Search

This study uses a new biometric measure, computer-coded smiling, to better understand the processes observed in a classic television-advertising context study (Kamins, Marks, and Skinner 1991). This conceptual replication tests the original study's program–ad matching effect on informational ads, which are more common than the sad ads the original study tested, using a cognitive outcome measure, recall. In both studies, positive ads perform equally well across positive and nonpositive program contexts, but nonpositive ads perform better in a matching nonpositive program context. These results strengthen the case for program–ad matching when using neutral informational and negative television commercials. [ABSTRACT FROM PUBLISHER]

The objective is to determine the optimal tactics in treatment of combined trauma using the objective assessment of the severity of injuries and the overall condition of the casualties.Material and methods. The article presents the materials on biometric analysis of treatment of casualties with combined trauma in the program of multi-staged surgical treatment.Results. In the process of multi-staged surgical treatment, we can determine the prognosis, the nature of the traumatic disease with the help of a biometric comprehensive study that will optimize comprehensive treatment program and rehabilitation of casualties in the trauma center of the 1st level.Conclusion. The conditions for the application of the full spectrum of surgical interventions in the maxillofacial region in the acute period of traumatic disease are created in favorable prognosis. The choice of treatment in favorable or unfavorable prognosis is a multistaged surgical tactics of treatment – Damage control surgery.

The purpose of this study is to examine the interaction effect between online Cause-Related Marketing (CRM) advertisements and brand reputation on consumers' brand evaluation in the hotel industry. Two experimental studies were conducted: (1) a survey-based experiment that demonstrates the moderating role of brand reputation, and (2) a laboratory experiment using psychophysiological measurements of emotional reactions that additionally examines the impact of consumers' emotional responses to CRM advertisements on hotel's pro-social initiatives, thereby complementing Study I's findings. [ABSTRACT FROM AUTHOR]

With the increasing demands of the remote surveillance system, the gait based personal identification research has obtained more and more attention from biometric recognition researchers. The gait sequence is easier to be affected by factors than other biometric feathers. In order to achieve better performance of the gait based identification system, in the paper, a local discriminant gait recognition method is proposed by integrating weighted adaptive center symmetric local binary pattern (WACS-LBP) with local linear discriminate projection (LLDP). The proposed method consists of two stages. In the first stage, the robust local weighted histogram feature vector is extracted from each gait image by WACS-LBP. In the second stage, the dimensionality of the extracted feature vector is reduced by LLDP. The highlights of the proposed method are (1) the extracted feature is robust to rotation invariant, and is also tolerant to illumination and pose changes; (2) the low dimensional feature vector reduced by LLDP can preserve the discriminating ability; and (3) the small-sample-size (SSS) problem is avoided naturally. The proposed method is validated and compared with the existing algorithms on a public gait database. The experimental results show that the proposed method is not only effective, but also can be clearly interpreted. [ABSTRACT FROM AUTHOR]

Ear recognition is an emerging research area in image‐based biometrics. The commercial availability of lenslet light field cameras able to capture full spatio‐angular information has brought momentum to biometric and forensic research exploiting this new type of imaging sensors. This study is the first to consider the usage of light field cameras for ear recognition, proposing both an appropriate content database and an ear recognition solution. The novel Lenslet Light Field Ear DataBase (LLFEDB) includes 536 light field images corresponding to four different poses, from 67 subjects, captured with a Lytro ILLUM lenslet light field camera. The LLFEDB includes critical cases such as ear images partly occluded by ear piercing, earing, hair and combination of occlusions. The novel light field‐based ear recognition solution proposed exploits the richer spatio‐angular information available in the light field images. A comparative performance evaluation study using the LLFEDB, and focusing on the most recent light field and non‐light field based descriptors for ear recognition, shows a very promising performance of the proposed descriptor, outperforming all the assessed descriptors. [ABSTRACT FROM AUTHOR]

Meta-analysis of diagnostic studies is still a rapidly developing area of biostatistical research. Especially, there is an increasing interest in methods to compare different diagnostic tests to a common gold standard. Restricting to the case of two diagnostic tests, in these meta-analyses the parameters of interest are the differences of sensitivities and specificities (with their corresponding confidence intervals) between the two diagnostic tests while accounting for the various associations across single studies and between the two tests. We propose statistical models with a quadrivariate response (where sensitivity of test 1, specificity of test 1, sensitivity of test 2, and specificity of test 2 are the four responses) as a sensible approach to this task. Using a quadrivariate generalized linear mixed model naturally generalizes the common standard bivariate model of meta-analysis for a single diagnostic test. If information on several thresholds of the tests is available, the quadrivariate model can be further generalized to yield a comparison of full receiver operating characteristic (ROC) curves. We illustrate our model by an example where two screening methods for the diagnosis of type 2 diabetes are compared. [ABSTRACT FROM AUTHOR]

