bluuebunny/crossref_metadata_2025 huggingface dataset:
Recursive Estimation of Dynamic Linear Models
Authors: Snyder R. D.
Published: December 2018

A dynamic, linear model for the analysis of univariate time series is proposed. It encompasses many of the common statistical models as special cases such as multiple regression, exponential smoothing and mixed autoregressive-moving average processes. its distinguishing feature is that it relies on only one primary source of randomness. It therefore not only provides a simpler framework for the study of dynamic models but also eliminates the need for the contentious system variance matrix which has been credited with hampering the use of recursive forecasting methods in practice. The associated Kalman filter is also derived.
Asymptotic Properties of the Generalized Median in the Case of Multivariate Normality
Authors: Oja H. , Niinimaa A.
Published: December 2018

The generalized affine invariant median proposed by Oja (1983) is, in the case of multivariate normality, shown to be asymptotically at least as efficient as the spatial median.
Exponential Dispersion Models
Authors: Jørgensen Bent
Published: December 2018

We study general properties of the class of exponential dispersion models, which is the multivariate generalization of the error distribution of Nelder and Wedderburn’s (1972) generalized linear models. Since any given moment generating function generates an exponential dispersion model, there exists a multitude of exponential dispersion models, and some new examples are introduced. General results on convolution and asymptotic normality of exponential dispersion models are presented. Asymptotic theory is discussed, including a new small-dispersion asymptotic framework, which extends the domain of application of large-sample theory. Procedures for constructing new exponential dispersion models for correlated data are introduced, including models for longitudinal data and variance components. The results of the paper unify and generalize standard results for distributions such as the Poisson, the binomial, the negative binomial, the normal, the gamma, and the inverse Gaussian distributions.
Kernel Smoothing in Partial Linear Models
Authors: Speckman Paul
Published: December 2018

Kernel smoothing is studied in partial linear models, i.e. semiparametric models of the form yi=ξi′β+f(ti)+εi(1⩽i⩽n), where the ξi are fixed known p vectors, β is an unknown vector parameter and f is a smooth but unknown function. Two methods of estimating β and f are considered, one related to partial smoothing splines and the other motivated by partial residual analysis. Under suitable assumptions, the asymptotic bias and variance are obtained for both methods, and it is shown that estimating β by partial residuals results in improved bias with no asymptotic loss in variance. Application to analysis of covariance is made, and several examples are presented.
Hierarchical Statistical Models and a Generalized Likelihood Ratio Test
Authors: Hosoya Yuzo
Published: December 2018

This paper deals with statistical inference for statistical models which have a nested structure. The emphasis here is on the use of a classical test of significance and on a confidence set construction approach for model identification, and the paper proposes a generalized likelihood ratio test with equal marginal error rate as a basic tool. The power performance of this test is characterized and some applications are illustrated.
A Smoothed Em Approach to Indirect Estimation Problems, with Particular Reference to Stereology and Emission Tomography
Authors: Silverman B. W. , Jones M. C. , Wilson J. D. , Nychka D. W.
Published: December 2018

There are many practical problems where the observed data are not drawn directly from the density g of real interest, but rather from another distribution derived from g by the application of an integral operator. The estimation of g then entails both statistical and numerical difficulties. A natural statistical approach is by maximum likelihood, conveniently implemented using the EM algorithm, but this provides unsatisfactory reconstructions of g. In this paper, we modify the maximum likelihood–EM approach by introducing a simple smoothing step at each EM iteration. In our experience, this algorithm converges in relatively few iterations to good estimates of g that do not depend on the choice of starting configuration. Some theoretical background is given that relates this smoothed EM algorithm to a maximum penalized likelihood approach. Two applications are considered in detail. The first is the classical stereology problem of determining particle size distributions from data collected on a plane section through a composite medium. The second concerns the recovery of the structure of a section of the human body from external observations obtained by positron emission tomography; for this problem, we also suggest several technical improvements on existing methodology.
Gene Hunting with Gradients of Likelihoods
Authors: Thomas Alun
Published: December 2018

When analysing the mode of inheritance of a genetic trait, or mapping its location on the human genome, computationally intensive likelihood calculations are required. The standard method, called peeling, is reviewed here and an extension to the calculation of gradients is implemented. The gradients are used for numerical searches for maximum likelihood estimates, and also for producing contour plots of log-likelihood surfaces. This is illustrated with an analysis of the mode of inheritance, of the eye disease blue dot cataract, and of its proximity to a genetic marker on chromosome 16.
Computing Extended Maximum Likelihood Estimates for Linear Parameter Models
Authors: Clarkson Douglas B. , Jennrich Robert I.
Published: December 2018

