previous studies, and never with a fully multivariate model accounting for within-subjects effects (changes over time), between-subjects effects (e.g., levels of risk and motivation), and within/between-subjects interactions (timexriskxmotivation). The results provide evidence of positive inmate change in response to prison TC treatment, but the patterns of results varied depending upon: (a) specific indicators of psychological and social functioning, motivation, and treatment process; (b) the time periods examined (1, 6, and 12 months during treatment); and (c) baseline levels of risk and motivation. Significant interactions between time and type of inmate suggest important new directions for research, theory, and practice in offender-based substance abuse treatment. Copyright (c) 2010 Elsevier Ireland Ltd. All rights reserved.

Factors influencing riverine fish assemblages in Massachusetts

USGS Publications Warehouse

Armstrong, David S.; Richards, Todd A.; Levin, Sara B.

2011-01-01

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Massachusetts Department of Environmental Protection, and the Massachusetts Department of Fish and Game, conducted an investigation of fish assemblages in small- to medium-sized Massachusetts streams. The objective of this study was to determine relations between fish-assemblage characteristics and anthropogenic factors, including impervious cover and estimated flow alteration, relative to the effects of environmental factors, including physical-basin characteristics and land use. The results of this investigation supersede those of a preliminary analysis published in 2010. Fish data were obtained for 669 fish-sampling sites from the Massachusetts Division of Fisheries and Wildlife fish-community database. A review of the literature was used to select fish metrics - species richness, abundance of individual species, and abundances of species grouped on life history traits - responsive to flow alteration. The contributing areas to the fish-sampling sites were delineated and used with a geographic information system to determine a set of environmental and anthropogenic factors that were tested for use as explanatory variables in regression models. Reported and estimated withdrawals and return flows were used together with simulated unaltered streamflows to estimate altered streamflows and indicators of flow alteration for each fish-sampling site. Altered streamflows and indicators of flow alteration were calculated on the basis of methods developed in a previous U.S. Geological Survey study in which unaltered daily streamflows were simulated for a 44-year period (water years 1961-2004), and streamflow alterations were estimated by use of water-withdrawal and wastewater-return data previously reported to the State for the 2000-04 period and estimated domestic-well withdrawals and septic-system discharges. A variable selection process, conducted using principal components analysis and Spearman rank correlation, was used to select a set of 15 non-redundant environmental and anthropogenic factors to test for use as explanatory variables in the regression analyses. Twenty-one fish species were used in a multivariate analysis of fish-assemblage patterns. Results of nonmetric multidimensional scaling and hierarchical cluster analysis were used to group fish species into fluvial and macrohabitat generalist habitat-use classes. Two analytical techniques, quantile regression and generalized linear modeling, were applied to characterize the association between fish-response variables and environmental and anthropogenic explanatory variables. Quantile regression demonstrated that as percent impervious cover and an indicator of percent alteration of August median flow from groundwater withdrawals increase, the relative abundance and species richness of fluvial fish decrease. The quantile regression plots indicate that (1) as many as seven fluvial fish species are expected in streams with little flow alteration or impervious cover, (2) no more than four fluvial fish species are expected in streams where flow alterations from groundwater withdrawals exceed 50 percent of the August median flow or the percent area of impervious cover exceeds 15 percent, and (3) few fluvial fish remain at high rates of withdrawal (approaching 100 percent) or high rates of impervious cover (between 25 and 30 percent). Three generalized linear models (GLMs) were developed to quantify the response of fluvial fish to multiple environmental and anthropogenic variables. All variables in the GLM equations were demonstrated to be significant (p less than 0.05, with most less than 0.01). Variables in the fluvial-fish relative-abundance model were channel slope, estimated percent alteration of August median flow from groundwater withdrawals, percent wetland in a 240-meter buffer strip, and percent impervious cover. Variables in the fluvial-fish species-richness model were drainage area, channel slope, total undammed reach length, percent wetland in a 240-meter buffer strip, and percent impervious cover. Variables in the brook trout relativeabundance model were drainage area, percent open water, and percent impervious cover. The variability explained by the GLM models, as measured by the pseudo R2, ranged from 18.2 to 34.6, and correlations between observed and predicted values ranged from 0.50 to 0.60. Results of GLM models indicated that, keeping all other variables the same, a one-unit (1 percent) increase in the percent depletion of August median flow would result in a 0.9-percent decrease in the relative abundance (in counts per hour) of fluvial fish. The results of GLM models also indicated that a unit increase in impervious cover (1 percent) resulted in a 3.7-percent decrease in the relative abundance of fluvial fish, a 5.4-percent decrease in fluvial-fish species richness, and an 8.7-percent decrease in brook trout relative abundance.

Healthcare resource utilization, costs of care, and treatment of mycosis fungoides cutaneous T-cell lymphoma patterns in a large managed care population: a retrospective US claims-based analysis.

PubMed

Tsang, Yuen; Gu, Tao; Sharma, Gaurav; Raspa, Susan; Drake, Bill; Tan, Hiangkiat

2018-05-07

To evaluate health care utilization, treatment patterns and costs among patients with mycosis fungoides-cutaneous T-cell lymphoma (MF-CTCL). This retrospective cohort study queried the HealthCore Integrated Research Database to identify patients ≥18 years with ≥2 diagnoses of MF-CTCL (ICD-9-CM code 202.1x, 202.2x) between 07 January 2006 and 07 January 2013. Index date was defined as first MF-CTCL diagnosis. Patients were continuously enrolled ≥6 months before and ≥12 months after index date. Severe MF-CTCL was identified via systemic therapy use postindex. Generalized linear model (GLM) was used to estimate the relationship between MF-CTCL severity and healthcare costs controlling for selected factors. A total of 1981 MF-CTCL patients were evaluated: 493 (24.9%) severe and 1488 (75.1%) with mild to moderate disease. GLM analysis indicated severe MF-CTCL patients incurred higher all-cause healthcare total costs compared to patients with mild-to-moderate MF-CTCL (coefficient estimate: 4.19, p < .0001). About 51% of patients did not receive any MF-CTCL-specific treatment within 60 days after MF-CTCL diagnosis. MF-CTCL severity was associated with greater healthcare resource utilization and costs. These findings suggest that about half of MF-CTCL patients do not receive MF-CTCL-specific treatment within 60 days following initial diagnosis. Future studies are needed to understand reasons for delayed treatment initiation.

Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis

PubMed Central

Abdulrahman, Hunar; Henson, Richard N.

2016-01-01

Functional magnetic resonance imaging (fMRI) studies typically employ rapid, event-related designs for behavioral reasons and for reasons associated with statistical efficiency. Efficiency is calculated from the precision of the parameters (Betas) estimated from a General Linear Model (GLM) in which trial onsets are convolved with a Hemodynamic Response Function (HRF). However, previous calculations of efficiency have ignored likely variability in the neural response from trial to trial, for example due to attentional fluctuations, or different stimuli across trials. Here we compare three GLMs in their efficiency for estimating average and individual Betas across trials as a function of trial variability, scan noise and Stimulus Onset Asynchrony (SOA): “Least Squares All” (LSA), “Least Squares Separate” (LSS) and “Least Squares Unitary” (LSU). Estimation of responses to individual trials in particular is important for both functional connectivity using “Beta-series correlation” and “multi-voxel pattern analysis” (MVPA). Our simulations show that the ratio of trial-to-trial variability to scan noise impacts both the optimal SOA and optimal GLM, especially for short SOAs < 5 s: LSA is better when this ratio is high, whereas LSS and LSU are better when the ratio is low. For MVPA, the consistency across voxels of trial variability and of scan noise is also critical. These findings not only have important implications for design of experiments using Beta-series regression and MVPA, but also statistical parametric mapping studies that seek only efficient estimation of the mean response across trials. PMID:26549299

Simultaneous Determination of Metformin, Glipizide, Repaglinide, and Glimepiride or Metformin and Pioglitazone by a Validated LC Method: Application in the Presence of Metformin Impurity (1-Cyanoguanidine).

PubMed

El-Zaher, Asmaa A; Elkady, Ehab F; Elwy, Hanan M; Saleh, Mahmoud A

2016-07-01

A rapid, simple, and precise RPLC method was developed for the simultaneous determination of the widely used oral antidiabetic, metformin hydrochloride (MTF), with some commonly coadministered oral antidiabetics from different pharmacological classes-glipizide (GPZ), pioglitazone hydrochloride (PGZ), glimepiride (GLM), and repaglinide (RPG)-in bulk, laboratory-prepared mixtures and pharmaceutical formulations in the presence of metformin-reported impurity [1-cyanoguanidine (CNG)]. Chromatographic separation was achieved using isocratic elution mode with a mobile phase of acetonitrile: 0.02 M potassium dihydrogen phosphate (pH 3.17; 50-50, v/v) flowing through a CN Phenomenex column (Phenosphere Next, 250 × 4.6 mm, 5 μm) at a rate of 1.5 mL/min at ambient temperature. UV detection was carried out at 220 nm. The method was validated according to International Conference on Harmonization guidelines. Linearity, accuracy, and precision were satisfactory for concentration ranges: 0.175-350 μg/mL for MTF, 0.0525-105 μg/mL for GPZ, 0.125-250 μg/mL for PGZ, and 0.05-100 μg/mL for GLM and RPG. Correlation coefficients were >0.99 for all analytes. LOQs were 0.009 μg/mL for MTF, 0.009 μg/mL for GPZ, 0.04 μg/mL for GLM, 0.124 μg/mL for PGZ, and 0.044 μg/mL for RPG. The developed method is specific, accurate, and suitable for the QC and routine analysis of the cited drugs in their pharmaceutical products.

Investigations in thunderstorm energetics using satellite instrumentation and Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Brunner, K. N.; Bitzer, P. M.

2017-12-01

The electrical energy dissipated by lightning is a fundamental question in lightning physics and may be used in severe weather applications. However, the electrical energy, flash area/extent and spectral energy density (radiance) are all influenced by the geometry of the lightning channel. We present details of a Monte Carlo based model simulating the optical emission from lightning and compare with observations. Using time-of-arrival techniques and the electric field change measurements from the Huntsville Alabama Marx Meter Array (HAMMA), the 4D lightning channel is reconstructed. The located sources and lightning channel emit optical emission, calibrated by the ground based electric field, that scatters until absorbed or a cloud boundary is reached within the model. At cloud top, the simulation is gridded as LIS pixels (events) and contiguous events (groups). The radiance is related via the LIS calibration and the estimated lightning electrical energy is calculated at the LIS/GLM time resolution. Previous Monte Carlo simulations have relied on a simplified lightning channel and scattering medium. This work considers the cloud a stratified medium of graupel/ice and inhomogeneous at flash scale. The impact of cloud inhomogeneity on the scattered optical emission at cloud top and at the time resolution of LIS and GLM are also considered. The simulation results and energy metrics provide an estimation of the electrical energy using GLM and LIS on the International Space Station (ISS-LIS).

The GOES-R GeoStationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas

2011-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved capability for the Advanced Baseline Imager (ABI). The Geostationary Lighting Mapper (GLM) will map total lightning activity (in-cloud and cloud-to-ground lighting flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms (environmental data records), cal/val performance monitoring tools, and new applications using GLM alone, in combination with the ABI, merged with ground-based sensors, and decision aids augmented by numerical weather prediction model forecasts. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. An international field campaign planned for 2011-2012 will produce concurrent observations from a VHF lightning mapping array, Meteosat multi-band imagery, Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) overpasses, and related ground and in-situ lightning and meteorological measurements in the vicinity of Sao Paulo. These data will provide a new comprehensive proxy data set for algorithm and application development.

Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch readiness in December 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models will be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) forecast offices in Southern and Eastern Region. This effort is designed to help improve our understanding of the application of these data in operational settings.

The optimal hormonal replacement modality selection for multiple organ procurement from brain-dead organ donors

PubMed Central

Mi, Zhibao; Novitzky, Dimitri; Collins, Joseph F; Cooper, David KC

2015-01-01

The management of brain-dead organ donors is complex. The use of inotropic agents and replacement of depleted hormones (hormonal replacement therapy) is crucial for successful multiple organ procurement, yet the optimal hormonal replacement has not been identified, and the statistical adjustment to determine the best selection is not trivial. Traditional pair-wise comparisons between every pair of treatments, and multiple comparisons to all (MCA), are statistically conservative. Hsu’s multiple comparisons with the best (MCB) – adapted from the Dunnett’s multiple comparisons with control (MCC) – has been used for selecting the best treatment based on continuous variables. We selected the best hormonal replacement modality for successful multiple organ procurement using a two-step approach. First, we estimated the predicted margins by constructing generalized linear models (GLM) or generalized linear mixed models (GLMM), and then we applied the multiple comparison methods to identify the best hormonal replacement modality given that the testing of hormonal replacement modalities is independent. Based on 10-year data from the United Network for Organ Sharing (UNOS), among 16 hormonal replacement modalities, and using the 95% simultaneous confidence intervals, we found that the combination of thyroid hormone, a corticosteroid, antidiuretic hormone, and insulin was the best modality for multiple organ procurement for transplantation. PMID:25565890

Granulomatous lobular mastitis: two case reports with focus on radiologic and histopathologic features.

PubMed

Khamapirad, Tuenchit; Hennan, Kimberly; Leonard, Morton; Eltorky, Mahmoud; Qiu, Suimin

2007-04-01

Granulomatous lobular mastitis (GLM) is a rare, benign condition with an unknown etiology that can appear as cancer on mammogram, ultrasound, and magnetic resonance imaging. The terminology of GLM was first named by Going et al (J Clin Pathol 1987;40:535-540) in 1987 after he noted the lobule centered distribution on histologic exam. We present 2 case reports of GLM that clinically and radiographically appeared as malignant lesions. The first case was a 31-year-old woman with a 1-month history of breast mass, and the second case was a 33-year-old woman with a 2-week history of breast mass. Both cases were histologically diagnosed as GLM. Retrospectively, we identified ultrasound and magnetic resonance imaging characteristics that may be used in the future to classify the breast mass before biopsy.

[Granulomatous lobular mastitis: a clinicopathologic study of 68 cases].

PubMed

Cheng, Juan; Du, Yu-tang; Ding, Hua-ye

2010-10-01

To study the clinical and pathologic features of granulomatous lobular mastitis (GLM). Sixty-eight cases of GLM were retrieved from the archival file. The clinical data and histologic features were retrospectively reviewed. Sixty-eight patients presented with breast mass. Ulceration in overlying breast skin was seen in 9 cases. Most of the patients had history of breast feeding. None of them had evidence of specific infections involving the breast. The clinical and radiologic features mimicked malignancy. Histologically, GLM was characterized by the presence of non-necrotizing granulomas, usually admixed with neutrophils and associated with benign ductolobular units. The ductolobular architecture was still preserved. The duration of follow up ranged from 6 to 36 months. Four patients suffered from disease recurrence. GLM shows clinical and radiologic features reminiscent of breast cancer. Correct diagnosis requires histologic examination of the biopsy specimens.

Body mass index and the impact of a health promotion intervention on health services use and expenditures.

PubMed

Meng, Hongdao; Liebel, Dianne; Wamsley, Brenda R

2011-06-01

To examine the effect of body mass index (BMI) on the impact of a health promotion intervention on health services use and expenditures among Medicare beneficiaries with disabilities. We analyzed data from 452 Medicare beneficiaries who participated in a Medicare demonstration. The intervention included the following components: patient education, health promotion coaching, medication management, and physician care management. We performed the analysis by using generalized linear models (GLM) to examine the impact of BMI and the intervention on total health care expenditures. The intervention was cost neutral over the 2-year study period. Participants in the intervention group used less home health aide services (p = .03) and had fewer nursing home days (p = .05). The intervention appeared to have smaller effects on expenditures as BMI level increased. The findings suggest that a health promotion intervention may achieve better beneficiary outcomes without an increase in resource use in this Medicare population.

The ecological associations of surface-dwelling lizards in Qom Province in the Northwest of Central Plateau of Iran.

PubMed

Ebrahimi, Mehregan; Ahmadzadeh, Faraham; Mostafavi, Hossein; Mehrabian, Aahmad Reza; Abdoli, Asghar; Mahini, Abdolrasoul Salman

2013-01-01

We used pitfall trapping to investigate the effects of elevation, plant density and soil structure on species diversity and the impact of these habitat factors on lizard habitat selectivity in the Qom Province in the Central Plateau of Iran. From a total of 12 1-ha plots, we captured 363 individuals of 15 species of lizards (six species of Lacertidae, five species of Agamidae, two species of Gekkonidae, one species of Varanidae and one species of Scincidae). A generalized linear model (GLM) determined that elevation was the most important factor impacting species diversity. The highest species diversity was at the intermediate elevation (1289 m). Abundance of 6 out of 15 species showed strong relationships with some habitat factors. These relationships were demonstrated by habitat selectivity index (Ivlev's index). Our result supports other surveys that showed that elevation plays an important role in determining lizard species diversity.

The Ecological Associations of Surface-Dwelling Lizards in Qom Province in the Northwest of Central Plateau of Iran

PubMed Central

Ebrahimi, Mehregan; Ahmadzadeh, Faraham; Mostafavi, Hossein; Mehrabian, Aahmad Reza; Abdoli, Asghar; Mahini, Abdolrasoul Salman

2013-01-01

We used pitfall trapping to investigate the effects of elevation, plant density and soil structure on species diversity and the impact of these habitat factors on lizard habitat selectivity in the Qom Province in the Central Plateau of Iran. From a total of 12 1-ha plots, we captured 363 individuals of 15 species of lizards (six species of Lacertidae, five species of Agamidae, two species of Gekkonidae, one species of Varanidae and one species of Scincidae). A generalized linear model (GLM) determined that elevation was the most important factor impacting species diversity. The highest species diversity was at the intermediate elevation (1289 m). Abundance of 6 out of 15 species showed strong relationships with some habitat factors. These relationships were demonstrated by habitat selectivity index (Ivlev's index). Our result supports other surveys that showed that elevation plays an important role in determining lizard species diversity. PMID:24349557

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data.

PubMed

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-05-15

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

THERAPEUTIC STRATEGY FOR GRANULOMATOUS LOBULAR MASTITIS: A CLINICOPATHOLOGICAL STUDY OF 12 PATIENTS

PubMed Central

AKAHANE, KAZUHISA; TSUNODA, NOBUYUKI; KATO, MASAMICHI; NODA, SUMIYO; SHIMOYAMA, YOSHIE; ISHIGAKI, SATOKO; SATAKE, HIROKO; NAKAMURA, SHIGEO; NAGINO, MASATO

2013-01-01

ABSTRACT Granulomatous lobular mastitis (GLM) is a rare inflammatory pseudotumor. No therapeutic modality for this disease has been established because of its rarity. The purpose of this study is to evaluate the treatment strategies of GLM. Twelve women who met the histological criteria for GLM were retrospectively studied. The clinical data and the presentation, histopathology, and management of the disease were analyzed by reviewing the patients’ medical records. The diagnosis of GLM was confirmed histologically by core needle biopsy in 9 cases, by vacuum-assisted biopsy in 2 cases, and by excisional biopsy in 1 case. Ten patients received corticosteroid treatment and another two patients were treated with local excision or incision and drainage. The median initial dosage of corticosteroid (Prednisolone) was 30 mg/day (range: 15–60 mg/day), and the dosages were tapered according to improvement. The median duration of corticosteroid treatment was 5 months (range: 1–12 months). The median follow-up period was 22 months (range: 6–104 months), and no patient treated with corticosteroid demonstrated recurrence. However, patients treated with excision or incision and drainage had recurrences. These results suggest that steroid treatment may be the first choice in treatment strategies for GLM. PMID:24640175

Therapeutic strategy for granulomatous lobular mastitis: a clinicopathological study of 12 patients.

PubMed

Akahane, Kazuhisa; Tsunoda, Nobuyuki; Kato, Masamichi; Noda, Sumiyo; Shimoyama, Yoshie; Ishigakis, Satoko; Satake, Hiroko; Nakamura, Shigeo; Nagino, Masato

2013-08-01

Granulomatous lobular mastitis (GLM) is a rare inflammatory pseudotumor. No therapeutic modality for this disease has been established because of its rarity. The purpose of this study is to evaluate the treatment strategies of GLM. Twelve women who met the histological criteria for GLM were retrospectively studied. The clinical data and the presentation, histopathology, and management of the disease were analyzed by reviewing the patients' medical records. The diagnosis of GLM was confirmed histologically by core needle biopsy in 9 cases, by vacuum-assisted biopsy in 2 cases, and by excisional biopsy in 1 case. Ten patients received corticosteroid treatment and another two patients were treated with local excision or incision and drainage. The median initial dosage of corticosteroid (Prednisolone) was 30 mg/day (range: 15-60 mg/day), and the dosages were tapered according to improvement. The median duration of corticosteroid treatment was 5 months (range: 1-12 months). The median follow-up period was 22 months (range: 6-104 months), and no patient treated with corticosteroid demonstrated recurrence. However, patients treated with excision or incision and drainage had recurrences. These results suggest that steroid treatment may be the first choice in treatment strategies for GLM.

A first look at lightning energy determined from GLM

NASA Astrophysics Data System (ADS)

Bitzer, P. M.; Burchfield, J. C.; Brunner, K. N.

2017-12-01

The Geostationary Lightning Mapper (GLM) was launched in November 2016 onboard GOES-16 has been undergoing post launch and product post launch testing. While these have typically focused on lightning metrics such as detection efficiency, false alarm rate, and location accuracy, there are other attributes of the lightning discharge that are provided by GLM data. Namely, the optical energy radiated by lightning may provide information useful for lightning physics and the relationship of lightning energy to severe weather development. This work presents initial estimates of the lightning optical energy detected by GLM during this initial testing, with a focus on observations during field campaign during spring 2017 in Huntsville. This region is advantageous for the comparison due to the proliferation of ground-based lightning instrumentation, including a lightning mapping array, interferometer, HAMMA (an array of electric field change meters), high speed video cameras, and several long range VLF networks. In addition, the field campaign included airborne observations of the optical emission and electric field changes. The initial estimates will be compared with previous observations using TRMM-LIS. In addition, a comparison between the operational and scientific GLM data sets will also be discussed.

Online Statistical Modeling (Regression Analysis) for Independent Responses

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

The Use of the Deep Convective Cloud Technique (DCCT) to Monitor On-Orbit Performance of the Geostationary Lightning Mapper (GLM): Use of Lightning Imaging Sensor (LIS) Data as Proxy

NASA Technical Reports Server (NTRS)

Buechler, Dennis E.; Christian, H. J.; Koshak, William J.; Goodman, Steve J.

2013-01-01

The Geostationary Lightning Mapper (GLM) on the next generation Geostationary Operational Environmental Satellite-R (GOES-R) will not have onboard calibration capability to monitor its performance. The Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite has been providing observations of total lightning over the Earth's Tropics since 1997. The GLM design is based on LIS heritage, making it a good proxy dataset. This study examines the performance of LIS throughout its time in orbit. This was accomplished through application of the Deep Convective Cloud Technique (DCCT) (Doelling et al., 2004) to LIS background pixel radiance data. The DCCT identifies deep convective clouds by their cold Infrared (IR) brightness temperatures and using them as invariant targets in the solar reflective portion of the solar spectrum. The GLM and LIS operate in the near-IR at a wavelength of 777.4 nm. In the present study the IR data is obtained from the Visible Infrared Sensor (VIRS) which is collocated with LIS onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The DCCT is applied to LIS observations for July and August of each year from 1998-2010. The resulting distributions of LIS background DCC pixel radiance for each July August are very similar, indicating stable performance. The mean radiance of the DCCT analysis does not show a long term trend and the maximum deviation of the July August mean radiance for each year is within 0.7% of the overall mean. These results demonstrate that there has been no discernible change in LIS performance throughout its lifetime. A similar approach will used for monitoring the performance of GLM, with cold clouds identified using IR data from the Advanced Baseline Imager (ABI) which will also be located on GOES-R. Since GLM is based on LIS design heritage, the LIS results indicate that GLM should also experience stable performance over its lifetime.

Simultaneous Observation of Lightning and Terrestrial Gamma-ray Flashes

NASA Astrophysics Data System (ADS)

Alnussirat, S.; Christian, H. J., Jr.; Fishman, G. J.; Burchfield, J. C.

2017-12-01

The relative timing between TGFs and lightning optical emissions is a critical parameter that may elucidate the production mechanism(s) of TGFs. In this work, we study the correlation between optical emissions detected by the Geostationary Lightning Mapper (GLM) and TGFs triggered by the Fermi-GBM. The GLM is the only instrument that detects total lightning activities (IC and CG) continuously (day and night) over a large area of the Earth, with very high efficiency and location accuracy. The unique optical emission data from the GLM will enable us to study, for the first time, the lightning activity before and after the TGF production. From this investigation, we hope to clarify the production mechanism of TGFs and the characteristics of thundercloud cells that produce them. A description of the GLM concept and operation will be presented and as well as the preliminary results of the TGF-optical emission correlation.

Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?

PubMed

Emmert, Kirsten; Kopel, Rotem; Sulzer, James; Brühl, Annette B; Berman, Brian D; Linden, David E J; Horovitz, Silvina G; Breimhorst, Markus; Caria, Andrea; Frank, Sabine; Johnston, Stephen; Long, Zhiying; Paret, Christian; Robineau, Fabien; Veit, Ralf; Bartsch, Andreas; Beckmann, Christian F; Van De Ville, Dimitri; Haller, Sven

2016-01-01

An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first- (single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction. In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-of-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success. Copyright © 2015 Elsevier Inc. All rights reserved.

An Analysis of Total Lightning Characteristics in Tornadic Storms: Preparing for the Capabilities of the GLM

NASA Astrophysics Data System (ADS)

Reimel, Karly Jackson

Numerous studies have found that severe weather is often preceded by a rapid increase in the total lightning flash rate. This rapid increase results from numerous intra-cloud flashes forming around the periphery of an intensifying updraft. The relationship between flash rates and updraft intensity is extremely useful to forecasters in severe weather warning decision making processes, but total lightning data has not always been widely available. The Geostationary Lightning Mapper (GLM) will be the first instrument to detect lightning from geostationary orbit, where it will provide a continuous view of lightning over the entire western hemisphere. To prepare for the capabilities of this new instrument, this thesis analyzes the relationship between total lightning trends and tornadogenesis. Four supercellular and two non-supercellular tornadic storms are analyzed and compared to determine how total lightning characteristics differ between dynamically different tornadic storms. Supercellular tornadoes require a downdraft to form while landspout tornadoes form within an intensifying updraft acting on pre-existing vertical vorticity. Results of this analysis suggest that the supercellular tornadoes we studied show a decrease in flash rate and a decrease in lightning mapping array (LMA) source density heights prior to the tornado. This decrease may indicate the formation of a downdraft. In contrast, lightning flash rates increase during landspout formation in conjunction with an intensifying updraft. The total lightning trends appear to follow the evolution of an updraft rather than directly responding to tornadogenesis. To further understand how storm microphysics and dynamics impact the relationship between lightning behavior and tornadogenesis, two of the tornadic supercells were analyzed over Colorado and two were analyzed over Alabama. Colorado storms typically exhibit higher flash rates and anomalous charge structures in comparison to the environmentally different Alabama storms that are typically normal polarity and produce fewer flashes. The difference in microphysical characteristics does not appear to affect the relationship between total lightning trends and tornadogenesis. The capabilities of GLM are yet to be determined because the instrument is still in its calibration/validation stages. However, as part of the GLM cal/val team, we were in a unique position to examine the first-light GLM data and contribute to the assessment of its performance for noteworthy thunderstorm events during the Spring/Summer seasons of 2017. The final chapter of this thesis displays a preliminary analysis of GLM data. A first look into GLM performance is established by comparing GLM data with data from other lightning detecting instruments. Overall, GLM appears to detect fewer flashes than other lightning detecting networks and instruments in Colorado storms, more so for intense storms compared to weaker storms.

Out-of-pocket expenditure by private households for dental services - empirical evidence from Austria.

PubMed

Sanwald, Alice; Theurl, Engelbert

2016-12-01

Dental services differ from other health services in several dimensions. One important difference is that a substantial share of costs of dental services-especially costs beyond routine dental treatment-is paid directly by the patient out-of-pocket. This study analyses the socio-economic determinants of out-of-pocket expenditure for dental services (OOPE) in Austria at the household level. Cross-sectional information on OOPE and household characteristics provided by the Austrian household budget survey 2009/10 was analysed. A two-part model (Logit/GLM) and one-part GLM was applied. The probability of OOPE is strongly affected by the life cycle (structure) of the household. It is higher for higher age classes, higher income, and partially higher levels of education. The type of public insurance has an influence on expenditure probability while the existence of private health insurance has no significant effect. In contrast to the highly statistically significant coefficients in the first stage, the covariates of the second stage remain predominantly insignificant. According to the results, the level of expenditure is driven mainly by the level of education and income. The results of the one-part GLM confirm the results of the two-part model. The results allow new insights into the determinants of OOPE for dental care. The household level turns out to be an adequate basis to study the determinants of OOPE, although caution should be applied before jumping to conclusions for the individual level.

Satellite Proving Ground for the GOES-R Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Gurka, James; Bruning, E. C.; Blakeslee, J. R.; Rabin, Robert; Buechler, D.

2009-01-01

The key mission of the Satellite Proving Ground is to demonstrate new satellite observing data, products and capabilities in the operational environment to be ready on Day 1 to use the GOES-R suite of measurements. Algorithms, tools, and techniques must be tested, validated, and assessed by end users for their utility before they are finalized and incorporated into forecast operations. The GOES-R Proving Ground for the Geostationary Lightning Mapper (GLM) focuses on evaluating how the infusion of the new technology, algorithms, decision aids, or tailored products integrate with other available tools (weather radar and ground strike networks; nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing forecasts and warning products. Additionally, the testing concept fosters operation and development staff interactions which will improve training materials and support documentation development. Real-time proxy total lightning data from regional VHF lightning mapping arrays (LMA) in Northern Alabama, Central Oklahoma, Cape Canaveral Florida, and the Washington, DC Greater Metropolitan Area are the cornerstone for the GLM Proving Ground. The proxy data will simulate the 8 km Event, Group and Flash data that will be generated by GLM. Tailored products such as total flash density at 1-2 minute intervals will be provided for display in AWIPS-2 to select NWS forecast offices and national centers such as the Storm Prediction Center. Additional temporal / spatial combinations are being investigated in coordination with operational needs and case-study proxy data and prototype visualizations may also be generated from the NASA heritage Lightning Imaging Sensor and Optical Transient Detector data. End users will provide feedback on the utility of products in their operational environment, identify use cases and spatial/temporal scales of interest, and provide feedback to the developers for adjusted or new products.

Health care resource utilization and direct medical costs for patients with schizophrenia initiating treatment with atypical versus typical antipsychotics in Tianjin, China.

PubMed

He, Xiaoning; Wu, Jing; Jiang, Yawen; Liu, Li; Ye, Wenyu; Xue, Haibo; Montgomery, William

2015-04-09

It is uncertain whether the extra acquisition costs of atypical antipsychotics over typical antipsychotics are offset by their other reduced resource use especially in hospital services in China. This study compared the psychiatric-related health care resource utilization and direct medical costs for patients with schizophrenia initiating atypical or typical antipsychotics in Tianjin, China. Data were obtained from the Tianjin Urban Employee Basic Medical Insurance database (2008-2010). Adult patients with schizophrenia with ≥1 prescription for antipsychotics after ≥90-day washout and 12-month continuous enrollment after first prescription was included. Psychiatric-related resource utilization and direct medical costs of the atypical and typical cohorts were estimated during the 12-month follow-up period. Logistic regressions, ordinary least square (OLS), and generalized linear models (GLM) were employed to estimate differences of resource utilization and costs between the two cohorts. One-to-one propensity score matching was conducted as a sensitivity analysis. 1131 patients initiating either atypical (N = 648) or typical antipsychotics (N = 483) were identified. Compared with the typical cohort, the atypical cohort had a lower likelihood of hospitalization (45.8% vs. 56.7%, P < 0.001; adjusted OR: 0.58, P < 0.001) over the follow-up period. Medication costs for the atypical cohort were higher than the typical cohort ($438 vs. $187, P < 0.001); however, their non-medication medical costs were significantly lower ($1223 vs. $1704, P < 0.001). The total direct medical costs were similar between the atypical and typical cohorts before ($1661 vs. $1892, P = 0.100) and after matching ($1711 vs. 1868, P = 0.341), consistent with the results from OLS and GLM models for matched cohorts. The atypical cohort had similar total direct medical costs compared to the typical cohort. Higher medication costs associated with atypical antipsychotics were offset by a reduction in non-medication medical costs, driven by fewer hospitalizations.

Environmentally driven synchronies of Mediterranean cephalopod populations

NASA Astrophysics Data System (ADS)

Keller, Stefanie; Quetglas, Antoni; Puerta, Patricia; Bitetto, Isabella; Casciaro, Loredana; Cuccu, Danila; Esteban, Antonio; Garcia, Cristina; Garofalo, Germana; Guijarro, Beatriz; Josephides, Marios; Jadaud, Angelique; Lefkaditou, Evgenia; Maiorano, Porzia; Manfredi, Chiara; Marceta, Bojan; Micallef, Reno; Peristeraki, Panagiota; Relini, Giulio; Sartor, Paolo; Spedicato, Maria Teresa; Tserpes, George; Hidalgo, Manuel

2017-03-01

The Mediterranean Sea is characterized by large scale gradients of temperature, productivity and salinity, in addition to pronounced mesoscale differences. Such a heterogeneous system is expected to shape the population dynamics of marine species. On the other hand, prevailing environmental and climatic conditions at whole basin scale may force spatially distant populations to fluctuate in synchrony. Cephalopods are excellent case studies to test these hypotheses owing to their high sensitivity to environmental conditions. Data of two cephalopod species with contrasting life histories (benthic octopus vs nectobenthic squid), obtained from scientific surveys carried out throughout the Mediterranean during the last 20 years were analyzed. The objectives of this study and the methods used to achieve them (in parentheses) were: (i) to investigate synchronies in spatially separated populations (decorrelation analysis); (ii) detect underlying common abundance trends over distant regions (dynamic factor analysis, DFA); and (iii) analyse putative influences of key environmental drivers such as productivity and sea surface temperature on the population dynamics at regional scale (general linear models, GLM). In accordance with their contrasting spatial mobility, the distance from where synchrony could no longer be detected (decorrelation scale) was higher in squid than in octopus (349 vs 217 km); for comparison, the maximum distance between locations was 2620 km. The DFA revealed a general increasing trend in the abundance of both species in most areas, which agrees with the already reported worldwide proliferation of cephalopods. DFA results also showed that population dynamics are more similar in the eastern than in the western Mediterranean basin. According to the GLM models, cephalopod populations were negatively affected by productivity, which would be explained by an increase of competition and predation by fishes. While warmer years coincided with declining octopus numbers, areas of high sea surface temperature showed higher densities of squid. Our results are relevant for regional fisheries management and demonstrate that the regionalisation objectives envisaged under the new Common Fishery Policy may not be adequate for Mediterranean cephalopod stocks.

Investigating the Contribution of Climate Variables to Estimates of Net Primary Productivity in a Tropical Ecosystem in India

NASA Astrophysics Data System (ADS)

Tripathi, P.; Behera, M. D.; Behera, S. K.; Sahu, N.

2016-12-01

Investigating the impact of climate variables on net primary productivity is crucial to evaluate the ecosystem health and the status of forest type response to climate change. The objective of this paper is (1) to analyze the spatio-temporal pattern of net primary productivity (NPP) in a tropical forest ecosystem situated along the Himalayan foothills in India and (2) to investigate the continuous and delayed effects of climatic variables. Weapplied simple Monteith equation based Light use efficiency model for two dominant plant functional types; sal (Shorea robusta) forest and teak (Tectona grandis) plantation to estimate the NPP for a decadal period from 2001 to 2010. The impact of climate variables on NPP for these 10 years was seen by applying two correlation analyses; generalized linear modelling (GLM) and time lag correlation approach.The impact of different climate variables was observed to vary throughout the study period.A decline in mean NPP during 2002-2003, 2005 and 2008 to 2010 could be attributed to drought, increased vapour pressure deficit, and decreased humidity and solar radiation. In time lag correlation analysis, precipitation and humidity were observed to be the major variables affecting NPP; whereas combination of temperature, humidity and VPD showed dominant effect on NPP in GLM. Shorea robusta forest showed slightly higher NPP than that of Tectona grandis plantation throughout the study period. Highest decrease in NPP was observed during 2010,pertaining to lower solar radiation, humidity and precipitation along with increased VPD.Higher gains in NPP by sal during all years indicates their better adaptability to climate compared to teak. Contribution of different climatic variables through some link process is revealed in statistical analysis clearly indicates the co-dominance of all the variables in explaining NPP. Lacking of site specific meteorological observations and microclimate put constraint on broad level analyses.

Integrating EEG and fMRI in epilepsy.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Bertoldo, Alessandra; Manganotti, Paolo; Fiaschi, Antonio; Toffolo, Gianna Maria

2011-02-14

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes. Copyright © 2010 Elsevier Inc. All rights reserved.

Linear mixed-effects modeling approach to FMRI group analysis

PubMed Central

Chen, Gang; Saad, Ziad S.; Britton, Jennifer C.; Pine, Daniel S.; Cox, Robert W.

2013-01-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance–covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance–covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. PMID:23376789

Linear mixed-effects modeling approach to FMRI group analysis.

PubMed

Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

2013-06-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. Published by Elsevier Inc.

Multisite rainfall downscaling and disaggregation in a tropical urban area

NASA Astrophysics Data System (ADS)

Lu, Y.; Qin, X. S.

2014-02-01

A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception.

PubMed

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness.

Understanding the experience of caring for children with haemophilia: cross-sectional study of caregivers in the United States.

PubMed

DeKoven, M; Karkare, S; Kelley, L A; Cooper, D L; Pham, H; Powers, J; Lee, W C; Wisniewski, T

2014-07-01

Congenital haemophilia is an inherited bleeding disorder typically diagnosed at birth or shortly thereafter. Haemophilia imposes a significant burden on patients and their caregivers. The aim of the study was to quantify the overall burden of haemophilia on caregivers in the USA using a novel disease-specific questionnaire and the previously validated CarerQol. Targeted literature review and a previous survey conducted by the authors was used to develop an online questionnaire with six burden domains of interest to caregivers (emotional stress, financial, sacrifice, medical management, child's pain and transportation) and several visual analogue scales (VAS). Content validity of the questionnaire was confirmed by three haemophilia caregivers. The study sample consisted of caregivers of children with haemophilia identified via a previously developed opt-in research database. Descriptive statistics were employed for demographic and clinical characteristics; a generalized linear model (GLM) was used to identify factors influencing caregiver burden. A total of 310 caregivers completed the survey (45.5% response rate). Most of the participating caregivers were mothers of a child with haemophilia (88%), between 35 and 44 years of age (48%), and with a college education or a postgraduate degree (63%). 'Child's pain' was identified as the most burdensome domain to caregivers (median score = 3.50 out of 5), followed by 'emotional stress' (2.67), 'financial' (2.40), 'transportation' (2.33), 'sacrifice' (2.17) and 'medical management' (2.00) domains. Although higher income exhibited a protective effect, episodes of bleeds, current presence of an inhibitor and lower caregiver productivity in the past month negatively affected caregiver burden per GLM results. Training and educational programs should potentially be developed to address caregiver burden. © 2014 John Wiley & Sons Ltd.

Alcohol Dose Effects on Brain Circuits During Simulated Driving: An fMRI Study

PubMed Central

Meda, Shashwath A.; Calhoun, Vince D.; Astur, Robert S.; Turner, Beth M.; Ruopp, Kathryn; Pearlson, Godfrey D.

2009-01-01

Driving while intoxicated remains a major public health hazard. Driving is a complex task involving simultaneous recruitment of multiple cognitive functions. The investigators studied the neural substrates of driving and their response to different blood alcohol concentrations (BACs), using functional magnetic resonance imaging (fMRI) and a virtual reality driving simulator. We used independent component analysis (ICA) to isolate spatially independent and temporally correlated driving-related brain circuits in 40 healthy, adult moderate social drinkers. Each subject received three individualized, separate single-blind doses of beverage alcohol to produce BACs of 0.05% (moderate), 0.10% (high), or 0% (placebo). 3 T fMRI scanning and continuous behavioral measurement occurred during simulated driving. Brain function was assessed and compared using both ICA and a conventional general linear model (GLM) analysis. ICA results replicated and significantly extended our previous 1.5T study (Calhoun et al. [2004a]: Neuropsychopharmacology 29:2097–2017). GLM analysis revealed significant dose-related functional differences, complementing ICA data. Driving behaviors including opposite white line crossings and mean speed independently demonstrated significant dose-dependent changes. Behavior-based factors also predicted a frontal-basal-temporal circuit to be functionally impaired with alcohol dosage across baseline scaled, good versus poorly performing drivers. We report neural correlates of driving behavior and found dose-related spatio-temporal disruptions in critical driving-associated regions including the superior, middle and orbito frontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia, and cerebellum. Overall, results suggest that alcohol (especially at high doses) causes significant impairment of both driving behavior and brain functionality related to motor planning and control, goal directedness, error monitoring, and memory. PMID:18571794

Trauma center staffing, infrastructure, and patient characteristics that influence trauma center need.

PubMed

Faul, Mark; Sasser, Scott M; Lairet, Julio; Mould-Millman, Nee-Kofi; Sugerman, David

2015-01-01

The most effective use of trauma center resources helps reduce morbidity and mortality, while saving costs. Identifying critical infrastructure characteristics, patient characteristics and staffing components of a trauma center associated with the proportion of patients needing major trauma care will help planners create better systems for patient care. We used the 2009 National Trauma Data Bank-Research Dataset to determine the proportion of critically injured patients requiring the resources of a trauma center within each Level I-IV trauma center (n=443). The outcome variable was defined as the portion of treated patients who were critically injured. We defined the need for critical trauma resources and interventions ("trauma center need") as death prior to hospital discharge, admission to the intensive care unit, or admission to the operating room from the emergency department as a result of acute traumatic injury. Generalized Linear Modeling (GLM) was used to determine how hospital infrastructure, staffing Levels, and patient characteristics contributed to trauma center need. Nonprofit Level I and II trauma centers were significantly associated with higher levels of trauma center need. Trauma centers that had a higher percentage of transferred patients or a lower percentage of insured patients were associated with a higher proportion of trauma center need. Hospital infrastructure characteristics, such as bed capacity and intensive care unit capacity, were not associated with trauma center need. A GLM for Level III and IV trauma centers showed that the number of trauma surgeons on staff was associated with trauma center need. Because the proportion of trauma center need is predominantly influenced by hospital type, transfer frequency, and insurance status, it is important for administrators to consider patient population characteristics of the catchment area when planning the construction of new trauma centers or when coordinating care within state or regional trauma systems.

Metrology of human-based and other qualitative measurements

NASA Astrophysics Data System (ADS)

Pendrill, Leslie; Petersson, Niclas

2016-09-01

The metrology of human-based and other qualitative measurements is in its infancy—concepts such as traceability and uncertainty are as yet poorly developed. This paper reviews how a measurement system analysis approach, particularly invoking as performance metric the ability of a probe (such as a human being) acting as a measurement instrument to make a successful decision, can enable a more general metrological treatment of qualitative observations. Measures based on human observations are typically qualitative, not only in sectors, such as health care, services and safety, where the human factor is obvious, but also in customer perception of traditional products of all kinds. A principal challenge is that the usual tools of statistics normally employed for expressing measurement accuracy and uncertainty will probably not work reliably if relations between distances on different portions of scales are not fully known, as is typical of ordinal or other qualitative measurements. A key enabling insight is to connect the treatment of decision risks associated with measurement uncertainty to generalized linear modelling (GLM). Handling qualitative observations in this way unites information theory, the perceptive identification and choice paradigms of psychophysics. The Rasch invariant measure psychometric GLM approach in particular enables a proper treatment of ordinal data; a clear separation of probe and item attribute estimates; simple expressions for instrument sensitivity; etc. Examples include two aspects of the care of breast cancer patients, from diagnosis to rehabilitation. The Rasch approach leads in turn to opportunities of establishing metrological references for quality assurance of qualitative measurements. In psychometrics, one could imagine a certified reference for knowledge challenge, for example, a particular concept in understanding physics or for product quality of a certain health care service. Multivariate methods, such as Principal Component Regression, can also be improved by exploiting the increased resolution of the Rasch approach.

