Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

PubMed

Quirós, Elia; Felicísimo, Angel M; Cuartero, Aurora

2009-01-01

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

Integrating Growth Variability of the Ilium, Fifth Lumbar Vertebra, and Clavicle with Multivariate Adaptive Regression Splines Models for Subadult Age Estimation.

PubMed

Corron, Louise; Marchal, François; Condemi, Silvana; Telmon, Norbert; Chaumoitre, Kathia; Adalian, Pascal

2018-05-31

Subadult age estimation should rely on sampling and statistical protocols capturing development variability for more accurate age estimates. In this perspective, measurements were taken on the fifth lumbar vertebrae and/or clavicles of 534 French males and females aged 0-19 years and the ilia of 244 males and females aged 0-12 years. These variables were fitted in nonparametric multivariate adaptive regression splines (MARS) models with 95% prediction intervals (PIs) of age. The models were tested on two independent samples from Marseille and the Luis Lopes reference collection from Lisbon. Models using ilium width and module, maximum clavicle length, and lateral vertebral body heights were more than 92% accurate. Precision was lower for postpubertal individuals. Integrating punctual nonlinearities of the relationship between age and the variables and dynamic prediction intervals incorporated the normal increase in interindividual growth variability (heteroscedasticity of variance) with age for more biologically accurate predictions. © 2018 American Academy of Forensic Sciences.

Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree

NASA Astrophysics Data System (ADS)

Heddam, Salim; Kisi, Ozgur

2018-04-01

In the present study, three types of artificial intelligence techniques, least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5T) are applied for modeling daily dissolved oxygen (DO) concentration using several water quality variables as inputs. The DO concentration and water quality variables data from three stations operated by the United States Geological Survey (USGS) were used for developing the three models. The water quality data selected consisted of daily measured of water temperature (TE, °C), pH (std. unit), specific conductance (SC, μS/cm) and discharge (DI cfs), are used as inputs to the LSSVM, MARS and M5T models. The three models were applied for each station separately and compared to each other. According to the results obtained, it was found that: (i) the DO concentration could be successfully estimated using the three models and (ii) the best model among all others differs from one station to another.

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Comparative Research of Navy Voluntary Education at Operational Commands

DTIC Science & Technology

2017-03-01

return on investment, ROI, logistic regression, multivariate analysis, descriptive statistics, Markov, time-series, linear programming 15. NUMBER...21  B.  DESCRIPTIVE STATISTICS TABLES ...............................................25  C.  PRIVACY CONSIDERATIONS...THIS PAGE INTENTIONALLY LEFT BLANK xi LIST OF TABLES Table 1.  Variables and Descriptions . Adapted from NETC (2016). .......................21

Prediction of energy expenditure from heart rate and accelerometry in children and adolescents using multivariate adaptive regression splines modeling

USDA-ARS?s Scientific Manuscript database

Free-living measurements of 24-h total energy expenditure (TEE) and activity energy expenditure (AEE) are required to better understand the metabolic, physiological, behavioral, and environmental factors affecting energy balance and contributing to the global epidemic of childhood obesity. The spec...

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

PubMed

Yilmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat

2018-05-23

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. Copyright © 2017 Elsevier B.V. All rights reserved.

Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers

USDA-ARS?s Scientific Manuscript database

Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and ma...

Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Parmar, Kulwinder Singh

2016-03-01

This study investigates the accuracy of least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in modeling river water pollution. Various combinations of water quality parameters, Free Ammonia (AMM), Total Kjeldahl Nitrogen (TKN), Water Temperature (WT), Total Coliform (TC), Fecal Coliform (FC) and Potential of Hydrogen (pH) monitored at Nizamuddin, Delhi Yamuna River in India were used as inputs to the applied models. Results indicated that the LSSVM and MARS models had almost same accuracy and they performed better than the M5Tree model in modeling monthly chemical oxygen demand (COD). The average root mean square error (RMSE) of the LSSVM and M5Tree models was decreased by 1.47% and 19.1% using MARS model, respectively. Adding TC input to the models did not increase their accuracy in modeling COD while adding FC and pH inputs to the models generally decreased the accuracy. The overall results indicated that the MARS and LSSVM models could be successfully used in estimating monthly river water pollution level by using AMM, TKN and WT parameters as inputs.

Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

PubMed

Garcia Nieto, P J; Sánchez Lasheras, F; de Cos Juez, F J; Alonso Fernández, J R

2011-11-15

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. Copyright © 2011 Elsevier B.V. All rights reserved.

Understanding adaptive gait in lower-limb amputees: insights from multivariate analyses

PubMed Central

2013-01-01

Background In this paper we use multivariate statistical techniques to gain insights into how adaptive gait involving obstacle crossing is regulated in lower-limb amputees compared to able-bodied controls, with the aim of identifying underlying characteristics that differ between the two groups and consequently highlighting gait deficits in the amputees. Methods Eight unilateral trans-tibial amputees and twelve able-bodied controls completed adaptive gait trials involving negotiating various height obstacles; with amputees leading with their prosthetic limb. Spatiotemporal variables that are regularly used to quantify how gait is adapted when crossing obstacles were determined and subsequently analysed using multivariate statistical techniques. Results and discussion There were fundamental differences in the adaptive gait between the two groups. Compared to controls, amputees had a reduced approach velocity, reduced foot placement distance before and after the obstacle and reduced foot clearance over it, and reduced lead-limb knee flexion during the step following crossing. Logistic regression analysis highlighted the variables that best distinguished between the gait of the two groups and multiple regression analysis (with approach velocity as a controlling factor) helped identify what gait adaptations were driving the differences seen in these variables. Getting closer to the obstacle before crossing it appeared to be a strategy to ensure the heel of the lead-limb foot passed over the obstacle prior to the foot being lowered to the ground. Despite adopting such a heel clearance strategy, the lead-foot was positioned closer to the obstacle following crossing, which was likely a result of a desire to attain a limb/foot angle and orientation at instant of landing that minimised loads on the residuum (as evidenced by the reduced lead-limb knee flexion during the step following crossing). These changes in foot placement meant the foot was in a different part of swing at point of crossing and this explains why foot clearance was considerably reduced in amputees. Conclusions These results highlight that trans-tibial amputees use quite different gait adaptations to cross obstacles compared with controls (at least when leading with their prosthetic limb), indicating they are governed by different constraints; seemingly related to how they land on/load their prosthesis after crossing the obstacle. PMID:23958032

Domain-Invariant Partial-Least-Squares Regression.

PubMed

Nikzad-Langerodi, Ramin; Zellinger, Werner; Lughofer, Edwin; Saminger-Platz, Susanne

2018-05-11

Multivariate calibration models often fail to extrapolate beyond the calibration samples because of changes associated with the instrumental response, environmental condition, or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical devices, while generic methods for calibration-model adaptation are largely missing. To fill this gap, we here introduce domain-invariant partial-least-squares (di-PLS) regression, which extends ordinary PLS by a domain regularizer in order to align the source and target distributions in the latent-variable space. We show that a domain-invariant weight vector can be derived in closed form, which allows the integration of (partially) labeled data from the source and target domains as well as entirely unlabeled data from the latter. We test our approach on a simulated data set where the aim is to desensitize a source calibration model to an unknown interfering agent in the target domain (i.e., unsupervised model adaptation). In addition, we demonstrate unsupervised, semisupervised, and supervised model adaptation by di-PLS on two real-world near-infrared (NIR) spectroscopic data sets.

Vulnerability of carbon storage in North American boreal forests to wildfires during the 21st century

Treesearch

M.S. Balshi; A.D. McGuire; P. Duffy; M. Flannigan; D.W. Kicklighter; J. Melillo

2009-01-01

We use a gridded data set developed with a multivariate adaptive regression spline approach to determine how area burned varies each year with changing climatic and fuel moisture conditions. We apply the process-based Terrestrial Ecosystem Model to evaluate the role of future fire on the carbon dynamics of boreal North America in the context of changing atmospheric...

Multivariate Adaptive Regression Splines (Preprint)

DTIC Science & Technology

1990-08-01

fold cross -validation would take about ten time as long, and MARS is not all that fast to begin with. Friedman has a number of examples showing...standardized mean squared error of prediction (MSEP), the generalized cross validation (GCV), and the number of selected terms (TERMS). In accordance with...and mi= 10 case were almost exclusively spurious cross product terms and terms involving the nuisance variables x6 through xlo. This large number of

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

NASA Astrophysics Data System (ADS)

Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

2015-10-01

The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

Clinical factors and the decision to transfuse chronic dialysis patients.

PubMed

Whitman, Cynthia B; Shreay, Sanatan; Gitlin, Matthew; van Oijen, Martijn G H; Spiegel, Brennan M R

2013-11-01

Red blood cell transfusion was previously the principle therapy for anemia in CKD but became less prevalent after the introduction of erythropoiesis-stimulating agents. This study used adaptive choice-based conjoint analysis to identify preferences and predictors of transfusion decision-making in CKD. A computerized adaptive choice-based conjoint survey was administered between June and August of 2012 to nephrologists, internists, and hospitalists listed in the American Medical Association Masterfile. The survey quantified the relative importance of 10 patient attributes, including hemoglobin levels, age, occult blood in stool, severity of illness, eligibility for transplant, iron indices, erythropoiesis-stimulating agents, cardiovascular disease, and functional status. Triggers of transfusions in common dialysis scenarios were studied, and based on adaptive choice-based conjoint-derived preferences, relative importance by performing multivariable regression to identify predictors of transfusion preferences was assessed. A total of 350 providers completed the survey (n=305 nephrologists; mean age=46 years; 21% women). Of 10 attributes assessed, absolute hemoglobin level was the most important driver of transfusions, accounting for 29% of decision-making, followed by functional status (16%) and cardiovascular comorbidities (12%); 92% of providers transfused when hemoglobin was 7.5 g/dl, independent of other factors. In multivariable regression, Veterans Administration providers were more likely to transfuse at 8.0 g/dl (odds ratio, 5.9; 95% confidence interval, 1.9 to 18.4). Although transplant eligibility explained only 5% of decision-making, nephrologists were five times more likely to value it as important compared with non-nephrologists (odds ratio, 5.2; 95% confidence interval, 2.4 to 11.1). Adaptive choice-based conjoint analysis was useful in predicting influences on transfusion decisions. Hemoglobin level, functional status, and cardiovascular comorbidities most strongly influenced transfusion decision-making, but preference variations were observed among subgroups.

Coping with post-war mental health problems among survivors of violence in Northern Uganda: Findings from the WAYS study.

PubMed

Amone-P'Olak, Kennedy; Omech, Bernard

2018-05-01

Cognitive emotion regulation strategies and mental health problems were assessed in a sample of war-affected youth in Northern Uganda. Univariable and multivariable regression models were fitted to assess the influence of CERS on mental health problems. Maladaptive cognitive emotion regulation strategies (e.g., rumination) were significantly associated with more mental health problems while adaptive cognitive emotion regulation strategies (e.g., putting into perspective) were associated with reporting fewer symptoms of mental health problems. The youth with significant scores on mental health problems (scores ≥ 85th percentile) reported more frequent use of maladaptive than adaptive strategies. Interventions to reduce mental health problems should focus on enhancing the use of adaptive strategies.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

More insights into early brain development through statistical analyses of eigen-structural elements of diffusion tensor imaging using multivariate adaptive regression splines

PubMed Central

Chen, Yasheng; Zhu, Hongtu; An, Hongyu; Armao, Diane; Shen, Dinggang; Gilmore, John H.; Lin, Weili

2013-01-01

The aim of this study was to characterize the maturational changes of the three eigenvalues (λ1 ≥ λ2 ≥ λ3) of diffusion tensor imaging (DTI) during early postnatal life for more insights into early brain development. In order to overcome the limitations of using presumed growth trajectories for regression analysis, we employed Multivariate Adaptive Regression Splines (MARS) to derive data-driven growth trajectories for the three eigenvalues. We further employed Generalized Estimating Equations (GEE) to carry out statistical inferences on the growth trajectories obtained with MARS. With a total of 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects, we found that the growth velocities of the three eigenvalues were highly correlated, but significantly different from each other. This paradox suggested the existence of mechanisms coordinating the maturations of the three eigenvalues even though different physiological origins may be responsible for their temporal evolutions. Furthermore, our results revealed the limitations of using the average of λ2 and λ3 as the radial diffusivity in interpreting DTI findings during early brain development because these two eigenvalues had significantly different growth velocities even in central white matter. In addition, based upon the three eigenvalues, we have documented the growth trajectory differences between central and peripheral white matter, between anterior and posterior limbs of internal capsule, and between inferior and superior longitudinal fasciculus. Taken together, we have demonstrated that more insights into early brain maturation can be gained through analyzing eigen-structural elements of DTI. PMID:23455648

A single determinant dominates the rate of yeast protein evolution.

PubMed

Drummond, D Allan; Raval, Alpan; Wilke, Claus O

2006-02-01

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

Digital controllers for VTOL aircraft

NASA Technical Reports Server (NTRS)

Stengel, R. F.; Broussard, J. R.; Berry, P. W.

1976-01-01

Using linear-optimal estimation and control techniques, digital-adaptive control laws have been designed for a tandem-rotor helicopter which is equipped for fully automatic flight in terminal area operations. Two distinct discrete-time control laws are designed to interface with velocity-command and attitude-command guidance logic, and each incorporates proportional-integral compensation for non-zero-set-point regulation, as well as reduced-order Kalman filters for sensor blending and noise rejection. Adaptation to flight condition is achieved with a novel gain-scheduling method based on correlation and regression analysis. The linear-optimal design approach is found to be a valuable tool in the development of practical multivariable control laws for vehicles which evidence significant coupling and insufficient natural stability.

Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers

NASA Astrophysics Data System (ADS)

Forghani, Ali; Peralta, Richard C.

2017-10-01

The study presents a procedure using solute transport and statistical models to evaluate the performance of aquifer storage and recovery (ASR) systems designed to earn additional water rights in freshwater aquifers. The recovery effectiveness (REN) index quantifies the performance of these ASR systems. REN is the proportion of the injected water that the same ASR well can recapture during subsequent extraction periods. To estimate REN for individual ASR wells, the presented procedure uses finely discretized groundwater flow and contaminant transport modeling. Then, the procedure uses multivariate adaptive regression splines (MARS) analysis to identify the significant variables affecting REN, and to identify the most recovery-effective wells. Achieving REN values close to 100% is the desire of the studied 14-well ASR system operator. This recovery is feasible for most of the ASR wells by extracting three times the injectate volume during the same year as injection. Most of the wells would achieve RENs below 75% if extracting merely the same volume as they injected. In other words, recovering almost all the same water molecules that are injected requires having a pre-existing water right to extract groundwater annually. MARS shows that REN most significantly correlates with groundwater flow velocity, or hydraulic conductivity and hydraulic gradient. MARS results also demonstrate that maximizing REN requires utilizing the wells located in areas with background Darcian groundwater velocities less than 0.03 m/d. The study also highlights the superiority of MARS over regular multiple linear regressions to identify the wells that can provide the maximum REN. This is the first reported application of MARS for evaluating performance of an ASR system in fresh water aquifers.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

PubMed

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

Clinical Factors and the Decision to Transfuse Chronic Dialysis Patients

PubMed Central

Whitman, Cynthia B.; Shreay, Sanatan; Gitlin, Matthew; van Oijen, Martijn G. H.

2013-01-01

Summary Background and objectives Red blood cell transfusion was previously the principle therapy for anemia in CKD but became less prevalent after the introduction of erythropoiesis-stimulating agents. This study used adaptive choice-based conjoint analysis to identify preferences and predictors of transfusion decision-making in CKD. Design, setting, participants, & measurements A computerized adaptive choice-based conjoint survey was administered between June and August of 2012 to nephrologists, internists, and hospitalists listed in the American Medical Association Masterfile. The survey quantified the relative importance of 10 patient attributes, including hemoglobin levels, age, occult blood in stool, severity of illness, eligibility for transplant, iron indices, erythropoiesis-stimulating agents, cardiovascular disease, and functional status. Triggers of transfusions in common dialysis scenarios were studied, and based on adaptive choice-based conjoint-derived preferences, relative importance by performing multivariable regression to identify predictors of transfusion preferences was assessed. Results A total of 350 providers completed the survey (n=305 nephrologists; mean age=46 years; 21% women). Of 10 attributes assessed, absolute hemoglobin level was the most important driver of transfusions, accounting for 29% of decision-making, followed by functional status (16%) and cardiovascular comorbidities (12%); 92% of providers transfused when hemoglobin was 7.5 g/dl, independent of other factors. In multivariable regression, Veterans Administration providers were more likely to transfuse at 8.0 g/dl (odds ratio, 5.9; 95% confidence interval, 1.9 to 18.4). Although transplant eligibility explained only 5% of decision-making, nephrologists were five times more likely to value it as important compared with non-nephrologists (odds ratio, 5.2; 95% confidence interval, 2.4 to11.1). Conclusions Adaptive choice-based conjoint analysis was useful in predicting influences on transfusion decisions. Hemoglobin level, functional status, and cardiovascular comorbidities most strongly influenced transfusion decision-making, but preference variations were observed among subgroups. PMID:23929931

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

HEALTH CONDITIONS LINKED TO AGE-RELATED MACULAR DEGENERATION ASSOCIATED WITH DARK ADAPTATION.

PubMed

Laíns, Inês; Miller, John B; Mukai, Ryo; Mach, Steven; Vavvas, Demetrios; Kim, Ivana K; Miller, Joan W; Husain, Deeba

2018-06-01

To determine the association between dark adaption (DA) and different health conditions linked with age-related macular degeneration (AMD). Cross-sectional study, including patients with AMD and a control group. Age-related macular degeneration was graded according to the Age-Related Eye Disease Study (AREDS) classification. We obtained data on medical history, medications, and lifestyle. Dark adaption was assessed with the extended protocol (20 minutes) of AdaptDx (MacuLogix). For analyses, the right eye or the eye with more advanced AMD was selected. Multivariate linear and logistic regressions were performed, accounting for age and AMD stage. Seventy-eight subjects (75.6% AMD; 24.4% controls) were included. Multivariate assessments revealed that body mass index (BMI; β = 0.30, P = 0.045), taking AREDS vitamins (β = 5.51, P < 0.001), and family history of AMD (β = 2.68, P = 0.039) were significantly associated with worse rod intercept times. Abnormal DA (rod intercept time ≥ 6.5 minutes) was significantly associated with family history of AMD (β = 1.84, P = 0.006), taking AREDS supplements (β = 1.67, P = 0.021) and alcohol intake (β = 0.07, P = 0.017). Besides age and AMD stage, a higher body mass index, higher alcohol intake, and a family history of AMD seem to impair DA. In this cohort, the use of AREDS vitamins was also statistically linked with impaired DA, most likely because of an increased severity of disease in subjects taking them.

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A reduced adaptive observer for multivariable systems. [using reduced dynamic ordering

NASA Technical Reports Server (NTRS)

Carroll, R. L.; Lindorff, D. P.

1973-01-01

An adaptive observer for multivariable systems is presented for which the dynamic order of the observer is reduced, subject to mild restrictions. The observer structure depends directly upon the multivariable structure of the system rather than a transformation to a single-output system. The number of adaptive gains is at most the sum of the order of the system and the number of input parameters being adapted. Moreover, for the relatively frequent specific cases for which the number of required adaptive gains is less than the sum of system order and input parameters, the number of these gains is easily determined by inspection of the system structure. This adaptive observer possesses all the properties ascribed to the single-input single-output adpative observer. Like the other adaptive observers some restriction is required of the allowable system command input to guarantee convergence of the adaptive algorithm, but the restriction is more lenient than that required by the full-order multivariable observer. This reduced observer is not restricted to cycle systems.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Concentration-Dependent Antagonism and Culture Conversion in Pulmonary Tuberculosis

PubMed Central

Pasipanodya, Jotam G.; Denti, Paolo; Sirgel, Frederick; Lesosky, Maia; Gumbo, Tawanda; Meintjes, Graeme; McIlleron, Helen; Wilkinson, Robert J.

2017-01-01

Abstract Background. There is scant evidence to support target drug exposures for optimal tuberculosis outcomes. We therefore assessed whether pharmacokinetic/pharmacodynamic (PK/PD) parameters could predict 2-month culture conversion. Methods. One hundred patients with pulmonary tuberculosis (65% human immunodeficiency virus coinfected) were intensively sampled to determine rifampicin, isoniazid, and pyrazinamide plasma concentrations after 7–8 weeks of therapy, and PK parameters determined using nonlinear mixed-effects models. Detailed clinical data and sputum for culture were collected at baseline, 2 months, and 5–6 months. Minimum inhibitory concentrations (MICs) were determined on baseline isolates. Multivariate logistic regression and the assumption-free multivariate adaptive regression splines (MARS) were used to identify clinical and PK/PD predictors of 2-month culture conversion. Potential PK/PD predictors included 0- to 24-hour area under the curve (AUC0-24), maximum concentration (Cmax), AUC0-24/MIC, Cmax/MIC, and percentage of time that concentrations persisted above the MIC (%TMIC). Results. Twenty-six percent of patients had Cmax of rifampicin <8 mg/L, pyrazinamide <35 mg/L, and isoniazid <3 mg/L. No relationship was found between PK exposures and 2-month culture conversion using multivariate logistic regression after adjusting for MIC. However, MARS identified negative interactions between isoniazid Cmax and rifampicin Cmax/MIC ratio on 2-month culture conversion. If isoniazid Cmax was <4.6 mg/L and rifampicin Cmax/MIC <28, the isoniazid concentration had an antagonistic effect on culture conversion. For patients with isoniazid Cmax >4.6 mg/L, higher isoniazid exposures were associated with improved rates of culture conversion. Conclusions. PK/PD analyses using MARS identified isoniazid Cmax and rifampicin Cmax/MIC thresholds below which there is concentration-dependent antagonism that reduces 2-month sputum culture conversion. PMID:28205671

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

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

PubMed

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

2014-11-01

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

A multivariate ecogeographic analysis of macaque craniodental variation.

PubMed

Grunstra, Nicole D S; Mitteroecker, Philipp; Foley, Robert A

2018-06-01

To infer the ecogeographic conditions that underlie the evolutionary diversification of macaques, we investigated the within- and between-species relationships of craniodental dimensions, geography, and environment in extant macaque species. We studied evolutionary processes by contrasting macroevolutionary patterns, phylogeny, and within-species associations. Sixty-three linear measurements of the permanent dentition and skull along with data about climate, ecology (environment), and spatial geography were collected for 711 specimens of 12 macaque species and analyzed by a multivariate approach. Phylogenetic two-block partial least squares was used to identify patterns of covariance between craniodental and environmental variation. Phylogenetic reduced rank regression was employed to analyze spatial clines in morphological variation. Between-species associations consisted of two distinct multivariate patterns. The first represents overall craniodental size and is negatively associated with temperature and habitat, but positively with latitude. The second pattern shows an antero-posterior tooth size contrast related to diet, rainfall, and habitat productivity. After controlling for phylogeny, however, the latter dimension was diminished. Within-species analyses neither revealed significant association between morphology, environment, and geography, nor evidence of isolation by distance. We found evidence for environmental adaptation in macaque body and craniodental size, primarily driven by selection for thermoregulation. This pattern cannot be explained by the within-species pattern, indicating an evolved genetic basis for the between-species relationship. The dietary signal in relative tooth size, by contrast, can largely be explained by phylogeny. This cautions against adaptive interpretations of phenotype-environment associations when phylogeny is not explicitly modelled. © 2018 Wiley Periodicals, Inc.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression

USDA-ARS?s Scientific Manuscript database

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly ...

Ensemble habitat mapping of invasive plant species

USGS Publications Warehouse

Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.

2010-01-01

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.

Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS

USGS Publications Warehouse

Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.

2011-01-01

The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-01

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.

PubMed

Golkarian, Ali; Naghibi, Seyed Amir; Kalantar, Bahareh; Pradhan, Biswajeet

2018-02-17

Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-07-01

Soil temperature (T s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T s. For this aim, the monthly mean data of the T s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T min, T max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R s, R n, R a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u 2); and water vapor pressure (Vp) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R 2) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T max, T min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R 2 = 0.995) and for RH, V p, P, and u 2 inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R 2 = 0.996), respectively.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Pan evaporation modeling using six different heuristic computing methods in different climates of China

NASA Astrophysics Data System (ADS)

Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

2017-01-01

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

Factors Associated With Caregivers' Resilience in a Terminal Cancer Care Setting.

PubMed

Hwang, In Cheol; Kim, Young Sung; Lee, Yong Joo; Choi, Youn Seon; Hwang, Sun Wook; Kim, Hyo Min; Koh, Su-Jin

2018-04-01

Resilience implies characteristics such as self-efficacy, adaptability to change, optimism, and the ability to recover from traumatic stress. Studies on resilience in family caregivers (FCs) of patients with terminal cancer are rare. This study aims to examine the factors associated with FCs' resilience in a terminal cancer care setting. This is a cross-sectional study of 273 FCs from 7 hospice and palliative care units in Korea. Resilience was categorized as high and low, and factors associated with resilience were grouped or categorized into subscales. A multivariate logistic regression analysis was used to examine relevant factors. High FCs' resilience was significantly associated with FCs' health status, depression, and social support. In a multivariate regression model, FCs' perception of good health (adjusted odds ratio [aOR] = 2.26, 95% confidence interval [CI] = 1.16-4.40), positive social support (aOR = 3.70, 95% CI = 1.07-12.87), and absence of depression (aOR = 3.12, 95% CI = 1.59-6.13) remained significantly associated with high FCs' resilience. Lack of family support is associated with and may be a cause of diminished resilience. And more concern should be paid to FCs to improve FCs' health and emotional status. Education programs might be effective for improving caregivers' resilience. Further research with supportive interventions is indicated.

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

Coping and violence exposure as predictors of psychological functioning in domestic violence survivors.

PubMed

Lewis, Carla S; Griffing, Sascha; Chu, Melissa; Jospitre, Tania; Sage, Robert E; Madry, Lorraine; Primm, Beny J

2006-04-01

This study examines the differential effects of adult and childhood physical and psychological abuse, abuse-specific coping, and psychological adjustment in battered women seeking emergency shelter. Multivariate regression analyses confirmed the devastating impact of psychological abuse (childhood and concurrent) on battered women's adjustment. The results corroborated prior research suggesting a cumulative vulnerability to psychological victimization in a substantial proportion of residents. Unexpectedly, frequency of physical violence was unrelated to women's distress. The study argues that modes of coping traditionally considered adaptive (e.g., engaged, proactive) may be unsafe for battered women and children. The multifaceted nature of survivors' coping choices is discussed.

Alternatives for using multivariate regression to adjust prospective payment rates

PubMed Central

Sheingold, Steven H.

1990-01-01

Multivariate regression analysis has been used in structuring three of the adjustments to Medicare's prospective payment rates. Because the indirect-teaching adjustment, the disproportionate-share adjustment, and the adjustment for large cities are responsible for distributing approximately $3 billion in payments each year, the specification of regression models for these adjustments is of critical importance. In this article, the application of regression for adjusting Medicare's prospective rates is discussed, and the implications that differing specifications could have for these adjustments are demonstrated. PMID:10113271

Robust, Adaptive Functional Regression in Functional Mixed Model Framework.

PubMed

Zhu, Hongxiao; Brown, Philip J; Morris, Jeffrey S

2011-09-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets.

Robust, Adaptive Functional Regression in Functional Mixed Model Framework

PubMed Central

Zhu, Hongxiao; Brown, Philip J.; Morris, Jeffrey S.

2012-01-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets. PMID:22308015

A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines.

PubMed

Sánchez Lasheras, Fernando; García Nieto, Paulino José; de Cos Juez, Francisco Javier; Mayo Bayón, Ricardo; González Suárez, Victor Manuel

2015-03-23

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

PubMed Central

Lasheras, Fernando Sánchez; Nieto, Paulino José García; de Cos Juez, Francisco Javier; Bayón, Ricardo Mayo; Suárez, Victor Manuel González

2015-01-01

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines. PMID:25806876

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures.

PubMed

Hegazy, Maha A; Lotfy, Hayam M; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-05

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations. Copyright © 2016 Elsevier B.V. All rights reserved.

Doing better to do good: the impact of strategic adaptation on nursing home performance.

PubMed

Zinn, Jacqueline S; Mor, Vincent; Feng, Zhanlian; Intrator, Orna

2007-06-01

To test the hypothesis that a greater commitment to strategic adaptation, as exhibited by more extensive implementation of a subacute/rehabilitation care strategy in nursing homes, will be associated with superior performance. Online Survey, Certification, and Reporting (OSCAR) data from 1997 to 2004, and the area resource file (ARF). The extent of strategic adaptation was measured by an aggregate weighted implementation score. Nursing home performance was measured by occupancy rate and two measures of payer mix. We conducted multivariate regression analyses using a cross-sectional time series generalized estimating equation (GEE) model to examine the effect of nursing home strategic implementation on each of the three performance measures, controlling for market and organizational characteristics that could influence nursing home performance. DATA COLLECTION/ABSTRACTION METHODS: OSCAR data was merged with relevant ARF data. The results of our analysis provide strong support for the hypothesis. From a theoretical perspective, our findings confirm that organizations that adjust strategies and structures to better fit environmental demands achieve superior performance. From a managerial perspective, these results support the importance of proactive strategic leadership in the nursing home industry.

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

A generalized multivariate regression model for modelling ocean wave heights

NASA Astrophysics Data System (ADS)

Wang, X. L.; Feng, Y.; Swail, V. R.

2012-04-01

In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.

Value of Information Analysis for Time-lapse Seismic Data by Simulation-Regression

NASA Astrophysics Data System (ADS)

Dutta, G.; Mukerji, T.; Eidsvik, J.

2016-12-01

A novel method to estimate the Value of Information (VOI) of time-lapse seismic data in the context of reservoir development is proposed. VOI is a decision analytic metric quantifying the incremental value that would be created by collecting information prior to making a decision under uncertainty. The VOI has to be computed before collecting the information and can be used to justify its collection. Previous work on estimating the VOI of geophysical data has involved explicit approximation of the posterior distribution of reservoir properties given the data and then evaluating the prospect values for that posterior distribution of reservoir properties. Here, we propose to directly estimate the prospect values given the data by building a statistical relationship between them using regression. Various regression techniques such as Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and k-Nearest Neighbors (k-NN) are used to estimate the VOI, and the results compared. For a univariate Gaussian case, the VOI obtained from simulation-regression has been shown to be close to the analytical solution. Estimating VOI by simulation-regression is much less computationally expensive since the posterior distribution of reservoir properties given each possible dataset need not be modeled and the prospect values need not be evaluated for each such posterior distribution of reservoir properties. This method is flexible, since it does not require rigid model specification of posterior but rather fits conditional expectations non-parametrically from samples of values and data.

Evolutionary grinding model for nanometric control of surface roughness for aspheric optical surfaces.

PubMed

Han, Jeong-Yeol; Kim, Sug-Whan; Han, Inwoo; Kim, Geon-Hee

2008-03-17

A new evolutionary grinding process model has been developed for nanometric control of material removal from an aspheric surface of Zerodur substrate. The model incorporates novel control features such as i) a growing database; ii) an evolving, multi-variable regression equation; and iii) an adaptive correction factor for target surface roughness (Ra) for the next machine run. This process model demonstrated a unique evolutionary controllability of machining performance resulting in the final grinding accuracy (i.e. averaged difference between target and measured surface roughness) of -0.2+/-2.3(sigma) nm Ra over seven trial machine runs for the target surface roughness ranging from 115 nm to 64 nm Ra.

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Adaptation and application of multivariate AMBI (M-AMBI) in US coastal waters

EPA Science Inventory

The multivariate AMBI (M-AMBI) is an extension of the AZTI Marine Biotic Index (AMBI) that has been used extensively in Europe, but not in the United States. In a previous study, we adapted AMBI for use in US coastal waters (US AMBI), but saw biases in salinity and score distribu...

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

Estimating Soil Cation Exchange Capacity from Soil Physical and Chemical Properties

NASA Astrophysics Data System (ADS)

Bateni, S. M.; Emamgholizadeh, S.; Shahsavani, D.

2014-12-01

The soil Cation Exchange Capacity (CEC) is an important soil characteristic that has many applications in soil science and environmental studies. For example, CEC influences soil fertility by controlling the exchange of ions in the soil. Measurement of CEC is costly and difficult. Consequently, several studies attempted to obtain CEC from readily measurable soil physical and chemical properties such as soil pH, organic matter, soil texture, bulk density, and particle size distribution. These studies have often used multiple regression or artificial neural network models. Regression-based models cannot capture the intricate relationship between CEC and soil physical and chemical attributes and provide inaccurate CEC estimates. Although neural network models perform better than regression methods, they act like a black-box and cannot generate an explicit expression for retrieval of CEC from soil properties. In a departure with regression and neural network models, this study uses Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) to estimate CEC from easily measurable soil variables such as clay, pH, and OM. CEC estimates from GEP and MARS are compared with measurements at two field sites in Iran. Results show that GEP and MARS can estimate CEC accurately. Also, the MARS model performs slightly better than GEP. Finally, a sensitivity test indicates that organic matter and pH have respectively the least and the most significant impact on CEC.

Prognostic Value of Pretherapeutic Tumor-to-Blood Standardized Uptake Ratio in Patients with Esophageal Carcinoma.

PubMed

Bütof, Rebecca; Hofheinz, Frank; Zöphel, Klaus; Stadelmann, Tobias; Schmollack, Julia; Jentsch, Christina; Löck, Steffen; Kotzerke, Jörg; Baumann, Michael; van den Hoff, Jörg

2015-08-01

Despite ongoing efforts to develop new treatment options, the prognosis for patients with inoperable esophageal carcinoma is still poor and the reliability of individual therapy outcome prediction based on clinical parameters is not convincing. The aim of this work was to investigate whether PET can provide independent prognostic information in such a patient group and whether the tumor-to-blood standardized uptake ratio (SUR) can improve the prognostic value of tracer uptake values. (18)F-FDG PET/CT was performed in 130 consecutive patients (mean age ± SD, 63 ± 11 y; 113 men, 17 women) with newly diagnosed esophageal cancer before definitive radiochemotherapy. In the PET images, the metabolically active tumor volume (MTV) of the primary tumor was delineated with an adaptive threshold method. The blood standardized uptake value (SUV) was determined by manually delineating the aorta in the low-dose CT. SUR values were computed as the ratio of tumor SUV and blood SUV. Uptake values were scan-time-corrected to 60 min after injection. Univariate Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), distant metastases-free survival (DM), and locoregional tumor control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. In multivariate Cox regression with respect to OS, including T stage, N stage, and smoking state, MTV- and SUR-based parameters were significant prognostic factors for OS with similar effect size. Multivariate analysis with respect to DM revealed smoking state, MTV, and all SUR-based parameters as significant prognostic factors. The highest hazard ratios (HRs) were found for scan-time-corrected maximum SUR (HR = 3.9) and mean SUR (HR = 4.4). None of the PET parameters was associated with LRC. Univariate Cox regression with respect to LRC revealed a significant effect only for N stage greater than 0 (P = 0.048). PET provides independent prognostic information for OS and DM but not for LRC in patients with locally advanced esophageal carcinoma treated with definitive radiochemotherapy in addition to clinical parameters. Among the investigated uptake-based parameters, only SUR was an independent prognostic factor for OS and DM. These results suggest that the prognostic value of tracer uptake can be improved when characterized by SUR instead of SUV. Further investigations are required to confirm these preliminary results. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Vibration-based structural health monitoring using adaptive statistical method under varying environmental condition

NASA Astrophysics Data System (ADS)

Jin, Seung-Seop; Jung, Hyung-Jo

2014-03-01

It is well known that the dynamic properties of a structure such as natural frequencies depend not only on damage but also on environmental condition (e.g., temperature). The variation in dynamic characteristics of a structure due to environmental condition may mask damage of the structure. Without taking the change of environmental condition into account, false-positive or false-negative damage diagnosis may occur so that structural health monitoring becomes unreliable. In order to address this problem, an approach to construct a regression model based on structural responses considering environmental factors has been usually used by many researchers. The key to success of this approach is the formulation between the input and output variables of the regression model to take into account the environmental variations. However, it is quite challenging to determine proper environmental variables and measurement locations in advance for fully representing the relationship between the structural responses and the environmental variations. One alternative (i.e., novelty detection) is to remove the variations caused by environmental factors from the structural responses by using multivariate statistical analysis (e.g., principal component analysis (PCA), factor analysis, etc.). The success of this method is deeply depending on the accuracy of the description of normal condition. Generally, there is no prior information on normal condition during data acquisition, so that the normal condition is determined by subjective perspective with human-intervention. The proposed method is a novel adaptive multivariate statistical analysis for monitoring of structural damage detection under environmental change. One advantage of this method is the ability of a generative learning to capture the intrinsic characteristics of the normal condition. The proposed method is tested on numerically simulated data for a range of noise in measurement under environmental variation. A comparative study with conventional methods (i.e., fixed reference scheme) demonstrates the superior performance of the proposed method for structural damage detection.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Principal component analysis-based pattern analysis of dose-volume histograms and influence on rectal toxicity.

PubMed

Söhn, Matthias; Alber, Markus; Yan, Di

2007-09-01

The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as "eigenmodes," which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe approximately 94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses ( approximately 40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches.

Matching pollution with adaptive changes in mangrove plants by multivariate statistics. A case study, Rhizophora mangle from four neotropical mangroves in Brazil.

PubMed

Souza, Iara da Costa; Morozesk, Mariana; Duarte, Ian Drumond; Bonomo, Marina Marques; Rocha, Lívia Dorsch; Furlan, Larissa Maria; Arrivabene, Hiulana Pereira; Monferrán, Magdalena Victoria; Matsumoto, Silvia Tamie; Milanez, Camilla Rozindo Dias; Wunderlin, Daniel Alberto; Fernandes, Marisa Narciso

2014-08-01

Roots of mangrove trees have an important role in depurating water and sediments by retaining metals that may accumulate in different plant tissues, affecting physiological processes and anatomy. The present study aimed to evaluate adaptive changes in root of Rhizophora mangle in response to different levels of chemical elements (metals/metalloids) in interstitial water and sediments from four neotropical mangroves in Brazil. What sets this study apart from other studies is that we not only investigate adaptive modifications in R. mangle but also changes in environments where this plant grows, evaluating correspondence between physical, chemical and biological issues by a combined set of multivariate statistical methods (pattern recognition). Thus, we looked to match changes in the environment with adaptations in plants. Multivariate statistics highlighted that the lignified periderm and the air gaps are directly related to the environmental contamination. Current results provide new evidences of root anatomical strategies to deal with contaminated environments. Multivariate statistics greatly contributes to extrapolate results from complex data matrixes obtained when analyzing environmental issues, pointing out parameters involved in environmental changes and also evidencing the adaptive response of the exposed biota. Copyright © 2014 Elsevier Ltd. All rights reserved.

Correlates of health-related quality of life in children with drug resistant epilepsy.

PubMed

Conway, Lauryn; Smith, Mary Lou; Ferro, Mark A; Speechley, Kathy N; Connoly, Mary B; Snead, O Carter; Widjaja, Elysa

2016-08-01

Health-related quality of life (HRQL) is compromised in children with epilepsy. The current study aimed to identify correlates of HRQL in children with drug resistant epilepsy. Data came from 115 children enrolled in the Impact of Pediatric Epilepsy Surgery on Health-Related Quality of Life Study (PEPSQOL), a multicenter prospective cohort study. Individual, clinical, and family factors were evaluated. HRQL was measured using the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE), a parent-rated epilepsy-specific instrument, with composite scores ranging from 0 to 100. A series of univariable linear regression analyses were conducted to identify significant associations with HRQL, followed by a multivariable regression analysis. Children had a mean age of 11.85 ± 3.81 years and 65 (56.5%) were male. The mean composite QOLCE score was 60.18 ± 16.69. Child age, sex, age at seizure onset, duration of epilepsy, caregiver age, caregiver education, and income were not significantly associated with HRQL. Univariable regression analyses revealed that a higher number of anti-seizure medications (p = 0.020), lower IQ (p = 0.002), greater seizure frequency (p = 0.048), caregiver unemployment (p = 0.010), higher caregiver depressive and anxiety symptoms (p < 0.001 for both), poorer family adaptation, fewer family resources, and a greater number of family demands (p < 0.001 for all) were associated with lower HRQL. Multivariable regression analysis showed that lower child IQ (β = 0.20, p = 0.004), fewer family resources (β = 0.43, p = 0.012), and caregiver unemployment (β = 6.53, p = 0.018) were associated with diminished HRQL in children. The results emphasize the importance of child cognition and family variables in the HRQL of children with drug-resistant epilepsy. The findings speak to the importance of offering comprehensive care to children and their families to address the nonmedical features that impact on HRQL. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Investigating low adaptive behaviour and presence of the triad of impairments characteristic of autistic spectrum disorder as indicators of risk for challenging behaviour among adults with intellectual disabilities.