Methods are given for computing extended maximum likelihood estimates in which one or more parameter estimates are infinite at the supremum of the likelihood. The results are given for a broad class of regression-like models based on independent observations with linearly related parameters including, in particular, the generalized linear models. The estimation consists of two steps: A linear programming step to identify the infinite components and a more conventional function optimization step to optimize the remaining finite components. Provision is made for nuisance parameters. Two algorithms are presented and examples illustrating their use are given.
Semiparametric Estimation in Logistic Measurement Error Models
Authors: Carroll R. J. , Wand M. P.
Published: December 2018

We describe semiparametric estimation and inference in a logistic regression model with measurement error in the predictors. The particular measurement error model consists of a primary data set in which only the response Y and a fallible surrogate W of the true predictor X are observed, plus a smaller validation data set for which (Y, X, W) are observed. Except for the underlying assumption of a logistic model in the true predictor, no parametric distributional assumption is made about the true predictor or its surrogate. We develop a semiparametric parameter estimate of the logistic regression parameter which is asymptotically normally distributed and computationally feasible. The estimate relies on kernel regression techniques. For scalar predictors, by a detailed analysis of the mean-squared error of the parameter estimate, we obtain a representation for an optimal bandwidth.
A Characterization of Optimal Designs for Observational Studies
Authors: Rosenbaum Paul R.
Published: December 2018

Subclassification and matching are often used to adjust for covariates in observational studies. Here, the form of an ‘optimal’ subclassification is determined, i.e. a subclassification which makes the treated and control subjects in the same subclass as similar as possible. This optimal form is simple, intuitive and quite practical, though it is rarely used. The optimal form has several consequences. First, it suggests that theoretical investigations may assume that observational studies have this form, for if they do not they may be changed to this form without a loss, and often with a gain. Second, in searching for an optimal subclassification, the search may be confined to subclassifications of this form, for one is sure to be optimal. Finally, it is shown that finding an optimal subclassification of this form may often be reduced to finding the minimum cost flow in a certain network, i.e. to a standard combinatorial optimization problem for which good algorithms exist.
A New Method of Prediction for Spatial Regression Models with Correlated Errors
Authors: Vecchia A. V.
Published: December 2018

This paper deals with minimum mean-squared error, unbiased linear interpolation of a continuous domain spatial process based on a sparse set of irregularly spaced observations. The process is assumed to be governed by a linear regression model with errors that follow a second-order stationary Gaussian random field. A new method of prediction is developed that is compatible with the parameter estimation procedures of Vecchia. The result is a new likelihood-based method for joint parameter estimation and prediction that can be applied to large or small data sets with irregularly spaced data. Simulated and observed data sets are analysed to illustrate the methods.
Pooling 2 Times 2 Tables: Asymptotic Moments of Estimators
Authors: Gart John J.
Published: December 2018

Consider J independent pairs of mutually independent binomial variates which can be considered as 72 times 2 tables. We assume a common measure of association, such as the odds ratio, between the tables. This paper studies the asymptotic moments of the ‘pooled' estimators, i.e. those constructed from the data summed over all the tables. For balanced designs, or proportional subclass numbers, it is shown that the pooled estimator of the common odds ratio is asymptotically biased towards 1, while those of the common ratio and difference are asymptotically unbiased. The asymptotic variances are also compared. A simple test for complete homogeneity is suggested. The various results are illustrated in numerical examples.
Bayesian Computation Via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods
Authors: Smith A. F. M. , Roberts G. O.
Published: December 2018

The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the context of some canonical examples. Other Markov chain Monte Carlo simulation methods are also briefly described, and comments are made on the advantages of sample-based approaches for Bayesian inference summaries.
Semiparametric Analysis of Variance with Tensor Product Thin Plate Splines
Authors: Gu Chong , Wahba Grace
Published: December 2018

We consider fitting multivariate models to response data based on analysis-of-variance (ANOVA)-like decompositions of functions of several variables, f(t  1,..., td) = C + σα  f  α(ta + σα < β f  αβ(t  α,  t  β) + · · · · A theory for fitting (some components of) these models with polynomial smoothing splines exists when each tα is in a subset of the real line. In this case the various estimated components turn out to be certain tensor sums and products of polynomial splines. This approach may not be natural when one or more of the 'variables' are geographic, in particular where nature does not know north from east. In this case splines of radial structure, such as thin plate splines, for the geographic component, are more natural. In this paper we extend this theory of polynomial smoothing spline ANOVA models to include variables which take values in Euclidean k-space, and fits which turn out to include sums and products of both polynomial and thin plate smoothing splines. The cases of most interest would be k = 2 and k = 3. The formulation, interpretation and calculation of the models are discussed, and an application of the technique is illustrated. This work can be used to build predictive ANOVA-like models which describe a response as a function of spatial, temporal and other variables and to explore their interactions. The models can be fitted by using the existing publicly available code RKPACK.
Bayesian Predictive Inference for a Finite Population Proportion: Two-Stage Cluster Sampling
Authors: Nandram B. , Sedransk J.
Published: December 2018