The role of drinking water sources, consumption of vegetables and seafood in relation to blood arsenic concentrations of Jamaican children with and without Autism Spectrum Disorders

PubMed Central

Rahbar, Mohammad H.; Samms-Vaughan, Maureen; Ardjomand-Hessabi, Manouchehr; Loveland, Katherine A.; Dickerson, Aisha S.; Chen, Zhongxue; Bressler, Jan; Shakespeare-Pellington, Sydonnie; Grove, Megan L.; Bloom, Kari; Wirth, Julie; Pearson, Deborah A.; Boerwinkle, Eric

2012-01-01

Arsenic is a toxic metal with harmful effects on human health, particularly on cognitive function. Autism Spectrum Disorders (ASDs) are lifelong neurodevelopmental and behavioral disorders manifesting in infancy or early childhood. We used data from 130 children between 2-8 years (65 pairs of ASD cases with age- and sex-matched control), to compare the mean total blood arsenic concentrations in children with and without ASDs in Kingston, Jamaica. Based on univariable analysis, we observed a significant difference between ASD cases and controls (4.03μg/L for cases vs. 4.48μg/L for controls, P < 0.01). In the final multivariable General Linear Model (GLM), after controlling for car ownership, maternal age, parental education levels, source of drinking water, consumption of “yam, sweet potato, or dasheen”, “carrot or pumpkin”, “callaloo, broccoli, or pak choi”, cabbage, avocado, and the frequency of seafood consumption per week, we did not find a significant association between blood arsenic concentrations and ASD status (4.36μg/L for cases vs. 4.65μg/L for controls, P = 0.23). Likewise, in a separate final multivariable GLM, we found that source of drinking water, eating avocado, and eating “callaloo, broccoli, or pak choi” were significantly associated with higher blood arsenic concentrations (all three P < 0.05). Based on our findings, we recommend assessment of arsenic levels in water, fruits, and vegetables, as well as increased awareness among the Jamaican population regarding potential risks for various exposures to arsenic. PMID:22819887

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

PubMed Central

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

Costs explained by function rather than diagnosis--results from the SNAC Nordanstig elderly cohort in Sweden.

PubMed

Lindholm, C; Gustavsson, A; Jönsson, L; Wimo, A

2013-05-01

Because the prevalence of many brain disorders rises with age, and brain disorders are costly, the economic burden of brain disorders will increase markedly during the next decades. The purpose of this study is to analyze how the costs to society vary with different levels of functioning and with the presence of a brain disorder. Resource utilization and costs from a societal viewpoint were analyzed versus cognition, activities of daily living (ADL), instrumental activities of daily living (IADL), brain disorder diagnosis and age in a population-based cohort of people aged 65 years and older in Nordanstig in Northern Sweden. Descriptive statistics, non-parametric bootstrapping and a generalized linear model (GLM) were used for the statistical analyses. Most people were zero users of care. Societal costs of dementia were by far the highest, ranging from SEK 262,000 (mild) to SEK 519,000 per year (severe dementia). In univariate analysis, all measures of functioning were significantly related to costs. When controlling for ADL and IADL in the multivariate GLM, cognition did not have a statistically significant effect on total cost. The presence of a brain disorder did not impact total cost when controlling for function. The greatest shift in costs was seen when comparing no dependency in ADL and dependency in one basic ADL function. It is the level of functioning, rather than the presence of a brain disorder diagnosis, which predicts costs. ADLs are better explanatory variables of costs than Mini mental state examination. Most people in a population-based cohort are zero users of care. Copyright © 2012 John Wiley & Sons, Ltd.

Phosphatase-inert glucosamine 6-phosphate mimics serve as actuators of the glmS riboswitch.

PubMed

Fei, Xiang; Holmes, Thomas; Diddle, Julianna; Hintz, Lauren; Delaney, Dan; Stock, Alex; Renner, Danielle; McDevitt, Molly; Berkowitz, David B; Soukup, Juliane K

2014-12-19

The glmS riboswitch is unique among gene-regulating riboswitches and catalytic RNAs. This is because its own metabolite, glucosamine-6-phosphate (GlcN6P), binds to the riboswitch and catalytically participates in the RNA self-cleavage reaction, thereby providing a novel negative feedback mechanism. Given that a number of pathogens harbor the glmS riboswitch, artificial actuators of this potential RNA target are of great interest. Structural/kinetic studies point to the 2-amino and 6-phosphate ester functionalities in GlcN6P as being crucial for this actuation. As a first step toward developing artificial actuators, we have synthesized a series of nine GlcN6P analogs bearing phosphatase-inert surrogates in place of the natural phosphate ester. Self-cleavage assays with the Bacillus cereus glmS riboswitch give a broad SAR. Two analogs display significant activity, namely, the 6-deoxy-6-phosphonomethyl analog (5) and the 6-O-malonyl ether (13). Kinetic profiles show a 22-fold and a 27-fold higher catalytic efficiency, respectively, for these analogs vs glucosamine (GlcN). Given their nonhydrolyzable phosphate surrogate functionalities, these analogs are arguably the most robust artificial glmS riboswitch actuators yet reported. Interestingly, the malonyl ether (13, extra O atom) is much more effective than the simple malonate (17), and the "sterically true" phosphonate (5) is far superior to the chain-truncated (7) or chain-extended (11) analogs, suggesting that positioning via Mg coordination is important for activity. Docking results are consistent with this view. Indeed, the viability of the phosphonate and 6-O-malonyl ether mimics of GlcN6P points to a potential new strategy for artificial actuation of the glmS riboswitch in a biological setting, wherein phosphatase-resistance is paramount.

Influenza forecasting with Google Flu Trends.

PubMed

Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E

2013-01-01

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

Statistical Modeling of Fire Occurrence Using Data from the Tōhoku, Japan Earthquake and Tsunami.

PubMed

Anderson, Dana; Davidson, Rachel A; Himoto, Keisuke; Scawthorn, Charles

2016-02-01

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models-generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out-of-sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out-of-sample predictions of the total number of ignitions in the region within one or two. Prefecture-level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response-covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates. © 2015 Society for Risk Analysis.

Data Assimilation of Lightning using 1D+3D/4D WRF Var Assimilation Schemes with Non-Linear Observation Operators

NASA Astrophysics Data System (ADS)

Navon, M. I.; Stefanescu, R.; Fuelberg, H. E.; Marchand, M.

2012-12-01

NASA's launch of the GOES-R Lightning Mapper (GLM) in 2015 will provide continuous, full disc, high resolution total lightning (IC + CG) data. The data will be available at a horizontal resolution of approximately 9 km. Compared to other types of data, the assimilation of lightning data into operational numerical models has received relatively little attention. Previous efforts of lightning assimilation mostly have employed nudging. This paper will describe the implementation of 1D+3D/4D Var assimilation schemes of existing ground-based WTLN (Worldwide Total Lightning Network) lightning observations using non-linear observation operators in the incremental WRFDA system. To mimic the expected output of GLM, the WTLN data were used to generate lightning super-observations characterized by flash rates/81 km2/20 min. A major difficulty associated with variational approaches is the complexity of the observation operator that defines the model equivalent of lightning. We use Convective Available Potential Energy (CAPE) as a proxy between lightning data and model variables. This operator is highly nonlinear. Marecal and Mahfouf (2003) have shown that nonlinearities can prevent direct assimilation of rainfall rates in the ECMWF 4D-VAR (using the incremental formulation proposed by Courtier et al. (1994)) from being successful. Using data from the 2011 Tuscaloosa, AL tornado outbreak, we have proved that the direct assimilation of lightning data into the WRF 3D/4D - Var systems is limited due to this incremental approach. Severe threshold limits must be imposed on the innovation vectors to obtain an improved analysis. We have implemented 1D+3D/4D Var schemes to assimilate lightning observations into the WRF model. Their use avoids innovation vector constrains from preventing the inclusion of a greater number of lightning observations Their use also minimizes the problem that nonlinearities in the moist convective scheme can introduce discontinuities in the cost function between inner and outer loops of the incremental 3-D/4-D VAR minimization. The first part of this paper will describe the methodology and performance analysis of the 1D-Var retrieval scheme that adjusts the WRF temperature profiles closer to an observed value as in Mahfouf et al. (2005). The second part will show the positive impact of these 1D-Var pseudo - temperature observations on both model 3D/4D-Var WRF analyses and short-range forecasts for three cases - the Tuscaloosa tornado outbreak (April 27, 2011) with intense but localized lightning, a second severe storm outbreak with more widespread but less intense lightning (June 27, 2011), and a northeaster containing much less lightning.

Fly's Eye GLM Simulator

NASA Image and Video Library

2017-07-27

The Fly’s Eye GLM Simulator (FEGS) is an airborne array of multi-spectral radiometers optimized to measure the optical emission from lightning. The instrument was designed by the Lightning Group in the Earth Science Office at the Marshall Space Flight Center as part of the validation effort for the first Geostationary Lightning Mapper (GLM) onboard GOES-16. From March to May of 2017, FEGS was flown on the NASA Armstrong Flight Research Center ER-2 along with a payload of other instruments during the GOES-16 Validation Flight Campaign. Data collected during the campaign are being analyzed by scientists at NASA and collaborating institutions to test the accuracy of GLM and other GOES-16 instruments. FEGS adds the capability to investigate sub-millisecond lightning energetics to the NASA Airborne Earth Science program. When flown with its complimentary suite of instruments, the FEGS package observes lightning radiation signatures that span from radio frequencies to gamma-ray emission. Learn more about the GOES-16 Validation Flight Campaign here: https://www.youtube.com/watch?v=rCTIk...

Using patient-specific hemodynamic response function in epileptic spike analysis of human epilepsy: a study based on EEG-fNIRS.

PubMed

Peng, Ke; Nguyen, Dang Khoa; Vannasing, Phetsamone; Tremblay, Julie; Lesage, Frédéric; Pouliot, Philippe

2016-02-01

Functional near-infrared spectroscopy (fNIRS) can be combined with electroencephalography (EEG) to continuously monitor the hemodynamic signal evoked by epileptic events such as seizures or interictal epileptiform discharges (IEDs, aka spikes). As estimation methods assuming a canonical shape of the hemodynamic response function (HRF) might not be optimal, we sought to model patient-specific HRF (sHRF) with a simple deconvolution approach for IED-related analysis with EEG-fNIRS data. Furthermore, a quadratic term was added to the model to account for the nonlinearity in the response when IEDs are frequent. Prior to analyzing clinical data, simulations were carried out to show that the HRF was estimable by the proposed deconvolution methods under proper conditions. EEG-fNIRS data of five patients with refractory focal epilepsy were selected due to the presence of frequent clear IEDs and their unambiguous focus localization. For each patient, both the linear sHRF and the nonlinear sHRF were estimated at each channel. Variability of the estimated sHRFs was seen across brain regions and different patients. Compared with the SPM8 canonical HRF (cHRF), including these sHRFs in the general linear model (GLM) analysis led to hemoglobin activations with higher statistical scores as well as larger spatial extents on all five patients. In particular, for patients with frequent IEDs, nonlinear sHRFs were seen to provide higher sensitivity in activation detection than linear sHRFs. These observations support using sHRFs in the analysis of IEDs with EEG-fNIRS data. Copyright © 2015 Elsevier Inc. All rights reserved.

N-acetylglucosamine-Mediated Expression of nagA and nagB in Streptococcus pneumoniae.

PubMed

Afzal, Muhammad; Shafeeq, Sulman; Manzoor, Irfan; Henriques-Normark, Birgitta; Kuipers, Oscar P

2016-01-01

In this study, we have explored the transcriptomic response of Streptococcus pneumoniae D39 to N-acetylglucosamine (NAG). Transcriptome comparison of S. pneumoniae D39 wild-type grown in chemically defined medium (CDM) in the presence of 0.5% NAG to that grown in the presence of 0.5% glucose revealed elevated expression of many genes/operons, including nagA, nagB, manLMN , and nanP . We have further confirmed the NAG-dependent expression of nagA, nagB, manLMN , and nanP by β-galactosidase assays. nagA, nagB and glmS are putatively regulated by a transcriptional regulator NagR. We predicted the operator site of NagR ( dre site) in P nagA , P nagB , and P glmS , which was further confirmed by mutating the predicted dre site in the respective promoters ( nagA, nagB , and glmS ). Growth comparison of Δ nagA , Δ nagB , and Δ glmS with the D39 wild-type demonstrates that nagA and nagB are essential for S. pneumoniae D39 to grow in the presence of NAG as a sole carbon source. Furthermore, deletion of ccpA shows that CcpA has no effect on the expression of nagA, nagB , and glmS in the presence of NAG in S . pneumoniae .

The Risk Need Responsivity Model of Offender Rehabilitation: Is There Really a Need for a Paradigm Shift?

ERIC Educational Resources Information Center

Looman, Jan; Abracen, Jeffrey

2013-01-01

The current paper critically reviews the Risk-Need-Responsivity (RNR) and Good Lives Model (GLM) approaches to correctional treatment. Research, or the lack thereof, is discussed in terms of whether there is a need for a new model of offender rehabilitation. We argue that although there is a wealth of research in support of RNR approaches, there…

Vacuum-assisted biopsy and steroid therapy for granulomatous lobular mastitis: report of three cases.

PubMed

Kuba, Sayaka; Yamaguchi, Junzo; Ohtani, Hiroshi; Shimokawa, Isao; Maeda, Shigeto; Kanematsu, Takashi

2009-01-01

We report the cases of three patients with granulomatous lobular mastitis (GLM), who were treated successfully with low-dose steroid therapy. Furthermore, the findings of our review of 271 patients reported in the literature suggest that steroid therapy is the treatment of choice for GLM.

Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia-A generalised linear model with break-point analysis.

PubMed

Alkhaldy, Ibrahim

2017-04-01

The aim of this study was to examine the role of environmental factors in the temporal distribution of dengue fever in Jeddah, Saudi Arabia. The relationship between dengue fever cases and climatic factors such as relative humidity and temperature was investigated during 2006-2009 to determine whether there is any relationship between dengue fever cases and climatic parameters in Jeddah City, Saudi Arabia. A generalised linear model (GLM) with a break-point was used to determine how different levels of temperature and relative humidity affected the distribution of the number of cases of dengue fever. Break-point analysis was performed to modelled the effect before and after a break-point (change point) in the explanatory parameters under various scenarios. Akaike information criterion (AIC) and cross validation (CV) were used to assess the performance of the models. The results showed that maximum temperature and mean relative humidity are most probably the better predictors of the number of dengue fever cases in Jeddah. In this study three scenarios were modelled: no time lag, 1-week lag and 2-weeks lag. Among these scenarios, the 1-week lag model using mean relative humidity as an explanatory variable showed better performance. This study showed a clear relationship between the meteorological variables and the number of dengue fever cases in Jeddah. The results also demonstrated that meteorological variables can be successfully used to estimate the number of dengue fever cases for a given period of time. Break-point analysis provides further insight into the association between meteorological parameters and dengue fever cases by dividing the meteorological parameters into certain break-points. Copyright © 2016 Elsevier B.V. All rights reserved.

Timing of repetition suppression of event-related potentials to unattended objects.

PubMed

Stefanics, Gabor; Heinzle, Jakob; Czigler, István; Valentini, Elia; Stephan, Klaas Enno

2018-05-26

Current theories of object perception emphasize the automatic nature of perceptual inference. Repetition suppression (RS), the successive decrease of brain responses to repeated stimuli, is thought to reflect the optimization of perceptual inference through neural plasticity. While functional imaging studies revealed brain regions that show suppressed responses to the repeated presentation of an object, little is known about the intra-trial time course of repetition effects to everyday objects. Here we used event-related potentials (ERP) to task-irrelevant line-drawn objects, while participants engaged in a distractor task. We quantified changes in ERPs over repetitions using three general linear models (GLM) that modelled RS by an exponential, linear, or categorical "change detection" function in each subject. Our aim was to select the model with highest evidence and determine the within-trial time-course and scalp distribution of repetition effects using that model. Model comparison revealed the superiority of the exponential model indicating that repetition effects are observable for trials beyond the first repetition. Model parameter estimates revealed a sequence of RS effects in three time windows (86-140ms, 322-360ms, and 400-446ms) and with occipital, temporo-parietal, and fronto-temporal distribution, respectively. An interval of repetition enhancement (RE) was also observed (320-340ms) over occipito-temporal sensors. Our results show that automatic processing of task-irrelevant objects involves multiple intervals of RS with distinct scalp topographies. These sequential intervals of RS and RE might reflect the short-term plasticity required for optimization of perceptual inference and the associated changes in prediction errors (PE) and predictions, respectively, over stimulus repetitions during automatic object processing. This article is protected by copyright. All rights reserved. © 2018 The Authors European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

NASA Astrophysics Data System (ADS)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

Corrigenda of 'explicit wave-averaged primitive equations using a generalized Lagrangian Mean'

NASA Astrophysics Data System (ADS)

Ardhuin, F.; Rascle, N.; Belibassakis, K. A.

2017-05-01

Ardhuin et al. (2008) gave a second-order approximation in the wave slope of the exact Generalized Lagrangian Mean (GLM) equations derived by Andrews and McIntyre (1978), and also performed a coordinate transformation, going from GLM to a 'GLMz' set of equations. That latter step removed the wandering of the GLM mean sea level away from the Eulerian-mean sea level, making the GLMz flow non-divergent. That step contained some inaccuarate statements about the coordinate transformation, while the rest of the paper contained an error on the surface dynamic boundary condition for viscous stresses. I am thankful to Mathias Delpey and Hidenori Aiki for pointing out these errors, which are corrected below.

Experience of treatment of patients with granulomatous lobular mastitis.

PubMed

Hur, Sung Mo; Cho, Dong Hui; Lee, Se Kyung; Choi, Min-Young; Bae, Soo Youn; Koo, Min Young; Kim, Sangmin; Choe, Jun-Ho; Kim, Jung-Han; Kim, Jee Soo; Nam, Seok-Jin; Yang, Jung-Hyun; Lee, Jeong Eon

2013-07-01

To present the author's experience with various treatment methods of granulomatous lobular mastitis (GLM) and to determine effective treatment methods of GLM. Fifty patients who were diagnosed with GLM were classified into five groups based on the initial treatment methods they underwent, which included observation (n = 8), antibiotics (n = 3), steroid (n = 13), drainage (n = 14), and surgical excision (n = 12). The treatment processes in each group were examined and their clinical characteristics, treatment processes, and results were analyzed respectively. Success rates with each initial treatment were observation, 87.5%; antibiotics, 33.3%; steroids, 30.8%; drainage, 28.6%; and surgical excision, 91.7%. In most cases of observation, the lesions were small and the symptoms were mild. A total of 23 patients underwent surgical excision during treatment. Surgical excision showed particularly fast recovery, high success rate (90.3%) and low recurrence rate (8.7%). The clinical course of GLM is complex and the outcome of each treatment type are variable. Surgery may play an important role when a lesion is determined to be mass-forming or appears localized as an abscess pocket during breast examination or imaging study.

The relationship of motor skills and social communicative skills in school-aged children with autism spectrum disorder.

PubMed

MacDonald, Megan; Lord, Catherine; Ulrich, Dale A

2013-07-01

Motor skill deficits are present and persist in school-aged children with autism spectrum disorder (ASD; Staples & Reid, 2010). Yet the focus of intervention is on core impairments, which are part of the diagnostic criteria for ASD, deficits in social communication skills. The purpose of this study is to determine whether the functional motor skills, of 6- to 15-year-old children with high-functioning ASD, predict success in standardized social communicative skills. It is hypothesized that children with better motor skills will have better social communicative skills. A total of 35 children with ASD between the ages of 6-15 years participated in this study. The univariate GLM (general linear model) tested the relationship of motor skills on social communicative skills holding constant age, IQ, ethnicity, gender, and clinical ASD diagnosis. Object-control motor skills significantly predicted calibrated ASD severity (p < .05). Children with weaker motor skills have greater social communicative skill deficits. How this relationship exists behaviorally, needs to be explored further.

The effects of music on pain perception of stroke patients during upper extremity joint exercises.

PubMed

Kim, Soo Ji; Koh, Iljoo

2005-01-01

The purpose of this study was to determine the effects of music therapy on pain perception of stroke patients during upper extremity joint exercises. Ten stroke patients (1 male and 9 females) ranging in age from 61 to 73 participated in the study. Music conditions used in the study consisted of: (a) song, (b) karaoke accompaniment (same music to condition A except singers' voices), and (c) no music. Exercise movements in this study included hand, wrist, and shoulder joints. During the 8-week period music therapy sessions, subjects repeated 3 conditions according to the randomized orders and subjects rated their perceived pain on a scale immediately after each condition. The General Linear Model (GLM) Repeated Measures ANOVA revealed that there were no significant differences in pain rating across the three music conditions. However, positive affects and verbal responses, while performing upper extremity exercises with both music and karaoke accompaniment music, were observed using video observations.

Broadleaf Mahonia attenuates granulomatous lobular mastitis‑associated inflammation by inhibiting CCL‑5 expression in macrophages.

PubMed

Wang, Zhiyu; Wang, Neng; Liu, Xiaoyan; Wang, Qi; Xu, Biao; Liu, Pengxi; Zhu, Huayu; Chen, Jianping; Situ, Honglin; Lin, Yi

2018-01-01

Granulomatous lobular mastitis (GLM) is a type of chronic mammary inflammation with unclear etiology. Currently systematic corticosteroids and methitrexate are considered as the main drugs for GLM treatment, but a high toxicity and risk of recurrence greatly limit their application. It is therefore an urgent requirement that safe and efficient natural drugs are found to improve the GLM prognosis. Broadleaf Mahonia (BM) is a traditional Chinese herb that is believed to have anti‑inflammatory properties according to ancient records of traditional Chinese medicine. The present study investigated this belief and demonstrated that BM significantly inhibited the expression of interleukin‑1β (IL‑1β), IL‑6, cyclooxygenase‑2 and inducible nitric oxide synthase in RAW264.7 cells, but had little influence on the cell viability, cell cycle and apoptosis. Meanwhile, the lipopolysaccharide‑induced elevation of reactive oxygen species and nitric oxide was also blocked following BM treatment, accompanied with decreased activity of nuclear factor‑κB and MAPK signaling. A cytokine array further validated that BM exhibited significant inhibitory effects on several chemoattractants, including chemokine (C‑C motif) ligand (CCL)‑2, CCL‑3, CCL‑5 and secreted tumor necrosis factor receptor 1, among which CCL‑5 exhibited the highest inhibition ratio in cell and clinical GLM specimens. Collectively, the results show that BM is a novel effective anti‑inflammatory herb in vitro and ex vivo, and that CCL‑5 may be closely associated with GLM pathogenesis.

Broadleaf Mahonia attenuates granulomatous lobular mastitis-associated inflammation by inhibiting CCL-5 expression in macrophages

PubMed Central

Wang, Zhiyu; Wang, Neng; Liu, Xiaoyan; Wang, Qi; Xu, Biao; Liu, Pengxi; Zhu, Huayu; Chen, Jianping; Situ, Honglin; Lin, Yi

2018-01-01

Granulomatous lobular mastitis (GLM) is a type of chronic mammary inflammation with unclear etiology. Currently systematic corticosteroids and methitrexate are considered as the main drugs for GLM treatment, but a high toxicity and risk of recurrence greatly limit their application. It is therefore an urgent requirement that safe and efficient natural drugs are found to improve the GLM prognosis. Broadleaf Mahonia (BM) is a traditional Chinese herb that is believed to have anti-inflammatory properties according to ancient records of traditional Chinese medicine. The present study investigated this belief and demonstrated that BM significantly inhibited the expression of interleukin-1β (IL-1β), IL-6, cyclooxygenase-2 and inducible nitric oxide synthase in RAW264.7 cells, but had little influence on the cell viability, cell cycle and apoptosis. Meanwhile, the lipopolysaccharide-induced elevation of reactive oxygen species and nitric oxide was also blocked following BM treatment, accompanied with decreased activity of nuclear factor-κB and MAPK signaling. A cytokine array further validated that BM exhibited significant inhibitory effects on several chemoattractants, including chemokine (C-C motif) ligand (CCL)-2, CCL-3, CCL-5 and secreted tumor necrosis factor receptor 1, among which CCL-5 exhibited the highest inhibition ratio in cell and clinical GLM specimens. Collectively, the results show that BM is a novel effective anti-inflammatory herb in vitro and ex vivo, and that CCL-5 may be closely associated with GLM pathogenesis. PMID:29138800

Protocol for a multicentred randomised controlled trial investigating the use of personalised golimumab dosing tailored to inflammatory load in ulcerative colitis: the GOAL-ARC study (GLM dose Optimisation to Adequate Levels to Achieve Response in Colitis) led by the INITIAtive group (NCT 0268772)

PubMed Central

Sheridan, Juliette; Coe, Carol Ann; Doran, Peter; Egan, Laurence; Cullen, Garret; Kevans, David; Leyden, Jan; Galligan, Marie; O’Toole, Aoibhlinn; McCarthy, Jane; Doherty, Glen

2018-01-01

Introduction Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD), often leading to an impaired quality of life in affected patients. Current treatment modalities include antitumour necrosis factor (anti-TNF) monoclonal antibodies (mABs) including infliximab, adalimumab and golimumab (GLM). Several recent retrospective and prospective studies have demonstrated that fixed dosing schedules of anti-TNF agents often fails to consistently achieve adequate circulating therapeutic drug levels (DL) with consequent risk of immunogenicity treatment failure and potential risk of hospitalisation and colectomy in patients with UC. The design of GLM dose Optimisation to Adequate Levels to Achieve Response in Colitis aims to address the impact of dose escalation of GLM immediately following induction and during the subsequent maintenance phase in response to suboptimal DL or persisting inflammatory burden as represented by raised faecal calprotectin (FCP). Aim The primary aim of the study is to ascertain if monitoring of FCP and DL of GLM to guide dose optimisation (during maintenance) improves rates of patient continuous clinical response and reduces disease activity in UC. Methods and analysis A randomised, multicentred two-arm trial studying the effect of dose optimisation of GLM based on FCP and DL versus treatment as per SMPC. Eligible patients will be randomised in a 1:1 ratio to 1 of 2 treatment groups and shall be treated over a period of 46 weeks. Ethics and dissemination The study protocol was approved by the Research Ethics committee of St. Vincent’s University Hospital. The results will be published in a peer-reviewed journal and shared with the worldwide medical community. Trial registration numbers EudraCT number: 2015-004724-62; Clinicaltrials.gov Identifier: NCT0268772; Pre-results. PMID:29379609

Measuring and modeling for the assessment of the genetic background behind cognitive processes in donkeys.

PubMed

Navas, Francisco Javier; Jordana, Jordi; León, José Manuel; Arando, Ander; Pizarro, Gabriela; McLean, Amy Katherine; Delgado, Juan Vicente

2017-08-01

New productive niches can offer new commercial perspectives linked to donkeys' products and human therapeutic or leisure applications. However, no assessment for selection criteria has been carried out yet. First, we assessed the animal inherent features and environmental factors that may potentially influence several cognitive processes in donkeys. Then, we aimed at describing a practical methodology to quantify such cognitive processes, seeking their inclusion in breeding and conservation programmes, through a multifactorial linear model. Sixteen cognitive process-related traits were scored on a problem-solving test in a sample of 300 Andalusian donkeys for three consecutive years from 2013 to 2015. The linear model assessed the influence and interactions of four environmental factors, sex as an animal-inherent factor, age as a covariable, and the interactions between these factors. Analyses of variance were performed with GLM procedure of SPSS Statistics for Windows, Version 24.0 software to assess the relative importance of each factor. All traits were significantly (P<0.05) affected by all factors in the model except for sex that was not significant for some of the cognitive processes, and stimulus which was not significant (P<0.05) for all of them except for the coping style related ones. The interaction between all factors within the model was non-significant (P<0.05) for almost all cognitive processes. The development of complex multifactorial models to study cognitive processes may counteract the inherent variability in behavior genetics and the estimation and prediction of related breeding parameters, key for the implementation of successful conservation programmes in apparently functionally misplaced endangered breeds. Copyright © 2017 Elsevier Ltd. All rights reserved.

Towards a threshold climate for emergency lower respiratory hospital admissions.

PubMed

Islam, Muhammad Saiful; Chaussalet, Thierry J; Koizumi, Naoru

2017-02-01

Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset (10 years, 2000-2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and 'day of the week' as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥27°C), low relative humidity (≤ 40%), high Pm10 level (≥70-µg/m 3 ), low wind speed (≤ 2 knots) and high rainfall (≥30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations. Copyright © 2016 Elsevier Inc. All rights reserved.

The Geostationary Lightning Mapper (GLM) for the GOES-R Series Next Generation Operational Environmental Satellite Constellation

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard; Koshak, William; Petersen, Walter; Carey, Larry; Mach, Douglas; Buechler, Dennis; Bateman, Monte; McCaul, Eugene; Bruning, Eric;

Lapatinib sensitivities of two novel trastuzumab-resistant HER2 gene-amplified gastric cancer cell lines.

PubMed

Oshima, Yukiko; Tanaka, Harunari; Murakami, Hiroki; Ito, Yuichi; Furuya, Tomomi; Kondo, Eisaku; Kodera, Yasuhiro; Nakanishi, Hayao

2014-01-01

Trastuzumab (Tmab) resistance is a major clinical problem to be resolved in patients with HER2-positive gastric cancers. However, in contrast to the situation for HER2-positive breast cancer lines, the Tmab-resistant gastric cancer preclinical models that are needed to develop a new therapy to overcome this problem are not yet available. We developed three new cell lines from HER2 gene-amplified gastric cancer cell lines (GLM-1, GLM-4, NCI N-87) by a new in vivo selection method consisting of the repeated culture of small residual peritoneal metastasis but not subcutaneous tumor after Tmab treatment. We then evaluated the anti-tumor efficacy of lapatinib for these Tmab-resistant cells. We successfully isolated two Tmab-resistant cell lines (GLM1-HerR2(3), GLM4-HerR2) among the three tested cell lines. These resistant cells differed from the parental cells in their flat morphology and rapid growth in vitro, but HER2, P95HER2 expression, and Tmab binding were essentially the same for the parental and resistant cells. MUC4 expression was up- or downregulated depending on the cell line. These resistant cells were still sensitive to lapatinib, similar to the parental cells, in vitro. This growth inhibition of the Tmab-resistant cells by lapatinib was due to both G1 cell-cycle arrest and apoptosis induction via effective blockade of the PI3K/Akt and MAPK pathways. A preclinical study confirmed that the Tmab-resistant tumors are significantly susceptible to lapatinib. These results suggest that lapatinib has antitumor activity against the Tmab-resistant gastric cancer cell lines, and that these cell lines are useful for understanding the mechanism of Tmab resistance and for developing a new molecular therapy for Tmab-resistant HER2-positive gastric cancers.

Blood manganese concentrations in Jamaican children with and without autism spectrum disorders

PubMed Central

2014-01-01

Background Manganese is an essential element for human health and development. Previous studies have shown neurotoxic effects in children exposed to higher levels of manganese. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that impairs social interaction and communication. Several studies have hypothesized that ASD is caused through environmental exposures during crucial stages in brain development. We investigated the possible association between blood manganese concentrations (BMC) and ASD. We also identified factors associated with BMC in typically developing (TD) Jamaican children. Methods We used data from 109 ASD cases with their 1:1 age- and sex-matched TD controls to compare mean BMC in Jamaican children (2–8 years of age) with and without ASD. We administered a pre-tested questionnaire to assess demographic and socioeconomic information, medical history, and potential exposure to manganese. Finally, we collected 2 mL of whole blood from each child for analysis of manganese levels. Using General Linear Models (GLM), we assessed the association between BMC and ASD status. Furthermore, we used two independent sample t-tests to identify factors associated with BMC in TD children. Results In univariable GLM analysis, we found no significant association between BMC and ASD, (10.9 μg/L for cases vs. 10.5 μg/L for controls; P = 0.29). In a multivariable GLM adjusting for paternal age, parental education, place of child’s birth (Kingston parish), consumption of root vegetables, cabbage, saltwater fish, and cakes/buns, there was still no significant association between BMC and ASD status, (11.5 μg/L for cases vs. 11.9 μg/L for controls; P = 0.48). Our findings also indicated TD children who ate fresh water fish had a higher BMC than children who did not (11.0 μg/L vs. 9.9 μg/L; P = 0.03) as younger TD children (i.e., 2 ≤ age ≤4), (12.0 μg/L vs. 10.2 μg/L; P = 0.01). Conclusions While these results cannot be used to assess early exposure at potentially more susceptible time period, our findings suggest that there is no significant association between manganese exposures and ASD case status in Jamaica. Our findings also indicate that BMC in Jamaican children resemble those of children in the developed world and are much lower than those in the developing countries. PMID:25149876

Spatial modelling of disease using data- and knowledge-driven approaches.

PubMed

Stevens, Kim B; Pfeiffer, Dirk U

2011-09-01

The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution. Copyright © 2011. Published by Elsevier Ltd.

Population Structure, Diversity and Trait Association Analysis in Rice (Oryza sativa L.) Germplasm for Early Seedling Vigor (ESV) Using Trait Linked SSR Markers

PubMed Central

Anandan, Annamalai; Anumalla, Mahender; Pradhan, Sharat Kumar; Ali, Jauhar

2016-01-01

Early seedling vigor (ESV) is the essential trait for direct seeded rice to dominate and smother the weed growth. In this regard, 629 rice genotypes were studied for their morphological and physiological responses in the field under direct seeded aerobic situation on 14th, 28th and 56th days after sowing (DAS). It was determined that the early observations taken on 14th and 28th DAS were reliable estimators to study ESV as compared to56th DAS. Further, 96 were selected from 629 genotypes by principal component (PCA) and discriminate function analyses. The selected genotypes were subjected to decipher the pattern of genetic diversity in terms of both phenotypic and genotypic by using ESV QTL linked simple sequence repeat (SSR) markers. To assess the genetic structure, model and distance based approaches were used. Genotyping of 96 rice lines using 39 polymorphic SSRs produced a total of 128 alleles with the phenotypic information content (PIC) value of 0.24. The model based population structure approach grouped the accession into two distinct populations, whereas unrooted tree grouped the genotypes into three clusters. Both model based and structure based approach had clearly distinguished the early vigor genotypes from non-early vigor genotypes. Association analysis revealed that 16 and 10 SSRs showed significant association with ESV traits by general linear model (GLM) and mixed linear model (MLM) approaches respectively. Marker alleles on chromosome 2 were associated with shoot dry weight on 28 DAS, vigor index on 14 and 28 DAS. Improvement in the rate of seedling growth will be useful for identifying rice genotypes acquiescent to direct seeded conditions through marker-assisted selection. PMID:27031620

Investigation of micromixing by acoustically oscillated sharp-edges

PubMed Central

Nama, Nitesh; Huang, Po-Hsun; Huang, Tony Jun; Costanzo, Francesco

2016-01-01

Recently, acoustically oscillated sharp-edges have been utilized to achieve rapid and homogeneous mixing in microchannels. Here, we present a numerical model to investigate acoustic mixing inside a sharp-edge-based micromixer in the presence of a background flow. We extend our previously reported numerical model to include the mixing phenomena by using perturbation analysis and the Generalized Lagrangian Mean (GLM) theory in conjunction with the convection-diffusion equation. We divide the flow variables into zeroth-order, first-order, and second-order variables. This results in three sets of equations representing the background flow, acoustic response, and the time-averaged streaming flow, respectively. These equations are then solved successively to obtain the mean Lagrangian velocity which is combined with the convection-diffusion equation to predict the concentration profile. We validate our numerical model via a comparison of the numerical results with the experimentally obtained values of the mixing index for different flow rates. Further, we employ our model to study the effect of the applied input power and the background flow on the mixing performance of the sharp-edge-based micromixer. We also suggest potential design changes to the previously reported sharp-edge-based micromixer to improve its performance. Finally, we investigate the generation of a tunable concentration gradient by a linear arrangement of the sharp-edge structures inside the microchannel. PMID:27158292

Investigation of micromixing by acoustically oscillated sharp-edges.

PubMed

Nama, Nitesh; Huang, Po-Hsun; Huang, Tony Jun; Costanzo, Francesco

2016-03-01

Recently, acoustically oscillated sharp-edges have been utilized to achieve rapid and homogeneous mixing in microchannels. Here, we present a numerical model to investigate acoustic mixing inside a sharp-edge-based micromixer in the presence of a background flow. We extend our previously reported numerical model to include the mixing phenomena by using perturbation analysis and the Generalized Lagrangian Mean (GLM) theory in conjunction with the convection-diffusion equation. We divide the flow variables into zeroth-order, first-order, and second-order variables. This results in three sets of equations representing the background flow, acoustic response, and the time-averaged streaming flow, respectively. These equations are then solved successively to obtain the mean Lagrangian velocity which is combined with the convection-diffusion equation to predict the concentration profile. We validate our numerical model via a comparison of the numerical results with the experimentally obtained values of the mixing index for different flow rates. Further, we employ our model to study the effect of the applied input power and the background flow on the mixing performance of the sharp-edge-based micromixer. We also suggest potential design changes to the previously reported sharp-edge-based micromixer to improve its performance. Finally, we investigate the generation of a tunable concentration gradient by a linear arrangement of the sharp-edge structures inside the microchannel.

GOES-R Geostationary Lightning Mapper Performance Specifications and Algorithms

NASA Technical Reports Server (NTRS)

Mach, Douglas M.; Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Petersen, William A.; Boldi, Robert A.; Carey, Lawrence D.; Bateman, Monte G.; Buchler, Dennis E.; McCaul, E. William, Jr.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series will carry a GLM that will provide continuous day and night observations of lightning. The mission objectives for the GLM are to: (1) Provide continuous, full-disk lightning measurements for storm warning and nowcasting, (2) Provide early warning of tornadic activity, and (2) Accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997- present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. The science data will consist of lightning "events", "groups", and "flashes". The algorithm is being designed to be an efficient user of the computational resources. This may include parallelization of the code and the concept of sub-dividing the GLM FOV into regions to be processed in parallel. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama, Oklahoma, Central Florida, and the Washington DC Metropolitan area) are being used to develop the prelaunch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution.

Experience of treatment of patients with granulomatous lobular mastitis

PubMed Central

Hur, Sung Mo; Cho, Dong Hui; Lee, Se Kyung; Choi, Min-Young; Bae, Soo Youn; Koo, Min Young; Kim, Sangmin; Choe, Jun-Ho; Kim, Jung-Han; Kim, Jee Soo; Nam, Seok-Jin; Yang, Jung-Hyun

2013-01-01

Purpose To present the author's experience with various treatment methods of granulomatous lobular mastitis (GLM) and to determine effective treatment methods of GLM. Methods Fifty patients who were diagnosed with GLM were classified into five groups based on the initial treatment methods they underwent, which included observation (n = 8), antibiotics (n = 3), steroid (n = 13), drainage (n = 14), and surgical excision (n = 12). The treatment processes in each group were examined and their clinical characteristics, treatment processes, and results were analyzed respectively. Results Success rates with each initial treatment were observation, 87.5%; antibiotics, 33.3%; steroids, 30.8%; drainage, 28.6%; and surgical excision, 91.7%. In most cases of observation, the lesions were small and the symptoms were mild. A total of 23 patients underwent surgical excision during treatment. Surgical excision showed particularly fast recovery, high success rate (90.3%) and low recurrence rate (8.7%). Conclusion The clinical course of GLM is complex and the outcome of each treatment type are variable. Surgery may play an important role when a lesion is determined to be mass-forming or appears localized as an abscess pocket during breast examination or imaging study. PMID:23833753

GLM Post Launch Testing and Airborne Science Field Campaign

NASA Astrophysics Data System (ADS)

Goodman, S. J.; Padula, F.; Koshak, W. J.; Blakeslee, R. J.

2017-12-01

The Geostationary Operational Environmental Satellite (GOES-R) series provides the continuity for the existing GOES system currently operating over the Western Hemisphere. The Geostationary Lightning Mapper (GLM) is a wholly new instrument that provides a capability for total lightning detection (cloud and cloud-to-ground flashes). The first satellite in the GOES-R series, now GOES-16, was launched in November 2016 followed by in-orbit post launch testing for approximately 12 months before being placed into operations replacing the GOES-E satellite in December. The GLM will map total lightning continuously throughout day and night with near-uniform spatial resolution of 8 km with a product latency of less than 20 sec over the Americas and adjacent oceanic regions. The total lightning is very useful for identifying hazardous and severe thunderstorms, monitoring storm intensification and tracking evolution. Used in tandem with radar, satellite imagery, and surface observations, total lightning data has great potential to increase lead time for severe storm warnings, improve aviation safety and efficiency, and increase public safety. In this paper we present initial results from the post-launch in-orbit performance testing, airborne science field campaign conducted March-May, 2017 and assessments of the GLM instrument and science products.

Assessing the Lifetime Performance of the Lightning Imaging Sensor (LIS): Implications for the Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Buechler, D. E.; Christian, H. J.; Koshak, W. J.; Goodman, S. J.

2011-01-01

The Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite has been providing observations of total lightning over the Earth s Tropics for 13 years. This study examines the performance of the LIS throughout its time in orbit. Application of the Deep Convective Cloud Technique (DCCT) (Doelling et al., 2004) was performed on the LIS background pixels to assess the stability of the LIS instrument. The DCCT analysis indicates that the maximum deviation of the monthly mean radiance is within 2% of the overall mean, indicating stable performance over the period. In addition, an examination of the number of flashes detected over time similarly shows no significant trend (after adjusting for the orbit boost that occurred in August 2001). These and other results indicate that there has been no discernible change in LIS performance throughout its lifetime. A similar approach will used for monitoring the performance of the Geostationary Lightning Mapper (GLM) onboard the next generation Geostationary Operational Environmental Satellite-R (GOES-R). Since GLM is based on LIS design heritage, the LIS results indicate that GLM may also experience stable performance over its lifetime.

Edge effect on post-dispersal artificial seed predation in the southeastern Amazonia, Brazil.

PubMed

Penido, G; Ribeiro, V; Fortunato, D S

2015-05-01

This paper evaluates the post-dispersal artificial seed predation rates in two areas of the southeastern Amazon forest-savanna boundary, central Brazil. We conducted the survey in a disturbance regime controlled research site to verify if exists an edge effect in these rates and if the disturbance (in this case annual fire and no fire) affects seed predation. We placed 800 peanuts seeds in each area at regular distance intervals from the fragment`s edge. Data were analyzed by a likelihood ratio model selection in generalized linear models (GLM). The complete model (with effects from edge distance and site and its interaction) was significative (F3=4.43; p=0.005). Seeds had a larger predation rates in fragment's interior in both areas, but in the controlled area (no disturbance) this effect was less linear. This suggests an edge effect for post-dispersal seed predation, and that disturbances might alter these effects. Even if we exclude the site effect (grouping both areas together) there is still a strong edge effect on seed predation rates (F3=32.679; p>0.001). We did not verify predator's species in this study; however, the presence of several species of ants was extremely common in the seeds. The detection of an edge effect in only a short survey time suggests that there is heterogeneity in predation rates and that this variation might affect plant recruitment in fragmented areas of the Amazon forest. Henceforth, this seed predation should be taken in consideration in reforestation projects, where the main source of plants species is from seed distribution.

Modeling suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in North and South America’s coastal waters

USGS Publications Warehouse

Evangelista, Paul H.; Young, Nicholas E.; Schofield, Pamela J.; Jarnevich, Catherine S.

2016-01-01

We used two common correlative species-distribution models to predict suitable habitat of invasive red lionfish Pterois volitans (Linnaeus, 1758) in the western Atlantic and eastern Pacific Oceans. The Generalized Linear Model (GLM) and the Maximum Entropy (Maxent) model were applied using the Software for Assisted Habitat Modeling. We compared models developed using native occurrences, using non-native occurrences, and using both native and non-native occurrences. Models were trained using occurrence data collected before 2010 and evaluated with occurrence data collected from the invaded range during or after 2010. We considered a total of 22 marine environmental variables. Models built with non-native only or both native and non-native occurrence data outperformed those that used only native occurrences. Evaluation metrics based on the independent test data were highest for models that used both native and non-native occurrences. Bathymetry was the strongest environmental predictor for all models and showed increasing suitability as ocean floor depth decreased, with salinity ranking the second strongest predictor for models that used native and both native and non-native occurrences, indicating low habitat suitability for salinities <30. Our model results also suggest that red lionfish could continue to invade southern latitudes in the western Atlantic Ocean and may establish localized populations in the eastern Pacific Ocean. We reiterate the importance in the choice of the training data source (native, non-native, or native/non-native) used to develop correlative species distribution models for invasive species.