Most future industrial and organizational (I-O) psychology practitioners and researchers initially enroll in an introductory I-O psychology course during their junior or senior year of undergraduate studies, making introductory textbooks their first in-depth exposure to the field and an important knowledge base. We reviewed and analyzed the 6,654 unique items (e.g., journal articles, book chapters) published in 1,682 unique sources (e.g., scholarly journals, edited books, popular press publications) and authored by 8,603 unique individuals cited in six popular I-O psychology textbooks. Results showed that 39% of the top-cited sources are not traditional academic peer-reviewed journals, 77% of the top-cited articles were published in cross-disciplinary journals, and 58% of the top-cited authors are affiliated with business schools and not psychology departments. These results suggest that the science–practice divide in I-O psychology may develop later—perhaps after graduates obtain employment as either practitioners or researchers. Also, results suggest I-O psychology is closer to business and management than social psychology and psychology in general. We discuss additional implications for the science–practice divide, how to define and measure scholarly impact, and the future of I-O psychology as a field, including the movement of I-O psychologists to business schools and the sustainability of I-O psychology programs in psychology departments. [ABSTRACT FROM PUBLISHER]

Biometrics, unique measurable physiological and behavioral characteristics, are used to identify individuals in a variety of scenarios, including forensic investigations. However, data on the longevity of these indicators are incomplete. This study demonstrated that iris and fingerprint biometric data can be obtained up to four days postmortem in warmer seasons and 50 + days in the winter. It has been generally believed, but never studied, that iris recognition is only obtainable within the first 24 hours after death. However, this study showed that they remain viable for longer (2-34 days) depending upon the environmental conditions. Temperature, precipitation, insects, and scavenger activity were the primary factors affecting the retention of biometrics in decomposing human remains. While this study is an initial step in determining the utility of physiological biometrics across postmortem time, biometric research has the potential to make important contributions to human identification and the law enforcement, military, and medicolegal communities. [ABSTRACT FROM AUTHOR]

We used 39 morphological and anatomical needle traits in the biometric comparisonsAbies nebrodensiswithA. albaandA. cephalonica. The multivariate analyses were utilised and a closer relationship ofA. nebrodensistoA. cephalonicathan toA. albawas detected, in contrast to what has been shown for cone characteristics. [ABSTRACT FROM PUBLISHER]

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out. [ABSTRACT FROM AUTHOR]

Through the HUMMINGBIRD framework outlined here, we seek to encourage a novel multidisciplinary approach to biometric analysis with the goal of enhancing both understanding and accuracy of identification. [ABSTRACT FROM AUTHOR]

In phase II and/or III clinical trial study, there are several competing treatments, the goal is to assess the performances of the treatments at the end of the study, the trial design aims to minimize risks to the patients in the trial, according to some given allocation optimality criterion. Recently, a new type of clinical trial, the staggered-start trial has been proposed in some studies, in which different treatments enter the same trial at different times. Some basic questions for this trial are whether optimality can still be kept? under what conditions? and if so how to allocate the the coming patients to treatments to achieve such optimality? Here we propose and study a class of adaptive designs of staggered-start clinical trials, in which for given optimality criterion object, we show that as long as the initial sizes at the beginning of the successive trials are not too large relative to the total sample size, the proposed design can still achieve optimality criterion asymptotically for the allocation proportions as the ordinary trials; if these initial sample sizes have about the same magnitude as the total sample size, full optimality cannot be achieved. The proposed method is simple to use and is illustrated with several examples and a simulation study. [ABSTRACT FROM AUTHOR]

Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild . The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem. As expected, the achieved results are slightly worse, roughly 8 percentage points lower, than those obtained by state-of-the-art facial GC, but they suggest the validity of the periocular area particularly in difficult scenarios where the whole face is not visible, or has been altered. A final experiment combines in a multi-scale approach features extracted from the periocular, face and head and shoulders areas, fusing them in a two stage ensemble of classifiers. The accuracy reported beats any previous results on the difficult The Images of Groups dataset, reaching 92.46%, with a GC error reduction of almost 20% compared to the best face based GC results in the literature. [ABSTRACT FROM AUTHOR]

Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Mitchell Trauring in the journal Nature in 1963. The first objective of this paper is to document the significant progress that has been achieved in the field of biometric recognition in the past 50 years since Trauring’s landmark paper. This progress has enabled current state-of-the-art biometric systems to accurately recognize individuals based on biometric trait(s) acquired under controlled environmental conditions from cooperative users. Despite this progress, a number of challenging issues continue to inhibit the full potential of biometrics to automatically recognize humans. The second objective of this paper is to enlist such challenges, analyze the solutions proposed to overcome them, and highlight the research opportunities in this field. One of the foremost challenges is the design of robust algorithms for representing and matching biometric samples obtained from uncooperative subjects under unconstrained environmental conditions (e.g., recognizing faces in a crowd). In addition, fundamental questions such as the distinctiveness and persistence of biometric traits need greater attention. Problems related to the security of biometric data and robustness of the biometric system against spoofing and obfuscation attacks, also remain unsolved. Finally, larger system-level issues like usability, user privacy concerns, integration with the end application, and return on investment have not been adequately addressed. Unlocking the full potential of biometrics through inter-disciplinary research in the above areas will not only lead to widespread adoption of this promising technology, but will also result in wider user acceptance and societal impact. [ABSTRACT FROM AUTHOR]