Given binary data from a two-stage cluster sample, we present a method to carry out Bayesian predictive inference for a finite population proportion. Our probabilistic specification should be useful for many surveys of this type and yields simple analytical expressions for the prior and posterior mean and variance. Within cluster k, we assume that the Yki are a random sample from the Bernoulli distribution with probability θk. Conditional on β and τ, θ1,..., θN are a random sample from a beta distribution. Finally, β has a discrete distribution with specified probabilities. We use data from the National Health Interview Survey to illustrate the methodology and to show how to choose values for the parameters in the prior distribution.
Tools for the Symbolic Computation of Asymptotic Expansions
Authors: Andrews David F. , Stafford James E.
Published: December 2018

This paper describes a collection of procedures for the systematic computation of asymptotic expansions that are common in statistical theory and practice: expansions of functions of sums of independent and identically distributed random variables. The procedures permit the expansion of maximum likelihood estimates, the associated deviance or drop in likelihood and more general functions of random variables with distributions involving one or more parameters. The procedures are illustrated with examples involving general and specific laws.
Improved Score Tests for Generalized Linear Models
Authors: Cordeiro Gauss M. , Ferrari Silvia L. de Paula , Paula Gilberto A.
Published: December 2018

We present, in matrix notation, an asymptotic expansion for the null distribution of the score statistic in generalized linear models, corrected up to order n  −1 where n is the sample size. The expansion has advantages for numerical purposes because it requires only simple operations on matrices. It is also sufficiently simple to be used analytically to obtain several closed form expressions, analogous to Bartlett corrections, in a variety of important score tests. Improved score tests for these models are discussed and various applications to some special tests are given. We show by simulation that two asymptotically equivalent adjusted score tests seem to improve, at least for continuous data, on the usual score test.
Heteroscedastic G-Optimal Designs
Authors: Wong Weng Kee , Cook R. Dennis
Published: December 2018

This paper addresses the issue of constructing large sample G-optimal designs when the variability of the response varies across a compact design space. A useful characterization theorem is presented along with a computer algorithm for generating (heteroscedastic) G-optimal designs. to facilitate comparisons between D- and G-optimal designs, Atwood's inequality for comparing D- and G-efficiencies in homoscedastic models is generalized to heteroscedastic models. Some robustness properties of these designs are presented.
Estimation of Variance in Crossover Designs
Authors: Kunert J. , Utzig B. P.
Published: December 2018

There is concern that the usual analysis of crossover designs with more than two treatments is subject to bias due to correlations between the measurements on the same experimental units. It has been shown by Kunert in the special case of balanced Latin squares that this bias can be present but that it is limited. Extending the work of Kunert we show in the present paper that there is a constant X* which guarantees that the estimate for the variance of any treatment contrast from the usual model multiplied by X* has an expectation which is at least as big as the true variance. This result holds for any within-unit covariance structure and it is valid for a class of commonly applied designs, allowing for fewer periods than treatments. The constant X* depends on the number of units, periods and treatments but not on the data or the unknown covariance matrix. We also deal with the effect that our result can have on tests for hypotheses about treatment contrasts.
A Note on Nonparametric Estimation of the Distribution Function from Interval-Censored and Truncated Observations
Authors: Frydman Halina
Published: December 2018

This paper is concerned with the nonparametric estimation of the distribution function F from interval-censored and truncated data as discussed by Turnbull. It is observed that Turnbull's characterization of F̂, the maximum likelihood estimator of F, is not applicable in general when the data are interval censored and truncated. It is shown, however, that Turnbull's characterization can be easily modified to obtain the appropriate characterization of F̂.
Time Series Analysis of Circular Data
Authors: Fisher N. I. , Lee A. J.
Published: December 2018

The paper considers the problem of modelling and analysing a time series of measurements which take the form of unit two-dimensional vectors. Several general classes of existing and new models are studied in terms of feasibility and flexibility. An approach is recommended which uses two of these classes, and which takes advantage of several standard time series algorithms that are available in modern software packages. An application is given to the analysis of a series of data on wind directions.
Variances for Maximum Penalized Likelihood Estimates Obtained Via the Em Algorithm
Authors: Segal Mark R. , Bacchetti Peter , Jewell Nicholas P.
Published: December 2018

We address the problem of providing variances for parameter estimates obtained under a penalized likelihood formulation through use of the EM algorithm. The solution proposed represents a synthesis of two existent techniques. Firstly, we exploit the supplemented EM algorithm developed by Meng and Rubin that provides variance estimates for maximum likelihood estimates obtained via the EM algorithm. Their procedure relies on evaluating the Jacobian of the mapping induced by the EM algorithm. Secondly, we utilize results from Green that provide expressions for Jacobians of mappings induced by EM algorithms applied to a penalized likelihood. The resultant procedure requires no more code than that needed for the penalized EM algorithm itself. The technique is demonstrated with an illustrative example.
Automated Kernel Smoothing of Dependent Data by Using Time Series Cross-Validation
Authors: Hart Jeffrey D.
Published: December 2018

