A split-pot experiment with sorghum to test a root water uptake partitioning model

Correct modeling of root water uptake partitioning over depth is an important issue in hydrological and crop growth models. Recently a physically based model to describe root water uptake was developed at single root scale and upscaled to the root system scale considering a homogeneous distribution of roots per soil layer. Root water uptake partitioning is calculated over soil layers or compartments as a function of respective soil hydraulic conditions, specifically the soil matric flux potential, root characteristics and a root system efficiency factor to compensate for within-layer root system heterogeneities. The performance of this model was tested in an experiment performed in two-compartment split-pot lysimeters with sorghum plants. The compartments were submitted to different irrigation cycles resulting in contrasting water contents over time. The root system efficiency factor was determined to be about 0.05. Release of water from roots to soil was predicted and observed on several occasions during the experiment; however, model predictions suggested root water release to occur more often and at a higher rate than observed. This may be due to not considering internal root system resistances, thus overestimating the ease with which roots can act as conductors of water. Excluding these erroneous predictions from the dataset, statistical indices show model performance to be of good quality.

Loss of Ergodicity in the Transition from Annealed to Quenched Disorder in a Finite Kinetic Ising Model

We consider a kinetic Ising model which represents a generic agent-based model for various types of socio-economic systems. We study the case of a finite (and not necessarily large) number of agents N as well as the asymptotic case when the number of agents tends to infinity. The main ingredient are individual decision thresholds which are either fixed over time (corresponding to quenched disorder in the Ising model, leading to nonlinear deterministic dynamics which are generically non-ergodic) or which may change randomly over time (corresponding to annealed disorder, leading to ergodic dynamics). We address the question how increasing the strength of annealed disorder relative to quenched disorder drives the system from non-ergodic behavior to ergodicity. Mathematically rigorous analysis provides an explicit and detailed picture for arbitrary realizations of the quenched initial thresholds, revealing an intriguing ""jumpy"" transition from non-ergodicity with many absorbing sets to ergodicity. For large N we find a critical strength of annealed randomness, above which the system becomes asymptotically ergodic. Our theoretical results suggests how to drive a system from an undesired socio-economic equilibrium (e. g. high level of corruption) to a desirable one (low level of corruption).

A Comparison Between Pulse Pressure Variation and Right End Diastolic Volume Index as Guides to Resuscitation in a Model of Hemorrhagic Shock in Pigs

Background: Different hemodynamic parameters including static indicators of cardiac preload as right ventricular end-diastolic volume index (RVEDVI) and dynamic parameters as pulse pressure variation (PPV) have been used in the decision-making process regarding volume expansion in critically ill patients. The objective of this study was to compare fluid resuscitation guided by either PPV or RVEDVI after experimentally induced hemorrhagic shock. Methods: Twenty-six anesthetized and mechanically ventilated pigs were allocated into control (group I), PPV (group II), or RVEDVI (group III) group. Hemorrhagic shock was induced by blood withdrawal to target mean arterial pressure of 40 mm Hg, maintained for 60 minutes. Parameters were measured at baseline, time of shock, 60 minutes after shock, immediately after resuscitation with hydroxyethyl starch 6% (130/0.4), 1 hour and 2 hours thereafter. The endpoint of fluid resuscitation was determined as the baseline values of PPV and RVEDVI. Statistical analysis of data was based on analysis of variance for repeated measures followed by the Bonferroni test (p < 0.05). Results: Volume and time to resuscitation were higher in group III than in group II (group III = 1,305 +/- 331 mL and group II = 965 +/- 245 mL, p < 0.05; and group III = 24.8 +/- 4.7 minutes and group II = 8.8 +/- 1.3 minutes, p < 0.05, respectively). All static and dynamic parameters and biomarkers of tissue oxygenation were affected by hemorrhagic shock and nearly all parameters were restored after resuscitation in both groups. Conclusion: In the proposed model of hemorrhagic shock, resuscitation to the established endpoints was achieved within a smaller amount of time and with less volume when guided by PPV than when guided by pulmonary artery catheter-derived RVEDVI.