PubMed

Felce, D; Kerr, M

2013-02-01

Identification of possible personal indicators of risk for challenging behaviour has generally been through association in cross-sectional prevalence studies, but few analyses have controlled for intercorrelation between potential risk factors. The aim was to investigate the extent to which gender, age, presence of the triad of impairments characteristic of autism and level of adaptive behaviour were independently associated with level of challenging behaviour among adults with intellectual disabilities. Five datasets were merged to produce information on challenging behaviour, adaptive behaviour, presence of the triad of impairments, gender and age of 818 adults. Variables were entered into a multivariate linear regression, which also tested the interaction between the presence of the triad of impairments and level of adaptive behaviour. Presence of the triad of impairments, level of adaptive behaviour, their interaction, and age, but not gender, significantly and independently contributed to the prediction of challenging behaviour. Presence/absence of the triad of impairments moderated the effect of adaptive behaviour on challenging behaviour. The inverse relationship found in the absence of the triad of impairments was virtually removed when present. This study has shown that it is necessary to control for intercorrelation between potential risk factors for challenging behaviour and to explore how interaction between them might moderate associations. © 2012 The Author. Journal of Intellectual Disability Research © 2012 Blackwell Publishing Ltd.

Summer and winter habitat suitability of Marco Polo argali in southeastern Tajikistan: A modeling approach.

PubMed

Salas, Eric Ariel L; Valdez, Raul; Michel, Stefan

2017-11-01

We modeled summer and winter habitat suitability of Marco Polo argali in the Pamir Mountains in southeastern Tajikistan using these statistical algorithms: Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines. Using sheep occurrence data collected from 2009 to 2015 and a set of selected habitat predictors, we produced summer and winter habitat suitability maps and determined the important habitat suitability predictors for both seasons. Our results demonstrated that argali selected proximity to riparian areas and greenness as the two most relevant variables for summer, and the degree of slope (gentler slopes between 0° to 20°) and Landsat temperature band for winter. The terrain roughness was also among the most important variables in summer and winter models. Aspect was only significant for winter habitat, with argali preferring south-facing mountain slopes. We evaluated various measures of model performance such as the Area Under the Curve (AUC) and the True Skill Statistic (TSS). Comparing the five algorithms, the AUC scored highest for Boosted Regression Tree in summer (AUC = 0.94) and winter model runs (AUC = 0.94). In contrast, Random Forest underperformed in both model runs.

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

PubMed Central

Shao, Yuehjen E.

2014-01-01

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. PMID:24723804

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

Predictors of health of pre-registration nursing and midwifery students: Findings from a cross-sectional survey.

PubMed

Deasy, Christine; Coughlan, Barry; Pironom, Julie; Jourdan, Didier; Mannix-McNamara, Patricia

2016-01-01

Student nurses/midwives evidence less than exemplary lifestyle habits and poor emotional health, despite exposure to health education/promotion during their educational preparation. Knowledge of the factors that predict nursing/midwifery students' health could inform strategies to enhance their health and increase their credibility as future health promoters/educators. To establish the predictors of nursing/midwifery student emotional health. Cross-sectional survey. The research took place at a university in Ireland. We involved a total sample (n=473) student nurses/midwives. Participants completed the General Health Questionnaire, Lifestyle Behaviour Questionnaire and Ways of Coping Questionnaire to determine their self-reported emotional health, lifestyle behaviour and coping processes. Multivariate regression was performed to identify the predictors of student emotional health (dependent variable). The independent variables were demographics, coping, lifestyle behaviour and students' perceptions of determinants of their health. Many respondents reported significant emotional distress (48.71%) and unhealthy lifestyle behaviours including smoking (27.94%), physical inactivity (34.29%), alcohol consumption (91.7%) and unhealthy diet (28.05%). Multivariate regressions indicated that the predictors of emotional distress included gender, year of study, smoking, passive coping and beliefs that their student life was stressful or/and that worry stress and boredom adversely impacted their diet. Targeting student's beliefs regarding influences upon their health, promotion of positive lifestyles and adaptive coping is necessary to facilitate health gain of future health professionals. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

A Novel approach to monitor chlorophyll-a concentration using an adaptive model from MODIS data at 250 metres spatial resolution

NASA Astrophysics Data System (ADS)

El Alem, A.; Chokmani, K.; Laurion, I.; El Adlouni, S.

2013-12-01

Occurrence and extent of Harmful Algal Bloom (HAB) has increased in inland water bodies around the world. The appearance of these blooms reflects the advanced state of eutrophication of several aquatic systems caused by urban, agricultural, and industrial development. Algal blooms, especially those cyanobacterial origins, are capable to produce and release toxins, threatening human and animal health, quality of drinking water, and recreational water bodies. Conventional monitoring networks, based on infrequent sampling in a few fixed monitoring stations, cannot provide the information needed as HABs are spatially and temporally heterogeneous. Remote sensing represents an interesting alternative to provide the required spatial and temporal coverage. The usefulness of air-borne and satellite remote sensing data to detect HABs was demonstrated since three decades ago, and since several empirical and semi-empirical models, using satellite imagery, were developed to estimate chlorophyll-a concentration [Chl-a] as a proxy to detect bloom proliferations. However, most of those models presented several weaknesses that are generally linked to the range of [Chl-a] to be estimated. Indeed, models originally calibrated for high [Chl-a] fail to estimate low concentrations and vice versa. In this study, an adaptive model to estimate [Chl-a], spread over a wide range of concentrations, is developed for optically complex inland water bodies based on combination of water spectral response classification and three developed semi-empirical algorithms using a multivariate regression. Three distinct water types (low, medium, and high [Chl-a]) are first identified using the Classification and Regression Tree (CART) method performed on remote sensing reflectance over a dataset of 44 [Chl-a] samples collected from Lakes over Quebec province. Based on the water classification, a specific multivariate model to each water type is developed using the same dataset and the MODIS data at 250-m spatial resolution. By pre-clustering inland water bodies, the results were very interesting as the determination coefficients as well as the relative RMSE of the cross-validation were of 0.99, 0.98 and 0.95 and of 0.5%, 8% and 17% for high, medium, and low [Chl-a], respectively. On the other hand, the adaptive model reached a global success rate of 92% using an independent, semi-qualitative, [Chl-a] samples collected over more than twenty inland water bodies for the years 2009 and 2010 over the Quebec province.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Is Heart Rate Variability Better Than Routine Vital Signs for Prehospital Identification of Major Hemorrhage

DTIC Science & Technology

2015-01-01

different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and routine vital signs to test the hypothesis that...study sponsors did not have any role in the study design, data collection, analysis and interpretation of data, report writing, or the decision to...primary outcome was hemorrhagic injury plus different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and

Assessing Multivariate Constraints to Evolution across Ten Long-Term Avian Studies

PubMed Central

Teplitsky, Celine; Tarka, Maja; Møller, Anders P.; Nakagawa, Shinichi; Balbontín, Javier; Burke, Terry A.; Doutrelant, Claire; Gregoire, Arnaud; Hansson, Bengt; Hasselquist, Dennis; Gustafsson, Lars; de Lope, Florentino; Marzal, Alfonso; Mills, James A.; Wheelwright, Nathaniel T.; Yarrall, John W.; Charmantier, Anne

2014-01-01

Background In a rapidly changing world, it is of fundamental importance to understand processes constraining or facilitating adaptation through microevolution. As different traits of an organism covary, genetic correlations are expected to affect evolutionary trajectories. However, only limited empirical data are available. Methodology/Principal Findings We investigate the extent to which multivariate constraints affect the rate of adaptation, focusing on four morphological traits often shown to harbour large amounts of genetic variance and considered to be subject to limited evolutionary constraints. Our data set includes unique long-term data for seven bird species and a total of 10 populations. We estimate population-specific matrices of genetic correlations and multivariate selection coefficients to predict evolutionary responses to selection. Using Bayesian methods that facilitate the propagation of errors in estimates, we compare (1) the rate of adaptation based on predicted response to selection when including genetic correlations with predictions from models where these genetic correlations were set to zero and (2) the multivariate evolvability in the direction of current selection to the average evolvability in random directions of the phenotypic space. We show that genetic correlations on average decrease the predicted rate of adaptation by 28%. Multivariate evolvability in the direction of current selection was systematically lower than average evolvability in random directions of space. These significant reductions in the rate of adaptation and reduced evolvability were due to a general nonalignment of selection and genetic variance, notably orthogonality of directional selection with the size axis along which most (60%) of the genetic variance is found. Conclusions These results suggest that genetic correlations can impose significant constraints on the evolution of avian morphology in wild populations. This could have important impacts on evolutionary dynamics and hence population persistence in the face of rapid environmental change. PMID:24608111

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

PubMed Central

Xiao, Li; Wei, Hui; Himmel, Michael E.; Jameel, Hasan; Kelley, Stephen S.

2014-01-01

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass. PMID:25147552

MicroRNA let-7, T cells, and patient survival in colorectal cancer

PubMed Central

Dou, Ruoxu; Nishihara, Reiko; Cao, Yin; Hamada, Tsuyoshi; Mima, Kosuke; Masuda, Atsuhiro; Masugi, Yohei; Shi, Yan; Gu, Mancang; Li, Wanwan; da Silva, Annacarolina; Nosho, Katsuhiko; Zhang, Xuehong; Meyerhardt, Jeffrey A.; Giovannucci, Edward L.; Chan, Andrew T.; Fuchs, Charles S.; Qian, Zhi Rong; Ogino, Shuji

2016-01-01

Experimental evidence suggests that the let-7 family of noncoding RNAs suppresses adaptive immune responses, contributing to immune evasion by the tumor. We hypothesized that the amount of let-7a and let-7b expression in colorectal carcinoma might be associated with limited T-lymphocyte infiltrates in the tumor microenvironment and worse clinical outcome. Utilizing the molecular pathological epidemiology resources of 795 rectal and colon cancers in two U.S.-nationwide prospective cohort studies, we measured tumor-associated let-7a and let-7b expression levels by quantitative reverse-transcription PCR, and CD3+, CD8+, CD45RO (PTPRC)+, and FOXP3+ cell densities by tumor tissue microarray immunohistochemistry and computer-assisted image analysis. Logistic regression analysis and Cox proportional hazards regression were used to assess associations of let-7a (and let-7b) expression (quartile predictor variables) with T-cell densities (binary outcome variables) and mortality, respectively, controlling for tumor molecular features, including microsatellite instability, CpG island methylator phenotype, LINE-1 methylation, and KRAS, BRAF, and PIK3CA mutations. Compared with cases in the lowest quartile of let-7a expression, those in the highest quartile were associated with lower densities of CD3+ [multivariate odds ratio (OR), 0.40; 95% confidence interval (CI), 0.23 to 0.67; Ptrend = 0.003] and CD45RO+ cells (multivariate OR, 0.31; 95% CI, 0.17 to 0.58; Ptrend = 0.0004), and higher colorectal cancer-specific mortality (multivariate hazard ratio, 1.82; 95% CI, 1.42 to 3.13; Ptrend = 0.001). In contrast, let-7b expression was not significantly associated with T-cell density or colorectal cancer prognosis. Our data support the role of let-7a in suppressing antitumor immunity in colorectal cancer, and suggest let-7a as a potential target of immunotherapy. PMID:27737877

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Application of correlation constrained multivariate curve resolution alternating least-squares methods for determination of compounds of interest in biodiesel blends using NIR and UV-visible spectroscopic data.

PubMed

de Oliveira, Rodrigo Rocha; de Lima, Kássio Michell Gomes; Tauler, Romà; de Juan, Anna

2014-07-01

This study describes two applications of a variant of the multivariate curve resolution alternating least squares (MCR-ALS) method with a correlation constraint. The first application describes the use of MCR-ALS for the determination of biodiesel concentrations in biodiesel blends using near infrared (NIR) spectroscopic data. In the second application, the proposed method allowed the determination of the synthetic antioxidant N,N'-Di-sec-butyl-p-phenylenediamine (PDA) present in biodiesel mixtures from different vegetable sources using UV-visible spectroscopy. Well established multivariate regression algorithm, partial least squares (PLS), were calculated for comparison of the quantification performance in the models developed in both applications. The correlation constraint has been adapted to handle the presence of batch-to-batch matrix effects due to ageing effects, which might occur when different groups of samples were used to build a calibration model in the first application. Different data set configurations and diverse modes of application of the correlation constraint are explored and guidelines are given to cope with different type of analytical problems, such as the correction of matrix effects among biodiesel samples, where MCR-ALS outperformed PLS reducing the relative error of prediction RE (%) from 9.82% to 4.85% in the first application, or the determination of minor compound with overlapped weak spectroscopic signals, where MCR-ALS gave higher (RE (%)=3.16%) for prediction of PDA compared to PLS (RE (%)=1.99%), but with the advantage of recovering the related pure spectral profile of analytes and interferences. The obtained results show the potential of the MCR-ALS method with correlation constraint to be adapted to diverse data set configurations and analytical problems related to the determination of biodiesel mixtures and added compounds therein. Copyright © 2014 Elsevier B.V. All rights reserved.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Social integration of Latin-American immigrants in Spain: the influence of the community context.

PubMed

Fuente, Asur; Herrero, Juan

2012-11-01

The main goal of this study is to analyze the degree to which several community elements such as insecurity, discrimination and informal community support might have an influence on the social integration of Latin-American immigrants, a group at risk of social exclusion in Spain. Multivariate linear regression analyses results showed that informal community support is positively related to social integration whereas insecurity is negatively related. The statistical relationship between discrimination and social integration disappears once levels of informal community support are taken into account. A better understanding of the factors that either promote or inhibit the social integration progress of immigrant population is important to orientate public policies and intervention programs that contribute to the adaptation of this population to the host society.

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

PubMed Central

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-01

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

PubMed

García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

2016-01-28

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

Regional flow duration curves: Geostatistical techniques versus multivariate regression

USGS Publications Warehouse

Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.

2016-01-01

A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

Estimation of Subpixel Snow-Covered Area by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Kuter, S.; Akyürek, Z.; Weber, G.-W.

2016-10-01

Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th

EXTENDING MULTIVARIATE DISTANCE MATRIX REGRESSION WITH AN EFFECT SIZE MEASURE AND THE ASYMPTOTIC NULL DISTRIBUTION OF THE TEST STATISTIC

PubMed Central

McArtor, Daniel B.; Lubke, Gitta H.; Bergeman, C. S.

2017-01-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains. PMID:27738957

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

PubMed

McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

2017-12-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Life stressors, coping strategies, and social supports in patients with irritable bowel syndrome

PubMed Central

Roohafza, Hamidreza; Keshteli, Ammar Hassanzadeh; Daghaghzadeh, Hamed; Afshar, Hamid; Erfani, Zahra; Adibi, Peyman

2016-01-01

Background: The frequency and the perceived intensity of life stressors, coping strategies, and social supports are very important in everybody's well-being. This study intended to estimate the relation of irritable bowel syndrome (IBS) and these factors. Materials and Methods: This was a cross-sectional study carried out in Isfahan on 2013. Data were extracted from the framework of the study on the epidemiology of psychological, alimentary health, and nutrition. Symptoms of IBS were evaluated by Talley bowel disease questionnaire. Stressful life event, modified COPE scale, and Multidimensional Scale of Perceived Social Support were also used. About 4763 subjects were completed questionnaires. Analyzing data were done by t-test and multivariate logistic regression. Results: Of all returned questionnaire, 1024 (21.5%) were diagnosed with IBS. IBS and clinically-significant IBS (IBS-S) groups have significantly experienced a higher level of perceived intensity of stressors and had a higher frequency of stressors. The mean score of social supports and the mean scores of three coping strategies (problem engagement, support seeking, and positive reinterpretation and growth) were significantly lower in subjects with either IBS-S or IBS than in those with no IBS. Multivariate logistic regression revealed a significant association between frequency of stressors and perceived intensity of stressors with IBS (odds ratio [OR] =1.09 and OR = 1.02, respectively) or IBS-S (OR = 1.09 and OR = 1.03, respectively). Conclusions: People with IBS had higher numbers of stressors, higher perception of the intensity of stressors, less adaptive coping strategies, and less social supports which should be focused in psychosocial interventions. PMID:27761433

Life stressors, coping strategies, and social supports in patients with irritable bowel syndrome.

PubMed

Roohafza, Hamidreza; Keshteli, Ammar Hassanzadeh; Daghaghzadeh, Hamed; Afshar, Hamid; Erfani, Zahra; Adibi, Peyman

2016-01-01

The frequency and the perceived intensity of life stressors, coping strategies, and social supports are very important in everybody's well-being. This study intended to estimate the relation of irritable bowel syndrome (IBS) and these factors. This was a cross-sectional study carried out in Isfahan on 2013. Data were extracted from the framework of the study on the epidemiology of psychological, alimentary health, and nutrition. Symptoms of IBS were evaluated by Talley bowel disease questionnaire. Stressful life event, modified COPE scale, and Multidimensional Scale of Perceived Social Support were also used. About 4763 subjects were completed questionnaires. Analyzing data were done by t -test and multivariate logistic regression. Of all returned questionnaire, 1024 (21.5%) were diagnosed with IBS. IBS and clinically-significant IBS (IBS-S) groups have significantly experienced a higher level of perceived intensity of stressors and had a higher frequency of stressors. The mean score of social supports and the mean scores of three coping strategies (problem engagement, support seeking, and positive reinterpretation and growth) were significantly lower in subjects with either IBS-S or IBS than in those with no IBS. Multivariate logistic regression revealed a significant association between frequency of stressors and perceived intensity of stressors with IBS (odds ratio [OR] =1.09 and OR = 1.02, respectively) or IBS-S (OR = 1.09 and OR = 1.03, respectively). People with IBS had higher numbers of stressors, higher perception of the intensity of stressors, less adaptive coping strategies, and less social supports which should be focused in psychosocial interventions.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Correlates of Incident Cognitive Impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study

PubMed Central

Gillett, Sarah R.; Thacker, Evan L.; Letter, Abraham J.; McClure, Leslie A.; Wadley, Virginia G.; Unverzagt, Frederick W.; Kissela, Brett M.; Kennedy, Richard E.; Glasser, Stephen P.; Levine, Deborah A.; Cushman, Mary

2015-01-01

Objective To identify approximately 500 cases of incident cognitive impairment (ICI) in a large, national sample adapting an existing cognitive test-based case definition and to examine relationships of vascular risk factors with ICI. Method Participants were from the REGARDS study, a national sample of 30,239 African-American and white Americans. Participants included in this analysis had normal cognitive screening and no history of stroke at baseline, and at least one follow-up cognitive assessment with a three test battery (TTB). Regression-based norms were applied to TTB scores to identify cases of ICI. Logistic regression was used to model associations with baseline vascular risk factors. Results We identified 495 participants with ICI out of 17,630 eligible participants. In multivariable modeling, income (OR 1.83 CI 1.27,2.62), stroke belt residence (OR 1.45 CI 1.18,1.78), history of transient ischemic attack (OR 1.90 CI 1.29,2.81), coronary artery disease(OR 1.32 CI 1.02,1.70), diabetes (OR 1.48 CI 1.17,1.87), obesity (OR 1.40 CI 1.05,1.86), and incident stroke (OR 2.73 CI 1.52,4.90) were associated with ICI. Conclusions We adapted a previously validated cognitive test-based case definition to identify cases of ICI. Many previously identified risk factors were associated with ICI, supporting the criterion-related validity of our definition. PMID:25978342

An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.

2017-01-01

Simulation-optimization method entails a large number of model simulations, which is computationally intensive or even prohibitive if the model simulation is extremely time-consuming. Statistical models have been examined as a surrogate of the high-fidelity physical model during simulation-optimization process to tackle this problem. Among them, Multivariate Adaptive Regression Splines (MARS), a non-parametric adaptive regression method, is superior in overcoming problems of high-dimensions and discontinuities of the data. Furthermore, the stability and accuracy of MARS model can be improved by bootstrap aggregating methods, namely, bagging. In this paper, Bagging MARS (BMARS) method is integrated to a surrogate-based simulation-optimization framework to calibrate a three-dimensional MODFLOW model, which is developed to simulate the groundwater flow in an arid hardrock-alluvium region in northwestern Oman. The physical MODFLOW model is surrogated by the statistical model developed using BMARS algorithm. The surrogate model, which is fitted and validated using training dataset generated by the physical model, can approximate solutions rapidly. An efficient Sobol' method is employed to calculate global sensitivities of head outputs to input parameters, which are used to analyze their importance for the model outputs spatiotemporally. Only sensitive parameters are included in the calibration process to further improve the computational efficiency. Normalized root mean square error (NRMSE) between measured and simulated heads at observation wells is used as the objective function to be minimized during optimization. The reasonable history match between the simulated and observed heads demonstrated feasibility of this high-efficient calibration framework.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

NASA Astrophysics Data System (ADS)

Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

2018-05-01

Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

Field applications of stand-off sensing using visible/NIR multivariate optical computing

NASA Astrophysics Data System (ADS)

Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.

2001-02-01

12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

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

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

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

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

USGS Publications Warehouse

Balshi, M. S.; McGuire, A.D.; Duffy, P.; Flannigan, M.; Walsh, J.; Melillo, J.

2009-01-01

Fire is a common disturbance in the North American boreal forest that influences ecosystem structure and function. The temporal and spatial dynamics of fire are likely to be altered as climate continues to change. In this study, we ask the question: how will area burned in boreal North America by wildfire respond to future changes in climate? To evaluate this question, we developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5?? (latitude ?? longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was substantially more predictable in the western portion of boreal North America than in eastern Canada. Burned area was also not very predictable in areas of substantial topographic relief and in areas along the transition between boreal forest and tundra. At the scale of Alaska and western Canada, the empirical fire models explain on the order of 82% of the variation in annual area burned for the period 1960-2002. July temperature was the most frequently occurring predictor across all models, but the fuel moisture codes for the months June through August (as a group) entered the models as the most important predictors of annual area burned. To predict changes in the temporal and spatial dynamics of fire under future climate, the empirical fire models used output from the Canadian Climate Center CGCM2 global climate model to predict annual area burned through the year 2100 across Alaska and western Canada. Relative to 1991-2000, the results suggest that average area burned per decade will double by 2041-2050 and will increase on the order of 3.5-5.5 times by the last decade of the 21st century. To improve the ability to better predict wildfire across Alaska and Canada, future research should focus on incorporating additional effects of long-term and successional vegetation changes on area burned to account more fully for interactions among fire, climate, and vegetation dynamics. ?? 2009 The Authors Journal compilation ?? 2009 Blackwell Publishing Ltd.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Testing the adaptive plasticity of Iris pumila leaf traits to natural light conditions using phenotypic selection analysis

NASA Astrophysics Data System (ADS)

Tucić, Branka; Tomić, Vladimir; Avramov, Stevan; Pemac, Danijela

1998-12-01

A multivariate selection analysis has been used to test the adaptiveness of several Iris pumila leaf traits that display plasticity to natural light conditions. Siblings of a synthetic population comprising 31 families of two populations from contrasting light habitats were grown at an open dune site and in the understory of a Pinus nigra stand in order to score variation in phenotypic expression of six leaf traits: number of senescent leaves, number of live leaves, leaf length, leaf width, leaf angle, and specific leaf area. The ambient light conditions affected the values of all traits studied except for specific leaf area. In accordance to ecophysiological expectations for an adaptive response to light, both leaf length and width were significantly greater while the angle between sequential leaves was significantly smaller in the woodland understory than at the exposed dune site. The relationship between leaf traits and vegetative fitness (total leaf area) differed across light habitats as predicted by functional hypotheses. The standardized linear selection gradient ( β') for leaf length and width were positive in sign in both environments, but their magnitude for leaf length was higher in the shade than under full sunlight. Since plasticity of leaf length in the woodland shade has been recognized as adaptive, fitness cost of producing plastic change in leaf length was assessed. In both of the available methods used, the two-step and the multivariate regression procedures, a rather high negative association between the fitness value and the plasticity of leaf length was obtained, indicating a cost of plasticity. The selection gradient for leaf angle was weak and significant only in the woodland understory. Genetic correlations between trait expressions in contrasting light environments were negative in sign and low in magnitude, implying a significant genetic variation for plasticity in these leaf traits. Furthermore, leaf length and leaf width were found to be genetically positively coupled, which indicates that there is a potential for these two traits to evolve toward their optimal phenotypic values even faster than would be expected if they were genetically independent.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Robustness of reduced-order multivariable state-space self-tuning controller

NASA Technical Reports Server (NTRS)

Yuan, Zhuzhi; Chen, Zengqiang

1994-01-01

In this paper, we present a quantitative analysis of the robustness of a reduced-order pole-assignment state-space self-tuning controller for a multivariable adaptive control system whose order of the real process is higher than that of the model used in the controller design. The result of stability analysis shows that, under a specific bounded modelling error, the adaptively controlled closed-loop real system via the reduced-order state-space self-tuner is BIBO stable in the presence of unmodelled dynamics.

The Impact of Coping Flexibility on the Risk of Depressive Symptoms

PubMed Central

Kato, Tsukasa

2015-01-01

Objective According to the dual-process theory, coping flexibility is defined as the ability to produce and implement a new coping strategy in place of an ineffective coping strategy. Specifically, coping flexibility includes two processes: evaluation coping and adaptive coping. Evaluation coping refers to sensitivity to feedback about the efficacy of a coping strategy, and adaptive coping involves the willingness to implement alternative coping strategies. The coping flexibility hypothesis (CFH) postulates that more flexible coping will be associated with more adaptive outcomes; importantly, there are numerous theories and studies that support the CFH. The main purpose of this study was to test the CFH based on dual-process theory. Methods A total of 1,770 Japanese college students participated and, completed a set of questionnaires that measured coping flexibility (evaluation coping and adaptive coping) and depressive symptoms. Depressive symptoms were measured via the Center for Epidemiologic Studies Depression Scale. Results The proportions of women and men who reported depressive symptoms were 58.69% (95% CIs [55.74, 61.66]) and 54.17% (95% CIs [50.37, 57.95]), respectively when a cut-off score of 16 on the CES-D was used. A multivariable logistic regression analysis revealed that evaluation coping (OR = 0.86, 95% CIs [0.83, 0.0.89]) and adaptive coping (OR = 0.91, 95% CIs [0.88, 0.93]) were significantly associated with lower levels of depressive symptoms. Conclusion The results of the present study indicated that the CFH based on dual-process theory was supported in a Japanese sample. PMID:26011626

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

Optical scatterometry of quarter-micron patterns using neural regression

NASA Astrophysics Data System (ADS)

Bischoff, Joerg; Bauer, Joachim J.; Haak, Ulrich; Hutschenreuther, Lutz; Truckenbrodt, Horst

1998-06-01

With shrinking dimensions and increasing chip areas, a rapid and non-destructive full wafer characterization after every patterning cycle is an inevitable necessity. In former publications it was shown that Optical Scatterometry (OS) has the potential to push the attainable feature limits of optical techniques from 0.8 . . . 0.5 microns for imaging methods down to 0.1 micron and below. Thus the demands of future metrology can be met. Basically being a nonimaging method, OS combines light scatter (or diffraction) measurements with modern data analysis schemes to solve the inverse scatter issue. For very fine patterns with lambda-to-pitch ratios grater than one, the specular reflected light versus the incidence angle is recorded. Usually, the data analysis comprises two steps -- a training cycle connected the a rigorous forward modeling and the prediction itself. Until now, two data analysis schemes are usually applied -- the multivariate regression based Partial Least Squares method (PLS) and a look-up-table technique which is also referred to as Minimum Mean Square Error approach (MMSE). Both methods are afflicted with serious drawbacks. On the one hand, the prediction accuracy of multivariate regression schemes degrades with larger parameter ranges due to the linearization properties of the method. On the other hand, look-up-table methods are rather time consuming during prediction thus prolonging the processing time and reducing the throughput. An alternate method is an Artificial Neural Network (ANN) based regression which combines the advantages of multivariate regression and MMSE. Due to the versatility of a neural network, not only can its structure be adapted more properly to the scatter problem, but also the nonlinearity of the neuronal transfer functions mimic the nonlinear behavior of optical diffraction processes more adequately. In spite of these pleasant properties, the prediction speed of ANN regression is comparable with that of the PLS-method. In this paper, the viability and performance of ANN-regression will be demonstrated with the example of sub-quarter-micron resist metrology. To this end, 0.25 micrometer line/space patterns have been printed in positive photoresist by means of DUV projection lithography. In order to evaluate the total metrology chain from light scatter measurement through data analysis, a thorough modeling has been performed. Assuming a trapezoidal shape of the developed resist profile, a training data set was generated by means of the Rigorous Coupled Wave Approach (RCWA). After training the model, a second data set was computed and deteriorated by Gaussian noise to imitate real measuring conditions. Then, these data have been fed into the models established before resulting in a Standard Error of Prediction (SEP) which corresponds to the measuring accuracy. Even with putting only little effort in the design of a back-propagation network, the ANN is clearly superior to the PLS-method. Depending on whether a network with one or two hidden layers was used, accuracy gains between 2 and 5 can be achieved compared with PLS regression. Furthermore, the ANN is less noise sensitive, for there is only a doubling of the SEP at 5% noise for ANN whereas for PLS the accuracy degrades rapidly with increasing noise. The accuracy gain also depends on the light polarization and on the measured parameters. Finally, these results have been proven experimentally, where the OS-results are in good accordance with the profiles obtained from cross- sectioning micrographs.

Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

PubMed

Shafizadeh-Moghadam, Hossein; Valavi, Roozbeh; Shahabi, Himan; Chapi, Kamran; Shirzadi, Ataollah

2018-07-01

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits.

PubMed

van Heerwaarden, Joost; van Zanten, Martijn; Kruijer, Willem

2015-10-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation.

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

State-space self-tuner for on-line adaptive control

NASA Technical Reports Server (NTRS)

Shieh, L. S.

1994-01-01

Dynamic systems, such as flight vehicles, satellites and space stations, operating in real environments, constantly face parameter and/or structural variations owing to nonlinear behavior of actuators, failure of sensors, changes in operating conditions, disturbances acting on the system, etc. In the past three decades, adaptive control has been shown to be effective in dealing with dynamic systems in the presence of parameter uncertainties, structural perturbations, random disturbances and environmental variations. Among the existing adaptive control methodologies, the state-space self-tuning control methods, initially proposed by us, are shown to be effective in designing advanced adaptive controllers for multivariable systems. In our approaches, we have embedded the standard Kalman state-estimation algorithm into an online parameter estimation algorithm. Thus, the advanced state-feedback controllers can be easily established for digital adaptive control of continuous-time stochastic multivariable systems. A state-space self-tuner for a general multivariable stochastic system has been developed and successfully applied to the space station for on-line adaptive control. Also, a technique for multistage design of an optimal momentum management controller for the space station has been developed and reported in. Moreover, we have successfully developed various digital redesign techniques which can convert a continuous-time controller to an equivalent digital controller. As a result, the expensive and unreliable continuous-time controller can be implemented using low-cost and high performance microprocessors. Recently, we have developed a new hybrid state-space self tuner using a new dual-rate sampling scheme for on-line adaptive control of continuous-time uncertain systems.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

In this first of a series of statistical methodology commentaries for the clinician, we discuss the use of multivariate linear regression. Copyright © 2012 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

On Restructurable Control System Theory

NASA Technical Reports Server (NTRS)

Athans, M.

1983-01-01

The state of stochastic system and control theory as it impacts restructurable control issues is addressed. The multivariable characteristics of the control problem are addressed. The failure detection/identification problem is discussed as a multi-hypothesis testing problem. Control strategy reconfiguration, static multivariable controls, static failure hypothesis testing, dynamic multivariable controls, fault-tolerant control theory, dynamic hypothesis testing, generalized likelihood ratio (GLR) methods, and adaptive control are discussed.

Adaptation of a Weighted Regression Approach to Evaluate Water Quality Trends in an Estuary

EPA Science Inventory

To improve the description of long-term changes in water quality, we adapted a weighted regression approach to analyze a long-term water quality dataset from Tampa Bay, Florida. The weighted regression approach, originally developed to resolve pollutant transport trends in rivers...

Adaptation of a weighted regression approach to evaluate water quality trends in anestuary

EPA Science Inventory

To improve the description of long-term changes in water quality, a weighted regression approach developed to describe trends in pollutant transport in rivers was adapted to analyze a long-term water quality dataset from Tampa Bay, Florida. The weighted regression approach allows...

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

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

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

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Socioeconomic disparities in the quality of life in children with cancer or brain tumors: the mediating role of family factors.

PubMed

Litzelman, Kristin; Barker, Emily; Catrine, Kristine; Puccetti, Diane; Possin, Peggy; Witt, Whitney P

2013-05-01

This study aimed to determine if and to what extent (i) socioeconomic disparities exist in the health-related quality of life (QOL) of children with cancer or brain tumors and healthy children; and (ii) family functioning and burden mediate the relationship between socioeconomic status and children's QOL. In this cross-sectional study, parents of children ages 2-18 with (n = 71) and without (n = 135) cancer or brain tumors completed in-person interviewer-assisted surveys assessing sociodemographics (including income and parental education), child QOL (measure: PedsQL), family functioning (measure: Family Adaptability and Cohesion Evaluation Scale IV) and burden (measure: Impact on the Family Scale). For children with cancer, clinical characteristics were captured through medical record abstraction. Multiple linear regression was used to determine the relationship between income and child QOL; the interaction between group status and income was assessed. Staged multivariate regression models were used to assess the role of family factors in this relationship among children with cancer. In multivariate analyses, the effect of income differed by cancer status; lower income was associated with worse QOL in children with cancer but not among healthy children. Among children with cancer, this relationship was significantly attenuated by family burden. Significant socioeconomic disparities exist in the QOL of children with cancer. Family factors partially explain the relationship between low income and poor QOL outcomes among these children. Lower-income families may have fewer resources to cope with their child's cancer. Increased support, monitoring, and referrals to reduce burden for these families may lead to improved QOL in children with cancer. Copyright © 2012 John Wiley & Sons, Ltd.

Factors associated with reduction in use of neoadjuvant androgen suppression therapy before radical prostatectomy.

PubMed

O'Shaughnessy, Matthew J; Jarosek, Stephanie L; Virnig, Beth A; Konety, Badrinath R; Elliott, Sean P

2013-04-01

To determine whether the prescribing patterns for nonindicated androgen suppression therapy (AST), using neoadjuvant AST as the model, changed according to the prevailing clinical evidence, changes in reimbursement, or evidence of increased harm from treatment. We identified 34,976 men with prostate cancer who had undergone radical prostatectomy within 12 months of diagnosis from the Surveillance, Epidemiology, and End Results-Medicare data set (1992-2007), and their clinical and demographic parameters were assessed. We measured the Medicare claims for receipt of AST before radical prostatectomy and calculated the annual rates of neoadjuvant AST, which were adjusted for confounding variables using multivariate logistic regression analysis, and compared them with the prevailing published clinical data on the outcomes of neoadjuvant AST, changes in reimbursement, or published data on clinical harm from treatment. The use of neoadjuvant AST increased from 7.8% in 1992 to a peak of 17.6% in 1996 and then decreased steadily to 4.6% in 2007. This rate change was significant on multivariate regression analysis, with a single join point in 1996 (P <.001), and corresponded to published data showing improved surgical margin rates and pathologic downstaging in the early 1990s and data showing no improvement in disease recurrence or overall survival beginning in 1997. Changes in reimbursement and evidence of harm from AST were not associated with the decreased use of neoadjuvant AST. Using neoadjuvant AST as the model for the nonindicated use of AST, physicians reduced AST use in response to high-level evidence showing a lack of benefit, despite the high reimbursement. This suggests that physicians adapt to emerging evidence and use evidence-based practice. Copyright © 2013 Elsevier Inc. All rights reserved.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Serum dehydroepiandrosterone sulphate, psychosocial factors and musculoskeletal pain in workers.

PubMed

Marinelli, A; Prodi, A; Pesel, G; Ronchese, F; Bovenzi, M; Negro, C; Larese Filon, F

2017-12-30

The serum level of dehydroepiandrosterone sulphate (DHEA-S) has been suggested as a biological marker of stress. To assess the association between serum DHEA-S, psychosocial factors and musculoskeletal (MS) pain in university workers. The study population included voluntary workers at the scientific departments of the University of Trieste (Italy) who underwent periodical health surveillance from January 2011 to June 2012. DHEA-S level was analysed in serum. The assessment tools included the General Health Questionnaire (GHQ) and a modified Nordic musculoskeletal symptoms questionnaire. The relation between DHEA-S, individual characteristics, pain perception and psychological factors was assessed by means of multivariable linear regression analysis. There were 189 study participants. The study population was characterized by high reward and low effort. Pain perception in the neck, shoulder, upper limbs, upper back and lower back was reported by 42, 32, 19, 29 and 43% of people, respectively. In multivariable regression analysis, gender, age and pain perception in the shoulder and upper limbs were significantly related to serum DHEA-S. Effort and overcommitment were related to shoulder and neck pain but not to DHEA-S. The GHQ score was associated with pain perception in different body sites and inversely to DHEA-S but significance was lost in multivariable regression analysis. DHEA-S was associated with age, gender and perception of MS pain, while effort-reward imbalance dimensions and GHQ score failed to reach the statistical significance in multivariable regression analysis. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

Tang, Weidong; Ruan, Feng; Chen, Qi; Chen, Suping; Shao, Xuebo; Gao, Jianbo; Zhang, Mao

2016-07-01

Acute organophosphorus pesticide poisoning (AOPP) is becoming a significant problem and a potential cause of human mortality because of the abuse of organophosphate compounds. This study aims to determine the independent prognostic factors of AOPP by using multivariate logistic regression analysis. The clinical data for 71 subjects with AOPP admitted to our hospital were retrospectively analyzed. This information included the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, admission blood cholinesterase levels, 6-h post-admission blood cholinesterase levels, cholinesterase activity, blood pH, and other factors. Univariate analysis and multivariate logistic regression analyses were conducted to identify all prognostic factors and independent prognostic factors, respectively. A receiver operating characteristic curve was plotted to analyze the testing power of independent prognostic factors. Twelve of 71 subjects died. Admission blood lactate levels, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, blood pH, and APACHE II scores were identified as prognostic factors for AOPP according to the univariate analysis, whereas only 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, and blood pH were independent prognostic factors identified by multivariate logistic regression analysis. The receiver operating characteristic analysis suggested that post-admission 6-h lactate clearance rates were of moderate diagnostic value. High 6-h post-admission blood lactate levels, low blood pH, and low post-admission 6-h lactate clearance rates were independent prognostic factors identified by multivariate logistic regression analysis. Copyright © 2016 by Daedalus Enterprises.

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals.

PubMed

Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M

2016-05-01

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

Sharpening method of satellite thermal image based on the geographical statistical model

NASA Astrophysics Data System (ADS)

Qi, Pengcheng; Hu, Shixiong; Zhang, Haijun; Guo, Guangmeng

2016-04-01

To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

EPA Science Inventory

Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

PubMed Central

van Zanten, Martijn

2015-01-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation. PMID:26496492

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

Predicting volumes in four Hawaii hardwoods...first multivariate equations developed

Treesearch

David A. Sharpnack

1966-01-01

Multivariate regression equations were developed for predicting board-foot (Int. 1/ 4-inch log rule ) and cubic-foot volumes in each 8.15-foot section of trees of four Hawaii hardwood species. The species are koa (Acacia koa), ohia (Metrosideros polymorpha), robusta eucalyptus (Eucalyptus robusta), and...

A Multivariate Test of the Bott Hypothesis in an Urban Irish Setting

ERIC Educational Resources Information Center

Gordon, Michael; Downing, Helen

1978-01-01

Using a sample of 686 married Irish women in Cork City the Bott hypothesis was tested, and the results of a multivariate regression analysis revealed that neither network connectedness nor the strength of the respondent's emotional ties to the network had any explanatory power. (Author)

Moral distress and burnout syndrome: are there relationships between these phenomena in nursing workers?

PubMed

Dalmolin, Graziele de Lima; Lunardi, Valéria Lerch; Lunardi, Guilherme Lerch; Barlem, Edison Luiz Devos; Silveira, Rosemary Silva da

2014-01-01

to identify relationships between moral distress and Burnout in the professional performance from the perceptions of the experiences of nursing workers. this is a survey type study with 375 nursing workers working in three different hospitals of southern Rio Grande do Sul, with the application of adaptations of the Moral Distress Scale and the Maslach Burnout Inventory, validated and standardized for use in Brazil. Data validation occurred through factor analysis and Cronbach's alpha. For the data analysis bivariate analysis using Pearson's correlation and multivariate analysis using multiple regression were performed. the existence of a weak correlation between moral distress and Burnout was verified. A possible positive correlation between Burnout and therapeutic obstinacy, and a negative correlation between professional fulfillment and moral distress were identified. the need was identified for further studies that include mediating and moderating variables that may explain more clearly the models studied.