Review of Statistical Methods for Analysing Healthcare Resources and Costs

PubMed Central

Mihaylova, Borislava; Briggs, Andrew; O'Hagan, Anthony; Thompson, Simon G

2011-01-01

We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single-distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)-part and Tobit models, (VII) survival methods, (VIII) non-parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near-normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work. Copyright © 2010 John Wiley & Sons, Ltd. PMID:20799344

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

PubMed

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

Is the kinetic equation for turbulent gas-particle flows ill posed?

PubMed

Reeks, M; Swailes, D C; Bragg, A D

2018-02-01

This paper is about the kinetic equation for gas-particle flows, in particular its well-posedness and realizability and its relationship to the generalized Langevin model (GLM) probability density function (PDF) equation. Previous analyses, e.g. [J.-P. Minier and C. Profeta, Phys. Rev. E 92, 053020 (2015)PLEEE81539-375510.1103/PhysRevE.92.053020], have concluded that this kinetic equation is ill posed, that in particular it has the properties of a backward heat equation, and as a consequence, its solution will in the course of time exhibit finite-time singularities. We show that this conclusion is fundamentally flawed because it ignores the coupling between the phase space variables in the kinetic equation and the time and particle inertia dependence of the phase space diffusion tensor. This contributes an extra positive diffusion that always outweighs the negative diffusion associated with the dispersion along one of the principal axes of the phase space diffusion tensor. This is confirmed by a numerical evaluation of analytic solutions of these positive and negative contributions to the particle diffusion coefficient along this principal axis. We also examine other erroneous claims and assumptions made in previous studies that demonstrate the apparent superiority of the GLM PDF approach over the kinetic approach. In so doing, we have drawn attention to the limitations of the GLM approach, which these studies have ignored or not properly considered, to give a more balanced appraisal of the benefits of both PDF approaches.

The GOES-R Series Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas M.

2011-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), which will have just completed Critical Design Review and move forward into the construction phase of instrument development. The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (an engineering development unit and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms

Initial Navigation Alignment of Optical Instruments on GOES-R

NASA Astrophysics Data System (ADS)

Isaacson, P.; DeLuccia, F.; Reth, A. D.; Igli, D. A.; Carter, D.

2016-12-01

The GOES-R satellite is the first in NOAA's next-generation series of geostationary weather satellites. In addition to a number of space weather sensors, it will carry two principal optical earth-observing instruments, the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). During launch, currently scheduled for November of 2016, the alignment of these optical instruments is anticipated to shift from that measured during pre-launch characterization. While both instruments have image navigation and registration (INR) processing algorithms to enable automated geolocation of the collected data, the launch-derived misalignment may be too large for these approaches to function without an initial adjustment to calibration parameters. The parameters that may require adjustment are for Line of Sight Motion Compensation (LMC), and the adjustments will be estimated on orbit during the post-launch test (PLT) phase. We have developed approaches to estimate the initial alignment errors for both ABI and GLM image products. Our approaches involve comparison of ABI and GLM images collected during PLT to a set of reference ("truth") images using custom image processing tools and other software (the INR Performance Assessment Tool Set, or "IPATS") being developed for other INR assessments of ABI and GLM data. IPATS is based on image correlation approaches to determine offsets between input and reference images, and these offsets are the fundamental input to our estimate of the initial alignment errors. Initial testing of our alignment algorithms on proxy datasets lends high confidence that their application will determine the initial alignment errors to within sufficient accuracy to enable the operational INR processing approaches to proceed in a nominal fashion. We will report on the algorithms, implementation approach, and status of these initial alignment tools being developed for the GOES-R ABI and GLM instruments.

Identifying antimalarial compounds targeting dihydrofolate reductase-thymidylate synthase (DHFR-TS) by chemogenomic profiling.

PubMed

Aroonsri, Aiyada; Akinola, Olugbenga; Posayapisit, Navaporn; Songsungthong, Warangkhana; Uthaipibull, Chairat; Kamchonwongpaisan, Sumalee; Gbotosho, Grace O; Yuthavong, Yongyuth; Shaw, Philip J

2016-07-01

The mode of action of many antimalarial drugs is unknown. Chemogenomic profiling is a powerful method to address this issue. This experimental approach entails disruption of gene function and phenotypic screening for changes in sensitivity to bioactive compounds. Here, we describe the application of reverse genetics for chemogenomic profiling in Plasmodium. Plasmodium falciparum parasites harbouring a transgenic insertion of the glmS ribozyme downstream of the dihydrofolate reductase-thymidylate synthase (DHFR-TS) gene were used for chemogenomic profiling of antimalarial compounds to identify those which target DHFR-TS. DHFR-TS expression can be attenuated by exposing parasites to glucosamine. Parasites with attenuated DHFR-TS expression were significantly more sensitive to antifolate drugs known to target DHFR-TS. In contrast, no change in sensitivity to other antimalarial drugs with different modes of action was observed. Chemogenomic profiling was performed using the Medicines for Malaria Venture (Switzerland) Malaria Box compound library, and two compounds were identified as novel DHFR-TS inhibitors. We also tested the glmS ribozyme in Plasmodium berghei, a rodent malaria parasite. The expression of reporter genes with downstream glmS ribozyme could be attenuated in transgenic parasites comparable with that obtained in P. falciparum. The chemogenomic profiling method was applied in a P. berghei line expressing a pyrimethamine-resistant Toxoplasma gondii DHFR-TS reporter gene under glmS ribozyme control. Parasites with attenuated expression of this gene were significantly sensitised to antifolates targeting DHFR-TS, but not other drugs with different modes of action. In conclusion, these data show that the glmS ribozyme reverse genetic tool can be applied for identifying primary targets of antimalarial compounds in human and rodent malaria parasites. Copyright © 2016 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.

Some Essential Environmental Ingredients for Sex Offender Reintegration

ERIC Educational Resources Information Center

Boer, Douglas P.

2013-01-01

Until the systematic work on the Good Lives Model (GLM) produced by Tony Ward, not a great deal of conceptual structure existed to provide sex offender treatment specialists with a theoretical underpinning for their work in helping offenders develop a better life as a way to prevent reoffending. However, the work of Ward and colleagues initially…

In the eye of the beholder: the effect of rater variability and different rating scales on QTL mapping.

PubMed

Poland, Jesse A; Nelson, Rebecca J

2011-02-01

The agronomic importance of developing durably resistant cultivars has led to substantial research in the field of quantitative disease resistance (QDR) and, in particular, mapping quantitative trait loci (QTL) for disease resistance. The assessment of QDR is typically conducted by visual estimation of disease severity, which raises concern over the accuracy and precision of visual estimates. Although previous studies have examined the factors affecting the accuracy and precision of visual disease assessment in relation to the true value of disease severity, the impact of this variability on the identification of disease resistance QTL has not been assessed. In this study, the effects of rater variability and rating scales on mapping QTL for northern leaf blight resistance in maize were evaluated in a recombinant inbred line population grown under field conditions. The population of 191 lines was evaluated by 22 different raters using a direct percentage estimate, a 0-to-9 ordinal rating scale, or both. It was found that more experienced raters had higher precision and that using a direct percentage estimation of diseased leaf area produced higher precision than using an ordinal scale. QTL mapping was then conducted using the disease estimates from each rater using stepwise general linear model selection (GLM) and inclusive composite interval mapping (ICIM). For GLM, the same QTL were largely found across raters, though some QTL were only identified by a subset of raters. The magnitudes of estimated allele effects at identified QTL varied drastically, sometimes by as much as threefold. ICIM produced highly consistent results across raters and for the different rating scales in identifying the location of QTL. We conclude that, despite variability between raters, the identification of QTL was largely consistent among raters, particularly when using ICIM. However, care should be taken in estimating QTL allele effects, because this was highly variable and rater dependent.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes.

PubMed

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-07-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran's eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes

PubMed Central

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-01-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran’s eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances. PMID:26257867

Retinotopic Maps, Spatial Tuning, and Locations of Human Visual Areas in Surface Coordinates Characterized with Multifocal and Blocked fMRI Designs

PubMed Central

Henriksson, Linda; Karvonen, Juha; Salminen-Vaparanta, Niina; Railo, Henry; Vanni, Simo

2012-01-01

The localization of visual areas in the human cortex is typically based on mapping the retinotopic organization with functional magnetic resonance imaging (fMRI). The most common approach is to encode the response phase for a slowly moving visual stimulus and to present the result on an individual's reconstructed cortical surface. The main aims of this study were to develop complementary general linear model (GLM)-based retinotopic mapping methods and to characterize the inter-individual variability of the visual area positions on the cortical surface. We studied 15 subjects with two methods: a 24-region multifocal checkerboard stimulus and a blocked presentation of object stimuli at different visual field locations. The retinotopic maps were based on weighted averaging of the GLM parameter estimates for the stimulus regions. In addition to localizing visual areas, both methods could be used to localize multiple retinotopic regions-of-interest. The two methods yielded consistent retinotopic maps in the visual areas V1, V2, V3, hV4, and V3AB. In the higher-level areas IPS0, VO1, LO1, LO2, TO1, and TO2, retinotopy could only be mapped with the blocked stimulus presentation. The gradual widening of spatial tuning and an increase in the responses to stimuli in the ipsilateral visual field along the hierarchy of visual areas likely reflected the increase in the average receptive field size. Finally, after registration to Freesurfer's surface-based atlas of the human cerebral cortex, we calculated the mean and variability of the visual area positions in the spherical surface-based coordinate system and generated probability maps of the visual areas on the average cortical surface. The inter-individual variability in the area locations decreased when the midpoints were calculated along the spherical cortical surface compared with volumetric coordinates. These results can facilitate both analysis of individual functional anatomy and comparisons of visual cortex topology across studies. PMID:22590626

The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy.

PubMed

Kirilina, Evgeniya; Jelzow, Alexander; Heine, Angela; Niessing, Michael; Wabnitz, Heidrun; Brühl, Rüdiger; Ittermann, Bernd; Jacobs, Arthur M; Tachtsidis, Ilias

2012-05-15

A major methodological challenge of functional near-infrared spectroscopy (fNIRS) is its high sensitivity to haemodynamic fluctuations in the scalp. Superficial fluctuations contribute on the one hand to the physiological noise of fNIRS, impairing the signal-to-noise ratio, and may on the other hand be erroneously attributed to cerebral changes, leading to false positives in fNIRS experiments. Here we explore the localisation, time course and physiological origin of task-evoked superficial signals in fNIRS and present a method to separate them from cortical signals. We used complementary fNIRS, fMRI, MR-angiography and peripheral physiological measurements (blood pressure, heart rate, skin conductance and skin blood flow) to study activation in the frontal lobe during a continuous performance task. The General Linear Model (GLM) was applied to analyse the fNIRS data, which included an additional predictor to account for systemic changes in the skin. We found that skin blood volume strongly depends on the cognitive state and that sources of task-evoked systemic signals in fNIRS are co-localized with veins draining the scalp. Task-evoked superficial artefacts were mainly observed in concentration changes of oxygenated haemoglobin and could be effectively separated from cerebral signals by GLM analysis. Based on temporal correlation of fNIRS and fMRI signals with peripheral physiological measurements we conclude that the physiological origin of the systemic artefact is a task-evoked sympathetic arterial vasoconstriction followed by a decrease in venous volume. Since changes in sympathetic outflow accompany almost any cognitive and emotional process, we expect scalp vessel artefacts to be present in a wide range of fNIRS settings used in neurocognitive research. Therefore a careful separation of fNIRS signals originating from activated brain and from scalp is a necessary precondition for unbiased fNIRS brain activation maps. Copyright © 2012 Elsevier Inc. All rights reserved.

Adolescents of the US National Longitudinal Lesbian Family Study: the impact of having a known or an unknown donor on the stability of psychological adjustment.

PubMed

Bos, H M W; Gartrell, N K

2011-03-01

The current study is based on the US National Longitudinal Lesbian Family Study (NLLFS), which was designed to document the development of the first generation of lesbian families with children conceived through donor insemination. Data were collected in five waves, first at insemination or during pregnancy, and subsequently when the index children were 2, 5, 10 and 17 years old. The study is ongoing, with a 93% retention rate to date. The purpose of the current investigation was to assess changes in psychological adjustment of the index offspring between the time that they were 10 and 17 years old (T4 and T5) and to examine the effects of having a known or an as-yet-unknown donor. The total T5 sample consisted of 78 adolescents. The mothers in 74 families completed a Child Behaviour Checklist (CBCL) on their offspring at both T4 and T5: 26 of these offspring had been conceived through known sperm donors and 48 through unknown donors. Changes in psychological adjustment were assessed through computations of stability coefficients between T4 and T5 on all CBCL subscales, and by means of a general linear model (GLM). On 10 out of 11 CBCL subscales, the stability coefficients were not significantly different for adolescents with known and unknown donors. Findings from the GLM showed that no main effect for donor type was found; for offspring in both donor groups thought problems and rule-breaking behaviour were higher and scores on social problems and aggressive behaviour were lower at T5 than T4. The development of psychological well-being in the offspring of lesbian mothers over a 7-year period from childhood through adolescence is the same for those who were conceived through known and unknown donors.

A longitudinal study of the individual- and group-level problematic gaming and associations with problem gambling among Swedish adolescents.

PubMed

Vadlin, Sofia; Åslund, Cecilia; Nilsson, Kent W

2018-04-01

The aims of this study were to investigate the long-term stability of problematic gaming among adolescents and whether problematic gaming at wave 1 (W1) was associated with problem gambling at wave 2 (W2), three years later. Data from the SALVe cohort, including adolescents in Västmanland born in 1997 and 1999, were accessed and analyzed in two waves W2, N  =   1576; 914 (58%) girls). At W1, the adolescents were 13 and 15 years old, and at W2, they were 16 and 18 years old. Adolescents self-rated on the Gaming Addiction Identification Test (GAIT), Problem Gambling Severity Index (PGSI), and gambling frequencies. Stability of gaming was determined using Gamma correlation, Spearman's rho, and McNemar. Logistic regression analysis and general linear model (GLM) analysis were performed and adjusted for sex, age, and ethnicity, frequency of gambling activities and gaming time at W1, with PGSI as the dependent variable, and GAIT as the independent variable, to investigate associations between problematic gaming and problem gambling. Problematic gaming was relative stable over time, γ = 0.739, p  ≤   .001, ρ = 0.555, p  ≤   .001, and McNemar p  ≤   .001. Furthermore, problematic gaming at W1 increased the probability of having problem gambling three years later, logistic regression OR = 1.886 (95% CI 1.125-3.161), p  =   .016, GLM F  = 10.588, η 2  = 0.007, p =  .001. Problematic gaming seems to be relatively stable over time. Although associations between problematic gaming and later problem gambling were found, the low explained variance indicates that problematic gaming in an unlikely predictor for problem gambling within this sample.

Differentiation between Vergence and Saccadic Functional Activity within the Human Frontal Eye Fields and Midbrain Revealed through fMRI

PubMed Central

Alkan, Yelda; Biswal, Bharat B.; Alvarez, Tara L.

2011-01-01

Purpose Eye movement research has traditionally studied solely saccade and/or vergence eye movements by isolating these systems within a laboratory setting. While the neural correlates of saccadic eye movements are established, few studies have quantified the functional activity of vergence eye movements using fMRI. This study mapped the neural substrates of vergence eye movements and compared them to saccades to elucidate the spatial commonality and differentiation between these systems. Methodology The stimulus was presented in a block design where the ‘off’ stimulus was a sustained fixation and the ‘on’ stimulus was random vergence or saccadic eye movements. Data were collected with a 3T scanner. A general linear model (GLM) was used in conjunction with cluster size to determine significantly active regions. A paired t-test of the GLM beta weight coefficients was computed between the saccade and vergence functional activities to test the hypothesis that vergence and saccadic stimulation would have spatial differentiation in addition to shared neural substrates. Results Segregated functional activation was observed within the frontal eye fields where a portion of the functional activity from the vergence task was located anterior to the saccadic functional activity (z>2.3; p<0.03). An area within the midbrain was significantly correlated with the experimental design for the vergence but not the saccade data set. Similar functional activation was observed within the following regions of interest: the supplementary eye field, dorsolateral prefrontal cortex, ventral lateral prefrontal cortex, lateral intraparietal area, cuneus, precuneus, anterior and posterior cingulates, and cerebellar vermis. The functional activity from these regions was not different between the vergence and saccade data sets assessed by analyzing the beta weights of the paired t-test (p>0.2). Conclusion Functional MRI can elucidate the differences between the vergence and saccade neural substrates within the frontal eye fields and midbrain. PMID:22073141

SLC22A1-ABCB1 haplotype profiles predict imatinib pharmacokinetics in Asian patients with chronic myeloid leukemia.

PubMed

Singh, Onkar; Chan, Jason Yongsheng; Lin, Keegan; Heng, Charles Chuah Thuan; Chowbay, Balram

2012-01-01

This study aimed to explore the influence of SLC22A1, PXR, ABCG2, ABCB1 and CYP3A5 3 genetic polymorphisms on imatinib mesylate (IM) pharmacokinetics in Asian patients with chronic myeloid leukemia (CML). Healthy subjects belonging to three Asian populations (Chinese, Malay, Indian; n = 70 each) and CML patients (n = 38) were enrolled in a prospective pharmacogenetics study. Imatinib trough (C(0h)) and clearance (CL) were determined in the patients at steady state. Haplowalk method was applied to infer the haplotypes and generalized linear model (GLM) to estimate haplotypic effects on IM pharmacokinetics. Association of haplotype copy numbers with IM pharmacokinetics was defined by Mann-Whitney U test. Global haplotype score statistics revealed a SLC22A1 sub-haplotypic region encompassing three polymorphisms (rs3798168, rs628031 and IVS7+850C>T), to be significantly associated with IM clearance (p = 0.013). Haplotype-specific GLM estimated that the haplotypes AGT and CGC were both associated with 22% decrease in clearance compared to CAC [CL (10(-2) L/hr/mg): CAC vs AGT: 4.03 vs 3.16, p = 0.017; CAC vs CGC: 4.03 vs 3.15, p = 0.017]. Patients harboring 2 copies of AGT or CGC haplotypes had 33.4% lower clearance and 50% higher C(0h) than patients carrying 0 or 1 copy [CL (10(-2) L/hr/mg): 2.19 vs 3.29, p = 0.026; C(0h) (10(-6) 1/ml): 4.76 vs 3.17, p = 0.013, respectively]. Further subgroup analysis revealed SLC22A1 and ABCB1 haplotypic combinations to be significantly associated with clearance and C(0h) (p = 0.002 and 0.009, respectively). This exploratory study suggests that SLC22A1-ABCB1 haplotypes may influence IM pharmacokinetics in Asian CML patients.

Preliminary assessment of factors influencing riverine fish communities in Massachusetts.

USGS Publications Warehouse

Armstrong, David S.; Richards, Todd A.; Brandt, Sara L.

2010-01-01

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation (MDCR), Massachusetts Department of Environmental Protection (MDEP), and the Massachusetts Department of Fish and Game (MDFG), conducted a preliminary investigation of fish communities in small- to medium-sized Massachusetts streams. The objective of this investigation was to determine relations between fish-community characteristics and anthropogenic alteration, including flow alteration and impervious cover, relative to the effect of physical basin and land-cover (environmental) characteristics. Fish data were obtained for 756 fish-sampling sites from the Massachusetts Division of Fisheries and Wildlife fish-community database. A review of the literature was used to select a set of fish metrics responsive to flow alteration. Fish metrics tested include two fish-community metrics (fluvial-fish relative abundance and fluvial-fish species richness), and five indicator species metrics (relative abundance of brook trout, blacknose dace, fallfish, white sucker, and redfin pickerel). Streamflows were simulated for each fish-sampling site using the Sustainable Yield Estimator application (SYE). Daily streamflows and the SYE water-use database were used to determine a set of indicators of flow alteration, including percent alteration of August median flow, water-use intensity, and withdrawal and return-flow fraction. The contributing areas to the fish-sampling sites were delineated and used with a Geographic Information System (GIS) to determine a set of environmental characteristics, including elevation, basin slope, percent sand and gravel, percent wetland, and percent open water, and a set of anthropogenic-alteration variables, including impervious cover and dam density. Two analytical techniques, quantile regression and generalized linear modeling, were applied to determine the association between fish-response variables and the selected environmental and anthropogenic explanatory variables. Quantile regression indicated that flow alteration and impervious cover were negatively associated with both fluvial-fish relative abundance and fluvial-fish species richness. Three generalized linear models (GLMs) were developed to quantify the response of fish communities to multiple environmental and anthropogenic variables. Flow-alteration variables are statistically significant for the fluvial-fish relative-abundance model. Impervious cover is statistically significant for the fluvial-fish relative-abundance, fluvial-fish species richness, and brook trout relative-abundance models. The variables in the equations were demonstrated to be significant, and the variability explained by the models, as measured by the correlation between observed and predicted values, ranges from 39 to 65 percent. The GLM models indicated that, keeping all other variables the same, a one-unit (1 percent) increase in the percent depletion or percent surcharging of August median flow would result in a 0.4-percent decrease in the relative abundance (in counts per hour) of fluvial fish and that the relative abundance of fluvial fish was expected to be about 55 percent lower in net-depleted streams than in net-surcharged streams. The GLM models also indicated that a unit increase in impervious cover resulted in a 5.5-percent decrease in the relative abundance of fluvial fish and a 2.5-percent decrease in fluvial-fish species richness.

The Health Care Cost Implications of Overweight and Obesity during Childhood

PubMed Central

Au, Nicole

2012-01-01

Objective To investigate whether childhood overweight at age 4–5 increases publicly funded health care costs during childhood, and to explore the role of timing and duration of overweight on health costs. Data Sources The Longitudinal Study of Australian Children (2004–2008) and linked records from Medicare, Australia's public health insurance provider (2004–2009). Study Design The influence of overweight status on non-hospital Medicare costs incurred by children over a 5-year period was estimated using two-part models and one-part generalized linear models (GLM). All models controlled for demographic, socioeconomic, and parental characteristics. Principal Findings Being overweight at age 4–5 is associated with significantly higher pharmaceutical and medical care costs. The results imply that for all children aged 4 and 5 in 2004–2005, those who were overweight had a combined 5-year Medicare bill that was AUD$9.8 million higher than that of normal weight children. Results from dynamic analyses show that costs of childhood overweight occur contemporaneously, and the duration of overweight is positively associated with medical costs for children who became overweight after age 5. Conclusions This study reveals that the financial burden to the public health system of childhood overweight and obesity occurs even during the first 5 years of primary school. PMID:22092082

Controlling for endogeneity in attributable costs of vancomycin-resistant enterococci from a Canadian hospital.

PubMed

Lloyd-Smith, Patrick

2017-12-01

Decisions regarding the optimal provision of infection prevention and control resources depend on accurate estimates of the attributable costs of health care-associated infections. This is challenging given the skewed nature of health care cost data and the endogeneity of health care-associated infections. The objective of this study is to determine the hospital costs attributable to vancomycin-resistant enterococci (VRE) while accounting for endogeneity. This study builds on an attributable cost model conducted by a retrospective cohort study including 1,292 patients admitted to an urban hospital in Vancouver, Canada. Attributable hospital costs were estimated with multivariate generalized linear models (GLMs). To account for endogeneity, a control function approach was used. The analysis sample included 217 patients with health care-associated VRE. In the standard GLM, the costs attributable to VRE are $17,949 (SEM, $2,993). However, accounting for endogeneity, the attributable costs were estimated to range from $14,706 (SEM, $7,612) to $42,101 (SEM, $15,533). Across all model specifications, attributable costs are 76% higher on average when controlling for endogeneity. VRE was independently associated with increased hospital costs, and controlling for endogeneity lead to higher attributable cost estimates. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Evaluating the Small-World-Ness of a Sampled Network: Functional Connectivity of Entorhinal-Hippocampal Circuitry

NASA Astrophysics Data System (ADS)

She, Qi; Chen, Guanrong; Chan, Rosa H. M.

2016-02-01

The amount of publicly accessible experimental data has gradually increased in recent years, which makes it possible to reconsider many longstanding questions in neuroscience. In this paper, an efficient framework is presented for reconstructing functional connectivity using experimental spike-train data. A modified generalized linear model (GLM) with L1-norm penalty was used to investigate 10 datasets. These datasets contain spike-train data collected from the entorhinal-hippocampal region in the brains of rats performing different tasks. The analysis shows that entorhinal-hippocampal network of well-trained rats demonstrated significant small-world features. It is found that the connectivity structure generated by distance-dependent models is responsible for the observed small-world features of the reconstructed networks. The models are utilized to simulate a subset of units recorded from a large biological neural network using multiple electrodes. Two metrics for quantifying the small-world-ness both suggest that the reconstructed network from the sampled nodes estimates a more prominent small-world-ness feature than that of the original unknown network when the number of recorded neurons is small. Finally, this study shows that it is feasible to adjust the estimated small-world-ness results based on the number of neurons recorded to provide a more accurate reference of the network property.

Comparisons of GLM and LMA Observations

NASA Astrophysics Data System (ADS)

Thomas, R. J.; Krehbiel, P. R.; Rison, W.; Stanley, M. A.; Attanasio, A.

2017-12-01

Observations from 3-dimensional VHF lightning mapping arrays (LMAs) provide a valuable basis for evaluating the spatial accuracy and detection efficiencies of observations from the recently launched, optical-based Geosynchronous Lightning Mapper (GLM). In this presentation, we describe results of comparing the LMA and GLM observations. First, the observations are compared spatially and temporally at the individual event (pixel) level for sets of individual discharges. For LMA networks in Florida, Colorado, and Oklahoma, the GLM observations are well correlated time-wise with LMA observations but are systematically offset by one- to two pixels ( 10 to 15 or 20 km) in a southwesterly direction from the actual lightning activity. The graphical comparisons show a similar location uncertainty depending on the altitude at which the scattered light is emitted from the parent cloud, due to being observed at slant ranges. Detection efficiencies (DEs) can be accurately determined graphically for intervals where individual flashes in a storm are resolved time-wise, and DEs and false alarm rates can be automated using flash sorting algorithms for overall and/or larger storms. This can be done as a function of flash size and duration, and generally shows high detection rates for larger flashes. Preliminary results during the May 1 2017 ER-2 overflight of Colorado storms indicate decreased detection efficiency if the storm is obscured by an overlying cloud layer.

Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

NASA Astrophysics Data System (ADS)

Lowe, Rachel; Bailey, Trevor C.; Stephenson, David B.; Graham, Richard J.; Coelho, Caio A. S.; Sá Carvalho, Marilia; Barcellos, Christovam

2011-03-01

This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5°×2.5° longitude-latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

Identifying factors affecting the safety of mid-block bicycle lanes considering mixed 2-wheeled traffic flow.

PubMed

Bai, Lu; Chan, Ching-Yao; Liu, Pan; Xu, Chengcheng

2017-10-03

Electric bikes (e-bikes) have been one of the fastest growing trip modes in Southeast Asia over the past 2 decades. The increasing popularity of e-bikes raised some safety concerns regarding urban transport systems. The primary objective of this study was to identify whether and how the generalized linear regression model (GLM) could be used to relate cyclists' safety with various contributing factors when riding in a mid-block bike lane. The types of 2-wheeled vehicles in the study included bicycle-style electric bicycles (BSEBs), scooter-style electric bicycles (SSEBs), and regular bicycles (RBs). Traffic conflict technology was applied as a surrogate measure to evaluate the safety of 2-wheeled vehicles. The safety performance model was developed by adopting a generalized linear regression model for relating the frequency of rear-end conflicts between e-bikes and regular bikes to the operating speeds of BSEBs, SSEBs, and RBs in mid-block bike lanes. The frequency of rear-end conflicts between e-bikes and bikes increased with an increase in the operating speeds of e-bikes and the volume of e-bikes and bikes and decreased with an increase in the width of bike lanes. The large speed difference between e-bikes and bikes increased the frequency of rear-end conflicts between e-bikes and bikes in mid-block bike lanes. A 1% increase in the average operating speed of e-bikes would increase the expected number of rear-end conflicts between e-bikes and bikes by 1.48%. A 1% increase in the speed difference between e-bikes and bikes would increase the expected number of rear-end conflicts between e-bikes/bikes by 0.16%. The conflict frequency in mid-block bike lanes can be modeled using generalized linear regression models. The factors that significantly affected the frequency of rear-end conflicts included the operating speeds of e-bikes, the speed difference between e-bikes and regular bikes, the volume of e-bikes, the volume of bikes, and the width of bike lanes. The safety performance model can help better understand the causes of crash occurrences in mid-block bike lanes.

Background sampling and transferability of species distribution model ensembles under climate change

NASA Astrophysics Data System (ADS)

Iturbide, Maialen; Bedia, Joaquín; Gutiérrez, José Manuel

2018-07-01

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning. A popular application of these models is the projection of species distributions under climate change conditions. Yet there are still a range of methodological SDM factors which limit the transferability of these models, contributing significantly to the overall uncertainty of the resulting projections. An important source of uncertainty often neglected in climate change studies comes from the use of background data (a.k.a. pseudo-absences) for model calibration. Here, we study the sensitivity to pseudo-absence sampling as a determinant factor for SDM stability and transferability under climate change conditions, focusing on European wide projections of Quercus robur as an illustrative case study. We explore the uncertainty in future projections derived from ten pseudo-absence realizations and three popular SDMs (GLM, Random Forest and MARS). The contribution of the pseudo-absence realization to the uncertainty was higher in peripheral regions and clearly differed among the tested SDMs in the whole study domain, being MARS the most sensitive - with projections differing up to a 40% for different realizations - and GLM the most stable. As a result we conclude that parsimonious SDMs are preferable in this context, avoiding complex methods (such as MARS) which may exhibit poor model transferability. Accounting for this new source of SDM-dependent uncertainty is crucial when forming multi-model ensembles to undertake climate change projections.

Bio-physical model provides insight into dispersal of plaice (Pleuronectes platessa L.) from putative spawning grounds to nursery areas on the west coast of Ireland

NASA Astrophysics Data System (ADS)

Zölck, Melanie; Brophy, Deirdre; Mohn, Christian; Minto, Cóilín; McGrath, David

2015-05-01

In this study we use an individual-based coupled physical biological model (ICPBM) to reconstruct the dispersal pathways of 0-group juveniles (young of the year) collected from nursery grounds in Galway Bay and to identify probable spawning ground locations for plaice on the west coast of Ireland. The relative importance of passive transport, behaviour and individual growth rates on successful larval delivery, from three putative spawning grounds to suitable nursery areas, was also investigated. Using a hydrodynamic Regional Ocean Modelling System (ROMS), combined with a particle tracking model, three model scenarios were tested: a passive tracer scenario (PTS), a linear growth scenario (LGS) and a temperature-dependent growth scenario (TDS). Hydrodynamic conditions were modelled and biological information (pelagic larval durations and size at settlement) incorporated. The LGS and TDS included vertical migration and tidally synchronised behaviour. Generalized Linear Model (GLM) comparisons showed that incorporation of behaviour and temperature-dependent growth, resulted in approximately two to three times more particles being delivered to sites of suitable depth for settlement (≤ 10 m), compared to passive transport alone (p < 0.001, LGS 19-78%; TDS 40-81%). The probability of successful delivery also varied significantly depending on the location, year and week of release (p < 0.05). A comparison of temperature histories between particles that were delivered to shallow inshore areas and those that failed to reach depths suitable for settlement indicated that dispersal to coastal nursery areas is facilitated by entrainment into a cool coastal current system. This study identifies a probable plaice spawning area in western Ireland and reconfirms the importance of including behaviour and growth in dispersal simulations. The model results suggest that differences in growth can influence larval delivery to potentially suitable nursery areas.

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

NASA Technical Reports Server (NTRS)

Solakiewiz, Richard; Koshak, William

2008-01-01

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornado activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William; Petersen, Walt; Buechler, Dennis; Krehbiel, Paul; Gatlin, Patrick; Zubrick, Steven

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational.The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) sate]lite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications. Abstract for the 3 rd Conference on Meteorological

Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors.

PubMed

Marini, C; Fossa, F; Paoli, C; Bellingeri, M; Gnone, G; Vassallo, P

2015-03-01

Habitat modeling is an important tool to investigate the quality of the habitat for a species within a certain area, to predict species distribution and to understand the ecological processes behind it. Many species have been investigated by means of habitat modeling techniques mainly to address effective management and protection policies and cetaceans play an important role in this context. The bottlenose dolphin (Tursiops truncatus) has been investigated with habitat modeling techniques since 1997. The objectives of this work were to predict the distribution of bottlenose dolphin in a coastal area through the use of static morphological features and to compare the prediction performances of three different modeling techniques: Generalized Linear Model (GLM), Generalized Additive Model (GAM) and Random Forest (RF). Four static variables were tested: depth, bottom slope, distance from 100 m bathymetric contour and distance from coast. RF revealed itself both the most accurate and the most precise modeling technique with very high distribution probabilities predicted in presence cells (90.4% of mean predicted probabilities) and with 66.7% of presence cells with a predicted probability comprised between 90% and 100%. The bottlenose distribution obtained with RF allowed the identification of specific areas with particularly high presence probability along the coastal zone; the recognition of these core areas may be the starting point to develop effective management practices to improve T. truncatus protection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Social connections and happiness among the elder population of Taiwan.

PubMed

Hsu, H-C; Chang, W-C

2015-01-01

The purpose of this study was to examine the association between social connections and happiness among members of the elder population of Taiwan. Longitudinal panel data collected in three waves from 1999 to 2007 that are selected from national samples of Taiwanese older people were used for the analysis (n = 4731 persons). Happiness was defined as a dichotomous variable. Social connection variables included living arrangements, contacts with children/grandchildren/parents/relatives/friends, telephone contacts, providing instrumental and informational support, receiving instrumental and emotional support, and social participation. We controlled for the variables demographics, physical and mental health, economic satisfaction, and lifestyle. A generalized linear model (GLM) was applied in the analysis. Happiness remained stable over time. Receiving more emotional support and participating in social events were related to happiness at the beginning, while the effect of social participation was offset over time. Living arrangements, telephone contacts, providing social support, and receiving instrumental support were not significant. The quality of social relationships experienced is possibly more important than the quantity of social interaction for older people, and having social relationships outside the informal social network may increase happiness.

Chosen risk level during car-following in adverse weather conditions.

PubMed

Hjelkrem, Odd André; Ryeng, Eirin Olaussen

2016-10-01

This study examines how precipitation, light conditions and surface conditions affect the drivers' risk perception. An indicator CRI (Chosen Risk Index) is defined, which describes the chosen risk level for drivers in a car-following situation. The dataset contains about 70 000 observations of driver behaviour and weather status on a rural road. Based on the theory of risk homeostasis and an assumption that driving behaviour in situations with daylight, dry road and no precipitation reflects drivers' target level of risk, generalised linear models (GLM) were estimated for cars and trucks separately to reveal the effect of adverse weather conditions on risk perception. The analyses show that both car and truck drivers perceive the highest risk when driving on snow covered roads. For car drivers, a snow covered road in combination with moderate rain or light snow are the factors which lowers the CRI the most. For trucks, snow cover and partially covered roads significantly lowers the CRI, while precipitation did not seem to impose any higher risk. Interaction effects were found for car drivers only. Copyright © 2016 Elsevier Ltd. All rights reserved.

Characterization of acute phase proteins and oxidative stress response to road transportation in the dog.

PubMed

Fazio, Francesco; Casella, Stefania; Giannetto, Claudia; Giudice, Elisabetta; Piccione, Giuseppe

2015-01-01

Haptoglobin (Hp), serum amyloid A (SAA), C-reactive protein (CRP), white blood cells (WBC), reactive oxygen metabolites (ROMs), the antioxidant barrier (Oxy-adsorbent) and thiol groups of plasma compounds (SHp) were measured in ten dogs that had been transported a distance of about 230 km within 2 h (experimental group) and in ten dogs that had not been subjected to road transportation (control group). Blood was collected via cephalic venipuncture before road transportation (T0), after road transportation (T1), and more than 6 (T6) and 24 (T24) hours after road transportation in the experimental group (Group A) and at the same time points in the control group (Group B). The GLM (general linear model) Repeated Measures procedure showed a significant difference between the two groups (P<0.0001) and a significant rise (P<0.0001) in the concentrations of Hp, SAA, CRP, WBC, ROMs, Oxy-adsorbent and SHp after road transportation in Group A, underlining that physiological and homeostatic mechanisms are modified differently at various sampling times.

Implementation and effect of life space crisis intervention in special schools with residential treatment for students with emotional and behavioral disorders (EBD).

PubMed

DOosterlinck, Franky; Goethals, Ilse; Broekaert, Eric; Boekaert, Eric; Schuyten, Gilberte; De Maeyer, Jessica

2008-03-01

The increase of violence in present-day society calls for adequate crisis interventions for students with behavioral problems. Life Space Crisis Intervention (LSCI) is a systematic and formatted response to a student's crisis, based on cognitive, behavioral, psychodynamic and developmental theory. The following research article evaluates a LSCI Program with students referred to special schools with residential treatment because of severe behavioral problems. The evaluation was conducted using a quasi experimental pre-test-post-test control group design. Thirty-one match paired students were pre-tested before the interventions started and post-tested after a period of 11 months. Five standardized questionnaires were examined to assess the effectiveness of the LSCI Program. General Linear Model (GLM) with repeated measures was used to analyze all data. For the total group of subjects (n = 62) it was found that students' perception about their athletic competence decrease significantly after 11 months in residential care. A positive effect of LSCI was found on direct aggression and social desirability.

Investigating bias in squared regression structure coefficients

PubMed Central

Nimon, Kim F.; Zientek, Linda R.; Thompson, Bruce

2015-01-01

The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients. PMID:26217273

The performance of projective standardization for digital subtraction radiography.

PubMed

Mol, André; Dunn, Stanley M

2003-09-01

We sought to test the performance and robustness of projective standardization in preserving invariant properties of subtraction images in the presence of irreversible projection errors. Study design Twenty bone chips (1-10 mg each) were placed on dentate dry mandibles. Follow-up images were obtained without the bone chips, and irreversible projection errors of up to 6 degrees were introduced. Digitized image intensities were normalized, and follow-up images were geometrically reconstructed by 2 operators using anatomical and fiduciary landmarks. Subtraction images were analyzed by 3 observers. Regression analysis revealed a linear relationship between radiographic estimates of mineral loss and actual mineral loss (R(2) = 0.99; P <.05). The effect of projection error was not significant (general linear model [GLM]: P >.05). There was no difference between the radiographic estimates from images standardized with anatomical landmarks and those standardized with fiduciary landmarks (Wilcoxon signed rank test: P >.05). Operator variability was low for image analysis alone (R(2) = 0.99; P <.05), as well as for the entire procedure (R(2) = 0.98; P <.05). The predicted detection limit was smaller than 1 mg. Subtraction images registered by projective standardization yield estimates of osseous change that are invariant to irreversible projection errors of up to 6 degrees. Within these limits, operator precision is high and anatomical landmarks can be used to establish correspondence.

Utilizing ISS Camera Systems for Scientific Analysis of Lightning Characteristics and comparison with ISS-LIS and GLM

NASA Astrophysics Data System (ADS)

Schultz, C. J.; Lang, T. J.; Leake, S.; Runco, M.; Blakeslee, R. J.

2017-12-01

Video and still frame images from cameras aboard the International Space Station (ISS) are used to inspire, educate, and provide a unique vantage point from low-Earth orbit that is second to none; however, these cameras have overlooked capabilities for contributing to scientific analysis of the Earth and near-space environment. The goal of this project is to study how georeferenced video/images from available ISS camera systems can be useful for scientific analysis, using lightning properties as a demonstration. Camera images from the crew cameras and high definition video from the Chiba University Meteor Camera were combined with lightning data from the National Lightning Detection Network (NLDN), ISS-Lightning Imaging Sensor (ISS-LIS), the Geostationary Lightning Mapper (GLM) and lightning mapping arrays. These cameras provide significant spatial resolution advantages ( 10 times or better) over ISS-LIS and GLM, but with lower temporal resolution. Therefore, they can serve as a complementarity analysis tool for studying lightning and thunderstorm processes from space. Lightning sensor data, Visible Infrared Imaging Radiometer Suite (VIIRS) derived city light maps, and other geographic databases were combined with the ISS attitude and position data to reverse geolocate each image or frame. An open-source Python toolkit has been developed to assist with this effort. Next, the locations and sizes of all flashes in each frame or image were computed and compared with flash characteristics from all available lightning datasets. This allowed for characterization of cloud features that are below the 4-km and 8-km resolution of ISS-LIS and GLM which may reduce the light that reaches the ISS-LIS or GLM sensor. In the case of video, consecutive frames were overlaid to determine the rate of change of the light escaping cloud top. Characterization of the rate of change in geometry, more generally the radius, of light escaping cloud top was integrated with the NLDN, ISS-LIS and GLM to understand how the peak rate of change and the peak area of each flash aligned with each lightning system in time. Flash features like leaders could be inferred from the video frames as well. Testing is being done to see if leader speeds may be accurately calculated under certain circumstances.

Preparations for Integrating Space-Based Total Lightning Observations into Forecast Operations

NASA Technical Reports Server (NTRS)

Stano, Geoffrey T.; Fuell, Kevin K.; Molthan, Andrew L.

2016-01-01

NASA's Short-term Prediction Research and Transition (SPoRT) Center has been a leader in collaborating with the United States National Weather Service (NWS) offices to integrate ground-based total lightning (intra-cloud and cloud-to-ground) observations into the real-time operational environment. For much of these collaborations, the emphasis has been on training, dissemination of data to the NWS AWIPS system, and focusing on the utility of these data in the warning decision support process. A shift away from this paradigm has occurred more recently for several reasons. For one, SPoRT's collaborations have expanded to new partners, including emergency managers and the aviation community. Additionally, and most importantly, is the impending launch of the GOES-R Geostationary Lightning Mapper (GLM). This has led to collaborative efforts to focus on additional forecast needs, new data displays, develop training for GLM uses based on the lessons learned from ground-based lightning mapping arrays, and ways to better relate total lightning data to other meteorological parameters. This presentation will focus on these efforts to prepare the operational end user community for GLM with an eye towards sharing lessons learned as EUMETSAT prepares for the Meteosat Third Generation Lightning Imager. This will focus on both software and training needs. In particular, SPoRT has worked closely with the Meteorological Development Laboratory to create the total lightning tracking tool. This software allows for NWS forecasters to manually track storms of interest and display a time series trend of observations. This tool also has been expanded to work on any gridded data set allowing for easy visual comparisons of multiple parameters in addition to total lightning. A new web display has been developed for the ground-based observations that can be easily extended to satellite observations. This paves the way for new collaborations outside of the NWS, both domestically and internationally, as the web display will be functional on PCs and mobile devices. Furthermore, SPoRT has helped developed the software plug-in to visualize GLM data. Examples using the official GLM proxy product will be used to provide a glimpse as to what real-time GLM and likely MTG-LI data will be in the near future.

Impact of a clinical pharmacy program on changes in hemoglobin A1c, diabetes-related hospitalizations, and diabetes-related emergency department visits for patients with diabetes in an underserved population.