The problem of selecting the bandwidth of a kernel regression estimator when the observed data are serially correlated is considered. The bandwidth is selected by using a version of cross-validation that seeks a good one-step-ahead predictor of the data. This method, referred to as time series cross-validation (TSCV), simultaneously estimates an optimal bandwidth and, given a time series model for the errors, the autocorrelation function of the data. In addition, different time series models having the same number of parameters can be compared by using TSCV. Boundary kernels play a key role in the proposed methodology since one-step-ahead prediction entails extrapolating past the available data. Boundary kernels are used at the bandwidth selection stage, but ‘proper’ kernels are used to estimate the regression function. It is shown that smooth (i.e. at least continuous) boundary kernels have some robustness to misspecification of the error model. This means that a simple correlation model, such as the first-order autoregressive process, will often suffice for selecting a bandwidth. A simulation study and real data examples indicate the usefulness of TSCV for smoothing time series data.
Representations of Knowledge in Complex Systems
Authors: Grenander Ulf , Miller Michael I.
Published: December 2018

Modern sensor technologies, especially in biomedicine, produce increasingly detailed and informative image ensembles, many extremely complex. It will be argued that pattern theory can supply mathematical representations of subject-matter knowledge that can be used as a basis for algorithmic ‘understanding’ of such pictures. After a brief survey of the basic principles of pattern theory we shall illustrate them by an application to a concrete situation: high magnification (greater than 15000×) electron micrographs of cardiac muscle cells. The aim is to build algorithms for automatic hypothesis formation concerning the number, location, orientation and shape of mitochondria and membranes. For this we construct a pattern theoretic model in the form of a prior probability measure on the space of configurations describing these hypotheses. This measure is synthesized by solving sequentially a jump–diffusion equation of generalized Langevin form. The jumps occur for the creation–annihilation of hypotheses, corresponding to a jump from one continuum to another in configuration (hypothesis) space. These continua (subhypotheses) are expressed in terms of products of low dimensional Lie groups acting on the generators of a template. We use a modified Bayes approach to obtain the hypothesis formation, also organized by solving a generalized Langevin equation. to justify this it is shown that the resulting jump-diffusion process is ergodic so that the solution converges to the desired probability measure. to speed up the convergence we reduce the computation of the drift term in the stochastic differential equation analytically to a curvilinear integral, with the random term computed almost instantaneously. The algorithms thus obtained are implemented, both for mitochondria and membranes, on a 4000 processor parallel machine. Photographs of the graphics illustrate how automatic hypothesis formation is achieved. This approach is applied to deformable neuroanatomical atlases and tracking recognition from narrow band and high resolution sensor arrays.
Asymptotically Unbiased Estimation in Generalized Linear Models with Random Effects
Authors: Kuk Anthony Y. C.
Published: December 2018

Obtaining estimates that are nearly unbiased has proven to be difficult when random effects are incorporated into a generalized linear model. In this paper, we propose a general method of adjusting any conveniently defined initial estimates to result in estimates which are asymptotically unbiased and consistent. The method is motivated by iterative bias correction and can be applied in principle to any parametric model. A simulation-based approach of implementing the method is described and the relationship of the method proposed with other sampling-based methods is discussed. Results from a small scale simulation study show that the method proposed can lead to estimates which are nearly unbiased even for the variance components while the standard errors are only slightly inflated. A new analysis of the famous salamander mating data is described which reveals previously undetected between-animal variation among the male salamanders and results in better prediction of mating outcomes.
Semiparametric Efficient Estimation of a Conditional Density with Missing or Mismeasured Covariates
Authors: Robins James M. , Hsieh Fushing , Newey Whitney
Published: December 2018

Pepe and Fleming, and Carroll and Wand have recently proposed estimators in a parametric model for the density of a random variable Y conditional on a vector of covariates (X, V) when data on one of the regressors X is missing for some study subjects. We propose a new class of estimators that remains consistent and asymptotically normal even when the probability that X is missing depends on the observed V and Y, includes an estimator whose asymptotic variance attains the semiparametric variance bound for the model and, when the data are missing completely at random, includes an estimator that is asymptotically equivalent to the inefficient estimators proposed by Pepe and Fleming and by Carroll and Wand. The optimal estimator in our class depends on the unknown probability law generating the data. When the vector V of non-missing regressors has at most two continuous components, we propose an adaptive semiparametric efficient estimator and compare the performance of the proposed semiparametric efficient estimator with the estimators proposed by Pepe and Fleming and Carroll and Wand in a small simulation study. When V has many continuous components, we propose an alternative class of adaptive estimators that should have high efficiency.
Sequential Changepoint Detection in Quality Control and Dynamical Systems
Authors: Lai Tze Leung
Published: December 2018