Moral distress and Burnout syndrome: are there relationships between these phenomena in nursing workers?1

PubMed Central

Dalmolin, Graziele de Lima; Lunardi, Valéria Lerch; Lunardi, Guilherme Lerch; Barlem, Edison Luiz Devos; da Silveira, Rosemary Silva

2014-01-01

Objective to identify relationships between moral distress and Burnout in the professional performance from the perceptions of the experiences of nursing workers. Methods this is a survey type study with 375 nursing workers working in three different hospitals of southern Rio Grande do Sul, with the application of adaptations of the Moral Distress Scale and the Maslach Burnout Inventory, validated and standardized for use in Brazil. Data validation occurred through factor analysis and Cronbach's alpha. For the data analysis bivariate analysis using Pearson's correlation and multivariate analysis using multiple regression were performed. Results the existence of a weak correlation between moral distress and Burnout was verified. A possible positive correlation between Burnout and therapeutic obstinacy, and a negative correlation between professional fulfillment and moral distress were identified. Conclusion the need was identified for further studies that include mediating and moderating variables that may explain more clearly the models studied. PMID:24553701

Geospatial Resource Access Analysis In Hedaru, Tanzania

NASA Astrophysics Data System (ADS)

Clark, Dylan G.; Premkumar, Deepak; Mazur, Robert; Kisimbo, Elibariki

2013-12-01

Populations around the world are facing increased impacts of anthropogenic-induced environmental changes and rapid population movements. These environmental and social shifts are having an elevated impact on the livelihoods of agriculturalists and pastoralists in developing countries. This appraisal integrates various tools—usually used independently— to gain a comprehensive understanding of the regional livelihood constraints in the rural Hedaru Valley of northeastern Tanzania. Conducted in three villages with different natural resources, using three primary methods: 1) participatory mapping of infrastructures; 2) administration of quantitative, spatially-tied surveys (n=80) and focus groups (n=14) that examined land use, household health, education, and demographics; 3) conducting quantitative time series analysis of Landsat- based Normalized Difference Vegetation Index images. Through various geospatial and multivariate linear regression analyses, significant geospatial trends emerged. This research added to the academic understanding of the region while establishing pathways for climate change adaptation strategies.

A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique.

PubMed

García Nieto, P J; Alonso Fernández, J R; Sánchez Lasheras, F; de Cos Juez, F J; Díaz Muñiz, C

2012-07-15

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational water uses. The aim of this study is to improve our previous and successful work about cyanotoxins prediction from some experimental cyanobacteria concentrations in the Trasona reservoir (Asturias, Northern Spain) using the multivariate adaptive regression splines (MARS) technique at a local scale. In fact, this new improvement consists of using not only biological variables, but also the physical-chemical ones. As a result, the coefficient of determination has improved from 0.84 to 0.94, that is to say, more accurate predictive calculations and a better approximation to the real problem were obtained. Finally the agreement of the MARS model with experimental data confirmed the good performance. Copyright © 2012 Elsevier B.V. All rights reserved.

Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments.

PubMed

Duan, Fenghai; Xu, Ye

2017-01-01

To analyze a microarray experiment to identify the genes with expressions varying after the diagnosis of breast cancer. A total of 44 928 probe sets in an Affymetrix microarray data publicly available on Gene Expression Omnibus from 249 patients with breast cancer were analyzed by the nonparametric multivariate adaptive splines. Then, the identified genes with turning points were grouped by K-means clustering, and their network relationship was subsequently analyzed by the Ingenuity Pathway Analysis. In total, 1640 probe sets (genes) were reliably identified to have turning points along with the age at diagnosis in their expression profiling, of which 927 expressed lower after turning points and 713 expressed higher after the turning points. K-means clustered them into 3 groups with turning points centering at 54, 62.5, and 72, respectively. The pathway analysis showed that the identified genes were actively involved in various cancer-related functions or networks. In this article, we applied the nonparametric multivariate adaptive splines method to a publicly available gene expression data and successfully identified genes with expressions varying before and after breast cancer diagnosis.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Need and availability of assistive devices to compensate for impaired hand function of individuals with tetraplegia.

PubMed

Wäckerlin, Stephanie; Gemperli, Armin; Sigrist-Nix, Diana; Arnet, Ursina

2018-06-04

Context/Objective To evaluate the availability and self-declared unmet need of assistive devices to compensate for impaired hand function of individuals with tetraplegia in Switzerland. Design Cross-sectional survey. Setting Community. Participants Individuals with tetraplegia, aged 16 years or older, living in Switzerland. Interventions not applicable. Outcome Measures The self-report availability and unmet need of 18 assistive devices for impaired hand function was analyzed descriptively. The availability of devices was further evaluated stratified by sex, age, SCI severity, independence in grooming, time since injury, living situation, working status, and income. Associations between availability of devices and person characteristics were investigated using logistic regression analysis. Results Overall 32.7% of participants had any assistive device for impaired hand function at their disposal. The most frequent devices were adapted cutlery (14.8%), type supports (14.1%), environmental control systems (11.4%), and writing orthosis (10.6%). In the bivariate analysis several factors showed significant associations with at least one assistive device. Nevertheless, when controlling for potential confounding in multivariate analysis only independence in grooming (adapted cutlery, environmental control systems, type support, speech recognition software), SCI severity (writing orthosis, type support), and sex (adapted kitchenware) remained significantly associated with the availability of the mentioned assistive devices. The self-declared unmet need was generally low (0.7% - 4.3%), except for adapted kitchenware with a moderate unmet need (8.9%). Conclusion This study indicates that most individuals with tetraplegia in Switzerland are adequately supplied with assistive devices to compensate for impaired hand function. The availability depends mainly on SCI severity and independence in grooming.

Career Adaptability Development in Adolescence: Multiple Predictors and Effect on Sense of Power and Life Satisfaction

ERIC Educational Resources Information Center

Hirschi, Andreas

2009-01-01

This longitudinal panel study investigated predictors of career adaptability development and its effect on development of sense of power and experience of life satisfaction among 330 Swiss eighth graders. A multivariate measure of career adaptability consisting of career choice readiness, planning, exploration, and confidence was applied. Based on…

Adaptation potential of naturally ventilated barns to high temperature extremes: The OptiBarn project

NASA Astrophysics Data System (ADS)

Menz, Christoph

2016-04-01

Climate change interferes with various aspects of the socio-economic system. One important aspect is its influence on animal husbandry, especially dairy faming. Dairy cows are usually kept in naturally ventilated barns (NVBs) which are particular vulnerable to extreme events due to their low adaptation capabilities. An effective adaptation to high outdoor temperatures for example, is only possible under certain wind and humidity conditions. High temperature extremes are expected to increase in number and strength under climate change. To assess the impact of this change on NVBs and dairy cows also the changes in wind and humidity needs to be considered. Hence we need to consider the multivariate structure of future temperature extremes. The OptiBarn project aims to develop sustainable adaptation strategies for dairy housings under climate change for Europe, by considering the multivariate structure of high temperature extremes. In a first step we identify various multivariate high temperature extremes for three core regions in Europe. With respect to dairy cows in NVBs we will focus on the wind and humidity field during high temperature events. In a second step we will use the CORDEX-EUR-11 ensemble to evaluate the capability of the RCMs to model such events and assess their future change potential. By transferring the outdoor conditions to indoor climate and animal wellbeing the results of this assessment can be used to develop technical, architectural and animal specific adaptation strategies for high temperature extremes.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

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

Treesearch

L.R. Grosenbaugh

1967-01-01

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

Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.

PubMed

Black, L E; Brion, G M; Freitas, S J

2007-06-01

Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.

[Risk factors for iron deficiency anemia in infants aged 6 to 12 months and its effects on neuropsychological development].

PubMed

Xu, Kang; Zhang, Cui-Mei; Huang, Lian-Hong; Fu, Si-Mao; Liu, Yu-Ling; Chen, Ang; Ou, Jun-Bin

2015-08-01

To study the risk factors for moderate and severe iron deficiency anemia (IDA) in infants aged 6-12 months, and to preliminarily investigate the effects of IDA on the neuromotor development and temperament characteristics of infants. A total of 326 infants aged 6-12 months with IDA were classified into three groups: mild IDA (n=176), moderate IDA (n=111), and severe IDA (n=39) according to the severity of anemia. The risk factors for moderate or severe IDA were investigated by multivariate logistic regression analysis. Three hundred and forty-six infants without IDA who showed matched age, sex, and other backgrounds were selected as the control group. The Gesell Development Diagnosis Scale was used to evaluate children's mental development. The Temperament Scale for infants was used for evaluating children's temperament. The univariate analysis showed that the severity of IDA was associated with sex, birth weight, gestational age, multiple birth, maternal anemia during pregnancy, and mother's lack of knowledge about IDA (P<0.05). Setting the mild IDA group as control, the multivariate logistic regression analysis showed that multiple birth, premature birth, low birth weight (<2500 g), maternal anemia during pregnancy, breast feeding, and mother's lack of knowledge about IDA were the risk factors for severe IDA (OR>1; P<0.05); premature birth, breast feeding, and mixed feeding were the risk factors for moderate IDA (OR>1; P<0.05). The IDA group had significantly lower scores in Gesell general development quotient, gross motor, adaptive behavior, and fine motor than the control group (P<0.05). The IDA group had higher percentages of children with difficulty and intermediate difficulty temperaments than the control group (P<0.05). The IDA group had significantly higher scores in activity level, rhythmicity, adaptability, and perseverance than the control group (P<0.05). The severity of IDA is associated with premature birth, multiple birth, low birth weight, feeding pattern, maternal anemia during pregnancy and mother's lack of knowledge about IDA in infants aged 6-12 months. Infants with IDA have delayed neuromotor development and most of them have negative temperaments. More attention should be paid to mental and behavior problems for the infants. It is necessary to provide guidance for their parents in feeding and education.

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Kisi, Ozgur; Singh, Vijay P.

2017-02-01

Drought forecasting using standardized metrics of rainfall is a core task in hydrology and water resources management. Standardized Precipitation Index (SPI) is a rainfall-based metric that caters for different time-scales at which the drought occurs, and due to its standardization, is well-suited for forecasting drought at different periods in climatically diverse regions. This study advances drought modelling using multivariate adaptive regression splines (MARS), least square support vector machine (LSSVM), and M5Tree models by forecasting SPI in eastern Australia. MARS model incorporated rainfall as mandatory predictor with month (periodicity), Southern Oscillation Index, Pacific Decadal Oscillation Index and Indian Ocean Dipole, ENSO Modoki and Nino 3.0, 3.4 and 4.0 data added gradually. The performance was evaluated with root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (r2). Best MARS model required different input combinations, where rainfall, sea surface temperature and periodicity were used for all stations, but ENSO Modoki and Pacific Decadal Oscillation indices were not required for Bathurst, Collarenebri and Yamba, and the Southern Oscillation Index was not required for Collarenebri. Inclusion of periodicity increased the r2 value by 0.5-8.1% and reduced RMSE by 3.0-178.5%. Comparisons showed that MARS superseded the performance of the other counterparts for three out of five stations with lower MAE by 15.0-73.9% and 7.3-42.2%, respectively. For the other stations, M5Tree was better than MARS/LSSVM with lower MAE by 13.8-13.4% and 25.7-52.2%, respectively, and for Bathurst, LSSVM yielded more accurate result. For droughts identified by SPI ≤ - 0.5, accurate forecasts were attained by MARS/M5Tree for Bathurst, Yamba and Peak Hill, whereas for Collarenebri and Barraba, M5Tree was better than LSSVM/MARS. Seasonal analysis revealed disparate results where MARS/M5Tree was better than LSSVM. The results highlight the importance of periodicity in drought forecasting and also ascertains that model accuracy scales with geographic/seasonal factors due to complexity of drought and its relationship with inputs and data attributes that can affect the evolution of drought events.

Coordination patterns related to high clinical performance in a simulated anesthetic crisis.

PubMed

Manser, Tanja; Harrison, Thomas Kyle; Gaba, David M; Howard, Steven K

2009-05-01

Teamwork is an integral component in the delivery of safe patient care. Several studies highlight the importance of effective teamwork and the need for teams to respond dynamically to changing task requirements, for example, during crisis situations. In this study, we address one of the many facets of "effective teamwork" in medical teams by investigating coordination patterns related to high performance in the management of a simulated malignant hyperthermia (MH) scenario. We hypothesized that (a) anesthesia crews dynamically adapt their work and coordination patterns to the occurrence of a simulated MH crisis and that (b) crews with higher clinical performance scores (based on a time-based scoring system for critical MH treatment steps) exhibit different coordination patterns. This observational study investigated differences in work and coordination patterns of 24 two-person anesthesia crews in a simulated MH scenario. Clinical and coordination behavior were coded using a structured observation system consisting of 36 mutually exclusive observation categories for clinical activities, coordination activities, teaching, and other communication. Clinical performance scores for treating the simulated episode of MH were calculated using a time-based scoring system for critical treatment steps. Coordination patterns in response to the occurrence of a crisis situation were analyzed using multivariate analysis of variance and the relationship between coordination patterns and clinical performance was investigated using hierarchical regression analyses. Qualitative analyses of the three highest and lowest performing crews were conducted to complement the quantitative analysis. First, a multivariate analysis of variance revealed statistically significant changes in the proportion of time spent on clinical and coordination activities once the MH crisis was declared (F [5,19] = 162.81, P < 0.001, eta(p)(2) = 0.98). Second, hierarchical regression analyses controlling for the effects of cognitive aid use showed that higher performing anesthesia crews exhibit statistically significant less task distribution (beta = -0.539, P < 0.01) and significantly more situation assessment (beta = 0.569, P < 0.05). Additional qualitative video analysis revealed, for example, that lower scoring crews were more likely to split into subcrews (i.e., both anesthesiologists worked with other members of the perioperative team without maintaining a shared plan among the two-person anesthesia crew). Our results of the relationship of coordination patterns and clinical performance will inform future research on adaptive coordination in medical teams and support the development of specific training to improve team coordination and performance.

Enhanced ID Pit Sizing Using Multivariate Regression Algorithm

NASA Astrophysics Data System (ADS)

Krzywosz, Kenji

2007-03-01

EPRI is funding a program to enhance and improve the reliability of inside diameter (ID) pit sizing for balance-of plant heat exchangers, such as condensers and component cooling water heat exchangers. More traditional approaches to ID pit sizing involve the use of frequency-specific amplitude or phase angles. The enhanced multivariate regression algorithm for ID pit depth sizing incorporates three simultaneous input parameters of frequency, amplitude, and phase angle. A set of calibration data sets consisting of machined pits of various rounded and elongated shapes and depths was acquired in the frequency range of 100 kHz to 1 MHz for stainless steel tubing having nominal wall thickness of 0.028 inch. To add noise to the acquired data set, each test sample was rotated and test data acquired at 3, 6, 9, and 12 o'clock positions. The ID pit depths were estimated using a second order and fourth order regression functions by relying on normalized amplitude and phase angle information from multiple frequencies. Due to unique damage morphology associated with the microbiologically-influenced ID pits, it was necessary to modify the elongated calibration standard-based algorithms by relying on the algorithm developed solely from the destructive sectioning results. This paper presents the use of transformed multivariate regression algorithm to estimate ID pit depths and compare the results with the traditional univariate phase angle analysis. Both estimates were then compared with the destructive sectioning results.

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

PubMed

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Survey of adaptive control using Liapunov design

NASA Technical Reports Server (NTRS)

Lindorff, D. P.; Carroll, R. L.

1973-01-01

A survey of the literature in which Liapunov's second method is used in determining the control law is presented, with emphasis placed on the model-tracking adaptive control problem. Forty references are listed. Following a brief tutorial exposition of the adaptive control problem, the techniques for treating reduction of order, disturbance and time-varying parameters, multivariable systems, identification, and adaptive observers are discussed. The method is critically evaluated, particularly with respect to possibilities for application.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis.

PubMed

Liu, Fei; Ye, Lanhan; Peng, Jiyu; Song, Kunlin; Shen, Tingting; Zhang, Chu; He, Yong

2018-02-27

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

Digital soil classification and elemental mapping using imaging Vis-NIR spectroscopy: How to explicitly quantify stagnic properties of a Luvisol under Norway spruce

NASA Astrophysics Data System (ADS)

Kriegs, Stefanie; Buddenbaum, Henning; Rogge, Derek; Steffens, Markus

2015-04-01

Laboratory imaging Vis-NIR spectroscopy of soil profiles is a novel technique in soil science that can determine quantity and quality of various chemical soil properties with a hitherto unreached spatial resolution in undisturbed soil profiles. We have applied this technique to soil cores in order to get quantitative proof of redoximorphic processes under two different tree species and to proof tree-soil interactions at microscale. Due to the imaging capabilities of Vis-NIR spectroscopy a spatially explicit understanding of soil processes and properties can be achieved. Spatial heterogeneity of the soil profile can be taken into account. We took six 30 cm long rectangular soil columns of adjacent Luvisols derived from quaternary aeolian sediments (Loess) in a forest soil near Freising/Bavaria using stainless steel boxes (100×100×300 mm). Three profiles were sampled under Norway spruce and three under European beech. A hyperspectral camera (VNIR, 400-1000 nm in 160 spectral bands) with spatial resolution of 63×63 µm² per pixel was used for data acquisition. Reference samples were taken at representative spots and analysed for organic carbon (OC) quantity and quality with a CN elemental analyser and for iron oxides (Fe) content using dithionite extraction followed by ICP-OES measurement. We compared two supervised classification algorithms, Spectral Angle Mapper and Maximum Likelihood, using different sets of training areas and spectral libraries. As established in chemometrics we used multivariate analysis such as partial least-squares regression (PLSR) in addition to multivariate adaptive regression splines (MARS) to correlate chemical data with Vis-NIR spectra. As a result elemental mapping of Fe and OC within the soil core at high spatial resolution has been achieved. The regression model was validated by a new set of reference samples for chemical analysis. Digital soil classification easily visualizes soil properties within the soil profiles. By combining both techniques, detailed soil maps, elemental balances and a deeper understanding of soil forming processes at the microscale become feasible for complete soil profiles.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Structural equation models based on multivariate diversity assessment of diploid and tetraploid hulled wheat species

USDA-ARS?s Scientific Manuscript database

Hulled wheats are largely untapped genetic resources with >10,000 years of genetic memory and diversity that can be used for wheat quality improvement, development of healthy products, and adaptation to climate change. Multivariate diversity was assessed in the diploid Triticum monococcum L. var mon...

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

USGS Publications Warehouse

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

2003-01-01

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

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

PubMed

Sun, Yu; Reynolds, Hayley M; Wraith, Darren; Williams, Scott; Finnegan, Mary E; Mitchell, Catherine; Murphy, Declan; Haworth, Annette

2018-04-26

There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 10 3 cells/mm 2 and a relative deviation of 13.3 ± 0.8%. Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

Aggression and related stressful life events among Chinese adolescents living in rural areas: A cross-sectional study.

PubMed

Huang, Juan; Tang, Jie; Tang, Lina; Chang, Hong Juan; Ma, Yuqiao; Yan, Qiuge; Yu, Yizhen

2017-03-15

Aggression is a serious problem for both individuals and society. Despite progress in aggression research, its persistence among adolescents living in rural areas remains to be investigated. We evaluate the prevalence of aggression and the association between stressful life events and aggression in a nationwide, school-based sample of adolescents living in rural areas of China. A sample of 13,495 Chinese rural students (7065 boys and 6430 girls; 11-20 years old) was selected from 15 representative rural areas from 5 provinces in China using stratified randomized cluster sampling. Aggression, stressful life events, neglect, emotional management, social support, and demographic characteristics were assessed via self-report questionnaires. Multivariate logistic regressions were used to estimate the association of stressful life events and aggression after controlling for confounds. The prevalence of aggressive behavior among Chinese adolescents living in rural areas was 24.3%. Regression analyses indicated that the odds of aggression were negatively influenced by chronic long-term stressors related to interpersonal problems (OR=2.03, 95% CI [1.75-2.36]), health adaptation difficulties (OR=1.21, 95% CI [1.09-1.34]), and other troubles (OR=1.93, 95% CI [1.74-2.14]), even after adjustment for parental neglect, emotional management, social support, and other relevant factors. This study was cross-sectional; thus, it is necessary to validate the causal relationship between stressful life events and aggression via follow-up studies. Aggression was prevalent among Chinese adolescents living in rural areas, and interpersonal problems, health adaption difficulties, and other troubles were considered potential independent risk factors for aggression. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 mum (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5 . These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn't account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level.

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

Multivariate decoding of brain images using ordinal regression.

PubMed

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

2013-11-01

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

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert M.

2013-01-01

A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

USGS Publications Warehouse

Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

2017-01-01

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

NASA Astrophysics Data System (ADS)

Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

2017-12-01

Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning.

PubMed

Harteveld, Dalphy O C; Grant, Michael R; Pscheidt, Jay W; Peever, Tobin L

2017-11-01

Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the U.S. Pacific Northwest (PNW). Apothecia develop from mummified berries overwintering on soil surfaces and produce ascospores that infect tissue emerging from floral and vegetative buds. Disease control currently relies on fungicides applied on a calendar basis rather than inoculum availability. To establish a prediction model for ascospore release, apothecial development was tracked in three fields, one in western Oregon and two in northwestern Washington in 2015 and 2016. Air and soil temperature, precipitation, soil moisture, leaf wetness, relative humidity and solar radiation were monitored using in-field weather stations and Washington State University's AgWeatherNet stations. Four modeling approaches were compared: logistic regression, multivariate adaptive regression splines, artificial neural networks, and random forest. A supervised learning approach was used to train the models on two data sets: training (70%) and testing (30%). The importance of environmental factors was calculated for each model separately. Soil temperature, soil moisture, and solar radiation were identified as the most important factors influencing ascospore release. Random forest models, with 78% accuracy, showed the best performance compared with the other models. Results of this research helps PNW blueberry growers to optimize fungicide use and reduce production costs.

Factors Associated with Suicidal Ideation and Suicide Attempt among School-Going Urban Adolescents in Peru

PubMed Central

Sharma, Bimala; Nam, Eun Woo; Kim, Ha Yun; Kim, Jong Koo

2015-01-01

The study examines the prevalence of suicidal ideation and suicide attempt, and associated factors among school-going urban adolescents in Peru. A cross-sectional survey was conducted in a sample of 916 secondary school adolescents in 2014. A structured questionnaire adapted from Global School-based Student Health Survey was used to obtain information. Data were analyzed using logistic regression models at 5% level of significance. Overall, 26.3% reported having suicidal ideation, and 17.5% reported having attempted suicide during the past 12 months. Multivariate logistic regression analysis showed that female sex, being in a fight, being insulted, being attacked, perceived unhappiness, smoking and sexual intercourse initiation were significantly associated with increased risk of suicidal ideation, while female sex, being in a fight, being insulted, being attacked, perceived unhappiness, alcohol and illicit drug use were related to suicide attempt. The prevalence of suicidal ideation and suicide attempts observed in the survey area is relatively high. Female adolescents are particularly vulnerable to report suicidal ideation and suicide attempt. Interventions that address the issue of violence against adolescents, fighting with peers, health risk behaviors particularly initiation of smoking, alcohol and illicit drug use and encourage supportive role of parents may reduce the risk of suicidal behaviors. PMID:26610536

[Influences of environmental factors and interaction of several chemokines gene-environmental on systemic lupus erythematosus].

PubMed

Ye, Dong-qing; Hu, Yi-song; Li, Xiang-pei; Huang, Fen; Yang, Shi-gui; Hao, Jia-hu; Yin, Jing; Zhang, Guo-qing; Liu, Hui-hui

2004-11-01

To explore the impact of environmental factors, daily lifestyle, psycho-social factors and the interactions between environmental factors and chemokines genes on systemic lupus erythematosus (SLE). Case-control study was carried out and environmental factors for SLE were analyzed by univariate and multivariate unconditional logistic regression. Interactions between environmental factors and chemokines polymorphism contributing to systemic lupus erythematosus were also analyzed by logistic regression model. There were nineteen factors associated with SLE when univariate unconditional logistic regression was used. However, when multivariate unconditional logistic regression was used, only five factors showed having impacts on the disease, in which drinking well water (OR=0.099) was protective factor for SLE, and multiple drug allergy (OR=8.174), over-exposure to sunshine (OR=18.339), taking antibiotics (OR=9.630) and oral contraceptives were risk factors for SLE. When unconditional logistic regression model was used, results showed that there was interaction between eating irritable food and -2518MCP-1G/G genotype (OR=4.387). No interaction between environmental factors was found that contributing to SLE in this study. Many environmental factors were related to SLE, and there was an interaction between -2518MCP-1G/G genotype and eating irritable food.

The role of self-determined motivation in the understanding of exercise-related behaviours, cognitions and physical self-evaluations.

PubMed

Thøgersen-Ntoumani, Cecilie; Ntoumanis, Nikos

2006-04-01

Grounded in self-determination theory (Deci & Ryan, 1985), the purpose of the present study was to examine whether amotivation, self-determined and controlling types of motivation could predict a range of exercise-related behaviours, cognitions and physical self-evaluations. Exercisers (n = 375) from ten health clubs in the North of England completed questionnaires measuring exercise motivation, exercise stages of change, number of relapses from exercise, future intention to exercise, barriers self-efficacy, physical self-worth and social physique anxiety. Controlling for age and sex, multiple and logistic regression analyses supported our hypotheses by showing self-determined motivation (i.e. intrinsic motivation and identified regulation) to predict more adaptive behavioural, cognitive and physical self-evaluation patterns than external regulation and amotivation. Introjected regulation was related to both adaptive and maladaptive outcomes. Furthermore, a multivariate analysis of variance revealed that exercisers in the maintenance stage of change displayed significantly more self-determined motivation to exercise than those in the preparation and action stages. The results illustrate the importance of promoting self-determined motivation in exercisers to improve the quality of their experiences, as well as to foster their exercise behaviour. Future research should examine the mechanisms that promote self-determined motivation in exercise.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

PubMed

Abram, Samantha V; Helwig, Nathaniel E; Moodie, Craig A; DeYoung, Colin G; MacDonald, Angus W; Waller, Niels G

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

PubMed Central

Abram, Samantha V.; Helwig, Nathaniel E.; Moodie, Craig A.; DeYoung, Colin G.; MacDonald, Angus W.; Waller, Niels G.

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks. PMID:27516732

Neurodevelopmental Status and Adaptive Behaviors in Preschool Children with Chronic Kidney Disease

ERIC Educational Resources Information Center

Duquette, Peter J.; Hooper, Stephen R.; Icard, Phil F.; Hower, Sarah J.; Mamak, Eva G.; Wetherington, Crista E.; Gipson, Debbie S.

2009-01-01

This study examines the early neurodevelopmental function of infants and preschool children who have chronic kidney disease (CKD). Fifteen patients with CKD are compared to a healthy control group using the "Mullen Scales of Early Learning" (MSEL) and the "Vineland Adaptive Behavior Scale" (VABS). Multivariate analysis reveals…

Adaptive Control Of Remote Manipulator

NASA Technical Reports Server (NTRS)

Seraji, Homayoun

1989-01-01

Robotic control system causes remote manipulator to follow closely reference trajectory in Cartesian reference frame in work space, without resort to computationally intensive mathematical model of robot dynamics and without knowledge of robot and load parameters. System, derived from linear multivariable theory, uses relatively simple feedforward and feedback controllers with model-reference adaptive control.

Predictive equations for the estimation of body size in seals and sea lions (Carnivora: Pinnipedia)

PubMed Central

Churchill, Morgan; Clementz, Mark T; Kohno, Naoki

2014-01-01

Body size plays an important role in pinniped ecology and life history. However, body size data is often absent for historical, archaeological, and fossil specimens. To estimate the body size of pinnipeds (seals, sea lions, and walruses) for today and the past, we used 14 commonly preserved cranial measurements to develop sets of single variable and multivariate predictive equations for pinniped body mass and total length. Principal components analysis (PCA) was used to test whether separate family specific regressions were more appropriate than single predictive equations for Pinnipedia. The influence of phylogeny was tested with phylogenetic independent contrasts (PIC). The accuracy of these regressions was then assessed using a combination of coefficient of determination, percent prediction error, and standard error of estimation. Three different methods of multivariate analysis were examined: bidirectional stepwise model selection using Akaike information criteria; all-subsets model selection using Bayesian information criteria (BIC); and partial least squares regression. The PCA showed clear discrimination between Otariidae (fur seals and sea lions) and Phocidae (earless seals) for the 14 measurements, indicating the need for family-specific regression equations. The PIC analysis found that phylogeny had a minor influence on relationship between morphological variables and body size. The regressions for total length were more accurate than those for body mass, and equations specific to Otariidae were more accurate than those for Phocidae. Of the three multivariate methods, the all-subsets approach required the fewest number of variables to estimate body size accurately. We then used the single variable predictive equations and the all-subsets approach to estimate the body size of two recently extinct pinniped taxa, the Caribbean monk seal (Monachus tropicalis) and the Japanese sea lion (Zalophus japonicus). Body size estimates using single variable regressions generally under or over-estimated body size; however, the all-subset regression produced body size estimates that were close to historically recorded body length for these two species. This indicates that the all-subset regression equations developed in this study can estimate body size accurately. PMID:24916814

Low incidence of chest wall pain with a risk-adapted lung stereotactic body radiation therapy approach using three or five fractions based on chest wall dosimetry.

PubMed

Coroller, Thibaud P; Mak, Raymond H; Lewis, John H; Baldini, Elizabeth H; Chen, Aileen B; Colson, Yolonda L; Hacker, Fred L; Hermann, Gretchen; Kozono, David; Mannarino, Edward; Molodowitch, Christina; Wee, Jon O; Sher, David J; Killoran, Joseph H

2014-01-01

To examine the frequency and potential of dose-volume predictors for chest wall (CW) toxicity (pain and/or rib fracture) for patients receiving lung stereotactic body radiotherapy (SBRT) using treatment planning methods to minimize CW dose and a risk-adapted fractionation scheme. We reviewed data from 72 treatment plans, from 69 lung SBRT patients with at least one year of follow-up or CW toxicity, who were treated at our center between 2010 and 2013. Treatment plans were optimized to reduce CW dose and patients received a risk-adapted fractionation of 18 Gy×3 fractions (54 Gy total) if the CW V30 was less than 30 mL or 10-12 Gy×5 fractions (50-60 Gy total) otherwise. The association between CW toxicity and patient characteristics, treatment parameters and dose metrics, including biologically equivalent dose, were analyzed using logistic regression. With a median follow-up of 20 months, 6 (8.3%) patients developed CW pain including three (4.2%) grade 1, two (2.8%) grade 2 and one (1.4%) grade 3. Five (6.9%) patients developed rib fractures, one of which was symptomatic. No significant associations between CW toxicity and patient and dosimetric variables were identified on univariate nor multivariate analysis. Optimization of treatment plans to reduce CW dose and a risk-adapted fractionation strategy of three or five fractions based on the CW V30 resulted in a low incidence of CW toxicity. Under these conditions, none of the patient characteristics or dose metrics we examined appeared to be predictive of CW pain.

[Correlation between gaseous exchange rate, body temperature, and mitochondrial protein content in the liver of mice].

PubMed

Muradian, Kh K; Utko, N O; Mozzhukhina, T H; Pishel', I M; Litoshenko, O Ia; Bezrukov, V V; Fraĭfel'd, V E

2002-01-01

Correlative and regressive relations between the gaseous exchange, thermoregulation and mitochondrial protein content were analyzed by two- and three-dimensional statistics in mice. It has been shown that the pair wise linear methods of analysis did not reveal any significant correlation between the parameters under exploration. However, it became evident at three-dimensional and non-linear plotting for which the coefficients of multivariable correlation reached and even exceeded 0.7-0.8. The calculations based on partial differentiation of the multivariable regression equations allow to conclude that at certain values of VO2, VCO2 and body temperature negative relations between the systems of gaseous exchange and thermoregulation become dominating.

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Hypnotizability as a Function of Repression, Adaptive Regression, and Mood

ERIC Educational Resources Information Center

Silver, Maurice Joseph

1974-01-01

Forty male undergraduates were assessed in a personality assessment session and a hypnosis session. The personality traits studied were repressive style and adaptive regression, while the transitory variable was mood prior to hypnosis. Hypnotizability was a significant interactive function of repressive style and mood, but not of adaptive…

Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

ERIC Educational Resources Information Center

Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan

2017-01-01

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

Survey of adaptive control using Liapunov design

NASA Technical Reports Server (NTRS)

Lindorff, D. P.; Carroll, R. L.

1972-01-01

A survey was made of the literature devoted to the synthesis of model-tracking adaptive systems based on application of Liapunov's second method. The basic synthesis procedure is introduced and a critical review of extensions made to the theory since 1966 is made. The extensions relate to design for relative stability, reduction of order techniques, design with disturbance, design with time variable parameters, multivariable systems, identification, and an adaptive observer.

A Comparison of Conventional Linear Regression Methods and Neural Networks for Forecasting Educational Spending.

ERIC Educational Resources Information Center

Baker, Bruce D.; Richards, Craig E.

1999-01-01

Applies neural network methods for forecasting 1991-95 per-pupil expenditures in U.S. public elementary and secondary schools. Forecasting models included the National Center for Education Statistics' multivariate regression model and three neural architectures. Regarding prediction accuracy, neural network results were comparable or superior to…

"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Barriers to health-care and psychological distress among mothers living with HIV in Quebec (Canada).

PubMed

Blais, Martin; Fernet, Mylène; Proulx-Boucher, Karène; Lebouché, Bertrand; Rodrigue, Carl; Lapointe, Normand; Otis, Joanne; Samson, Johanne

2015-01-01

Health-care providers play a major role in providing good quality care and in preventing psychological distress among mothers living with HIV (MLHIV). The objectives of this study are to explore the impact of health-care services and satisfaction with care providers on psychological distress in MLHIV. One hundred MLHIV were recruited from community and clinical settings in the province of Quebec (Canada). Prevalence estimation of clinical psychological distress and univariate and multivariable logistic regression models were performed to predict clinical psychological distress. Forty-five percent of the participants reported clinical psychological distress. In the multivariable regression, the following variables were significantly associated with psychological distress while controlling for sociodemographic variables: resilience, quality of communication with the care providers, resources, and HIV disclosure concerns. The multivariate results support the key role of personal, structural, and medical resources in understanding psychological distress among MLHIV. Interventions that can support the psychological health of MLHIV are discussed.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Barriers to Physical Activity in East Harlem, New York

PubMed Central

Fox, Ashley M.; Mann, Devin M.; Ramos, Michelle A.; Kleinman, Lawrence C.; Horowitz, Carol R.

2012-01-01

Background. East Harlem is an epicenter of the intertwining epidemics of obesity and diabetes in New York. Physical activity is thought to prevent and control a number of chronic illnesses, including diabetes, both independently and through weight control. Using data from a survey collected on adult (age 18+) residents of East Harlem, this study evaluated whether perceptions of safety and community-identified barriers were associated with lower levels of physical activity in a diverse sample. Methods. We surveyed 300 adults in a 2-census tract area of East Harlem and took measurements of height and weight. Physical activity was measured in two ways: respondents were classified as having met the weekly recommended target of 2.5 hours of moderate physical activity (walking) per week (or not) and reporting having engaged in at least one recreational physical activity (or not). Perceived barriers were assessed through five items developed by a community advisory board and perceptions of neighborhood safety were measured through an adapted 7-item scale. Two multivariate logistic regression models with perceived barriers and concerns about neighborhood safety were modeled separately as predictors of engaging in recommended levels of exercise and recreational physical activity, controlling for respondent weight and sociodemographic characteristics. Results. The most commonly reported perceived barriers to physical activity identified by nearly half of the sample were being too tired or having little energy followed by pain with exertion and lack of time. Multivariate regression found that individuals who endorsed a greater number of perceived barriers were less likely to report having met their weekly recommended levels of physical activity and less likely to engage in recreational physical activity controlling for covariates. Concerns about neighborhood safety, though prevalent, were not associated with physical activity levels. Conclusions. Although safety concerns were prevalent in this low-income, minority community, it was individual barriers that correlated with lower physical activity levels. PMID:22848797

Cognitive Development of Toddlers: Does Parental Stimulation Matter?

PubMed

Malhi, Prahbhjot; Menon, Jagadeesh; Bharti, Bhavneet; Sidhu, Manjit

2018-02-01

To examine the impact of quality of early stimulation on cognitive functioning of toddlers living in a developing country. The developmental functioning of 150 toddlers in the age range of 12-30 mo (53% boys; Mean = 1.76 y, SD = 0.48) was assessed by the mental developmental index of the Developmental Assessment Scale for Indian Infants (DASII). The StimQ questionnaire- toddler version was used to measure cognitive stimulation at home. The questionnaire consists of four subscales including availability of learning materials (ALM), reading activities (READ), parent involvement in developmental activities (PIDA), and parent verbal responsivity (PVR). Multivariate regression analysis was used to predict cognitive scores using demographic (age of child), socio-economic status (SES) (income, parental education), and home environment (subscale scores of StimQ) as independent variables. Mean Mental Development Index (MDI) score was 91.5 (SD = 13.41), nearly one-fifth (17.3%) of the toddlers had MDI scores less than 80 (cognitive delay). Children with cognitive delay, relative to typically developing (TD, MDI score ≥ 80) cohort of toddlers, had significantly lower scores on all the subscales of StimQ and the total StimQ score. Despite the overall paucity of learning materials available to toddlers, typical developing toddlers were significantly more likely to have access to symbolic toys (P = 0.004), art materials (P = 0.032), adaptive/fine motor toys (P = 0.018), and life size toys (P = 0.036). Multivariate regression analysis results indicated that controlling for confounding socio-economic status variables, higher parental involvement in developmental activities (PIDA score) and higher parental verbal responsivity (PVR score) emerged as significant predictors of higher MDI scores and explained 34% of variance in MDI scores (F = 23.66, P = 0.001). Disparities in child development emerge fairly early and these differences are not all linked to economic disparities. There is a need to develop evidence-based parenting interventions for primary prevention of developmental problems, especially in resource poor countries.

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

PubMed Central

Decker, Anna L.; Hubbard, Alan; Crespi, Catherine M.; Seto, Edmund Y.W.; Wang, May C.

2015-01-01

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice. PMID:26046009

Ecosystem Evapotranspiration as a Response to Climate and Vegetation Coverage Changes in Northwest Yunnan, China

PubMed Central

Yang, Hao; Luo, Peng; Wang, Jun; Mou, Chengxiang; Mo, Li; Wang, Zhiyuan; Fu, Yao; Lin, Honghui; Yang, Yongping; Bhatta, Laxmi Dutt

2015-01-01

Climate and human-driven changes play an important role in regional droughts. Northwest Yunnan Province is a key region for biodiversity conservation in China, and it has experienced severe droughts since the beginning of this century; however, the extent of the contributions from climate and human-driven changes remains unclear. We calculated the ecosystem evapotranspiration (ET) and water yield (WY) of northwest Yunnan Province, China from 2001 to 2013 using meteorological and remote sensing observation data and a Surface Energy Balance System (SEBS) model. Multivariate regression analyses were used to differentiate the contribution of climate and vegetation coverage to ET. The results showed that the annual average vegetation coverage significantly increased over time with a mean of 0.69 in spite of the precipitation fluctuation. Afforestation/reforestation and other management efforts attributed to vegetation coverage increase in NW Yunnan. Both ET and WY considerably fluctuated with the climate factors, which ranged from 623.29 mm to 893.8 mm and –51.88 mm to 384.40 mm over the time period. Spatially, ET in the southeast of NW Yunnan (mainly in Lijiang) increased significantly, which was in line with the spatial trend of vegetation coverage. Multivariate linear regression analysis indicated that climatic factors accounted for 85.18% of the ET variation, while vegetation coverage explained 14.82%. On the other hand, precipitation accounted for 67.5% of the WY. We conclude that the continuous droughts in northwest Yunnan were primarily climatically driven; however, man-made land cover and vegetation changes also increased the vulnerability of local populations to drought. Because of the high proportion of the water yield consumed for subsistence and poor infrastructure for water management, local populations have been highly vulnerable to climate drought conditions. We suggest that conservation of native vegetation and development of water-conserving agricultural practices should be implemented as adaptive strategies to mitigate climate change. PMID:26237220

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

Li, B; Fujita, A; Buch, K

Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIsmore » over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.« less

Ecosystem Evapotranspiration as a Response to Climate and Vegetation Coverage Changes in Northwest Yunnan, China.