PubMed

Chung, Nancy; Rascati, Karen; Lopez, Debra; Jokerst, Jason; Garza, Aida

2014-09-01

Diabetes mellitus is associated with substantial morbidity and mortality. With the rise in prevalence of diabetes, there has been an increased need for clinical pharmacy services focused on diabetes management in ambulatory clinics. However, more data IS needed to determine the overall impact that clinical pharmacists have on preventing diabetes-related inpatient admissions and emergency department (ED) visits for patients with diabetes, especially in an underserved population. To assess the impact of clinical pharmacy services on the change in hemoglobin A1c measurements, the number of diabetes-related hospitalizations, and the number of diabetes-related ED visits for patients with uncontrolled diabetes. This was a retrospective study that evaluated outcomes for patients referred to a clinical pharmacist for management of diabetes, compared with patients who were not seen by a clinical pharmacist. Adult patients aged between 18 and 89 years with a diagnosis of type 1 or type 2 diabetes mellitus were identified, using the electronic medical records from CommUnityCare outpatient clinics in central Texas during the period July 1, 2007, through July 1, 2011. Patients enrolled had poor glycemic control, defined as an A1c ≥9% at baseline (index), with at least 3 visits with a clinical pharmacist or 3 visits to usual care. Patients with at least 1 year of pre-index data, 1 year of post-index follow-up, and a post-index A1c measure were included in the study. Propensity score (PS) matching was used to create a 1:1 cohort. T-tests were used to calculate results for the main outcome variables (change in A1c, change in number of diabetes-related hospitalizations, and change in number of diabetes-related ED visits). In addition, general linear models (GLM) were used to control for baseline demographic and clinical characteristics. A total of 782 patients met inclusion criteria, 557 in the usual care (control) group and 225 in the clinical pharmacy (intervention) group. PS matching provided a 1:1 matched sample of 220 patients per cohort. When assessing the change in the number of diabetes-related hospitalizations from the pre-index year to the post-index year, patients in the control group had an increase of 8 hospitalizations (8 visits per 220 patients, mean = 0.036, SD = 0.284), while the intervention group had a decrease of 1 hospitalization (-1 visit per 220 patients, mean = -0.005, SD=0.278). Both the t-test (P = 0.06) and GLM model (P = 0.06) indicated that the difference was statistically significant. When assessing the change in the number of diabetes-related ED visits from the pre-index year to the post-index year, we found patients in the control group had an increase of 16 ED visits (16 visits per 220 patients, mean = 0.073, SD = 0.584), while the intervention group had an increase of 4 ED visits (4 visits per 220 patients, mean = -0.018, SD=0.641). Both the t-test (P = 0.18) and GLM model (P = 0.28) indicated that the difference was not statistically significant. A1c levels were reduced in the post-index period for both groups. For the control group, A1c reduction was 1.50 (from 11.17 to 9.67, SD = 2.49). For the intervention group, A1c reduction was 1.90 (from 11.09 to 9.19, SD = 2.44). Both the t-test (P = 0.04) and GLM model (P = 0.05) indicated that the A1c difference was statistically significant. Underserved patients with baseline uncontrolled diabetes who were managed by a clinical pharmacist in the outpatient setting had a higher decrease in A1c compared with usual care. The changes in diabetes-related hospitalizations and diabetes-related ED visits were in the hypothesized direction, but the comparison for ED visits was not statistically significant.

Predicting potentially toxigenic Pseudo-nitzschia blooms in the Chesapeake Bay

USGS Publications Warehouse

Anderson, C.R.; Sapiano, M.R.P.; Prasad, M.B.K.; Long, W.; Tango, P.J.; Brown, C.W.; Murtugudde, R.

2010-01-01

Harmful algal blooms are now recognized as a significant threat to the Chesapeake Bay as they can severely compromise the economic viability of important recreational and commercial fisheries in the largest estuary of the United States. This study describes the development of empirical models for the potentially domoic acid-producing Pseudo-nitzschia species complex present in the Bay, developed from a 22-year time series of cell abundance and concurrent measurements of hydrographic and chemical properties. Using a logistic Generalized Linear Model (GLM) approach, model parameters and performance were compared over a range of Pseudo-nitzschia bloom thresholds relevant to toxin production by different species. Small-threshold blooms (???10cellsmL-1) are explained by time of year, location, and variability in surface values of phosphate, temperature, nitrate plus nitrite, and freshwater discharge. Medium- (100cellsmL-1) to large- threshold (1000cellsmL-1) blooms are further explained by salinity, silicic acid, dissolved organic carbon, and light attenuation (Secchi) depth. These predictors are similar to other models for Pseudo-nitzschia blooms on the west coast, suggesting commonalities across ecosystems. Hindcasts of bloom probabilities at a 19% bloom prediction point yield a Heidke Skill Score of -53%, a Probability of Detection ~75%, a False Alarm Ratio of ~52%, and a Probability of False Detection ~9%. The implication of possible future changes in Baywide nutrient stoichiometry on Pseudo-nitzschia blooms is discussed. ?? 2010 Elsevier B.V.

A statistical model to estimate the impact of a hepatitis A vaccination programme.

PubMed

Oviedo, Manuel; Pilar Muñoz, M; Domínguez, Angela; Borras, Eva; Carmona, Gloria

2008-11-11

A program of routine hepatitis A+B vaccination in preadolescents was introduced in 1998 in Catalonia, a region situated in the northeast of Spain. The objective of this study was to quantify the reduction in the incidence of hepatitis A in order to differentiate the natural reduction of the incidence of hepatitis A from that produced due to the vaccination programme and to predict the evolution of the disease in forthcoming years. A generalized linear model (GLM) using negative binomial regression was used to estimate the incidence rates of hepatitis A in Catalonia by year, age group and vaccination. Introduction of the vaccine reduced cases by 5.5 by year (p-value<0.001), but there was a significant interaction between the year of report and vaccination that smoothed this reduction (p-value<0.001). The reduction was not equal in all age groups, being greater in the 12-18 years age group, which fell from a mean rate of 8.15 per 100,000 person/years in the pre-vaccination period (1992-1998) to 1.4 in the vaccination period (1999-2005). The model predicts the evolution accurately for the group of vaccinated subjects. Negative binomial regression is more appropriate than Poisson regression when observed variance exceeds the observed mean (overdispersed count data), can cause a variable apparently contribute more on the model of what really makes it.

Alexander Technique Training Coupled With an Integrative Model of Behavioral Prediction in Teachers With Low Back Pain.

PubMed

Kamalikhah, Tahereh; Morowatisharifabad, Mohammad Ali; Rezaei-Moghaddam, Farid; Ghasemi, Mohammad; Gholami-Fesharaki, Mohammad; Goklani, Salma

2016-09-01

Individuals suffering from chronic low back pain (CLBP) experience major physical, social, and occupational disruptions. Strong evidence confirms the effectiveness of Alexander technique (AT) training for CLBP. The present study applied an integrative model (IM) of behavioral prediction for improvement of AT training. This was a quasi-experimental study of female teachers with nonspecific LBP in southern Tehran in 2014. Group A contained 42 subjects and group B had 35 subjects. In group A, AT lessons were designed based on IM constructs, while in group B, AT lessons only were taught. The validity and reliability of the AT questionnaire were confirmed using content validity (CVR 0.91, CVI 0.96) and Cronbach's α (0.80). The IM constructs of both groups were measured after the completion of training. Statistical analysis used independent and paired samples t-tests and the univariate generalized linear model (GLM). Significant differences were recorded before and after intervention (P < 0.001) for the model constructs of intention, perceived risk, direct attitude, behavioral beliefs, and knowledge in both groups. Direct attitude and behavioral beliefs in group A were higher than in group B after the intervention (P < 0.03). The educational framework provided by IM for AT training improved attitude and behavioral beliefs that can facilitate the adoption of AT behavior and decreased CLBP.

Noninvasive Classification of Hepatic Fibrosis Based on Texture Parameters From Double Contrast-Enhanced Magnetic Resonance Images

PubMed Central

Bahl, Gautam; Cruite, Irene; Wolfson, Tanya; Gamst, Anthony C.; Collins, Julie M.; Chavez, Alyssa D.; Barakat, Fatma; Hassanein, Tarek; Sirlin, Claude B.

2016-01-01

Purpose To demonstrate a proof of concept that quantitative texture feature analysis of double contrast-enhanced magnetic resonance imaging (MRI) can classify fibrosis noninvasively, using histology as a reference standard. Materials and Methods A Health Insurance Portability and Accountability Act (HIPAA)-compliant Institutional Review Board (IRB)-approved retrospective study of 68 patients with diffuse liver disease was performed at a tertiary liver center. All patients underwent double contrast-enhanced MRI, with histopathology-based staging of fibrosis obtained within 12 months of imaging. The MaZda software program was used to compute 279 texture parameters for each image. A statistical regularization technique, generalized linear model (GLM)-path, was used to develop a model based on texture features for dichotomous classification of fibrosis category (F ≤2 vs. F ≥3) of the 68 patients, with histology as the reference standard. The model's performance was assessed and cross-validated. There was no additional validation performed on an independent cohort. Results Cross-validated sensitivity, specificity, and total accuracy of the texture feature model in classifying fibrosis were 91.9%, 83.9%, and 88.2%, respectively. Conclusion This study shows proof of concept that accurate, noninvasive classification of liver fibrosis is possible by applying quantitative texture analysis to double contrast-enhanced MRI. Further studies are needed in independent cohorts of subjects. PMID:22851409

N-acetylglucosamine-Mediated Expression of nagA and nagB in Streptococcus pneumoniae

PubMed Central

Afzal, Muhammad; Shafeeq, Sulman; Manzoor, Irfan; Henriques-Normark, Birgitta; Kuipers, Oscar P.

2016-01-01

In this study, we have explored the transcriptomic response of Streptococcus pneumoniae D39 to N-acetylglucosamine (NAG). Transcriptome comparison of S. pneumoniae D39 wild-type grown in chemically defined medium (CDM) in the presence of 0.5% NAG to that grown in the presence of 0.5% glucose revealed elevated expression of many genes/operons, including nagA, nagB, manLMN, and nanP. We have further confirmed the NAG-dependent expression of nagA, nagB, manLMN, and nanP by β-galactosidase assays. nagA, nagB and glmS are putatively regulated by a transcriptional regulator NagR. We predicted the operator site of NagR (dre site) in PnagA, PnagB, and PglmS, which was further confirmed by mutating the predicted dre site in the respective promoters (nagA, nagB, and glmS). Growth comparison of ΔnagA, ΔnagB, and ΔglmS with the D39 wild-type demonstrates that nagA and nagB are essential for S. pneumoniae D39 to grow in the presence of NAG as a sole carbon source. Furthermore, deletion of ccpA shows that CcpA has no effect on the expression of nagA, nagB, and glmS in the presence of NAG in S. pneumoniae. PMID:27900287

Comparing the validity of the payment card and structured haggling willingness to pay methods: The case of a diabetes prevention program in rural Kenya.

PubMed

Kangethe, Anne; Franic, Duska M; Corso, Phaedra S

2016-11-01

The objective of this study was to compare the theoretical validity of two willingness-to-pay (WTP) methods, the commonly used payment card (PC) and the recently developed structured haggling (SH), for estimating the potential benefits of a diabetes prevention program in rural Kenya. A convenience sample of adult residents from a rural county in Kenya (Kiambu), with no history of diabetes, was randomly assigned to one of two WTP methods, PC or SH, using structured face-to-face interviews from December 2011 to February 2012. A total of 376 respondents completed the interviews using PC (n = 185) or SH (n = 191). More than 95% of respondents were willing to pay something for program access. The study showed that both methods were feasible in rural Kenya. SH resulted in a higher annual mean WTP than PC, Ksh504.05 (US$7.25) versus Ksh619.95 (US$5.90), respectively (p < 0.01). Based on theory, it was hypothesized that certain predisposing factors would result in greater WTP. Greater socio-economic status (measured using income proxies) resulted in greater unconditional WTP for both the PC and SH groups (t-tests and bivariate correlations) and conditional WTP (GLM models). GLM for PC showed being male, employed and having distant relatives with diabetes were significant predictors for WTP, while for SH being educated, employed and owning a vehicle were significant predictors. Both PC and SH showed theoretical validity in rural Kenya. However, the use of SH over PC in rural Kenya may be the better choice given that SH more closely mirrors marketplace transactions in this setting and the use of SH resulted in more significant variables in the GLM models. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison Study of Lightning observations from VHF interferometer and Geostationary Lightning Mapper

NASA Astrophysics Data System (ADS)

Kudo, A.; Stock, M.; Ushio, T.

2017-12-01

We compared the optical observation from Geostationary Lightning Mapper (GLM) which is mounted on the geostationary meteorological satellite GOES-16 launched last year, and the radio observations from the ground-based VHF broad band interferometer. GLM detects 777.4 nm wavelength infrared optical signals from thunderstorm cells which are illuminated by the heated path during lightning discharge, and was developed mainly for the purpose of increasing the lead time for warning of severe weather and clarifying the discharge mechanism. Its detection has 2 ms frame rate, and 8 km square of space resolution at nadir. The VHF broad band interferometer is able to capture the electromagnetic waves from 20 MHz to 75 MHz and estimate the direction of arrival of the radiation sources using the interferometry technique. This system also has capability of observing the fast discharge process which cannot be captured by other systems, so it is expected to able to make detailed comparison. The recording duration of the system is 1 second. We installed the VHF broad band interferometer which consists of three VHF antenna and one fast antenna at Huntsville, Alabama from April 22nd to May 15th and in this total observation period, 720 triggers of data were observed by the interferometer. For comparison, we adopted the data from April 27th , April 30th. Most April 27th data has GLM "event" detection which is coincident time period. In time-elevation plot comparison, we found GLM detection timing was well coincide with interferometer during K-changes or return strokes and few detection during breakdown process. On the other hand, no GLM detection near the site for all data in April 30th and we are triyng to figure out the reason. We would like to thank University of Alabama Huntsville, New Mexico Institute of Mining and Technology, and RAIRAN Pte. Ltd for the help during the campaign.

Geostationary Lightning Mapper for GOES-R

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2007-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).

Random parameter models for accident prediction on two-lane undivided highways in India.

PubMed

Dinu, R R; Veeraragavan, A

2011-02-01

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation. The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models. The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations. The paper is concluded with a discussion on modeling results and the limitations of the present study. Copyright © 2010 Elsevier Ltd. All rights reserved.

Total Quality Management in the Department of Defense

DTIC Science & Technology

1989-09-01

DTI ELECT SDu TOTAL QUALITY MANAGEMENT IN THE DEPARTMENT OF DEFENSE THESIS BRUCE E. SPRINGS, B.S. CAPTAIN, USAF AFIT/GLN/LSR/ 89S -57 I1- DEPARTMENT...13 0 3 AFIT/GLM/LSR/89S-57 TOTAL QUALITY MANAGEMENT IN THE DEPARTMENT OF DEFENSE THESIS BRUCE E. SPRINGS, B.S. CAPTAIN, USAF AFIT/GLH/LSR/89S-57...Defense. # AFIT/GLM/LSR/89S-57 TOTAL QUALITY MANAGEMENT IN THE DEPARTMENT OF DEFENSE THESIS Presented to the Faculty of the School of Systems and Logistics

A Feasibility Study of the Collection of Unscheduled Maintenance Data Using Statistical Sampling Techniques.

DTIC Science & Technology

1985-09-01

TECHNIQUES THESIS Robert A. Heinlein Captain, USAF AFIT/GLM/LSM/855-32.- _ DTIC MU’noN ’ST.,TEMENT A A-ZELECTE Approved lt public teleo*I Al \\ Z #&N0V21...343" A FEASIBILITY STUDY OF THE COLLECTION OF UNSCHEDULED MAINTENANCE DATA USING STrATISTICAL SAMPLING TECHNIQUES THESIS L .9 Robe-t A. Heinlein...a AFIT/GLM/LSM/85S-32 A FEASIBILITY STUDY OF THE COLLECTION OF UNSCHEDULED MAINTENANCE DATA USING STATISTICAL SAMPLING TECHNIQUES THESIS

Mini-Tn7 Insertion in Bacteria With Multiple glmS-Linked attTn7 Sites: Example Burkholderia Mallei ATCC 23344

DTIC Science & Technology

2006-06-27

INTRODUCTION Burkholderia mallei is the etiological agent of glanders and a highly evolved obligate zoonotic mammalian pathogen that naturally affects horses...mini-Tn7 insertion in bacteria with multiple glmS-linked attTn7 sites: example Burkholderia mallei ATCC 23344 Kyoung-Hee Choi1, David DeShazer2... glanders is a rare disease, B. mallei has received renewed attention because of its listing as a category B agent by the Centers for Disease Control

Protecting Future Biodiversity via Re-allocation of Future Land-use Change Patterns

NASA Astrophysics Data System (ADS)

Chini, L. P.; Hurtt, G. C.; Jantz, S.; Brooks, T.; Leon, C.; Waldhoff, S.; Edmonds, J.

2013-12-01

Future scenarios, such as the Representative Concentration Pathways (RCPs), are typically designed to meet a radiative forcing target while also producing enough food and energy for a growing population. In the assessment process, impacts of these scenarios for other important variables such as biodiversity loss are considered 'downstream', after the future climate has been simulated within Earth System Models. However, the direct land-use impacts associated with future scenarios often have as much impact on these issues as the changing climate; in addition, many different patterns of land-use can result in the same radiative forcing target. In the case of biodiversity loss, one of the greatest contributors to species extinction is the loss of habitat such as primary forest, which is a direct result of land-use change decisions. By considering issues such as the preservation of future biodiversity 'up-front' in the scenario process, we can design a scenario that not only meets a radiative forcing target and feeds a growing planet, but also preserves as much habitat as possible through careful spatial allocation of future land-use change. Our Global Land-use Model (GLM) is used to provide 'harmonized' land-use data for the RCP process. GLM preserves as much information as possible from the Integrated Assessment Models (IAMs) while spatially allocating regional IAM land-use change data, ensuring a continuous transition from historical to future land-use states, and producing annual, gridded (0.5°×0.5°), fractional land-use states and all associated transitions. In this presentation we will present results from new GLM simulations in which land-use change decisions are constrained to meet the mutual goals of protecting important eco-regions (e.g. biodiversity hotspots) from future land-use change, providing enough food and fiber for a growing planet, and remaining consistent with the radiative forcing targets of the future scenarios. Trade-offs between agricultural demand and biodiversity protection were needed in some scenarios, but by constraining the land-use decisions to protect future biodiversity, an estimated 10-25% of species could be saved from loss between 2005 and 2100 (Jantz et al. 2013, in prep).

Landslide susceptibility near highways is increased by one order of magnitude in the Andes of southern Ecuador, Loja province

NASA Astrophysics Data System (ADS)

Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2014-03-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic and geological predictors as possible confounders. A spatial block bootstrap was used to obtain non-parametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21 with lower 95% confidence bounds > 13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than one order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province

NASA Astrophysics Data System (ADS)

Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2015-01-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic, and geological predictors as possible confounders. A spatial block bootstrap was used to obtain nonparametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21, with lower 95% confidence bounds >13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than 1 order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Therapist turnover and new program sustainability in mental health clinics as a function of organizational culture, climate, and service structure.

PubMed

Glisson, Charles; Schoenwald, Sonja K; Kelleher, Kelly; Landsverk, John; Hoagwood, Kimberly Eaton; Mayberg, Stephen; Green, Philip

2008-03-01

The present study incorporates organizational theory and organizational characteristics in examining issues related to the successful implementation of mental health services. Following the theoretical foundations of socio-technical and cultural models of organizational effectiveness, organizational climate, culture, legal and service structures, and workforce characteristics are examined as correlates of therapist turnover and new program sustainability in a nationwide sample of mental health clinics. Results of General Linear Modeling (GLM) with the organization as the unit of analysis revealed that organizations with the best climates as measured by the Organizational Social Context (OSC) profiling system, had annual turnover rates (10%) that were less than half the rates found in organizations with the worst climates (22%). In addition, organizations with the best culture profiles sustained new treatment or service programs over twice as long (50 vs. 24 months) as organizations with the worst cultures. Finally, clinics with separate children's services units had higher turnover rates than clinics that served adults and children within the same unit. The findings suggest that strategies to support the implementation of new mental health treatments and services should attend to organizational culture and climate, and to the compatibility of organizational service structures with the demand characteristics of treatments.

Relative abundance and size of coastal sharks derived from commercial shark longline catch and effort data.

PubMed

Carlson, J K; Hale, L F; Morgan, A; Burgess, G

2012-04-01

In the north-west Atlantic Ocean, stock assessments conducted for some commercially harvested coastal sharks indicate declines from 64 to 80% with respect to virgin population levels. While the status of commercially important species is available, abundance trend information for other coastal shark species in the north-west Atlantic Ocean are unavailable. Using a generalized linear modelling (GLM) approach, a relative abundance index was derived from 1994 to 2009 using observer data collected in a commercial bottom longline fishery. Trends in abundance and average size were estimated for bull shark Carcharhinus leucas, spinner shark Carcharhinus brevipinna, tiger shark Galeocerdo cuvier and lemon shark Negaprion brevirostris. Increases in relative abundance for all shark species ranged from 14% for C. brevipinna, 12% for C. leucas, 6% for N. brevirostris and 3% for G. cuvier. There was no significant change in the size at capture over the time period considered for all species. While the status of shark populations should not be based exclusively on abundance trend information, but ultimately on stock assessment models, results from this study provide some cause for optimism on the status of these coastal shark species. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Characterization of a Glucosamine/Glucosaminide N-Acetyltransferase of Clostridium acetobutylicum▿†

PubMed Central

Reith, Jan; Mayer, Christoph

2011-01-01

Many bacteria, in particular Gram-positive bacteria, contain high proportions of non-N-acetylated amino sugars, i.e., glucosamine (GlcN) and/or muramic acid, in the peptidoglycan of their cell wall, thereby acquiring resistance to lysozyme. However, muramidases with specificity for non-N-acetylated peptidoglycan have been characterized as part of autolytic systems such as of Clostridium acetobutylicum. We aim to elucidate the recovery pathway for non-N-acetylated peptidoglycan fragments and present here the identification and characterization of an acetyltransferase of novel specificity from C. acetobutylicum, named GlmA (for glucosamine/glucosaminide N-acetyltransferase). The enzyme catalyzes the specific transfer of an acetyl group from acetyl coenzyme A to the primary amino group of GlcN, thereby generating N-acetylglucosamine. GlmA is also able to N-acetylate GlcN residues at the nonreducing end of glycosides such as (partially) non-N-acetylated peptidoglycan fragments and β-1,4-glycosidically linked chitosan oligomers. Km values of 114, 64, and 39 μM were determined for GlcN, (GlcN)2, and (GlcN)3, respectively, and a 3- to 4-fold higher catalytic efficiency was determined for the di- and trisaccharides. GlmA is the first cloned and biochemically characterized glucosamine/glucosaminide N-acetyltransferase and a member of the large GCN5-related N-acetyltransferases (GNAT) superfamily of acetyltransferases. We suggest that GlmA is required for the recovery of non-N-acetylated muropeptides during cell wall rescue in C. acetobutylicum. PMID:21784938

Effects of different omega-3 sources, fish oil, krill oil, and green-lipped mussel against cytokine-mediated canine cartilage degradation.

PubMed

Buddhachat, Kittisak; Siengdee, Puntita; Chomdej, Siriwadee; Soontornvipart, Kumpanart; Nganvongpanit, Korakot

2017-05-01

Our purpose was to evaluate the protective effect of three marine omega-3 sources, fish oil (FO), krill oil (KO), and green-lipped mussel (GLM) against cartilage degradation. Canine cartilage explants were stimulated with either 10 ng/mL interleukin-1β (IL-1β) or IL-1β/oncostatin M (10 ng/mL each) and then treated with various concentrations of docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA; 3 and 30 μg/mL), FO, KO, or GLM (250, 500, and 1000 μg/mL) for 28 days. Gene expression was then investigated in primary canine chondrocytes. Our results showed that DHA and EPA as well as omega-3 sources could suppress matrix degradation in cytokine-induced cartilage explants by significantly reducing the increase of sulfated glycosaminoglycans (s-GAGs) and preserving uronic acid and hydroxyproline content (except GLM). These agents were not able to reduce IL-1β-induced IL1B and TNFA expression but were able to down-regulate the expression of the catabolic genes MMP1, MMP3, and MMP13 and up-regulate the anabolic genes AGG and COL2A1; FO and KO were especially effective. Our findings indicated that FO and KO were superior to GLM for their protective effect against proteoglycan and collagen degradation. Hence, FO and KO could serve as promising sources of chondroprotective agents.

Viscosity of fiber preloads affects food intake in adolescents.

PubMed

Vuksan, V; Panahi, S; Lyon, M; Rogovik, A L; Jenkins, A L; Leiter, L A

2009-09-01

Dietary fiber that develops viscosity in the gastrointestinal tract is capable of addressing various aspects of food intake control. The aim of this study was to assess subsequent food intake and appetite in relation to the level of viscosity following three liquid preloads each containing 5 g of either a high (novel viscous polysaccharide; NVP), medium (glucomannan; GLM), or low (cellulose; CE) viscosity fiber. In this double-blind, randomized, controlled and crossover trial, 31 healthy weight adolescents (25 F:6 M; age 16.1+/-0.6 years; BMI 22.2+/-3.7 kg/m(2)) consumed one of the three preloads 90 min prior to an ad libitum pizza meal. Preloads were identical in taste, appearance, nutrient content and quantity of fiber, but different in their viscosities (10, 410, and 700 poise for CE, GLM, and NVP, respectively). Pizza intake was significantly lower (p=0.008) after consumption of the high-viscosity NVP (278+/-111 g) compared to the medium-viscosity GLM (313+/-123 g) and low-viscosity CE (316+/-138 g) preloads, with no difference between the GLM and CE preloads. Appetite scores, physical symptoms and 24-h intake did not differ among treatment groups. A highly viscous NVP preload leads to reduced subsequent food intake, in terms of both gram weight and calories, in healthy weight adolescents. This study provides preliminary evidence of an independent contribution of viscosity on food intake and may form a basis for further studies on factors influencing food intake in adolescents.

New GOES-R Risk Reduction Activities at CIRA

NASA Astrophysics Data System (ADS)

Rogers, M. A.; Miller, S. D.; Grasso, L. D.; Haynes, J. M.; NOH, Y. J.; Forsythe, J.; Zupanski, M.; Lindsey, D. T.

2017-12-01

A team of atmospheric scientists at the Cooperative Institute for Research in the Atmosphere (CIRA) at the Colorado State University has been selected by the National Oceanic and Atmospheric Administration's (NOAA) GOES-R Risk Reduction (GOES-R3) science program to develop applications to enhance the utilization of the GOES-R sensors, including the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The selected project topics follow NOAA's Research and Development Objectives listed in its 5-year Strategic Plan. The projects will be carried out over a three-year period which started on 1 July 2017 and will end on 30 June 2019. CIRA is working on five GOES-R3 application developments: 1) Developing an Environmental Awareness Repertoire of ABI Imagery (`DEAR-ABII') to Advise the Operational Weather Forecaster. DEAR-ABII maximizes the vast potential of the new GOES-R/GOES-16 sensor technology. 2) GOES-R ABI channel differencing used to reveal cloud-free zones of `precursors of convective initiation'. This product identifies where convective initiation may occur in cloud free skies. 3) Improving the ABI Cloud Layers Product for Multiple Layer Cloud Systems and Aviation Forecast Applications. This project aims to improve the GOES-16 cloud layer product by providing information on the boundaries of cloud layers even when one layer overlies another. 4) Using the New Capabilities of GOES-R to Improve Blended, Multisensor Water Vapor Products for Forecasters. GOES-R TPW retrievals will be merged with TPW derived from polar orbiter and surface data to improve the operational NOAA blended TPW product. 5) Data assimilation of GLM observations in HWRF/GSI system. Assimilation of GOES-R GLM observations for the NOAA operational hurricane model with the goal to improve operational hurricane forecasting. Examples for each of these applications will be presented.

The Goes-R Geostationary Lightning Mapper (GLM): Algorithm and Instrument Status

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas

2010-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved capability for the Advanced Baseline Imager (ABI). The Geostationary Lighting Mapper (GLM) will map total lightning activity (in-cloud and cloud-to-ground lighting flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. A joint field campaign with Brazilian researchers in 2010-2011 will produce concurrent observations from a VHF lightning mapping array, Meteosat multi-band imagery, Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) overpasses, and related ground and in-situ lightning and meteorological measurements in the vicinity of Sao Paulo. These data will provide a new comprehensive proxy data set for algorithm and application development.

Development of a spatially distributed model of fish population density for habitat assessment of rivers

NASA Astrophysics Data System (ADS)

Sui, Pengzhe; Iwasaki, Akito; Ryo, Masahiro; Saavedra, Oliver; Yoshimura, Chihiro

2013-04-01

Flow conditions play an important role in sustaining biodiversity of river ecosystem. However, their relations to freshwater fishes, especially to fish population density, have not been clearly described. This study, therefore, aimed to propose a new methodology to quantitatively link habitat conditions, including flow conditions and other physical conditions, to population density of fish species. We developed a basin-scale fish distribution model by integrating the concept of habitat suitability assessment with a distributed hydrological model (DHM) in order to estimate fish population density with particular attention to flow conditions. Generalized linear model (GLM) was employed to evaluate the relationship between population density of fish species and major environmental factors. The target basin was Sagami River in central Japan, where the river reach was divided into 10 sections by estuary, confluences of tributaries, and river-crossing structures (dams, weirs). The DHM was employed to simulate river discharge from 1998 to 2005, which was used to calculate 10 flow indices including mean discharge, 25th and 75th percentile discharge, duration of low and high flows, number of floods. In addition, 5 water quality parameters and 13 other physical conditions (such as basin area, river width, mean diameter of riverbed material, and number of river-crossing structures upstream and downstream) of each river section were considered as environmental variables. In case of Sagami River, 10 habitat variables among them were then selected based on their correlations to avoid multicollinearity. Finally, the best GLM was developed for each species based on Akaike's information criterion. As results, population densities of 16 fish species in Sagami River were modelled, and correlation coefficients between observed and calculated population densities for 10 species were more than 0.70. The key habitat factors for population density varied among fish species. Minimum discharge (MID) was found to be positively correlated to 9 among 16 fish species. For duration of high and low flows (DHF and DLF), longer DHF/DLF was corresponded to lower population density for 7/6 fish species, respectively, such as Rhinogobius kurodai and Plecoglossus altivelis altivelis. Among physical habitat conditions, sinuosity index (SI, the ratio between actual river section length and straight line length) seems to be the most important parameter for fish population density in Sagami River basin, since it affects 12 out of 16 fish species, followed by mean longitudinal slope (S) and number of downstream dams (NLD). Above results demonstrated the applicability of fish distribution model to provide quantitative information on flow conditions required to maintain fish population, which enabled us to evaluate and project ecological consequences of water resource management policy, such as flood management and water withdrawal.

Is NIPARS Working as Advertised? An Analysis of NIPARS Program Customer Service

DTIC Science & Technology

1992-09-01

AD-A259 733IN I II I ll IMiiiI Gil III 11 AFIT/GLM/LSM/92S- 17 IS NIPARS WORKING AS ADVERTISED ? AN ANALYSIS OFNIPARS PROGRAM CUSTOMER SERVICE THESIS...and/or Dist Speoiai. AFIT/GLM/LSM/92S-17 IS NIPARS WORKING AS ADVERTISED ? AN ANALYSIS OF NIPARS PROGRAM CUSTOMER SERVICE THESIS Presented to the...measures. x1i IS NIPARS WORKING AS ADVERTISED ? AN ANALYSIS OF NIPARS PROGRAM CUSTOMER SERVICE L Introduction 1.1 Overview Foreign policy must start with

Changes in beverage consumption among adolescents from public schools in the first decade of the century XXI.

PubMed

Monteiro, Luana Silva; Vasconcelos, Thaís Meirelles de; Veiga, Gloria Valéria da; Pereira, Rosângela Alves

2016-01-01

To evaluate the changes in beverage consumption among adolescents between 2003 and 2008. Two school-based cross-sectional studies were carried out with public school students (12 to 19 years-old) from Niterói, Rio de Janeiro, Brazil. Data from three food records were used to estimate daily, weekdays and weekend average consumption (volume and percent contribution for total daily energy intake) of milk and milk-based beverages, sugar sweetened beverages, fresh squeezed fruit juices, caffeinated and alcoholic beverages. Beverage consumption age-adjusted means for weekdays and weekends were compared using linear regression (Generalized Linear Models - GLM). A total of 433 adolescents were examined in 2003, and 510 in 2008. The prevalence of overweight was 17% in 2003 and 22% in 2008 (p > 0.05). Milk was the most consumed beverage, being reported by 89% of adolescents, followed by sodas (75%). In general, in the five-year period, there was an increase in the prevalence of consumption of alcoholic drinks, guarana syrup refreshment, and processed fruit drinks, especially on weekdays. The soft drink was the largest contributor to the total energy consumption, corresponding on average to 4% of daily energy intake. The main changes in the beverage consumption among adolescents from Niterói, in the first decade of the XXI century, were the tendency to reduce the consumption of milk and the increase in the consumption of processed and alcoholic beverages.

Predicting potentially toxigenic Pseudo-nitzschia blooms in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Anderson, Clarissa R.; Sapiano, Mathew R. P.; Prasad, M. Bala Krishna; Long, Wen; Tango, Peter J.; Brown, Christopher W.; Murtugudde, Raghu

2010-11-01

Harmful algal blooms are now recognized as a significant threat to the Chesapeake Bay as they can severely compromise the economic viability of important recreational and commercial fisheries in the largest estuary of the United States. This study describes the development of empirical models for the potentially domoic acid-producing Pseudo-nitzschia species complex present in the Bay, developed from a 22-year time series of cell abundance and concurrent measurements of hydrographic and chemical properties. Using a logistic Generalized Linear Model (GLM) approach, model parameters and performance were compared over a range of Pseudo-nitzschia bloom thresholds relevant to toxin production by different species. Small-threshold blooms (≥10 cells mL -1) are explained by time of year, location, and variability in surface values of phosphate, temperature, nitrate plus nitrite, and freshwater discharge. Medium- (100 cells mL -1) to large- threshold (1000 cells mL -1) blooms are further explained by salinity, silicic acid, dissolved organic carbon, and light attenuation (Secchi) depth. These predictors are similar to other models for Pseudo-nitzschia blooms on the west coast, suggesting commonalities across ecosystems. Hindcasts of bloom probabilities at a 19% bloom prediction point yield a Heidke Skill Score of ~53%, a Probability of Detection ˜ 75%, a False Alarm Ratio of ˜ 52%, and a Probability of False Detection ˜9%. The implication of possible future changes in Baywide nutrient stoichiometry on Pseudo-nitzschia blooms is discussed.

Comparison of gene expression and fatty acid profiles in concentrate and forage finished beef.

PubMed

Buchanan, J W; Garmyn, A J; Hilton, G G; VanOverbeke, D L; Duan, Q; Beitz, D C; Mateescu, R G

2013-01-01

Fatty acid profiles and intramuscular expression of genes involved in fatty acid metabolism were characterized in concentrate- (CO) and forage- (FO) based finishing systems. Intramuscular samples from the adductor were taken at slaughter from 99 heifers finished on a CO diet and 58 heifers finished on a FO diet. Strip loins were obtained at fabrication to evaluate fatty acid profiles of LM muscle for all 157 heifers by using gas chromatography fatty acid methyl ester analysis. Composition was analyzed for differences by using the General Linear Model (GLM) procedure in SAS. Differences in fatty acid profile included a greater atherogenic index, greater percentage total MUFA, decreased omega-3 to omega-6 ratio, decreased percentage total PUFA, and decreased percentage omega-3 fatty acids in CO- compared with FO-finished heifers (P<0.05). Fatty acid profiles from intramuscular samples were ranked by the atherogenic index, and 20 heifers with either a high (HAI; n=10) or low (LAI; n=10) atherogenic index were selected for gene expression analysis using real-time PCR (RT-PCR). Gene expression data for the 20 individuals were analyzed as a 2 by 2 factorial arrangement of treatments using the GLM procedure in SAS. There was no significant diet × atherogenic index interaction identified for any gene (P>0.05). Upregulation was observed for PPARγ, fatty acid synthase (FASN), and fatty acid binding protein 4 (FABP4) in FO-finished compared with CO-finished heifers in both atherogenic index categories (P<0.05). Upregulation of diglyceride acyl transferase 2 (DGAT2) was observed in FO-finished heifers with a HAI (P<0.05). Expression of steroyl Co-A desaturase (SCD) was upregulated in CO-finished heifers with a LAI, and downregulated in FO-finished heifers with a HAI (P<0.05). Expression of adiponectin (ADIPOQ) was significantly downregulated in CO-finished heifers with a HAI compared with all other categories (P<0.05). The genes identified in this study which exhibit differential regulation in response to diet or in animals with extreme fatty acid profiles may provide genetic markers for selecting desirable fatty acid profiles in future selection programs.

Effects of air pollution on respiratory hospital admissions in İstanbul, Turkey, 2013 to 2015.

PubMed

Çapraz, Özkan; Deniz, Ali; Doğan, Nida

2017-08-01

We examined the associations between the daily variations of air pollutants and hospital admissions for respiratory diseases in İstanbul, the largest city of Turkey. A time series analysis of counts of daily hospital admissions and outdoor air pollutants was performed using single-pollutant Poisson generalized linear model (GLM) while controlling for time trends and meteorological factors over a 3-year period (2013-2015) at different time lags (0-9 days). Effects of the pollutants (Excess Risk, ER) on current-day (lag 0) hospital admissions to the first ten days (lag 9) were determined. Data on hospital admissions, daily mean concentrations of air pollutants of PM 10 , PM 2.5 and NO 2 and daily mean concentrations of temperature and humidity of İstanbul were used in the study. The analysis was conducted among people of all ages, but also focused on different sexes and different age groups including children (0-14 years), adults (35-44 years) and elderly (≥65 years). We found significant associations between air pollution and respiratory related hospital admissions in the city. Our findings showed that the relative magnitude of risks for an association of the pollutants with the total respiratory hospital admissions was in the order of: PM 2.5 , NO 2 , and PM 10 . The highest association of each pollutant with total hospital admission was observed with PM 2.5 at lag 4 (ER = 1.50; 95% CI = 1.09-1.99), NO 2 at lag 4 (ER = 1.27; 95% CI = 1.02-1.53) and PM 10 at lag 0 (ER = 0.61; 95% CI = 0.33-0.89) for an increase of 10 μg/m3 in concentrations of the pollutants. In conclusion, our study showed that short-term exposure to air pollution was positively associated with increased respiratory hospital admissions in İstanbul during 2013-2015. As the first air pollution hospital admission study using GLM in İstanbul, these findings may have implications for local environmental and social policies. Copyright © 2017 Elsevier Ltd. All rights reserved.

What could we learn from the sub-analysis of a single nation cohort in a worldwide study? Lessons from the results observed in the Italian cohort of the GO-MORE trial.

PubMed

Giacomelli, Roberto; Ruscitti, Piero; Bombardieri, Stefano; Cuomo, Giovanna; De Vita, Salvatore; Galeazzi, Mauro; Mecchia, Monica

2017-01-01

GO-MORE Trial investigated the use of Golimumab (GLM) in 3280 rheumatoid arthritis (RA) patients worldwide. At present, the burden of arthritis is greater in poorer countries than in developed countries due to socioeconomic disparities, thus suggesting the usefulness of subgroup investigations. We aimed to evaluate GLM as add-on therapy for RA patients in the Italian cohort of GO-MORE trial and compared the clinical characteristics between Italian patients and the enrolled patients worldwide. Ninety-eight Italian patients with active RA, fulfilling the 1987 ACR criteria were enrolled. Statistical analyses were performed to assess: i. the differences in baseline characteristics; ii. the efficacy after 6 months; between Italian and Rest of the World GO-MORE populations. Compared to the worldwide population, Italian patients showed a lower value of disease activity and a significantly short disease duration. Unlike the worldwide patients, the large majority of Italian patients received biologic therapy after the failure of the first synthetic DMARD and were not treated by high methotrexate dosage. After 6 months of GLM treatment, no differences were observed in the therapeutic response. Italian patients reported a positive autoinjection experience mirroring the worldwide results. The analysis of the Italian GO-MORE subset confirms that differences among patients may be shown, depending on different approaches in different health systems. GLM in the Italian patients showed a favourable benefit/risk profile and the positive autoinjection experience may help with patient's compliance and survival of the treatment.

Demography of a breeding population of whimbrel (Numenius phaeopus) near Churchill, Manitoba, Canada

NASA Astrophysics Data System (ADS)

Perz, Johanna

I used a GIS raster layer of an area in the Churchill, Manitoba region to investigate the effect of breeding habitat on demography and density of Whimbrel from 2010 through 2013. Program MARK was used to quantify adult and daily nest survival. Apparent annual survival of 0.73 +/- 0.06 SE (95% CI = 0.60-0.83) did not significantly differ between sexes or habitats and was lower than expected based on longevity records and estimates for other large-bodied shorebirds. Nest success, corrected for exposure days, was highly variable, ranging from a low of 3% (95% CI = 0-12%) in 2011 to a high of 71% (95% CI = 54-83%) in 2013. The highest rate of nest survival occurred in the spring with the warmest mean temperature. I developed a generalized linear model (GLM) with a negative-binomial distribution from random plots that were surveyed for abundance to extrapolate a local breeding population size of 410 +/- 230 SE and density of 3.2 birds per square km +/- 1.8 SE. The result of my study suggests that other aspects of habitat not captured by the land cover categories may be more important to population dynamics.

Determination of HER2 amplification status in breast cancer cells using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bi, Xiaohong; Rexer, Brent; Arteaga, Carlos L.; Guo, Mingsheng; Li, Ming; Mahadevan-Jansen, Anita

2010-02-01

The overexpression of HER2 (human epidermal growth factor receptor 2) in breast cancer is associated with increased disease recurrence and worse prognosis. Current diagnosis of HER2 positive breast cancer is time consuming with an estimated 20% inaccuracy. Raman spectroscopy is a proven method for pathological diagnosis based on the molecular composition of tissues. This study aimed to determine the feasibility of Raman spectroscopy to differentially identify the amplification of HER2 in cells. Three cell lines including BT474 (HER2 overexpressing breast cancer cell), MCF-10A (human breast epithelial cell), and MCF-10A with overexpressing HER2, were investigated using a bench top confocal Raman system. A diagnostic algorithm based on generalized linear model (GLM) with elastic-net penalties was established to discriminate 318 spectra collected from the cells, and to identify the spectra regions that differentiate the cell lines. The algorithm was able to differentially identify BT474 breast cancer cells with an overall sensitivity of 100% and specificity of 99%. The results demonstrate the capability of Raman spectroscopy to determine HER2 status in cells. Raman spectroscopy shows promise for application in the diagnosis of HER2 positive breast cancer in clinical practice.

Determination of mercury and vanadium concentration in Johnius belangerii (C) fish in Musa estuary in Persian Gulf.

PubMed

Fard, Neamat Jaafarzadeh Haghighi; Ravanbakhsh, Maryam; Ramezani, Zahra; Ahmadi, Mehdi; Angali, Kambiz Ahmadi; Javid, Ahmad Zare

2015-08-15

The main aim of this study was to determine the concentrations of mercury and vanadium in Johnius belangerii (C) fish in the Musa estuary. A total of 67 fishes were caught from the Musa estuary during five intervals of 15days in the summer of 2013. After biometric measurements were conducted, the concentrations of mercury and vanadium were measured in the muscle tissue of fish using a direct method analyzer (DMA) and a graphite furnace atomic absorption spectrophotometer, respectively. The mean concentration of mercury and vanadium in the muscle tissue of fish was 3.154±1.981 and 2.921±0.873mg/kg w.w, respectively. The generalized linear model (GLM) analysis showed a significantly positive relationship among mercury concentration, length, and weight (P=0.000). In addition, there was a significantly negative relationship between vanadium concentration and fish length (P=0.000). A reverse association was found between concentrations of mercury and vanadium. Mercury concentration exceeded the allowable standards of the Environmental Protection Agency (EPA), the World Health Organization (WHO), and the Food and Drug Administration (FDA) in J. belangerii (C). Copyright © 2015 Elsevier Ltd. All rights reserved.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Enhancing the Biological Relevance of Secretome-Based Proteomics by Linking Tumor Cell Proliferation and Protein Secretion.

PubMed

Gregori, Josep; Méndez, Olga; Katsila, Theodora; Pujals, Mireia; Salvans, Cándida; Villarreal, Laura; Arribas, Joaquin; Tabernero, Josep; Sánchez, Alex; Villanueva, Josep

2014-07-15

Secretome profiling has become a methodology of choice for the identification of tumor biomarkers. We hypothesized that due to the dynamic nature of secretomes cellular perturbations could affect their composition but also change the global amount of protein secreted per cell. We confirmed our hypothesis by measuring the levels of secreted proteins taking into account the amount of proteome produced per cell. Then, we established a correlation between cell proliferation and protein secretion that explained the observed changes in global protein secretion. Next, we implemented a normalization correcting the statistical results of secretome studies by the global protein secretion of cells into a generalized linear model (GLM). The application of the normalization to two biological perturbations on tumor cells resulted in drastic changes in the list of statistically significant proteins. Furthermore, we found that known epithelial-to-mesenchymal transition (EMT) effectors were only statistically significant when the normalization was applied. Therefore, the normalization proposed here increases the sensitivity of statistical tests by increasing the number of true-positives. From an oncology perspective, the correlation between protein secretion and cellular proliferation suggests that slow-growing tumors could have high-protein secretion rates and consequently contribute strongly to tumor paracrine signaling.