When evaluating the usefulness of clinical information for the diagnosis of disease, multiple raters provide a diagnosis for the same set of data. These ratings provide important insights into the performance of the diagnosis, determining the accuracy of each rater's diagnosis compared to the truth standard and the level of agreement among the raters. We demonstrate that the intraclass correlation coefficient (ICC) is dependent on the sensitivities and specificities of the raters involved in the study. Given the sensitivity and specificity of any number of raters, along with the prevalence of disease, the expected ICC can be determined. [ABSTRACT FROM AUTHOR]

Objective: To develop customized biometric charts to better define abnormal fetal growth.Methods: A total of 1056 singleton fetuses from the Raine Study underwent serial ultrasound biometry (abdominal circumference [AC], head circumference, and femur length) at 18, 24, 28, 34, and 38 weeks' gestation. Customized biometry trajectories were developed adjusting for epidemiological influences upon fetal biometry using covariates available at 18 weeks gestation. Prediction accuracy (areas under the receiver operating characteristic curve [AUC] and 95% confidence interval [95%CI]) was evaluated by repeated random sub-sampling cross-validation methodology.Results: The model for derived estimated fetal weight (EFW) performed well for EFW less than 10th predicted percentile (AUC = 0.695, 95%CI, 0.692-0.699) and EFW greater than 90th predicted percentile (AUC = 0.705, 95%CI, 0.702-0.708). Fetal AC was also well predicted for growth restriction (AUC = 0.789, 95%CI, 0.784-0.794) and macrosomia (AUC = 0.796, 95%CI, 0.793-0.799). Population-derived, sex-specific charts misclassified 7.9% of small fetuses and 10.7% of large fetuses as normal. Conversely, 9.2% of those classified as abnormally grown by population-derived charts were considered normal by customized charts, potentially leading to complications of unnecessary intervention.Conclusions: Customized fetal biometric charts may offer improved ability for clinicians to detect deviations from optimal fetal growth and influence pregnancy management. [ABSTRACT FROM AUTHOR]

Intrauterine growth restriction (IUGR) is a condition which has been difficult to assess at an early stage, resulting in the delivery of children who have poor genetic growth potential. Currently, IUGR classification is based upon the system of ultrasound biometry. Doppler velocimetry allows the measurement of hemodynamic flow of major fetal vessels, comparing the flow indices and patterns of normal and IUGR cases. In this review, the effectiveness of Doppler velocimetry in assessing blood flow in major vessels including the umbilical artery, ductus venosus, and middle cerebral artery was studied for both diagnostic and prognostic screening of IUGR. The umbilical artery is the most frequently studied vessel in Doppler velocimetry due to its accessibility and the strength of its associations with fetal outcomes. Abnormalities in the ductus venosus waveform can be indicative of increased resistance in the right atrium due to placental abnormalities. The middle cerebral artery is the most studied fetal cerebral artery and can detect cerebral blood flow and direction, which is why these three vessels were selected to be examined in this context. A potential mathematical model could be developed to incorporate these Doppler measurements which are indicative of IUGR, in order to reduce perinatal mortality. The purpose of the proposed algorithm is to integrate Doppler velocimetry with biophysical profiling in order to determine the optimal timing of delivery, thus reducing the risks of adverse perinatal outcomes. [ABSTRACT FROM AUTHOR]

In (Kingma [2010]), I argued that Boorse's ([1977], [1997]) biostatistical theory of health (BST) is unable to accommodate diseases that are the normal result of harmful environments. Hausman ([2011]) disagrees: if the BST compares normal dispositional function against the whole population or reference class, rather than against organisms in similar circumstances as I proposed, then my ([2010]) challenge can be avoided. In this paper, I argue that Hausman's response fails: his proposal cannot accommodate a series of common physiological processes, such as sleep and those involved in reproduction. In the course of this argument, I also offer a detailed discussion of the concept of functional efficiency (Hausman [2011], [2012]), and reveal a link between the problem of environments (Kingma [2010]) and that of reference classes (Kingma [2007]). [ABSTRACT FROM AUTHOR]