PubMed

Yang, Hao; Luo, Peng; Wang, Jun; Mou, Chengxiang; Mo, Li; Wang, Zhiyuan; Fu, Yao; Lin, Honghui; Yang, Yongping; Bhatta, Laxmi Dutt

2015-01-01

Climate and human-driven changes play an important role in regional droughts. Northwest Yunnan Province is a key region for biodiversity conservation in China, and it has experienced severe droughts since the beginning of this century; however, the extent of the contributions from climate and human-driven changes remains unclear. We calculated the ecosystem evapotranspiration (ET) and water yield (WY) of northwest Yunnan Province, China from 2001 to 2013 using meteorological and remote sensing observation data and a Surface Energy Balance System (SEBS) model. Multivariate regression analyses were used to differentiate the contribution of climate and vegetation coverage to ET. The results showed that the annual average vegetation coverage significantly increased over time with a mean of 0.69 in spite of the precipitation fluctuation. Afforestation/reforestation and other management efforts attributed to vegetation coverage increase in NW Yunnan. Both ET and WY considerably fluctuated with the climate factors, which ranged from 623.29 mm to 893.8 mm and -51.88 mm to 384.40 mm over the time period. Spatially, ET in the southeast of NW Yunnan (mainly in Lijiang) increased significantly, which was in line with the spatial trend of vegetation coverage. Multivariate linear regression analysis indicated that climatic factors accounted for 85.18% of the ET variation, while vegetation coverage explained 14.82%. On the other hand, precipitation accounted for 67.5% of the WY. We conclude that the continuous droughts in northwest Yunnan were primarily climatically driven; however, man-made land cover and vegetation changes also increased the vulnerability of local populations to drought. Because of the high proportion of the water yield consumed for subsistence and poor infrastructure for water management, local populations have been highly vulnerable to climate drought conditions. We suggest that conservation of native vegetation and development of water-conserving agricultural practices should be implemented as adaptive strategies to mitigate climate change.

[The Predictive Factors of Stent Failure in the Treatment of Malignant Extrinsc Ureteral Obstruction Using Internal Ureteral Stents].

PubMed

Matsuura, Hiroshi; Arase, Shigeki; Hori, Yasuhide; Tochigi, Hiromi

2017-12-01

In this study, we retrospectively reviewed the experiences at our single institute in the treatment of malignant extrinsic ureteral obstruction (MUO) using ureteral stents to investigate the clinical outcomes and the predictive factors of stent failure. In 52 ureters of 38 patients who had radiologically significant hydronephrosis due to MUO, internal ureteral stents (The BARD(R) INLAY(TM) ureteral stent set) were inserted. The median follow-up interval after the initial stent insertion was 124.5 days (4-1,120). Stent failure occurred in 8 ureters (15.4%) of the 7 patients. The median interval from the first stent insertion to stent failure was 88 days (1-468). A Cox regression multivariate analysis showed that the significant predictors of stent failure were bladder invasion. Based on the possibility of stent failure, the adaptation of the internal ureteral stent placement should be considered especially in a patient with MUO combined with bladder invasion.

Surface Estimation, Variable Selection, and the Nonparametric Oracle Property.

PubMed

Storlie, Curtis B; Bondell, Howard D; Reich, Brian J; Zhang, Hao Helen

2011-04-01

Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting.

Surface Estimation, Variable Selection, and the Nonparametric Oracle Property

PubMed Central

Storlie, Curtis B.; Bondell, Howard D.; Reich, Brian J.; Zhang, Hao Helen

2010-01-01

Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties. In particular, we define a selection procedure to be nonparametric oracle (np-oracle) if it consistently selects the correct subset of predictors and at the same time estimates the smooth surface at the optimal nonparametric rate, as the sample size goes to infinity. In this paper, we propose a model selection procedure for nonparametric models, and explore the conditions under which the new method enjoys the aforementioned properties. Developed in the framework of smoothing spline ANOVA, our estimator is obtained via solving a regularization problem with a novel adaptive penalty on the sum of functional component norms. Theoretical properties of the new estimator are established. Additionally, numerous simulated and real examples further demonstrate that the new approach substantially outperforms other existing methods in the finite sample setting. PMID:21603586

Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

PubMed

Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

2015-04-01

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

[Loneliness and self-management abilities in the visually impaired elderly].

PubMed

Alma, M A; Van der Mei, S F; Feitsma, W N; Groothoff, J W; Van Tilburg, T G; Suurmeijer, T P B M

2013-06-01

To describe the degree of loneliness among the visually impaired elderly and to make a comparison with a matched reference group of the normally sighted elderly. In addition, we examined self-management abilities (SMAs) as determinants of loneliness among the visually impaired elderly. In a cross-sectional study, 173 visually impaired elderly persons completed telephone interviews. Loneliness and SMAs were assessed with the Loneliness Scale of De Jong Gierveld and the SMAS-30, respectively. The prevalence of loneliness among the visually impaired elderly was higher compared to the reference group (50% vs 29%; p < .001). Multivariate hierarchical regression analysis showed that the SMA self-efficacy, partner status, and self-esteem were determinants of loneliness. Severity and duration of visual impairment had no effect on loneliness. The relationship between SMAs (i.e., self-efficacy) and loneliness is promising, since SMAs can be learned through training. Consequently, self-management training may reduce feelings of loneliness. An adapted version of this paper was published in Journal of Aging and Health, doi: 10.1177/0898264311399758.

Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees

PubMed Central

Choi, Ickwon; Chung, Amy W.; Suscovich, Todd J.; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J.; Francis, Donald; Robb, Merlin L.; Michael, Nelson L.; Kim, Jerome H.; Alter, Galit; Ackerman, Margaret E.; Bailey-Kellogg, Chris

2015-01-01

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates. PMID:25874406

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

Procedures for using signals from one sensor as substitutes for signals of another

NASA Technical Reports Server (NTRS)

Suits, G.; Malila, W.; Weller, T.

1988-01-01

Long-term monitoring of surface conditions may require a transfer from using data from one satellite sensor to data from a different sensor having different spectral characteristics. Two general procedures for spectral signal substitution are described in this paper, a principal-components procedure and a complete multivariate regression procedure. They are evaluated through a simulation study of five satellite sensors (MSS, TM, AVHRR, CZCS, and HRV). For illustration, they are compared to another recently described procedure for relating AVHRR and MSS signals. The multivariate regression procedure is shown to be best. TM can accurately emulate the other sensors, but they, on the other hand, have difficulty in accurately emulating its shortwave infrared bands (TM5 and TM7).

Multivariate Analysis of Seismic Field Data

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

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Non-proportional odds multivariate logistic regression of ordinal family data.

PubMed

Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

Estimating future burned areas under changing climate in the EU-Mediterranean countries.

PubMed

Amatulli, Giuseppe; Camia, Andrea; San-Miguel-Ayanz, Jesús

2013-04-15

The impacts of climate change on forest fires have received increased attention in recent years at both continental and local scales. It is widely recognized that weather plays a key role in extreme fire situations. It is therefore of great interest to analyze projected changes in fire danger under climate change scenarios and to assess the consequent impacts of forest fires. In this study we estimated burned areas in the European Mediterranean (EU-Med) countries under past and future climate conditions. Historical (1985-2004) monthly burned areas in EU-Med countries were modeled by using the Canadian Fire Weather Index (CFWI). Monthly averages of the CFWI sub-indices were used as explanatory variables to estimate the monthly burned areas in each of the five most affected countries in Europe using three different modeling approaches (Multiple Linear Regression - MLR, Random Forest - RF, Multivariate Adaptive Regression Splines - MARS). MARS outperformed the other methods. Regression equations and significant coefficients of determination were obtained, although there were noticeable differences from country to country. Climatic conditions at the end of the 21st Century were simulated using results from the runs of the regional climate model HIRHAM in the European project PRUDENCE, considering two IPCC SRES scenarios (A2-B2). The MARS models were applied to both scenarios resulting in projected burned areas in each country and in the EU-Med region. Results showed that significant increases, 66% and 140% of the total burned area, can be expected in the EU-Med region under the A2 and B2 scenarios, respectively. Copyright © 2013 Elsevier B.V. All rights reserved.

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

Drought Patterns Forecasting using an Auto-Regressive Logistic Model

NASA Astrophysics Data System (ADS)

del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

2014-12-01

Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

Multivariable Control Law Design for the AFTI/F-16 with a Failed Control Surface Using a Parameter-Adaptive Controller.

DTIC Science & Technology

1987-12-01

Appendix D: Macro Listings D-1 Appendix E: MATRIXx Simulation E-1 Bibiliography Vita iv e List of Figures Figure Page 1-1 Self -Tuning Regulator 6 2-1 AFTI...Command 59 4-25 Yaw Rate Command - Three Pulses 60 4-26 Adaptive Yaw Rate Respose - Three Pulses 61 4-27 Adaptive Pitch Angle Response - Three Pulses 62 4...several types of adaptive controllers (regulators). Three of the simplest controllers are gain scheduling, model reference, and self -tuning

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

NASA Astrophysics Data System (ADS)

Eyarkai Nambi, Vijayaram; Thangavel, Kuladaisamy; Manickavasagan, Annamalai; Shahir, Sultan

2017-01-01

Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

Feasibility and efficacy of sepsis management guidelines in a pediatric intensive care unit in Saudi Arabia: a quality improvement initiative.

PubMed

Hasan, Gamal M; Al-Eyadhy, Ayman A; Temsah, Mohamed-Hani A; Al-Haboob, Ali A; Alkhateeb, Mohammad A; Al-Sohime, Fahad

2018-04-25

Evaluation of feasibility and effectiveness of Surviving Sepsis Campaign (SSC) Guidelines implementation at a Pediatric Intensive Care Unit (PICU) in Saudi Arabia to reduce severe sepsis associated mortality. Retrospective data analysis for a prospective quality improvement (QI) initiative. PICU at King Saud University Medical City, Saudi Arabia. Children ≤14 years of age admitted to the PICU from July 2010 to March 2011 with suspected or proven sepsis. Comparisons were made to a previously admitted group of patients with sepsis from October 2009 to June 2010. Adaptation and implementation of the Surviving Sepsis Campaign-Clinical Practice Guidelines (SSC-CPGs) through AGREE instrument and ADAPTE process. We reported pre- and post-implementation outcome of interest for this QI initiative, annual sepsis-related mortality rate. Furthermore, we reported follow-up of annual mortality rate until December 2016. Sixty-five patients was included in the study (42 in post-guidelines implementation group and 23 in pre-guidelines implementation group). Mortality was insignificantly lower in the post-implementation group (26.2% vs. 47.8%; P = 0.079). However, when adjusted for severity, identified by number of failing organs in the multivariate regression analysis, the mortality difference was favorable for the post-implementation group (P = 0.006). The lower sepsis-related mortality rate was also sustained, with an average mortality rate of 15.11% for the subsequent years (2012-16). Adaptation and implementation of SSC Guidelines in our setting support its feasibility and potential benefits. However, a larger study is recommended to explore detailed compliance rates.

Assessing the weighted multi-objective adaptive surrogate model optimization to derive large-scale reservoir operating rules with sensitivity analysis

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Wang, Xu; Liu, Pan; Lei, Xiaohui; Li, Zejun; Gong, Wei; Duan, Qingyun; Wang, Hao

2017-01-01

The optimization of large-scale reservoir system is time-consuming due to its intrinsic characteristics of non-commensurable objectives and high dimensionality. One way to solve the problem is to employ an efficient multi-objective optimization algorithm in the derivation of large-scale reservoir operating rules. In this study, the Weighted Multi-Objective Adaptive Surrogate Model Optimization (WMO-ASMO) algorithm is used. It consists of three steps: (1) simplifying the large-scale reservoir operating rules by the aggregation-decomposition model, (2) identifying the most sensitive parameters through multivariate adaptive regression splines (MARS) for dimensional reduction, and (3) reducing computational cost and speeding the searching process by WMO-ASMO, embedded with weighted non-dominated sorting genetic algorithm II (WNSGAII). The intercomparison of non-dominated sorting genetic algorithm (NSGAII), WNSGAII and WMO-ASMO are conducted in the large-scale reservoir system of Xijiang river basin in China. Results indicate that: (1) WNSGAII surpasses NSGAII in the median of annual power generation, increased by 1.03% (from 523.29 to 528.67 billion kW h), and the median of ecological index, optimized by 3.87% (from 1.879 to 1.809) with 500 simulations, because of the weighted crowding distance and (2) WMO-ASMO outperforms NSGAII and WNSGAII in terms of better solutions (annual power generation (530.032 billion kW h) and ecological index (1.675)) with 1000 simulations and computational time reduced by 25% (from 10 h to 8 h) with 500 simulations. Therefore, the proposed method is proved to be more efficient and could provide better Pareto frontier.

Using a family systems approach to investigate cancer risk communication within melanoma families.

PubMed

Harris, Julie N; Hay, Jennifer; Kuniyuki, Alan; Asgari, Maryam M; Press, Nancy; Bowen, Deborah J

2010-10-01

The family provides an important communication nexus for information and support exchange about family cancer history, and adoption of family-wide cancer risk reduction strategies. The goals of this study were to (1) use the family systems theory to identify characteristics of this sample of families at increased risk of developing melanoma and (2) to relate familial characteristics to the frequency and style of familial risk communication. Participants were first-degree relatives (n=313) of melanoma patients, recruited into a family web-based intervention study. We used multivariable logistic regression models to analyze the association between family functioning and family communication. Most participants were female (60%), with an average age of 51 years. Fifty percent of participants reported that they spoke to their relatives about melanoma risk and people were more likely to speak to their female family members. Familial adaptation, cohesion, coping, and health beliefs were strongly associated with an open style of risk communication within families. None were associated with a blocked style of risk communication. Only cohesion and adaptation were associated with the amount of risk communication that occurred within families. Overall, individuals who came from families that were more highly cohesive, adaptable, and shared strong beliefs about melanoma risk were more likely to communicate openly about melanoma. The fact that this association was not consistent across blocked communication and communication frequency highlights the multifaceted nature of this process. Future research should focus on the interplay between different facets of communication. Copyright © 2010 John Wiley & Sons, Ltd.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

PubMed

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Body proportions of circumpolar peoples as evidenced from skeletal data: Ipiutak and Tigara (Point Hope) versus Kodiak Island Inuit.

PubMed

Holliday, Trenton W; Hilton, Charles E

2010-06-01

Given the well-documented fact that human body proportions covary with climate (presumably due to the action of selection), one would expect that the Ipiutak and Tigara Inuit samples from Point Hope, Alaska, would be characterized by an extremely cold-adapted body shape. Comparison of the Point Hope Inuit samples to a large (n > 900) sample of European and European-derived, African and African-derived, and Native American skeletons (including Koniag Inuit from Kodiak Island, Alaska) confirms that the Point Hope Inuit evince a cold-adapted body form, but analyses also reveal some unexpected results. For example, one might suspect that the Point Hope samples would show a more cold-adapted body form than the Koniag, given their more extreme environment, but this is not the case. Additionally, univariate analyses seldom show the Inuit samples to be more cold-adapted in body shape than Europeans, and multivariate cluster analyses that include a myriad of body shape variables such as femoral head diameter, bi-iliac breadth, and limb segment lengths fail to effectively separate the Inuit samples from Europeans. In fact, in terms of body shape, the European and the Inuit samples tend to be cold-adapted and tend to be separated in multivariate space from the more tropically adapted Africans, especially those groups from south of the Sahara. Copyright 2009 Wiley-Liss, Inc.

Clinical management provided by board-certificated physiatrists in early rehabilitation is a significant determinant of functional improvement in acute stroke patients: a retrospective analysis of Japan rehabilitation database.

PubMed

Kinoshita, Shoji; Kakuda, Wataru; Momosaki, Ryo; Yamada, Naoki; Sugawara, Hidekazu; Watanabe, Shu; Abo, Masahiro

2015-05-01

Early rehabilitation for acute stroke patients is widely recommended. We tested the hypothesis that clinical outcome of stroke patients who receive early rehabilitation managed by board-certificated physiatrists (BCP) is generally better than that provided by other medical specialties. Data of stroke patients who underwent early rehabilitation in 19 acute hospitals between January 2005 and December 2013 were collected from the Japan Rehabilitation Database and analyzed retrospectively. Multivariate linear regression analysis using generalized estimating equations method was performed to assess the association between Functional Independence Measure (FIM) effectiveness and management provided by BCP in early rehabilitation. In addition, multivariate logistic regression analysis was also performed to assess the impact of management provided by BCP in acute phase on discharge destination. After setting the inclusion criteria, data of 3838 stroke patients were eligible for analysis. BCP provided early rehabilitation in 814 patients (21.2%). Both the duration of daily exercise time and the frequency of regular conferencing were significantly higher for patients managed by BCP than by other specialties. Although the mortality rate was not different, multivariate regression analysis showed that FIM effectiveness correlated significantly and positively with the management provided by BCP (coefficient, .35; 95% confidence interval [CI], .012-.059; P < .005). In addition, multivariate logistic analysis identified clinical management by BCP as a significant determinant of home discharge (odds ratio, 1.24; 95% CI, 1.08-1.44; P < .005). Our retrospective cohort study demonstrated that clinical management provided by BCP in early rehabilitation can lead to functional recovery of acute stroke. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Physical function in older men with hyperkyphosis.

PubMed

Katzman, Wendy B; Harrison, Stephanie L; Fink, Howard A; Marshall, Lynn M; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M; Kado, Deborah M

2015-05-01

Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71-98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5-1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

PubMed

Liu, Zitao; Hauskrecht, Milos

2017-11-01

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

Treesearch

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2010-01-01

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

Per capita community-level effects of an invasive grass, Microstegium vimineum, on vegetation in mesic forests in northern Mississippi (USA)

Treesearch

J. Stephen Brewer

2010-01-01

Quantifying per capita impacts of invasive species on resident communities requires integrating regression analyses with experiments under natural conditions. Using multivariate and univariate approaches, I regressed the abundance of 105 resident species of groundcover plants and tree seedlings against the abundance and height of an invasive grass, Microstegium...

Quantitative analysis of binary polymorphs mixtures of fusidic acid by diffuse reflectance FTIR spectroscopy, diffuse reflectance FT-NIR spectroscopy, Raman spectroscopy and multivariate calibration.

PubMed

Guo, Canyong; Luo, Xuefang; Zhou, Xiaohua; Shi, Beijia; Wang, Juanjuan; Zhao, Jinqi; Zhang, Xiaoxia

2017-06-05

Vibrational spectroscopic techniques such as infrared, near-infrared and Raman spectroscopy have become popular in detecting and quantifying polymorphism of pharmaceutics since they are fast and non-destructive. This study assessed the ability of three vibrational spectroscopy combined with multivariate analysis to quantify a low-content undesired polymorph within a binary polymorphic mixture. Partial least squares (PLS) regression and support vector machine (SVM) regression were employed to build quantitative models. Fusidic acid, a steroidal antibiotic, was used as the model compound. It was found that PLS regression performed slightly better than SVM regression in all the three spectroscopic techniques. Root mean square errors of prediction (RMSEP) were ranging from 0.48% to 1.17% for diffuse reflectance FTIR spectroscopy and 1.60-1.93% for diffuse reflectance FT-NIR spectroscopy and 1.62-2.31% for Raman spectroscopy. The results indicate that diffuse reflectance FTIR spectroscopy offers significant advantages in providing accurate measurement of polymorphic content in the fusidic acid binary mixtures, while Raman spectroscopy is the least accurate technique for quantitative analysis of polymorphs. Copyright © 2017 Elsevier B.V. All rights reserved.

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

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

PubMed

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

2017-05-07

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

Transition from a multiport technique to a single-port technique for lung cancer surgery: is lymph node dissection inferior using the single-port technique?†.

PubMed

Liu, Chia-Chuan; Shih, Chih-Shiun; Pennarun, Nicolas; Cheng, Chih-Tao

2016-01-01

The feasibility and radicalism of lymph node dissection for lung cancer surgery by a single-port technique has frequently been challenged. We performed a retrospective cohort study to investigate this issue. Two chest surgeons initiated multiple-port thoracoscopic surgery in a 180-bed cancer centre in 2005 and shifted to a single-port technique gradually after 2010. Data, including demographic and clinical information, from 389 patients receiving multiport thoracoscopic lobectomy or segmentectomy and 149 consecutive patients undergoing either single-port lobectomy or segmentectomy for primary non-small-cell lung cancer were retrieved and entered for statistical analysis by multivariable linear regression models and Box-Cox transformed multivariable analysis. The mean number of total dissected lymph nodes in the lobectomy group was 28.5 ± 11.7 for the single-port group versus 25.2 ± 11.3 for the multiport group; the mean number of total dissected lymph nodes in the segmentectomy group was 19.5 ± 10.8 for the single-port group versus 17.9 ± 10.3 for the multiport group. In linear multivariable and after Box-Cox transformed multivariable analyses, the single-port approach was still associated with a higher total number of dissected lymph nodes. The total number of dissected lymph nodes for primary lung cancer surgery by single-port video-assisted thoracoscopic surgery (VATS) was higher than by multiport VATS in univariable, multivariable linear regression and Box-Cox transformed multivariable analyses. This study confirmed that highly effective lymph node dissection could be achieved through single-port VATS in our setting. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Automated time series forecasting for biosurveillance.

PubMed

Burkom, Howard S; Murphy, Sean Patrick; Shmueli, Galit

2007-09-30

For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams. The methods are (1) a non-adaptive regression model using a long historical baseline, (2) an adaptive regression model with a shorter, sliding baseline, and (3) the Holt-Winters method for generalized exponential smoothing. Criteria for comparing the forecasts were the root-mean-square error, the median absolute per cent error (MedAPE), and the median absolute deviation. The median-based criteria showed best overall performance for the Holt-Winters method. The MedAPE measures over the 16 test series averaged 16.5, 11.6, and 9.7 for the non-adaptive regression, adaptive regression, and Holt-Winters methods, respectively. The non-adaptive regression forecasts were degraded by changes in the data behaviour in the fixed baseline period used to compute model coefficients. The mean-based criterion was less conclusive because of the effects of poor forecasts on a small number of calendar holidays. The Holt-Winters method was also most effective at removing serial autocorrelation, with most 1-day-lag autocorrelation coefficients below 0.15. The forecast methods were compared without tuning them to the behaviour of individual series. We achieved improved predictions with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.

Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

NASA Astrophysics Data System (ADS)

Khan, Firdos; Pilz, Jürgen

2016-04-01

South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological stations. The proposed model will be validated by using the (National Centers for Environmental Prediction / National Center for Atmospheric Research) NCEP/NCAR predictors for the period of 1960-1990 and validated for 1990-2000. To investigate the efficiency of the proposed model, it will be compared with the multivariate multiple regression model and with dynamical downscaling climate models by using different climate indices that describe the frequency, intensity and duration of the variables of interest. KEY WORDS: Climate change, Copula, Monsoon, Quantile regression, Spatio-temporal distribution.

A gap-filling model for eddy covariance latent heat flux: Estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example

NASA Astrophysics Data System (ADS)

Chen, Yi-Ying; Chu, Chia-Ren; Li, Ming-Hsu

2012-10-01

SummaryIn this paper we present a semi-parametric multivariate gap-filling model for tower-based measurement of latent heat flux (LE). Two statistical techniques, the principal component analysis (PCA) and a nonlinear interpolation approach were integrated into this LE gap-filling model. The PCA was first used to resolve the multicollinearity relationships among various environmental variables, including radiation, soil moisture deficit, leaf area index, wind speed, etc. Two nonlinear interpolation methods, multiple regressions (MRS) and the K-nearest neighbors (KNNs) were examined with random selected flux gaps for both clear sky and nighttime/cloudy data to incorporate into this LE gap-filling model. Experimental results indicated that the KNN interpolation approach is able to provide consistent LE estimations while MRS presents over estimations during nighttime/cloudy. Rather than using empirical regression parameters, the KNN approach resolves the nonlinear relationship between the gap-filled LE flux and principal components with adaptive K values under different atmospheric states. The developed LE gap-filling model (PCA with KNN) works with a RMSE of 2.4 W m-2 (˜0.09 mm day-1) at a weekly time scale by adding 40% artificial flux gaps into original dataset. Annual evapotranspiration at this study site were estimated at 736 mm (1803 MJ) and 728 mm (1785 MJ) for year 2008 and 2009, respectively.

Critical stakeholder determinants to the implementation of intersectoral community approaches targeting childhood obesity.

PubMed

van der Kleij, R M J J; Crone, M R; Reis, R; Paulussen, T G W M

2016-12-01

Several intersectoral community approaches targeting childhood obesity (IACOs) have been launched in the Netherlands. Translation of these approaches into practice is however arduous and implementation. We therefore studied the implementation of five IACOs in the Netherlands for one-and-a-half years. IACO implementation was evaluated via an adapted version of the MIDI questionnaire, consisting of 18 theory-based constructs. A response rate of 62% was obtained. A hierarchical multivariate linear regression model was used to analyse our data; the final regression model predicted 65% of the variance in adherence. Higher levels of self-efficacy, being an implementer embedded in community B, and having more than 1 year of experience with IACO implementation were associated with higher degrees of adherence. Formal ratification of implementation by management and being prescribed a higher number of activities were related to lower degrees of adherence. We advise that, when designing implementation strategies, emphasis should be placed on the enhancement of professionals' self-efficacy, limitation of the number of activities prescribed and allocation of sufficient time to get acquainted and experienced with IACO implementation. Longitudinal studies are needed to further evaluate interaction between and change within critical determinants while progressing through the innovation process. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Adaptive functioning and its associated factors among girl children residing in slum areas of Bhubaneswar, India.

PubMed

Panigrahi, Ansuman; Das, Sai C; Sahoo, Prabhudarsan

2018-01-01

Adaptive functioning develops throughout early childhood, and its limitation is a reflection that the child has developmental or emotional problems or even mental retardation. Little is known about the adaptive functioning or developmental status of slum children. The present cross-sectional study was undertaken during the year 2014 to assess the status of adaptive functioning among girl children aged between 3 and 9 years residing in slum areas of Bhubaneswar and to explore the factors associated with poor adaptive functioning. Stratified multi-stage cluster random sampling technique was used to select the study population; 256 mother-child pairs from 256 households in selected slum areas were studied. Demographic information was collected, and adaptive functioning was assessed using the modified Vineland Social Maturity Scale. Univariate and multivariate analyses was carried out using Statistical Package for Social Sciences (SPSS) version 21. One-fifth (54, 21%) of the girls sampled had poor adaptive functioning, and 44 (17%) had poor cognitive functioning. Multivariate analysis revealed that the age of the child, parents' education, presence of stunting in children and attending school/early childhood centre were strong predictors of adaptive functioning in slum children. One-fifth of girls from slums are developmentally vulnerable; parental education, stunting and early childhood education or exposure to schooling are modifiable factors influencing children's adaptive functioning. Health, education and welfare sectors need to be aware of this so that a multi-pronged approach can be planned to properly address this issue in one of the most disadvantaged sections of the society. © 2017 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

Rainfall Variability, Adaptation through Irrigation, and Sustainable Management of Water Resources in India

NASA Astrophysics Data System (ADS)

Fishman, R.

2013-12-01

Most studies of the impact of climate change on agriculture account for shifts in temperature and total seasonal (or monthly) precipitation. However, climate change is also projected to increase intra-seasonal precipitation variability in many parts of the world. To provide first estimates of the potential impact, I paired daily rainfall and rice yield data during the period 1970-2004, from across India, where about a fifth of the world's rice is produced, and yields have always been highly dependent on the erratic monsoon rainfall. Multivariate regression models revealed that the number of rainless days during the wet season has a statistically robust negative impact on rice yields that exceeds that of total seasonal rainfall. Moreover, a simulation of climate change impacts found that the negative impact of the projected increase in the number of rainless days will trump the positive impact of the projected increase in total precipitation, and reverse the net precipitation effect on rice production from positive (+3%) to negative (-10%). The results also indicate that higher irrigation coverage is correlated with reduced sensitivity to rainfall variability, suggesting the expansion of irrigation can effectively adapt agriculture to these climate change impacts. However, taking into account limitations on water resource availability in India, I calculate that under current irrigation practices, sustainable use of water can mitigate less than a tenth of the impact.

Using "big data" to guide implementation of a web and mobile adaptive learning platform for medical students.

PubMed

Menon, Ashwin; Gaglani, Shiv; Haynes, M Ryan; Tackett, Sean

2017-09-01

Adaptive learning platforms (ALPs) can revolutionize medical education by making learning more efficient, but their potential has not been realized because students do not use them persistently. We applied educational data mining methods to study United States medical students who used an ALP called Osmosis ( www.osmosis.org ) from 1 August 2014 to 31 July 2015. Multivariate logistic regressions modeled persistence on Osmosis as the dependent variable and Osmosis-collected variables as predictors. The 6787 students included in our analysis responded to a total of 887,193 items, with 2138 (31.5%) using Osmosis persistently. Number of items per student, mobile device use, subscription payment, and group membership were independently associated with persisting (p < 0.001 in all models). Persistent users rated quality more favorably (p < 0.01) but were not more confident in answer selections (p = 0.80). While persisters were more accurate than non-persisters (55% (SD 18%) vs 52% (SD 22%), p < 0.001), after adjusting for number of items, lower accuracy was associated with persistent use (OR 0.93 [95% CI 0.90-0.97], p < 0.01). Our study of a large sample of U.S. medical students illustrates big data medical education research and provides guidance for improving implementation of ALPs and further investigation.

Multivariate analysis of the heterogeneous geochemical processes controlling arsenic enrichment in a shallow groundwater system.

PubMed

Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin

2014-01-01

The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file.

Analysis of Forest Foliage Using a Multivariate Mixture Model

NASA Technical Reports Server (NTRS)

Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.

1997-01-01

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Multivariate Dynamical Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts

NASA Technical Reports Server (NTRS)

Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah

2014-01-01

The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in intergrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.

Multivariate Dynamic Modeling to Investigate Human Adaptation to Space Flight: Initial Concepts

NASA Technical Reports Server (NTRS)

Shelhamer, Mark; Mindock, Jennifer; Zeffiro, Tom; Krakauer, David; Paloski, William H.; Lumpkins, Sarah

2014-01-01

The array of physiological changes that occur when humans venture into space for long periods presents a challenge to future exploration. The changes are conventionally investigated independently, but a complete understanding of adaptation requires a conceptual basis founded in integrative physiology, aided by appropriate mathematical modeling. NASA is in the early stages of developing such an approach.

The importance of extent of choroid plexus cauterization in addition to endoscopic third ventriculostomy for infantile hydrocephalus: a retrospective North American observational study using propensity score-adjusted analysis.

PubMed

Fallah, Aria; Weil, Alexander G; Juraschka, Kyle; Ibrahim, George M; Wang, Anthony C; Crevier, Louis; Tseng, Chi-Hong; Kulkarni, Abhaya V; Ragheb, John; Bhatia, Sanjiv

2017-12-01

OBJECTIVE Combined endoscopic third ventriculostomy (ETC) and choroid plexus cauterization (CPC)-ETV/CPC- is being investigated to increase the rate of shunt independence in infants with hydrocephalus. The degree of CPC necessary to achieve improved rates of shunt independence is currently unknown. METHODS Using data from a single-center, retrospective, observational cohort study involving patients who underwent ETV/CPC for treatment of infantile hydrocephalus, comparative statistical analyses were performed to detect a difference in need for subsequent CSF diversion procedure in patients undergoing partial CPC (describes unilateral CPC or bilateral CPC that only extended from the foramen of Monro [FM] to the atrium on one side) or subtotal CPC (describes CPC extending from the FM to the posterior temporal horn bilaterally) using a rigid neuroendoscope. Propensity scores for extent of CPC were calculated using age and etiology. Propensity scores were used to perform 1) case-matching comparisons and 2) Cox multivariable regression, adjusting for propensity score in the unmatched cohort. Cox multivariable regression adjusting for age and etiology, but not propensity score was also performed as a third statistical technique. RESULTS Eighty-four patients who underwent ETV/CPC had sufficient data to be included in the analysis. Subtotal CPC was performed in 58 patients (69%) and partial CPC in 26 (31%). The ETV/CPC success rates at 6 and 12 months, respectively, were 49% and 41% for patients undergoing subtotal CPC and 35% and 31% for those undergoing partial CPC. Cox multivariate regression in a 48-patient cohort case-matched by propensity score demonstrated no added effect of increased extent of CPC on ETV/CPC survival (HR 0.868, 95% CI 0.422-1.789, p = 0.702). Cox multivariate regression including all patients, with adjustment for propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.845, 95% CI 0.462-1.548, p = 0.586). Cox multivariate regression including all patients, with adjustment for age and etiology, but not propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.908, 95% CI 0.495-1.664, p = 0.755). CONCLUSIONS Using multiple comparative statistical analyses, no difference in need for subsequent CSF diversion procedure was detected between patients in this cohort who underwent partial versus subtotal CPC. Further investigation regarding whether there is truly no difference between partial versus subtotal extent of CPC in larger patient populations and whether further gain in CPC success can be achieved with complete CPC is warranted.

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

Determining the response of sea level to atmospheric pressure forcing using TOPEX/POSEIDON data

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Pihos, Greg

1994-01-01

The static response of sea level to the forcing of atmospheric pressure, the so-called inverted barometer (IB) effect, is investigated using TOPEX/POSEIDON data. This response, characterized by the rise and fall of sea level to compensate for the change of atmospheric pressure at a rate of -1 cm/mbar, is not associated with any ocean currents and hence is normally treated as an error to be removed from sea level observation. Linear regression and spectral transfer function analyses are applied to sea level and pressure to examine the validity of the IB effect. In regions outside the tropics, the regression coefficient is found to be consistently close to the theoretical value except for the regions of western boundary currents, where the mesoscale variability interferes with the IB effect. The spectral transfer function shows near IB response at periods of 30 degrees is -0.84 +/- 0.29 cm/mbar (1 standard deviation). The deviation from = 1 cm /mbar is shown to be caused primarily by the effect of wind forcing on sea level, based on multivariate linear regression model involving both pressure and wind forcing. The regression coefficient for pressure resulting from the multivariate analysis is -0.96 +/- 0.32 cm/mbar. In the tropics the multivariate analysis fails because sea level in the tropics is primarily responding to remote wind forcing. However, after removing from the data the wind-forced sea level estimated by a dynamic model of the tropical Pacific, the pressure regression coefficient improves from -1.22 +/- 0.69 cm/mbar to -0.99 +/- 0.46 cm/mbar, clearly revealing an IB response. The result of the study suggests that with a proper removal of the effect of wind forcing the IB effect is valid in most of the open ocean at periods longer than 20 days and spatial scales larger than 500 km.

Total body weight loss of ≥ 10 % is associated with improved hepatic fibrosis in patients with nonalcoholic steatohepatitis.

PubMed

Glass, Lisa M; Dickson, Rolland C; Anderson, Joseph C; Suriawinata, Arief A; Putra, Juan; Berk, Brian S; Toor, Arifa

2015-04-01

Given the rising epidemics of obesity and metabolic syndrome, nonalcoholic steatohepatitis (NASH) is now the most common cause of liver disease in the developed world. Effective treatment for NASH, either to reverse or prevent the progression of hepatic fibrosis, is currently lacking. To define the predictors associated with improved hepatic fibrosis in NASH patients undergoing serial liver biopsies at prolonged biopsy interval. This is a cohort study of 45 NASH patients undergoing serial liver biopsies for clinical monitoring in a tertiary care setting. Biopsies were scored using the NASH Clinical Research Network guidelines. Fibrosis regression was defined as improvement in fibrosis score ≥1 stage. Univariate analysis utilized Fisher's exact or Student's t test. Multivariate regression models determined independent predictors for regression of fibrosis. Forty-five NASH patients with biopsies collected at a mean interval of 4.6 years (±1.4) were included. The mean initial fibrosis stage was 1.96, two patients had cirrhosis and 12 patients (26.7 %) underwent bariatric surgery. There was a significantly higher rate of fibrosis regression among patients who lost ≥10 % total body weight (TBW) (63.2 vs. 9.1 %; p = 0.001) and who underwent bariatric surgery (47.4 vs. 4.5 %; p = 0.003). Factors such as age, gender, glucose intolerance, elevated ferritin, and A1AT heterozygosity did not influence fibrosis regression. On multivariate analysis, only weight loss of ≥10 % TBW predicted fibrosis regression [OR 8.14 (CI 1.08-61.17)]. Results indicate that regression of fibrosis in NASH is possible, even in advanced stages. Weight loss of ≥10 % TBW predicts fibrosis regression.

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Fully probabilistic control design in an adaptive critic framework.

PubMed

Herzallah, Randa; Kárný, Miroslav

2011-12-01

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem; in particular, very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic control algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this paper. Copyright © 2011 Elsevier Ltd. All rights reserved.

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

PubMed

Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

2016-05-15

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples. Published by Elsevier B.V.

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Negative Events in Childhood Predict Trajectories of Internalizing Symptoms Up to Young Adulthood: An 18-Year Longitudinal Study

PubMed Central

Melchior, Maria; Touchette, Évelyne; Prokofyeva, Elena; Chollet, Aude; Fombonne, Eric; Elidemir, Gulizar; Galéra, Cédric

2014-01-01

Background Common negative events can precipitate the onset of internalizing symptoms. We studied whether their occurrence in childhood is associated with mental health trajectories over the course of development. Methods Using data from the TEMPO study, a French community-based cohort study of youths, we studied the association between negative events in 1991 (when participants were aged 4–16 years) and internalizing symptoms, assessed by the ASEBA family of instruments in 1991, 1999, and 2009 (n = 1503). Participants' trajectories of internalizing symptoms were estimated with semi-parametric regression methods (PROC TRAJ). Data were analyzed using multinomial regression models controlled for participants' sex, age, parental family status, socio-economic position, and parental history of depression. Results Negative childhood events were associated with an increased likelihood of concurrent internalizing symptoms which sometimes persisted into adulthood (multivariate ORs associated with > = 3 negative events respectively: high and decreasing internalizing symptoms: 5.54, 95% CI: 3.20–9.58; persistently high internalizing symptoms: 8.94, 95% CI: 2.82–28.31). Specific negative events most strongly associated with youths' persistent internalizing symptoms included: school difficulties (multivariate OR: 5.31, 95% CI: 2.24–12.59), parental stress (multivariate OR: 4.69, 95% CI: 2.02–10.87), serious illness/health problems (multivariate OR: 4.13, 95% CI: 1.76–9.70), and social isolation (multivariate OR: 2.24, 95% CI: 1.00–5.08). Conclusions Common negative events can contribute to the onset of children's lasting psychological difficulties. PMID:25485875

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Evolution of an adaptive behavior and its sensory receptors promotes eye regression in blind cavefish.

PubMed

Yoshizawa, Masato; Yamamoto, Yoshiyuki; O'Quin, Kelly E; Jeffery, William R

2012-12-27

How and why animals lose eyesight during adaptation to the dark and food-limited cave environment has puzzled biologists since the time of Darwin. More recently, several different adaptive hypotheses have been proposed to explain eye degeneration based on studies in the teleost Astyanax mexicanus, which consists of blind cave-dwelling (cavefish) and sighted surface-dwelling (surface fish) forms. One of these hypotheses is that eye regression is the result of indirect selection for constructive characters that are negatively linked to eye development through the pleiotropic effects of Sonic Hedgehog (SHH) signaling. However, subsequent genetic analyses suggested that other mechanisms also contribute to eye regression in Astyanax cavefish. Here, we introduce a new approach to this problem by investigating the phenotypic and genetic relationships between a suite of non-visual constructive traits and eye regression. Using quantitative genetic analysis of crosses between surface fish, the Pachón cavefish population and their hybrid progeny, we show that the adaptive vibration attraction behavior (VAB) and its sensory receptors, superficial neuromasts (SN) specifically found within the cavefish eye orbit (EO), are genetically correlated with reduced eye size. The quantitative trait loci (QTL) for these three traits form two clusters of congruent or overlapping QTL on Astyanax linkage groups (LG) 2 and 17, but not at the shh locus on LG 13. Ablation of EO SN in cavefish demonstrated a major role for these sensory receptors in VAB expression. Furthermore, experimental induction of eye regression in surface fish via shh overexpression showed that the absence of eyes was insufficient to promote the appearance of VAB or EO SN. We conclude that natural selection for the enhancement of VAB and EO SN indirectly promotes eye regression in the Pachón cavefish population through an antagonistic relationship involving genetic linkage or pleiotropy among the genetic factors underlying these traits. This study demonstrates a trade-off between the evolution of a non-visual sensory system and eye regression during the adaptive evolution of Astyanax to the cave environment.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Seasonally-Dynamic Presence-Only Species Distribution Models for a Cryptic Migratory Bat Impacted by Wind Energy Development

PubMed Central

Hayes, Mark A.; Cryan, Paul M.; Wunder, Michael B.