A mesocosm study using four native Hawaiian plants to assess nitrogen accumulation under varying surface water nitrogen concentrations.

PubMed

Unser, C U; Bruland, G L; Hood, A; Duin, K

2010-01-01

Accumulation of nitrogen (N) by native Hawaiian riparian plants from surface water was measured under a controlled experimental mesocosm setting. Four species, Cladium jamaicense, Cyperus javanicus, Cyperus laevigatus, and Cyperus polystachyos were tested for their ability to survive in coconut fiber coir log media with exposure to differing N concentrations. It was hypothesized that the selected species would have significantly different tissue total nitrogen (TN) concentrations, aboveground biomass, and TN accumulation rates because of habitat preference and physiological growth differences. A general linear model (GLM) analysis of variance (ANOVA) determined that species differences accounted for the greatest proportion of variance in tissue TN concentration, aboveground biomass growth, and accumulation rates, when compared with the other main effects (i.e. N concentration, time) and their interactions. A post hoc test of means demonstrated that C. jamaicense had significantly higher tissue TN concentration, aboveground biomass growth, and accumulation rates than the other species under all N concentrations. It was also hypothesized that tissue TN concentrations and biomass growth would increase in plants exposed to elevated N concentrations, however data did not support this hypothesis. Nitrogen accumulation rates by species were controlled by differences in plant biomass growth.

A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging

PubMed Central

Nakae, Ken; Ikegaya, Yuji; Ishikawa, Tomoe; Oba, Shigeyuki; Urakubo, Hidetoshi; Koyama, Masanori; Ishii, Shin

2014-01-01

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems. PMID:25393874

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

USGS Publications Warehouse

West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-01-01

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

PubMed

West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-10-11

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Comparison of modeling methods to predict the spatial distribution of deep-sea coral and sponge in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Rooper, Christopher N.; Zimmermann, Mark; Prescott, Megan M.

2017-08-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska's marine waters, and are associated with many different species of fishes and invertebrates. These ecosystems are vulnerable to the effects of commercial fishing activities and climate change. We compared four commonly used species distribution models (general linear models, generalized additive models, boosted regression trees and random forest models) and an ensemble model to predict the presence or absence and abundance of six groups of benthic invertebrate taxa in the Gulf of Alaska. All four model types performed adequately on training data for predicting presence and absence, with regression forest models having the best overall performance measured by the area under the receiver-operating-curve (AUC). The models also performed well on the test data for presence and absence with average AUCs ranging from 0.66 to 0.82. For the test data, ensemble models performed the best. For abundance data, there was an obvious demarcation in performance between the two regression-based methods (general linear models and generalized additive models), and the tree-based models. The boosted regression tree and random forest models out-performed the other models by a wide margin on both the training and testing data. However, there was a significant drop-off in performance for all models of invertebrate abundance ( 50%) when moving from the training data to the testing data. Ensemble model performance was between the tree-based and regression-based methods. The maps of predictions from the models for both presence and abundance agreed very well across model types, with an increase in variability in predictions for the abundance data. We conclude that where data conforms well to the modeled distribution (such as the presence-absence data and binomial distribution in this study), the four types of models will provide similar results, although the regression-type models may be more consistent with biological theory. For data with highly zero-inflated distributions and non-normal distributions such as the abundance data from this study, the tree-based methods performed better. Ensemble models that averaged predictions across the four model types, performed better than the GLM or GAM models but slightly poorer than the tree-based methods, suggesting ensemble models might be more robust to overfitting than tree methods, while mitigating some of the disadvantages in predictive performance of regression methods.

The Regional Geochemistry of Soils and Willow in a Metamorphic Bedrock Terrain, Seward Peninsula, Alaska, 2005, and Its Possible Relation to Moose

USGS Publications Warehouse

Gough, L.P.; Lamothe, P.J.; Sanzolone, R.F.; Drew, L.J.; Maier, J.A.K.

2009-01-01

In 2005 willow leaves (all variants of Salix pulchra) and A-, B-, and C-horizon soils were sampled at 10 sites along a transect near the Quarry prospect and 11 sites along a transect near the Big Hurrah mine for the purpose of defining the spatial variability of elements and the regional geochemistry of willow and soil over Paleozoic metamorphic rocks potentially high in cadmium (Cd). Willow, a favorite browse of moose (Alces alces), has been shown by various investigators to bioaccumulate Cd. Moose in this region show clinical signs of tooth wear and breakage and are declining in population for unknown reasons. A trace element imbalance in their diet has been proposed as a possible cause for these observations. Cadmium, in high enough concentrations, is one dietary trace element that potentially could produce such symptoms. We report both the summary statistics for elements in willow and soils and the results of an unbalanced, one-way, hierarchical analysis of variance (ANOVA) (general linear model, GLM), which was constructed to measure the geochemical variability in willow (and soil) at various distance scales across the Paleozoic geologic unit high in bioavailable Cd. All of the geochemical data are presented in the Appendices. The two locations are separated by approximately 80 kilometers (km); sites within a location are approximately 0.5 kilometers apart. Duplicate soil samples collected within a site were separated by 0.05 km or slightly less. Results of the GLM are element specific and range from having very little regional variability to having most of their variance at the top (greater than 80 km) level. For willow, a significant proportion of the total variance occurred at the 'between locations' level for ash yield, barium (Ba), Cd, calcium (Ca), cobalt (Co), nickel (Ni), and zinc (Zn). For soils, concentrations of elements in all three soil horizons were similar in that most of the variability in the geochemical data occurred at the 'between locations' and the 'among sites at a location' GLM levels. Most of the variation in concentrations of Cd in soils occurred among sites (separated by 0.5 km) at both locations across all soil horizons and not between the two locations. Cd distribution across the landscape may be due to variation in soil mineralogy, especially the amount of graphite in soil, which has been associated with Cd. Although samples were collected on the same geologic unit, the geochemistry of soils was demonstrated to be uniform with depth but highly variable between locations separated by 80 km. This exploratory study establishes the presence of elevated levels of Cd in willow growing over Paleozoic bedrock in the Seward Peninsula. Further work is needed to definitively link these high Cd levels in willow browse to the health of moose.

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

PubMed Central

Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

2016-01-01

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation. PMID:27065832

The Extratropical Transition of Tropical Storm Cindy From a GLM, ISS LIS and GPM Perspective

NASA Technical Reports Server (NTRS)

Heuscher, Lena; Gatlin, Patrick; Petersen, Walt; Liu, Chuntao; Cecil, Daniel J.

2017-01-01

The distribution of lightning with respect to tropical convective precipitation systems has been well established in previous studies and more recently by the successful Tropical Rainfall Measuring Mission (TRMM). However, TRMM did not provide information about precipitation features poleward of +/-38 deg latitude. Hence we focus on the evolution of lightning within extra-tropical cyclones traversing the mid-latitudes, especially its oceans. To facilitate such studies, lightning data from the Geostationary Lightning Mapper (GLM) onboard GOES-16 was combined with precipitation features obtained from the Global Precipitation Measurement (GPM) mission constellation of satellites.

Why inputs matter: Selection of climatic variables for species distribution modelling in the Himalayan region

NASA Astrophysics Data System (ADS)

Bobrowski, Maria; Schickhoff, Udo

2017-04-01

Betula utilis is a major constituent of alpine treeline ecotones in the western and central Himalayan region. The objective of this study is to provide first time analysis of the potential distribution of Betula utilis in the subalpine and alpine belts of the Himalayan region using species distribution modelling. Using Generalized Linear Models (GLM) we aim at examining climatic factors controlling the species distribution under current climate conditions. Furthermore we evaluate the prediction ability of climate data derived from different statistical methods. GLMs were created using least correlated bioclimatic variables derived from two different climate models: 1) interpolated climate data (i.e. Worldclim, Hijmans et al., 2005) and 2) quasi-mechanistical statistical downscaling (i.e. Chelsa; Karger et al., 2016). Model accuracy was evaluated by the ability to predict the potential species distribution range. We found that models based on variables of Chelsa climate data had higher predictive power, whereas models using Worldclim climate data consistently overpredicted the potential suitable habitat for Betula utilis. Although climatic variables of Worldclim are widely used in modelling species distribution, our results suggest to treat them with caution when remote regions like the Himalayan mountains are in focus. Unmindful usage of climatic variables for species distribution models potentially cause misleading projections and may lead to wrong implications and recommendations for nature conservation. References: Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N., Linder, H.P. & Kessler, M. (2016) Climatologies at high resolution for the earth land surface areas. arXiv:1607.00217 [physics].

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion.

PubMed

Xu, Qinneng; Gel, Yulia R; Ramirez Ramirez, L Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient.

Bayesian deconvolution of [corrected] fMRI data using bilinear dynamical systems.

PubMed

Makni, Salima; Beckmann, Christian; Smith, Steve; Woolrich, Mark

2008-10-01

In Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993], a particular case of the Linear Dynamical Systems (LDSs) was used to model the dynamic behavior of the BOLD response in functional MRI. This state-space model, called bilinear dynamical system (BDS), is used to deconvolve the fMRI time series in order to estimate the neuronal response induced by the different stimuli of the experimental paradigm. The BDS model parameters are estimated using an expectation-maximization (EM) algorithm proposed by Ghahramani and Hinton [Ghahramani, Z., Hinton, G.E. 1996. Parameter Estimation for Linear Dynamical Systems. Technical Report, Department of Computer Science, University of Toronto]. In this paper we introduce modifications to the BDS model in order to explicitly model the spatial variations of the haemodynamic response function (HRF) in the brain using a non-parametric approach. While in Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993] the relationship between neuronal activation and fMRI signals is formulated as a first-order convolution with a kernel expansion using basis functions (typically two or three), in this paper, we argue in favor of a spatially adaptive GLM in which a local non-parametric estimation of the HRF is performed. Furthermore, in order to overcome the overfitting problem typically associated with simple EM estimates, we propose a full Variational Bayes (VB) solution to infer the BDS model parameters. We demonstrate the usefulness of our model which is able to estimate both the neuronal activity and the haemodynamic response function in every voxel of the brain. We first examine the behavior of this approach when applied to simulated data with different temporal and noise features. As an example we will show how this method can be used to improve interpretability of estimates from an independent component analysis (ICA) analysis of fMRI data. We finally demonstrate its use on real fMRI data in one slice of the brain.

Lightning Jump Algorithm Development for the GOES·R Geostationary Lightning Mapper

NASA Technical Reports Server (NTRS)

Schultz. E.; Schultz. C.; Chronis, T.; Stough, S.; Carey, L.; Calhoun, K.; Ortega, K.; Stano, G.; Cecil, D.; Bateman, M.;

Golimumab in refractory uveitis related to spondyloarthritis. Multicenter study of 15 patients.

PubMed

Calvo-Río, Vanesa; Blanco, Ricardo; Santos-Gómez, Montserrat; Rubio-Romero, Esteban; Cordero-Coma, Miguel; Gallego-Flores, Adela; Veroz, Raúl; Torre, Ignacio; Hernández, Félix Francisco; Atanes, Antonio; Loricera, Javier; González-Vela, M C; Palmou, Natalia; Hernández, José L; González-Gay, Miguel A

2016-08-01

To assess the efficacy of golimumab (GLM) in refractory uveitis associated to spondyloarthritis (SpA). Multicenter study of SpA-related uveitis refractory to at least 1 immunosuppressive drug. The main outcome variables were degree of anterior and posterior chamber inflammation, visual acuity, and macular thickness. A total of 15 patients (13 men/2 women; 18 affected eyes; mean age 39 ± 6 years) were evaluated. The underlying SpA subtypes were ankylosing spondylitis (n = 8), psoriatic arthritis (n = 6) and non-radiographic axial SpA (n = 1). The ocular involvement patterns were recurrent anterior uveitis in 8 patients and chronic anterior uveitis in 7. Before GLM they have received methotrexate (n = 13), sulfasalazine (n = 6), pulses of methylprednisolone (n = 4), azathioprine (n = 3), leflunomide (n = 2), and cyclosporine (n = 1). Overall, 10 of them had also been treated with TNF-α blockers; etanercept (n = 7), adalimumab (n = 7), infliximab (n = 6), and certolizumab (n = 1). GLM was given at the standard dose (50mg/sc/monthly) as monotherapy (n = 7) or in combination with conventional immunosuppressive drugs (n = 8), mainly methotrexate. Most patients had rapid and progressive improvement of intraocular inflammation parameters. The median number of cells in the anterior chamber at 2 years [0 (0-0)] was significantly reduced compared to baseline findings [1 (0-3); p = 0.04]. The mean best corrected visual acuity value also improved (0.84 ± 0.3 at 2 years versus 0.62 ± 0.3 at baseline; p = 0.03). Only minor side effects were observed after a mean follow-up of 23 ± 7 months. Our results indicate that GLM may be a useful therapeutic option in refractory SpA-related uveitis. Copyright © 2016 Elsevier Inc. All rights reserved.

UV reflectance is associated with environmental conditions in Palaearctic Pieris napi (Lepidoptera: Pieridae).

PubMed

Stella, David; Pecháček, Pavel; Meyer-Rochow, Victor Benno; Kleisner, Karel

2018-06-01

The subject of our investigation was the visual features of wing color with special focus on the UV reflectance in the green-veined white butterfly (Pieris napi). Previous studies had concluded that UV reflectance on dorsal wing surfaces is found only in the female P. napi. Based on UV sensitive photography, we analyzed a correlation between 12 geographic and environmental factors and UV reflectance patterns on 3 patches on the forewings of 407 P. napi specimens from the Palaearctic region. Results had shown that females significantly differ from males: they exhibit a 25% higher UV reflectance. To investigate whether and how UV reflectance levels on the forewings and hindwings of both sexes are influenced by the environment, we performed a principal component analysis (PCA) with several environmental variables. For several variables (in particular, latitude and longitude, mean annual temperature and precipitation, and temperature annual range and altitude), the generalized linear model (GLM) model revealed a significant correlation in both sexes. This suggests a link between UV reflectance levels and the environment and distribution of P. napi. We found that stronger UV reflectance is associated with generally more hostile environments and concluded that large-scale environmental factors influence the UV reflectance on the forewings of both male and female green-veined white butterflies. © 2016 Institute of Zoology, Chinese Academy of Sciences.

Dry Matter Production, Nutrient Cycled and Removed, and Soil Fertility Changes in Yam-Based Cropping Systems with Herbaceous Legumes in the Guinea-Sudan Zone of Benin.

PubMed

Maliki, Raphiou; Sinsin, Brice; Floquet, Anne; Cornet, Denis; Malezieux, Eric; Vernier, Philippe

2016-01-01

Traditional yam-based cropping systems (shifting cultivation, slash-and-burn, and short fallow) often result in deforestation and soil nutrient depletion. The objective of this study was to determine the impact of yam-based systems with herbaceous legumes on dry matter (DM) production (tubers, shoots), nutrients removed and recycled, and the soil fertility changes. We compared smallholders' traditional systems (1-year fallow of Andropogon gayanus-yam rotation, maize-yam rotation) with yam-based systems integrated herbaceous legumes (Aeschynomene histrix/maize intercropping-yam rotation, Mucuna pruriens/maize intercropping-yam rotation). The experiment was conducted during the 2002 and 2004 cropping seasons with 32 farmers, eight in each site. For each of them, a randomized complete block design with four treatments and four replicates was carried out using a partial nested model with five factors: Year, Replicate, Farmer, Site, and Treatment. Analysis of variance (ANOVA) using the general linear model (GLM) procedure was applied to the dry matter (DM) production (tubers, shoots), nutrient contribution to the systems, and soil properties at depths 0-10 and 10-20 cm. DM removed and recycled, total N, P, and K recycled or removed, and soil chemical properties (SOM, N, P, K, and pH water) were significantly improved on yam-based systems with legumes in comparison with traditional systems.

Dry Matter Production, Nutrient Cycled and Removed, and Soil Fertility Changes in Yam-Based Cropping Systems with Herbaceous Legumes in the Guinea-Sudan Zone of Benin

PubMed Central

Sinsin, Brice; Floquet, Anne; Cornet, Denis; Malezieux, Eric; Vernier, Philippe

2016-01-01

Traditional yam-based cropping systems (shifting cultivation, slash-and-burn, and short fallow) often result in deforestation and soil nutrient depletion. The objective of this study was to determine the impact of yam-based systems with herbaceous legumes on dry matter (DM) production (tubers, shoots), nutrients removed and recycled, and the soil fertility changes. We compared smallholders' traditional systems (1-year fallow of Andropogon gayanus-yam rotation, maize-yam rotation) with yam-based systems integrated herbaceous legumes (Aeschynomene histrix/maize intercropping-yam rotation, Mucuna pruriens/maize intercropping-yam rotation). The experiment was conducted during the 2002 and 2004 cropping seasons with 32 farmers, eight in each site. For each of them, a randomized complete block design with four treatments and four replicates was carried out using a partial nested model with five factors: Year, Replicate, Farmer, Site, and Treatment. Analysis of variance (ANOVA) using the general linear model (GLM) procedure was applied to the dry matter (DM) production (tubers, shoots), nutrient contribution to the systems, and soil properties at depths 0–10 and 10–20 cm. DM removed and recycled, total N, P, and K recycled or removed, and soil chemical properties (SOM, N, P, K, and pH water) were significantly improved on yam-based systems with legumes in comparison with traditional systems. PMID:27446635

Serious psychological distress as a barrier to dental care in community-dwelling adults in the United States.

PubMed

Xiang, Xiaoling; Lee, Wonik; Kang, Sung-wan

2015-01-01

To examine whether serious psychological distress (SPD), a nonspecific indicator of past year mental health problems, was associated with subsequent dental care utilization, dental expenditures, and unmet dental needs. We analyzed data from panel 13 thru 15 of the Medical Expenditure Panel Survey -Household Component (n=31,056). SPD was defined as a score of 13 or higher on the Kessler Psychological Distress Scale (K6). Logistic regression, zero-inflated negative binomial model, and generalized linear model (GLM) with a gamma distribution were used to test the study hypotheses. Adults with SPD had, in the subsequent year, 35 percent lower odds of adhering to annual dental checkups and a twofold increase in the odds of having unmet dental needs. Although adults with SPD did not have significantly more dental visits than those without SPD, they spent 20 percent more on dental care. SPD was a modest independent risk factor for lack of subsequent preventive dental care, greater unmet dental needs, and greater dental expenditures. In addition to expanding adult dental coverage, it is important to develop and evaluate interventions to increase the utilization of dental care particularly preventive dental services among people with mental illness in order to improve oral health and reduce dental expenditures among this vulnerable population. © 2014 American Association of Public Health Dentistry.

Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

PubMed

Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

2018-06-01

Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

Leadership styles of nursing home administrators and their association with staff turnover.

PubMed

Donoghue, Christopher; Castle, Nicholas G

2009-04-01

The purpose of this study was to examine the associations between nursing home administrator (NHA) leadership style and staff turnover. We analyzed primary data from a survey of 2,900 NHAs conducted in 2005. The Online Survey Certification and Reporting database and the Area Resource File were utilized to extract organizational and local economic characteristics of the facilities. A general linear model (GLM) was used to estimate the effects of NHA leadership style, organizational characteristics, and local economic characteristics on nursing home staff turnover for registered nurses (RNs), licensed practical nurses (LPNs), and nurse's aides (NAs). The complete model estimates indicate that NHAs who are consensus managers (leaders who solicit, and act upon, the most input from their staff) are associated with the lowest turnover levels, 7% for RNs, 3% for LPNs, and 44% for NAs. Shareholder managers (leaders who neither solicit input when making a decision nor provide their staffs with relevant information for making decisions on their own) are associated with the highest turnover levels, 32% for RNs, 56% for LPNs, and 168% for NAs. The findings indicate that NHA leadership style is associated with staff turnover, even when the effects of organizational and local economic conditions are held constant. Because leadership strategies are amenable to change, the findings of this study may be used to develop policies for lowering staff turnover.

FIACH: A biophysical model for automatic retrospective noise control in fMRI.

PubMed

Tierney, Tim M; Weiss-Croft, Louise J; Centeno, Maria; Shamshiri, Elhum A; Perani, Suejen; Baldeweg, Torsten; Clark, Christopher A; Carmichael, David W

2016-01-01

Different noise sources in fMRI acquisition can lead to spurious false positives and reduced sensitivity. We have developed a biophysically-based model (named FIACH: Functional Image Artefact Correction Heuristic) which extends current retrospective noise control methods in fMRI. FIACH can be applied to both General Linear Model (GLM) and resting state functional connectivity MRI (rs-fcMRI) studies. FIACH is a two-step procedure involving the identification and correction of non-physiological large amplitude temporal signal changes and spatial regions of high temporal instability. We have demonstrated its efficacy in a sample of 42 healthy children while performing language tasks that include overt speech with known activations. We demonstrate large improvements in sensitivity when FIACH is compared with current methods of retrospective correction. FIACH reduces the confounding effects of noise and increases the study's power by explaining significant variance that is not contained within the commonly used motion parameters. The method is particularly useful in detecting activations in inferior temporal regions which have proven problematic for fMRI. We have shown greater reproducibility and robustness of fMRI responses using FIACH in the context of task induced motion. In a clinical setting this will translate to increasing the reliability and sensitivity of fMRI used for the identification of language lateralisation and eloquent cortex. FIACH can benefit studies of cognitive development in young children, patient populations and older adults. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Integrals of motion from quantum toroidal algebras

NASA Astrophysics Data System (ADS)

Feigin, B.; Jimbo, M.; Mukhin, E.

2017-11-01

We identify the Taylor coefficients of the transfer matrices corresponding to quantum toroidal algebras with the elliptic local and non-local integrals of motion introduced by Kojima, Shiraishi, Watanabe, and one of the authors. That allows us to prove the Litvinov conjectures on the Intermediate Long Wave model. We also discuss the ({gl_m, {gl_n) duality of XXZ models in quantum toroidal setting and the implications for the quantum KdV model. In particular, we conjecture that the spectrum of non-local integrals of motion of Bazhanov, Lukyanov, and Zamolodchikov is described by Gaudin Bethe ansatz equations associated to affine {sl}2 . Dedicated to the memory of Petr Petrovich Kulish.

Increased spruce tree growth in Central Europe since 1960s.

PubMed

Cienciala, Emil; Altman, Jan; Doležal, Jiří; Kopáček, Jiří; Štěpánek, Petr; Ståhl, Göran; Tumajer, Jan

2018-04-01

Tree growth response to recent environmental changes is of key interest for forest ecology. This study addressed the following questions with respect to Norway spruce (Picea abies, L. Karst.) in Central Europe: Has tree growth accelerated during the last five decades? What are the main environmental drivers of the observed tree radial stem growth and how much variability can be explained by them? Using a nationwide dendrochronological sampling of Norway spruce in the Czech Republic (1246 trees, 266 plots), novel regional tree-ring width chronologies for 40(±10)- and 60(±10)-year old trees were assembled, averaged across three elevation zones (break points at 500 and 700m). Correspondingly averaged drivers, including temperature, precipitation, nitrogen (N) deposition and ambient CO 2 concentration, were used in a general linear model (GLM) to analyze the contribution of these in explaining tree ring width variability for the period from 1961 to 2013. Spruce tree radial stem growth responded strongly to the changing environment in Central Europe during the period, with a mean tree ring width increase of 24 and 32% for the 40- and 60-year old trees, respectively. The indicative General Linear Model analysis identified CO 2 , precipitation during the vegetation season, spring air temperature (March-May) and N-deposition as the significant covariates of growth, with the latter including interactions with elevation zones. The regression models explained 57% and 55% of the variability in the two tree ring width chronologies, respectively. Growth response to N-deposition showed the highest variability along the elevation gradient with growth stimulation/limitation at sites below/above 700m. A strong sensitivity of stem growth to CO 2 was also indicated, suggesting that the effect of rising ambient CO 2 concentration (direct or indirect by increased water use efficiency) should be considered in analyses of long-term growth together with climatic factors and N-deposition. Copyright © 2017 Elsevier B.V. All rights reserved.

High occurrence of Helicobacter pylori in raw goat, sheep and cow milk inferred by glmM gene: a risk of food-borne infection?

PubMed

Quaglia, N C; Dambrosio, A; Normanno, G; Parisi, A; Patrono, R; Ranieri, G; Rella, A; Celano, G V

2008-05-10

Helicobacter pylori is an organism widespread in humans and sometimes responsible for serious illnesses, such as gastric and duodenal ulcers, MALToma and even gastric cancer. It has been hypothesized that the infection route by H. pylori involves multiple pathways including food-borne transmission, as the microorganism has been detected from foods such as sheep and cow milk. This work reports the results of a survey conducted in order to investigate the presence of H. pylori in raw goat, sheep and cow milk produced in Southern Italy, employing a Nested Polymerase Chain Reaction (Nested-PCR) assay for the detection of the phosphoglucosamine mutase gene (glmM), as screening method followed by conventional bacteriological isolation. Out of the 400 raw milk samples examined, 139 (34.7%) resulted positive for the presence of glmM gene, but no strains were isolated. In this work H. pylori DNA has been firstly detected from 41 (25.6%) raw goat milk samples. The results deserve further investigations on the contamination source/s of the milk samples and on the major impact that it may have on consumers.

Effectiveness and safety of golimumab in patients with rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis under real-life clinical conditions: non-interventional GO-NICE study in Germany.

PubMed

Krüger, Klaus; Burmester, Gerd R; Wassenberg, Siegfried; Bohl-Bühler, Martin; Thomas, Matthias H

2018-06-14

The Non Interventional Evaluation with Golumimab (GO-NICE) study aimed to document patient and treatment characteristics as well as clinical effectiveness and safety in adult patients newly treated with the tumour necrosis factor inhibitor golimumab (GLM). Prospective non-interventional study with 24-month observation per patient. 158 office-based and clinical-based physicians in Germany. GLM administered in the 50 mg dose subcutaneously in monthly intervals under real-life conditions. Of the 1613 included patients, 1458 patients were eligible for final analysis: 474 patients with rheumatoid arthritis (RA, 54.9±13.4 years, 72.8% women, 64.7% biologic-naïve), 501 with psoriatic arthritis (PsA, 50.5±12.1 years, 54.1% women, 56.5% biologic-naïve) and 483 with ankylosing spondylitis (AS, 43.6±12.3 years, 66.5% men, 61.0% biologic-naïve). 664 patients completed follow-up (2-year retention rate 45.5%). Disease Activity Score 28-joint count erythrocyte sedimentation rate (DAS28-ESR) decreased from 5.0 to 2.9 after 24 months (p<0.0001) in patients with RA, and Bath Ankylosing Spondylitis Disease Index score decreased from 5.1 to 2.4 (p<0.0001) in patients with AS. Response rate calculated in patients with PsA by modified Psoriatic Arthritis Response Criteria was 67.9% after 24 months. Most adverse events were of mild or moderate nature, and no new safety signals were detected. According to the physicians' clinical assessments, treatment with GLM was successful (no adverse drug reaction and a clear or moderate therapeutic effect in an individual patient) in 55.0%-56.6% of patients with RA, PsA and AS, respectively, at month 3, increasing from 74.5% to 76.1% at month 24. GLM subcutaneously once monthly led to substantial improvements in clinical effectiveness in patients with various inflammatory rheumatic diseases who could be followed up in a real-life setting in Germany. The treatment was well tolerated, and the safety profile of GLM was consistent with that observed in the previous randomised controlled trials. NCT01313858. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Atmospheric studies related to aerospace activities and remote sensing technology

NASA Technical Reports Server (NTRS)

Sze, N. D.; Isaacs, R. G.; Ko, M.; Mcelroy, M. B.

1981-01-01

Parallel investigations were conducted relating to: the sensitivity of 1-D photochemical model simulated column ozone perturbations due to a projected fleet of 1000 aircraft cruising 7 hours per day at altitudes of 15-16 and 18-19 km to uncertainties in kinetic rate constant data determining modeled OH concentrations and eddy diffusivity profile parameterization and a comparison of the inherent strengths and weaknesses of Eulerian and Langrangian averaging processes in the development of multidimensional models and investigation of approaches to applying the Generalized Lagrangian Mean (GLM) formalism to zonal-mean models. The role of multiple scattering and Earth curvature in the evaluation of diurnally dependent photodissociation rates and trace species variations was examined.

The Geostationary Lighting Mapper (GLM) for GOES-R: A New Operational Capability to Improve Storm Forecasts and Warnings

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R.; Koshak, William J.; Petersen, W. A.; Carey, L.; Mah, D.

2010-01-01

The next generation Geostationary Operational Environmental Satellite (GOES-R) series is a follow on to the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved spectral (3x), spatial (4x), and temporal (5x) resolution for the Advanced Baseline Imager (ABI). The GLM, an optical transient detector and imager operating in the near-IR at 777.4 nm will map all (in-cloud and cloud-to-ground) lighting flashes continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions, from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) national centers and forecast offices via the GOES-R Proving Ground to help improve our understanding of the application of these data in operational settings and facilitate Day-1 user readiness for this new capability.

The Sao Paulo Lightning Mapping Array (SPLMA): Prospects to GOES-R GLM and CHUVA

NASA Technical Reports Server (NTRS)

Albrecht, Rachel I.; Carrey, Larry; Blakeslee, Richard J.; Bailey, Jeffrey C.; Goodman, Steven J.; Bruning, Eric C.; Koshak, William; Morales, Carlos A.; Machado, Luiz A. T.; Angelis, Carlos F.;

Inhibitors of acetyltransferase domain of N-acetylglucosamine-1-phosphate-uridyltransferase/glucosamine-1-phosphate-acetyltransferase (GlmU). Part 1: Hit to lead evaluation of a novel arylsulfonamide series.

PubMed

Green, Oluyinka M; McKenzie, Andrew R; Shapiro, Adam B; Otterbein, Ludovic; Ni, Haihong; Patten, Arthur; Stokes, Suzanne; Albert, Robert; Kawatkar, Sameer; Breed, Jason

2012-02-15

A novel arylsulfonamide-containing series of compounds represented by 1, discovered by highthroughput screening, inhibit the acetyltransferase domain of N-acetylglucosamine-1-phosphate-uridyltransferase/glucosamine-1-phosphate-acetyltransferase (GlmU). X-ray structure determination confirmed that inhibitor binds at the site occupied by acetyl-CoA, indicating that series is competitive with this substrate. This letter documents our early hit-to-lead evaluation of the chemical series and some of the findings that led to improvement in in-vitro potency against Gram-negative and Gram-positive bacterial isozymes, exemplified by compound 40. Copyright © 2012 Elsevier Ltd. All rights reserved.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

Effect of Vitamin D Receptor Activation on the AGE/RAGE System and Myeloperoxidase in Chronic Kidney Disease Patients

PubMed Central

Pizzini, Patrizia; Cutrupi, Sebastiano; Tripepi, Rocco; Vilasi, Antonio; Tripepi, Giovanni; Mallamaci, Francesca

2017-01-01

Vitamin D receptor (VDR) activation has been reported to increase circulating levels of the advanced glycation end products (AGE) and their decoy receptor (RAGE). However, until now, the effect of VDR activation on AGE and RAGE has not been tested in the setting of a randomized, double-blind clinical trial. We have therefore analyzed the effect of VDR activation by paricalcitol on pentosidine, S100A12/ENRAGE, and RAGE and on established biomarkers of oxidative stress like myeloperoxidase in CKD patients in the PENNY trial. At baseline, human S100A12/ENRAGE, RAGE, and myeloperoxidase, but not pentosidine, were intercorrelated, and the association between S100A12/ENRAGE and myeloperoxidase (r = 0.71, P < 0.001) was the strongest among these correlations. Paricalcitol failed to modify biomarkers of the AGE/RAGE system and myeloperoxidase in unadjusted and adjusted analyses by the generalized linear model (GLM). No effect modification by other risk factors was registered. Paricalcitol does not modify biomarkers of the AGE/RAGE system and myeloperoxidase in CKD patients. The apparent increase in RAGE levels by VDR activation reported in previous uncontrolled studies is most likely due to confounding factors rather than to VDR activation per se. This trial is registered with NCT01680198. PMID:29362665

Comparison of Melatonin Profile and Alertness of Firefighters with Different Work Schedules

PubMed Central

Kazemi, Reza; Zare, Sajad

2018-01-01

Introduction: A two-shift work schedule with different rotations is common among firefighters in Iranian petrochemical companies. This study compared salivary melatonin and sleepiness on the last night before turning to day shift at 19:00, 23:00, 3:00, and 7:00 among petrochemical firefighters (PFFs) working seven and four consecutive night shifts. Methods: Sixty four PFFs working in the petrochemical industry were selected. To measure melatonin, saliva samples were taken, whereas the KSS index was used to assess sleepiness. Chi-square and independent samples t-test were carried out to analyze the data, and generalized linear model (GLM) was employed to determine the effect of confounding factors such as lighting and caffeine. Results: The levels of melatonin at 3:00 and 7:00, and the overall changes during the shift in the two shift patterns under the study were different (P < 0.05). Sleepiness was significantly different only at 3:00 in the two studied shift patterns, while the effects of lighting and caffeine on melatonin changes were not significant (P > 0.05). Conclusion: It seems that a slow shift rotation is better because it reduces the secretion of melatonin (hence reducing sleepiness during the night) and changes the peak of melatonin secretion to the daytime, which is a sign of adaptation.

A comparison of donor and control group quality of life.

PubMed

Tumin, Makmor; Abdul Talib Abdul Mutalib, Muzalwana; Mohd Satar, Nurulhuda; Abdullah, Nawi; Chong, Chin-Sieng; Ng, Kok-Peng; Lim, Soo-Kun

2014-03-03

Informed consent of prospective donors should include information about the quality of life (QoL) of existing donors, especially those within the relevant country. This study aimed to provide information on Malaysian organ donors' QoL relative to a control group. Using a shorter version of the SF-36, QoL of 80 donors from the University of Malaya Medical Center (UMMC), Malaysia was surveyed and compared to QoL of 80 selected healthy individuals. ANOVA and General Linear Model (GLM) procedure were each applied for the QoL comparison, which was based on gender and age. Donors recorded a better QoL relative to the control group. Comparison across gender revealed that differences are more obvious for males than females. Donor/control comparison across age groups reveals that donors aged 56 and above reported significantly better QoL in most domains relative to other age groups. Information on donor QoL should be made available to the public to present a comprehensive picture of the consequences of organ donation. Nonetheless, we also argue that, despite the merits of organ donation, caution is required before concluding that donors have better QoL because the present research outcomes may reflect a self-selection bias in which respondents only included donors engaging in regular follow-ups.

Tempo-Spatial Variations of Ambient Ozone-Mortality Associations in the USA: Results from the NMMAPS Data.

PubMed

Liu, Tao; Zeng, Weilin; Lin, Hualiang; Rutherford, Shannon; Xiao, Jianpeng; Li, Xing; Li, Zhihao; Qian, Zhengmin; Feng, Baixiang; Ma, Wenjun

2016-08-26

Although the health effects of ambient ozone have been widely assessed, their tempo-spatial variations remain unclear. We selected 20 communities (ten each from southern and northern USA) based on the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) dataset. A generalized linear model (GLM) was used to estimate the season-specific association between each 10 ppb (lag0-2 day average) increment in daily 8 h maximum ozone concentration and mortality in every community. The results showed that in the southern communities, a 10 ppb increment in ozone was linked to an increment of mortality of -0.07%, -0.17%, 0.40% and 0.27% in spring, summer, autumn and winter, respectively. For the northern communities, the excess risks (ERs) were 0.74%, 1.21%, 0.52% and -0.65% in the spring, summer, autumn and winter seasons, respectively. City-specific ozone-related mortality effects were positively related with latitude, but negatively related with seasonal average temperature in the spring, summer and autumn seasons. However, a reverse relationship was found in the winter. We concluded that there were different seasonal patterns of ozone effects on mortality between southern and northern US communities. Latitude and seasonal average temperature were identified as modifiers of the ambient ozone-related mortality risks.

Changes in humpback whale song occurrence in response to an acoustic source 200 km away.

PubMed

Risch, Denise; Corkeron, Peter J; Ellison, William T; Parijs, Sofie M Van

2012-01-01

The effect of underwater anthropogenic sound on marine mammals is of increasing concern. Here we show that humpback whale (Megaptera novaeangliae) song in the Stellwagen Bank National Marine Sanctuary (SBNMS) was reduced, concurrent with transmissions of an Ocean Acoustic Waveguide Remote Sensing (OAWRS) experiment approximately 200 km away. We detected the OAWRS experiment in SBNMS during an 11 day period in autumn 2006. We compared the occurrence of song for 11 days before, during and after the experiment with song over the same 33 calendar days in two later years. Using a quasi-Poisson generalized linear model (GLM), we demonstrate a significant difference in the number of minutes with detected song between periods and years. The lack of humpback whale song during the OAWRS experiment was the most substantial signal in the data. Our findings demonstrate the greatest published distance over which anthropogenic sound has been shown to affect vocalizing baleen whales, and the first time that active acoustic fisheries technology has been shown to have this effect. The suitability of Ocean Acoustic Waveguide Remote Sensing technology for in-situ, long term monitoring of marine ecosystems should be considered, bearing in mind its possible effects on non-target species, in particular protected species.

Severity of cough in idiopathic pulmonary fibrosis is associated with MUC5 B genotype

PubMed Central

2014-01-01

Background A polymorphism (rs35705950) in the promoter region of the mucin MUC5B is associated with both familial and sporadic forms of idiopathic pulmonary fibrosis. (IPF) We hypothesize that this common MUC5B variant will impact the expression of cough, a frequent disabling symptom seen in subjects with IPF. Methods We genotyped 136 subjects with IPF. All living subjects were provided with a Leicester Cough Questionnaire (LCQ) to measure cough severity. We assessed allele effects of the MUC5B polymorphism on the LCQ scores using SAS General Linear Models (GLM) in the patients with IPF. Results In the 68 of the total 136 IPF patients who returned the LCQ, MUC5B minor allele frequency (T) is consistent with prior published studies (31%). We found a significant independent effect of the T allele on the LCQ score (p = 0.002 for subjects with IPF). This effect is independent of other common causes of cough, including gastroesophogeal reflux disease and upper airway cough syndrome. Conclusions Cough severity, a common disabling phenotypic component of IPF, is significantly associated with the presence of the minor allele of a MUC5B promoter polymorphism. This study highlights a possible genetic mechanism for phenotypic heterogeneity in pulmonary fibrosis. PMID:24667072

Tempo-Spatial Variations of Ambient Ozone-Mortality Associations in the USA: Results from the NMMAPS Data

PubMed Central

Liu, Tao; Zeng, Weilin; Lin, Hualiang; Rutherford, Shannon; Xiao, Jianpeng; Li, Xing; Li, Zhihao; Qian, Zhengmin; Feng, Baixiang; Ma, Wenjun

2016-01-01

Although the health effects of ambient ozone have been widely assessed, their tempo-spatial variations remain unclear. We selected 20 communities (ten each from southern and northern USA) based on the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) dataset. A generalized linear model (GLM) was used to estimate the season-specific association between each 10 ppb (lag0-2 day average) increment in daily 8 h maximum ozone concentration and mortality in every community. The results showed that in the southern communities, a 10 ppb increment in ozone was linked to an increment of mortality of −0.07%, −0.17%, 0.40% and 0.27% in spring, summer, autumn and winter, respectively. For the northern communities, the excess risks (ERs) were 0.74%, 1.21%, 0.52% and −0.65% in the spring, summer, autumn and winter seasons, respectively. City-specific ozone-related mortality effects were positively related with latitude, but negatively related with seasonal average temperature in the spring, summer and autumn seasons. However, a reverse relationship was found in the winter. We concluded that there were different seasonal patterns of ozone effects on mortality between southern and northern US communities. Latitude and seasonal average temperature were identified as modifiers of the ambient ozone-related mortality risks. PMID:27571094

The role of environmental variables in structuring landscape-scale species distributions in seafloor habitats.

PubMed

Kraan, Casper; Aarts, Geert; Van der Meer, Jaap; Piersma, Theunis

2010-06-01

Ongoing statistical sophistication allows a shift from describing species' spatial distributions toward statistically disentangling the possible roles of environmental variables in shaping species distributions. Based on a landscape-scale benthic survey in the Dutch Wadden Sea, we show the merits of spatially explicit generalized estimating equations (GEE). The intertidal macrozoobenthic species, Macoma balthica, Cerastoderma edule, Marenzelleria viridis, Scoloplos armiger, Corophium volutator, and Urothoe poseidonis served as test cases, with median grain-size and inundation time as typical environmental explanatory variables. GEEs outperformed spatially naive generalized linear models (GLMs), and removed much residual spatial structure, indicating the importance of median grain-size and inundation time in shaping landscape-scale species distributions in the intertidal. GEE regression coefficients were smaller than those attained with GLM, and GEE standard errors were larger. The best fitting GEE for each species was used to predict species' density in relation to median grain-size and inundation time. Although no drastic changes were noted compared to previous work that described habitat suitability for benthic fauna in the Wadden Sea, our predictions provided more detailed and unbiased estimates of the determinants of species-environment relationships. We conclude that spatial GEEs offer the necessary methodological advances to further steps toward linking pattern to process.

Changes in Humpback Whale Song Occurrence in Response to an Acoustic Source 200 km Away

PubMed Central

Risch, Denise; Corkeron, Peter J.; Ellison, William T.; Van Parijs, Sofie M.

2012-01-01

The effect of underwater anthropogenic sound on marine mammals is of increasing concern. Here we show that humpback whale (Megaptera novaeangliae) song in the Stellwagen Bank National Marine Sanctuary (SBNMS) was reduced, concurrent with transmissions of an Ocean Acoustic Waveguide Remote Sensing (OAWRS) experiment approximately 200 km away. We detected the OAWRS experiment in SBNMS during an 11 day period in autumn 2006. We compared the occurrence of song for 11 days before, during and after the experiment with song over the same 33 calendar days in two later years. Using a quasi-Poisson generalized linear model (GLM), we demonstrate a significant difference in the number of minutes with detected song between periods and years. The lack of humpback whale song during the OAWRS experiment was the most substantial signal in the data. Our findings demonstrate the greatest published distance over which anthropogenic sound has been shown to affect vocalizing baleen whales, and the first time that active acoustic fisheries technology has been shown to have this effect. The suitability of Ocean Acoustic Waveguide Remote Sensing technology for in-situ, long term monitoring of marine ecosystems should be considered, bearing in mind its possible effects on non-target species, in particular protected species. PMID:22253769

Spatial and Temporal EEG-fMRI Changes During Preictal and Postictal Phases in a Patient With Posttraumatic Epilepsy.

PubMed

Storti, Silvia F; Del Felice, Alessandra; Formaggio, Emanuela; Boscolo Galazzo, Ilaria; Bongiovanni, Luigi G; Cerini, Roberto; Fiaschi, Antonio; Manganotti, Paolo

2015-07-01

The combined use of electroencephalography (EEG) and functional magnetic resonance imaging (EEG-fMRI) in epilepsy allows the noninvasive hemodynamic characterization of epileptic discharge-related neuronal activations. The aim of this study was to investigate pathophysiologic mechanisms underlying epileptic activity by exploring the spatial and temporal distribution of fMRI signal modifications during seizure in a single patient with posttraumatic epilepsy. EEG and fMRI data were acquired during two scanning sessions: a spontaneous critical episode was observed during the first, and interictal events were recorded during the second. The EEG-fMRI data were analyzed using the general linear model (GLM). Blood oxygenation level-dependent (BOLD) localization derived from the preictal and artifact-free postictal phase was concordant with the BOLD localization of the interictal epileptiform discharges identified in the second session, pointing to a left perilesional mesiofrontal area. Of note, BOLD signal modifications were already visible several seconds before seizure onset. In brief, BOLD activations from the preictal, postictal, and interictal epileptiform discharge analysis appear to be concordant with the clinically driven localization hypothesis, whereas a widespread network of activations is detected during the ictal phase in a partial seizure. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Task-dependent modulation of regions in the left temporal cortex during auditory sentence comprehension.