Combined glucosamine and chondroitin sulfate provides functional and structural benefit in the anterior cruciate ligament transection model

Evidence that combined glucosamine sulfate and chondroitin sulfate (Gluchon) or isolated glucosamine (Glu) modifies joint damage in osteoarthritis (OA) is still lacking. We studied joint pain and cartilage damage using the anterior cruciate ligament transection (ACLT) model. Wistar rats were subjected to ACLT of the right knee ( OA) or sham operation. Groups received either Glu (500 mg/kg), Gluchon (500 mg/kg glucosamine +400 mg/kg chondroitin) or vehicle (non-treated-NT) per os starting 7 days prior to ACLT until sacrifice at 70 days. Joint pain was evaluated daily using the rat-knee joint articular incapacitation test. Structural joint damage was assessed using histology and biochemistry as the chondroitin sulfate ( CS) content of cartilage by densitometry (microgram per milligram dried cartilage), comparing to standard CS. The molar weight (Mw) of the CS samples, used as a qualitative biochemical parameter, was obtained by comparing their relative mobility on a polyacrylamide gel electrophoresis to standard CS. Gluchon, but not Glu, significantly reduced joint pain (P<0.05) compared to NT. There was an increase in CS content in the OA group (77.7 +/- 8.3 mu g/mg) compared to sham (53.5 +/- 11.2 mu g/mg) (P<0.05). The CS from OA samples had higher Mw (4:62 +/- 0:24 x 10(4) g/mol) compared to sham (4:18 +/- 0:19 x 10(4) g/mol) (P<0.05). Gluchon administration significantly reversed both the increases in CS content (54.4 +/- 12.1 mu g/mg) and Mw (4:18 +/- 0:2 x 104 g/mol) as compared to NT. Isolated Glu decreased CS content though not reaching statistical significance. Cartilage histology alterations were also significantly prevented by Gluchon administration. Gluchon provides clinical (analgesia) and structural benefits in the ACLT model. This is the first demonstration that biochemical alterations occurring in parallel to histological damage in OA are prevented by Gluchon administration.

A Dynamic Model of Hand-and-Foot Syndrome in Patients Receiving Capecitabine

For the purpose of developing a longitudinal model to predict hand-and-foot syndrome (HFS) dynamics in patients receiving capecitabine, data from two large phase III studies were used. Of 595 patients in the capecitabine arms, 400 patients were randomly selected to build the model, and the other 195 were assigned for model validation. A score for risk of developing HFS was modeled using the proportional odds model, a sigmoidal maximum effect model driven by capecitabine accumulation as estimated through a kinetic-pharmacodynamic model and a Markov process. The lower the calculated creatinine clearance value at inclusion, the higher was the risk of HFS. Model validation was performed by visual and statistical predictive checks. The predictive dynamic model of HFS in patients receiving capecitabine allows the prediction of toxicity risk based on cumulative capecitabine dose and previous HFS grade. This dose-toxicity model will be useful in developing Bayesian individual treatment adaptations and may be of use in the clinic.

Exploring the knowledge contained in neuroimages: Statistical discriminant analysis and automatic segmentation of the most significant changes

Objective: The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models. Methods: A multivariate statistical methodology was first used to identify the most discriminating hyperplane separating two groups of images (from healthy controls and patients with schizophrenia) contained in the input data. After this, the present work makes explicit the differences found by the multivariate statistical method by subtracting the discriminant models of controls and patients, weighted by the pooled variance between the two groups. A variational level-set technique was used to segment clusters of these differences. We obtain a label of each anatomical change using the Talairach atlas. Results: In this work all the data was analysed simultaneously rather than assuming a priori regions of interest. As a consequence of this, by using active contour models, we were able to obtain regions of interest that were emergent from the data. The results were evaluated using, as gold standard, well-known facts about the neuroanatomical changes related to schizophrenia. Most of the items in the gold standard was covered in our result set. Conclusions: We argue that such investigation provides a suitable framework for characterising the high complexity of magnetic resonance images in schizophrenia as the results obtained indicate a high sensitivity rate with respect to the gold standard. (C) 2010 Elsevier B.V. All rights reserved.