2015-01-01

Understanding seasonal distribution and movement patterns of animals that migrate long distances is an essential part of monitoring and conserving their populations. Compared to migratory birds and other more conspicuous migrants, we know very little about the movement patterns of many migratory bats. Hoary bats (Lasiurus cinereus), a cryptic, wide-ranging, long-distance migrant, comprise a substantial proportion of the tens to hundreds of thousands of bat fatalities estimated to occur each year at wind turbines in North America. We created seasonally-dynamic species distribution models (SDMs) from 2,753 museum occurrence records collected over five decades in North America to better understand the seasonal geographic distributions of hoary bats. We used 5 SDM approaches: logistic regression, multivariate adaptive regression splines, boosted regression trees, random forest, and maximum entropy and consolidated outputs to generate ensemble maps. These maps represent the first formal hypotheses for sex- and season-specific hoary bat distributions. Our results suggest that North American hoary bats winter in regions with relatively long growing seasons where temperatures are moderated by proximity to oceans, and then move to the continental interior for the summer. SDMs suggested that hoary bats are most broadly distributed in autumn—the season when they are most susceptible to mortality from wind turbines; this season contains the greatest overlap between potentially suitable habitat and wind energy facilities. Comparing wind-turbine fatality data to model outputs could test many predictions, such as ‘risk from turbines is highest in habitats between hoary bat summering and wintering grounds’. Although future field studies are needed to validate the SDMs, this study generated well-justified and testable hypotheses of hoary bat migration patterns and seasonal distribution. PMID:26208098

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests

NASA Astrophysics Data System (ADS)

Vaglio Laurin, Gaia; Puletti, Nicola; Chen, Qi; Corona, Piermaria; Papale, Dario; Valentini, Riccardo

2016-10-01

Estimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservation and selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources which are often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring is needed. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its application in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scan the forest vertical profile, and can provide structure information which is also linked to biodiversity. In the last decade the remote sensing of biodiversity has received great attention, but few studies focused on the use of lidar for assessing tree species richness in tropical forests. This research aims at estimating aboveground biomass and tree species richness using discrete return airborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate Adaptive Regression Splines (MARS), which does not require assumptions on data distribution or on the relationships between variables, being suitable for studying ecological variables. We compared the MARS regression results with those obtained by multilinear regression and found that both algorithms were effective, but MARS provided higher accuracy either for biomass (R2 = 0.72) and species richness (R2 = 0.64). We also noted strong correlation between biodiversity and biomass field values. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, the preliminary indications produced by our study suggest that instrument such as lidar, specifically useful for pinpointing forest structure, can also be exploited as a support for tree species richness assessment.

Seasonally-dynamic presence-only species distribution models for a cryptic migratory bat impacted by wind energy development

USGS Publications Warehouse

Hayes, Mark A.; Cryan, Paul M.; Wunder, Michael B.

2015-01-01

Understanding seasonal distribution and movement patterns of animals that migrate long distances is an essential part of monitoring and conserving their populations. Compared to migratory birds and other more conspicuous migrants, we know very little about the movement patterns of many migratory bats. Hoary bats (Lasiurus cinereus), a cryptic, wide-ranging, long-distance migrant, comprise a substantial proportion of the tens to hundreds of thousands of bat fatalities estimated to occur each year at wind turbines in North America. We created seasonally-dynamic species distribution models (SDMs) from 2,753 museum occurrence records collected over five decades in North America to better understand the seasonal geographic distributions of hoary bats. We used 5 SDM approaches: logistic regression, multivariate adaptive regression splines, boosted regression trees, random forest, and maximum entropy and consolidated outputs to generate ensemble maps. These maps represent the first formal hypotheses for sex- and season-specific hoary bat distributions. Our results suggest that North American hoary bats winter in regions with relatively long growing seasons where temperatures are moderated by proximity to oceans, and then move to the continental interior for the summer. SDMs suggested that hoary bats are most broadly distributed in autumn—the season when they are most susceptible to mortality from wind turbines; this season contains the greatest overlap between potentially suitable habitat and wind energy facilities. Comparing wind-turbine fatality data to model outputs could test many predictions, such as ‘risk from turbines is highest in habitats between hoary bat summering and wintering grounds’. Although future field studies are needed to validate the SDMs, this study generated well-justified and testable hypotheses of hoary bat migration patterns and seasonal distribution.

Development of Ensemble Model Based Water Demand Forecasting Model

NASA Astrophysics Data System (ADS)

Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

2014-05-01

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

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

PubMed

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

2017-08-01

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

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases.

PubMed

Wendling, T; Jung, K; Callahan, A; Schuler, A; Shah, N H; Gallego, B

2018-06-03

There is growing interest in using routinely collected data from health care databases to study the safety and effectiveness of therapies in "real-world" conditions, as it can provide complementary evidence to that of randomized controlled trials. Causal inference from health care databases is challenging because the data are typically noisy, high dimensional, and most importantly, observational. It requires methods that can estimate heterogeneous treatment effects while controlling for confounding in high dimensions. Bayesian additive regression trees, causal forests, causal boosting, and causal multivariate adaptive regression splines are off-the-shelf methods that have shown good performance for estimation of heterogeneous treatment effects in observational studies of continuous outcomes. However, it is not clear how these methods would perform in health care database studies where outcomes are often binary and rare and data structures are complex. In this study, we evaluate these methods in simulation studies that recapitulate key characteristics of comparative effectiveness studies. We focus on the conditional average effect of a binary treatment on a binary outcome using the conditional risk difference as an estimand. To emulate health care database studies, we propose a simulation design where real covariate and treatment assignment data are used and only outcomes are simulated based on nonparametric models of the real outcomes. We apply this design to 4 published observational studies that used records from 2 major health care databases in the United States. Our results suggest that Bayesian additive regression trees and causal boosting consistently provide low bias in conditional risk difference estimates in the context of health care database studies. Copyright © 2018 John Wiley & Sons, Ltd.

Geographic dimensions of heat-related mortality in seven U.S. cities.

PubMed

Hondula, David M; Davis, Robert E; Saha, Michael V; Wegner, Carleigh R; Veazey, Lindsay M

2015-04-01

Spatially targeted interventions may help protect the public when extreme heat occurs. Health outcome data are increasingly being used to map intra-urban variability in heat-health risks, but there has been little effort to compare patterns and risk factors between cities. We sought to identify places within large metropolitan areas where the mortality rate is highest on hot summer days and determine if characteristics of high-risk areas are consistent from one city to another. A Poisson regression model was adapted to quantify temperature-mortality relationships at the postal code scale based on 2.1 million records of daily all-cause mortality counts from seven U.S. cities. Multivariate spatial regression models were then used to determine the demographic and environmental variables most closely associated with intra-city variability in risk. Significant mortality increases on extreme heat days were confined to 12-44% of postal codes comprising each city. Places with greater risk had more developed land, young, elderly, and minority residents, and lower income and educational attainment, but the key explanatory variables varied from one city to another. Regression models accounted for 14-34% of the spatial variability in heat-related mortality. The results emphasize the need for public health plans for heat to be locally tailored and not assume that pre-identified vulnerability indicators are universally applicable. As known risk factors accounted for no more than one third of the spatial variability in heat-health outcomes, consideration of health outcome data is important in efforts to identify and protect residents of the places where the heat-related health risks are the highest. Copyright © 2015 Elsevier Inc. All rights reserved.

Seasonally-Dynamic Presence-Only Species Distribution Models for a Cryptic Migratory Bat Impacted by Wind Energy Development.

PubMed

Hayes, Mark A; Cryan, Paul M; Wunder, Michael B

2015-01-01

Understanding seasonal distribution and movement patterns of animals that migrate long distances is an essential part of monitoring and conserving their populations. Compared to migratory birds and other more conspicuous migrants, we know very little about the movement patterns of many migratory bats. Hoary bats (Lasiurus cinereus), a cryptic, wide-ranging, long-distance migrant, comprise a substantial proportion of the tens to hundreds of thousands of bat fatalities estimated to occur each year at wind turbines in North America. We created seasonally-dynamic species distribution models (SDMs) from 2,753 museum occurrence records collected over five decades in North America to better understand the seasonal geographic distributions of hoary bats. We used 5 SDM approaches: logistic regression, multivariate adaptive regression splines, boosted regression trees, random forest, and maximum entropy and consolidated outputs to generate ensemble maps. These maps represent the first formal hypotheses for sex- and season-specific hoary bat distributions. Our results suggest that North American hoary bats winter in regions with relatively long growing seasons where temperatures are moderated by proximity to oceans, and then move to the continental interior for the summer. SDMs suggested that hoary bats are most broadly distributed in autumn-the season when they are most susceptible to mortality from wind turbines; this season contains the greatest overlap between potentially suitable habitat and wind energy facilities. Comparing wind-turbine fatality data to model outputs could test many predictions, such as 'risk from turbines is highest in habitats between hoary bat summering and wintering grounds'. Although future field studies are needed to validate the SDMs, this study generated well-justified and testable hypotheses of hoary bat migration patterns and seasonal distribution.

Multivariate analysis of nystatin and metronidazole in a semi-solid matrix by means of diffuse reflectance NIR spectroscopy and PLS regression.

PubMed

Baratieri, Sabrina C; Barbosa, Juliana M; Freitas, Matheus P; Martins, José A

2006-01-23

A multivariate method of analysis of nystatin and metronidazole in a semi-solid matrix, based on diffuse reflectance NIR measurements and partial least squares regression, is reported. The product, a vaginal cream used in the antifungal and antibacterial treatment, is usually, quantitatively analyzed through microbiological tests (nystatin) and HPLC technique (metronidazole), according to pharmacopeial procedures. However, near infrared spectroscopy has demonstrated to be a valuable tool for content determination, given the rapidity and scope of the method. In the present study, it was successfully applied in the prediction of nystatin (even in low concentrations, ca. 0.3-0.4%, w/w, which is around 100,000 IU/5g) and metronidazole contents, as demonstrated by some figures of merit, namely linearity, precision (mean and repeatability) and accuracy.

MANCOVA for one way classification with homogeneity of regression coefficient vectors

NASA Astrophysics Data System (ADS)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

The association between a body shape index and cardiovascular risk in overweight and obese children and adolescents.

PubMed

Mameli, Chiara; Krakauer, Nir Y; Krakauer, Jesse C; Bosetti, Alessandra; Ferrari, Chiara Matilde; Moiana, Norma; Schneider, Laura; Borsani, Barbara; Genoni, Teresa; Zuccotti, Gianvincenzo

2018-01-01

A Body Shape Index (ABSI) and normalized hip circumference (Hip Index, HI) have been recently shown to be strong risk factors for mortality and for cardiovascular disease in adults. We conducted an observational cross-sectional study to evaluate the relationship between ABSI, HI and cardiometabolic risk factors and obesity-related comorbidities in overweight and obese children and adolescents aged 2-18 years. We performed multivariate linear and logistic regression analyses with BMI, ABSI, and HI age and sex normalized z scores as predictors to examine the association with cardiometabolic risk markers (systolic and diastolic blood pressure, fasting glucose and insulin, total cholesterol and its components, transaminases, fat mass % detected by bioelectrical impedance analysis) and obesity-related conditions (including hepatic steatosis and metabolic syndrome). We recruited 217 patients (114 males), mean age 11.3 years. Multivariate linear regression showed a significant association of ABSI z score with 10 out of 15 risk markers expressed as continuous variables, while BMI z score showed a significant correlation with 9 and HI only with 1. In multivariate logistic regression to predict occurrence of obesity-related conditions and above-threshold values of risk factors, BMI z score was significantly correlated to 7 out of 12, ABSI to 5, and HI to 1. Overall, ABSI is an independent anthropometric index that was significantly associated with cardiometabolic risk markers in a pediatric population affected by overweight and obesity.

Influence of professional preparation and class structure on sexuality topics taught in middle and high schools.

PubMed

Rhodes, Darson L; Kirchofer, Gregg; Hammig, Bart J; Ogletree, Roberta J

2013-05-01

This study examined the impact of professional preparation and class structure on sexuality topics taught and use of practice-based instructional strategies in US middle and high school health classes. Data from the classroom-level file of the 2006 School Health Policies and Programs were used. A series of multivariable logistic regression models were employed to determine if sexuality content taught was dependent on professional preparation and /or class structure (HE only versus HE/another subject combined). Additional multivariable logistic regression models were employed to determine if use of practice-based instructional strategies was dependent upon professional preparation and/or class structure. Years of teaching health topics and size of the school district were included as covariates in the multivariable logistic regression models. Findings indicated professionally prepared health educators were significantly more likely to teach 7 of the 13 sexuality topics as compared to nonprofessionally prepared health educators. There was no statistically significant difference in the instructional strategies used by professionally prepared and nonprofessionally prepared health educators. Exclusively health education classes versus combined classes were significantly more likely to have included 6 of the 13 topics and to have incorporated practice-based instructional strategies in the curricula. This study indicated professional preparation and class structure impacted sexuality content taught. Class structure also impacted whether opportunities for students to practice skills were made available. Results support the need for continued advocacy for professionally prepared health educators and health only courses. © 2013, American School Health Association.

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

NASA Technical Reports Server (NTRS)

Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

2011-01-01

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

Association between cardiovascular risk factors and carotid intima-media thickness in prepubertal Brazilian children.

PubMed

Gazolla, Fernanda Mussi; Neves Bordallo, Maria Alice; Madeira, Isabel Rey; de Miranda Carvalho, Cecilia Noronha; Vieira Monteiro, Alexandra Maria; Pinheiro Rodrigues, Nádia Cristina; Borges, Marcos Antonio; Collett-Solberg, Paulo Ferrez; Muniz, Bruna Moreira; de Oliveira, Cecilia Lacroix; Pinheiro, Suellen Martins; de Queiroz Ribeiro, Rebeca Mathias

2015-05-01

Early exposure to cardiovascular risk factors creates a chronic inflammatory state that could damage the endothelium followed by thickening of the carotid intima-media. To investigate the association of cardiovascular risk factors and thickening of the carotid intima. Media in prepubertal children. In this cross-sectional study, carotid intima-media thickness (cIMT) and cardiovascular risk factors were assessed in 129 prepubertal children aged from 5 to 10 year. Association was assessed by simple and multivariate logistic regression analyses. In simple logistic regression analyses, body mass index (BMI) z-score, waist circumference, and systolic blood pressure (SBP) were positively associated with increased left, right, and average cIMT, whereas diastolic blood pressure was positively associated only with increased left and average cIMT (p<0.05). In multivariate logistic regression analyses increased left cIMT was positively associated to BMI z-score and SBP, and increased average cIMT was only positively associated to SBP (p<0.05). BMI z-score and SBP were the strongest risk factors for increased cIMT.

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients.

PubMed

Jacobsen, Ramune; Vadstrup, Eva; Røder, Michael; Frølich, Anne

2012-01-01

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

Cytokine and cytokine receptor genes of adaptive immune response are differentially associated with breast cancer risk in American women of African and European ancestry

PubMed Central

Quan, Lei; Gong, Zhihong; Yao, Song; Bandera, Elisa V.; Zirpoli, Gary; Hwang, Helena; Roberts, Michelle; Ciupak, Gregory; Davis, Warren; Sucheston, Lara; Pawlish, Karen; Bovbjerg, Dana H.; Jandorf, Lina; Cabasag, Citadel; Coignet, Jean-Gabriel; Ambrosone, Christine B.; Hong, Chi-Chen

2014-01-01

Disparities in breast cancer biology are evident between American women of African ancestry (AA) and European ancestry (EA), and may be due, in part, to differences in immune function. To assess the potential role of constitutional host immunity on breast carcinogenesis, we tested associations between breast cancer risk and 47 single nucleotide polymorphisms (SNPs) in 26 cytokine-related genes of the adaptive immune system using 650 EA (n=335 cases) and 864 AA (n=458 cases) women from the Women's Circle of Health Study (WCHS). With additional participant accrual to the WCHS, promising SNPs from the initial analysis were evaluated in a larger sample size (1307 EAs and 1365 AAs). Multivariate logistic regression found SNPs in genes important for T helper type 1 (Th1) immunity (IFNGR2 rs1059293, IL15RA rs2296135, LTA rs1041981), Th2 immunity (IL4R rs1801275), and T regulatory cell-mediated immunosuppression (TGFB1 rs1800469), associated with breast cancer risk, mainly among AAs. The combined effect of these five SNPs was highly significant among AAs (P-trend=0.0005). When stratified by estrogen receptor (ER) status, LTA rs1041981 was associated with ER positive breast cancers among EAs and marginally among AAs. Among AA women only, IL15 rs10833 and IL15RA rs2296135 were associated with ER positive tumors, and IL12RB1 rs375947, IL15 rs10833 and TGFB1 rs1800469 were associated with ER negative tumors. Our study systematically identified genetic variants in the adaptive immune response pathway associated with breast cancer risk, which appears to differ by ancestry groups, menopausal status and ER status. PMID:23996684

Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body, Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Ndembo Longo, Jean; Vanclooster, Marnik

2016-03-01

A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones.

Successive Projections Algorithm-Multivariable Linear Regression Classifier for the Detection of Contaminants on Chicken Carcasses in Hyperspectral Images

NASA Astrophysics Data System (ADS)

Wu, W.; Chen, G. Y.; Kang, R.; Xia, J. C.; Huang, Y. P.; Chen, K. J.

2017-07-01

During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.

Aging, not menopause, is associated with higher prevalence of hyperuricemia among older women.

PubMed

Krishnan, Eswar; Bennett, Mihoko; Chen, Linjun

2014-11-01

This work aims to study the associations, if any, of hyperuricemia, gout, and menopause status in the US population. Using multiyear data from the National Health and Nutrition Examination Survey, we performed unmatched comparisons and one to three age-matched comparisons of women aged 20 to 70 years with and without hyperuricemia (serum urate ≥6 mg/dL). Analyses were performed using survey-weighted multiple logistic regression and conditional logistic regression, respectively. Overall, there were 1,477 women with hyperuricemia. Age and serum urate were significantly correlated. In unmatched analyses (n = 9,573 controls), postmenopausal women were older, were heavier, and had higher prevalence of renal impairment, hypertension, diabetes, and hyperlipidemia. In multivariable regression, after accounting for age, body mass index, glomerular filtration rate, and diuretic use, menopause was associated with hyperuricemia (odds ratio, 1.36; 95% CI, 1.05-1.76; P = 0.002). In corresponding multivariable regression using age-matched data (n = 4,431 controls), the odds ratio for menopause was 0.94 (95% CI, 0.83-1.06). Current use of hormone therapy was not associated with prevalent hyperuricemia in both unmatched and matched analyses. Age is a better statistical explanation for the higher prevalence of hyperuricemia among older women than menopause status.

Salivary miRNA profiles identify children with autism spectrum disorder, correlate with adaptive behavior, and implicate ASD candidate genes involved in neurodevelopment.

PubMed

Hicks, Steven D; Ignacio, Cherry; Gentile, Karen; Middleton, Frank A

2016-04-22

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that lacks adequate screening tools, often delaying diagnosis and therapeutic interventions. Despite a substantial genetic component, no single gene variant accounts for >1 % of ASD incidence. Epigenetic mechanisms that include microRNAs (miRNAs) may contribute to the ASD phenotype by altering networks of neurodevelopmental genes. The extracellular availability of miRNAs allows for painless, noninvasive collection from biofluids. In this study, we investigated the potential for saliva-based miRNAs to serve as diagnostic screening tools and evaluated their potential functional importance. Salivary miRNA was purified from 24 ASD subjects and 21 age- and gender-matched control subjects. The ASD group included individuals with mild ASD (DSM-5 criteria and Autism Diagnostic Observation Schedule) and no history of neurologic disorder, pre-term birth, or known chromosomal abnormality. All subjects completed a thorough neurodevelopmental assessment with the Vineland Adaptive Behavior Scales at the time of saliva collection. A total of 246 miRNAs were detected and quantified in at least half the samples by RNA-Seq and used to perform between-group comparisons with non-parametric testing, multivariate logistic regression and classification analyses, as well as Monte-Carlo Cross-Validation (MCCV). The top miRNAs were examined for correlations with measures of adaptive behavior. Functional enrichment analysis of the highest confidence mRNA targets of the top differentially expressed miRNAs was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), as well as the Simons Foundation Autism Database (AutDB) of ASD candidate genes. Fourteen miRNAs were differentially expressed in ASD subjects compared to controls (p <0.05; FDR <0.15) and showed more than 95 % accuracy at distinguishing subject groups in the best-fit logistic regression model. MCCV revealed an average ROC-AUC value of 0.92 across 100 simulations, further supporting the robustness of the findings. Most of the 14 miRNAs showed significant correlations with Vineland neurodevelopmental scores. Functional enrichment analysis detected significant over-representation of target gene clusters related to transcriptional activation, neuronal development, and AutDB genes. Measurement of salivary miRNA in this pilot study of subjects with mild ASD demonstrated differential expression of 14 miRNAs that are expressed in the developing brain, impact mRNAs related to brain development, and correlate with neurodevelopmental measures of adaptive behavior. These miRNAs have high specificity and cross-validated utility as a potential screening tool for ASD.

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients.

PubMed

Yue, Yong; Osipov, Arsen; Fraass, Benedick; Sandler, Howard; Zhang, Xiao; Nissen, Nicholas; Hendifar, Andrew; Tuli, Richard

2017-02-01

To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%). Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.

Predictors of psychiatric readmission among patients with bipolar disorder at an academic safety-net hospital.

PubMed

Hamilton, Jane E; Passos, Ives C; de Azevedo Cardoso, Taiane; Jansen, Karen; Allen, Melissa; Begley, Charles E; Soares, Jair C; Kapczinski, Flavio

2016-06-01

Even with treatment, approximately one-third of patients with bipolar disorder relapse into depression or mania within 1 year. Unfavorable clinical outcomes for patients with bipolar disorder include increased rates of psychiatric hospitalization and functional impairment. However, only a few studies have examined predictors of psychiatric hospital readmission in a sample of patients with bipolar disorder. The purpose of this study was to examine predictors of psychiatric readmission within 30 days, 90 days and 1 year of discharge among patients with bipolar disorder using a conceptual model adapted from Andersen's Behavioral Model of Health Service Use. In this retrospective study, univariate and multivariate logistic regression analyses were conducted in a sample of 2443 adult patients with bipolar disorder who were consecutively admitted to a public psychiatric hospital in the United States from 1 January to 31 December 2013. In the multivariate models, several enabling and need factors were significantly associated with an increased risk of readmission across all time periods examined, including being uninsured, having ⩾3 psychiatric hospitalizations and having a lower Global Assessment of Functioning score. Additional factors associated with psychiatric readmission within 30 and 90 days of discharge included patient homelessness. Patient race/ethnicity, bipolar disorder type or a current manic episode did not significantly predict readmission across all time periods examined; however, patients who were male were more likely to readmit within 1 year. The 30-day and 1-year multivariate models showed the best model fit. Our study found enabling and need factors to be the strongest predictors of psychiatric readmission, suggesting that the prevention of psychiatric readmission for patients with bipolar disorder at safety-net hospitals may be best achieved by developing and implementing innovative transitional care initiatives that address the issues of multiple psychiatric hospitalizations, housing instability, insurance coverage and functional impairment. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail

2015-04-01

The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press. With a CD: data, software, guides. (2009). 2. Kanevski M. Spatial Predictions of Soil Contamination Using General Regression Neural Networks. Systems Research and Information Systems, Volume 8, number 4, 1999. 3. Robert S., Foresti L., Kanevski M. Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks. International Journal of Climatology, 33 pp. 1793-1804, 2013.

Distinct Patterns of Desynchronized Limb Regression in Malagasy Scincine Lizards (Squamata, Scincidae)

PubMed Central

Miralles, Aurélien; Hipsley, Christy A.; Erens, Jesse; Gehara, Marcelo; Rakotoarison, Andolalao; Glaw, Frank; Müller, Johannes; Vences, Miguel

2015-01-01

Scincine lizards in Madagascar form an endemic clade of about 60 species exhibiting a variety of ecomorphological adaptations. Several subclades have adapted to burrowing and convergently regressed their limbs and eyes, resulting in a variety of partial and completely limbless morphologies among extant taxa. However, patterns of limb regression in these taxa have not been studied in detail. Here we fill this gap in knowledge by providing a phylogenetic analysis of DNA sequences of three mitochondrial and four nuclear gene fragments in an extended sampling of Malagasy skinks, and microtomographic analyses of osteology of various burrowing taxa adapted to sand substrate. Based on our data we propose to (i) consider Sirenoscincus Sakata & Hikida, 2003, as junior synonym of Voeltzkowia Boettger, 1893; (ii) resurrect the genus name Grandidierina Mocquard, 1894, for four species previously included in Voeltzkowia; and (iii) consider Androngo Brygoo, 1982, as junior synonym of Pygomeles Grandidier, 1867. By supporting the clade consisting of the limbless Voeltzkowia mira and the forelimb-only taxa V. mobydick and V. yamagishii, our data indicate that full regression of limbs and eyes occurred in parallel twice in the genus Voeltzkowia (as hitherto defined) that we consider as a sand-swimming ecomorph: in the Voeltzkowia clade sensu stricto the regression first affected the hindlimbs and subsequently the forelimbs, whereas the Grandidierina clade first regressed the forelimbs and subsequently the hindlimbs following the pattern prevalent in squamates. Timetree reconstructions for the Malagasy Scincidae contain a substantial amount of uncertainty due to the absence of suitable primary fossil calibrations. However, our preliminary reconstructions suggest rapid limb regression in Malagasy scincids with an estimated maximal duration of 6 MYr for a complete regression in Paracontias, and 4 and 8 MYr respectively for complete regression of forelimbs in Grandidierina and hindlimbs in Voeltzkowia. PMID:26042667

Distinct patterns of desynchronized limb regression in malagasy scincine lizards (squamata, scincidae).

PubMed

Miralles, Aurélien; Hipsley, Christy A; Erens, Jesse; Gehara, Marcelo; Rakotoarison, Andolalao; Glaw, Frank; Müller, Johannes; Vences, Miguel

2015-01-01

Scincine lizards in Madagascar form an endemic clade of about 60 species exhibiting a variety of ecomorphological adaptations. Several subclades have adapted to burrowing and convergently regressed their limbs and eyes, resulting in a variety of partial and completely limbless morphologies among extant taxa. However, patterns of limb regression in these taxa have not been studied in detail. Here we fill this gap in knowledge by providing a phylogenetic analysis of DNA sequences of three mitochondrial and four nuclear gene fragments in an extended sampling of Malagasy skinks, and microtomographic analyses of osteology of various burrowing taxa adapted to sand substrate. Based on our data we propose to (i) consider Sirenoscincus Sakata & Hikida, 2003, as junior synonym of Voeltzkowia Boettger, 1893; (ii) resurrect the genus name Grandidierina Mocquard, 1894, for four species previously included in Voeltzkowia; and (iii) consider Androngo Brygoo, 1982, as junior synonym of Pygomeles Grandidier, 1867. By supporting the clade consisting of the limbless Voeltzkowia mira and the forelimb-only taxa V. mobydick and V. yamagishii, our data indicate that full regression of limbs and eyes occurred in parallel twice in the genus Voeltzkowia (as hitherto defined) that we consider as a sand-swimming ecomorph: in the Voeltzkowia clade sensu stricto the regression first affected the hindlimbs and subsequently the forelimbs, whereas the Grandidierina clade first regressed the forelimbs and subsequently the hindlimbs following the pattern prevalent in squamates. Timetree reconstructions for the Malagasy Scincidae contain a substantial amount of uncertainty due to the absence of suitable primary fossil calibrations. However, our preliminary reconstructions suggest rapid limb regression in Malagasy scincids with an estimated maximal duration of 6 MYr for a complete regression in Paracontias, and 4 and 8 MYr respectively for complete regression of forelimbs in Grandidierina and hindlimbs in Voeltzkowia.

Development and psychometric testing of the Adaptive Capacity Index, an instrument to measure adaptive capacity in individuals with advanced cancer.

PubMed

Olson, K; Rogers, W T; Cui, Y; Cree, M; Baracos, V; Rust, T; Mellott, I; Johnson, L; Macmillan, K; Bonville, N

2011-08-01

We have proposed that declines in adaptive capacity, defined as the ability to adapt to multiple stressors, may serve as an indicator of risk for fatigue. A comprehensive measure of adaptive capacity does not exist. In this paper we describe construction of an instrument to measure adaptive capacity, the Adaptive Capacity Index (ACI). Descriptive and psychometric. Six sites providing palliative care in Western Canada. ≥18 years old, diagnosed with advanced cancer, able to read and write English, Mini-Mental Status Exam score ≥22. Pilot study n=48; Main study n=225 stratified using the Edmonton Symptom Assessment Scale (ESAS) tiredness score (≥0 to ≤2 n=60; ≥3 to ≤6 n=108; ≥7 and ≤10 n=57). Following ethics approval, 17 experts in symptom management assisted with content validation and consenting individuals completed the Functional Assessment of Cancer Therapy-Fatigue (FACT-F), the Profile of Mood States-Vigor short form (POMS-Vsf), and the ACI. A research assistant collected demographic information and assigned an Eastern Cooperative Oncology Group (ECOG) score. Data were analyzed using descriptive and inferential statistics (i.e., exploratory factor analyses, correlation, multivariate analyses of variance, and multiple regression). Five 6-item ACI factors/subscales (Cognitive Function, Stamina/Muscle Endurance, Sleep Quality, Emotional Reactivity, and Social Interaction) were identified. The ACI-total scale and its subscales were internally consistent (Cronbach's alpha 0.76-0.89), and were significantly correlated with each other, and with each fatigue measure (Pearson's r ranging from -0.724 to 0.634). The ACI total score was sensitive to changes in the ESAS tiredness score. Stamina/Muscle Endurance, Cognitive Function, and Sleep Quality predicted 60.8% of the variance in FACT-F. Stamina/Muscle Endurance and Social Interaction predicted 36.8% of the variance in POMS-Vsf. Stamina/Muscle Endurance and Sleep Quality predicted 8% of the variance in ECOG. The ACI is reliable and has beginning evidence of validity. In future studies we will examine relationships between ACI subscale scores and subsequent increases in fatigue and explore linkages to physiological processes. We will also establish ACI norms for early and late stage cancers and explore variations in ACI subscale scores base on age or gender. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

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

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

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

The time frame of Epstein-Barr virus latent membrane protein-1 gene to disappear in nasopharyngeal swabs after initiation of primary radiotherapy is an independently significant prognostic factor predicting local control for patients with nasopharyngeal carcinoma

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

Lin, S.-Y.; Chang, K.-P.; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Linkou, Taiwan

Purpose: The presence of Epstein-Barr virus latent membrane protein-1 (LMP-1) gene in nasopharyngeal swabs indicates the presence of nasopharyngeal carcinoma (NPC) mucosal tumor cells. This study was undertaken to investigate whether the time taken for LMP-1 to disappear after initiation of primary radiotherapy (RT) was inversely associated with NPC local control. Methods and Materials: During July 1999 and October 2002, there were 127 nondisseminated NPC patients receiving serial examinations of nasopharyngeal swabbing with detection of LMP-1 during the RT course. The time for LMP-1 regression was defined as the number of days after initiation of RT for LMP-1 results tomore » turn negative. The primary outcome was local control, which was represented by freedom from local recurrence. Results: The time for LMP-1 regression showed a statistically significant influence on NPC local control both univariately (p < 0.0001) and multivariately (p = 0.004). In multivariate analysis, the administration of chemotherapy conferred a significantly more favorable local control (p = 0.03). Advanced T status ({>=} T2b), overall treatment time of external photon radiotherapy longer than 55 days, and older age showed trends toward being poor prognosticators. The time for LMP-1 regression was very heterogeneous. According to the quartiles of the time for LMP-1 regression, we defined the pattern of LMP-1 regression as late regression if it required 40 days or more. Kaplan-Meier plots indicated that the patients with late regression had a significantly worse local control than those with intermediate or early regression (p 0.0129). Conclusion: Among the potential prognostic factors examined in this study, the time for LMP-1 regression was the most independently significant factor that was inversely associated with NPC local control.« less

An adaptive Cartesian control scheme for manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

A adaptive control scheme for direct control of manipulator end-effectors to achieve trajectory tracking in Cartesian space is developed. The control structure is obtained from linear multivariable theory and is composed of simple feedforward and feedback controllers and an auxiliary input. The direct adaptation laws are derived from model reference adaptive control theory and are not based on parameter estimation of the robot model. The utilization of feedforward control and the inclusion of auxiliary input are novel features of the present scheme and result in improved dynamic performance over existing adaptive control schemes. The adaptive controller does not require the complex mathematical model of the robot dynamics or any knowledge of the robot parameters or the payload, and is computationally fast for online implementation with high sampling rates.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

PubMed

Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

2011-11-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.

Empirical Bayes approach to the estimation of "unsafety": the multivariate regression method.

PubMed

Hauer, E

1992-10-01

There are two kinds of clues to the unsafety of an entity: its traits (such as traffic, geometry, age, or gender) and its historical accident record. The Empirical Bayes approach to unsafety estimation makes use of both kinds of clues. It requires information about the mean and the variance of the unsafety in a "reference population" of similar entities. The method now in use for this purpose suffers from several shortcomings. First, a very large reference population is required. Second, the choice of reference population is to some extent arbitrary. Third, entities in the reference population usually cannot match the traits of the entity the unsafety of which is estimated. To alleviate these shortcomings the multivariate regression method for estimating the mean and variance of unsafety in reference populations is offered. Its logical foundations are described and its soundness is demonstrated. The use of the multivariate method makes the Empirical Bayes approach to unsafety estimation applicable to a wider range of circumstances and yields better estimates of unsafety. The application of the method to the tasks of identifying deviant entities and of estimating the effect of interventions on unsafety are discussed and illustrated by numerical examples.

A multivariate analysis of clinical and morphological prognostic factors in squamous cell carcinoma of the vulva.

PubMed

Smyczek-Gargya, B; Volz, B; Geppert, M; Dietl, J

1997-01-01

Clinical and histological data of 168 patients with squamous cell carcinoma of the vulva were analyzed with respect to survival. 151 patients underwent surgery, 12 patients were treated with primary radiation and in 5 patients no treatment was performed. Follow-up lasted from at least 2 up to 22 years' posttreatment. In univariate analysis, the following factors were highly significant: presurgery lymph node status, tumor infiltration beyond the vulva, tumor grading, histological inguinal lymph node status, pre- and postsurgery tumor stage, depth of invasion and tumor diameter. In the multivariate analysis (Cox regression), the most powerful factors were shown to be histological inguinal lymph node status, tumor diameter and tumor grading. The multivariate logistic regression analysis worked out as main prognostic factors for metastases of inguinal lymph nodes: presurgery inguinal lymph node status, tumor size, depth of invasion and tumor grading. Based on these results, tumor biology seems to be the decisive factor concerning recurrence and survival. Therefore, we suggest a more conservative treatment of vulvar carcinoma. Patients with confined carcinoma to the vulva, with a tumor diameter up to 3 cm and without clinical suspected lymph nodes, should be treated by wide excision/partial vulvectomy with ipsilateral lymphadenectomy.

Niche evolution and diversification in a Neotropical radiation of birds (Aves: Furnariidae).

PubMed

Seeholzer, Glenn F; Claramunt, Santiago; Brumfield, Robb T

2017-03-01

Rapid diversification may be caused by ecological adaptive radiation via niche divergence. In this model, speciation is coupled with niche divergence and lineage diversification is predicted to be correlated with rates of niche evolution. Studies of the role of niche evolution in diversification have generally focused on ecomorphological diversification but climatic-niche evolution may also be important. We tested these alternatives using a phylogeny of 298 species of ovenbirds (Aves: Furnariidae). We found that within Furnariidae, variation in species richness and diversification rates of subclades were best predicted by rate of climatic-niche evolution than ecomorphological evolution. Although both are clearly important, univariate regression and multivariate model averaging more consistently supported the climatic-niche as the best predictor of lineage diversification. Our study adds to the growing body of evidence, suggesting that climatic-niche divergence may be an important driver of rapid diversification in addition to ecomorphological evolution. However, this pattern may depend on the phylogenetic scale at which rate heterogeneity is examined. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods

NASA Astrophysics Data System (ADS)

Szeląg, Bartosz; Barbusiński, Krzysztof; Studziński, Jan; Bartkiewicz, Lidia

2017-11-01

In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance.

Social inclusion and length of stay as determinants of health among North Korean refugees in South Korea.

PubMed

Park, Kunhee; Cho, Youngtae; Yoon, In-Jin

2009-01-01

Although the number of North Koreans seeking asylum in South Korea has increased notably in recent years, studies on the health of North Koreans residing in South Korea are rare. This study examined the roles of social inclusion and the length of stay on refugees' self-rated health. Employing a data set (n = 1,111) created by the South Korean government, we conduct multivariate logistic regression analyses. We found that degree of familiarity with South Koreans, employed as an indicator of social inclusion, was significantly associated with North Korean refugees' self-rated health status. Further, self-rated health seemed to be poorest when the duration of stay in South Korea reached about 2-4 years. Self-rated health outcomes improved after this time period. Social inclusion through close contacts with South Koreans and overcoming an arduous adaptation period, as well as addressing economic deprivation, are important in promoting the health of North Korean refugees in South Korea. These findings should be considered in crafting better resettlement and training programs for this population.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Kidney transplantation from deceased donors with elevated serum creatinine.

PubMed

Gallinat, Anja; Leerhoff, Sabine; Paul, Andreas; Molmenti, Ernesto P; Schulze, Maren; Witzke, Oliver; Sotiropoulos, Georgios C

2016-12-01

Elevated donor serum creatinine has been associated with inferior graft survival in kidney transplantation (KT). The aim of this study was to evaluate the impact of elevated donor serum creatinine on short and long-term outcomes and to determine possible ways to optimize the use of these organs. All kidney transplants from 01-2000 to 12-2012 with donor creatinine ≥ 2 mg/dl were considered. Risk factors for delayed graft function (DGF) were explored with uni- and multivariate regression analyses. Donor and recipient data were analyzed with uni- and multivariate cox proportional hazard analyses. Graft and patient survival were calculated using the Kaplan-Meier method. Seventy-eight patients were considered. Median recipient age and waiting time on dialysis were 53 years and 5.1 years, respectively. After a median follow-up of 6.2 years, 63 patients are alive. 1, 3, and 5-year graft and patient survival rates were 92, 89, and 89 % and 96, 93, and 89 %, respectively. Serum creatinine level at procurement and recipient's dialysis time prior to KT were predictors of DGF in multivariate analysis (p = 0.0164 and p = 0.0101, respectively). Charlson comorbidity score retained statistical significance by multivariate regression analysis for graft survival (p = 0.0321). Recipient age (p = 0.0035) was predictive of patient survival by multivariate analysis. Satisfactory long-term kidney transplant outcomes in the setting of elevated donor serum creatinine ≥2 mg/dl can be achieved when donor creatinine is <3.5 mg/dl, and the recipient has low comorbidities, is under 56 years of age, and remains in dialysis prior to KT for <6.8 years.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

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

Estimation of railroad capacity using parametric methods.

DOT National Transportation Integrated Search

2013-12-01

This paper reviews different methodologies used for railroad capacity estimation and presents a user-friendly method to measure capacity. The objective of this paper is to use multivariate regression analysis to develop a continuous relation of the d...

On the degrees of freedom of reduced-rank estimators in multivariate regression

PubMed Central

Mukherjee, A.; Chen, K.; Wang, N.; Zhu, J.

2015-01-01

Summary We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than the sample size. The derived analytical form facilitates the investigation of theoretical properties and provides new insights into the empirical behaviour of the degrees of freedom. In particular, we examine the differences and connections between the proposed estimator and a commonly-used naive estimator. The use of the proposed estimator leads to efficient and accurate prediction risk estimation and model selection, as demonstrated by simulation studies and a data example. PMID:26702155

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

PubMed Central

Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim W.

2016-01-01

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors. PMID:27285611

Factors affecting the outcome of excimer laser photorefractive keratectomy: a preliminary multivariable regression analysis

NASA Astrophysics Data System (ADS)

Maguen, Ezra I.; Papaioannou, Thanassis; Nesburn, Anthony B.; Salz, James J.; Warren, Cathy; Grundfest, Warren S.

1996-05-01

Multivariable regression analysis was used to evaluate the combined effects of some preoperative and operative variables on the change of refraction following excimer laser photorefractive keratectomy for myopia (PRK). This analysis was performed on 152 eyes (at 6 months postoperatively) and 156 eyes (at 12 months postoperatively). The following variables were considered: intended refractive correction, patient age, treatment zone, central corneal thickness, average corneal curvature, and intraocular pressure. At 6 months after surgery, the cumulative R2 was 0.43 with 0.38 attributed to the intended correction and 0.06 attributed to the preoperative corneal curvature. At 12 months, the cumulative R2 was 0.37 where 0.33 was attributed to the intended correction, 0.02 to the preoperative corneal curvature, and 0.01 to both preoperative corneal thickness and to the patient age. Further model augmentation is necessary to account for the remaining variability and the behavior of the residuals.