Objectives: To compare translabial three-dimensional (3D) ultrasound with magnetic resonance imaging (MRI) for the measurement of levator hiatal biometry at rest in women with pelvic organ prolapse, and to determine the interobserver reliability between two independent observers for ultrasound and MRI measurements.Methods: Data were derived from a multicenter prospective cohort study in which women scheduled for conventional anterior colporrhaphy underwent translabial 3D ultrasound and MRI prior to surgery. Intraclass correlation coefficients (ICCs) were calculated to estimate interobserver reliability between two independent observers and determine the agreement between ultrasound and MRI measurements. Bland-Altman plots were created to assess the agreement between ultrasound and MRI measurements.Results: Data from 139 women from nine hospitals were included in the study. The interobserver reliability of ultrasound assessment at rest, during Valsalva maneuver and during contraction and of MRI assessment at rest were moderate or good. The agreement between ultrasound and MRI for the measurement of levator hiatal biometry at rest was moderate, with ICCs of 0.52 (95%CI, 0.32-0.66) for levator hiatal area, 0.44 (95%CI, 0.21-0.60) for anteroposterior diameter and 0.44 (95%CI, 0.22-0.60) for transverse diameter. Levator hiatal biometry measurements were statistically significantly larger on MRI than on translabial 3D ultrasound.Conclusions: The agreement between translabial 3D ultrasound and MRI for measurement of the levator hiatus at rest in women with pelvic organ prolapse was only moderate. The results of translabial 3D ultrasound and MRI should therefore not be used interchangeably in daily practice or in clinical research. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Objective: To construct a reference range for a new vertical measurement of the fetal head and to assess whether its combination with fetal head circumference (HC) can prevent the misdiagnosis of microcephaly in fetuses with an acrocephalic-like head deformation.Methods: A new vertical cranial biometric measurement was defined: the foramen magnum-to-cranium distance (FCD), measured between the foramen magnum and the upper inner cranial border along the posterior wall of the brainstem. The measurement was performed in a precise mid-sagittal plane using a three-dimensional multiplanar display of a sagittally acquired sonographic volume of the fetal head. The normal reference range was developed by measuring 396 healthy fetuses of low-risk singleton pregnancies between 15 and 40 gestational weeks. This reference was applied to 25 fetuses with microcephaly diagnosed prenatally (Fmic) based on HC ≥ 3 SD below the mean for gestational age. We determined an optimal FCD cut-off for combination with HC to detect all cases found with microcephaly at birth (micB), while excluding the fetuses with normal head circumference at birth (NHCB), who were described postnatally as having an acrocephalic-like cranial deformation.Results: In the healthy singleton fetuses, FCD increased with gestational age, with a quadratic equation providing an optimal fit to the data (adjusted R(2) = 0.934). The measurement could be assessed in 95.2% of cases. Of the 25 cases diagnosed with Fmic prenatally, on the basis of HC alone, 14 were micB and 11 were NHCB. We observed FCD below the mean - 2SD for gestational age in all 14 micB cases, but in only four of the 11 NHCB cases (P < 0.003). An acrocephalic-like cranial deformation was described at birth in five of the seven NHCB cases with normal FCD. The mean ± SD FCD Z-score of the micB cases was significantly lower (P < 0.001) than that of the false-positive ones: -3.85 ± 0.96 SD and -1.59 ± 1.45 SD, respectively. Based on HC measurement alone, the positive predictive value (PPV) was 56%. Combination of the HC and FCD criteria raised the PPV to 78%, decreasing the number of false positives from 11 to four, without missing any of the 14 micB cases.Conclusions: Fetal vertical cranial biometric assessment in the mid-sagittal plane is feasible and correlates well with gestational age. In our series, a vertical cranial deformation was a frequent cause of a false Fmic diagnosis made on the basis of HC alone. Combination of the new vertical cranial biometric measurement with HC measurement can exclude these cases and thus improve diagnostic accuracy for Fmic. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

We propose a new approach for sparse regression and marginal testing, for data with correlated features. Our procedure first clusters the features, and then chooses as the cluster prototype the most informative feature in that cluster. Then we apply either sparse regression (lasso) or marginal significance testing to these prototypes. While this kind of strategy is not entirely new, a key feature of our proposal is its use of the post-selection inference theory of Taylor and others (2014, Exact post-selection inference for forward stepwise and least angle regression, Preprint, arXiv:1401.3889) and Lee and others (2014, Exact post-selection inference with the lasso, Preprint, arXiv:1311.6238v5) to compute exact [Formula: see text]-values and confidence intervals that properly account for the selection of prototypes. We also apply the recent "knockoff" idea of Barber and Candès (2014, Controlling the false discovery rate via knockoffs, Preprint, arXiv:1404.5609) to provide exact finite sample control of the FDR of our regression procedure. We illustrate our proposals on both real and simulated data. [ABSTRACT FROM AUTHOR]

We propose a spatial Bayesian variable selection method for detecting blood oxygenation level dependent activation in functional magnetic resonance imaging (fMRI) data. Typical fMRI experiments generate large datasets that exhibit complex spatial and temporal dependence. Fitting a full statistical model to such data can be so computationally burdensome that many practitioners resort to fitting oversimplified models, which can lead to lower quality inference. We develop a full statistical model that permits efficient computation. Our approach eases the computational burden in two ways. We partition the brain into 3D parcels, and fit our model to the parcels in parallel. Voxel-level activation within each parcel is modeled as regressions located on a lattice. Regressors represent the magnitude of change in blood oxygenation in response to a stimulus, while a latent indicator for each regressor represents whether the change is zero or non-zero. A sparse spatial generalized linear mixed model captures the spatial dependence among indicator variables within a parcel and for a given stimulus. The sparse SGLMM permits considerably more efficient computation than does the spatial model typically employed in fMRI. Through simulation we show that our parcellation scheme performs well in various realistic scenarios. Importantly, indicator variables on the boundary between parcels do not exhibit edge effects. We conclude by applying our methodology to data from a task-based fMRI experiment. [ABSTRACT FROM AUTHOR]