PHOTOELASTIC ANALISYS OF A HUMAN VERTEBRA MODEL WITH PEDICULAR SCREW

Introduction: The vertebrae fixation system using pedicular screws is one of the most efficient methods to treat vertebral spine pathologies. When the screw is submitted to pullout strength, it causes internal tension near the medullar canal and this situation can be analyzed by using the photoelasticity technique. Objective: Were analyzed those internal tensions near the medullar canal of photoelastic vertebra models using different sizes of screws of the vertebral fixation system submitted to pullout strength. Methods: A lumbar vertebral model made of photoelastic material with three different USS1-type pedicular screw sizes (5, 6, and 7 mm) was used. The internal tensions around the screw were tested in 12 predetermined points by a plain transmission polaroscope. Results: The areas of greater tension concentration were between the medullar canal and the curves of the transverse process. Comparing the maximum average pulling tension, statistical differences were observed between screws 5 and 7, and 6 and 7. On the other hand, for screws 5 and 6, there were no significant differences. Conclusion: The study evidenced that the internal tensions are greater in irregular areas, next to the medullar canal, showing that this is a critical region.

A multilevel model for the study of breastfeeding determinants in Brazil

The benefits of breastfeeding for the children`s health have been highlighted in many studies. The innovative aspect of the present study lies in its use of a multilevel model, a technique that has rarely been applied to studies on breastfeeding. The data reported were collected from a larger study, the Family Budget Survey-Pesquisa de Orcamentos Familiares, carried out between 2002 and 2003 in Brazil that involved a sample of 48 470 households. A representative national sample of 1477 infants aged 0-6 months was used. The statistical analysis was performed using a multilevel model, with two levels grouped by region. In Brazil, breastfeeding prevalence was 58%. The factors that bore a negative influence on breastfeeding were over four residents living in the same household [odds ratio (OR) = 0.68, 90% confidence interval (CI) = 0.51-0.89] and mothers aged 30 years or more (OR = 0.68, 90% CI = 0.53-0.89). The factors that positively influenced breastfeeding were the following: higher socio-economic levels (OR = 1.37, 90% CI = 1.01-1.88), families with over two infants under 5 years (OR = 1.25, 90% CI = 1.00-1.58) and being a resident in rural areas (OR = 1.25, 90% CI = 1.00-1.58). Although majority of the mothers was aware of the value of maternal milk and breastfed their babies, the prevalence of breastfeeding remains lower than the rate advised by the World Health Organization, and the number of residents living in the same household along with mothers aged 30 years or older were both factors associated with early cessation of infant breastfeeding before 6 months.

Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.

Robust inference in an heteroscedastic measurement error model

In this paper we deal with robust inference in heteroscedastic measurement error models Rather than the normal distribution we postulate a Student t distribution for the observed variables Maximum likelihood estimates are computed numerically Consistent estimation of the asymptotic covariance matrices of the maximum likelihood and generalized least squares estimators is also discussed Three test statistics are proposed for testing hypotheses of interest with the asymptotic chi-square distribution which guarantees correct asymptotic significance levels Results of simulations and an application to a real data set are also reported (C) 2009 The Korean Statistical Society Published by Elsevier B V All rights reserved

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

Bayesian analysis of skew-t multivariate null intercept measurement error model

The multivariate skew-t distribution (J Multivar Anal 79:93-113, 2001; J R Stat Soc, Ser B 65:367-389, 2003; Statistics 37:359-363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew-normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763-771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

Hypotheses testing for structural calibration model

The main objective of this paper is to discuss maximum likelihood inference for the comparative structural calibration model (Barnett, in Biometrics 25:129-142, 1969), which is frequently used in the problem of assessing the relative calibrations and relative accuracies of a set of p instruments, each designed to measure the same characteristic on a common group of n experimental units. We consider asymptotic tests to answer the outlined questions. The methodology is applied to a real data set and a small simulation study is presented.

A transformational language for mutant description

Mutation testing has been used to assess the quality of test case suites by analyzing the ability in distinguishing the artifact under testing from a set of alternative artifacts, the so-called mutants. The mutants are generated from the artifact under testing by applying a set of mutant operators, which produce artifacts with simple syntactical differences. The mutant operators are usually based on typical errors that occur during the software development and can be related to a fault model. In this paper, we propose a language-named MuDeL (MUtant DEfinition Language)-for the definition of mutant operators, aiming not only at automating the mutant generation, but also at providing precision and formality to the operator definition. The proposed language is based on concepts from transformational and logical programming paradigms, as well as from context-free grammar theory. Denotational semantics formal framework is employed to define the semantics of the MuDeL language. We also describe a system-named mudelgen-developed to support the use of this language. An executable representation of the denotational semantics of the language is used to check the correctness of the implementation of mudelgen. At the very end, a mutant generator module is produced, which can be incorporated into a specific mutant tool/environment. (C) 2008 Elsevier Ltd. All rights reserved.