A multivariate regression model for detection of fumonisins content in maize from near infrared spectra.

PubMed

Giacomo, Della Riccia; Stefania, Del Zotto

2013-12-15

Fumonisins are mycotoxins produced by Fusarium species that commonly live in maize. Whereas fungi damage plants, fumonisins cause disease both to cattle breedings and human beings. Law limits set fumonisins tolerable daily intake with respect to several maize based feed and food. Chemical techniques assure the most reliable and accurate measurements, but they are expensive and time consuming. A method based on Near Infrared spectroscopy and multivariate statistical regression is described as a simpler, cheaper and faster alternative. We apply Partial Least Squares with full cross validation. Two models are described, having high correlation of calibration (0.995, 0.998) and of validation (0.908, 0.909), respectively. Description of observed phenomenon is accurate and overfitting is avoided. Screening of contaminated maize with respect to European legal limit of 4 mg kg(-1) should be assured. Copyright © 2013 Elsevier Ltd. All rights reserved.

Specific prognostic factors for secondary pancreatic infection in severe acute pancreatitis.

PubMed

Armengol-Carrasco, M; Oller, B; Escudero, L E; Roca, J; Gener, J; Rodríguez, N; del Moral, P; Moreno, P

1999-01-01

The aim of the present study was to investigate whether there are specific prognostic factors to predict the development of secondary pancreatic infection (SPI) in severe acute pancreatitis in order to perform a computed tomography-fine needle aspiration with bacteriological sampling at the right moment and confirm the diagnosis. Twenty-five clinical and laboratory parameters were determined sequentially in 150 patients with severe acute pancreatitis (SAP) and univariate, and multivariate regression analyses were done looking for correlation with the development of SPI. Only APACHE II score and C-reactive protein levels were related to the development of SPI in the multivariate analysis. A regression equation was designed using these two parameters, and empiric cut-off points defined the subgroup of patients at high risk of developing secondary pancreatic infection. The results showed that it is possible to predict SPI during SAP allowing bacteriological confirmation and early treatment of this severe condition.

Compulsive buying: Earlier illicit drug use, impulse buying, depression, and adult ADHD symptoms.

PubMed

Brook, Judith S; Zhang, Chenshu; Brook, David W; Leukefeld, Carl G

2015-08-30

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant's earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Compulsive Buying: Earlier Illicit Drug Use, Impulse Buying, Depression, and Adult ADHD Symptoms

PubMed Central

Brook, Judith S.; Zhang, Chenshu; Brook, David W.; Leukefeld, Carl G.

2015-01-01

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant’s earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. PMID:26165963

Self-reported mental health among US military personnel prior and subsequent to the terrorist attacks of September 11, 2001.

PubMed

Smith, Tyler C; Smith, Besa; Corbeil, Thomas E; Riddle, James R; Ryan, Margaret A K

2004-08-01

There is much concern over the potential for short- and long-term adverse mental health effects caused by the terrorist attacks on September 11, 2001. This analysis used data from the Millennium Cohort Study to identify subgroups of US military members who enrolled in the cohort and reported their mental health status before the traumatic events of September 11 and soon after September 11. While adjusting for confounding, multivariable logistic regression, analysis of variance, and multivariate ordinal, or polychotomous logistic regression were used to compare 18 self-reported mental health measures in US military members who enrolled in the cohort before September 11, 2001 with those military personnel who enrolled after September 11, 2001. In contrast to studies of other populations, military respondents reported fewer mental health problems in the months immediately after September 11, 2001.

Development and validation of multivariate calibration methods for simultaneous estimation of Paracetamol, Enalapril maleate and hydrochlorothiazide in pharmaceutical dosage form

NASA Astrophysics Data System (ADS)

Singh, Veena D.; Daharwal, Sanjay J.

2017-01-01

Three multivariate calibration spectrophotometric methods were developed for simultaneous estimation of Paracetamol (PARA), Enalapril maleate (ENM) and Hydrochlorothiazide (HCTZ) in tablet dosage form; namely multi-linear regression calibration (MLRC), trilinear regression calibration method (TLRC) and classical least square (CLS) method. The selectivity of the proposed methods were studied by analyzing the laboratory prepared ternary mixture and successfully applied in their combined dosage form. The proposed methods were validated as per ICH guidelines and good accuracy; precision and specificity were confirmed within the concentration range of 5-35 μg mL- 1, 5-40 μg mL- 1 and 5-40 μg mL- 1of PARA, HCTZ and ENM, respectively. The results were statistically compared with reported HPLC method. Thus, the proposed methods can be effectively useful for the routine quality control analysis of these drugs in commercial tablet dosage form.

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

DOEpatents

Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

2006-11-14

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

Socio Cultural and Geographical Determinants of Child Immunisation in Borno State, Nigeria

PubMed Central

2013-01-01

Immunisation has been an important strategy for disease prevention globally. Despite proven successes in other settings, child immunisation has continued to be problematic in developing countries including Nigeria. In addressing the problems, policy in Nigeria is largely directed at overcoming socio cultural issues surrounding parents’ rejection of vaccines. However, determinants of immunisation have geographical implications as well. A cross sectional survey was used to select 484 mothers/caregivers through a multi stage cluster sampling technique from the three senatorial districts of Borno State, Nigeria. Mothers or caregivers of children 12–23 months were interviewed using a structured questionnaire adapted from the Nigeria Demographic and Health Survey (2008). Socio cultural factors measured include mother’s education, religion, husband’s permission and sex of child while spatial variables include location i.e. whether rural or urban, and distance measured in terms of physical distance, cost and perception of physical distance. Descriptive statistics, univariate and multivariate logistic regressions were used to analyse the results. Data indicate that only 10.5% of children were fully immunised. Though immunisation uptake differed between the senatorial districts, this was not significant (P=0.1). In the bivariate analysis, mothers living in urban areas, <1 km to immunisation centre, their perception of travel distance and travel cost were the spatial predictors of immunisation while literacy and husband’s permission were the socio-cultural factors of significance. However, in the multivariate regression only two geographical factors i.e. living in an urban area [odds ratio (OR) 3.42, confidence interval (CI) 1.40–8.33] and mothers’ perception of distance (OR 4.52, CI 2.14–9.55) were protective against under immunisation while mother’s education was the only socio cultural variable of significance (OR 0.10, CI 0.03–0.41). It was concluded that while it is important to address socio cultural issues, policies directed at overcoming the friction of distance especially mobile clinics in rural areas are required to significantly improve immunisation uptake in the state. PMID:28299099

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.« less

The association between intimate partner domestic violence and the food security status of poor families in Brazil.

PubMed

Ribeiro-Silva, Rita de Cássia; Fiaccone, Rosemeire Leovigildo; Barreto, Maurício Lima; Santana, Mônica Leila Portela; dos Santos, Sandra Maria Chaves; da Conceição-Machado, Maria Ester Pereira; Aliaga, Marie Agnès

2016-05-01

To assess the association between physical intimate partner violence and household food security within households with schoolchildren. Cross-sectional study. Salvador, Bahia, north-eastern Brazil. The study was conducted in 1019 households with students. Violence between couples was evaluated using the Portuguese version of the revised Conflict Tactics Scales (CTS2), previously adapted and validated for use in Brazil. The Brazilian Food Insecurity Scale (BFIS) was used to identify food insecurity. We also obtained socio-economic information for each participant. We used multivariate Poisson regression to assess the associations of interest. According to the results of the BFIS, 62·5% of the households were found to experience food insecurity, including 19·5% moderate food insecurity and 6·5% severe food insecurity. The prevalence of minor physical violence was 9·6% (95% CI 7·8, 11·4%) and of severe physical violence was 4·7% (95% CI 3·4, 6·0%) among the couples. In the final multivariate model, it was found that couples reporting minor (prevalence ratio=1·23; 95% CI 1·12, 1·35) and severe (prevalence ratio=1·16; 95% CI 1·00, 1·34) physical violence were more likely to be experiencing household food insecurity, compared with those not reporting physical violence. Physical intimate partner violence was associated with food insecurity of households. The present study brings new data to the subject of the role of violence in the context of food insecurity.

Driving and off-road impairments underlying failure on road testing in Parkinson's disease.

PubMed

Devos, Hannes; Vandenberghe, Wim; Tant, Mark; Akinwuntan, Abiodun E; De Weerdt, Willy; Nieuwboer, Alice; Uc, Ergun Y

2013-12-01

Parkinson's disease (PD) affects driving ability. We aimed to determine the most critical impairments in specific road skills and in clinical characteristics leading to failure on a road test in PD. In this cross-sectional study, certified driving assessment experts evaluated specific driving skills in 104 active, licensed drivers with PD using a standardized, on-road checklist and issued a global decision of pass/fail. Participants also completed an off-road evaluation assessing demographic features, disease characteristics, motor function, vision, and cognition. The most important driving skills and off-road predictors of the pass/fail outcome were identified using multivariate stepwise regression analyses. Eighty-six (65%) passed and 36 (35%) failed the on-road driving evaluation. Persons who failed performed worse on all on-road items. When adjusted for age and gender, poor performances on lateral positioning at low speed, speed adaptations at high speed, and left turning maneuvers yielded the best model that determined the pass/fail decision (R(2) = 0.56). The fail group performed poorer on all motor, visual, and cognitive tests. Measures of visual scanning, motor severity, PD subtype, visual acuity, executive functions, and divided attention were independent predictors of pass/fail decisions in the multivariate model (R(2) = 0.60). Our study demonstrated that failure on a road test in PD is determined by impairments in specific driving skills and associated with deficits in motor, visual, executive, and visuospatial functions. These findings point to specific driving and off-road impairments that can be targeted in multimodal rehabilitation programs for drivers with PD. © 2013 Movement Disorder Society.

Prevalence and correlates of intimate partner violence in HIV-positive women engaged in transactional sex in Mombasa, Kenya

PubMed Central

Wilson, Kate S; Deya, Ruth; Masese, Linnet; Simoni, Jane M; Stoep, Ann Vander; Shafi, Juma; Jaoko, Walter; Hughes, James P; McClelland, R Scott

2016-01-01

We evaluated the prevalence and correlates of intimate partner violence in the past year by a regular male partner in HIV-positive female sex workers in Mombasa, Kenya. This cross-sectional study included HIV-positive women ≥ 18 years old who reported engagement in transactional sex at the time of enrolment in the parent cohort. We asked 13 questions adapted from the World Health Organization survey on violence against women about physical, sexual, or emotional violence in the past year by the current or most recent emotional partner (index partner). We used standardised instruments to assess socio-demographic and behavioural characteristics as possible correlates of intimate partner violence. Associations between intimate partner violence and these correlates were evaluated using univariate and multivariate logistic regression. Overall, 286/357 women (80.4%) had an index partner, and 52/357 (14.6%, 95% confidence interval 10.9%–18.2%) reported intimate partner violence by that partner in the past year. In multivariate analysis, women with severe alcohol problems (adjusted odds ratio 4.39, 1.16–16.61) and those experiencing controlling behaviours by the index partner (adjusted odds ratio 4.98, 2.31–10.74) were significantly more likely to report recent intimate partner violence. Recent intimate partner violence was common in HIV-positive female sex workers. Interventions targeting risk factors for intimate partner violence, including alcohol problems and partner controlling behaviours, could help to reduce recurrent violence and negative health outcomes in this key population. PMID:26464502

A meta-analysis of aortic root size in elite athletes.

PubMed

Iskandar, Aline; Thompson, Paul D

2013-02-19

The aorta is exposed to hemodynamic stress during exercise, but whether or not the aorta is larger in athletes is not clear. We performed a systematic literature review and meta-analysis to examine whethere athletes demonstrate increased aortic root dimensions compared with nonathlete controls. We searched MEDLINE and Scopus from inception through August 12, 2012, for English-language studies reporting the aortic root size in elite athletes. Two investigators independently extracted athlete and study characteristics. A multivariate linear mixed model was used to conduct meta-regression analyses. We identified 71 studies reporting aortic root dimensions in 8564 unique athletes, but only 23 of these studies met our criteria by reporting aortic root dimensions at the aortic valve annulus or sinus of Valsalva in elite athletes (n=5580). Athletes were compared directly with controls (n=727) in 13 studies. On meta-regression, the weighted mean aortic root diameter measured at the sinuses of Valsalva was 3.2 mm (P=0.02) larger in athletes than in the nonathletic controls, whereas aortic root size at the aortic valve annulus was 1.6 mm (P=0.04) greater in athletes than in controls. Elite athletes have a small but significantly larger aortic root diameter at the sinuses of Valsalva and aortic valve annulus, but this difference is minor and clinically insignificant. Clinicians evaluating athletes should know that marked aortic root dilatation likely represents a pathological process and not a physiological adaptation to exercise.

Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach.

PubMed

Cai, Yutong; Zijlema, Wilma L; Doiron, Dany; Blangiardo, Marta; Burton, Paul R; Fortier, Isabel; Gaye, Amadou; Gulliver, John; de Hoogh, Kees; Hveem, Kristian; Mbatchou, Stéphane; Morley, David W; Stolk, Ronald P; Elliott, Paul; Hansell, Anna L; Hodgson, Susan

2017-01-01

We investigated the effects of both ambient air pollution and traffic noise on adult asthma prevalence, using harmonised data from three European cohort studies established in 2006-2013 (HUNT3, Lifelines and UK Biobank).Residential exposures to ambient air pollution (particulate matter with aerodynamic diameter ≤10 µm (PM 10 ) and nitrogen dioxide (NO 2 )) were estimated by a pan-European Land Use Regression model for 2007. Traffic noise for 2009 was modelled at home addresses by adapting a standardised noise assessment framework (CNOSSOS-EU). A cross-sectional analysis of 646 731 participants aged ≥20 years was undertaken using DataSHIELD to pool data for individual-level analysis via a "compute to the data" approach. Multivariate logistic regression models were fitted to assess the effects of each exposure on lifetime and current asthma prevalence.PM 10 or NO 2 higher by 10 µg·m -3 was associated with 12.8% (95% CI 9.5-16.3%) and 1.9% (95% CI 1.1-2.8%) higher lifetime asthma prevalence, respectively, independent of confounders. Effects were larger in those aged ≥50 years, ever-smokers and less educated. Noise exposure was not significantly associated with asthma prevalence.This study suggests that long-term ambient PM 10 exposure is associated with asthma prevalence in western European adults. Traffic noise is not associated with asthma prevalence, but its potential to impact on asthma exacerbations needs further investigation. Copyright ©ERS 2017.

Gluten-free living in China: The characteristics, food choices and difficulties in following a gluten-free diet - An online survey.

PubMed

Lu, Zhenxing; Zhang, Haoyang; Luoto, Sanna; Ren, Xiang

2018-05-10

Individuals adhering to a gluten-free diet (GFD) have been scarcely researched within a Chinese population. This present study was conducted to assess the characteristics, food choices, shopping preferences, and emotions and attitudes of individuals following a GFD. Individuals following a GFD were invited to complete an online questionnaire about their demography and geography characteristics, gluten-free food choices, shopping preferences, GFD management and subjective difficulty towards the diet. Distribution of the characteristics was described, and univariate and multivariate logistic regressions performed to explore the relationship between the above-mentioned variables and subjective difficulty in following a GFD. 209 individuals following a GFD completed the questionnaire, most of whom were young, single and well-educated females from developed provinces or municipalities in China. Multiple regression showed that age, education level, advice on starting a GFD, duration before discovering a gluten intolerance, food choices and ways of GFD management were significantly associated with the subjective difficulty in following a GFD. Our findings offer a basic characteristics pattern of the population on a GFD in mainland China. Nearly one-third of GFD followers found the diet challenging to be followed. We suggest that sufficient celiac disease and gluten-induced disorder education be conducted among healthcare practitioners. Early diagnosis of gluten-induced disorders and defining an Asian-adapted GFD, as well as an increase in public awareness, may help adherence to a strict GFD in China. Copyright © 2018. Published by Elsevier Ltd.

Implementing smoking cessation guidelines for hospitalized Veterans: Cessation results from the VA-BEST trial☆

PubMed Central

Vander Weg, Mark W.; Holman, John E.; Rahman, Hafizur; Sarrazin, Mary Vaughan; Hillis, Stephen L.; Fu, Steven S.; Grant, Kathleen M.; Prochazka, Allan V.; Adams, Susan L.; Battaglia, Catherine T.; Buchanan, Lynne M.; Tinkelman, David; Katz, David A.

2018-01-01

Purpose To examine the impact of a nurse-initiated tobacco cessation intervention focused on providing guideline-recommended care to hospitalized smokers. Design Pre-post quasi-experimental trial. Setting General medical units of four US Department of Veterans Affairs hospitals. Subjects 898 adult Veteran smokers (503 and 395 were enrolled in the baseline and intervention periods, respectively). Intervention The intervention included academic detailing, adaptation of the computerized medical record, patient self-management support, and organizational support and feedback. Measures The primary outcome was self-reported 7-day point prevalence abstinence at six months. Analysis Tobacco use was compared for the pre-intervention and intervention periods with multivariable logistic regression using generalized estimating equations to account for clustering at the nurse level. Predictors of abstinence at six months were investigated with best subsets regression. Results Seven-day point prevalence abstinence during the intervention period did not differ significantly from the pre-intervention period at either three (adjusted odds ratio (AOR) and 95% confidence interval (CI95) = 0.78 [0.51–1.18]) or six months (AOR = 0.92; CI95 = 0.62–1.37). Predictors of abstinence included baseline self-efficacy for refraining from smoking when experiencing negative affect (p = 0.0004) and perceived likelihood of staying off cigarettes following discharge (p < 0.0001). Conclusions Tobacco use interventions in the VA inpatient setting likely require more substantial changes in clinician behavior and enhanced post-discharge follow-up to improve cessation outcomes. PMID:28476277

Machine Learning Intermolecular Potentials for 1,3,5-Triamino-2,4,6-trinitrobenzene (TATB) Using Symmetry-Adapted Perturbation Theory

DTIC Science & Technology

2018-04-25

unlimited. NOTICES Disclaimers The findings in this report are not to be construed as an official Department of the Army position unless so...this report, intermolecular potentials for 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) are developed using machine learning techniques. Three...potentials based on support vector regression, kernel ridge regression, and a neural network are fit using symmetry-adapted perturbation theory. The

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Systemic and Mucosal Differences in HIV Burden, Immune and Therapeutic Responses

PubMed Central

Wahl, Sharon M.; Redford, Maryann; Christensen, Shawna; Mack, Wendy; Cohn, Jon; Janoff, Edward N.; Mestecky, Jiri; Jenson, Hal B.; Navazesh, Mahvash; Cohen, Mardge; Reichelderfer, Patricia; Kovacs, Andrea

2011-01-01

Background Mucosal tissues represent major targets for HIV transmission, but differ in susceptibility and reservoir function by unknown mechanisms. Methods In a cross-sectional study, HIV RNA and infectious virus were compared between oral and genital compartments and blood in HIV-infected women, in association with clinical parameters, co-pathogens and putative innate and adaptive HIV inhibitors. Results HIV RNA was detectable in 24.5% of women from all 3 compartments, whereas 45% had RNA in only one or two sites. By comparison, infectious HIV, present in blood of the majority, was rare in mucosal sites. Innate mediators, SLPI and TSP, were highest in mucosae. Highly active antiretroviral therapy (HAART) was associated with an 80% decreased probability of shedding. Multivariate logistic regression models revealed that mucosal HIV RNA was associated with higher plasma RNA, infectious virus, and total mucosal IgA, but not IgG. There was a 37-fold increased probability of detecting RNA in both genital and oral specimens (P=0.008;P=0.02, respectively) among women in highest vs lowest IgA tertiles. Conclusions Mucosal sites exhibit distinct characteristics of infectious HIV, viral shedding and responses to therapy, dependent upon both systemic and local factors. Of the putative innate and adaptive mucosal defense factors examined, only IgA was associated with HIV RNA shedding. However, rather than being protective, there was a striking increase in probability of detectable HIV RNA shedding in women with highest total IgA. PMID:21239996

Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA

USGS Publications Warehouse

West, Amanda; Kumar, Sunil; Jarnevich, Catherine S.

2016-01-01

Regional analysis of large wildfire potential given climate change scenarios is crucial to understanding areas most at risk in the future, yet wildfire models are not often developed and tested at this spatial scale. We fit three historical climate suitability models for large wildfires (i.e. ≥ 400 ha) in Colorado andWyoming using topography and decadal climate averages corresponding to wildfire occurrence at the same temporal scale. The historical models classified points of known large wildfire occurrence with high accuracies. Using a novel approach in wildfire modeling, we applied the historical models to independent climate and wildfire datasets, and the resulting sensitivities were 0.75, 0.81, and 0.83 for Maxent, Generalized Linear, and Multivariate Adaptive Regression Splines, respectively. We projected the historic models into future climate space using data from 15 global circulation models and two representative concentration pathway scenarios. Maps from these geospatial analyses can be used to evaluate the changing spatial distribution of climate suitability of large wildfires in these states. April relative humidity was the most important covariate in all models, providing insight to the climate space of large wildfires in this region. These methods incorporate monthly and seasonal climate averages at a spatial resolution relevant to land management (i.e. 1 km2) and provide a tool that can be modified for other regions of North America, or adapted for other parts of the world.

Factors associated with oral hygiene practices among adults with systemic sclerosis

PubMed Central

Yuen, Hon K.; Hant, Faye N.; Hatfield, Corey; Summerlin, Lisa M.; Smith, Edwin A.; Silver, Richard M.

2013-01-01

OBJECTIVE To identify factors associated with oral hygiene practices in adults with systemic sclerosis (SSc) METHODS In this cross-sectional study, 178 dentate adults with SSc received an oral examination which included measurement of oral aperture, assessment of manual dexterity to perform oral hygiene, as well as completion of the Center of Epidemiological Studies Depression (CES-D) Scale, and an oral health-related questionnaire. RESULTS Multivariable logistic regression modeling showed male, minority and high CES-D scores (i.e., clinically significant symptoms of depression) were associated with less likelihood of participants brushing teeth at least twice daily, but the presence of self-reported dry mouth symptoms increased the likelihood of toothbrushing. Having a dental visit in the past 12 months, and use of an adapted flossing or interdental cleaning device were significantly associated with daily dental flossing; however, having difficulty flossing teeth reduced the likelihood of daily flossing. CONCLUSIONS Overall, demographic variables were strongly associated with toothbrushing frequency, whereas, flossing self-efficacy and barriers were strongly associated with dental flossing frequency in adults with SSc. The results suggest that dental health professionals should take mental health into consideration when educating patients with SSc to improve their oral hygiene, and consider making referrals for patients exhibiting suspected clinically significant depressive symptoms to mental health professionals for further evaluation and treatment. In addition, an appropriate adapted flossing or interdental cleaning device should be recommended to increase dental flossing practices in this patient population. PMID:24128049

Direct adaptive control of manipulators in Cartesian space

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

A new adaptive-control scheme for direct control of manipulator end effector to achieve trajectory tracking in Cartesian space is developed in this article. The control structure is obtained from linear multivariable theory and is composed of simple feedforward and feedback controllers and an auxiliary input. The direct adaptation laws are derived from model reference adaptive control theory and are not based on parameter estimation of the robot model. The utilization of adaptive feedforward control and the inclusion of auxiliary input are novel features of the present scheme and result in improved dynamic performance over existing adaptive control schemes. The adaptive controller does not require the complex mathematical model of the robot dynamics or any knowledge of the robot parameters or the payload, and is computationally fast for on-line implementation with high sampling rates. The control scheme is applied to a two-link manipulator for illustration.

The Association Between Internet Use and Ambulatory Care-Seeking Behaviors in Taiwan: A Cross-Sectional Study

PubMed Central

Chen, Tsung-Fu; Liang, Jyh-Chong; Lin, Tzu-Bin; Tsai, Chin-Chung

2016-01-01

Background Compared with the traditional ways of gaining health-related information from newspapers, magazines, radio, and television, the Internet is inexpensive, accessible, and conveys diverse opinions. Several studies on how increasing Internet use affected outpatient clinic visits were inconclusive. Objective The objective of this study was to examine the role of Internet use on ambulatory care-seeking behaviors as indicated by the number of outpatient clinic visits after adjusting for confounding variables. Methods We conducted this study using a sample randomly selected from the general population in Taiwan. To handle the missing data, we built a multivariate logistic regression model for propensity score matching using age and sex as the independent variables. The questionnaires with no missing data were then included in a multivariate linear regression model for examining the association between Internet use and outpatient clinic visits. Results We included a sample of 293 participants who answered the questionnaire with no missing data in the multivariate linear regression model. We found that Internet use was significantly associated with more outpatient clinic visits (P=.04). The participants with chronic diseases tended to make more outpatient clinic visits (P<.01). Conclusions The inconsistent quality of health-related information obtained from the Internet may be associated with patients’ increasing need for interpreting and discussing the information with health care professionals, thus resulting in an increasing number of outpatient clinic visits. In addition, the media literacy of Web-based health-related information seekers may also affect their ambulatory care-seeking behaviors, such as outpatient clinic visits. PMID:27927606

Landscape controls on total and methyl Hg in the Upper Hudson River basin, New York, USA

USGS Publications Warehouse

Burns, Douglas A.; Riva-Murray, K.; Bradley, P.M.; Aiken, G.R.; Brigham, M.E.

2012-01-01

Approaches are needed to better predict spatial variation in riverine Hg concentrations across heterogeneous landscapes that include mountains, wetlands, and open waters. We applied multivariate linear regression to determine the landscape factors and chemical variables that best account for the spatial variation of total Hg (THg) and methyl Hg (MeHg) concentrations in 27 sub-basins across the 493 km2 upper Hudson River basin in the Adirondack Mountains of New York. THg concentrations varied by sixfold, and those of MeHg by 40-fold in synoptic samples collected at low-to-moderate flow, during spring and summer of 2006 and 2008. Bivariate linear regression relations of THg and MeHg concentrations with either percent wetland area or DOC concentrations were significant but could account for only about 1/3 of the variation in these Hg forms in summer. In contrast, multivariate linear regression relations that included metrics of (1) hydrogeomorphology, (2) riparian/wetland area, and (3) open water, explained about 66% to >90% of spatial variation in each Hg form in spring and summer samples. These metrics reflect the influence of basin morphometry and riparian soils on Hg source and transport, and the role of open water as a Hg sink. Multivariate models based solely on these landscape metrics generally accounted for as much or more of the variation in Hg concentrations than models based on chemical and physical metrics, and show great promise for identifying waters with expected high Hg concentrations in the Adirondack region and similar glaciated riverine ecosystems.

The combination of ovarian volume and outline has better diagnostic accuracy than prostate-specific antigen (PSA) concentrations in women with polycystic ovarian syndrome (PCOs).

PubMed

Bili, Eleni; Bili, Authors Eleni; Dampala, Kaliopi; Iakovou, Ioannis; Tsolakidis, Dimitrios; Giannakou, Anastasia; Tarlatzis, Basil C

2014-08-01

The aim of this study was to determine the performance of prostate specific antigen (PSA) and ultrasound parameters, such as ovarian volume and outline, in the diagnosis of polycystic ovary syndrome (PCOS). This prospective, observational, case-controlled study included 43 women with PCOS, and 40 controls. Between day 3 and 5 of the menstrual cycle, fasting serum samples were collected and transvaginal ultrasound was performed. The diagnostic performance of each parameter [total PSA (tPSA), total-to-free PSA ratio (tPSA:fPSA), ovarian volume, ovarian outline] was estimated by means of receiver operating characteristic (ROC) analysis, along with area under the curve (AUC), threshold, sensitivity, specificity as well as positive (+) and negative (-) likelihood ratios (LRs). Multivariate logistical regression models, using ovarian volume and ovarian outline, were constructed. The tPSA and tPSA:fPSA ratio resulted in AUC of 0.74 and 0.70, respectively, with moderate specificity/sensitivity and insufficient LR+/- values. In the multivariate logistic regression model, the combination of ovarian volume and outline had a sensitivity of 97.7% and a specificity of 97.5% in the diagnosis of PCOS, with +LR and -LR values of 39.1 and 0.02, respectively. In women with PCOS, tPSA and tPSA:fPSA ratio have similar diagnostic performance. The use of a multivariate logistic regression model, incorporating ovarian volume and outline, offers very good diagnostic accuracy in distinguishing women with PCOS patients from controls. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

[Multivariate ordinal logistic regression analysis on the association between consumption of fried food and both esophageal cancer and precancerous lesions].

PubMed

Guo, L W; Liu, S Z; Zhang, M; Chen, Q; Zhang, S K; Sun, X B

2017-12-10

Objective: To investigate the effect of fried food intake on the pathogenesis of esophageal cancer and precancerous lesions. Methods: From 2005 to 2013, all the residents aged 40-69 years from 11 counties (cities) where cancer screening of upper gastrointestinal cancer had been conducted in rural areas of Henan province, were recruited as the subjects of study. Information on demography and lifestyle was collected. The residents under study were screened with iodine staining endoscopic examination and biopsy samples were diagnosed pathologically, under standardized criteria. Subjects with high risk were divided into the groups based on their different pathological degrees. Multivariate ordinal logistic regression analysis was used to analyze the relationship between the frequency of fried food intake and esophageal cancer and precancerous lesions. Results: A total number of 8 792 cases with normal esophagus, 3 680 with mild hyperplasia, 972 with moderate hyperplasia, 413 with severe hyperplasia carcinoma in situ, and 336 cases of esophageal cancer were recruited. Results from multivariate logistic regression analysis showed that, when compared with those who did not eat fried food, the intake of fried food (<2 times/week: OR =1.60, 95% CI : 1.40-1.83; ≥2 times/week: OR =2.58, 95% CI : 1.98-3.37) appeared a risk factor for both esophageal cancer or precancerous lesions after adjustment for age, sex, marital status, educational level, body mass index, smoking and alcohol intake. Conclusion: The intake of fried food appeared a risk factor for both esophageal cancer and precancerous lesions.

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

PubMed

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

Risk of hemorrhagic transformation after ischemic stroke in patients with antiphospholipid antibody syndrome.

PubMed

Mehta, Tapan; Hussain, Mohammed; Sheth, Khushboo; Ding, Yuchuan; McCullough, Louise D

2017-06-01

Several rheumatologic conditions including systemic lupus erythematosus, antiphospholipid antibody (APS) syndrome, rheumatoid arthritis, and scleroderma are known risk factors for stroke. The risk of hemorrhagic transformation after an acute ischemic stroke (AIS) in these patients is not known. We queried the Nationwide Inpatient Sample (NIS) data between 2010 and 2012 with ICD 9 diagnostic codes for AIS. The primary outcome was the development of hemorrhagic transformation. Multivariate predictors for hemorrhagic transformation were identified with a logistic regression model. Using SAS 9.2, Survey procedures were used to accommodate for hierarchical two stage cluster design of NIS. APS (OR 2.57, 95% CI 1.14-5.81, p = 0.0228) independently predicted risk of hemorrhagic transformation in multivariate regression analysis. Similarly, in multivariate regression models for the outcome variables of total charges of the hospitalization and length of stay (LOS), patients with APS had the highest charges ($56,286, p = 0.0228) and LOS (3.87 days, p = 0.0164) compared to other co-variates. Univariate analysis showed increased mortality in the APS compared to the non-APS group (11.68% vs. 7.16%, p = 0.0024). APS is an independent risk factor for hemorrhagic transformation in both thrombolytic and non-thrombolytic treated patients. APS is also associated with longer length and cost of hospital stay. Further research is warranted to identify the unique risk factors in these patients to identify strategies to reduce the risk of hemorrhagic transformation in this subgroup of the population.

[Relationship between highly sensitive cardiac troponin T and sepsis and outcome in critically ill patients].

PubMed

Wang, T T; Jiang, L

2017-10-01

Objective: To investigate the prognostic value of highly sensitive cardiac Troponin T (hs-cTn T) for sepsis in critically ill patients. Methods: Patients estimated to stay in the ICU of Fuxing Hospital for more than 24h were enrolled at from March 2014 to December 2014. Serum hs-cTn T was tested within two hours. Univariate and multivariate linear regression analyses were used to determine the association of variables with the hs-cTn T. Multivariable logistic regression analysis was used to evaluate the risk factors of 28-day mortality. Results: A total of 125 patients were finally enrolled including 68 patients with sepsis and 57 without. The levels of hs-cTn T in sepsis and non-sepsis groups were significantly different[52.0(32.5, 87.5) ng/L vs 14.0(6.5, 29.0) ng/L respectively, P <0.001]. In sepsis group, hs-cTn T among common sepsis, severe sepsis and septic shock were similar. Hs-cTn T was significantly higher in non-survivors than survivors [27(13, 52)ng/L vs 44.5(28.8, 83.5)ng/L, P <0.001]. Age, sepsis, serum creatinine were independent risk factors affecting hs-cTn T by multivariate linear regression analyses. But hs-cTn T was not a risk factor for death. Conclusion: Patients with sepsis had higher serum hs-cTn T than those without sepsis. but it was not found to be associated with the severity of sepsis.

Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.

PubMed

Hemakom, Apit; Goverdovsky, Valentin; Looney, David; Mandic, Danilo P

2016-04-13

An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain-computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate. © 2016 The Author(s).

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Statistical Evaluation of Time Series Analysis Techniques

NASA Technical Reports Server (NTRS)

Benignus, V. A.

1973-01-01

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

LANDSCAPE METRICS THAT ARE USEFUL FOR EXPLAINING ESTUARINE ECOLOGICAL RESPONSES

EPA Science Inventory

We investigated whether land use/cover characteristics of watersheds associated with estuaries exhibit a strong enough signal to make landscape metrics useful for predicting estuarine ecological condition. We used multivariate logistic regression models to discriminate between su...

Association between Daily Hospital Outpatient Visits for Accidents and Daily Ambient Air Temperatures in an Industrial City.

PubMed

Chau, Tang-Tat; Wang, Kuo-Ying

2016-01-01

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007-2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0-15 years old). Middle-aged people (16-65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8-1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place.

Association between Daily Hospital Outpatient Visits for Accidents and Daily Ambient Air Temperatures in an Industrial City

PubMed Central

Chau, Tang-Tat; Wang, Kuo-Ying

2016-01-01

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007–2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0–15 years old). Middle-aged people (16–65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8–1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place. PMID:26815039

Changes of visual-field global indices after cataract surgery in primary open-angle glaucoma patients.

PubMed

Seol, Bo Ram; Jeoung, Jin Wook; Park, Ki Ho

2016-11-01

To determine changes of visual-field (VF) global indices after cataract surgery and the factors associated with the effect of cataracts on those indices in primary open-angle glaucoma (POAG) patients. A retrospective chart review of 60 POAG patients who had undergone phacoemulsification and intraocular lens insertion was conducted. All of the patients were evaluated with standard automated perimetry (SAP; 30-2 Swedish interactive threshold algorithm; Carl Zeiss Meditec Inc.) before and after surgery. VF global indices before surgery were compared with those after surgery. The best-corrected visual acuity, intraocular pressure (IOP), number of glaucoma medications before surgery, mean total deviation (TD) values, mean pattern deviation (PD) value, and mean TD-PD value were also compared with the corresponding postoperative values. Additionally, postoperative peak IOP and mean IOP were evaluated. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the effect of cataract on global indices. Mean deviation (MD) after cataract surgery was significantly improved compared with the preoperative MD. Pattern standard deviation (PSD) and visual-field index (VFI) after surgery were similar to those before surgery. Also, mean TD and mean TD-PD were significantly improved after surgery. The posterior subcapsular cataract (PSC) type showed greater MD changes than did the non-PSC type in both the univariate and multivariate logistic regression analyses. In the univariate logistic regression analysis, the preoperative TD-PD value and type of cataract were associated with MD change. However, in the multivariate logistic regression analysis, type of cataract was the only associated factor. None of the other factors was associated with MD change. MD was significantly affected by cataracts, whereas PSD and VFI were not. Most notably, the PSC type showed better MD improvement compared with the non-PSC type after cataract surgery. Clinicians therefore should carefully analyze VF examination results for POAG patients with the PSC type.

Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study.

PubMed

Lee, Poh Foong; Kan, Donica Pei Xin; Croarkin, Paul; Phang, Cheng Kar; Doruk, Deniz

2018-01-01

There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms. Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models. Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03). The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history. Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution.

PubMed

Koch, Cosima; Posch, Andreas E; Goicoechea, Héctor C; Herwig, Christoph; Lendl, Bernhard

2014-01-07

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution - alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L(-1) for Penicillin V and 0.32 g L(-1) for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L(-1) for Penicillin V and 0.15 g L(-1) for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

USGS Publications Warehouse

Anderson, Ryan; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott M.; Morris, Richard V.; Ehlmann, Bethany L.; Dyar, M. Darby

2017-01-01

Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Beyond Reading Alone: The Relationship Between Aural Literacy And Asthma Management

PubMed Central

Rosenfeld, Lindsay; Rudd, Rima; Emmons, Karen M.; Acevedo-García, Dolores; Martin, Laurie; Buka, Stephen

2010-01-01

Objectives To examine the relationship between literacy and asthma management with a focus on the oral exchange. Methods Study participants, all of whom reported asthma, were drawn from the New England Family Study (NEFS), an examination of links between education and health. NEFS data included reading, oral (speaking), and aural (listening) literacy measures. An additional survey was conducted with this group of study participants related to asthma issues, particularly asthma management. Data analysis focused on bivariate and multivariable logistic regression. Results In bivariate logistic regression models exploring aural literacy, there was a statistically significant association between those participants with lower aural literacy skills and less successful asthma management (OR:4.37, 95%CI:1.11, 17.32). In multivariable logistic regression analyses, controlling for gender, income, and race in separate models (one-at-a-time), there remained a statistically significant association between those participants with lower aural literacy skills and less successful asthma management. Conclusion Lower aural literacy skills seem to complicate asthma management capabilities. Practice Implications Greater attention to the oral exchange, in particular the listening skills highlighted by aural literacy, as well as other related literacy skills may help us develop strategies for clear communication related to asthma management. PMID:20399060

Application of multivariate chemometric techniques for simultaneous determination of five parameters of cottonseed oil by single bounce attenuated total reflectance Fourier transform infrared spectroscopy.

PubMed

Talpur, M Younis; Kara, Huseyin; Sherazi, S T H; Ayyildiz, H Filiz; Topkafa, Mustafa; Arslan, Fatma Nur; Naz, Saba; Durmaz, Fatih; Sirajuddin

2014-11-01

Single bounce attenuated total reflectance (SB-ATR) Fourier transform infrared (FTIR) spectroscopy in conjunction with chemometrics was used for accurate determination of free fatty acid (FFA), peroxide value (PV), iodine value (IV), conjugated diene (CD) and conjugated triene (CT) of cottonseed oil (CSO) during potato chips frying. Partial least square (PLS), stepwise multiple linear regression (SMLR), principal component regression (PCR) and simple Beer׳s law (SBL) were applied to develop the calibrations for simultaneous evaluation of five stated parameters of cottonseed oil (CSO) during frying of French frozen potato chips at 170°C. Good regression coefficients (R(2)) were achieved for FFA, PV, IV, CD and CT with value of >0.992 by PLS, SMLR, PCR, and SBL. Root mean square error of prediction (RMSEP) was found to be less than 1.95% for all determinations. Result of the study indicated that SB-ATR FTIR in combination with multivariate chemometrics could be used for accurate and simultaneous determination of different parameters during the frying process without using any toxic organic solvent. Copyright © 2014 Elsevier B.V. All rights reserved.

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

PubMed

André, Silvère; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Duponchel, Ludovic

2017-03-01

In recent years, as a result of the process analytical technology initiative of the US Food and Drug Administration, many different works have been carried out on direct and in situ monitoring of critical parameters for mammalian cell cultures by Raman spectroscopy and multivariate regression techniques. However, despite interesting results, it cannot be said that the proposed monitoring strategies, which will reduce errors of the regression models and thus confidence limits of the predictions, are really optimized. Hence, the aim of this article is to optimize some critical steps of spectroscopic acquisition and data treatment in order to reach a higher level of accuracy and robustness of bioprocess monitoring. In this way, we propose first an original strategy to assess the most suited Raman acquisition time for the processes involved. In a second part, we demonstrate the importance of the interbatch variability on the accuracy of the predictive models with a particular focus on the optical probes adjustment. Finally, we propose a methodology for the optimization of the spectral variables selection in order to decrease prediction errors of multivariate regressions. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:308-316, 2017. © 2017 American Institute of Chemical Engineers.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

A Polyhedral Outer-approximation, Dynamic-discretization optimization solver, 1.x

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

Bent, Rusell; Nagarajan, Harsha; Sundar, Kaarthik

2017-09-25

In this software, we implement an adaptive, multivariate partitioning algorithm for solving mixed-integer nonlinear programs (MINLP) to global optimality. The algorithm combines ideas that exploit the structure of convex relaxations to MINLPs and bound tightening procedures

Introduction to uses and interpretation of principal component analyses in forest biology.