For the analysis of longitudinal or repeated measures data, generalized linear mixed-effects models provide a flexible and powerful tool to deal with heterogeneity among subject response profiles. However, the typical statistical design adopted in usual randomized controlled trials is an analysis of covariance type analysis using a pre-defined pair of "pre-post" data, in which pre-(baseline) data are used as a covariate for adjustment together with other covariates. Then, the major design issue is to calculate the sample size or the number of subjects allocated to each treatment group. In this paper, we propose a new repeated measures design and sample size calculations combined with generalized linear mixed-effects models that depend not only on the number of subjects but on the number of repeated measures before and after randomization per subject used for the analysis. The main advantages of the proposed design combined with the generalized linear mixed-effects models are (1) it can easily handle missing data by applying the likelihood-based ignorable analyses under the missing at random assumption and (2) it may lead to a reduction in sample size, compared with the simple pre-post design. The proposed designs and the sample size calculations are illustrated with real data arising from randomized controlled trials. [ABSTRACT FROM AUTHOR]

In clustered designs such as family studies, the exposure-outcome association is usually confounded by both cluster-constant and cluster-varying confounders. The influence of cluster-constant confounders can be eliminated by studying the exposure-outcome association within (conditional on) clusters, but additional regression modeling is usually required to control for observed cluster-varying confounders. A problem is that the working regression model may be misspecified, in which case the estimated within-cluster association may be biased. To reduce sensitivity to model misspecification we propose to augment the standard working model for the outcome with an auxiliary working model for the exposure. We derive a doubly robust conditional generalized estimating equation (DRCGEE) estimator for the within-cluster association. This estimator combines the two models in such a way that it is consistent if either model is correct, not necessarily both. Thus, the DRCGEE estimator gives the researcher two chances instead of only one to make valid inference on the within-cluster association. We have implemented the estimator in an R package and we use it to examine the association between smoking during pregnancy and cognitive abilities in offspring, in a sample of siblings. [ABSTRACT FROM AUTHOR]

An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate (WW ) is a linear function of the unobserved true covariate (X) plus other covariates (Z) in the regression model. In this paper, we consider models for W that include interactions between X and Z. We derive the conditional distribution of X given W and Z and use it to extend the method of regression calibration to this class of measurement error models. We apply the model to dietary data and test whether self-reported dietary intake includes an interaction between true intake and body mass index. We also perform simulations to compare the model to simpler approximate calibration models. [ABSTRACT FROM AUTHOR]

Recurrent event data are commonly encountered in clinical and epidemiological studies. A major complication arises when recurrent events are terminated by death. To assess the overall effects of covariates on the two types of events, we define a weighted composite endpoint as the cumulative number of recurrent and terminal events properly weighted by the relative severity of each event. We propose a semiparametric proportional rates model which specifies that the (possibly time-varying) covariates have multiplicative effects on the rate function of the weighted composite endpoint while leaving the form of the rate function and the dependence among recurrent and terminal events completely unspecified. We construct appropriate estimators for the regression parameters and the cumulative frequency function. We show that the estimators are consistent and asymptotically normal with variances that can be consistently estimated. We also develop graphical and numerical procedures for checking the adequacy of the model. We then demonstrate the usefulness of the proposed methods in simulation studies. Finally, we provide an application to a major cardiovascular clinical trial. [ABSTRACT FROM AUTHOR]

We propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves. [ABSTRACT FROM AUTHOR]

This paper is motivated by a phase I-II clinical trial of a targeted agent for advanced solid tumors. We study a stylized version of this trial with the goal to determine optimal actions in each of two cycles of therapy. A design is presented that generalizes the decision-theoretic two-cycle design of Lee and others (2015. Bayesian dose-finding in two treatment cycles based on the joint utility of efficacy and toxicity. Journal of the American Statistical Association, to appear) to accommodate ordinal outcomes. Backward induction is used to jointly optimize the actions taken for each patient in each of the two cycles, with the second action accounting for the patient's cycle 1 dose and outcomes. A simulation study shows that simpler designs obtained by dichotomizing the ordinal outcomes either perform very similarly to the proposed design, or have much worse performance in some scenarios. We also compare the proposed design with the simpler approaches of optimizing the doses in each cycle separately, or ignoring the distinction between cycles 1 and 2. [ABSTRACT FROM AUTHOR]

High-throughput time course expression profiles have been available in the last decade due to developments in measurement techniques and devices. Functional data analysis, which treats smoothed curves instead of originally observed discrete data, is effective for the time course expression profiles in terms of dimension reduction, robustness, and applicability to data measured at small and irregularly spaced time points. However, the statistical method of differential analysis for time course expression profiles has not been well established. We propose a functional logistic model based on elastic net regularization (F-Logistic) in order to identify the genes with dynamic alterations in case/control study. We employ a mixed model as a smoothing method to obtain functional data; then F-Logistic is applied to time course profiles measured at small and irregularly spaced time points. We evaluate the performance of F-Logistic in comparison with another functional data approach, i.e. functional ANOVA test (F-ANOVA), by applying the methods to real and synthetic time course data sets. The real data sets consist of the time course gene expression profiles for long-term effects of recombinant interferon β on disease progression in multiple sclerosis. F-Logistic distinguishes dynamic alterations, which cannot be found by competitive approaches such as F-ANOVA, in case/control study based on time course expression profiles. F-Logistic is effective for time-dependent biomarker detection, diagnosis, and therapy. [ABSTRACT FROM AUTHOR]

Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. [ABSTRACT FROM AUTHOR]

In the genomic era, group association tests are of great interest. Due to the overwhelming number of individual genomic features, the power of testing for association of a single genomic feature at a time is often very small, as are the effect sizes for most features. Many methods have been proposed to test association of a trait with a group of features within a functional unit as a whole, e.g. all SNPs in a gene, yet few of these methods account for the fact that generally a substantial proportion of the features are not associated with the trait. In this paper, we propose to model the association for each feature in the group as a mixture of features with no association and features with non-zero associations to explicitly account for the possibility that a fraction of features may not be associated with the trait while other features in the group are. The feature-level associations are first estimated by generalized linear models; the sequence of these estimated associations is then modeled by a hidden Markov chain. To test for global association, we develop a modified likelihood ratio test based on a log-likelihood function that ignores higher order dependency plus a penalty term. We derive the asymptotic distribution of the likelihood ratio test under the null hypothesis. Furthermore, we obtain the posterior probability of association for each feature, which provides evidence of feature-level association and is useful for potential follow-up studies. In simulations and data application, we show that our proposed method performs well when compared with existing group association tests especially when there are only few features associated with the outcome. [ABSTRACT FROM AUTHOR]

Meta-analysis plays an important role in summarizing and synthesizing scientific evidence derived from multiple studies. With high-dimensional data, the incorporation of variable selection into meta-analysis improves model interpretation and prediction. Existing variable selection methods require direct access to raw data, which may not be available in practical situations. We propose a new approach, sparse meta-analysis (SMA), in which variable selection for meta-analysis is based solely on summary statistics and the effect sizes of each covariate are allowed to vary among studies. We show that the SMA enjoys the oracle property if the estimated covariance matrix of the parameter estimators from each study is available. We also show that our approach achieves selection consistency and estimation consistency even when summary statistics include only the variance estimators or no variance/covariance information at all. Simulation studies and applications to high-throughput genomics studies demonstrate the usefulness of our approach. [ABSTRACT FROM AUTHOR]

Semiparametric methods are well established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However, often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings, unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem, we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials. [ABSTRACT FROM AUTHOR]

Many hormones, including stress hormones, are intermittently secreted as pulses. The pulsatile location process, describing times when pulses occur, is a regulator of the entire stress system. Characterizing the pulse location process is particularly difficult because the pulse locations are latent; only hormone concentration at sampled times is observed. In addition, for stress hormones the process may change both over the day and relative to common external stimuli. This potentially results in clustering in pulse locations across subjects. Current approaches to characterizing the pulse location process do not capture subject-to-subject clustering in locations. Here we show how a Bayesian Cox cluster process may be adapted as a model of the pulse location process. We show that this novel model of pulse locations is capable of detecting circadian rhythms in pulse locations, clustering of pulse locations between subjects, and identifying exogenous controllers of pulse events. We integrate our pulse location process into a model of hormone concentration, the observed data. A spatial birth-and-death Markov chain Monte Carlo algorithm is used for estimation. We exhibit the strengths of this model on simulated data and adrenocorticotropic and cortisol data collected to study the stress axis in depressed and non-depressed women. [ABSTRACT FROM AUTHOR]

The combination of a pyrenyl tetraamine with an isophthaloyl spacer has led to two new water-soluble carbohydrate receptors ('synthetic lectins'). Both systems show outstanding affinities for derivatives of N-acetylglucosamine (GlcNAc) in aqueous solution. One receptor binds the methyl glycoside GlcNAc-β-OMe with Ka≈20 000 m−1, whereas the other one binds an O-GlcNAcylated peptide with Ka≈70 000 m−1. These values substantially exceed those usually measured for GlcNAc-binding lectins. Slow exchange on the NMR timescale enabled structural determinations for several complexes. As expected, the carbohydrate units are sandwiched between the pyrenes, with the alkoxy and NHAc groups emerging at the sides. The high affinity of the GlcNAcyl-peptide complex can be explained by extra-cavity interactions, raising the possibility of a family of complementary receptors for O-GlcNAc in different contexts. [ABSTRACT FROM AUTHOR]

Background: Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes. Results: Simulation studies indicated that the proposed network difference measure (NetDifM) was stable and outperformed other methods existed, under various sample sizes and network topology structure. One application to real data about GWAS of leprosy successfully identified the specific gene interaction network contributing to leprosy. For additional gene expression data of ovarian cancer, two candidate subnetworks, PI3K-AKT and Notch signaling pathways, were considered and identified respectively. Conclusions: The proposed method, accounting for the vertex changes and edge changes simultaneously, is valid and powerful to capture the group difference of biological networks. [ABSTRACT FROM AUTHOR]