After a brief survey of a large variety of sequential detection procedures that are widely scattered in statistical references on quality control and engineering references on fault detection and signal processing, we study some open problems concerning these procedures and introduce a unified theory of sequential changepoint detection. This theory leads to a class of sequential detection rules which are not too demanding in computational and memory requirements for on-line implementation and yet are nearly optimal under several performance criteria.
Regression Models for Longitudinal Binary Responses with Informative Drop-Outs
Authors: Fitzmaurice Garrett M. , Molenberghs Geert , Lipsitz Stuart R.
Published: December 2018

This paper reviews both likelihood-based and non-likelihood (generalized estimating equations) regression models for longitudinal binary responses when there are drop-outs. Throughout, it is assumed that the regression parameters for the marginal expectations of the binary responses are of primary scientific interest. The association or time dependence between the responses is largely regarded as a nuisance characteristic of the data. The performance of the methods is compared, in terms of asymptotic bias, under mis-specification of the association between the responses and the missing data mechanism or drop-out process.
Equivalence with Respect to a Control: Stepwise Tests
Authors: Bofinger Eve , Bofinger Mark
Published: December 2018

The problem of selecting those treatments which are equivalent (or bioequivalent) to a control treatment is investigated by using single-step, step-down and step-up multiple-testing procedures. For each of the tests considered the null hypothesis is that of non-equivalence and the hypotheses are rejected in either a single step or a stepwise fashion while controlling the family wise error rate. The single-step procedure is based on ‘expanded’ confidence intervals, as discussed by Bofinger, the step-down procedure is based on the method of Naik and the step-up procedure on the method of Dunnett and Tamhane. Almost all the critical constants used in the procedures are based on tables already published.
On the Comparison of Multinomial and Poisson Log-Linear Models
Authors: Lang Joseph B.
Published: December 2018

We introduce a method for comparing multinomial and Poisson log-linear models which affords an explicit description of their equivalences and differences. The method involves specifying the model in terms of constraint equations, rather than the more common freedom equations. The Poisson and multinomial large sample distributions of log-linear model parameter estimators are derived and compared within this constraint equation context; reparameterizations are thereby avoided. As a by-product, the method provides the practitioner with the adjustment that is necessary to make valid inferences about all multinomial log-linear parameters when, as a matter of convenience, the Poisson log-linear model is fitted. This implies that valid large sample inferences about the multinomial cell probabilities can be made directly by using the Poisson log-linear model. to illustrate the utility of this approach, several examples are considered.
Quasi-Likelihood and Generalizing the Em Algorithm
Authors: Heyde C. C. , Morton R.
Published: December 2018

This paper is concerned with situations in which there are missing or otherwise incomplete data and the full likelihood may not be available. Extensions of the EM algorithm are developed to deal with estimation via general estimating functions and in particular the quasi-score. The E-step is replaced by projecting the quasi-score and the M-step requires the solution of an estimating equation. The standard EM algorithm can be obtained as a particular case if the likelihood is available.
Pseudosplines
Authors: Hastie Trevor
Published: December 2018

We describe a method for constructing a family of low rank, penalized scatterplot smoothers. These pseudosplines have shrinking behaviour that is similar to that of smoothing splines. They require two ingredients: A basis and a penalty sequence. The smoother is then computed by a generalized ridge regression. The family can be used to approximate existing high rank smoothers in terms of their dominant eigenvectors. Our motivating example uses linear combinations of orthogonal polynomials to approximate smoothing splines, where the linear combination and the penalty sequence depend on the particular instance of the smoother being approximated. As a leading application, we demonstrate the use of these pseudosplines in additive model computations. Additive models are typically fitted by an iterative smoothing algorithm, and any features other than the fit itself are difficult to compute. These include standard error curves, degrees of freedom, generalized cross-validation and influence diagnostics. By using a low rank pseudospline approximation for each of the smoothers involved, the entire additive fit can be approximated by a corresponding low rank approximation. This can be computed exactly and efficiently, and opens the door to a variety of computations that were not feasible before.
Multiple Tests of a Non-Hierarchical Finite Family of Hypotheses
Authors: Liu Wei
Published: December 2018

This paper considers the problem of simultaneously testing a non-hierarchical finite family of hypotheses. Several definitions have been introduced. They are used to shed interesting light on the interrelationship between the four types of test that have previously been proposed.
An Approach to Bayesian Sensitivity Analysis
Authors: Weiss Robert
Published: December 2018

This paper describes an approach to Bayesian sensitivity analysis that uses an influence statistic and an outlier statistic to assess the sensitivity of a model to perturbations. The basic outlier statistic is a Bayes factor, whereas the influence statistic depends strongly on the purpose of the analysis. The task of influence analysis is aided by having an interpretable influence statistic. Two alternative divergences, an L  1-distance and a χ2-divergence, are proposed and shown to be interpretable. The Bayes factor and the proposed influence measures are shown to be summaries of the posterior of a perturbation function.
Modeling the effects of corn and wheat resistant starch on texture properties and quality of resistant starch‐enrichment dough and biscuit
Authors: Pourmohammadi Kiana , Abedi Elahe , Farahmandi Shabnam , Mahmoudi Mohammad Reza , Hashemi Seyed Mohammad Bagher , Torri Luisa
Published: December 2018