Treesearch

J. G. Isebrands; Thomas R. Crow

1975-01-01

The application of principal component analysis for interpretation of multivariate data sets is reviewed with emphasis on (1) reduction of the number of variables, (2) ordination of variables, and (3) applications in conjunction with multiple regression.

Adaptive local linear regression with application to printer color management.

PubMed

Gupta, Maya R; Garcia, Eric K; Chin, Erika

2008-06-01

Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

PubMed Central

Martinez, Josue G.; Carroll, Raymond J.; Müller, Samuel; Sampson, Joshua N.; Chatterjee, Nilanjan

2012-01-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso. PMID:22347720

A correlate of HIV-1 control consisting of both innate and adaptive immune parameters best predicts viral load by multivariable analysis in HIV-1 infected viremic controllers and chronically-infected non-controllers.

PubMed

Tomescu, Costin; Liu, Qin; Ross, Brian N; Yin, Xiangfan; Lynn, Kenneth; Mounzer, Karam C; Kostman, Jay R; Montaner, Luis J

2014-01-01

HIV-1 infected viremic controllers maintain durable viral suppression below 2000 copies viral RNA/ml without anti-retroviral therapy (ART), and the immunological factor(s) associated with host control in presence of low but detectable viral replication are of considerable interest. Here, we utilized a multivariable analysis to identify which innate and adaptive immune parameters best correlated with viral control utilizing a cohort of viremic controllers (median 704 viral RNA/ml) and non-controllers (median 21,932 viral RNA/ml) that were matched for similar CD4+ T cell counts in the absence of ART. We observed that HIV-1 Gag-specific CD8+ T cell responses were preferentially targeted over Pol-specific responses in viremic controllers (p = 0.0137), while Pol-specific responses were positively associated with viral load (rho = 0.7753, p = 0.0001, n = 23). Viremic controllers exhibited significantly higher NK and plasmacytoid dendritic cells (pDC) frequency as well as retained expression of the NK CD16 receptor and strong target cell-induced NK cell IFN-gamma production compared to non-controllers (p<0.05). Despite differences in innate and adaptive immune function however, both viremic controllers (p<0.05) and non-controller subjects (p<0.001) exhibited significantly increased CD8+ T cell activation and spontaneous NK cell degranulation compared to uninfected donors. Overall, we identified that a combination of innate (pDC frequency) and adaptive (Pol-specific CD8+ T cell responses) immune parameters best predicted viral load (R2 = 0.5864, p = 0.0021, n = 17) by a multivariable analysis. Together, this data indicates that preferential Gag-specific over Pol-specific CD8+ T cell responses along with a retention of functional innate subsets best predict host control over viral replication in HIV-1 infected viremic controllers compared to chronically-infected non-controllers.

Albumin, a marker for post-operative myocardial damage in cardiac surgery.

PubMed

van Beek, Dianne E C; van der Horst, Iwan C C; de Geus, A Fred; Mariani, Massimo A; Scheeren, Thomas W L

2018-06-06

Low serum albumin (SA) is a prognostic factor for poor outcome after cardiac surgery. The aim of this study was to estimate the association between pre-operative SA, early post-operative SA and postoperative myocardial injury. This single center cohort study included adult patients undergoing cardiac surgery during 4 consecutive years. Postoperative myocardial damage was defined by calculating the area under the curve (AUC) of troponin (Tn) values during the first 72 h after surgery and its association with SA analyzed using linear regression and with multivariable linear regression to account for patient related and procedural confounders. The association between SA and the secondary outcomes (peri-operative myocardial infarction [PMI], requiring ventilation >24 h, rhythm disturbances, 30-day mortality) was studied using (multivariable) log binomial regression analysis. In total 2757 patients were included. The mean pre-operative SA was 29 ± 13 g/l and the mean post-operative SA was 26 ± 6 g/l. Post-operative SA levels (on average 26 min after surgery) were inversely associated with postoperative myocardial damage in both univariable analysis (regression coefficient - 0.019, 95%CI -0.022/-0.015, p < 0.005) and after adjustment for patient related and surgical confounders (regression coefficient - 0.014 [95% CI -0.020/-0.008], p < 0.0005). Post-operative albumin levels were significantly correlated with the amount of postoperative myocardial damage in patients undergoing cardiac surgery independent of typical confounders. Copyright © 2018. Published by Elsevier Inc.

Evolutionary dynamics of the leaf phenological cycle in an oak metapopulation along an elevation gradient.

PubMed

Firmat, C; Delzon, S; Louvet, J-M; Parmentier, J; Kremer, A

2017-12-01

It has been predicted that environmental changes will radically alter the selective pressures on phenological traits. Long-lived species, such as trees, will be particularly affected, as they may need to undergo major adaptive change over only one or a few generations. The traits describing the annual life cycle of trees are generally highly evolvable, but nothing is known about the strength of their genetic correlations. Tight correlations can impose strong evolutionary constraints, potentially hampering the adaptation of multivariate phenological phenotypes. In this study, we investigated the evolutionary, genetic and environmental components of the timing of leaf unfolding and senescence within an oak metapopulation along an elevation gradient. Population divergence, estimated from in situ and common-garden data, was compared to expectations under neutral evolution, based on microsatellite markers. This approach made it possible (1) to evaluate the influence of genetic correlation on multivariate local adaptation to elevation and (2) to identify traits probably exposed to past selective pressures due to the colder climate at high elevation. The genetic correlation was positive but very weak, indicating that genetic constraints did not shape the local adaptation pattern for leaf phenology. Both spring and fall (leaf unfolding and senescence, respectively) phenology timings were involved in local adaptation, but leaf unfolding was probably the trait most exposed to climate change-induced selection. Our data indicated that genetic variation makes a much smaller contribution to adaptation than the considerable plastic variation displayed by a tree during its lifetime. The evolutionary potential of leaf phenology is, therefore, probably not the most critical aspect for short-term population survival in a changing climate. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma

PubMed Central

Lee, Tsair-Fwu; Liou, Ming-Hsiang; Huang, Yu-Jie; Chao, Pei-Ju; Ting, Hui-Min; Lee, Hsiao-Yi

2014-01-01

To predict the incidence of moderate-to-severe patient-reported xerostomia among head and neck squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC) patients treated with intensity-modulated radiotherapy (IMRT). Multivariable normal tissue complication probability (NTCP) models were developed by using quality of life questionnaire datasets from 152 patients with HNSCC and 84 patients with NPC. The primary endpoint was defined as moderate-to-severe xerostomia after IMRT. The numbers of predictive factors for a multivariable logistic regression model were determined using the least absolute shrinkage and selection operator (LASSO) with bootstrapping technique. Four predictive models were achieved by LASSO with the smallest number of factors while preserving predictive value with higher AUC performance. For all models, the dosimetric factors for the mean dose given to the contralateral and ipsilateral parotid gland were selected as the most significant predictors. Followed by the different clinical and socio-economic factors being selected, namely age, financial status, T stage, and education for different models were chosen. The predicted incidence of xerostomia for HNSCC and NPC patients can be improved by using multivariable logistic regression models with LASSO technique. The predictive model developed in HNSCC cannot be generalized to NPC cohort treated with IMRT without validation and vice versa. PMID:25163814

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

Experimental study of adaptive pointing and tracking for large flexible space structures

NASA Technical Reports Server (NTRS)

Boussalis, D.; Bayard, D. S.; Ih, C.; Wang, S. J.; Ahmed, A.

1991-01-01

This paper describes an experimental study of adaptive pointing and tracking control for flexible spacecraft conducted on a complex ground experiment facility. The algorithm used in this study is based on a multivariable direct model reference adaptive control law. Several experimental validation studies were performed earlier using this algorithm for vibration damping and robust regulation, with excellent results. The current work extends previous studies by addressing the pointing and tracking problem. As is consistent with an adaptive control framework, the plant is assumed to be poorly known to the extent that only system level knowledge of its dynamics is available. Explicit bounds on the steady-state pointing error are derived as functions of the adaptive controller design parameters. It is shown that good tracking performance can be achieved in an experimental setting by adjusting adaptive controller design weightings according to the guidelines indicated by the analytical expressions for the error.

Real estate value prediction using multivariate regression models

NASA Astrophysics Data System (ADS)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Improving Cluster Analysis with Automatic Variable Selection Based on Trees

DTIC Science & Technology

2014-12-01

regression trees Daisy DISsimilAritY PAM partitioning around medoids PMA penalized multivariate analysis SPC sparse principal components UPGMA unweighted...unweighted pair-group average method ( UPGMA ). This method measures dissimilarities between all objects in two clusters and takes the average value

Victimization and Suicidality among Female College Students

ERIC Educational Resources Information Center

Leone, Janel M.; Carroll, James M.

2016-01-01

Objective: To investigate the predictive role of victimization in suicidality among college women. Participants: Female respondents to the American College Health Association National College Health Assessment II (N = 258). Methods: Multivariate logistic regression analyses examined the relationship between victimization and suicidality. Results:…

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

[Value of the albumin to globulin ratio in predicting severity and prognosis in myasthenia gravis patients].

PubMed

Yang, D H; Su, Z Q; Chen, Y; Chen, Z B; Ding, Z N; Weng, Y Y; Li, J; Li, X; Tong, Q L; Han, Y X; Zhang, X

2016-03-08

To assess the predictive value of the albumin to globulin ratio (AGR) in evaluation of disease severity and prognosis in myasthenia gravis patients. A total of 135 myasthenia gravis (MG) patients were enrolled between February 2009 and March 2015. The AGR was detected on the first day of hospitalization and ranked from lowest to highest, and the patients were divided into three equal tertiles according to the AGR values, which were T1 (AGR <1.34), T2 (1.34≤AGR≤1.53) and T3 (AGR>1.53). The Kaplan-Meier curve was used to evaluate the prognostic value of AGR. Cox model analysis was used to evaluate the relevant factors. Multivariate Logistic regression analysis was used to find the predictors of myasthenia crisis during hospitalization. The median length of hospital stay for each tertile was: for the T1 21 days (15-35.5), T2 18 days (14-27.5), and T3 16 days (12-22.5) (P<0.01), and Kaplan-Meier curves showed significant difference among the three groups. In the univariate model, serum albumin, creatinine, AGR and MGFA clinical classification were related to prognosis of myasthenia gravis. At the multivariate Cox regression analysis, the AGR (P<0.001) and MGFA clinical classification (P<0.001) were independent predictive factors of disease severity and prognosis in myasthenia gravis patients. Respectively, the hazard ratio (HR) were 4.655 (95% CI: 2.355-9.202) and 0.596 (95% CI: 0.492-0.723). Multivariate Logistic regression analysis showed the AGR (P<0.001) and MGFA clinical classification were related to myasthenia crisis. The AGR may represent a simple, potentially useful predictive biomarker for evaluating the disease severity and prognosis of patients with myasthenia gravis.

Factors associated with secondhand smoke exposure in different settings: Results from the German Health Update (GEDA) 2012.

PubMed

Fischer, Florian; Kraemer, Alexander

2016-04-14

The ubiquity of secondhand smoke (SHS) exposure at home or in private establishments, workplaces and public areas poses several challenges for the reduction of SHS exposure. This study aimed to describe the prevalence of SHS exposure in Germany and key factors associated with exposure. Results were also differentiated by place of exposure. A secondary data analysis based on the public use file of the German Health Update 2012 was conducted (n = 13,933). Only non-smokers were included in the analysis. In a multivariable logistic regression model the factors associated with SHS exposure were calculated. In addition, a further set of multivariable logistic regressions were calculated for factors associated with the place of SHS exposure (workplace, at home, bars/discotheques, restaurants, at the house of a friend). More than a quarter of non-smoking study participants were exposed to SHS. The main area of exposure was the workplace (40.9 %). The multivariable logistic regression indicated young age as the most important factor associated with SHS exposure. The odds for SHS exposure was higher in men than in women. The likelihood of SHS exposure decreased with higher education. SHS exposure and the associated factors varied between different places of exposure. Despite several actions to protect non-smokers which were implemented in Germany during the past years, SHS exposure still remains a relevant risk factor at a population level. According to the results of this study, particularly the workplace and other public places such as bars and discotheques have to be taken into account for the development of strategies to reduce SHS exposure.

Valuing the visual impact of wind farms: A calculus method for synthesizing choice experiments studies.

PubMed

Wen, Cheng; Dallimer, Martin; Carver, Steve; Ziv, Guy

2018-05-06

Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. A growing number of studies have applied nonmarket valuation methods like Choice Experiments (CE) to value the visual impact by eliciting respondents' willingness to pay (WTP) or willingness to accept (WTA) for hypothetical wind farms through survey questions. Several meta-analyses have been found in the literature to synthesize results from different valuation studies, but they have various limitations related to the use of the prevailing multivariate meta-regression analysis. In this paper, we propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. This method involves establishing WTA or WTP functions for individual studies, fitting the average derivative functions and deriving the general integral functions of WTP or WTA against wind farm attributes. Results indicate that respondents in different studies consistently showed increasing WTP for moving wind farms to greater distances, which can be fitted by non-linear (natural logarithm) functions. However, divergent preferences for the number of turbines and turbine height were found in different studies. We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing CE studies and the general integral functions of WTP or WTA against wind farm attributes are useful for future spatial modelling and benefit transfer studies. We also suggest that future multivariate meta-analyses should include non-linear components in the regression functions. Copyright © 2018. Published by Elsevier B.V.

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Association of ITPA polymorphisms rs6051702/rs1127354 instead of rs7270101/rs1127354 as predictor of ribavirin-associated anemia in chronic hepatitis C treated patients.

PubMed

D'Avolio, Antonio; De Nicolò, Amedeo; Cusato, Jessica; Ciancio, Alessia; Boglione, Lucio; Strona, Silvia; Cariti, Giuseppe; Troshina, Giulia; Caviglia, Gian Paolo; Smedile, Antonina; Rizzetto, Mario; Di Perri, Giovanni

2013-10-01

Functional variants rs7270101 and rs1127354 of inosine triphosphatase (ITPA) were recently found to protect against ribavirin (RBV)-induced hemolytic anemia. However, no definitive data are yet available on the role of no functional rs6051702 polymorphism. Since a simultaneous evaluation of the three ITPA SNPs for hemolytic anemia has not yet been investigated, we aimed to understand the contribution of each SNPs and its potential clinical use to predict anemia in HCV treated patients. A retrospective analysis included 379 HCV treated patients. The ITPA variants rs6051702, rs7270101 and rs1127354 were genotyped and tested for association with achieving anemia at week 4. We also investigated, using multivariate logistic regression, the impact of each single and paired associated polymorphism on anemia onset. All SNPs were associated with Hb decrease. The carrier of at least one variant allele in the functional ITPA SNPs was associated with a lower decrement of Hb, as compared to patients without a variant allele. In multivariate logistic regression analyses the carrier of a variant allele in the rs6051702/rs1127354 association (OR=0.11, p=1.75×10(-5)) and Hb at baseline (OR=1.51, p=1.21×10(-4)) were independently associated with protection against clinically significant anemia at week 4. All ITPA polymorphisms considered were shown to be significantly associated with anemia onset. A multivariate regression model based on ITPA genetic polymorphisms was developed for predicting the risk of anemia. Considering the characterization of pre-therapy anemia predictors, rs6051702 SNP in association to rs1127354 is more informative in order to avoid this relevant adverse event. Copyright © 2013 Elsevier B.V. All rights reserved.

Arteriopathy after transarterial chemo-lipiodolization for hepatocellular carcinoma.

PubMed

Matsui, Y; Figi, A; Horikawa, M; Jahangiri Noudeh, Y; Tomozawa, Y; Hashimoto, K; Kaufman, J A; Farsad, K

2017-12-01

The purpose of this study was to investigate the incidence of and the risk factors for arteriopathy in hepatic arteries after transarterial chemo-lipiodolization in patients with hepatocellular carcinoma and the subsequent treatment strategy changes due to arteriopathy. A total of 365 arteries in 167 patients (126 men and 41 women; mean age, 60.4±15.0 [SD] years [range: 18-87 years]) were evaluated for the development of arteriopathy after chemo-lipiodolization with epirubicin- or doxorubicin-Lipiodol ® emulsion. The development of arteriopathy after chemo-lipiodolization was assessed on arteriograms performed during subsequent transarterial treatments. The treatment strategy changes due to arteriopathy, including change in the chemo-lipiodolization method and the application of alternative therapies was also investigated. Univariate and multivariate binary logistic regression models were used to identify risk factors for arteriopathy and subsequent treatment strategy change. One hundred two (27.9%) arteriopathies were detected in 62/167 (37.1%) patients (45 men, 17 women) with a mean age of 63.3±7.1 [SD] years (age range, 50-86 years). The incidence of arteriopathy was highly patient dependent, demonstrating significant correlation in a fully-adjusted multivariate regression model (P<0.0001). Multivariate-adjusted regression analysis with adjustment for the patient effect showed a statistically significant association of super-selective chemo-lipiodolization (P=0.003) with the incidence of arteriopathy. Thirty of the 102 arteriopathies (29.4%) caused a change in treatment strategy. No factors were found to be significantly associated with the treatment strategy change. The incidence of arteriopathy after chemo-lipiodolization is 27.9%. Among them, 29.4% result in a change in treatment strategy. Copyright © 2017 Editions françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Effect of duration of denervation on outcomes of ansa-recurrent laryngeal nerve reinnervation.

PubMed

Li, Meng; Chen, Shicai; Wang, Wei; Chen, Donghui; Zhu, Minhui; Liu, Fei; Zhang, Caiyun; Li, Yan; Zheng, Hongliang

2014-08-01

To investigate the efficacy of laryngeal reinnervation with ansa cervicalis among unilateral vocal fold paralysis (UVFP) patients with different denervation durations. We retrospectively reviewed 349 consecutive UVFP cases of delayed ansa cervicalis to the recurrent laryngeal nerve (RLN) anastomosis. Potential influencing factors were analyzed in multivariable logistic regression analysis. Stratification analysis performed was aimed at one of the identified significant variables: denervation duration. Videostroboscopy, perceptual evaluation, acoustic analysis, maximum phonation time (MPT), and laryngeal electromyography (EMG) were performed preoperatively and postoperatively. Gender, age, preoperative EMG status and denervation duration were analyzed in multivariable logistic regression analysis. Stratification analysis was performed on denervation duration, which was divided into three groups according to the interval between RLN injury and reinnervation: group A, 6 to 12 months; group B, 12 to 24 months; and group C, > 24 months. Age, preoperative EMG, and denervation duration were identified as significant variables in multivariable logistic regression analysis. Stratification analysis on denervation duration showed significant differences between group A and C and between group B and C (P < 0.05)-but showed no significant difference between group A and B (P > 0.05) with regard to parameters overall grade, jitter, shimmer, noise-to-harmonics ratio, MPT, and postoperative EMG. In addition, videostroboscopic and laryngeal EMG data, perceptual and acoustic parameters, and MPT values were significantly improved postoperatively in each denervation duration group (P < 0.01). Although delayed laryngeal reinnervation is proved valid for UVFP, surgical outcome is better if the procedure is performed within 2 years after nerve injury than that over 2 years. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

Nomogram for prediction of level 2 axillary lymph node metastasis in proven level 1 node-positive breast cancer patients.

PubMed

Jiang, Yanlin; Xu, Hong; Zhang, Hao; Ou, Xunyan; Xu, Zhen; Ai, Liping; Sun, Lisha; Liu, Caigang

2017-09-22

The current management of the axilla in level 1 node-positive breast cancer patients is axillary lymph node dissection regardless of the status of the level 2 axillary lymph nodes. The goal of this study was to develop a nomogram predicting the probability of level 2 axillary lymph node metastasis (L-2-ALNM) in patients with level 1 axillary node-positive breast cancer. We reviewed the records of 974 patients with pathology-confirmed level 1 node-positive breast cancer between 2010 and 2014 at the Liaoning Cancer Hospital and Institute. The patients were randomized 1:1 and divided into a modeling group and a validation group. Clinical and pathological features of the patients were assessed with uni- and multivariate logistic regression. A nomogram based on independent predictors for the L-2-ALNM identified by multivariate logistic regression was constructed. Independent predictors of L-2-ALNM by the multivariate logistic regression analysis included tumor size, Ki-67 status, histological grade, and number of positive level 1 axillary lymph nodes. The areas under the receiver operating characteristic curve of the modeling set and the validation set were 0.828 and 0.816, respectively. The false-negative rates of the L-2-ALNM nomogram were 1.82% and 7.41% for the predicted probability cut-off points of < 6% and < 10%, respectively, when applied to the validation group. Our nomogram could help predict L-2-ALNM in patients with level 1 axillary lymph node metastasis. Patients with a low probability of L-2-ALNM could be spared level 2 axillary lymph node dissection, thereby reducing postoperative morbidity.

Predictors of unsuccessful outcome in cemented femoral revisions using bone impaction grafting; Cox regression analysis of 208 cases.

PubMed

Te Stroet, Martijn A J; Rijnen, Wim H C; Gardeniers, Jean W M; Schreurs, B Willem; Hannink, Gerjon

2016-09-29

Despite improvements in the technique of femoral impaction bone grafting, reconstruction failures still can occur. Therefore, the aim of our study was to determine risk factors for the endpoint re-revision for any reason. We used prospectively collected demographic, clinical and surgical data of all 202 patients who underwent 208 femoral revisions using the X-change Femoral Revision System (Stryker-Howmedica), fresh-frozen morcellised allograft and a cemented polished Exeter stem in our department from 1991 to 2007. Univariable and multivariable Cox regression analyses were performed to identify potential factors associated with re-revision. The mean follow-up was 10.6 (5-21) years. The cumulative re-revision rate was 6.3% (13/208). After univariable selection, sex, age, body mass index (BMI), American Association of Anesthesiologists (ASA) classification, type of removed femoral component, and mesh used for reconstruction were included in multivariable regression analysis.In the multivariable analysis, BMI was the only factor that was significantly associated with the risk of re-revision after bone impaction grafting (BMI ≥30 vs. BMI <30, HR = 6.54 [95% CI 1.89-22.65]; p = 0.003). BMI was the only factor associated with the risk of re-revision for any reason. Besides BMI also other factors, such as Endoklinik score and the type of removed femoral component, can provide guidance in the process of preclinical decision making. With the knowledge obtained from this study, preoperative patient selection, informed consent, and treatment protocols can be better adjusted to the individual patient who needs to undergo a femoral revision with impaction bone grafting.

Sick of our loans: Student borrowing and the mental health of young adults in the United States.

PubMed

Walsemann, Katrina M; Gee, Gilbert C; Gentile, Danielle

2015-01-01

Student loans are increasingly important and commonplace, especially among recent cohorts of young adults in the United States. These loans facilitate the acquisition of human capital in the form of education, but may also lead to stress and worries related to repayment. This study investigated two questions: 1) what is the association between the cumulative amount of student loans borrowed over the course of schooling and psychological functioning when individuals are 25-31 years old; and 2) what is the association between annual student loan borrowing and psychological functioning among currently enrolled college students? We also examined whether these relationships varied by parental wealth, college enrollment history (e.g. 2-year versus 4-year college), and educational attainment (for cumulative student loans only). We analyzed data from the National Longitudinal Survey of Youth 1997 (NLSY97), a nationally representative sample of young adults in the United States. Analyses employed multivariate linear regression and within-person fixed-effects models. Student loans were associated with poorer psychological functioning, adjusting for covariates, in both the multivariate linear regression and the within-person fixed effects models. This association varied by level of parental wealth in the multivariate linear regression models only, and did not vary by college enrollment history or educational attainment. The present findings raise novel questions for further research regarding student loan debt and the possible spillover effects on other life circumstances, such as occupational trajectories and health inequities. The study of student loans is even more timely and significant given the ongoing rise in the costs of higher education. Copyright © 2014 Elsevier Ltd. All rights reserved.

Serum Vitamin D Levels and Markers of Severity of Childhood Asthma in Costa Rica

PubMed Central

Brehm, John M.; Celedón, Juan C.; Soto-Quiros, Manuel E.; Avila, Lydiana; Hunninghake, Gary M.; Forno, Erick; Laskey, Daniel; Sylvia, Jody S.; Hollis, Bruce W.; Weiss, Scott T.; Litonjua, Augusto A.

2009-01-01

Rationale: Maternal vitamin D intake during pregnancy has been inversely associated with asthma symptoms in early childhood. However, no study has examined the relationship between measured vitamin D levels and markers of asthma severity in childhood. Objectives: To determine the relationship between measured vitamin D levels and both markers of asthma severity and allergy in childhood. Methods: We examined the relation between 25-hydroxyvitamin D levels (the major circulating form of vitamin D) and markers of allergy and asthma severity in a cross-sectional study of 616 Costa Rican children between the ages of 6 and 14 years. Linear, logistic, and negative binomial regressions were used for the univariate and multivariate analyses. Measurements and Main Results: Of the 616 children with asthma, 175 (28%) had insufficient levels of vitamin D (<30 ng/ml). In multivariate linear regression models, vitamin D levels were significantly and inversely associated with total IgE and eosinophil count. In multivariate logistic regression models, a log10 unit increase in vitamin D levels was associated with reduced odds of any hospitalization in the previous year (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.004–0.71; P = 0.03), any use of antiinflammatory medications in the previous year (OR, 0.18; 95% CI, 0.05–0.67; P = 0.01), and increased airway responsiveness (a ≤8.58-μmol provocative dose of methacholine producing a 20% fall in baseline FEV1 [OR, 0.15; 95% CI, 0.024–0.97; P = 0.05]). Conclusions: Our results suggest that vitamin D insufficiency is relatively frequent in an equatorial population of children with asthma. In these children, lower vitamin D levels are associated with increased markers of allergy and asthma severity. PMID:19179486

The Association Between Internet Use and Ambulatory Care-Seeking Behaviors in Taiwan: A Cross-Sectional Study.

PubMed

Hsieh, Ronan Wenhan; Chen, Likwang; Chen, Tsung-Fu; Liang, Jyh-Chong; Lin, Tzu-Bin; Chen, Yen-Yuan; Tsai, Chin-Chung

2016-12-07

Compared with the traditional ways of gaining health-related information from newspapers, magazines, radio, and television, the Internet is inexpensive, accessible, and conveys diverse opinions. Several studies on how increasing Internet use affected outpatient clinic visits were inconclusive. The objective of this study was to examine the role of Internet use on ambulatory care-seeking behaviors as indicated by the number of outpatient clinic visits after adjusting for confounding variables. We conducted this study using a sample randomly selected from the general population in Taiwan. To handle the missing data, we built a multivariate logistic regression model for propensity score matching using age and sex as the independent variables. The questionnaires with no missing data were then included in a multivariate linear regression model for examining the association between Internet use and outpatient clinic visits. We included a sample of 293 participants who answered the questionnaire with no missing data in the multivariate linear regression model. We found that Internet use was significantly associated with more outpatient clinic visits (P=.04). The participants with chronic diseases tended to make more outpatient clinic visits (P<.01). The inconsistent quality of health-related information obtained from the Internet may be associated with patients' increasing need for interpreting and discussing the information with health care professionals, thus resulting in an increasing number of outpatient clinic visits. In addition, the media literacy of Web-based health-related information seekers may also affect their ambulatory care-seeking behaviors, such as outpatient clinic visits. ©Ronan Wenhan Hsieh, Likwang Chen, Tsung-Fu Chen, Jyh-Chong Liang, Tzu-Bin Lin, Yen-Yuan Chen, Chin-Chung Tsai. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.12.2016.

Uni- and multi-variable modelling of flood losses: experiences gained from the Secchia river inundation event.

NASA Astrophysics Data System (ADS)

Carisi, Francesca; Domeneghetti, Alessio; Kreibich, Heidi; Schröter, Kai; Castellarin, Attilio

2017-04-01

Flood risk is function of flood hazard and vulnerability, therefore its accurate assessment depends on a reliable quantification of both factors. The scientific literature proposes a number of objective and reliable methods for assessing flood hazard, yet it highlights a limited understanding of the fundamental damage processes. Loss modelling is associated with large uncertainty which is, among other factors, due to a lack of standard procedures; for instance, flood losses are often estimated based on damage models derived in completely different contexts (i.e. different countries or geographical regions) without checking its applicability, or by considering only one explanatory variable (i.e. typically water depth). We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 200 km2 in Northern Italy. In the aftermath of this event, local authorities collected flood loss data, together with additional information on affected private households and industrial activities (e.g. buildings surface and economic value, number of company's employees and others). Based on these data we implemented and compared a quadratic-regression damage function, with water depth as the only explanatory variable, and a multi-variable model that combines multiple regression trees and considers several explanatory variables (i.e. bagging decision trees). Our results show the importance of data collection revealing that (1) a simple quadratic regression damage function based on empirical data from the study area can be significantly more accurate than literature damage-models derived for a different context and (2) multi-variable modelling may outperform the uni-variable approach, yet it is more difficult to develop and apply due to a much higher demand of detailed data.

The Association Between Spontaneous Hyperventilation, Delayed Cerebral Ischemia, and Poor Neurological Outcome in Patients with Subarachnoid Hemorrhage.

PubMed

Williamson, Craig A; Sheehan, Kyle M; Tipirneni, Renuka; Roark, Christopher D; Pandey, Aditya S; Thompson, B Gregory; Rajajee, Venkatakrishna

2015-12-01

The frequency and associations of spontaneous hyperventilation in subarachnoid hemorrhage (SAH) are unknown. Because hyperventilation decreases cerebral blood flow, it may exacerbate delayed cerebral ischemia (DCI) and worsen neurological outcome. This is a retrospective analysis of data from a prospectively collected cohort of SAH patients at an academic medical center. Spontaneous hyperventilation was defined by PaCO2 <35 mmHg and pH >7.45 and subdivided into moderate and severe groups. Clinical and demographic characteristics of patients with and without spontaneous hyperventilation were compared using χ (2) or t tests. Bivariate and multivariable logistic regression analyses were conducted to examine the association of moderate and severe hyperventilation with DCI and discharge neurological outcome. Of 207 patients, 113 (55 %) had spontaneous hyperventilation. Spontaneously hyperventilating patients had greater illness severity as measured by the Hunt-Hess, World Federation of Neurosurgical Societies (WFNS), and SAH sum scores. They were also more likely to develop the following complications: pneumonia, neurogenic myocardial injury, systemic inflammatory response syndrome (SIRS), radiographic vasospasm, DCI, and poor neurological outcome. In a multivariable logistic regression model including age, gender, WFNS, SAH sum score, pneumonia, neurogenic myocardial injury, etiology, and SIRS, only moderate [odds ratio (OR) 2.49, 95 % confidence interval (CI) 1.10-5.62] and severe (OR 3.12, 95 % CI 1.30-7.49) spontaneous hyperventilation were associated with DCI. Severe spontaneous hyperventilation (OR 4.52, 95 % CI 1.37-14.89) was also significantly associated with poor discharge outcome in multivariable logistic regression analysis. Spontaneous hyperventilation is common in SAH and is associated with DCI and poor neurological outcome.

Socioeconomic disparities in the utilization of mechanical thrombectomy for acute ischemic stroke in US hospitals.

PubMed

Brinjikji, W; Rabinstein, A A; McDonald, J S; Cloft, H J

2014-03-01

Previous studies have demonstrated that socioeconomic disparities in the treatment of cerebrovascular diseases exist. We studied a large administrative data base to study disparities in the utilization of mechanical thrombectomy for acute ischemic stroke. With the utilization of the Perspective data base, we studied disparities in mechanical thrombectomy utilization between patient race and insurance status in 1) all patients presenting with acute ischemic stroke and 2) patients presenting with acute ischemic stroke at centers that performed mechanical thrombectomy. We examined utilization rates of mechanical thrombectomy by race/ethnicity (white, black, and Hispanic) and insurance status (Medicare, Medicaid, self-pay, and private). Multivariate logistic regression analysis adjusting for potential confounding variables was performed to study the association between race/insurance status and mechanical thrombectomy utilization. The overall mechanical thrombectomy utilization rate was 0.15% (371/249,336); utilization rate at centers that performed mechanical thrombectomy was 1.0% (371/35,376). In the sample of all patients with acute ischemic stroke, multivariate logistic regression analysis demonstrated that uninsured patients had significantly lower odds of mechanical thrombectomy utilization compared with privately insured patients (OR = 0.52, 95% CI = 0.25-0.95, P = .03), as did Medicare patients (OR = 0.53, 95% CI = 0.41-0.70, P < .0001). Blacks had significantly lower odds of mechanical thrombectomy utilization compared with whites (OR = 0.35, 95% CI = 0.23-0.51, P < .0001). When considering only patients treated at centers performing mechanical thrombectomy, multivariate logistic regression analysis demonstrated that insurance was not associated with significant disparities in mechanical thrombectomy utilization; however, black patients had significantly lower odds of mechanical thrombectomy utilization compared with whites (OR = 0.41, 95% CI = 0.27-0.60, P < .0001). Significant socioeconomic disparities exist in the utilization of mechanical thrombectomy in the United States.

Association Between Duration of Breast Feeding and Metabolic Syndrome: The Korean National Health and Nutrition Examination Surveys.

PubMed

Choi, Se Rin; Kim, Yong Min; Cho, Min Su; Kim, So Hyun; Shim, Young Suk

2017-04-01

This study aimed to evaluate the association of the lifelong duration of breast feeding with metabolic syndrome (MetS) and its components in Korean parous women aged 19-50 years. A total of 4724 participants from the Korean National Health and Nutritional Survey were included. Subjects were divided into four groups according to the duration of breast feeding: ≤5, 6-11, 12-23, or ≥24 months groups. The adjusted odds ratios (ORs) of MetS and its components were assessed according to the duration of breast feeding. Women who breastfed for 6-11 months had an OR of 0.67 (95% confidence interval [CI], 0.54-0.86) for elevated blood pressure (BP) compared with those who breastfed for ≤5 months after adjustment for possible confounders in a multivariable logistic regression analyses. Women who breastfed for 12-23 months were associated with an OR of 0.68 (95% CI, 0.54-0.86) for elevated BP, an OR of 0.78 (95% CI, 0.62-0.97) for elevated glucose, and an OR of 0.73 (95% CI, 0.56-0.95) for MetS compared with those who breastfed for ≤5 months in a multivariable logistic regression analyses. Women who breastfed for ≥24 months had an OR of 0.62 (95% CI, 0.52-0.84) for elevated glucose, an OR of 0.76 (95% CI, 0.60-0.96) for elevated triglycerides, and an OR of 0.70 (95% CI, 0.53-0.92) for MetS compared with those who breastfed for ≤5 months in a multivariable logistic regression analyses. Our results suggest that lifelong breast feeding for ≥12 months may be associated with lower risk for MetS.

Vitamin D insufficiency and subclinical atherosclerosis in non-diabetic males living with HIV.

PubMed

Portilla, Joaquín; Moreno-Pérez, Oscar; Serna-Candel, Carmen; Escoín, Corina; Alfayate, Rocio; Reus, Sergio; Merino, Esperanza; Boix, Vicente; Giner, Livia; Sánchez-Payá, José; Picó, Antonio

2014-01-01

Vitamin D insufficiency (VDI) has been associated with increased cardiovascular risk in the non-HIV population. This study evaluates the relationship among serum 25-hydroxyvitamin D [25(OH)D] levels, cardiovascular risk factors, adipokines, antiviral therapy (ART) and subclinical atherosclerosis in HIV-infected males. A cross-sectional study in ambulatory care was made in non-diabetic patients living with HIV. VDI was defined as 25(OH)D serum levels <75 nmol/L. Fasting lipids, glucose, inflammatory markers (tumour necrosis factor-α, interleukin-6, high-sensitivity C-reactive protein) and endothelial markers (plasminogen activator inhibitor-1, or PAI-I) were measured. The common carotid artery intima-media thickness (C-IMT) was determined. A multivariate logistic regression analysis was made to identify factors associated with the presence of VDI, while multivariate linear regression analysis was used to identify factors associated with common C-IMT. Eighty-nine patients were included (age 42 ± 8 years), 18.9% were in CDC (US Centers for Disease Control and Prevention) stage C and 75 were on ART. VDI was associated with ART exposure, sedentary lifestyle, higher triglycerides levels and PAI-I. In univariate analysis, VDI was associated with greater common C-IMT. The multivariate linear regression model, adjusted by confounding factors, revealed an independent association between common C-IMT and patient age, time of exposure to protease inhibitors (PIs) and impaired fasting glucose (IFG). In contrast, there were no independent associations between common C-IMT and VDI or inflammatory and endothelial markers. VDI was not independently associated with subclinical atherosclerosis in non-diabetic males living with HIV. Older age, a longer exposure to PIs, and IFG were independent factors associated with common C-IMT in this population.

Application of Fluorescence Spectrometry With Multivariate Calibration to the Enantiomeric Recognition of Fluoxetine in Pharmaceutical Preparations.

PubMed

Poláček, Roman; Májek, Pavel; Hroboňová, Katarína; Sádecká, Jana

2016-04-01

Fluoxetine is the most prescribed antidepressant chiral drug worldwide. Its enantiomers have a different duration of serotonin inhibition. A novel simple and rapid method for determination of the enantiomeric composition of fluoxetine in pharmaceutical pills is presented. Specifically, emission, excitation, and synchronous fluorescence techniques were employed to obtain the spectral data, which with multivariate calibration methods, namely, principal component regression (PCR) and partial least square (PLS), were investigated. The chiral recognition of fluoxetine enantiomers in the presence of β-cyclodextrin was based on diastereomeric complexes. The results of the multivariate calibration modeling indicated good prediction abilities. The obtained results for tablets were compared with those from chiral HPLC and no significant differences are shown by Fisher's (F) test and Student's t-test. The smallest residuals between reference or nominal values and predicted values were achieved by multivariate calibration of synchronous fluorescence spectral data. This conclusion is supported by calculated values of the figure of merit.

Multivariate meta-analysis using individual participant data

PubMed Central

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

2016-01-01

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

Quantifying Multi-variables in Urban Watershed Adaptation: Challenges and Opportunities

EPA Science Inventory

Climate change and rapid socioeconomic developments are considered to be the principle variables affecting evolution of an urban watershed, the forms and sustainability of its built environment. In the traditional approach, we are accustomed to the assumption of a stationary cli...

Heat Waves and Climate Change: Applying the Health Belief Model to Identify Predictors of Risk Perception and Adaptive Behaviours in Adelaide, Australia

PubMed Central

Akompab, Derick A.; Bi, Peng; Williams, Susan; Grant, Janet; Walker, Iain A.; Augoustinos, Martha

2013-01-01

Heat waves are considered a health risk and they are likely to increase in frequency, intensity and duration as a consequence of climate change. The effects of heat waves on human health could be reduced if individuals recognise the risks and adopt healthy behaviours during a heat wave. The purpose of this study was to determine the predictors of risk perception using a heat wave scenario and identify the constructs of the health belief model that could predict adaptive behaviours during a heat wave. A cross-sectional study was conducted during the summer of 2012 among a sample of persons aged between 30 to 69 years in Adelaide. Participants’ perceptions were assessed using the health belief model as a conceptual frame. Their knowledge about heat waves and adaptive behaviours during heat waves was also assessed. Logistic regression analyses were performed to determine the predictors of risk perception to a heat wave scenario and adaptive behaviours during a heat wave. Of the 267 participants, about half (50.9%) had a high risk perception to heat waves while 82.8% had good adaptive behaviours during a heat wave. Multivariate models found that age was a significant predictor of risk perception. In addition, participants who were married (OR = 0.21; 95% CI, 0.07–0.62), who earned a gross annual household income of ≥$60,000 (OR = 0.41; 95% CI, 0.17–0.94) and without a fan (OR = 0.29; 95% CI, 0.11–0.79) were less likely to have a high risk perception to heat waves. Those who were living with others (OR = 2.87; 95% CI, 1.19–6.90) were more likely to have a high risk perception to heat waves. On the other hand, participants with a high perceived benefit (OR = 2.14; 95% CI, 1.00–4.58), a high “cues to action” (OR = 3.71; 95% CI, 1.63–8.43), who had additional training or education after high school (OR = 2.65; 95% CI, 1.25–5.58) and who earned a gross annual household income of ≥$60,000 (OR = 2.66; 95% CI, 1.07–6.56) were more likely to have good adaptive behaviours during a heat wave. The health belief model could be useful to guide the design and implementation of interventions to promote adaptive behaviours during heat waves. PMID:23759952

Alternative High School Students: Prevalence and Correlates of Overweight

ERIC Educational Resources Information Center

Kubik, Martha Y.; Davey, Cynthia; Fulkerson, Jayne A.; Sirard, John; Story, Mary; Arcan, Chrisa

2009-01-01

Objective: To determine prevalence and correlates of overweight among adolescents attending alternative high schools (AHS). Methods: AHS students (n=145) from 6 schools completed surveys and anthropometric measures. Cross-sectional associations were assessed using mixed model multivariate logistic regression. Results: Among students, 42% were…

Innovation Analysis | Energy Analysis | NREL

Science.gov Websites

. New empirical methods for estimating technical and commercial impact (based on patent citations and Commercial Breakthroughs, NREL employed regression models and multivariate simulations to compare social in the marketplace and found that: Web presence may provide a better representation of the commercial

Parenting Characteristics Associated with Anxiety and Depression: A Multivariate Approach

ERIC Educational Resources Information Center

Anhalt, Karla; Morris, Tracy L.