Objective: The process of getting the abstracts published as journal articles is an exhaustive work due to the collection and analysis of clinical information, meticulously preparing manuscripts, statistical analysis of data, and subsequent critical peer review process. We investigate publication rate of abstracts presented at national and international statistics congresses in present study. For this purpose we considered abstracts presented at 5th International Conference of the ERCIM Working Group on Computing&Statistics (ERCIM 2012) and 14th National Biostatistics Congress of Turkey in 2012. Material and Methods: We invited participants who had participated ERCIM 2012 and 14th National Biostatistics Congress to our study by sending e-mail to their email adresses that obtain from abstract books. Results: The present analysis found that 19.40% of abstracts presented at 14th National Biostatistics Congress and 51.90% of abstracts presented at ERCIM 2012 were published as full peer-reviewed journal articles. Percentage of presentations was not sent to any journal to publish for 14th National Biostatistics Congress (51.60%) is higher than ERCIM 2012 (18.50%). In addition, percentage of presentation was accepted to be published in the journal for 14th National Biostatistics Congress (19.40%) is lower than ERCIM 2012 (51.90%). Conclusion: Even if all of the presentations presented at the congress is not published, importance of congress is undeniable by the reason of scientific-social communications made at congress and meetings where new ideas are born. [ABSTRACT FROM AUTHOR]

The significant advantages that biometric recognition technologies offer are in danger of being left aside in everyday life due to concerns over the misuse of such data. The biometric data employed so far focuses on the permanence of the characteristics involved. A concept known as 'the right to be forgotten' is gaining momentum in international law and this should further hamper the adoption of permanent biometric recognition technologies. However, a multitude of common applications are short-term and, therefore, non-permanent biometric characteristics would suffice for them. In this paper we discuss 'transient biometrics,' i.e. recognition via biometric characteristics that will change in the short term and show that images of the fingernail plate can be used as a transient biometric with a useful life-span of less than 6 months. A direct approach is proposed that requires no training and a relevant evaluation dataset is made publicly available. [ABSTRACT FROM AUTHOR]

Background: In order to address and support biostatistics for health research, the Health Sciences Research Office of the University of the Witwatersrand sought to introduce training in biomedical statistics to sustain research and postgraduate education. The experiences encountered in setting up such statistical support in a limited resource, developing country are discussed here. Methods: Two cross-sectional surveys (a) statistical needs assessment (2009) and (b) feedback (2010-11) on the statistical support through biostatistics courses and consultations were conducted. These surveys were supplemented with information such as graduations, research publication output and costs of setting up the support. Results: Seventy-three percent of respondents favoured short courses with "hands-on" practice. Eighty-nine percent agreed that these courses should be run and coordinated by the Health Sciences Research Office instead of the departments. There was use of varied statistical packages requiring one package for standardised support. The numbers of postgraduate students attending short courses in statistics increased from 2010 to 2012 as did the numbers attending statistical consultations. Graduations and publication outputs increased over this period of time although this may not be directly linked solely to the biostatistical support system introduced. Conclusions: There is a distinct need for biostatistics training in developing countries and the process described in this study could be replicated in any health sciences institution, especially in a resource-restricted environment. [ABSTRACT FROM AUTHOR]

Background: During total knee arthroplasty (TKA), excision of the popliteus tendon leads to extensive static gaps and reduced mobility. The purpose of this study was to determine the positional relationship between the femoral insertion of the popliteus tendon and the bone cutting lines of various TKA systems.Methods: This study included 21 cadaveric right femurs presenting no macroscopic deformity. The lateral image of the femur and the template of the femoral component were overlaid to determine the preservation/excision of the popliteus tendon insertion. TKA systems used were Genesis II, NexGen, low contact stress (LCS), PFC Σ, Scorpio, and Vanguard. The knees in which the insertion was preserved in all implants or excised in at least one implant were classified into intact or the high-risk groups, respectively.Results: The popliteus tendon was preserved in all specimens with the LCS system. In contrast, the popliteus tendon insertion was excised in ≥1/3 of the specimens with the other systems. The anteroposterior diameter was significantly larger in the intact group than that in the high-risk group (58.1 ± 4.5 mm vs. 53.7 ± 2.7 mm; p = 0.018). The high-risk group included more knees from female cadavers than the intact group (70 vs. 9 %; p = 0.008).Conclusion: During primary TKA, the femoral insertion of the popliteus tendon could be inevitably excised, regardless of technical problems. We demonstrate that the unique design of the LCS system preserves the popliteus tendon insertion. In addition, small knees and females may be risk factors for excision of the insertion. [ABSTRACT FROM AUTHOR]