PubMed

Zhang, Linjun; Yue, Qiuhai; Zhang, Yang; Shu, Hua; Li, Ping

2015-01-01

Numerous studies have revealed the essential role of the left lateral temporal cortex in auditory sentence comprehension along with evidence of the functional specialization of the anterior and posterior temporal sub-areas. However, it is unclear whether task demands (e.g., active vs. passive listening) modulate the functional specificity of these sub-areas. In the present functional magnetic resonance imaging (fMRI) study, we addressed this issue by applying both independent component analysis (ICA) and general linear model (GLM) methods. Consistent with previous studies, intelligible sentences elicited greater activity in the left lateral temporal cortex relative to unintelligible sentences. Moreover, responses to intelligibility in the sub-regions were differentially modulated by task demands. While the overall activation patterns of the anterior and posterior superior temporal sulcus and middle temporal gyrus (STS/MTG) were equivalent during both passive and active tasks, a middle portion of the STS/MTG was found to be selectively activated only during the active task under a refined analysis of sub-regional contributions. Our results not only confirm the critical role of the left lateral temporal cortex in auditory sentence comprehension but further demonstrate that task demands modulate functional specialization of the anterior-middle-posterior temporal sub-areas. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Fitting monthly Peninsula Malaysian rainfall using Tweedie distribution

NASA Astrophysics Data System (ADS)

Yunus, R. M.; Hasan, M. M.; Zubairi, Y. Z.

2017-09-01

In this study, the Tweedie distribution was used to fit the monthly rainfall data from 24 monitoring stations of Peninsula Malaysia for the period from January, 2008 to April, 2015. The aim of the study is to determine whether the distributions within the Tweedie family fit well the monthly Malaysian rainfall data. Within the Tweedie family, the gamma distribution is generally used for fitting the rainfall totals, however the Poisson-gamma distribution is more useful to describe two important features of rainfall pattern, which are the occurrences (dry months) and the amount (wet months). First, the appropriate distribution of the monthly rainfall was identified within the Tweedie family for each station. Then, the Tweedie Generalised Linear Model (GLM) with no explanatory variable was used to model the monthly rainfall data. Graphical representation was used to assess model appropriateness. The QQ plots of quantile residuals show that the Tweedie models fit the monthly rainfall data better for majority of the stations in the west coast and mid land than those in the east coast of Peninsula. This significant finding suggests that the best fitted distribution depends on the geographical location of the monitoring station. In this paper, a simple model is developed for generating synthetic rainfall data for use in various areas, including agriculture and irrigation. We have showed that the data that were simulated using the Tweedie distribution have fairly similar frequency histogram to that of the actual data. Both the mean number of rainfall events and mean amount of rain for a month were estimated simultaneously for the case that the Poisson gamma distribution fits the data reasonably well. Thus, this work complements previous studies that fit the rainfall amount and the occurrence of rainfall events separately, each to a different distribution.

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

GLM Proxy Data Generation: Methods for Stroke/Pulse Level Inter-Comparison of Ground-Based Lightning Reference Networks

NASA Technical Reports Server (NTRS)

Cummins, Kenneth L.; Carey, Lawrence D.; Schultz, Christopher J.; Bateman, Monte G.; Cecil, Daniel J.; Rudlosky, Scott D.; Petersen, Walter Arthur; Blakeslee, Richard J.; Goodman, Steven J.

2011-01-01

In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala s Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

GLM Proxy Data Generation: Methods for Stroke/Pulse Level Inter-comparison of Ground-based Lightning Reference Networks

NASA Astrophysics Data System (ADS)

Cummins, K. L.; Carey, L. D.; Schultz, C. J.; Bateman, M. G.; Cecil, D. J.; Rudlosky, S. D.; Petersen, W. A.; Blakeslee, R. J.; Goodman, S. J.

2011-12-01

In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala's Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments.

PubMed

Pinti, Paola; Merla, Arcangelo; Aichelburg, Clarisse; Lind, Frida; Power, Sarah; Swingler, Elizabeth; Hamilton, Antonia; Gilbert, Sam; Burgess, Paul W; Tachtsidis, Ilias

2017-07-15

Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that "brain-first" rather than "behaviour-first" analysis is possible and that the present method can provide a novel solution to analyse real-world fNIRS data, filling the gap between real-life testing and functional neuroimaging. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Inundation and Fire Shape the Structure of Riparian Forests in the Pantanal, Brazil

PubMed Central

Arruda, Wellinton de Sá; Oldeland, Jens; Paranhos Filho, Antonio Conceição; Pott, Arnildo; Cunha, Nicolay L.; Ishii, Iria Hiromi; Damasceno-Junior, Geraldo Alves

2016-01-01

Inundation and fire can affect the structure of riparian vegetation in wetlands. Our aim was to verify if there are differences in richness, abundance, basal area, composition and topographic preference of woody species in riparian forests related to the fire history, flooding duration, or the interaction between both. The study was conducted in the riparian forests of the Paraguay River some of which were burned three times between 2001 and 2011. We sampled trees with a girth of at least 5 cm at breast height in 150 5 × 10 m plots (79 burned and 71 unburned). We also measured height of the flood mark and estimated the flooding duration of each plot. We performed Generalized Linear Mixed Models to verify differences in richness, basal area, and abundance of individuals associated to interaction of fire and inundation. We used an analysis of similarity (ANOSIM) and indicator species analysis to identify differences in composition of species and the association with burned and unburned area according to different levels of inundation. Finally, we used a hierarchical set of Generalized Linear Models (GLM), the so-called HOF models, to analyse each species’ specific response to inundation based on topography and to determine their preferred optimal topographic position for both burned as well as unburned areas. Richness was positively associated with elevation only in burned areas while abundance was negatively influenced by inundation only in burned areas. Basal area was negatively associated with time of inundation independent of fire history. There were 15 species which were significant indicators for at least one combination of the studied factors. We found nine species in burned areas and 15 in unburned areas, with response curves in HOF models along the inundation gradient. From these, five species shifted their optimal position along the inundation gradient in burned areas. The interaction of fire and inundation did not appear to affect the basal area, but it did affect the richness, number of individuals, success of some species, and seemed to shape the boundary of these forests as shown by the difference in the positioning of these species along the inundation gradient. PMID:27280879

Inundation and Fire Shape the Structure of Riparian Forests in the Pantanal, Brazil.

PubMed

Arruda, Wellinton de Sá; Oldeland, Jens; Paranhos Filho, Antonio Conceição; Pott, Arnildo; Cunha, Nicolay L; Ishii, Iria Hiromi; Damasceno-Junior, Geraldo Alves

2016-01-01

Inundation and fire can affect the structure of riparian vegetation in wetlands. Our aim was to verify if there are differences in richness, abundance, basal area, composition and topographic preference of woody species in riparian forests related to the fire history, flooding duration, or the interaction between both. The study was conducted in the riparian forests of the Paraguay River some of which were burned three times between 2001 and 2011. We sampled trees with a girth of at least 5 cm at breast height in 150 5 × 10 m plots (79 burned and 71 unburned). We also measured height of the flood mark and estimated the flooding duration of each plot. We performed Generalized Linear Mixed Models to verify differences in richness, basal area, and abundance of individuals associated to interaction of fire and inundation. We used an analysis of similarity (ANOSIM) and indicator species analysis to identify differences in composition of species and the association with burned and unburned area according to different levels of inundation. Finally, we used a hierarchical set of Generalized Linear Models (GLM), the so-called HOF models, to analyse each species' specific response to inundation based on topography and to determine their preferred optimal topographic position for both burned as well as unburned areas. Richness was positively associated with elevation only in burned areas while abundance was negatively influenced by inundation only in burned areas. Basal area was negatively associated with time of inundation independent of fire history. There were 15 species which were significant indicators for at least one combination of the studied factors. We found nine species in burned areas and 15 in unburned areas, with response curves in HOF models along the inundation gradient. From these, five species shifted their optimal position along the inundation gradient in burned areas. The interaction of fire and inundation did not appear to affect the basal area, but it did affect the richness, number of individuals, success of some species, and seemed to shape the boundary of these forests as shown by the difference in the positioning of these species along the inundation gradient.

Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework

USGS Publications Warehouse

Sojda, Richard S.; Towler, Erin; Roberts, Mike; Rajagopalan, Balaji

2013-01-01

[1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

Interaction between mercury (Hg), arsenic (As) and selenium (Se) affects the activity of glutathione S-transferase in breast milk; possible relationship with fish and sellfish intake.

PubMed

Gaxiola-Robles, Ramón; Labrada-Martagón, Vanessa; Celis de la Rosa, Alfredo de Jesús; Acosta-Vargas, Baudilio; Méndez-Rodríguez, Lía Celina; Zenteno-Savín, Tania

2014-08-01

Breast milk is regarded as an ideal source of nutrients for the growth and development of neonates, but it can also be a potential source of pollutants. Mothers can be exposed to different contaminants as a result of their lifestyle and environmental pollution. Mercury (Hg) and arsenic (As) could adversely affect the development of fetal and neonatal nervous system. Some fish and shellfish are rich in selenium (Se), an essential trace element that forms part of several enzymes related to the detoxification process, including glutathione S-transferase (GST). The goal of this study was to determine the interaction between Hg, As and Se and analyze its effect on the activity of GST in breast milk. Milk samples were collected from women between day 7 and 10 postpartum. The GST activity was determined spectrophotometrically; total Hg, As and Se concentrations were measured by atomic absorption spectrometry. To explain the possible association of Hg, As and Se concentrations with GST activity in breast milk, generalized linear models were constructed. The model explained 44% of the GST activity measured in breast milk. The GLM suggests that GST activity was positively correlated with Hg, As and Se concentrations. The activity of the enzyme was also explained by the frequency of consumption of marine fish and shellfish in the diet of the breastfeeding women. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Modulation of Temporal Precision in Thalamic Population Responses to Natural Visual Stimuli

PubMed Central

Desbordes, Gaëlle; Jin, Jianzhong; Alonso, Jose-Manuel; Stanley, Garrett B.

2010-01-01

Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise – on a time scale of 10–25 ms – both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment. PMID:21151356

Genome-Wide Association Study Identifying Candidate Genes Influencing Important Agronomic Traits of Flax (Linum usitatissimum L.) Using SLAF-seq

PubMed Central

Xie, Dongwei; Dai, Zhigang; Yang, Zemao; Sun, Jian; Zhao, Debao; Yang, Xue; Zhang, Liguo; Tang, Qing; Su, Jianguang

2018-01-01

Flax (Linum usitatissimum L.) is an important cash crop, and its agronomic traits directly affect yield and quality. Molecular studies on flax remain inadequate because relatively few flax genes have been associated with agronomic traits or have been identified as having potential applications. To identify markers and candidate genes that can potentially be used for genetic improvement of crucial agronomic traits, we examined 224 specimens of core flax germplasm; specifically, phenotypic data for key traits, including plant height, technical length, number of branches, number of fruits, and 1000-grain weight were investigated under three environmental conditions before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome-wide association study (GWAS) for these five agronomic traits. Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the important agronomic traits. Our analyses identified a total of 42 SNP loci that showed significant correlations with the five important agronomic flax traits. Next, candidate genes were screened in the 10 kb zone of each of the 42 SNP loci. These SNP loci were then analyzed by a more stringent screening via co-identification using both a general linear model (GLM) and a mixed linear model (MLM) as well as co-occurrences in at least two of the three environments, whereby 15 final candidate genes were obtained. Based on these results, we determined that UGT and PL are candidate genes for plant height, GRAS and XTH are candidate genes for the number of branches, Contig1437 and LU0019C12 are candidate genes for the number of fruits, and PHO1 is a candidate gene for the 1000-seed weight. We propose that the identified SNP loci and corresponding candidate genes might serve as a biological basis for improving crucial agronomic flax traits. PMID:29375606

Genome-Wide Association Study Identifying Candidate Genes Influencing Important Agronomic Traits of Flax (Linum usitatissimum L.) Using SLAF-seq.

PubMed

Xie, Dongwei; Dai, Zhigang; Yang, Zemao; Sun, Jian; Zhao, Debao; Yang, Xue; Zhang, Liguo; Tang, Qing; Su, Jianguang

2017-01-01

Flax ( Linum usitatissimum L.) is an important cash crop, and its agronomic traits directly affect yield and quality. Molecular studies on flax remain inadequate because relatively few flax genes have been associated with agronomic traits or have been identified as having potential applications. To identify markers and candidate genes that can potentially be used for genetic improvement of crucial agronomic traits, we examined 224 specimens of core flax germplasm; specifically, phenotypic data for key traits, including plant height, technical length, number of branches, number of fruits, and 1000-grain weight were investigated under three environmental conditions before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome-wide association study (GWAS) for these five agronomic traits. Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the important agronomic traits. Our analyses identified a total of 42 SNP loci that showed significant correlations with the five important agronomic flax traits. Next, candidate genes were screened in the 10 kb zone of each of the 42 SNP loci. These SNP loci were then analyzed by a more stringent screening via co-identification using both a general linear model (GLM) and a mixed linear model (MLM) as well as co-occurrences in at least two of the three environments, whereby 15 final candidate genes were obtained. Based on these results, we determined that UGT and PL are candidate genes for plant height, GRAS and XTH are candidate genes for the number of branches, Contig1437 and LU0019C12 are candidate genes for the number of fruits, and PHO1 is a candidate gene for the 1000-seed weight. We propose that the identified SNP loci and corresponding candidate genes might serve as a biological basis for improving crucial agronomic flax traits.

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

PubMed Central

Monk, Jacquomo; Ierodiaconou, Daniel; Harvey, Euan; Rattray, Alex; Versace, Vincent L.

2012-01-01

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions. PMID:22536325

Helicobacter pylori Infection in Rural and Urban Dyspeptic Patients from Venezuela

PubMed Central

Contreras, Monica; Fernández-Delgado, Milagro; Reyes, Nelson; García-Amado, María Alexandra; Rojas, Héctor; Michelangeli, Fabian

2015-01-01

The goal of this work was to assess the Helicobacter pylori prevalence in a rural mestizo population and compare it to an urban population from Venezuela. The study was performed in gastric juice samples of 71 dyspeptic patients from Caracas (urban) and 39 from Tucupita (rural), in the Orinoco Delta region. Helicobacter pylori was detected by amplification of 16S rRNA, glmM, and ureA genes in 55.0% patients from urban and 87.2% from rural populations. cagA was found positive in 51% and 62% urban and rural patients, respectively. Non-H. pylori Helicobacter species were not detected in the urban population, but was found in 7.7% of patients in the rural study site. Frequency values of the 16S rRNA, glmM, and ureA genes were higher in the rural population. The odds ratio for each gene was 15.18 for 16S rRNA, 2.34 for glmM, 2.89 for ureA, and 1.53 cagA, showing significant differences except for cagA when gene frequency was compared in both populations. These results demonstrate a higher frequency of H. pylori and gastric non-H. pylori Helicobacter infection in a rural mestizo population with low hygienic standards as compared with city dwellers, representing a potential risk for the development of gastroduodenal diseases. PMID:26195456

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion

PubMed Central

Ramirez Ramirez, L. Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

Background The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Methods Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. Results DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. Conclusions The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient. PMID:28464015

Model-free fMRI group analysis using FENICA.

PubMed

Schöpf, V; Windischberger, C; Robinson, S; Kasess, C H; Fischmeister, F PhS; Lanzenberger, R; Albrecht, J; Kleemann, A M; Kopietz, R; Wiesmann, M; Moser, E

2011-03-01

Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects. Copyright © 2010 Elsevier Inc. All rights reserved.

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

NASA Technical Reports Server (NTRS)

Houchin, Scott; Porter, Brian; Graybill, Justin; Slingerland, Philip

2017-01-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. This paper describes the software design and implementation of IPATS and provides preliminary test results.

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs

PubMed Central

Truccolo, Wilson

2017-01-01

Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity. PMID:28234899

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

PubMed

Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson

2017-02-01

Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity.

[Which factors determine the altitudinal distribution of tropical Andean riverine fishes]?

PubMed

De La Barra, Evans; Zubieta, José; Aguilera, Gastón; Maldonado, Mabel; Pouilly, Marc; Oberdorff, Thierry

2016-03-01

Altitudinal gradients represent an appropriate system to assess whether there is a relationship between richness patterns, environmental variables, and the ecological processes that determine the species type and number inhabiting a given area. In mountain streams freshwater fishes, the most prevalent relationship is a monotonic decrease in species richness with elevation. The objective of this study was to evaluate four hypotheses that can explain the negative relationship between local fish species richness and altitude, 1) the hypothesis of decreasing energy availability, 2) the hypothesis of increasing climate severity, 3) the hypothesis of habitat diversity, and 4) the hypothesis of isolation by physical severity of the environment. Fish and macro-invertebrates were collected following standard methods from 83 sites (between 200-4 000 meters) of two river basins in the Bolivian Amazon. The first hypothesis was tested by analyzing relationships between the density of macro-invertebrates, the richness of invertivorous fish species and altitude; while the second and third hypotheses were assessed by a multiple regression analysis (GLM) between fish species richness and several local and regional factors. Besides, assemblage dissimilarity between sites along the altitudinal gradient was analyzed using βsim and βness indices. Fish richness decreases linearly with increasing altitude. The density of macro-invertebrates tends to increase at higher altitudes, contrary to invertivorous fish species richness, suggesting that energy availability is not a limiting factor for fish species colonization. The GLM explained 86 % of the variation in fish species richness, with a significant contribution of water temperature, maximum slope in the river mainstem, and stream width. There is a higher species turnover (βsim) between sites at low elevation. Inversely, βness shows higher values in the upper parts, corresponding to change in assemblages mainly due to species loss. Taken together, these results suggest that climatic and physical severities create strong barriers to colonization, further explaining the decrease in fish richness along the altitudinal gradient.

Evaluation of NASA SPoRT's Pseudo-Geostationary Lightning Mapper Products in the 2011 Spring Program

NASA Technical Reports Server (NTRS)

Stano, Geoffrey T.; Carcione, Brian; Siewert, Christopher; Kuhlman, Kristin M.

2012-01-01

NASA's Short-term Prediction Research and Transition (SPoRT) program is a contributing partner with the GOES-R Proving Ground (PG) preparing forecasters to understand and utilize the unique products that will be available in the GOES-R era. This presentation emphasizes SPoRT s actions to prepare the end user community for the Geostationary Lightning Mapper (GLM). This preparation is a collaborative effort with SPoRT's National Weather Service partners, the National Severe Storms Laboratory (NSSL), and the Hazardous Weather Testbed s Spring Program. SPoRT continues to use its effective paradigm of matching capabilities to forecast problems through collaborations with our end users and working with the developers at NSSL to create effective evaluations and visualizations. Furthermore, SPoRT continues to develop software plug-ins so that these products will be available to forecasters in their own decision support system, AWIPS and eventually AWIPS II. In 2009, the SPoRT program developed the original pseudo geostationary lightning mapper (PGLM) flash extent product to demonstrate what forecasters may see with GLM. The PGLM replaced the previous GLM product and serves as a stepping-stone until the AWG s official GLM proxy is ready. The PGLM algorithm is simple and can be applied to any ground-based total lightning network. For 2011, the PGLM used observations from four ground-based networks (North Alabama, Kennedy Space Center, Oklahoma, and Washington D.C.). While the PGLM is not a true proxy product, it is intended as a tool to train forecasters about total lightning as well as foster discussions on product visualizations and incorporating GLM-resolution data into forecast operations. The PGLM has been used in 2010 and 2011 and is likely to remain the primary lightning training tool for the GOES-R program for the near future. This presentation will emphasize the feedback received during the 2011 Spring Program. This will discuss several topics. Based on feedback from the 2010 Spring Program, SPoRT created two variant PGLM products, which NSSL produced locally and provided in real-time within AWIPS for 2011. The first is the flash initiation density (FID) product, which creates a gridded display showing the number of flashes that originated in each 8 8 km grid box. The second product is the maximum flash density (MFD). This shows the highest PGLM value for each grid point over a specific period of time, ranging from 30 to 120 minutes. In addition to the evaluation of these two new products, the evaluation of the PGLM itself will be covered. The presentation will conclude with forecaster feedback for additional improvements requested for future evaluations, such as within the 2012 Spring Program.

Self-dual form of Ruijsenaars-Schneider models and ILW equation with discrete Laplacian

NASA Astrophysics Data System (ADS)

Zabrodin, A.; Zotov, A.

2018-02-01

We discuss a self-dual form or the Bäcklund transformations for the continuous (in time variable) glN Ruijsenaars-Schneider model. It is based on the first order equations in N + M complex variables which include N positions of particles and M dual variables. The latter satisfy equations of motion of the glM Ruijsenaars-Schneider model. In the elliptic case it holds M = N while for the rational and trigonometric models M is not necessarily equal to N. Our consideration is similar to the previously obtained results for the Calogero-Moser models which are recovered in the non-relativistic limit. We also show that the self-dual description of the Ruijsenaars-Schneider models can be derived from complexified intermediate long wave equation with discrete Laplacian by means of the simple pole ansatz likewise the Calogero-Moser models arise from ordinary intermediate long wave and Benjamin-Ono equations.

Day 6 blastocyst is associated with increased birth weight in full-term singleton newborns after frozen-thawed transfer.

PubMed

Cai, Jiali; Liu, Lanlan; Xu, Yingpei; Liu, Zhenfang; Jiang, Xiaoming; Li, Ping; Sha, Aiguo; Ren, Jianzhi

2018-06-13

The purpose of the study is to compare the newborns weight in singleton term birth following transfer of thawed blastocysts-frozen on either day 5 or day 6 after in vitro fertilization. The retrospective study included 1444 frozen-thawed blastocyst transfer (FBT) cycles resulting in live singleton births between Jan 2013 and Dec 2016. The main outcomes measured were absolute birth weight, z-score adjusted for gestational age and gender, and incidence of large-for-gestational-age (LGA) newborns. Generalized linear model (GLM) and logistic regression were used in multivariate analyses. Both the absolute birth weight (3416.49 ± 404.74 vs 3349.22 ± 416.17) and the z-score (0.6 ± 0.93 vs 0.41 ± 0.93) were significantly higher on day 6 FBT in comparison with day 5 FBT. The incidence of LGA newborns was also increased on day 6 FBT (22.8 vs 14.7%, P = 0.006). Adjusted for maternal age, BMI, PCOS diagnosis, present of vanishing twin, and embryo quality, the odds ratio (95% confidence interval) for LGA on day 6 FBT comparing with day 5 FBT was 1.76 (1.18-2.64). Day 6 FBT is associated with increased birth weight and contributes to the incidence of LGA newborns in FBT.

Smoked Cannabis' Psychomotor and Neurocognitive Effects in Occasional and Frequent Smokers

PubMed Central

Desrosiers, Nathalie A.; Ramaekers, Johannes G.; Chauchard, Emeline; Gorelick, David A.; Huestis, Marilyn A.

2015-01-01

Δ9-Tetrahydrocannabinol (THC), the primary psychoactive constituent in cannabis, impairs psychomotor performance, cognition and driving ability; thus, driving under the influence of cannabis is a public safety concern. We documented cannabis' psychomotor, neurocognitive, subjective and physiological effects in occasional and frequent smokers to investigate potential differences between these smokers. Fourteen frequent (≥4x/week) and 11 occasional (<2x/week) cannabis smokers entered a secure research unit ∼19 h prior to smoking one 6.8% THC cigarette. Cognitive and psychomotor performance was evaluated with the critical tracking (CTT), divided attention (DAT), n-back (working memory) and Balloon Analog Risk (BART) (risk-taking) tasks at −1.75, 1.5, 3.5, 5.5 and 22.5 h after starting smoking. GLM (General Linear Model) repeated measures ANOVA was utilized to compare scores. Occasional smokers had significantly more difficulty compensating for CTT tracking error compared with frequent smokers 1.5 h after smoking. Divided attention performance declined significantly especially in occasional smokers, with session × group effects for tracking error, hits, false alarms and reaction time. Cannabis smoking did not elicit session × group effects on the n-back or BART. Controlled cannabis smoking impaired psychomotor function, more so in occasional smokers, suggesting some tolerance to psychomotor impairment in frequent users. These data have implications for cannabis-associated impairment in driving under the influence of cannabis cases. PMID:25745105

Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and Other Meteorological Measurements

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawrence D.; Cecil, Daniel J.; Bateman, Monte

2012-01-01

The lightning jump algorithm has a robust history in correlating upward trends in lightning to severe and hazardous weather occurrence. The algorithm uses the correlation between the physical principles that govern an updraft's ability to produce microphysical and kinematic conditions conducive for electrification and its role in the development of severe weather conditions. Recent work has demonstrated that the lightning jump algorithm concept holds significant promise in the operational realm, aiding in the identification of thunderstorms that have potential to produce severe or hazardous weather. However, a large amount of work still needs to be completed in spite of these positive results. The total lightning jump algorithm is not a stand-alone concept that can be used independent of other meteorological measurements, parameters, and techniques. For example, the algorithm is highly dependent upon thunderstorm tracking to build lightning histories on convective cells. Current tracking methods show that thunderstorm cell tracking is most reliable and cell histories are most accurate when radar information is incorporated with lightning data. In the absence of radar data, the cell tracking is a bit less reliable but the value added by the lightning information is much greater. For optimal application, the algorithm should be integrated with other measurements that assess storm scale properties (e.g., satellite, radar). Therefore, the recent focus of this research effort has been assessing the lightning jump's relation to thunderstorm tracking, meteorological parameters, and its potential uses in operational meteorology. Furthermore, the algorithm must be tailored for the optically-based GOES-R Geostationary Lightning Mapper (GLM), as what has been observed using Very High Frequency Lightning Mapping Array (VHF LMA) measurements will not exactly translate to what will be observed by GLM due to resolution and other instrument differences. Herein, we present some of the promising aspects and challenges encountered in utilizing objective tracking and GLM proxy data, as well as recent results that demonstrate the value added information gained by combining the lightning jump concept with traditional meteorological measurements.

Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis.

PubMed

Xie, Yuanchang; Lord, Dominique; Zhang, Yunlong

2007-09-01

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.

Multiple causes of nonstationarity in the Weihe annual low-flow series

NASA Astrophysics Data System (ADS)

Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong-Yu; Li, Lingqi

2018-02-01

Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

NASA SPoRT GOES-R Proving Ground Activities

NASA Technical Reports Server (NTRS)

Stano, Geoffrey T.; Fuell, Kevin K.; Jedloec, Gary J.

2010-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) program is a partner with the GOES-R Proving Ground (PG) helping prepare forecasters understand the unique products to come from the GOES-R instrument suite. SPoRT is working collaboratively with other members of the GOES-R PG team and Algorithm Working Group (AWG) scientists to develop and disseminate a suite of proxy products that address specific forecast problems for the WFOs, Regional and National Support Centers, and other NOAA users. These products draw on SPoRT s expertise with the transition and evaluation of products into operations from the MODIS instrument and the North Alabama Lightning Mapping Array (NALMA). The MODIS instrument serves as an excellent proxy for the Advanced Baseline Imager (ABI) that will be aboard GOES-R. SPoRT has transitioned and evaluated several multi-channel MODIS products. The true and false color products are being used in natural hazard detection by several SPoRT partners to provide better observation of land features, such as fires, smoke plumes, and snow cover. Additionally, many of SPoRT s partners are coastal offices and already benefit from the MODIS sea surface temperature composite. This, along with other surface feature observations will be developed into ABI proxy products for diagnostic use in the forecast process as well as assimilation into forecast models. In addition to the MODIS instrument, the NALMA has proven very valuable to WFOs with access to these total lightning data. These data provide situational awareness and enhanced warning decision making to improve lead times for severe thunderstorm and tornado warnings. One effort by SPoRT scientists includes a lightning threat product to create short-term model forecasts of lightning activity. Additionally, SPoRT is working with the AWG to create GLM proxy data from several of the ground based total lightning networks, such as the NALMA. The evaluation will focus on the vastly improved spatial coverage of the GLM, but with the trade-off of lower resolution compared to the NALMA. In addition to the above tasks, SPoRT will make these data available in the NWS next generation display software, AWIPS II. This has already been successfully completed for the two basic GLM proxies. SPoRT will use these products to train forecasters on the capabilities of GOES-R and foster feedback to develop additional products, visualizations, and requirements beneficial to end users needs. These developments and feedback will be made available to the GOES-R Proving Ground for the upcoming 2010 Spring Program in Norman, Oklahoma.

A network extension of species occupancy models in a patchy environment applied to the Yosemite toad (Anaxyrus canorus)

USGS Publications Warehouse

Berlow, Eric L.; Knapp, Roland A.; Ostoja, Steven M.; Williams, Richard J.; McKenny, Heather; Matchett, John R.; Guo, Qinghau; Fellers, Gary M.; Kleeman, Patrick; Brooks, Matthew L.; Joppa, Lucas

2013-01-01

A central challenge of conservation biology is using limited data to predict rare species occurrence and identify conservation areas that play a disproportionate role in regional persistence. Where species occupy discrete patches in a landscape, such predictions require data about environmental quality of individual patches and the connectivity among high quality patches. We present a novel extension to species occupancy modeling that blends traditionalpredictions of individual patch environmental quality with network analysis to estimate connectivity characteristics using limited survey data. We demonstrate this approach using environmental and geospatial attributes to predict observed occupancy patterns of the Yosemite toad (Anaxyrus (= Bufo) canorus) across >2,500 meadows in Yosemite National Park (USA). A. canorus, a Federal Proposed Species, breeds in shallow water associated with meadows. Our generalized linear model (GLM) accurately predicted ~84% of true presence-absence data on a subset of data withheld for testing. The predicted environmental quality of each meadow was iteratively ‘boosted’ by the quality of neighbors within dispersal distance. We used this park-wide meadow connectivity network to estimate the relative influence of an individual Meadow’s ‘environmental quality’ versus its ‘network quality’ to predict: a) clusters of high quality breeding meadows potentially linked by dispersal, b) breeding meadows with high environmental quality that are isolated from other such meadows, c) breeding meadows with lower environmental quality where long-term persistence may critically depend on the network neighborhood, and d) breeding meadows with the biggest impact on park-wide breeding patterns. Combined with targeted data on dispersal, genetics, disease, and other potential stressors, these results can guide designation of core conservation areas for A. canorus in Yosemite National Park.

Sensitivity of Statistical Downscaling Techniques to Reanalysis Choice and Implications for Regional Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Manzanas, R., Sr.; Brands, S.; San Martin, D., Sr.; Gutiérrez, J. M., Sr.

2014-12-01

This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a Generalized Linear Model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested -under a cross-validation scheme- separately for two distinct reanalyses (ERA-Interim and JRA-25) for the period 1981-2000. When the observed and downscaled time-series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a Global Climate Model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to "delta-change" estimates differing by up to a 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is shown to importantly contribute to the uncertainty of local-scale climate change projections, and, consequently, should be treated with equal care as other, well-known, sources of uncertainty -e.g., the choice of the GCM and/or downscaling method.- Implications of the results for the entire tropics, as well as for the Model Output Statistics downscaling approach are also briefly discussed.

The extratropical transition of Tropical Storm Cindy from a GLM, ISS LIS and GPM perspective

NASA Astrophysics Data System (ADS)

Heuscher, L.; Gatlin, P. N.; Petersen, W. A.; Liu, C.; Cecil, D. J.

2017-12-01

The distribution of lightning with respect to tropical convective precipitation systems has been well established in previous studies, and more recently by the successful Tropical Rainfall Measuring Mission (TRMM). However, TRMM did not provide information about precipitation features pole-ward of ±38° latitude. Hence not much is known about the evolution of lightning within extra-tropical cyclones traversing the mid-latitudes, especially its oceans. To facilitate such studies we have combined lightning data from the Geostationary Lightning Mapper (GLM) onboard GOES-16 and the Lightning Imaging Sensor (LIS) onboard the International Space Station (ISS) together with precipitation features obtained from the Global Precipitation Measurement (GPM) mission constellation of satellites. We used this lightning-enriched precipitation feature dataset to investigate the lightning and precipitation characteristics of Tropical Storm Cindy (20 June - 24 June 2017) from its organization in the central Gulf of Mexico to its landfall along the northern Gulf and transition to an extra-tropical cyclone. We analyzed lightning observations from GLM and ISS LIS in relation to microwave brightness temperatures from GPM constellation satellite overpasses of Cindy. We find that the 37 and 89 GHz brightness temperatures decreased as Cindy strengthened and continued to decrease after landfall and as Cindy took on more baroclinic characteristics during which time its overall lightning activity increased by a factor of six. In this regard, the study provides a new observationally-based view of the tropical to extra-tropical transition and its impact on lightning production.

The Goes-R Geostationary Lightning Mapper (GLM)

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Mach, Douglas

2011-01-01

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved storm diagnostic capability with the Advanced Baseline Imager. The GLM will map total lightning activity (in-cloud and cloud-to-ground lighting flashes) continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. In this paper we will report on new Nowcasting and storm warning applications being developed and evaluated at various NOAA Testbeds.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia.

PubMed

Almalki, Mohammed J; FitzGerald, Gerry; Clark, Michele

2012-09-12

Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. A cross-sectional survey was used in this study. Data were collected using Brooks' survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes.

Time till death affects spider mobility and web-building behavior during web construction in an orb-web spider.

PubMed

Anotaux, Mylène; Toscani, Camille; Leborgne, Raymond; Chaline, Nicolas; Pasquet, Alain

2016-04-01

It is well known that age influences organism mobility. This was demonstrated in vertebrates (such as mammals and birds) but has been less studied in invertebrates with the exception of Drosophila and the nematode Caenorhabditis elegans. Here we studied the influence of age on the mobility of the orb-weaving spider Zygiella x-notata during web construction. The orb-web is a good model because it has a characteristic geometrical structure and video tracking can be used to easily follow the spider's movements during web building. We investigated the influence of age (specifically chronological age, life span, and time till death) on different parameters of spider mobility during the construction of the capture spiral (distance traveled, duration of construction, spider velocity, spider movement, and spider inactivity) with a generalized linear model (GLM) procedure adjusted for the spider mass. The results showed that neither chronological age, nor life span affected the mobility parameters. However, when the time till death decreased, there was a decrease in the distance traveled, the duration of the construction of the capture spiral, and the spider movement. The spider velocity and the time of inactivity were not affected. These results could be correlated with a decrease in the length of the silky thread deposited for the construction of the capture spiral. Spiders with a shorter time till death built smaller web using less silk. Thus, our study suggests strongly that time till death affects spider mobility during web construction but not the chronological age and thus may be a good indicator of senescence.

Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Ohl, Frank W; Anemüller, Jörn

2015-05-15

The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves. Copyright © 2015 Elsevier B.V. All rights reserved.

Infection of foxes by Echinococcocus multilocularis in urban and suburban areas of Nancy, France: influence of feeding habits and environment.

PubMed

Robardet, E; Giraudoux, P; Caillot, C; Boue, F; Cliquet, F; Augot, D; Barrat, J

2008-03-01

This study evaluated the impact of biological and environmental factors on the infection of red foxes (Vulpes vulpes) by Echinococcus multilocularis in an endemic area of north-east France. From January 2004 to April 2006, 127 foxes were examined for E. multilocularis and their stomach contents analysed. The effect of year, season, age, sex and urbanisation level on E. multilocularis presence was estimated using a General Linear Model (GLM) with logit link, (i.e. logistic regression). Urbanisation level was the only influencing factor, with a decreasing gradient from rural [54%, CI 95% (40-68)] to peri-urban [31%, CI 95% (15-52)] and urban area [4%, CI 95% (0.7-15)]. The consumption of Arvicola terrestris and Microtus sp., grassland species, the main presumed intermediate hosts of E. multilocularis, was studied by the same approach. The two species were consumed less in the urban area and more in autumn than in spring. Anthropogenic food consumption was linked to urbanisation and to age. The frequency of anthropogenic food consumption decreased in the rural area. A global model explaining the presence of E. multilocularis and including urbanisation level and diet was then elaborated. Independently of urbanisation, there was a suggestion of less E. multilocularis infection with anthropogenic food consumption. Red foxes consuming Microtus sp. and A. terrestris had higher worm burden than those that did not. The results suggest that the decreasing gradient observed from rural to urban area is linked to behaviour and feeding habits.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia

PubMed Central

2012-01-01

Background Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. Methods A cross-sectional survey was used in this study. Data were collected using Brooks’ survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Results Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Conclusions Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes. PMID:22970764

Time till death affects spider mobility and web-building behavior during web construction in an orb-web spider

PubMed Central

Anotaux, Mylène; Toscani, Camille; Leborgne, Raymond; Chaline, Nicolas; Pasquet, Alain

2016-01-01

Abstract It is well known that age influences organism mobility. This was demonstrated in vertebrates (such as mammals and birds) but has been less studied in invertebrates with the exception of Drosophila and the nematode Caenorhabditis elegans. Here we studied the influence of age on the mobility of the orb-weaving spider Zygiella x-notata during web construction. The orb-web is a good model because it has a characteristic geometrical structure and video tracking can be used to easily follow the spider’s movements during web building. We investigated the influence of age (specifically chronological age, life span, and time till death) on different parameters of spider mobility during the construction of the capture spiral (distance traveled, duration of construction, spider velocity, spider movement, and spider inactivity) with a generalized linear model (GLM) procedure adjusted for the spider mass. The results showed that neither chronological age, nor life span affected the mobility parameters. However, when the time till death decreased, there was a decrease in the distance traveled, the duration of the construction of the capture spiral, and the spider movement. The spider velocity and the time of inactivity were not affected. These results could be correlated with a decrease in the length of the silky thread deposited for the construction of the capture spiral. Spiders with a shorter time till death built smaller web using less silk. Thus, our study suggests strongly that time till death affects spider mobility during web construction but not the chronological age and thus may be a good indicator of senescence. PMID:29491899

The Evolution of a Long-Lived Mesoscale Convective System Observed by GLM

NASA Astrophysics Data System (ADS)

Peterson, M. J.; Rudlosky, S. D.; Antunes, L.

2017-12-01

Continuous Geostationary Lightning Mapper (GLM) observations are used to document total lightning activity over the life cycle of a long-lived Mesoscale Convective System (MCS). MCS's may be few in number, but they are important for the Global Electric Circuit (GEC) because they sustain high lightning flash rates and quasi steady state conduction currents (Wilson currents) over longer time periods than ordinary isolated convection. The optical characteristics of the flashes produced by MCS's change over time, providing additional insights into the precipitation structure, convective mode, and evolution of the storm system. These insights are particularly useful in areas void of radar observations. Intercalibrated passive microwave radiometer data from the Global Precipitation Measurement (GPM) constellation also are used to estimate changes in Wilson current generation as the system evolves. These results highlight the role of MCS's in the GEC, and showcase how optical flash descriptors relate to thunderstorm organization, maturity, and structure.

Geostationary Lightning Mapper: Lessons Learned from Post Launch Test

NASA Astrophysics Data System (ADS)

Edgington, S.; Tillier, C. E.; Demroff, H.; VanBezooijen, R.; Christian, H. J., Jr.; Bitzer, P. M.

2017-12-01

Pre-launch calibration and algorithm design for the GOES Geostationary Lightning Mapper resulted in a successful and trouble-free on-orbit activation and post-launch test sequence. Within minutes of opening the GLM aperture door on January 4th, 2017, lightning was detected across the entire field of view. During the six-month post-launch test period, numerous processing parameters on board the instrument and in the ground processing algorithms were fine-tuned. Demonstrated on-orbit performance exceeded pre-launch predictions. We provide an overview of the ground calibration sequence, on-orbit tuning of the instrument, tuning of the ground processing algorithms (event filtering and navigation). We also touch on new insights obtained from analysis of a large and growing archive of raw GLM data, containing 3e8 flash detections derived from over 1e10 full-disk images of the Earth.

Dietary cyanogen exposure and early child neurodevelopment: An observational study from the Democratic Republic of Congo.

PubMed

Kashala-Abotnes, Espérance; Sombo, Marie-Thérèse; Okitundu, Daniel L; Kunyu, Marcel; Bumoko Makila-Mabe, Guy; Tylleskär, Thorkild; Sikorskii, Alla; Banea, Jean-Pierre; Mumba Ngoyi, Dieudonné; Tshala-Katumbay, Désiré; Boivin, Michael J

2018-01-01

Dietary cyanogen exposure from ingesting bitter (toxic) cassava as a main source of food in sub-Saharan Africa is related to neurological impairments in sub-Saharan Africa. We explored possible association with early child neurodevelopmental outcomes. We undertook a cross-sectional neurodevelopmental assessment of 12-48 month-old children using the Mullen Scale of Early Learning (MSEL) and the Gensini Gavito Scale (GGS). We used the Hopkins Symptoms Checklist-10 (HSCL-10) and Goldberg Depression Anxiety Scale (GDAS) to screen for symptoms of maternal depression-anxiety. We used the cyanogen content in household cassava flour and urinary thiocyanate (SCN) as biomarkers of dietary cyanogen exposure. We employed multivariable generalized linear models (GLM) with Gamma link function to determine predictors of early child neurodevelopmental outcomes. The mean (SD) and median (IQR) of cyanogen content of cassava household flour were above the WHO cut-off points of 10 ppm (52.18 [32·79]) and 50 (30-50) ppm, respectively. Mean (SD) urinary levels of thiocyanate and median (IQR) were respectively 817·81 (474·59) and 688 (344-1032) μmole/l in mothers, and 617·49 (449·48) and 688 (344-688) μmole/l in children reflecting individual high levels as well as a community-wide cyanogenic exposure. The concentration of cyanide in cassava flour was significantly associated with early child neurodevelopment, motor development and cognitive ability as indicated by univariable linear regression (p < 0.05). After adjusting for biological and socioeconomic predictors at multivariable analyses, fine motor proficiency and child neurodevelopment remained the main predictors associated with the concentration of cyanide in cassava flour: coefficients of -0·08 to -.15 (p < 0·01). We also found a significant association between child linear growth, early child neurodevelopment, cognitive ability and motor development at both univariable and multivariable linear regression analyses coefficients of 1.44 to 7.31 (p < 0·01). Dietary cyanogen exposure is associated with early child neurodevelopment, cognitive abilities and motor development, even in the absence of clinically evident paralysis. There is a need for community-wide interventions for better cassava processing practices for detoxification, improved nutrition, and neuro-rehabilitation, all of which are essential for optimal development in exposed children.

The impact of immigration and vaccination in reducing the incidence of hepatitis B in Catalonia (Spain)

PubMed Central

2012-01-01

Background The Hepatitis B virus (HBV) infection is a major cause of liver disease and liver cancer worldwide according to the World Health Organization. Following acute HBV infection, 1-5% of infected healthy adults and up to 90% of infected infants become chronic carriers and have an increased risk of cirrhosis and primary hepatocellular carcinoma. The aim of this study was to investigate the relationship between the reduction in acute hepatitis B incidence and the universal vaccination programme in preadolescents in Catalonia (Spain), taking population changes into account, and to construct a model to forecast the future incidence of cases that permits the best preventive strategy to be adopted. Methods Reported acute hepatitis B incidence in Catalonia according to age, gender, vaccination coverage, percentage of immigrants and the year of report of cases was analysed. A statistical analysis was made using three models: generalized linear models (GLM) with Poisson or negative binomial distribution and a generalized additive model (GAM). Results The higher the vaccination coverage, the lower the reported incidence of hepatitis B (p <0.01). In groups with vaccination coverage > 70%, the reduction in incidence was 2-fold higher than in groups with a coverage <70% (p <0.01). The increase in incidence was significantly-higher in groups with a high percentage of immigrants and more than 15% (p <0.01) in immigrant males of working age (19-49 years). Conclusions The results of the adjusted models in this study confirm that the global incidence of hepatitis B has declined in Catalonia after the introduction of the universal preadolescent vaccination programme, but the incidence increased in male immigrants of working age. Given the potential severity of hepatitis B for the health of individuals and for the community, universal vaccination programmes should continue and programmes in risk groups, especially immigrants, should be strengthened. PMID:22867276

Statistical Modeling of Daily Stream Temperature for Mitigating Fish Mortality

NASA Astrophysics Data System (ADS)

Caldwell, R. J.; Rajagopalan, B.