2008-01-01

This study examined the association between perceived parenting factors and symptoms of social anxiety, generalized anxiety and depression. Participants rated experiences with their mothers and fathers with regard to parental care, overprotection, criticism, parent-adolescent attachment, and family sociability. Regression analyses examined the…

Predictors of Political Activism among Social Work Students

ERIC Educational Resources Information Center

Swank, Eric W.

2012-01-01

This article identifies factors inspiring greater political participation among undergraduate social work students (N=125). When separating students into self-identified liberals and conservatives, the study uses resource, mobilizing, and framing variables to explain greater levels of activism. After several multivariate regressions, this article…

College Student Invulnerability Beliefs and HIV Vaccine Acceptability

ERIC Educational Resources Information Center

Ravert, Russell D.; Zimet, Gregory D.

2009-01-01

Objective: To examine behavioral history, beliefs, and vaccine characteristics as predictors of HIV vaccine acceptability. Methods: Two hundred forty-five US under graduates were surveyed regarding their sexual history, risk beliefs, and likelihood of accepting hypothetical HIV vaccines. Results: Multivariate regression analysis indicated that…

Predicting major element mineral/melt equilibria - A statistical approach

NASA Technical Reports Server (NTRS)

Hostetler, C. J.; Drake, M. J.

1980-01-01

Empirical equations have been developed for calculating the mole fractions of NaO0.5, MgO, AlO1.5, SiO2, KO0.5, CaO, TiO2, and FeO in a solid phase of initially unknown identity given only the composition of the coexisting silicate melt. The approach involves a linear multivariate regression analysis in which solid composition is expressed as a Taylor series expansion of the liquid compositions. An internally consistent precision of approximately 0.94 is obtained, that is, the nature of the liquidus phase in the input data set can be correctly predicted for approximately 94% of the entries. The composition of the liquidus phase may be calculated to better than 5 mol % absolute. An important feature of this 'generalized solid' model is its reversibility; that is, the dependent and independent variables in the linear multivariate regression may be inverted to permit prediction of the composition of a silicate liquid produced by equilibrium partial melting of a polymineralic source assemblage.

Determination of enantiomeric composition of ibuprofen in pharmaceutical formulations by partial least-squares regression of strongly overlapped chromatographic profiles.

PubMed

Grisales, Jaiver Osorio; Arancibia, Juan A; Castells, Cecilia B; Olivieri, Alejandro C

2012-12-01

In this report, we demonstrate how chiral liquid chromatography combined with multivariate chemometric techniques, specifically unfolded-partial least-squares regression (U-PLS), provides a powerful analytical methodology. Using U-PLS, strongly overlapped enantiomer profiles in a sample could be successfully processed and enantiomeric purity could be accurately determined without requiring baseline enantioresolution between peaks. The samples were partially enantioseparated with a permethyl-β-cyclodextrin chiral column under reversed-phase conditions. Signals detected with a diode-array detector within a wavelength range from 198 to 241 nm were recorded, and the data were processed by a second-order multivariate algorithm to decrease detection limits. The R-(-)-enantiomer of ibuprofen in tablet formulation samples could be determined at the level of 0.5 mg L⁻¹ in the presence of 99.9% of the S-(+)-enantiomorph with relative prediction error within ±3%. Copyright © 2012 Elsevier B.V. All rights reserved.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

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

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

DOE PAGES

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; ...

2016-12-15

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.

PubMed

Gong, Xu; Cui, Jianli; Jiang, Ziping; Lu, Laijin; Li, Xiucun

2018-03-01

Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis. For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified 163 patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model. Of 163 skin flaps, 125 flaps survived completely without any complications. The pedicled flap necrosis rate in hands was 11.04%, which included partial flap necrosis (7.36%) and total flap necrosis (3.68%). Soft tissue defects in fingers were noted in 68.10% of all cases. The logistic regression analysis indicated that the soft tissue defect site (P = 0.046, odds ratio (OR) = 0.079, confidence interval (CI) (0.006, 0.959)), flap size (P = 0.020, OR = 1.024, CI (1.004, 1.045)) and postoperative wound infection (P < 0.001, OR = 17.407, CI (3.821, 79.303)) were statistically significant risk factors for pedicled flap necrosis of the hand. Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. © 2017 Royal Australasian College of Surgeons.

Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

PubMed Central

Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

2014-01-01

Purpose The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT. PMID:24586971

Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of Xerostomia after intensity-modulated radiotherapy for head and neck cancer.

PubMed

Lee, Tsair-Fwu; Chao, Pei-Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Wu, Jia-Ming; Wang, Hung-Yu; Horng, Mong-Fong; Chang, Chun-Ming; Lan, Jen-Hong; Huang, Ya-Yu; Fang, Fu-Min; Leung, Stephen Wan

2014-01-01

The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3(+) xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R(2), chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R(2) was satisfactory and corresponded well with the expected values. Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.

Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

Fast-NPS-A Markov Chain Monte Carlo-based analysis tool to obtain structural information from single-molecule FRET measurements

NASA Astrophysics Data System (ADS)

Eilert, Tobias; Beckers, Maximilian; Drechsler, Florian; Michaelis, Jens

2017-10-01

The analysis tool and software package Fast-NPS can be used to analyse smFRET data to obtain quantitative structural information about macromolecules in their natural environment. In the algorithm a Bayesian model gives rise to a multivariate probability distribution describing the uncertainty of the structure determination. Since Fast-NPS aims to be an easy-to-use general-purpose analysis tool for a large variety of smFRET networks, we established an MCMC based sampling engine that approximates the target distribution and requires no parameter specification by the user at all. For an efficient local exploration we automatically adapt the multivariate proposal kernel according to the shape of the target distribution. In order to handle multimodality, the sampler is equipped with a parallel tempering scheme that is fully adaptive with respect to temperature spacing and number of chains. Since the molecular surrounding of a dye molecule affects its spatial mobility and thus the smFRET efficiency, we introduce dye models which can be selected for every dye molecule individually. These models allow the user to represent the smFRET network in great detail leading to an increased localisation precision. Finally, a tool to validate the chosen model combination is provided. Programme Files doi:http://dx.doi.org/10.17632/7ztzj63r68.1 Licencing provisions: Apache-2.0 Programming language: GUI in MATLAB (The MathWorks) and the core sampling engine in C++ Nature of problem: Sampling of highly diverse multivariate probability distributions in order to solve for macromolecular structures from smFRET data. Solution method: MCMC algorithm with fully adaptive proposal kernel and parallel tempering scheme.

Comprehensive knowledge of HIV among women in rural Mozambique: development and validation of the HIV knowledge 27 scale.

PubMed

Ciampa, Philip J; Skinner, Shannon L; Patricio, Sérgio R; Rothman, Russell L; Vermund, Sten H; Audet, Carolyn M

2012-01-01

The relationship between HIV knowledge and HIV-related behaviors in settings like Mozambique has been limited by a lack of rigorously validated measures. A convenience sample of women seeking prenatal care at two clinics were administered an adapted, orally-administered, 27 item HIV-knowledge scale, the HK-27. Validation analyses were stratified by survey language (Portuguese and Echuabo). Kuder-Richardson (KR-20) coefficients estimated internal reliability. Construct validity was assessed with bivariate associations between HK-27 scores (% correct) and selected participant characteristics. The association between knowledge, self-reported HIV testing, and HIV infection were evaluated with multivariable logistic regression. Participants (N = 348) had a median age of 24; 188 spoke Portuguese, and 160 spoke Echuabo. Mean HK-27 scores were higher for Portuguese-speaking participants than Echuabo-speaking participants (68% correct vs. 42%, p<0.001). Internal reliability was strong (KR-20>0.8) for scales in both languages. Higher HK-27 scores were significantly (p≤0.05) correlated with more education, more media items in the home, a history of HIV testing, and participant work outside of the home for women of both languages. HK-27 scores were independently associated with completion of HIV testing in multivariable analysis (per 1% correct: aOR:1.02, 95%CI:0.01-0.03, p = 0.01), but not with HIV infection. HK-27 is a reliable and valid measure of HIV knowledge among Portuguese and Echuabo-speaking Mozambican women. The HK-27 demonstrated significant knowledge deficits among women in the study, and higher scores were associated with higher HIV testing probability. Future studies should evaluate the role of the HK-27 in longitudinal studies and in other populations.

Social support and common mental disorder among medical students.

PubMed

Silva, Adriano Gonçalves; Cerqueira, Ana Teresa de Abreu Ramos; Lima, Maria Cristina Pereira

2014-01-01

Different kinds of psychological distress have been identified for students in the health field, especially in the medical school. To estimate the prevalence of mental suffering among medical students in the Southeastern Brazil and asses its association with social support. It is a cross-sectional study. Structured questionnaires were applied for students from the 1st up to the 6th years of the medical school of Universidade Estadual Paulista "Júlio de Mesquita Filho", assessing demographic variables related to aspects of graduation and adaptation to the city. Psychological suffering was defined as a common mental disorder (CMD) assessed by the Self Reporting Questionnaire (SRQ-20). Social support was assessed by the social support scale of the Medical Outcomes Study (MOS). The association between the outcome and explanatory variables was assessed by the χ2 test and Logistic Regression, for the multivariate analyses, using p < 0.05. The response rate was of 80.7%, with no differences between sample and the population regarding gender (p = 0.78). The average age was 22 years old (standard deviation - SD = 2.2), mainly women (58.2%) and students who were living with friends (62%). The prevalence of CMD was 44.9% (95%CI 40.2 - 49.6). After the multivariate analyses, the explanatory variables that were associated with CMD were: feeling rejected in the past year (p < 0.001), thinking about leaving medical school (p < 0.001) and "interaction" in the MOS scale (p = 0.002). The prevalence of CMD among medical students was high and insufficient social support was an important risk factor. Our findings suggest that interventions to improve social interaction among those students could be beneficial, decreasing the prevalence of CMD in this group.

Relationship between water quality and macro-scale parameters (land use, erosion, geology, and population density) in the Siminehrood River Basin.

PubMed

Bostanmaneshrad, Farshid; Partani, Sadegh; Noori, Roohollah; Nachtnebel, Hans-Peter; Berndtsson, Ronny; Adamowski, Jan Franklin

2018-10-15

To date, few studies have investigated the simultaneous effects of macro-scale parameters (MSPs) such as land use, population density, geology, and erosion layers on micro-scale water quality variables (MSWQVs). This research focused on an evaluation of the relationship between MSPs and MSWQVs in the Siminehrood River Basin, Iran. In addition, we investigated the importance of water particle travel time (hydrological distance) on this relationship. The MSWQVs included 13 physicochemical and biochemical parameters observed at 15 stations during three seasons. Primary screening was performed by utilizing three multivariate statistical analyses (Pearson's correlation, cluster and discriminant analyses) in seven series of observed data. These series included three separate seasonal data, three two-season data, and aggregated three-season data for investigation of relationships between MSPs and MSWQVs. Coupled data (pairs of MSWQVs and MSPs) repeated in at least two out of three statistical analyses were selected for final screening. The primary screening results demonstrated significant relationships between land use and phosphorus, total solids and turbidity, erosion levels and electrical conductivity, and erosion and total solids. Furthermore, water particle travel time effects were considered through three geographical pattern definitions of distance for each MSP by using two weighting methods. To find effective MSP factors on MSWQVs, a multivariate linear regression analysis was employed. Then, preliminary equations that estimated MSWQVs were developed. The preliminary equations were modified to adaptive equations to obtain the final models. The final models indicated that a new metric, referred to as hydrological distance, provided better MSWQV estimation and water quality prediction compared to the National Sanitation Foundation Water Quality Index. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Quantifying Burnout among Emergency Medicine Professionals

PubMed Central

Wilson, William; Raj, Jeffrey Pradeep; Narayan, Girish; Ghiya, Murtuza; Murty, Shakuntala; Joseph, Bobby

2017-01-01

Background: Burnout is a syndrome explained as serious emotional depletion with poor adaptation at work due to prolonged occupational stress. It has three principal components namely emotional exhaustion(EE), depersonalization(DP) and diminished feelings of personal accomplishment(PA). Thus, we aimed at measuring the degree of burnout in doctors and nurses working in emergency medicine department (EMD) of 4 select tertiary care teaching hospitals in South India. Methods: A cross sectional survey was conducted among EMD professionals using a 30-item standardized pilot tested questionnaire as well as the Maslach burnout inventory. Univariate and Multivariate analyses were conducted using binary logistic regression models to identify predictors of burnout. Results: Total number of professionals interviewed were 105 of which 71.5% were women and 51.4% were doctors. Majority (78.1%) belonged to the age group 20-30 years. Prevalence of moderate to severe burnout in the 3 principal components EE, DP and PA were 64.8%, 71.4% and 73.3% respectively. After multivariate analysis, the risk factors [adjusted odds ratio (95% confidence intervals) for DP included facing more criticism [3.57(1.25,10.19)], disturbed sleep [6.44(1.45,28.49)] and being short tempered [3.14(1.09,9.09)]. While there were no statistically significant risk factors for EE, being affected by mortality [2.35(1.12,3.94)] and fear of medication errors [3.61(1.26, 10.37)] appeared to be significant predictors of PA. Conclusion: Degree of burn out among doctors and nurses is moderately high in all of the three principal components and some of the predictors identified were criticism, disturbed sleep, short tempered nature, fear of committing errors and witnessing death in EMD. PMID:29097859

Quantifying Burnout among Emergency Medicine Professionals.

PubMed

Wilson, William; Raj, Jeffrey Pradeep; Narayan, Girish; Ghiya, Murtuza; Murty, Shakuntala; Joseph, Bobby

2017-01-01

Burnout is a syndrome explained as serious emotional depletion with poor adaptation at work due to prolonged occupational stress. It has three principal components namely emotional exhaustion(EE), depersonalization(DP) and diminished feelings of personal accomplishment(PA). Thus, we aimed at measuring the degree of burnout in doctors and nurses working in emergency medicine department (EMD) of 4 select tertiary care teaching hospitals in South India. A cross sectional survey was conducted among EMD professionals using a 30-item standardized pilot tested questionnaire as well as the Maslach burnout inventory. Univariate and Multivariate analyses were conducted using binary logistic regression models to identify predictors of burnout. Total number of professionals interviewed were 105 of which 71.5% were women and 51.4% were doctors. Majority (78.1%) belonged to the age group 20-30 years. Prevalence of moderate to severe burnout in the 3 principal components EE, DP and PA were 64.8%, 71.4% and 73.3% respectively. After multivariate analysis, the risk factors [adjusted odds ratio (95% confidence intervals) for DP included facing more criticism [3.57(1.25,10.19)], disturbed sleep [6.44(1.45,28.49)] and being short tempered [3.14(1.09,9.09)]. While there were no statistically significant risk factors for EE, being affected by mortality [2.35(1.12,3.94)] and fear of medication errors [3.61(1.26, 10.37)] appeared to be significant predictors of PA. Degree of burn out among doctors and nurses is moderately high in all of the three principal components and some of the predictors identified were criticism, disturbed sleep, short tempered nature, fear of committing errors and witnessing death in EMD.

Estimators for longitudinal latent exposure models: examining measurement model assumptions.

PubMed

Sánchez, Brisa N; Kim, Sehee; Sammel, Mary D

2017-06-15

Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Disparities in physician-patient communication by obesity status.

PubMed

Richard, Patrick; Ferguson, Christine; Lara, Anthony S; Leonard, Jennifer; Younis, Mustafa

2014-01-01

This study aimed to examine variations in patient-physician communication by obesity status. We pooled data from the 2005-2007 Medical Expenditure Panel Survey (MEPS),_included only individuals who completed the self-administered questionnaire themselves, and restricted the sample to patients who received care from primary care physicians. We included a total of 6,628 unique individuals between the ages of 18 and 65 who had at least one office or hospital outpatient visit during the past 12 months. There are six outcomes of interest in this study. The patient-physician communication composite score is based on five questions that the MEPS adapted from the Consumer Assessment of Healthcare Providers and Systems Survey. The other five variables were: respect from providers, providers' listening skills, explanations from providers, time spent with patients, and patient involvement in treatment decisions. The key independent variable was obesity. Bivariate and multivariate models such as ordinary least squares (OLS) and logistic regression were used to examine the relationship between patient-physician communication and obesity status. Multivariate models showed that obese patients had a reduced physician-patient communication composite score of 0.19 (95% CI 0.03-0.34, p=0.02), physicians' show of respect OR 0.77 (95% CI 0.61-0.98, p=0.04), listening ability OR 0.82 (95% CI 0.65-1.02, p=0.07), and spending enough time OR 0.80 (95% CI 0.62-0.99, p=0.04) compared to non-obese patients. We found a negative association between physician-patient communication and patients' obesity status. These findings may inform public health practitioners in the design of effective initiatives that account for the needs and circumstances of obese individuals. © The Author(s) 2014.

Parental Restriction of Mature-rated Media and Its Association with Substance Use among Argentinian Adolescents

PubMed Central

Mejia, Raul; Pérez, Adriana; Peña, Lorena; Morello, Paola; Kollath-Cattano, Christy; Braun, Sandra; Thrashe, James F.; Sargent, James D.

2016-01-01

Objective To assess the independent relation between parental restrictions on mature-rated media (M-RM) and substance use among South American adolescents. Methods Cross-sectional school-based youth survey of n=3,172 students (mean age 12.8 years; 57.6% boys) in three large Argentinian cities. The anonymous survey queried tobacco, alcohol, and drug use using items adapted from global youth surveys. Adolescents reported M-RM restriction for internet and videogames use, television programming and movies rated for adults. Multivariate logistic regression models assessed the association between parental M-RM restriction and substance use after adjusting for hourly media use, measures of authoritative parenting style, sociodemographics, and sensation seeking. Results Substance use rates were 10% for current smoking, 32% for current drinking alcohol, 17% for past 30-day binge drinking, and 8% for illicit drug use (marijuana or cocaine). Half of respondents reported parental M-RM restriction (internet 52%, TV 43%, adult movies 34%, videogame 25%). Parental M-RM restriction was only modestly correlated with authoritative parenting measures. In multivariate analyses M-RM restriction on all four venues was strongly protective for all substance use outcomes. Compared with no restriction, odds ratios for substance use for full restrictions were 0.32 (0.18–0.59), 0.53 (0.38–0.07), 0.36 (0.22–0.59), and 0.49 (0.26–0.92) for current smoking, drinking, binge drinking, and illicit drug use respectively. The most important single M-RM venue was movies. Conclusion This study confirms the protective association between parental M-RM restriction during adolescence and multiple substance use outcomes, including illicit drugs. M-RM restriction is independent of traditional parenting measures. The preponderance of the evidence supports intervention development. PMID:26615087

GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA

PubMed Central

Zheng, Qi; Peng, Limin; He, Xuming

2015-01-01

Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal. PMID:26604424

Multivariate functions for predicting the sorption of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2016-11-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed a new solution for modeling the sorption and persistence of these munition constituents as multivariate mathematical functions correlating soil attribute data over a variety of taxonomically distinct soil types to contaminant behavior, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments measuring the sorption of TNT and RDX on taxonomically different soil types that were extensively physical and chemically characterized. Statistical decomposition of the log-transformed, and auto-scaled soil characterization data using the dimension-reduction technique PCA (principal component analysis) revealed a strong latent structure based in the multiple pairwise correlations among the soil properties. TNT and RDX sorption partitioning coefficients (KD-TNT and KD-RDX) were regressed against this latent structure using partial least squares regression (PLSR), generating a 3-factor, multivariate linear functions. Here, PLSR models predicted KD-TNT and KD-RDX values based on attributes contributing to endogenous alkaline/calcareous and soil fertility criteria, respectively, exhibited among the different soil types: We hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished soil types may provide the means for potentially predicting complex phenomena in soils. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to anticipate the environmental risks of training activities on impact sites. Published by Elsevier Ltd.

Multivariable regression analysis of list experiment data on abortion: results from a large, randomly-selected population based study in Liberia.

PubMed

Moseson, Heidi; Gerdts, Caitlin; Dehlendorf, Christine; Hiatt, Robert A; Vittinghoff, Eric

2017-12-21

The list experiment is a promising measurement tool for eliciting truthful responses to stigmatized or sensitive health behaviors. However, investigators may be hesitant to adopt the method due to previously untestable assumptions and the perceived inability to conduct multivariable analysis. With a recently developed statistical test that can detect the presence of a design effect - the absence of which is a central assumption of the list experiment method - we sought to test the validity of a list experiment conducted on self-reported abortion in Liberia. We also aim to introduce recently developed multivariable regression estimators for the analysis of list experiment data, to explore relationships between respondent characteristics and having had an abortion - an important component of understanding the experiences of women who have abortions. To test the null hypothesis of no design effect in the Liberian list experiment data, we calculated the percentage of each respondent "type," characterized by response to the control items, and compared these percentages across treatment and control groups with a Bonferroni-adjusted alpha criterion. We then implemented two least squares and two maximum likelihood models (four total), each representing different bias-variance trade-offs, to estimate the association between respondent characteristics and abortion. We find no clear evidence of a design effect in list experiment data from Liberia (p = 0.18), affirming the first key assumption of the method. Multivariable analyses suggest a negative association between education and history of abortion. The retrospective nature of measuring lifetime experience of abortion, however, complicates interpretation of results, as the timing and safety of a respondent's abortion may have influenced her ability to pursue an education. Our work demonstrates that multivariable analyses, as well as statistical testing of a key design assumption, are possible with list experiment data, although with important limitations when considering lifetime measures. We outline how to implement this methodology with list experiment data in future research.

Integrated environmental monitoring and multivariate data analysis-A case study.

PubMed

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

Genomic regression of claw keratin, taste receptor and light-associated genes provides insights into biology and evolutionary origins of snakes.

PubMed

Emerling, Christopher A

2017-10-01

Regressive evolution of anatomical traits often corresponds with the regression of genomic loci underlying such characters. As such, studying patterns of gene loss can be instrumental in addressing questions of gene function, resolving conflicting results from anatomical studies, and understanding the evolutionary history of clades. The evolutionary origins of snakes involved the regression of a number of anatomical traits, including limbs, taste buds and the visual system, and by analyzing serpent genomes, I was able to test three hypotheses associated with the regression of these features. The first concerns two keratins that are putatively specific to claws. Both genes that encode these keratins are pseudogenized/deleted in snake genomes, providing additional evidence of claw-specificity. The second hypothesis is that snakes lack taste buds, an issue complicated by conflicting results in the literature. I found evidence that different snakes have lost one or more taste receptors, but all snakes examined retained at least one gustatory channel. The final hypothesis addressed is that the earliest snakes were adapted to a dim light niche. I found evidence of deleted and pseudogenized genes with light-associated functions in snakes, demonstrating a pattern of gene loss similar to other dim light-adapted clades. Molecular dating estimates suggest that dim light adaptation preceded the loss of limbs, providing some bearing on interpretations of the ecological origins of snakes. Copyright © 2017 Elsevier Inc. All rights reserved.

Socioeconomic differences in adolescent stress: the role of psychological resources.

PubMed

Finkelstein, Daniel M; Kubzansky, Laura D; Capitman, John; Goodman, Elizabeth

2007-02-01

To investigate whether psychological resources influenced the association between parent education (PE), a marker of socioeconomic status (SES), and perceived stress. Cross-sectional analyses were conducted in a sample of 1167 non-Hispanic black and white junior and senior high school students from a Midwestern public school district in 2002-2003. Hierarchical multivariable regression analyses examined relationships between PE (high school graduate or less = E1, > high school, < college = E2, college graduate = E3, and professional degree = E4), and psychological resources (optimism and coping style) on teens' perceived stress. Greater optimism and adaptive coping were hypothesized to influence (i.e., mediate or moderate) the relationship between higher PE and lower stress. Relative to adolescents from families with a professionally educated parent, adolescents with lower parent education had higher perceived stress (E3 beta = 1.70, p < .01, E2 beta = 1.94, p < .01, E1 beta = 3.19, p < .0001). Both psychological resources were associated with stress: higher optimism (beta = -.58, p < .0001) and engagement coping (beta = -.19, p < .0001) were associated with less stress and higher disengagement coping was associated with more stress (beta = .09, p < .01). Adding optimism to the regression model attenuated the effect of SES by nearly 30%, suggesting that optimism partially mediates the inverse SES-stress relationship. Mediation was confirmed using a Sobel test (p < .01). Adolescents from families with lower parent education are less optimistic than teens from more educated families. This pessimism may be a mechanism through which lower SES increases stress in adolescence.

The effect of HMO penetration on physician retirement.

PubMed

Kletke, P R; Polsky, D; Wozniak, G D; Escarce, J J

2000-12-01

To examine the effect of HMO penetration on physician retirement. We linked together historical data from the Physician Masterfile of the American Medical Association for successive years to track changes in physicians' activity status between 1980 and 1997. We used a multivariate discrete-time survival model to examine how the probability of physician retirement was affected by the level of HMO penetration in the physician's market area, controlling for other physician and market characteristics. The study population included all active allopathic patient-care physicians in the United States who reached age 55 between the years of 1980 and 1996. The main outcome measure was physician retirements as reported on the Physician Masterfile. HMO penetration had a statistically significant positive effect on the retirement probabilities of generalists and medical/surgical specialists, but it s effect on hospital-based specialists and psychiatrists was not significant . For generalists regression-adjusted retirement probabilities were roughly 13 percent greater in high-penetration markets (HMO penetration of 45 percent ) than in low-penetration markets (HMO penetration of 5 percent ). For medical/surgical specialist s regression-adjusted retirement probabilities were roughly 17 percent greater in high-penetration markets than in low-penetration markets. Our findings suggest that many older physicians have found it preferable to retire rather than adapt their practices to an environment with a high degree of managed care penetration . Because the number of physicians entering the older age categories will increase rapidly over the next 20 years, the growth of managed care and other influences on physician retirement will play an increasingly important role in determining the size of the physician workforce.

Estimation of soil clay and organic matter using two quantitative methods (PLSR and MARS) based on reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Nawar, Said; Buddenbaum, Henning; Hill, Joachim

2014-05-01

A rapid and inexpensive soil analytical technique is needed for soil quality assessment and accurate mapping. This study investigated a method for improved estimation of soil clay (SC) and organic matter (OM) using reflectance spectroscopy. Seventy soil samples were collected from Sinai peninsula in Egypt to estimate the soil clay and organic matter relative to the soil spectra. Soil samples were scanned with an Analytical Spectral Devices (ASD) spectrometer (350-2500 nm). Three spectral formats were used in the calibration models derived from the spectra and the soil properties: (1) original reflectance spectra (OR), (2) first-derivative spectra smoothened using the Savitzky-Golay technique (FD-SG) and (3) continuum-removed reflectance (CR). Partial least-squares regression (PLSR) models using the CR of the 400-2500 nm spectral region resulted in R2 = 0.76 and 0.57, and RPD = 2.1 and 1.5 for estimating SC and OM, respectively, indicating better performance than that obtained using OR and SG. The multivariate adaptive regression splines (MARS) calibration model with the CR spectra resulted in an improved performance (R2 = 0.89 and 0.83, RPD = 3.1 and 2.4) for estimating SC and OM, respectively. The results show that the MARS models have a great potential for estimating SC and OM compared with PLSR models. The results obtained in this study have potential value in the field of soil spectroscopy because they can be applied directly to the mapping of soil properties using remote sensing imagery in arid environment conditions. Key Words: soil clay, organic matter, PLSR, MARS, reflectance spectroscopy.

A screening system for smear-negative pulmonary tuberculosis using artificial neural networks.

PubMed

de O Souza Filho, João B; de Seixas, José Manoel; Galliez, Rafael; de Bragança Pereira, Basilio; de Q Mello, Fernanda C; Dos Santos, Alcione Miranda; Kritski, Afranio Lineu

2016-08-01

Molecular tests show low sensitivity for smear-negative pulmonary tuberculosis (PTB). A screening and risk assessment system for smear-negative PTB using artificial neural networks (ANNs) based on patient signs and symptoms is proposed. The prognostic and risk assessment models exploit a multilayer perceptron (MLP) and inspired adaptive resonance theory (iART) network. Model development considered data from 136 patients with suspected smear-negative PTB in a general hospital. MLP showed higher sensitivity (100%, 95% confidence interval (CI) 78-100%) than the other techniques, such as support vector machine (SVM) linear (86%; 95% CI 60-96%), multivariate logistic regression (MLR) (79%; 95% CI 53-93%), and classification and regression tree (CART) (71%; 95% CI 45-88%). MLR showed a slightly higher specificity (85%; 95% CI 59-96%) than MLP (80%; 95% CI 54-93%), SVM linear (75%, 95% CI 49-90%), and CART (65%; 95% CI 39-84%). In terms of the area under the receiver operating characteristic curve (AUC), the MLP model exhibited a higher value (0.918, 95% CI 0.824-1.000) than the SVM linear (0.796, 95% CI 0.651-0.970) and MLR (0.782, 95% CI 0.663-0.960) models. The significant signs and symptoms identified in risk groups are coherent with clinical practice. In settings with a high prevalence of smear-negative PTB, the system can be useful for screening and also to aid clinical practice in expediting complementary tests for higher risk patients. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale.

PubMed

Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T

2018-03-01

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer. Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models. An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R 2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses. It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach. Copyright © 2017 Elsevier B.V. All rights reserved.

Prevalence of health literacy and its correlates among patients with type II diabetes in Kuwait: A population based study.

PubMed

Hussein, Shaimaa H; Almajran, Abdullah; Albatineh, Ahmed N

2018-05-03

The purpose of this study is to estimate the prevalence of health literacy among patients with type II diabetes and investigate its association with several covariates. No studies were conducted in the Arabian Gulf region characterizing such factors for this population. A cross sectional study was implemented in which 359 type II diabetes patients were recruited from diabetes centers across Kuwait. Health literacy was measured by STOFHLA. Multivariate linear regression was applied to investigate the relationship between health literacy and several covariates. About 44.5% had inadequate, 19.5% marginal, and 35.5% adequate health literacy. Patients with inadequate health literacy were more likely to be older, females, widowed, low education, with income less than 500 KD/month. Multivariate linear regression indicated residence, nationality, education level, and age were significantly associated with health literacy. Adding marital status and gender, hierarchical linear regression revealed that 43.4% of the variability was accounted for. Inadequate health literacy is high in Kuwait. Interventions should be implemented to improve health literacy. This will reduce the prevalence of diabetes-related complications, produce better diabetes outcomes, and improve patients' quality-of-life. Health literacy should be an integral part to health promotion and chronic diseases' management programs in Kuwait. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis of threats to research validity introduced by audio recording clinic visits: Selection bias, Hawthorne effect, both, or neither?

PubMed Central

Henry, Stephen G.; Jerant, Anthony; Iosif, Ana-Maria; Feldman, Mitchell D.; Cipri, Camille; Kravitz, Richard L.

2015-01-01

Objective To identify factors associated with participant consent to record visits; to estimate effects of recording on patient-clinician interactions Methods Secondary analysis of data from a randomized trial studying communication about depression; participants were asked for optional consent to audio record study visits. Multiple logistic regression was used to model likelihood of patient and clinician consent. Multivariable regression and propensity score analyses were used to estimate effects of audio recording on 6 dependent variables: discussion of depressive symptoms, preventive health, and depression diagnosis; depression treatment recommendations; visit length; visit difficulty. Results Of 867 visits involving 135 primary care clinicians, 39% were recorded. For clinicians, only working in academic settings (P=0.003) and having worked longer at their current practice (P=0.02) were associated with increased likelihood of consent. For patients, white race (P=0.002) and diabetes (P=0.03) were associated with increased likelihood of consent. Neither multivariable regression nor propensity score analyses revealed any significant effects of recording on the variables examined. Conclusion Few clinician or patient characteristics were significantly associated with consent. Audio recording had no significant effect on any dependent variables. Practice Implications Benefits of recording clinic visits likely outweigh the risks of bias in this setting. PMID:25837372

Blastocoele expansion degree predicts live birth after single blastocyst transfer for fresh and vitrified/warmed single blastocyst transfer cycles.

PubMed

Du, Qing-Yun; Wang, En-Yin; Huang, Yan; Guo, Xiao-Yi; Xiong, Yu-Jing; Yu, Yi-Ping; Yao, Gui-Dong; Shi, Sen-Lin; Sun, Ying-Pu

2016-04-01

To evaluate the independent effects of the degree of blastocoele expansion and re-expansion and the inner cell mass (ICM) and trophectoderm (TE) grades on predicting live birth after fresh and vitrified/warmed single blastocyst transfer. Retrospective study. Reproductive medical center. Women undergoing 844 fresh and 370 vitrified/warmed single blastocyst transfer cycles. None. Live-birth rate correlated with blastocyst morphology parameters by logistic regression analysis and Spearman correlations analysis. The degree of blastocoele expansion and re-expansion was the only blastocyst morphology parameter that exhibited a significant ability to predict live birth in both fresh and vitrified/warmed single blastocyst transfer cycles respectively by multivariate logistic regression and Spearman correlations analysis. Although the ICM grade was significantly related to live birth in fresh cycles according to the univariate model, its effect was not maintained in the multivariate logistic analysis. In vitrified/warmed cycles, neither ICM nor TE grade was correlated with live birth by logistic regression analysis. This study is the first to confirm that the degree of blastocoele expansion and re-expansion is a better predictor of live birth after both fresh and vitrified/warmed single blastocyst transfer cycles than ICM or TE grade. Copyright © 2016. Published by Elsevier Inc.

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses.

PubMed

Prunier, J G; Colyn, M; Legendre, X; Nimon, K F; Flamand, M C

2015-01-01

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses. © 2014 John Wiley & Sons Ltd.

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

PubMed

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

2015-08-01

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

Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection.

PubMed

Lee, Yeonok; Wu, Hulin

2012-01-01

Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.

The role of innate immunity in acute allograft rejection after lung transplantation.

PubMed

Palmer, Scott M; Burch, Lauranell H; Davis, R Duane; Herczyk, Walter F; Howell, David N; Reinsmoen, Nancy L; Schwartz, David A

2003-09-15

Although innate immunity is crucial to pulmonary host defense and can initiate immune and inflammatory responses independent of adaptive immunity, it remains unstudied in the context of transplant rejection. To investigate the role of innate immunity in the development of allograft rejection, we assessed the impact of two functional polymorphisms in the toll-like receptor 4 (TLR4) associated with endotoxin hyporesponsiveness on the development of acute rejection after human lung transplantation. Patients and donors were screened for the TLR4 Asp299Gly and Thr399Ile polymorphisms by polymerase chain reaction using sequence-specific primers. The rate of acute rejection at 6 months was significantly reduced in recipients, but not in donors, with the Asp299Gly or Thr399Ile alleles as compared with wild type (29 vs. 56%, respectively, p = 0.05). This association was confirmed in Cox proportional hazards and multivariate logistic regression models. Our results suggest activation of innate immunity in lung transplant recipients through TLR4 contributes to the development acute rejection after lung transplantation. Therapies directed at inhibition of innate immune responses mediated by TLR4 may represent a novel and effective means to prevent acute rejection after lung transplantation.

Learning-based computing techniques in geoid modeling for precise height transformation

NASA Astrophysics Data System (ADS)

Erol, B.; Erol, S.

2013-03-01

Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.

A cross-sectional, randomized cluster sample survey of household vulnerability to extreme heat among slum dwellers in ahmedabad, india.

PubMed

Tran, Kathy V; Azhar, Gulrez S; Nair, Rajesh; Knowlton, Kim; Jaiswal, Anjali; Sheffield, Perry; Mavalankar, Dileep; Hess, Jeremy

2013-06-18

Extreme heat is a significant public health concern in India; extreme heat hazards are projected to increase in frequency and severity with climate change. Few of the factors driving population heat vulnerability are documented, though poverty is a presumed risk factor. To facilitate public health preparedness, an assessment of factors affecting vulnerability among slum dwellers was conducted in summer 2011 in Ahmedabad, Gujarat, India. Indicators of heat exposure, susceptibility to heat illness, and adaptive capacity, all of which feed into heat vulnerability, was assessed through a cross-sectional household survey using randomized multistage cluster sampling. Associations between heat-related morbidity and vulnerability factors were identified using multivariate logistic regression with generalized estimating equations to account for clustering effects. Age, preexisting medical conditions, work location, and access to health information and resources were associated with self-reported heat illness. Several of these variables were unique to this study. As sociodemographics, occupational heat exposure, and access to resources were shown to increase vulnerability, future interventions (e.g., health education) might target specific populations among Ahmedabad urban slum dwellers to reduce vulnerability to extreme heat. Surveillance and evaluations of future interventions may also be worthwhile.

GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation

DOE PAGES

Jiang, Bo; Liang, Shunlin; Ma, Han; ...

2016-03-09

Mapping surface all-wave net radiation (R n) is critically needed for various applications. Several existing R n products from numerical models and satellite observations have coarse spatial resolutions and their accuracies may not meet the requirements of land applications. In this study, we develop the Global LAnd Surface Satellite (GLASS) daytime R n product at a 5 km spatial resolution. Its algorithm for converting shortwave radiation to all-wave net radiation using the Multivariate Adaptive Regression Splines (MARS) model is determined after comparison with three other algorithms. The validation of the GLASS R n product based on high-quality in situ measurementsmore » in the United States shows a coefficient of determination value of 0.879, an average root mean square error value of 31.61 Wm -2, and an average bias of 17.59 Wm -2. Furthermore, we also compare our product/algorithm with another satellite product (CERES-SYN) and two reanalysis products (MERRA and JRA55), and find that the accuracy of the much higher spatial resolution GLASS R n product is satisfactory. The GLASS R n product from 2000 to the present is operational and freely available to the public.« less

The Sensitivity of Adverse Event Cost Estimates to Diagnostic Coding Error

PubMed Central

Wardle, Gavin; Wodchis, Walter P; Laporte, Audrey; Anderson, Geoffrey M; Baker, Ross G

2012-01-01

Objective To examine the impact of diagnostic coding error on estimates of hospital costs attributable to adverse events. Data Sources Original and reabstracted medical records of 9,670 complex medical and surgical admissions at 11 hospital corporations in Ontario from 2002 to 2004. Patient specific costs, not including physician payments, were retrieved from the Ontario Case Costing Initiative database. Study Design Adverse events were identified among the original and reabstracted records using ICD10-CA (Canadian adaptation of ICD10) codes flagged as postadmission complications. Propensity score matching and multivariate regression analysis were used to estimate the cost of the adverse events and to determine the sensitivity of cost estimates to diagnostic coding error. Principal Findings Estimates of the cost of the adverse events ranged from $16,008 (metabolic derangement) to $30,176 (upper gastrointestinal bleeding). Coding errors caused the total cost attributable to the adverse events to be underestimated by 16 percent. The impact of coding error on adverse event cost estimates was highly variable at the organizational level. Conclusions Estimates of adverse event costs are highly sensitive to coding error. Adverse event costs may be significantly underestimated if the likelihood of error is ignored. PMID:22091908

GLASS daytime all-wave net radiation product: Algorithm development and preliminary validation

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

Jiang, Bo; Liang, Shunlin; Ma, Han

Mapping surface all-wave net radiation (R n) is critically needed for various applications. Several existing R n products from numerical models and satellite observations have coarse spatial resolutions and their accuracies may not meet the requirements of land applications. In this study, we develop the Global LAnd Surface Satellite (GLASS) daytime R n product at a 5 km spatial resolution. Its algorithm for converting shortwave radiation to all-wave net radiation using the Multivariate Adaptive Regression Splines (MARS) model is determined after comparison with three other algorithms. The validation of the GLASS R n product based on high-quality in situ measurementsmore » in the United States shows a coefficient of determination value of 0.879, an average root mean square error value of 31.61 Wm -2, and an average bias of 17.59 Wm -2. Furthermore, we also compare our product/algorithm with another satellite product (CERES-SYN) and two reanalysis products (MERRA and JRA55), and find that the accuracy of the much higher spatial resolution GLASS R n product is satisfactory. The GLASS R n product from 2000 to the present is operational and freely available to the public.« less