This work aimed to develop innovative dietary fiber‐enriched biscuits using resistant starch (RS Type III) from corn (CRS) and wheat (WRS) as wheat‐flour replacers at several ratios (0, 20, 40, 60, 80, and 100%). The effect of replacement on the physicochemical properties of dough and biscuits (moisture content, protein, amylose, resistant starch, specific volume), and their color, textural properties, microstructure, and sensory acceptability was investigated. The potential of multiple linear regression (MLR) to predict dough and biscuit properties was also assessed. Results showed that total resistant starch content was higher in biscuits containing WRS than in CRS samples, and that the chemical properties of flour had an important role in the formation of resistant starch after baking. Moisture content, protein, density, and textural characteristics reduced on increasing the resistant starch levels in both CRS and WRS samples compared with the control, while the sensory acceptability improved. Results also showed that MLR was suitable for modeling the response variables and predicting the effect of moisture and RS (before and after baking) content on texture, color, and overall liking of biscuits.

Practical applications

Industries and researchers have long investigated the manufacture of dietary biscuits with low sugar content and no unfavorable aftertaste. Replacement of sugar with other sweeteners would lead to a vast effect on the sensory and structural properties of biscuits. Due to high starch content in flour, and starch hydrolysis into glucose in the human body, replacement of sugar with sugar replacers would not lead to a dietary product. Therefore, one of the suitable techniques for making biscuits with the most favorable dietary properties is the replacement of flour with resistant starch. The results of this study will be used to evaluate production methods of dietary fiber‐enriched biscuits using resistant starch.
Development and evaluation of calcium hydroxide‐coated, pericardium‐based biomembranes for direct pulp capping
Authors: Klein‐Júnior Celso A. , Reston Eduardo , Plepis Ana M. , Martins Virginia C. , Pötter Isabel C. , Lundy Fionnuala , Hentschke Guilherme S. , Hentschke Vítor S. , Karim Ikhlas El
Published: December 2018

Aim

The aim of the present study was to develop a bovine pericardium biomembrane (BPB) and to evaluate pulp response in vivo.

Methods

A double‐layer bovine BPB/chitosan was manufactured, and the porous chitosan side was coated with calcium hydroxide. The microstructure of the matrices was evaluated with electron microscopy. To test pulp response, cavities were prepared on the occlusal surface of Wistar rats’ mandibular left first molars and capped with matrices, followed by appropriate adhesives/composite restorations. The animals were divided into three groups: group 1, calcium hydroxide alone; group 2, BPB without calcium hydroxide; and group 3, BPB coated with calcium hydroxide. Specimens were processed and histologically evaluated at 7, 14, and 30 days, postoperatively.

Results

Electron microscopy showed porous chitosan surface and a cohesive calcium hydroxide layer. Histological analysis showed that groups 1 and 3 had mild odontoblast layer disorganization, but normal pulp tissue appearance at 7, 14, and 30 days. At the same time points, group 2 showed a loss of general pulp tissue, pulp necrosis, and periapical abscess in some teeth.

Conclusion

Coated bovine pericardium‐based biomembranes resulted in favorable outcomes in cases of pulp exposure after a 30‐day observation period, and might protect against injuries caused by adhesive systems and composites.
Support for Tough Immigration Policy: Identity Defense or Concern for Law and Order?
Authors: Mukherjee Sahana , Adams Glenn , Molina Ludwin E.
Published: December 2018

Across two studies, U.S. participants read a fictional transcript of a law enforcement officer who observed a speeding infraction and made a discretionary traffic stop. The car carried occupants who displayed either high or low fit with Anglocentric constructions of U.S. identity and were of presumptive Mexican (Studies 1 and 2), Canadian (Study 1), or Irish (Study 2) origin. The officer decided over the course of the traffic stop that the occupants’ behavior aroused “reasonable suspicion” about documentation status, so he asked them to produce identification documents and detained them when they failed to do so. Participants indicated their suspicion about occupants’ documentation status and rated the appropriateness of law enforcement actions. Results indicate effects of origin across both studies for all outcomes: participants considered occupants of Mexican origin (vs. Canadian or Irish) as more suspicious, and rated law enforcement actions related to traffic and immigration violations as more appropriate when the interaction involved occupants of Mexican origin (vs. Canadian or Irish). Results indicate effects of fit across both studies for all outcomes: participants considered occupants who showed low‐fit (vs. high‐fit) as more suspicious, and rated law enforcement actions related to traffic and immigration violations as more appropriate when occupants showed low‐fit (vs. high‐fit). Discussion focuses on how participant support for punitive anti‐immigration measures is less about neutral enforcement of law than about racialized exclusion to defend an Anglocentric construction of U.S. identity.
Salvianolic acid B inhibits the development of diabetic peripheral neuropathy by suppressing autophagy and apoptosis
Authors: Wang Qian-qian , Zhai Cui , Wahafu Alafate , Zhu Yan-ting , Liu Yong-hui , Sun Lian-qing
Published: December 2018

Objectives

The aim of this study was to evaluate the neuroprotective effects of SalB on high glucose (HG)-induced excessive autophagy and apoptosis in vitro.