2011-12-01

Water allocations in the Central Valley Project (CVP) of California require the consideration of short- and long-term needs of many socioeconomic factors including, but not limited to, agriculture, urban use, flood mitigation/control, and environmental concerns. The Endangered Species Act (ESA) ensures that the decision-making process provides sufficient water to limit the impact on protected species, such as salmon, in the Sacramento River Valley. Current decision support tools in the CVP were deemed inadequate by the National Marine Fisheries Service due to the limited temporal resolution of forecasts for monthly stream temperature and fish mortality. Finer scale temporal resolution is necessary to account for the stream temperature variations critical to salmon survival and reproduction. In addition, complementary, long-range tools are needed for monthly and seasonal management of water resources. We will present a Generalized Linear Model (GLM) framework of maximum daily stream temperatures and related attributes, such as: daily stream temperature range, exceedance/non-exceedance of critical threshold temperatures, and the number of hours of exceedance. A suite of predictors that impact stream temperatures are included in the models, including current and prior day values of streamflow, water temperatures of upstream releases from Shasta Dam, air temperature, and precipitation. Monthly models are developed for each stream temperature attribute at the Balls Ferry gauge, an EPA compliance point for meeting temperature criteria. The statistical framework is also coupled with seasonal climate forecasts using a stochastic weather generator to provide ensembles of stream temperature scenarios that can be used for seasonal scale water allocation planning and decisions. Short-term weather forecasts can also be used in the framework to provide near-term scenarios useful for making water release decisions on a daily basis. The framework can be easily translated to other locations and is intended to be a complement to the physical stream temperature modeling efforts that are underway on the river.

Converting Parkinson-Specific Scores into Health State Utilities to Assess Cost-Utility Analysis.

PubMed

Chen, Gang; Garcia-Gordillo, Miguel A; Collado-Mateo, Daniel; Del Pozo-Cruz, Borja; Adsuar, José C; Cordero-Ferrera, José Manuel; Abellán-Perpiñán, José María; Sánchez-Martínez, Fernando Ignacio

2018-06-07

The aim of this study was to compare the Parkinson's Disease Questionnaire-8 (PDQ-8) with three multi-attribute utility (MAU) instruments (EQ-5D-3L, EQ-5D-5L, and 15D) and to develop mapping algorithms that could be used to transform PDQ-8 scores into MAU scores. A cross-sectional study was conducted. A final sample of 228 evaluable patients was included in the analyses. Sociodemographic and clinical data were also collected. Two EQ-5D questionnaires were scored using Spanish tariffs. Two models and three statistical techniques were used to estimate each model in the direct mapping framework for all three MAU instruments, including the most widely used ordinary least squares (OLS), the robust MM-estimator, and the generalized linear model (GLM). For both EQ-5D-3L and EQ-5D-5L, indirect response mapping based on an ordered logit model was also conducted. Three goodness-of-fit tests were employed to compare the models: the mean absolute error (MAE), the root-mean-square error (RMSE), and the intra-class correlation coefficient (ICC) between the predicted and observed utilities. Health state utility scores ranged from 0.61 (EQ-5D-3L) to 0.74 (15D). The mean PDQ-8 score was 27.51. The correlation between overall PDQ-8 score and each MAU instrument ranged from - 0.729 (EQ-5D-5L) to - 0.752 (EQ-5D-3L). A mapping algorithm based on PDQ-8 items had better performance than using the overall score. For the two EQ-5D questionnaires, in general, the indirect mapping approach had comparable or even better performance than direct mapping based on MAE. Mapping algorithms developed in this study enable the estimation of utility values from the PDQ-8. The indirect mapping equations reported for two EQ-5D questionnaires will further facilitate the calculation of EQ-5D utility scores using other country-specific tariffs.

Sao Paulo Lightning Mapping Array (SP-LMA): Network Assessment and Analyses for Intercomparison Studies and GOES-R Proxy Activities

NASA Technical Reports Server (NTRS)

Blakeslee, R. J.; Bailey, J. C.; Carey, L. D.; Goodman, S. J.; Rudlosky, S. D.; Albrecht, R.; Morales, C. A.; Anselmo, E. M.; Neves, J. R.

2013-01-01

A 12 station Lightning Mapping Array (LMA) network was deployed during October 2011in the vicinity of São Paulo, Brazil (SP-LMA) to contribute total lightning measurements to an international field campaign [CHUVA - Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)]. The SP-LMA was operational from November 2011 through March 2012. Sensor spacing was on the order of 15-30 km, with a network diameter on the order of 40-50km. The SP-LMA provides good 3-D lightning mapping out to150 km from the network center, with 2-D coverage considerably farther. In addition to supporting CHUVA science/mission objectives, the SP-LMA is supporting the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), on NOAA's Geostationary Operational Environmental Satellite-R (GOES-R: scheduled for a 2015 launch). These proxy data will be used to develop and validate operational algorithms so that they will be ready to use on "day1" following the GOES-R launch. The SP-LMA data also will be intercompared with lightning observations from other deployed lightning networks to advance our understanding of the capabilities/contributions of each of these networks toward GLM proxy and validation activities. This paper addresses the network assessment and analyses for intercomparison studies and GOES-R proxy activities

Impacts of Population Structure and Analytical Models in Genome-Wide Association Studies of Complex Traits in Forest Trees: A Case Study in Eucalyptus globulus

PubMed Central

Garcia, Martín N.; Acuña, Cintia; Borralho, Nuno M. G.; Grattapaglia, Dario; Marcucci Poltri, Susana N.

2013-01-01

The promise of association genetics to identify genes or genomic regions controlling complex traits has generated a flurry of interest. Such phenotype-genotype associations could be useful to accelerate tree breeding cycles, increase precision and selection intensity for late expressing, low heritability traits. However, the prospects of association genetics in highly heterozygous undomesticated forest trees can be severely impacted by the presence of cryptic population and pedigree structure. To investigate how to better account for this, we compared the GLM and five combinations of the Unified Mixed Model (UMM) on data of a low-density genome-wide association study for growth and wood property traits carried out in a Eucalyptus globulus population (n = 303) with 7,680 Diversity Array Technology (DArT) markers. Model comparisons were based on the degree of deviation from the uniform distribution and estimates of the mean square differences between the observed and expected p-values of all significant marker-trait associations detected. Our analysis revealed the presence of population and family structure. There was not a single best model for all traits. Striking differences in detection power and accuracy were observed among the different models especially when population structure was not accounted for. The UMM method was the best and produced superior results when compared to GLM for all traits. Following stringent correction for false discoveries, 18 marker-trait associations were detected, 16 for tree diameter growth and two for lignin monomer composition (S∶G ratio), a key wood property trait. The two DArT markers associated with S∶G ratio on chromosome 10, physically map within 1 Mbp of the ferulate 5-hydroxylase (F5H) gene, providing a putative independent validation of this marker-trait association. This study details the merit of collectively integrate population structure and relatedness in association analyses in undomesticated, highly heterozygous forest trees, and provides additional insights into the nature of complex quantitative traits in Eucalyptus. PMID:24282578

Image navigation and registration performance assessment tool set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

NASA Astrophysics Data System (ADS)

De Luccia, Frank J.; Houchin, Scott; Porter, Brian C.; Graybill, Justin; Haas, Evan; Johnson, Patrick D.; Isaacson, Peter J.; Reth, Alan D.

2016-05-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99. 73rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

STEM connections to the GOES-R Satellite Series

NASA Astrophysics Data System (ADS)

Mooney, M. E.; Schmit, T.

2015-12-01

GOES-R, a new Geostationary Operational Environmental Satellite (GOES) is scheduled to be launched in October of 2016. Its role is to continue western hemisphere satellite coverage while the existing GOES series winds down its 20-year operation. However, instruments on the next generation GOES-R satellite series will provide major improvements to the current GOES, both in the frequency of images acquired and the spectral and spatial resolution of the images, providing a perfect conduit for STEM education. Most of these improvements will be provided by the Advanced Baseline Imager (ABI). ABI will provide three times more spectral information, four times the spatial resolution, and more than five times faster temporal coverage than the current GOES. Another exciting addition to the GOES-R satellite series will be the Geostationary Lightning Mapper (GLM). The all new GLM on GOES-R will measure total lightning activity continuously over the Americas and adjacent ocean regions with near uniform spatial resolution of approximately 10 km! Due to ABI, GLM and improved spacecraft calibration and navigation, the next generation GOES-R satellite series will usher in an exciting era of satellite applications and opportunities for STEM education. This session will present and demonstrate exciting next-gen imagery advancements and new HTML5 WebApps that demonstrate STEM connections to these improvements. Participants will also be invited to join the GOES-R Education Proving Ground, a national network of educators who will receive stipends to attend 4 webinars during the spring of 2016, pilot a STEM lesson plan, and organize a school-wide launch awareness event.

Structure of the human gastric bacterial community in relation to Helicobacter pylori status.

PubMed

Maldonado-Contreras, Ana; Goldfarb, Kate C; Godoy-Vitorino, Filipa; Karaoz, Ulas; Contreras, Mónica; Blaser, Martin J; Brodie, Eoin L; Dominguez-Bello, Maria G

2011-04-01

The human stomach is naturally colonized by Helicobacter pylori, which, when present, dominates the gastric bacterial community. In this study, we aimed to characterize the structure of the bacterial community in the stomach of patients of differing H. pylori status. We used a high-density 16S rRNA gene microarray (PhyloChip, Affymetrix, Inc.) to hybridize 16S rRNA gene amplicons from gastric biopsy DNA of 10 rural Amerindian patients from Amazonas, Venezuela, and of two immigrants to the United States (from South Asia and Africa, respectively). H. pylori status was determined by PCR amplification of H. pylori glmM from gastric biopsy samples. Of the 12 patients, 8 (6 of the 10 Amerindians and the 2 non-Amerindians) were H. pylori glmM positive. Regardless of H. pylori status, the PhyloChip detected Helicobacteriaceae DNA in all patients, although with lower relative abundance in patients who were glmM negative. The G2-chip taxonomy analysis of PhyloChip data indicated the presence of 44 bacterial phyla (of which 16 are unclassified by the Taxonomic Outline of the Bacteria and Archaea taxonomy) in a highly uneven community dominated by only four phyla: Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Positive H. pylori status was associated with increased relative abundance of non-Helicobacter bacteria from the Proteobacteria, Spirochetes and Acidobacteria, and with decreased abundance of Actinobacteria, Bacteroidetes and Firmicutes. The PhyloChip detected richness of low abundance phyla, and showed marked differences in the structure of the gastric bacterial community according to H. pylori status.

Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

NASA Technical Reports Server (NTRS)

DeLuccia, Frank J.; Houchin, Scott; Porter, Brian C.; Graybill, Justin; Haas, Evan; Johnson, Patrick D.; Isaacson, Peter J.; Reth, Alan D.

2016-01-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Navigation and Registration Performance Assessment Tool Set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

NASA Technical Reports Server (NTRS)

De Luccia, Frank J.; Houchin, Scott; Porter, Brian C.; Graybill, Justin; Haas, Evan; Johnson, Patrick D.; Isaacson, Peter J.; Reth, Alan D.

2016-01-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24-hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24-hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Structure of the human gastric bacterial community in relation to Helicobacter pylori status

PubMed Central

Maldonado-Contreras, Ana; Goldfarb, Kate C; Godoy-Vitorino, Filipa; Karaoz, Ulas; Contreras, Mónica; Blaser, Martin J; Brodie, Eoin L; Dominguez-Bello, Maria G

2011-01-01

The human stomach is naturally colonized by Helicobacter pylori, which, when present, dominates the gastric bacterial community. In this study, we aimed to characterize the structure of the bacterial community in the stomach of patients of differing H. pylori status. We used a high-density 16S rRNA gene microarray (PhyloChip, Affymetrix, Inc.) to hybridize 16S rRNA gene amplicons from gastric biopsy DNA of 10 rural Amerindian patients from Amazonas, Venezuela, and of two immigrants to the United States (from South Asia and Africa, respectively). H. pylori status was determined by PCR amplification of H. pylori glmM from gastric biopsy samples. Of the 12 patients, 8 (6 of the 10 Amerindians and the 2 non-Amerindians) were H. pylori glmM positive. Regardless of H. pylori status, the PhyloChip detected Helicobacteriaceae DNA in all patients, although with lower relative abundance in patients who were glmM negative. The G2-chip taxonomy analysis of PhyloChip data indicated the presence of 44 bacterial phyla (of which 16 are unclassified by the Taxonomic Outline of the Bacteria and Archaea taxonomy) in a highly uneven community dominated by only four phyla: Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Positive H. pylori status was associated with increased relative abundance of non-Helicobacter bacteria from the Proteobacteria, Spirochetes and Acidobacteria, and with decreased abundance of Actinobacteria, Bacteroidetes and Firmicutes. The PhyloChip detected richness of low abundance phyla, and showed marked differences in the structure of the gastric bacterial community according to H. pylori status. PMID:20927139

Modelling fire frequency and area burned across phytoclimatic regions in Spain using reanalysis data and the Canadian Fire Weather Index System

NASA Astrophysics Data System (ADS)

Bedia, J.; Herrera, S.; Gutiérrez, J. M.

2013-09-01

We develop fire occurrence and burned area models in peninsular Spain, an area of high variability in climate and fuel types, for the period 1990-2008. We based the analysis on a phytoclimatic classification aiming to the stratification of the territory into homogeneous units in terms of climatic and fuel type characteristics, allowing to test model performance under different climatic and fuel conditions. We used generalized linear models (GLM) and multivariate adaptive regression splines (MARS) as modelling algorithms and temperature, relative humidity, precipitation and wind speed, taken from the ERA-Interim reanalysis, as well as the components of the Canadian Forest Fire Weather Index (FWI) System as predictors. We also computed the standardized precipitation-evapotranspiration index (SPEI) as an additional predictor for the models of burned area. We found two contrasting fire regimes in terms of area burned and number of fires: one characterized by a bimodal annual pattern, characterizing the Nemoral and Oro-boreal phytoclimatic types, and another one exhibiting an unimodal annual cycle, with the fire season concentrated in the summer months in the Mediterranean and Arid regions. The fire occurrence models attained good skill in most of the phytoclimatic zones considered, yielding in some zones notably high correlation coefficients between the observed and modelled inter-annual fire frequencies. Total area burned also exhibited a high dependence on the meteorological drivers, although their ability to reproduce the observed annual burned area time series was poor in most cases. We identified temperature and some FWI system components as the most important explanatory variables, and also SPEI in some of the burned area models, highlighting the adequacy of the FWI system for fire modelling applications and leaving the door opened to the development a more complex modelling framework based on these predictors. Furthermore, we demonstrate the potential usefulness of ERA-Interim reanalysis data for the reconstruction of historical fire-climate relationships at the scale of analysis. Fire frequency predictions may provide a preferable basis for past fire history reconstruction, long-term monitoring and the assessment of future climate impacts on fire regimes across regions, posing several advantages over burned area as response variable.

Assessment of the Pseudo Geostationary Lightning Mapper Products at the Spring Program and Summer Experiment

NASA Technical Reports Server (NTRS)

Stano, Geoffrey T.; Calhoun, Kristin K.; Terborg, Amanda M.

2014-01-01

Since 2010, the de facto Geostationary Lightning Mapper (GLM) demonstration product has been the Pseudo-Geostationary Lightning Mapper (PGLM) product suite. Originally prepared for the Hazardous Weather Testbed's Spring Program (specifically the Experimental Warning Program) when only four ground-based lightning mapping arrays were available, the effort now spans collaborations with several institutions and eight collaborative networks. For 2013, NASA's Short-term Prediction Research and Transition (SPoRT) Center and NOAA's National Severe Storms Laboratory have worked to collaborate with each network to obtain data in real-time. This has gone into producing the SPoRT variant of the PGLM that was demonstrated in AWIPS II for the 2013 Spring Program. Alongside the PGLM products, the SPoRT / Meteorological Development Laboratory's total lightning tracking tool also was evaluated to assess not just another visualization of future GLM data but how to best extract more information while in the operational environment. Specifically, this tool addressed the leading request by forecasters during evaluations; provide a time series trend of total lightning in real-time. In addition to the Spring Program, SPoRT is providing the PGLM "mosaic" to the Aviation Weather Center (AWC) and Storm Prediction Center. This is the same as what is used at the Hazardous Weather Testbed, but combines all available networks into one display for use at the national centers. This year, the mosaic was evaluated during the AWC's Summer Experiment. An important distinction between this and the Spring Program is that the Summer Experiment focuses on the national center perspective and not at the local forecast office level. Specifically, the Summer Experiment focuses on aviation needs and concerns and brings together operational forecaster, developers, and FAA representatives. This presentation will focus on the evaluation of SPoRT's pseudo-GLM products in these separate test beds. The emphasis will be on how future GLM observations can support operations at both the local and national scale and how the PGLM was used in combination with other lightning data sets. Evaluations for the PGLM were quite favorable with forecasters appreciating the high temporal resolution, the ability to look for rapid increases in lightning activity ahead of severe weather, as well as situational awareness for where convection is firing and for flight routing.

Determinants of climate change awareness level in upper Nyakach Division, Kisumu County, Kenya.

PubMed

Ajuang, Chadwick O; Abuom, Paul O; Bosire, Esna K; Dida, Gabriel O; Anyona, Douglas N

2016-01-01

Improving the understanding of climate change awareness is one of the top priorities in climate change research. While the African continent is among the regions with the highest vulnerability to climate change, research on climate knowledge and awareness is lacking. Kenya is already grappling with the impacts of climate change, which are projected to increase in a non-linear and non-predictable manner. This study sought to determine climate change awareness levels among households residing in Upper Nyakach Division, Kisumu County, Kenya using common climate change markers viz heavy rainfall, floods, droughts and temperature. A cross-sectional survey design was adopted in which 384 household heads were selected as respondents from 11 sub-locations; all located within Upper Nyakach Division. A questionnaire was used to collect data. Most (90.9 %) respondents had observed changes in the overall climate. Awareness level of climate change varied significantly across the 11 sub-locations. To further gain insight unto which variables were the most significant determinant of climate change awareness in upper Nyakach division, Kisumu county, a Generalized Linear Model (GLM) with Poisson error distribution was built. The model indicated that sex of the household head, education level and age significantly influenced respondents' awareness to climate change markers. Most (87 %) households reported rising temperatures over the past 20 years. Over half (55.2 %) the respondents had observed declining rains, with significant differences being observed across age groups. Up to 75 % of the respondents reported increased droughts frequency over the last 20 years, with significant differences observed across gender. Most (86.7 %) respondents reported having observed changes in water sources with significant differences reported across age groups. The respondents reported an increased prevalence of malaria with significant differences being observed among the education levels and households' main livelihoods. The general population of the Upper Nyakach Divison is aware of changing global climate. However, more effort is required in mitigating climate change as per the local settings. Awareness campaign aimed at increasing knowledge of climate change markers among community members is recommended.

Gene-Based Single Nucleotide Polymorphism Markers for Genetic and Association Mapping in Common Bean

PubMed Central

2012-01-01

Background In common bean, expressed sequence tags (ESTs) are an underestimated source of gene-based markers such as insertion-deletions (Indels) or single-nucleotide polymorphisms (SNPs). However, due to the nature of these conserved sequences, detection of markers is difficult and portrays low levels of polymorphism. Therefore, development of intron-spanning EST-SNP markers can be a valuable resource for genetic experiments such as genetic mapping and association studies. Results In this study, a total of 313 new gene-based markers were developed at target genes. Intronic variation was deeply explored in order to capture more polymorphism. Introns were putatively identified after comparing the common bean ESTs with the soybean genome, and the primers were designed over intron-flanking regions. The intronic regions were evaluated for parental polymorphisms using the single strand conformational polymorphism (SSCP) technique and Sequenom MassARRAY system. A total of 53 new marker loci were placed on an integrated molecular map in the DOR364 × G19833 recombinant inbred line (RIL) population. The new linkage map was used to build a consensus map, merging the linkage maps of the BAT93 × JALO EEP558 and DOR364 × BAT477 populations. A total of 1,060 markers were mapped, with a total map length of 2,041 cM across 11 linkage groups. As a second application of the generated resource, a diversity panel with 93 genotypes was evaluated with 173 SNP markers using the MassARRAY-platform and KASPar technology. These results were coupled with previous SSR evaluations and drought tolerance assays carried out on the same individuals. This agglomerative dataset was examined, in order to discover marker-trait associations, using general linear model (GLM) and mixed linear model (MLM). Some significant associations with yield components were identified, and were consistent with previous findings. Conclusions In short, this study illustrates the power of intron-based markers for linkage and association mapping in common bean. The utility of these markers is discussed in relation with the usefulness of microsatellites, the molecular markers by excellence in this crop. PMID:22734675

Spatial Spread of the Root Parasitic Weed Phelipanche aegyptiaca in Processing Tomatoes by Using Ecoinformatics and Spatial Analysis†

PubMed Central

Cohen, Yafit; Roei, Itai; Blank, Lior; Goldshtein, Eitan; Eizenberg, Hanan

2017-01-01

Egyptian broomrape (Phelipanche aegyptiaca) is one of the main threats to tomato production in Israel. The seed bank of P. aegyptiaca rapidly develops and spreads in the field. Knowledge about the spatio-temporal distribution of such weeds is required in advance of emergence, as they emerge late in their life cycle when they have already caused major crop damage. The aim of this study is to reveal the effects of two major internal infestation sources: crop rotation and infestation history; and one external source: proximity to infested tomato fields; on infestation of P. aegyptiaca in processing tomatoes. Ecoinformatics, spatial analysis and geostatistics were used to examine these effects. A regional survey was conducted to collect data on field history from 238 tomato fields between 2000 and 2012, in a major tomato-growing region in Israel. Multivariate logistic regression in the framework of generalized linear models (GLM) has demonstrated the importance of all three variables in predicting infestation in tomato fields. The parameters of the overall model indicated a high specificity between tomatoes and P. aegyptiaca, which is potentially responsible for aggravating infestation. In addition, P. aegyptiaca infestation levels were intensively mapped in 43 of the 238 tomato fields in the years 2010–2012. Geostatistical measures showed that 40% of the fields had clustered infestation spatial patterns with infestation clusters located along the fields’ borders. Strong linear and negative relationships were found between infestation level and distance from a neighboring infested field, strengthening the role of infested tomato fields in P. aegyptiaca spread. An experiment specifically designed for this study showed that during harvest, P. aegyptiaca seeds are blown from an infested field to a distance of at least 90 m, and may initiate infestation in neighboring fields. Integrating current knowledge about the role of agricultural practices on the spread of P. aegyptiaca with the results of this study enabled us to propose a mechanism for the spread of P. aegyptiaca. Given the major effect of agricultural practices on infestation levels, it is assumed that the spread of this weed can be suppressed by implementing sanitation and using decision support tools for herbicide application. PMID:28676803

Spatial Spread of the Root Parasitic Weed Phelipanche aegyptiaca in Processing Tomatoes by Using Ecoinformatics and Spatial Analysis.

PubMed

Cohen, Yafit; Roei, Itai; Blank, Lior; Goldshtein, Eitan; Eizenberg, Hanan

2017-01-01

Egyptian broomrape ( Phelipanche aegyptiaca ) is one of the main threats to tomato production in Israel. The seed bank of P. aegyptiaca rapidly develops and spreads in the field. Knowledge about the spatio-temporal distribution of such weeds is required in advance of emergence, as they emerge late in their life cycle when they have already caused major crop damage. The aim of this study is to reveal the effects of two major internal infestation sources: crop rotation and infestation history; and one external source: proximity to infested tomato fields; on infestation of P. aegyptiaca in processing tomatoes. Ecoinformatics, spatial analysis and geostatistics were used to examine these effects. A regional survey was conducted to collect data on field history from 238 tomato fields between 2000 and 2012, in a major tomato-growing region in Israel. Multivariate logistic regression in the framework of generalized linear models (GLM) has demonstrated the importance of all three variables in predicting infestation in tomato fields. The parameters of the overall model indicated a high specificity between tomatoes and P. aegyptiaca , which is potentially responsible for aggravating infestation. In addition, P. aegyptiaca infestation levels were intensively mapped in 43 of the 238 tomato fields in the years 2010-2012. Geostatistical measures showed that 40% of the fields had clustered infestation spatial patterns with infestation clusters located along the fields' borders. Strong linear and negative relationships were found between infestation level and distance from a neighboring infested field, strengthening the role of infested tomato fields in P. aegyptiaca spread. An experiment specifically designed for this study showed that during harvest, P. aegyptiaca seeds are blown from an infested field to a distance of at least 90 m, and may initiate infestation in neighboring fields. Integrating current knowledge about the role of agricultural practices on the spread of P. aegyptiaca with the results of this study enabled us to propose a mechanism for the spread of P. aegyptiaca . Given the major effect of agricultural practices on infestation levels, it is assumed that the spread of this weed can be suppressed by implementing sanitation and using decision support tools for herbicide application.

Noise exposure and cognitive performance: A study on personnel on board Royal Norwegian Navy vessels

PubMed Central

Irgens-Hansen, Kaja; Gundersen, Hilde; Sunde, Erlend; Baste, Valborg; Harris, Anette; Bråtveit, Magne; Moen, Bente E.

2015-01-01

Prior research shows that work on board vessels of the Royal Norwegian Navy (RNoN) is associated with noise exposure levels above recommended standards. Further, noise exposure has been found to impair cognitive performance in environmental, occupational, and experimental settings, although prior research in naval and maritime settings is sparse. The aim of this study was to evaluate cognitive performance after exposure to noise among personnel working on board vessels in the RNoN. Altogether 87 Navy personnel (80 men, 7 women; 31 ± 9 years) from 24 RNoN vessels were included. Noise exposure was recorded by personal noise dosimeters at a minimum of 4 h prior to testing, and categorized into 4 groups for the analysis: <72.6 dB(A), 72.6-77.0 dB(A), 77.1-85.2 dB(A), and >85.2 dB(A). The participants performed a visual attention test based on the Posner cue-target paradigm. Multivariable general linear model (GLM) analyses were performed to analyze whether noise exposure was associated with response time (RT) when adjusting for the covariates age, alertness, workload, noise exposure in test location, sleep the night before testing, use of hearing protection device (HPD), and percentage of errors. When adjusting for covariates, RT was significantly increased among personnel exposed to >85.2 dB(A) and 77.1-85.2 dB(A) compared to personnel exposed to <72.6 dB(A). PMID:26356374

Beaked whales demonstrate a marked acoustic response to the use of shipboard echosounders

PubMed Central

DeAngelis, Annamaria I.; Palka, Debra; Corkeron, Peter J.; Van Parijs, Sofie M.

2017-01-01

The use of commercial echosounders for scientific and industrial purposes is steadily increasing. In addition to traditional navigational and fisheries uses, commercial sonars are used extensively for oceanographic research, benthic habitat mapping, geophysical exploration, and ecosystem studies. Little is known about the effects of these acoustic sources on marine animals, though several studies have already demonstrated behavioural responses of cetaceans to shipboard echosounders. Some species of cetaceans are known to be particularly sensitive to acoustic disturbance, including beaked whales. In 2011 and 2013, we conducted cetacean assessment surveys in the western North Atlantic in which a suite of Simrad EK60 echosounders was used to characterize the distribution of prey along survey tracklines. Echosounders were alternated daily between active and passive mode, to determine whether their use affected visual and acoustic detection rates of beaked whales. A total of 256 groups of beaked whales were sighted, and 118 definitive acoustic detections were recorded. Regression analyses using generalized linear models (GLM) found that sea state and region were primary factors in determining visual sighting rates, while echosounder state was the primary driver for acoustic detections, with significantly fewer detections (only 3%) occurring when echosounders were active. These results indicate that beaked whales both detect and change their behaviour in response to commercial echosounders. The mechanism of this response is unknown, but could indicate interruption of foraging activity or vessel avoidance, with potential implications for management and mitigation of anthropogenic impacts. PMID:29308236

Effect of the National Stress Check Program on mental health among workers in Japan: A 1-year retrospective cohort study.

PubMed

Imamura, Kotaro; Asai, Yumi; Watanabe, Kazuhiro; Tsutsumi, Akizumi; Shimazu, Akihito; Inoue, Akiomi; Hiro, Hisanori; Odagiri, Yuko; Yoshikawa, Toru; Yoshikawa, Etsuko; Kawakami, Norito

2018-04-18

This retrospective cohort study evaluated the impact of the Stress Check Program, a recently introduced national policy and program aimed at reducing psychological distress among Japanese workers. A baseline survey was conducted from November 2015 to February 2016, the period when Japan began enforcing the Stress Check Program. A one-year follow-up survey was conducted in December 2016. In the follow-up survey, two exposure variables were collected: having taken the annual stress survey, and experiencing an improvement in the psychosocial work environment. Psychological distress was assessed using the Brief Job Stress Questionnaire (BJSQ) at baseline and 1-year follow-up. The two exposure variables were used to define four groups: "Neither", "Stress survey (SS) only", "Psychosocial work environment improvement (WI) only", and "Both". BJSQ results were analyzed using repeated measures general linear modeling (GLM). The study included 2,492 participants: 1,342 in the "Neither" group, 1,009 in the "SS only" group, 76 in the "WI only" group, and 65 in the "Both" group. Overall time-group interaction effects were not significant. The "Both" group showed significantly greater improvements in psychological distress than the "Neither" group (p = 0.02) at the 1-year follow-up, although the effect size was small (d = -0.14). Combination of the annual stress survey and improvement in psychosocial work environment may have been effective in reducing psychological distress in workers, although the effect size was small.

fNIRS Evidence of Prefrontal Regulation of Frustration in Early Childhood

PubMed Central

Perlman, Susan B.; Luna, Beatriz; Hein, Tyler C.; Huppert, Theodore J.

2013-01-01

The experience of frustration is common in early childhood, yet some children seem to possess a lower tolerance for frustration than others. Characterizing the biological mechanisms underlying a wide range of frustration tolerance observed in early childhood may inform maladaptive behavior and psychopathology that is associated with this construct. The goal of this study was to measure prefrontal correlates of frustration in 3–5 year-old children, who are not readily adaptable for typical neuroimaging approaches, using functional near infrared spectroscopy (fNIRS). fNIRS of frontal regions were measured as frustration was induced in children through a computer game where a desired and expected prize was “stolen” by an animated dog. A fNIRS general linear model (GLM) was used to quantify the correlation of brain regions with the task and identify areas that were statistically different between the winning and frustrating test conditions. A second-level voxel-based ANOVA analysis was then used to correlate the amplitude of each individual’s brain activation with measure of parent-reported frustration. Experimental results indicated increased activity in the middle prefrontal cortex during winning of a desired prize, while lateral prefrontal cortex activity increased during frustration. Further, activity increase in lateral prefrontal cortex during frustration correlated positively with parent-reported frustration tolerance. These findings point to the role of the lateral prefrontal cortex as a potential region supporting the regulation of emotion during frustration. PMID:23624495

Noise exposure and cognitive performance: A study on personnel on board Royal Norwegian Navy vessels.

PubMed

Irgens-Hansen, Kaja; Gundersen, Hilde; Sunde, Erlend; Baste, Valborg; Harris, Anette; Bråtveit, Magne; Moen, Bente E

2015-01-01

Prior research shows that work on board vessels of the Royal Norwegian Navy (RNoN) is associated with noise exposure levels above recommended standards. Further, noise exposure has been found to impair cognitive performance in environmental, occupational, and experimental settings, although prior research in naval and maritime settings is sparse. The aim of this study was to evaluate cognitive performance after exposure to noise among personnel working on board vessels in the RNoN. Altogether 87 Navy personnel (80 men, 7 women; 31 ± 9 years) from 24 RNoN vessels were included. Noise exposure was recorded by personal noise dosimeters at a minimum of 4 h prior to testing, and categorized into 4 groups for the analysis: <72.6 dB(A), 72.6-77.0 dB(A), 77.1-85.2 dB(A), and >85.2 dB(A). The participants performed a visual attention test based on the Posner cue-target paradigm. Multivariable general linear model (GLM) analyses were performed to analyze whether noise exposure was associated with response time (RT) when adjusting for the covariates age, alertness, workload, noise exposure in test location, sleep the night before testing, use of hearing protection device (HPD), and percentage of errors. When adjusting for covariates, RT was significantly increased among personnel exposed to >85.2 dB(A) and 77.1-85.2 dB(A) compared to personnel exposed to <72.6 dB(A).

Relationship between thyroid stimulating hormone and night shift work.

PubMed

Moon, So-Hyun; Lee, Bum-Joon; Kim, Seong-Jin; Kim, Hwan-Cheol

2016-01-01

Night shift work has well-known adverse effects on health. However, few studies have investigated the relationship between thyroid diseases and night shift work. This study aimed to examine night shift workers and their changes in thyroid stimulating hormones (TSH) levels over time. Medical check-up data (2011-2015) were obtained from 967 female workers at a university hospital in Incheon, Korea. Data regarding TSH levels were extracted from the records, and 2015 was used as a reference point to determine night shift work status. The relationships between TSH levels and night shift work in each year were analyzed using the general linear model (GLM). The generalized estimating equation (GEE) was used to evaluate the repeated measurements over the 5-year period. The GEE analysis revealed that from 2011 to 2015, night shift workers had TSH levels that were 0.303 mIU/L higher than the levels of non-night shift workers (95 % CI: 0.087-0.519 mIU/L, p  = 0.006) after adjusting for age and department. When we used TSH levels of 4.5 ≥ mIU/L to identify subclinical hypothyroidism, night shift workers exhibited a 1.399 fold higher risk of subclinical hypothyroidism (95 % CI: 1.050-1.863, p  = 0.022), compared to their non-night shift counterparts. This result of this study suggests that night shift workers may have an increased risk of thyroid diseases, compared to non-night shift workers.

Acute effects of exercise and active video games on adults' reaction time and perceived exertion.

PubMed

Guzmán, José F; López-García, Jesús

2016-11-01

The purpose of the present study was to examine the acute effects of resting, aerobic exercise practised alone, and aerobic exercise with active video games (AVG), on complex reaction time (CRT) and the post-exercise acute rate of perceived exertion (RPE) in young healthy adults. The experimental group was composed of 92 healthy young adults, 78 males and 13 females (age M = 21.9 ± 2.7 years) who completed two sessions, A and B. In session A, participants rode 30 min on an ergometer, while in session B they exercised for 30 min on an ergometer while playing an AVG on a Wii. The control group was composed of 30 young adults, 26 males and 4 females (age M = 21.4 ± 2.9 years) who rested for 30 min. In each session, a CRT task was performed before and after exercising or resting, and post-exercise global RPE was noted. Repeated measures general linear model (GLM) and Wilcoxon tests were performed. (1) Both aerobic exercise alone and aerobic exercise combined with AVG improved CRT, while resting did not; (2) aerobic exercise combined with AVG did not improve CRT more than aerobic exercise only; and (3) RPE was lower after aerobic exercise combined with AVG compared with aerobic exercise only. In young adults, exercise produces acute benefits on CRT, and practising exercise with AVG helps to decrease RPE.

Cost and utilization outcomes of patients receiving hospital-based palliative care consultation.

PubMed

Penrod, Joan D; Deb, Partha; Luhrs, Carol; Dellenbaugh, Cornelia; Zhu, Carolyn W; Hochman, Tsivia; Maciejewski, Matthew L; Granieri, Evelyn; Morrison, R Sean

2006-08-01

To compare per diem total direct, ancillary (laboratory and radiology) and pharmacy costs of palliative care (PC) compared to usual care (UC) patients during a terminal hospitalization; to examine the association between PC and ICU admission. Retrospective, observational cost analysis using a VA (payer) perspective. Two urban VA medical centers. Demographic and health characteristics of 314 veterans admitted during two years were obtained from VA administrative data. Hospital costs came from the VA cost accounting system. Generalized linear models (GLM) were estimated for total direct, ancillary and pharmacy costs. Predictors included patient age, principal diagnosis, comorbidity, whether patient stay was medical or surgical, site and whether the patient was seen by the palliative care consultation team. A probit regression was used to analyze probability of ICU admission. Propensity score matching was used to improve balance in observed covariates. PC patients were 42 percentage points (95% CI, -56% [corrected] to -31%) less likely to be admitted to ICU. Total direct costs per day were $239 (95% CI, -387 to -122) lower and ancillary costs were $98 (95% CI, -133 to -57) lower than costs for UC patients. There was no difference in pharmacy costs. The results were similar using propensity score matching. PC was associated with significantly lower likelihood of ICU use and lower inpatient costs compared to UC. Our findings coupled with those indicating better patient and family outcomes with PC suggest both a cost and quality incentive for hospitals to develop PC programs.

Pupillary behavior in relation to wavelength and age

PubMed Central

Lobato-Rincón, Luis-Lucio; Cabanillas-Campos, Maria del Carmen; Bonnin-Arias, Cristina; Chamorro-Gutiérrez, Eva; Murciano-Cespedosa, Antonio; Sánchez-Ramos Roda, Celia

2014-01-01

Pupil light reflex can be used as a non-invasive ocular predictor of cephalic autonomic nervous system integrity. Spectral sensitivity of the pupil's response to light has, for some time, been an interesting issue. It has generally, however, only been investigated with the use of white light and studies with monochromatic wavelengths are scarce. This study investigates the effects of wavelength and age within three parameters of the pupil light reflex (amplitude of response, latency, and velocity of constriction) in a large sample of younger and older adults (N = 97), in mesopic conditions. Subjects were exposed to a single light stimulus at four different wavelengths: white (5600°K), blue (450 nm), green (510 nm), and red (600 nm). Data was analyzed appropriately, and, when applicable, using the General Linear Model (GLM), Randomized Complete Block Design (RCBD), Student's t-test and/or ANCOVA. Across all subjects, pupillary response to light had the greatest amplitude and shortest latency in white and green light conditions. In regards to age, older subjects (46–78 years) showed an increased latency in white light and decreased velocity of constriction in green light compared to younger subjects (18–45 years old). This study provides data patterns on parameters of wavelength-dependent pupil reflexes to light in adults and it contributes to the large body of pupillometric research. It is hoped that this study will add to the overall evaluation of cephalic autonomic nervous system integrity. PMID:24795595

Pharmacokinetics and bioequivalence study of a fixed dose combination of rabeprazole and itopride in healthy Indian volunteers.

PubMed

Sahoo, Bijay Kumar; Das, Ayan; Agarwal, Sangita; Bhaumik, Uttam; Bose, Anirbandeep; Ghosh, Debotri; Roy, Bikash; Pal, Tapan Kumar

2009-01-01

The aim of the present study was to compare the pharmacokinetics of rabeprazole (CAS 117976-89-3) and itopride (CAS 122898-67-3) after oral administration of a rabeprazole (20 mg)-itopride (150 mg) fixed dose combination (FDC) in healthy human volunteers. The bioequivalence of two formulations (test and reference) was determined in 12 healthy Indian male volunteers (age: 25.25 +/- 4.69 years; weight: 60.50 +/- 5.04 kg) in a randomized, single-dose, two-period, two-treatment crossover study. Both formulations were administered orally as a single dose, with the treatments separated by a washout period of 1 week. Rabeprazole and itopride plasma levels were determined by a validated HPLC method using UV detection. The formulations were compared using the pharmacokinetic parameters area under the plasma concentration-time curve (AUC(0-t)), area under the plasma concentration-time curve from zero to infinity (AUC(0-infinity)) and peak plasma concentration (Cmax). General linear model (GLM) procedures were used in which sources of variation were subject, treatment and period. The results indicated that there were no statistically significant differences (P > 0.05) between the logarithmically transformed AUC(0-infinity) and Cmax values between test and reference formulation. The 90% confidence interval for the ratio of the logarithmically transformed AUC(0-t), AUC(0-infinity) and Cmax were within the bioequivalence limits of 0.8-1.25 and the relative bioavailability of rabeprazole and itopride test and reference formulations was 98.24 and 93.65%, respectively.

An ecosystem-based assessment of hairtail ( Trichiurus lepturus) harvested by multi-gears and management implications in Korean waters

NASA Astrophysics Data System (ADS)

Kang, Hee Joong; Zhang, Chang Ik; Lee, Eun Ji; Seo, Young Il

2015-06-01

Hairtail ( Trichiurus lepturus) has been traditionally harvested by multi-gear types in the Yellow Sea and the East China Sea, except for the East Sea (Sea of Japan) in Korean waters. Six different fishery types such as offshore stownet fishery, offshore longline fishery, large pair-trawl fishery, large purse seine fishery, large otter trawl fishery and offshore angling fishery target to harvest the hairtail stock accounting for about 90% of the total annual catch. We attempted to develop an ecosystem-based fisheries assessment approach, which determines the optimal allocation of catch quotas and fishing efforts for major fisheries. We conducted standardization of fishing effort for six types of hairtail fisheries using a general linear model (GLM), and then estimated maximum sustainable yield (MSY) and maximum economic yield (MEY). Estimated MSY and MEY for the hairtail stock were estimated as 100,151 mt and 97,485 mt, respectively. In addition, we carried out an ecosystem-based risk analysis to obtain species risk index (SRI), which was applied to adjusting the optimal proportion of fishing effort for six hairtail fisheries as a penalty or an incentive. As a result, fishing effort ratios were adjusted by SRI for the six fisheries types. Also, the total allowable catch (TAC) was estimated as 97,485 mt and the maximum net profit at TAC by the hairtail fisheries was estimated as 778 billion won (USD 765 million).

Associations of borderline personality disorder traits with stressful events and emotional reactivity in women with bulimia nervosa.

PubMed

Pearson, Carolyn M; Lavender, Jason M; Cao, Li; Wonderlich, Stephen A; Crosby, Ross D; Engel, Scott G; Mitchell, James E; Peterson, Carol B; Crow, Scott J

2017-07-01

Borderline personality disorder (BPD) traits are common among those with bulimia nervosa (BN). However, how these traits impact the state experience of precipitants of BN behavior, such as stressful events and emotional reactivity, has not been determined. Thus, the purpose of this naturalistic study was to examine this trait-state association in BN. Women with DSM-IV BN (N = 133) completed a baseline measure of personality pathology traits, and subsequently recorded their affective state and the frequency and perception of 3 types of stressful events (interpersonal, work/environment, and daily hassles) several times per day for 2 weeks using ecological momentary assessment (EMA). Pearson correlations assessed the associations between BPD traits (affective lability, identity problems, insecure attachment, and cognitive dysregulation) and (a) frequency of stressful events and (b) perception of stressful events. Generalized linear models (GLM) were used to evaluate the relationship between BPD traits and changes in negative affect following stressful events. Results revealed that while all traits were significantly associated with perceived stressfulness, certain BPD traits were significantly associated with the frequency of stressful events. Individuals with higher trait insecure attachment experienced larger increases in negative affect following interpersonal stressful events. These findings suggest that interventions focused on addressing stressful events and enhancing adaptive emotional responses to interpersonal events may be particularly useful for a subset of individuals with BN with BPD-related personality characteristics, including insecure attachment, affective lability, and identity problems. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Decreased hypothalamus volumes in generalized anxiety disorder but not in panic disorder.

PubMed

Terlevic, Robert; Isola, Miriam; Ragogna, Maria; Meduri, Martina; Canalaz, Francesca; Perini, Laura; Rambaldelli, Gianluca; Travan, Luciana; Crivellato, Enrico; Tognin, Stefania; Como, Giuseppe; Zuiani, Chiara; Bazzocchi, Massimo; Balestrieri, Matteo; Brambilla, Paolo

2013-04-25

The hypothalamus is a brain structure involved in the neuroendocrine aspect of stress and anxiety. Evidence suggests that generalized anxiety disorder (GAD) and panic disorder (PD) might be accompanied by dysfunction of the hypothalamus-pituitary-adrenal axis (HPA), but so far structural alterations were not studied. We investigated hypothalamic volumes in patients with either GAD or PD and in healthy controls. Twelve GAD patients, 11 PD patients and 21 healthy controls underwent a 1.5T MRI scan. Hypothalamus volumes were manually traced by a rater blind to subjects' identity. General linear model for repeated measures (GLM-RM) was used to compare groups on hypothalamic volumes, controlling for total intracranial volume, age and sex. The hypothalamus volume was significantly reduced (p=0.04) in GAD patients, with significant reductions in both the left (p=0.02) and right side (p=0.04). Patients with PD did not differ significantly (p=0.73). Anxiety scores were inversely correlated with hypothalamic volumes. The small sample size could reduce the generalizability of the results while the lack of stress hormone measurements renders functional assessment of the hypothalamus-pituitary-adrenal axis not feasible. The present study showed decreased hypothalamic volumes in GAD patients but not in those with PD. Future longitudinal studies should combine volumetric data with measurements of stress hormones to better elucidate the role of the HPA axis in GAD. Copyright © 2012 Elsevier B.V. All rights reserved.