The compiled data for this study represents the first Atlantic and Mediterranean-wide effort to pool all available biometric data for Atlantic bluefin tuna (Thunnus thynnus) with the collaboration of many countries and scientific groups. Biometric relationships were based on an extensive sampling (over 140,000 fish sampled), covering most of the fishing areas for this species in the North Atlantic Ocean and Mediterranean Sea. Sensitivity analyses were carried out to evaluate the representativeness of sampling and explore the most adequate procedure to fit the weight-length relationship (WLR). The selected model for the WLRs by stock included standardized data series (common measurement types) weighted by the inverse variability. There was little difference between annual stock-specific round weight-straight fork length relationships, with an overall difference of 6% in weight. The predicted weight by month was estimated as an additional component in the exponent of the weight-length function. The analyses of monthly variations of fish condition by stock, maturity state and geographic area reflect annual cycles of spawning and feeding behavior. We update and improve upon the biometric relationships for bluefin currently used by the International Commission for the Conservation of Atlantic Tunas, by incorporating substantially larger datasets than ever previously compiled, providing complete documentation of sources and employing robust statistical fitting. WLRs and other conversion factors estimated in this study differ from the ones used in previous bluefin stock assessments. [ABSTRACT FROM AUTHOR]

The gross biometric and histological study was conducted on the ovaries of 30 Jaffrabadi buffaloes collected fresh from the local abattoir. The average weight of left and right ovaries was 4.08 ± 0.38 and 4.32 ± 0.26 g; average length 3.89 ± 0.13 and 4.14 ± 0.18 cm; average width 1.56 ± 0.06 and 1.62 ± 0.09 cm, and average thickness was 1.82 ± 0.08 and 1.90 ± 0.06 cm, respectively. There was no significant (P >0.05) difference in various gross biometric observations of left and right ovaries. The stroma of the ovary had distinct cortex and medulla. The ovarian surface was lined by simple cuboidal epithelium. The average height of epithelium was 4.26 ± 0.33 μm. The ovarian follicles were observed in different developmental stages distributed in the cortex. The average diameter of primordial, primary and secondary follicles was recorded as 26.37 ± 0.84 μm, 38.92 ± 0.66 μm and 74.25 ± 0.52 μm, respectively. The thickness of zona pellucida was 7.68 ± 0.37 μm and 6.40 ± 0.22 μm in secondary and tertiary follicles, respectively. [ABSTRACT FROM AUTHOR]

Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large. [ABSTRACT FROM AUTHOR]

Iris segmentation is the first and critical step in an iris recognition system. A robust iris segmentation algorithm based on the self-adaptive Chan and Vese (SACV) level set model is proposed. First, the process of constructing the SACV model based on analyses of the corresponding requirements for the CV model is described when it is applied on iris segmentation. Second, the coarse segmentation of pupil and iris, which are localized based on image pixel gray information, is used as the initial contour of the SACV model. Third, the interference factors, such as eyelashes and eyelids, are detected and evaluated simultaneously to generate the interference degree and then the related parameter of SACV is set according to the interference degree. Finally, SACV is used to conduct the final fine segmentation of the pupil and iris. Experiments on four public iris image databases (e.g., CASIA-V1, CASIA-V3 Interval, CASIA-V3 Lamp, and MMU-V1) demonstrate the segmentation accuracy performance of the proposed algorithm, and at the same time, the proposed algorithm also displays robust performance in noisy situations, such as Gaussian, Poisson, salt-and-pepper, and speckle noises. Moreover, comparisons with the well-known methods further show that our algorithm can segment iris images more accurately. [ABSTRACT FROM AUTHOR]

The present study records Halophila baillonis and describes the specimens of the Environmental Protection Area of the Parnaíba River Delta, Piauí, semiarid coast of Brazil. In August 2014, we mapped and characterized the seagrass meadow, and georeferenced the extent of H. baillonis patches within the area. Ten ramets were collected manually for biometric measurements, and five samples were collected using a PVC corer of 10 cm in diameter, inserted at 15 cm depth, to obtain averages of shoot density and belowground/aboveground biomass ratios. Apart from H. baillonis, we recorded Halodule wrightii, Halodule sp. and Halophila decipiens, characterizing the seagrass meadow of Cajueiro da Praia as the most diverse on the Brazilian coast. Average density was between 96 and 1401 shoots m-2; total biomass ranged from 14.3 g dw m-2 to 27.1 g dw m-2; and leaf size 7.8 to 23.8 mm. In general, density, biometric and biomass values of the plants were greater than or at the upper range described in other areas, co-occurring with other seagrass species and on a variety of sediments, indicating a well-established bed. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Correlations between solar radiation, sea surface temperature (SST), biometry, and population density were assessed along a wide latitudinal gradient in the non-zooxanthellate solitary coral Caryophyllia inornata. Biometric parameters were more strongly correlated with temperature than with solar radiation as in previous studies on Mediterranean solitary corals. With increasing SST, populations were characterized by bigger polyps, and consequently by a higher percent area coverage and mass per square meter. Population abundance was not correlated with SST, similarly to the non-zooxanthellate solitary Leptopsammia pruvoti. Instead, the population density of the zooxanthellate solitary Balanophyllia europaea decreases with increasing SST. When extrapolating the relationships between biological parameters of the three species and SST, according to the projected seawater temperature increase, a higher tolerance to temperature for non-zooxanthellate species seems to be confirmed. [ABSTRACT FROM AUTHOR]