Methods

The proliferation and apoptosis of RSC96 cells were determined using the MTT assay and flow cytometry, respectively. Western blot analysis was performed to examine the expression of autophagy and apoptosis-related proteins. RT-PCR and flow cytometry were manipulated to examine the level of Bcl-2. The signals of autophagy markers were detected using immunofluorescence methods.

Key findings

We found that HG significantly reduced RSC96 cell's proliferation and induced apoptosis. What's more, HG increased the level of autophagy and apoptosis-related proteins. However, these effects were reversed by SalB. In addition, we also found that 3-MA decreased the expression of LC3A/B and Beclin1, while the JNK inhibitor SP600125 reduced the levels of phosphorylated JNK, LC3A/B and Beclin1.

Conclusions

High glucose not only induced apoptosis but also caused autophagic cell death by activating the JNK pathway. These effects prevented by SalB in an opposite manner.
Effects of Carvacrol, Thymol and essential oils containing such monoterpenes on wound healing: a systematic review
Authors: Costa Michelle Fonseca , Durço Aimée Obolari , Rabelo Thallita Kelly , Barreto Rosana de Souza Siqueira , Guimarães Adriana Gibara
Published: December 2018

Objectives

The treatment of wounds accounts for a considerable fraction of health expenses as well as serious socioeconomic problems. The use of natural substances stands out as a source of new therapeutic discoveries for the wound healing. Thus, this review compiled scientific findings on the applicability of carvacrol and thymol, or essential oils containing at least one of these compounds, for the treatment of wounds.

Methods

This review was performed at PubMed, SCOPUS, Web of Science databases using keywords as wound healing, thymol/carvacrol and essential oils. Thirteen studies were selected for discussion.

Key findings

Thymol/carvacrol was able to act in the three phases of wound healing. In the first phase, they showed modulatory effect of the inflammatory cytokines, oxidative stress and antimicrobial power. In the second phase, they promoted re-epithelialization, angiogenesis and development of granulation tissue. Finally, in the third phase, they improve the collagen deposition and modulated the growth of fibroblasts and keratinocytes.

Conclusions

These compounds present a high potential for the development of new therapeutic for wound repair. However, dose, efficacy and safety of these compounds for the treatment of wounds, as well as the mechanisms by which those effects can be observed, are challenges for future studies.
Gene expression is more strongly associated with behavioural specialization than with age or fertility in ant workers
Authors: Kohlmeier Philip , Alleman Austin R. , Libbrecht Romain , Foitzik Susanne , Feldmeyer Barbara
Published: December 2018

The ecological success of social insects is based on division of labour, not only between queens and workers, but also among workers. Whether a worker tends the brood or forages is influenced by age, fertility and nutritional status, with brood carers being younger, more fecund and more corpulent. Here, we experimentally disentangle behavioural specialization from age and fertility in Temnothorax longispinosus ant workers and analyse how these parameters are linked to whole‐body gene expression. A total of 3,644 genes were associated with behavioural specialization which is ten times more than associated with age and 50 times more than associated with fertility. Brood carers were characterized by an upregulation of three Vitellogenin (Vg) genes, one of which, Vg‐like A, was the most differentially expressed gene that was recently shown experimentally to control the switch from brood to worker care. The expression of Conventional Vg was unlinked to behavioural specialization, age or fertility, which contrasts to studies on bees and some ants. Diversity in Vg/Vg‐like copy number and expression bias across ants supports subfunctionalization of Vg genes and indicates that some regulatory mechanisms of division of labour diverged in different ant lineages. Simulations revealed that our experimental dissociation of co‐varying factors reduced transcriptomic noise, suggesting that confounding factors could potentially explain inconsistencies across transcriptomic studies of behavioural specialization in ants. Thus, our study reveals that worker gene expression is mainly linked to the worker's function for the colony and provides novel insights into the evolution of sociality in ants.
Snap shot view on anaemia in chronic kidney disease of uncertain aetiology
Authors: Fernando Warnakulasuriya B. N. T. , Hettiarachchi Thilini W , Sudeshika Thilini , Badurdeen Zeid , Abeysundara Hemalika , Ranasinghe Shirani , Rathnayake Manel P , Nanayakkara Nishantha
Published: December 2018

Aim

Anaemia is a well‐known complication of chronic kidney disease but there are no published studies on the pattern of anaemia in chronic kidney disease of uncertain aetiology (CKDu). This study aims to find out the prevalence, causes and associations of anaemia in CKDu to identify any unique features which are different from already described anaemia in chronic kidney disease.