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

PubMed Central

Calhoun, Vince D.; Liu, Jingyu; Adalı, Tülay

2009-01-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain. PMID:19059344

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.

PubMed

Calhoun, Vince D; Liu, Jingyu; Adali, Tülay

2009-03-01

Independent component analysis (ICA) has become an increasingly utilized approach for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the data (e.g. the brain's response to stimuli), ICA, by relying upon a general assumption of independence, allows the user to be agnostic regarding the exact form of the response. In addition, ICA is intrinsically a multivariate approach, and hence each component provides a grouping of brain activity into regions that share the same response pattern thus providing a natural measure of functional connectivity. There are a wide variety of ICA approaches that have been proposed, in this paper we focus upon two distinct methods. The first part of this paper reviews the use of ICA for making group inferences from fMRI data. We provide an overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software. In the next part of this paper, we provide an overview of the use of ICA to combine or fuse multimodal data. ICA has proven particularly useful for data fusion of multiple tasks or data modalities such as single nucleotide polymorphism (SNP) data or event-related potentials. As demonstrated by a number of examples in this paper, ICA is a powerful and versatile data-driven approach for studying the brain.

Different roles of the posterior inferior frontal gyrus in Chinese character form judgment differences between literate and illiterate individuals.

PubMed

Wu, Jinglong; Wang, Bin; Yan, Tianyi; Li, Xiujun; Bao, Xuexiang; Guo, Qiyong

2012-01-11

In the present study, we used event-related functional magnetic resonance imaging (fMRI) to explore the different roles of the posterior inferior frontal gyrus (pIFG) in Chinese character form judgment between literate and illiterate subjects. Using event-related fMRI, 24 healthy right-handed Chinese subjects (12 literates and 12 illiterates) were asked to perform Chinese character and figure form judgment tasks. The blood oxygen level-dependent (BOLD) differences in pIFG were examined with general linear modeling (GLM). We found differences in reaction times and accuracy between subjects as they performed these tasks. These behavioral differences reflect the different cognitive demands of character form judgment for literate and illiterate individuals. The results showed differences in the BOLD response patterns in the pIFG between the two discrimination tasks and the two subject groups. A comparison of the character and figure tasks showed that literate and illiterate subjects had similar BOLD responses in the inferior frontal gyrus. However, differences in behavioral performance suggest that the pIFG plays a different role in Chinese character form judgment for each subject group. In literate subjects, the left pIFG mediated access to phonology in achieving Chinese character form judgment, whereas the right pIFG participated in the processing of the orthography of Chinese characters. In illiterate subjects, the bilateral frontal gyrus participated in the visual-spatial processing of Chinese characters to achieve form judgment. Copyright © 2011 Elsevier B.V. All rights reserved.

Emergency multiple sclerosis hospital admissions attributable to chemical and acoustic pollution: Madrid (Spain), 2001-2009.

PubMed

Carmona, Rocío; Linares, Cristina; Recio, Alberto; Ortiz, Cristina; Díaz, Julio

2018-01-15

Multiple sclerosis (MS) is the most prevalent neurological disease among young adults in Spain. A number of recent studies have linked traffic-related pollution, both chemical and acoustic, to the aetiology and exacerbation of neurodegenerative diseases. To analyse the existence of a significant short-term association between daily emergency MS hospital admissions and chemical and acoustic pollution caused by traffic in Madrid. We conducted a longitudinal ecological time series study, in which the dependent variable was the number of daily emergency MS hospital admissions (ICD-9: 340) registered in Madrid from 1 January 2001 to 31 December 2009. The independent variables were daily mean concentrations (μg/m 3 ) of PM 2.5 , PM 10 , O 3 and NO 2 . Equivalent diurnal (Leqd), nocturnal (Leqn) and daily equivalent noise levels (Leq24) were also considered. In addition, we controlled for linear trends, seasonality and the autoregressive nature of the series itself. Day of the week was also added as a covariate. Significant environmental variables were determined using Poisson GLM models. Relative risk (RR) and attributable risk (AR) values were calculated for increases of 10μg/m 3 in the case of chemical pollutants and 1dB(A) in noise levels. While there was no association between chemical pollutants caused by traffic and MS admissions, such an association was in evidence for Leqd at lag zero. This association is linear without a threshold, with there being a level above 67dB(A) from which this effect is more pronounced. The RRs were as follows: for all Leqd values, 1.21 (95% CI: 1.16, 1.26); and for Leqd >67dB(A), 1.62 (95% CI: 1.24, 2.13). The above results indicate that traffic noise can exacerbate MS symptoms, leading to hospital admissions due to this cause. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting small mammal and flea abundance using landform and soil properties in a plague endemic area in Lushoto District, Tanzania.

PubMed

Meliyo, Joel L; Kimaro, Didas N; Msanya, Balthazar M; Mulungu, Loth S; Hieronimo, Proches; Kihupi, Nganga I; Gulinck, Hubert; Deckers, Jozef A

2014-07-01

Small mammals particularly rodents, are considered the primary natural hosts of plague. Literature suggests that plague persistence in natural foci has a root cause in soils. The objective of this study was to investigate the relationship between on the one hand landforms and associated soil properties, and on the other hand small mammals and fleas in West Usambara Mountains in Tanzania, a plague endemic area. Standard field survey methods coupled with Geographical Information System (GIS) technique were used to examine landform and soils characteristics. Soil samples were analysed in the laboratory for physico-chemical properties. Small mammals were trapped on pre-established landform positions and identified to genus/species level. Fleas were removed from the trapped small mammals and counted. Exploration of landform and soil data was done using ArcGIS Toolbox functions and descriptive statistical analysis. The relationships between landforms, soils, small mammals and fleas were established by generalised linear regression model (GLM) operated in R statistics software. Results show that landforms and soils influence the abundance of small mammals and fleas and their spatial distribution. The abundance of small mammals and fleas increased with increase in elevation. Small mammal species richness also increases with elevation. A landform-soil model shows that available phosphorus, slope aspect and elevation were statistically significant predictors explaining richness and abundance of small mammals. Fleas' abundance and spatial distribution were influenced by hill-shade, available phosphorus and base saturation. The study suggests that landforms and soils have a strong influence on the richness and evenness of small mammals and their fleas' abundance hence could be used to explain plague dynamics in the area.

Geographic consistency and variation in conflicting selection generated by pollinators and seed predators.

PubMed

Sun, Shi-Guo; Armbruster, W Scott; Huang, Shuang-Quan

2016-08-01

Floral traits that attract pollinators may also attract seed predators, which, in turn, may generate conflicting natural selection on such traits. Although such selection trade-offs are expected to vary geographically, few studies have investigated selection mediated by pollinators and seed predators across a geographic mosaic of environments and floral variation. Floral traits were investigated in 14 populations of the bumble-bee-pollinated herb, Pedicularis rex, in which tubular flowers are subtended by cupular bracts holding rain water. To study potentially conflicting selection on floral traits generated by pollinators and florivores, stigmatic pollen loads, initial seed set, pre-dispersal seed predation and final viable seed production were measured in 12-14 populations in the field. Generalized Linear Model (GLM) analyses indicated that the pollen load on stigmas was positively related to the exsertion of the corolla beyond the cupular bracts and size of the lower corolla lip, but so too was the rate of seed predation, creating conflicting selection on both floral traits. A geographic mosaic of selection mediated by seed predators, but not pollinators, was indicated by significant variation in levels of seed predation and the inclusion of two-, three- and four-way interaction terms between population and seed predation in the best model [lowest corrected Akaike Information Criterion (AICc)] explaining final seed production. These results indicate opposing selection in operation: pollinators generated selection for greater floral exsertion beyond the bracts, but seed predators generated selection for reduced exsertion above the protective pools of water, although the strength of the latter varied across populations. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI.

PubMed

Huang, Huiyuan; Ding, Zhongxiang; Mao, Dewang; Yuan, Jianhua; Zhu, Fangmei; Chen, Shuda; Xu, Yan; Lou, Lin; Feng, Xiaoyan; Qi, Le; Qiu, Wusi; Zhang, Han; Zang, Yu-Feng

2016-10-01

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning.

PubMed

Hao, Yongfu; Khoo, Hui Ming; von Ellenrieder, Nicolas; Zazubovits, Natalja; Gotman, Jean

2018-01-01

Presurgical evaluation that can precisely delineate the epileptogenic zone (EZ) is one important step for successful surgical resection treatment of refractory epilepsy patients. The noninvasive EEG-fMRI recording technique combined with general linear model (GLM) analysis is considered an important tool for estimating the EZ. However, the manual marking of interictal epileptic discharges (IEDs) needed in this analysis is challenging and time-consuming because the quality of the EEG recorded inside the scanner is greatly deteriorated compared to the usual EEG obtained outside the scanner. This is one of main impediments to the widespread use of EEG-fMRI in epilepsy. We propose a deep learning based semi-automatic IED detector that can find the candidate IEDs in the EEG recorded inside the scanner which resemble sample IEDs marked in the EEG recorded outside the scanner. The manual marking burden is greatly reduced as the expert need only edit candidate IEDs. The model is trained on data from 30 patients. Validation of IEDs detection accuracy on another 37 consecutive patients shows our method can improve the median sensitivity from 50.0% for the previously proposed template-based method to 84.2%, with false positive rate as 5 events/min. Reproducibility validation on 15 patients is applied to evaluate if our method can produce similar hemodynamic response maps compared with the manual marking ground truth results. We explore the concordance between the maximum hemodynamic response and the intracerebral EEG defined EZ and find that both methods produce similar percentage of concordance (76.9%, 10 out of 13 patients, electrode was absent in the maximum hemodynamic response in two patients). This tool will make EEG-fMRI analysis more practical for clinical usage.

Hindlimb muscle architecture in non-human great apes and a comparison of methods for analysing inter-species variation

PubMed Central

Myatt, Julia P; Crompton, Robin H; Thorpe, Susannah K S

2011-01-01

By relating an animal's morphology to its functional role and the behaviours performed, we can further develop our understanding of the selective factors and constraints acting on the adaptations of great apes. Comparison of muscle architecture between different ape species, however, is difficult because only small sample sizes are ever available. Further, such samples are often comprised of different age–sex classes, so studies have to rely on scaling techniques to remove body mass differences. However, the reliability of such scaling techniques has been questioned. As datasets increase in size, more reliable statistical analysis may eventually become possible. Here we employ geometric and allometric scaling techniques, and ancovas (a form of general linear model, GLM) to highlight and explore the different methods available for comparing functional morphology in the non-human great apes. Our results underline the importance of regressing data against a suitable body size variable to ascertain the relationship (geometric or allometric) and of choosing appropriate exponents by which to scale data. ancova models, while likely to be more robust than scaling for species comparisons when sample sizes are high, suffer from reduced power when sample sizes are low. Therefore, until sample sizes are radically increased it is preferable to include scaling analyses along with ancovas in data exploration. Overall, the results obtained from the different methods show little significant variation, whether in muscle belly mass, fascicle length or physiological cross-sectional area between the different species. This may reflect relatively close evolutionary relationships of the non-human great apes; a universal influence on morphology of generalised orthograde locomotor behaviours or, quite likely, both. PMID:21507000

Detecting the subtle shape differences in hemodynamic responses at the group level

PubMed Central

Chen, Gang; Saad, Ziad S.; Adleman, Nancy E.; Leibenluft, Ellen; Cox, Robert W.

2015-01-01

The nature of the hemodynamic response (HDR) is still not fully understood due to the multifaceted processes involved. Aside from the overall amplitude, the response may vary across cognitive states, tasks, brain regions, and subjects with respect to characteristics such as rise and fall speed, peak duration, undershoot shape, and overall duration. Here we demonstrate that the fixed-shape (FSM) or adjusted-shape (ASM) methods may fail to detect some shape subtleties (e.g., speed of rise or recovery, or undershoot). In contrast, the estimated-shape method (ESM) through multiple basis functions can provide the opportunity to identify some subtle shape differences and achieve higher statistical power at both individual and group levels. Previously, some dimension reduction approaches focused on the peak magnitude, or made inferences based on the area under the curve (AUC) or interaction, which can lead to potential misidentifications. By adopting a generic framework of multivariate modeling (MVM), we showcase a hybrid approach that is validated by simulations and real data. With the whole HDR shape integrity maintained as input at the group level, the approach allows the investigator to substantiate these more nuanced effects through the unique HDR shape features. Unlike the few analyses that were limited to main effect, two- or three-way interactions, we extend the modeling approach to an inclusive platform that is more adaptable than the conventional GLM. With multiple effect estimates from ESM for each condition, linear mixed-effects (LME) modeling should be used at the group level when there is only one group of subjects without any other explanatory variables. Under other situations, an approximate approach through dimension reduction within the MVM framework can be adopted to achieve a practical equipoise among representation, false positive control, statistical power, and modeling flexibility. The associated program 3dMVM is publicly available as part of the AFNI suite. PMID:26578853

Testing geographical and climatic controls on glacier retreat

NASA Astrophysics Data System (ADS)

Freudiger, Daphné; Stahl, Kerstin; Weiler, Markus

2015-04-01

Glacier melt provides an important part of the summer discharge in many mountainous basins. The understanding of the processes behind the glacier mass losses and glacier retreats observed during the last century is therefore relevant for a sustainable management of the water resources and reliable models for the prediction of future changes. The changes in glacier area of 49 sub-basins of the Rhine River in the Alps were analyzed for the time period 1900-2010 by comparing the glacier areas of Siegfried maps for the years 1900 and 1940 with satellite derived glacier areas for the years 1973, 2003 and 2010. The aim was to empirically investigate the controls of glacier retreat and its regional differences. All glaciers in the glacierized basins retreated over the last 110 years with some variations in the sub-periods. However, the relative changes in glacier area compared to 1900 differed for every sub-basin and some glaciers decreased much faster than others. These observed differences were related to a variety of different potential controls derived from different sources, including mean annual solar radiation on the glacier surface, average slope, mean glacier elevation, initial glacier area, average precipitation (summer and winter), and the precipitation catchment area of the glacier. We fitted a generalized linear model (GLM) and selected predictors that were significant to assess the individual effects of the potential controls. The fitted model explains more than 60% of the observed variance of the relative change in glacier area with the initial area alone only explaining a small proportion. Some interesting patterns emerge with higher average elevation resulting in higher area changes, but steeper slopes or solar radiation resulting in lower relative glacier area changes. Further controls that will be tested include snow transport by wind or avalanches as they play an important role for the glacier mass balance and potentially reduce the changes in glacier area. The derived predictors will be further analyzed and the observed general patterns will be compared to modeling studies of glacier changes.

Evaluation of a community-based sex offender treatment program using a good lives model approach.

PubMed

Harkins, Leigh; Flak, Vanja E; Beech, Anthony R; Woodhams, Jessica

2012-12-01

In this study the authors assessed a Good Lives model (GLM) approach to sex offender treatment and compare it to a standard Relapse Prevention program. The comparisons examined (a) attrition rates, (b) treatment change in areas targeted in treatment and achievement of a posttreatment treated profile, and (c) views of offenders and facilitators. There were no differences in the attrition rates or the rates of treatment change between the two programs, indicating that they were equally effective at retaining participants and achieving change on areas targeted within treatment. Both facilitators and program participants reported the Good Lives approach module's impact in a positive, future-focused manner. In contrast, those who attended the Relapse Prevention module did not report their perceptions and motivations in a manner that was focused on the positives in their future as frequently as those who attended the module with the Good Lives model approach.

Flash-Type Discrimination

NASA Technical Reports Server (NTRS)

Koshak, William J.

2010-01-01

This viewgraph presentation describes the significant progress made in the flash-type discrimination algorithm development. The contents include: 1) Highlights of Progress for GLM-R3 Flash-Type discrimination Algorithm Development; 2) Maximum Group Area (MGA) Data; 3) Retrieval Errors from Simulations; and 4) Preliminary Global-scale Retrieval.

Cystic Neutrophilic Granulomatous Mastitis: Further Characterization of a Distinctive Histopathologic Entity Not Always Demonstrably Attributable to Corynebacterium Infection.

PubMed

D'Alfonso, Timothy M; Moo, Tracy-Ann; Arleo, Elizabeth K; Cheng, Esther; Antonio, Lilian B; Hoda, Syed A

2015-10-01

Granulomatous lobular mastitis (GLM) is an uncommon condition that typically occurs in parous, reproductive-aged women and can simulate malignancy on the basis of clinical and imaging features. A distinctive histologic pattern termed cystic neutrophilic granulomatous mastitis (CNGM) is seen in some cases of GLM and has been associated with Corynebacterium infection. We sought to further characterize the clinical, imaging, and histopathologic features of CNGM by studying 12 cases and attempted to establish the relationship of this disease with Corynebacterium infection. Patients were women ranging in age from 25 to 49 years (median: 34 y), and all presented with a palpable mass that was painful in half of the cases. In 2 of 9 cases, imaging was highly suspicious for malignancy (BI-RADS 5). CNGM was characterized by lobulocentric granulomas with mixed inflammation and clear vacuoles lined by neutrophils within granulomas. Gram-positive bacilli were identified in 5/12 cases. In 4 patients, the disease process worsened after the diagnostic core biopsy, with the development of a draining sinus in 2 cases. No growth of bacteria was seen in any microbial cultures. No bacterial DNA was identified by 16S rDNA polymerase chain reaction for 1 case that showed gram-positive bacilli on histology. Patients were treated with variable combinations of surgery, antibiotics, and steroids. The time to significant resolution of symptoms ranged from 2 weeks to 6 months. Similar to other forms of GLM, CNGM can mimic malignancy clinically and on imaging. When encountered in a needle core biopsy sample, recognition of the characteristic histologic pattern and its possible association with Corynebacterium infection can help guide treatment.

Mur Ligase Inhibitors as Anti-bacterials: A Comprehensive Review.

PubMed

Sangshetti, Jaiprakash N; Joshi, Suyog S; Patil, Rajendra H; Moloney, Mark G; Shinde, Devanand B

2017-01-01

Exploring a new target for antibacterial drug discovery has gained much attention because of the emergence of Multidrug Resistance (MDR) strains of bacteria. To overcome this problem the development of novel antibacterial was considered as highest priority task and was one of the biggest challenge since multiple factors were involved. The bacterial peptidoglycan biosynthetic pathway has been well documented in the last few years and has been found to be imperative source for the development of novel antibacterial agents with high target specificity as they are essential for bacterial survival and have no homologs in humans. We have therefore reviewed the process of peptidoglycan biosynthesis which involves various steps like formation of UDP-Nacetylglucosamine (GlcNAc), UDP-N-acetylmuramic acid (MurNAc) and lipid intermediates (Lipid I and Lipid II) which are controlled by various enzymes like GlmS, GlmM, GlmU enzyme, followed by Mur Ligases (MurAMurF) and finally by MraY and MurG respectively. These four amide ligases MurC-MurF can be used as the source for the development of novel multi-target antibacterial agents as they shared and conserved amino acid regions, catalytic mechanisms and structural features. This review begins with the need for novel antibacterial agents and challenges in their development even after the development of bacterial genomic studies. An overview of the peptidoglycan monomer formation, as a source of disparity in this process is presented, followed by detailed discussion of structural and functional aspects of all Mur enzymes and different chemical classes of their inhibitors along with their SAR studies and inhibitory potential. This review finally emphasizes on different patents and novel Mur inhibitors in the development phase. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

An In Depth Look at Lightning Trends in Hurricane Harvey using Satellite and Ground-Based Measurements

NASA Astrophysics Data System (ADS)

Ringhausen, J.

2017-12-01

This research combines satellite measurements of lightning in Hurricane Harvey with ground-based lightning measurements to get a better sense of the total lightning occurring in the hurricane, both intra-cloud (IC) and cloud-to-ground (CG), and how it relates to the intensification and weakening of the tropical system. Past studies have looked at lightning trends in hurricanes using the space based Lightning Imaging Sensor (LIS) or ground-based lightning detection networks. However, both of these methods have drawbacks. For instance, LIS was in low earth orbit, which limited lightning observations to 90 seconds for a particular point on the ground; hence, continuous lightning coverage of a hurricane was not possible. Ground-based networks can have a decreased detection efficiency, particularly for ICs, over oceans where hurricanes generally intensify. With the launch of the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite, researchers can study total lightning continuously over the lifetime of a tropical cyclone. This study utilizes GLM to investigate total lightning activity in Hurricane Harvey temporally; this is augmented with spatial analysis relative to hurricane structure, similar to previous studies. Further, GLM and ground-based network data are combined using Bayesian techniques in a new manner to leverage the strengths of each detection method. This methodology 1) provides a more complete estimate of lightning activity and 2) enables the derivation of the IC:CG ratio (Z-ratio) throughout the time period of the study. In particular, details of the evolution of the Z-ratio in time and space are presented. In addition, lightning stroke spatiotemporal trends are compared to lightning flash trends. This research represents a new application of lightning data that can be used in future study of tropical cyclone intensification and weakening.

Initial Navigation Alignment of Optical Instruments on GOES-R

NASA Technical Reports Server (NTRS)

Isaacson, Peter J.; DeLuccia, Frank J.; Reth, Alan D.; Igli, David A.; Carter, Delano R.

2016-01-01

Post-launch alignment errors for the Advanced Baseline Imager (ABI) and Geospatial Lightning Mapper (GLM) on GOES-R may be too large for the image navigation and registration (INR) processing algorithms to function without an initial adjustment to calibration parameters. We present an approach that leverages a combination of user-selected image-to-image tie points and image correlation algorithms to estimate this initial launch-induced offset and calculate adjustments to the Line of Sight Motion Compensation (LMC) parameters. We also present an approach to generate synthetic test images, to which shifts and rotations of known magnitude are applied. Results of applying the initial alignment tools to a subset of these synthetic test images are presented. The results for both ABI and GLM are within the specifications established for these tools, and indicate that application of these tools during the post-launch test (PLT) phase of GOES-R operations will enable the automated INR algorithms for both instruments to function as intended.

The GlcN6P cofactor serves multiple catalytic roles in the glmS ribozyme

PubMed Central

Bingaman, Jamie L.; Zhang, Sixue; Stevens, David R.; Yennawar, Neela H.; Hammes-Schiffer, Sharon; Bevilacqua, Philip C.

2017-01-01

RNA enzymes have remarkably diverse biological roles despite having limited chemical diversity. Protein enzymes enhance their reactivity through recruitment of cofactors. The naturally occurring glmS ribozyme uses the glucosamine-6-phosphate (GlcN6P) organic cofactor for phosphodiester bond cleavage. Prior structural and biochemical studies implicated GlcN6P as the general acid. Here we describe new catalytic roles for GlcN6P through experiments and calculations. Large stereospecific normal thio effects and lack of metal ion rescue in the holoribozyme show that nucleobases and the cofactor play direct chemical roles and align the active site for self-cleavage. Large stereospecific inverse thio effects in the aporibozyme suggest that the GlcN6P cofactor disrupts an inhibitory interaction of the nucleophile. Strong metal ion rescue in the aporibozyme reveals this cofactor also provides electrostatic stabilization. Ribozyme organic cofactors thus perform myriad catalytic roles, allowing RNA to compensate for its limited functional diversity. PMID:28192411

Air Force Materiel Command: A Survey of Performance Measures

DTIC Science & Technology

2004-03-12

AIR FORCE MATERIEL COMMAND: A SURVEY OF PERFORMANCE MEASURES THESIS Marcia Leonard, Capt...AFIT/GLM/ENS/04-10 AIR FORCE MATERIEL COMMAND: A SURVEY OF PERFORMANCE MEASURES THESIS Presented to the Faculty...SURVEY OF PERFORMANCE MEASURES Marcia Leonard, BS Capt, USAF Approved: //signed// 12 March 2004

Decoding negative affect personality trait from patterns of brain activation to threat stimuli.

PubMed

Fernandes, Orlando; Portugal, Liana C L; Alves, Rita de Cássia S; Arruda-Sanchez, Tiago; Rao, Anil; Volchan, Eliane; Pereira, Mirtes; Oliveira, Letícia; Mourao-Miranda, Janaina

2017-01-15

Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Relating structural growth environment to white spruce sapling establishment at the Forest-Tundra Ecotone

NASA Astrophysics Data System (ADS)

Maguire, A.; Boelman, N.; Griffin, K. L.; Jensen, J.; Hiers, E.; Johnson, D. M.; Vierling, L. A.; Eitel, J.

2017-12-01

The effect of climate change on treeline position at the latitudinal Forest-Tundra ecotone (FTE) is poorly understood. While the FTE is expansive (stretching 13,000 km acros the panarctic), understanding relationships between climate and tree function may depend on very fine scale processes. High resolution tools are therefore needed to appropriately characterize the leading (northernmost) edge of the FTE. We hypothesized that microstructural metrics obtainable from lidar remote sensing may explain variation in the physical growth environment that governs sapling establishment. To test our hypothesis, we used terrestrial laser scanning (TLS) to collect highly spatially resolved 3-D structural information of white spruce (Picea glauca) saplings and their aboveground growth environment at the leading edge of a FTE in northern Alaska and Northwest Territories, Canada. Coordinates of sapling locations were extracted from the 3-D TLS data. Within each sampling plot, 20 sets of coordinates were randomly selected from regions where no saplings were present. Ground roughness, canopy roughness, average aspect, average slope, average curvature, wind shelter index, and wetness indexwere extracted from point clouds within a variable radius from all coordinates. Generalized linear models (GLM) were fit to determine which microstructural metrics were most strongly associated with sapling establishment. Preliminary analyses of three plots suggest that vegetation roughness, wetness index, ground roughness, and slope were the most important terrain metrics governing sapling presence (Figure 1). Comprehensive analyses will include eight plots and GLMs optimized for scale at which structural parameters affect sapling establishment. Spatial autocorrelation of sample locations will be accounted for in models. Because these analyses address how the physical growth environment affects sapling establishment, model outputs will provide information for improving understanding of the ecological processes that regulate treeline dynamics. Moreover, establishing relationships between the remotely sensed structural growth environment and tree establishment provides new ways of spatially scaling across larger areas to study ecological change at the FTE.

The role of Ixodes scapularis, Borrelia burgdorferi and wildlife hosts in Lyme disease prevalence: A quantitative review.

PubMed

Halsey, Samniqueka J; Allan, Brian F; Miller, James R

2018-04-16

Due to the ongoing expansion of Ixodes scapularis (blacklegged tick) throughout the northeastern and midwestern United States, there is need to identify the role wildlife hosts play in the establishment and maintenance of tick populations. To quantify and synthesize the patterns of I. scapularis and Borrelia burgdorferi sensu stricto and sensu lato prevalence relative to wildlife hosts, we reviewed the findings of independent studies conducted throughout the United States. We performed a comprehensive literature search from 1970 to 2017 using the ISS Web of Science Core Collection and the keywords "Ixodes scapularis," "Ixodes dammini" and "Borrelia burgdorferi." We identified 116 studies for inclusion in our meta-analysis, with 187,414 individual wildlife hosts captured and examined for I. scapularis and either the host or ticks collected subsequently tested for B. burgdorferi. We found that only 13% of the wildlife mammals sampled comprised species other than Odocoileus virginianus (white-tailed deer) and Peromyscus leucopus (white-footed mouse). To examine whether there were regional differences between the Northeast, Midwest and the Southeast U.S. in I. scapularis infestation rates on wildlife hosts, we used general linear models (glm), with post hoc pairwise comparisons. In most cases, detection of I. scapularis and B. burgdorferi was significantly higher in the Northeast than the Midwest. Using data on host-specific I. scapularis infestation prevalence, B. burgdorferi prevalence in feeding larvae, and host permissiveness, we developed an epizootiological model to determine the relative contributions of individual hosts to B. burgdorferi-infected nymphs. Our model provides additional evidence that wildlife hosts other than P. leucopus may contribute more to Lyme disease risk than commonly thought. To aid in understanding the ecology of Lyme disease, we propose that additional studies sample non-Peromyscus spp. hosts to obtain more detailed tick and pathogen infestation and infection estimates, respectively, for these less frequently sampled wildlife hosts. Copyright © 2018 Elsevier GmbH. All rights reserved.

Human thermal bioclimatic conditions associated with acute cardiovascular syndromes in Crete Island, Greece

NASA Astrophysics Data System (ADS)

Bleta, Anastasia G.; Nastos, Panagiotis T.

2013-04-01

The aim of this study is to quantify the association between bioclimatic conditions and daily counts of admissions for non-fatal acute cardiovascular (acute coronary syndrome, arrhythmia, decompensation of heart failure) syndromes (ACS) registered by the two main hospitals in Heraklion, Crete Island, during a five-year period 2008-2012. The bioclimatic conditions analyzed are based on human thermal bioclimatic indices such as the Physiological Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI). Mean daily meteorological parameters, such as air temperature, relative humidity, wind speed and cloudiness, were acquired from the meteorological station of Heraklion (Hellenic National Meteorological Service). These parameters were used as input variables in modeling the aforementioned thermal indices, in order to interpret the grade of the thermo-physiological stress. The PET and UTCI analysis was performed by the use of the radiation and bioclimate model, "RayMan", which is well-suited to calculate radiation fluxes and human biometeorological indices. Generalized linear models (GLM) were applied to time series of daily numbers of outpatients with ACS against bioclimatic variations, after controlling for possible confounders and adjustment for season and trends. The interpretation of the results of this analysis suggests a significant association between cold weather and increased coronary heart disease incidence, especially in the elderly and males. Additionally, heat stress plays an important role in the configuration of daily ACS outpatients, even in temperate climate, as that in Crete Island. In this point it is worth mentioning that Crete Island is frequently affected by Saharan outbreaks, which are associated in many cases with miscellaneous phenomena, such as Föhn winds - hot and dry winds - causing extreme bioclimatic conditions (strong heat stress). Taking into consideration the projected increased ambient temperature in the future, ACS exacerbation is very likely to happen during the warm period, against mitigation during the cold period of the year.

Electromagnetic Inverse Methods and Applications for Inhomogeneous Media Probing and Synthesis.

NASA Astrophysics Data System (ADS)

Xia, Jake Jiqing

The electromagnetic inverse scattering problems concerned in this thesis are to find unknown inhomogeneous permittivity and conductivity profiles in a medium from the scattering data. Both analytical and numerical methods are studied in the thesis. The inverse methods can be applied to geophysical medium probing, non-destructive testing, medical imaging, optical waveguide synthesis and material characterization. An introduction is given in Chapter 1. The first part of the thesis presents inhomogeneous media probing. The Riccati equation approach is discussed in Chapter 2 for a one-dimensional planar profile inversion problem. Two types of the Riccati equations are derived and distinguished. New renormalized formulae based inverting one specific type of the Riccati equation are derived. Relations between the inverse methods of Green's function, the Riccati equation and the Gel'fand-Levitan-Marchenko (GLM) theory are studied. In Chapter 3, the renormalized source-type integral equation (STIE) approach is formulated for inversion of cylindrically inhomogeneous permittivity and conductivity profiles. The advantages of the renormalized STIE approach are demonstrated in numerical examples. The cylindrical profile inversion problem has an application for borehole inversion. In Chapter 4 the renormalized STIE approach is extended to a planar case where the two background media are different. Numerical results have shown fast convergence. This formulation is applied to inversion of the underground soil moisture profiles in remote sensing. The second part of the thesis presents the synthesis problem of inhomogeneous dielectric waveguides using the electromagnetic inverse methods. As a particular example, the rational function representation of reflection coefficients in the GLM theory is used. The GLM method is reviewed in Chapter 5. Relations between modal structures and transverse reflection coefficients of an inhomogeneous medium are established in Chapter 6. A stratified medium model is used to derive the guidance condition and the reflection coefficient. Results obtained in Chapter 6 provide the physical foundation for applying the inverse methods for the waveguide design problem. In Chapter 7, a global guidance condition for continuously varying medium is derived using the Riccati equation. It is further shown that the discrete modes in an inhomogeneous medium have the same wave vectors as the poles of the transverse reflection coefficient. An example of synthesizing an inhomogeneous dielectric waveguide using a rational reflection coefficient is presented. A summary of the thesis is given in Chapter 8. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.).

Interactive Effects of Early Exclusive Breastfeeding and Pre-Pregnancy Maternal Weight Status on Young Children’s BMI – A Chinese Birth Cohort

PubMed Central

Yin, Baomin; Liang, Xiong; Adair, Linda; Thompson, Amanda; Zhang, Jianduan

2015-01-01

Objectives To assess if the maternal pre-pregnancy weight status (MPWS) alters the association of early infant feeding pattern (at one and third months) with infant body mass index (BMI) in the first two years of life. Methods A cohort of 2,220 neonates were recruited in a community-based study conducted in China. Body weight and length were measured at birth, at age one and two, with BMI calculated accordingly. The BMI z-scores (BMI-Z) were computed according to the World Health Organization Growth Standard (2006). Feeding patterns were classified as exclusive breastfeeding (EBF), mixed feeding (MF), and formula feeding (FF). General linear models (GLM) were employed to estimate main and interaction effects of EBF and MPWS on children’s BMI-Z. Results No main effect of MPWS was found on child BMI-Z at ages one and two, nor the feeding patterns. An interaction between MPWS and feeding patterns was detected (p<0.05). For children who were formula fed during the first month, those who were born to overweight/obesity (OW/OB) mothers had a significantly greater BMI-Z at ages one and two, compared with those with underweight/normal weight (UW/NW) mothers. FF children had greater BMI-Z at ages one and two compared with their EBF and MF counterparts, when they were born to OW/OB mothers. Conclusions Maternal pre-pregnancy weight control and early initiation of EBF for children are essential for healthy development in children’s BMI, hence the prevention of early life obesity. PMID:26641272

Impacts of fertilization on water quality of a drained pine plantation: a worst case scenario.

PubMed

Beltran, Bray J; Amatya, Devendra M; Youssef, Mohamed; Jones, Martin; Callahan, Timothy J; Skaggs, R Wayne; Nettles, Jami E

2010-01-01

Intensive plantation forestry will be increasingly important in the next 50 yr to meet the high demand for domestic wood in the United States. However, forest management practices can substantially influence downstream water quality and ecology. This study analyses, the effect of fertilization on effluent water quality of a low gradient drained coastal pine plantation in Carteret County, North Carolina using a paired watershed approach. The plantation consists of three watersheds, two mature (31-yr) and one young (8-yr) (age at treatment). One of the mature watersheds was commercially thinned in 2002. The mature unthinned watershed was designated as the control. The young and mature-thinned watersheds were fertilized at different rates with Arborite (Encee Chemical Sales, Inc., Bridgeton, NC), and boron. The outflow rates and nutrient concentrations in water drained from each of the watersheds were measured. Nutrient concentrations and loadings were analyzed using general linear models (GLM). Three large storm events occurred within 47 d of fertilization, which provided a worst case scenario for nutrient export from these watersheds to the receiving surface waters. Results showed that average nutrient concentrations soon after fertilization were significantly (alpha = 0.05) higher on both treatment watersheds than during any other period during the study. This increase in nutrient export was short lived and nutrient concentrations and loadings were back to prefertilization levels as soon as 3 mo after fertilization. Additionally, the mature-thinned watershed presented higher average nutrient concentrations and loadings when compared to the young watershed, which received a reduced fertilizer rate than the mature-thinned watershed.

Applying a new unequally weighted feature fusion method to improve CAD performance of classifying breast lesions

NASA Astrophysics Data System (ADS)

Zargari Khuzani, Abolfazl; Danala, Gopichandh; Heidari, Morteza; Du, Yue; Mashhadi, Najmeh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Higher recall rates are a major challenge in mammography screening. Thus, developing computer-aided diagnosis (CAD) scheme to classify between malignant and benign breast lesions can play an important role to improve efficacy of mammography screening. Objective of this study is to develop and test a unique image feature fusion framework to improve performance in classifying suspicious mass-like breast lesions depicting on mammograms. The image dataset consists of 302 suspicious masses detected on both craniocaudal and mediolateral-oblique view images. Amongst them, 151 were malignant and 151 were benign. The study consists of following 3 image processing and feature analysis steps. First, an adaptive region growing segmentation algorithm was used to automatically segment mass regions. Second, a set of 70 image features related to spatial and frequency characteristics of mass regions were initially computed. Third, a generalized linear regression model (GLM) based machine learning classifier combined with a bat optimization algorithm was used to optimally fuse the selected image features based on predefined assessment performance index. An area under ROC curve (AUC) with was used as a performance assessment index. Applying CAD scheme to the testing dataset, AUC was 0.75+/-0.04, which was significantly higher than using a single best feature (AUC=0.69+/-0.05) or the classifier with equally weighted features (AUC=0.73+/-0.05). This study demonstrated that comparing to the conventional equal-weighted approach, using an unequal-weighted feature fusion approach had potential to significantly improve accuracy in classifying between malignant and benign breast masses.

Marker traits association of agronomical traits correlated with stagnant flooding tolerance in rice

NASA Astrophysics Data System (ADS)

Sitaresmi, T.; Utami, D. W.; Suwarno, W. B.; Ardie, S. W.; Susanto, U.; Aswidinnoor, H.

2017-05-01

In deep-water areas, the water depth increases gradually throughout the year and maintains up to more than 50 cm of deep of water for long period. In these situations, elongation ability is necessary to allow the plants to keep up with rising floodwater. The elongation of internode during submergence is regulated by environmental and hormonal factors. The objective of this study was aimed to identify the SNP markers on 384 SNPs linked with agronomical and morphological traits related to stagnant flooding tolerance. The research were conducted at Indonesian Center for Rice Research and Indonesian Centre for Agricultural Biotechnology and Genetic Resources Research and Development. The phenotypical data was collected from F2 from bi-parental crossing of IR 42 and IRRI 119. IR 42 was sensitive parent, and IRRI 119 was tolerant. DNA extraction for rice was using a modified version of Murray and Thompson method using cetyl tri-methyl-ammonium bromide (CTAB). The genotyping was carried out using 384 SNPs Golden Gate Illumina assay. Association analysis between SNP markers and phenotypical data was performed using General Linear Model in Tassel versus 5.0 software program. Based on GLM analysis, the significant marker for plant height with P value < 0.05 are TBGI275345, TBGI275367, and TBGI424383. The significant marker for number of tiller are TBGI000722, TBGI258600, TBGI270843, TBGI271066, TBGI271076, TBGI272122, TBGI272241, and TBGI327790. Two of them, TBGI424383 and TBGI271066 were expected associated with family of protein kinase which play role in plant stress signalling.

Following The Trail: Factors Underlying the Sudden Expansion of the Egyptian Mongoose (Herpestes ichneumon) in Portugal

PubMed Central

Barros, Tânia; Carvalho, João; Pereira, Maria João Ramos; Ferreira, Joaquim P.; Fonseca, Carlos

2015-01-01

Species range-limits are influenced by a combination of several factors. In our study we aimed to unveil the drivers underlying the expansion of the Egyptian mongoose in Portugal, a carnivore that was confined to southern Portugal and largely increased its range during the last three decades. We evaluated the expansion of the species in three periods (1980-1990, 1990-2000 and 2000-2010), by projecting the presence/absence data of the species in each temporal range and proposed four hypotheses to explain this sudden expansion associated to changes in the barrier effects of human infrastructure and topographic features, and in the availability of suitable areas due to climate change or land use. An exploratory analysis was made using Spearman rank correlation, followed by a hierarchical partitioning analysis to select uncorrelated potential explanatory variables associated with the different hypotheses. We then ran Generalized Linear Models (GLM) for every period for each hypothesis and for every combination of hypotheses. Our main findings suggest that dynamic transitions of land-use coupled with temperature and rainfall variations over the decades are the main drivers promoting the mongoose expansion. The geographic barriers and the human infrastructures functioned as barriers for mongoose expansion and have shaped its distribution. The expansion of the Egyptian mongoose across the Portuguese territory was due to a variety of factors. Our results suggest a rapid shift in species range in response to land-use and climate changes, underlining the close link between species ranges and a changing environment. PMID:26266939

Following the trail: factors underlying the sudden expansion of the Egyptian mongoose (Herpestes ichneumon) in Portugal.

PubMed

Barros, Tânia; Carvalho, João; Pereira, Maria João Ramos; Ferreira, Joaquim P; Fonseca, Carlos

2015-01-01

Species range-limits are influenced by a combination of several factors. In our study we aimed to unveil the drivers underlying the expansion of the Egyptian mongoose in Portugal, a carnivore that was confined to southern Portugal and largely increased its range during the last three decades. We evaluated the expansion of the species in three periods (1980-1990, 1990-2000 and 2000-2010), by projecting the presence/absence data of the species in each temporal range and proposed four hypotheses to explain this sudden expansion associated to changes in the barrier effects of human infrastructure and topographic features, and in the availability of suitable areas due to climate change or land use. An exploratory analysis was made using Spearman rank correlation, followed by a hierarchical partitioning analysis to select uncorrelated potential explanatory variables associated with the different hypotheses. We then ran Generalized Linear Models (GLM) for every period for each hypothesis and for every combination of hypotheses. Our main findings suggest that dynamic transitions of land-use coupled with temperature and rainfall variations over the decades are the main drivers promoting the mongoose expansion. The geographic barriers and the human infrastructures functioned as barriers for mongoose expansion and have shaped its distribution. The expansion of the Egyptian mongoose across the Portuguese territory was due to a variety of factors. Our results suggest a rapid shift in species range in response to land-use and climate changes, underlining the close link between species ranges and a changing environment.

Gastro-intestinal parasite infections of Ankole cattle in an unhealthy landscape: An assessment of ecological predictors.

PubMed

Sun, Ping; Wronski, Torsten; Bariyanga, Jean D; Apio, Ann

2018-03-15

The distribution of gastro-intestinal (GI) parasites across landscapes is closely related to the spatial distribution of hosts. In GI parasites with environmental life stages, the vitality of parasites is also affected by ecological and landscape-related components of the environment. This is particularly relevant for domestic livestock species that are often kept across habitats with varying degrees of degradation, exposing them to a wide range of environmentally robust parasite species. In our study, we examined the effect of environmental and anthropogenic factors on the prevalence and intensity of GI parasites across a free-ranging stock of Ankole cattle in the Mutara rangelands of northeastern Rwanda. Prevalence and intensity of each parasite type (i.e., strongyle-type nematodes, Strongyloides spp., Moniezia spp., and Eimeria spp.) were used as dependent variables. Two fixed factors related to season and conservation-political history, together with three principal components (condensed from nine ecological variables) were used as independent covariates in a univariate General Linear Model (GLM). Major effects on the prevalence and intensity of strongyle-type nematodes and on the intensity of Eimeria spp. were found in that vegetation-related effects such as above-ground grass biomass in conjunction with a high degree of soil compaction had a negative relationship with these parasite types. These unexpected findings suggest that strongyle-type and coccidian infections increase with increasing rangeland degradation. Strongyle-type nematode prevalence and intensity were also negatively related to goat/sheep density, indicating a 'dilution effect' of GI infections between domestic livestock species. Copyright © 2018 Elsevier B.V. All rights reserved.

Frequency of Internet addiction and development of social skills in adolescents in an urban area of Lima.

PubMed

Zegarra Zamalloa, Carlos Orlando; Cuba Fuentes, Maria Sofía

2017-01-30

To determine the frequency of Internet addiction and its relationship with the development of social skills in adolescents in the town of Condevilla, district of San Martin de Porres, Lima - Peru. The degree of social skills and level of internet use was evaluated in adolescents from 10 to 19 years of 5th to 11th grades in two secondary schools in the town of Condevilla. Classrooms were randomly selected, and the questionnaires were applied to all adolescents. Two questionnaires were applied: Scale for Internet Addiction of Lima to determine the extent of Internet use, and the Social Skills Test from the Ministry of Health of Peru, which evaluates self-esteem, assertiveness, communication and decision-making. The analyses by Chi2 test and Fisher's exact test, as well as a generalized linear model (GLM) were performed using the binomial family. Both questionnaires were applied to 179 adolescents, of whom 49.2% were male. The main age was 13 years, 78.8% of which were in secondary school. Internet addiction was found in 12.9% of respondents, of whom the majority were male (78.3%, p = 0.003) and had a higher prevalence of low social skills (21.7%, p = 0.45). In multivariate analysis, the independent factors associated with Internet addiction were gender (p = 0.016) and to have low social skills compared to high social skills (p = 0.004). In adolescents, there is a relationship between internet addiction and low social skills, among which the area of communication is statistically significant.