Method

All (119) biopsy‐confirmed CKDu patients in two endemic clinics (Girandurukotte and Wilgamuwa) were selected as cases. Blood samples (10 mL) were collected from the peripheral veins into Potassium‐Ethylenediaminetetraacetic acid (K‐EDTA) tubes, plain tubes and Na‐citrated tubes. Serum was separated immediately by centrifugation at 3000 rpm for 10 min. Spot urine samples were collected into empty, sterile, polypropylene urine containers. All analyses were performed in IBM spss statistics version 23 (IBM Corp, Armonk, New York).

Results

The overall prevalence of anaemia in 119 non‐dialysis CKDu patients was 72.3% with the highest prevalence seen in females compared to males (P < 0.001). The prevalence of anaemia in CKDu patients with progression to renal failure was 66.7% – stage 1, 60% – stage 2, 50% – stage 3a, 95% – stage 3b, 79.2% – stage 4 and 100% – stage 5 (P = 0.005). Of CKDu patients, 44.3% had anaemia of chronic disease with iron deficiency. CKDu patients with anaemia had a high inflammatory score were seen in both early and late stages of CKDu. There were a similar proportion of patients with both early and late CKDu having unexplained anaemia.

Conclusion

The current study showed a significant association of anaemia with disease severity among CKDu patients. Iron deficiency is a crucial aetiology factor of anaemia in CKDu and inflammation likely to effects adversely on anaemia of CKDu.
Watanabea acidotolerans: A new trebouxiophyte lineage (Chlorophyta) inhabiting low pH environments from Europe to South America
Authors: Barcytė Dovilė , Hodač Ladislav
Published: December 2018

Here we studied a new Watanabea strain CAUP H 8901‐CRYO isolated from an extremely acidic (pH 2.6) pit lake in Czechia. We used morphological, ultrastructural and molecular data to ascertain its taxonomic position and phylogenetic relationships within the genus Watanabea. The morphology of the studied strain matched the formal Watanabea description, including its most characteristic feature, the production of two types of autospores. The new isolate morphologically and ultrastructurally resembled the Watanabea type species, W. reniformis. However, it differed from W. borysthenica by having a pyrenoid not surrounded by starch grains. Molecular phylogenetic analyses of nuclear 18S rDNA and plastid rbcL showed that the strain CAUP H 8901‐CRYO formed an independent lineage within the genus Watanabea. Furthermore, analyses of nuclear ITS2 secondary structure demonstrated an additional side loop coiling from the helix III, the unique feature of CAUP H 8901‐CRYO, not present in the authentic strain of W. reniformis SAG 211‐9b. The ITS2 secondary structures of the two strains also differed by one compensatory base change (CBC) and a number of additional nucleotide substitutions. The new acidotolerant strain and another Watanabea isolate from a naturally acidic lake in Argentina were identical in their plastid rbcL sequence. Consequently, here we proposed a new species, Watanabea acidotolerans, to accommodate a lineage of microalgae with a possible worldwide distribution and tolerance to low pH habitats.
Digital mapping of soil salinity at various depths using an EM38
Authors: Narjary Bhaskar , Meena Murli Dhar , Kumar Satyendra , Kamra Sushil Kumar , Sharma Dinesh Kumar , Triantafilis John
Published: December 2018

Problem definition

Spatial information on salinity is required at the farm level to enable suitable soil, crop and water management practices.

Rationale

To facilitate this, we used an electromagnetic (EM) induction instrument for rapid measurement of apparent soil electrical conductivity (EC
a—mS m–1) across the 11 ha area of the Central Soil Salinity Research Institute experimental farm in Nain, Haryana, India.

Methods

The ECa was measured using an EM38 in horizontal (ECah) and vertical (ECav) modes on a grid survey. Using the ECa data, we selected 21 locations using the response surface sampling design (RSSD) module of Electrical Conductivity Sampling Assessment and Prediction (ESAP) software. We collected soil samples at four depth increments, including two topsoil (0–0.15 and 0.15–0.30 m), a subsurface (0.3–0.6m) and a subsoil (0.6–0.9m) and measured the soil electrical conductivity (EC
e—dS m–1).

Results

We developed multiple linear regression to predict ECe using the ESAP software from ECah and ECav and two trend surface parameters (i.e., Easting and Northing) across the farm. The prediction accuracy and bias were compared at different depth increments, and results of the spatial distributions of ECe using ordinary kriging (OK) interpolation were described in terms of the crop and soil use and management implications.

Conclusions

We conclude the overall approach allows for generations of a digital soil maps (DSMs) of ECe which serve as baseline data that will allow the monitoring of any rehabilitation effort of salt‐affected soils according to their actual degree of salinity.