ADVANTAGES OF THE SAME-DIFFERENT METHOD OVER THE TRIANGULAR METHOD FOR THE MEASUREMENT OF TASTE DISCRIMINATION

The triangular method, the same-different rating method, and the standard same-different method were each used with 10 tasters to obtain estimates of the discriminability of two concentrations of a colorless beverage. There were no significant differences in d', an index of discriminability, among the three tasks. In the light of this parity, a choice amongst the methods should be driven by practical considerations. When receiver operating characteristics (ROCs) were fitted to the rating data, the model underlying published tables of d' was strongly supported, so that discriminability can be assessed from same-different data either by entering tables or by fitting ROC curves. Same-different methods appear more acceptable to tasters than the triangular method.     

Malignant cell detection and cervical cancer screening

A study was conducted to determine the isolated, single-cell detection characteristics of human observers as this information relates to cervical cancer screening. Two interrelated experiments were performed. First, the receiver operating characteristic (ROC) was obtained for slide screening. In this experiment, approximately 1,200 slides were examined. Second, ROCs were obtained for human observer cell discrimination, using a rating method. An individual's curves were computed, assuming a multiple decision criterion. In this experiment, 6,375 cells from the same specimens used in the slide screening experiment were studied. In both experiments, results were analyzed using a Gaussian signal-detection model. This approach provided analytical detection criteria and rigorous definition of ROCs. These experiments addressed the problem of where the screening information lies: (1) in individual cells alone or (2) with additional components in global or other a priori information. We quantified the detection requirements of (1) the Papanicolaou smear screening process, Az = 0.99, and (2) the capabilities of trained cytotechnologists on isolated single cells, Az = 0.87. System modeling using intermediate cell detection instead of "rare event" detection resulted in a reduction of the predicted number of cells required for analysis from approximately 60,000 to 750.     

Estimating haplotype frequencies and standard errors for multiple single nucleotide polymorphisms

Estimating haplotype frequencies becomes increasingly important in the mapping of complex disease genes, as millions of single nucleotide polymorphisms (SNPs) are being identified and genotyped. When genotypes at multiple SNP loci are gathered from unrelated individuals, haplotype frequencies can be accurately estimated using expectation-maximization (EM) algorithms (Excoffier and Slatkin, 1995; Hawley and Kidd, 1995; Long et al., 1995), with standard errors estimated using bootstraps. However, because the number of possible haplotypes increases exponentially with the number of SNPs, handling data with a large number of SNPs poses a computational challenge for the EM methods and for other haplotype inference methods. To solve this problem, Niu and colleagues, in their Bayesian haplotype inference paper (Niu et al., 2002), introduced a computational algorithm called progressive ligation (PL). But their Bayesian method has a limitation on the number of subjects (no more than 100 subjects in the current implementation of the method). In this paper, we propose a new method in which we use the same likelihood formulation as in Excoffier and Slatkin's EM algorithm and apply the estimating equation idea and the PL computational algorithm with some modifications. Our proposed method can handle data sets with large number of SNPs as well as large numbers of subjects. Simultaneously, our method estimates standard errors efficiently, using the sandwich-estimate from the estimating equation, rather than the bootstrap method. Additionally, our method admits missing data and produces valid estimates of parameters and their standard errors under the assumption that the missing genotypes are missing at random in the sense defined by Rubin (1976).     

TWO MODELS FOR ESTIMATING THE DISCRIMINABILITY OF FOODS AND BEVERAGES

In order to evaluate the suitability of signal detection theory methods for assessing the discriminability of foods and beverages, the discriminability of two dairy milk products that differed in fat content was measured with two detection-theoretic methods: the single-interval rating method, and the same-different method. The nominal fat contents of the milk products were 0.1 and 1.6%. Measures of discriminability for three observers were derived by fitting receiver operating characteristics (ROCs) based on equal-variance normal models to the ratings of each observer with a procedure that combined jackknifing and maximum-likelihood estimation. The fitted ROCs provided a good fit to the data indicating that the equal-variance models were appropriate for these tasks. The best-fitting estimates of d' obtained for each task were not significantly different, demonstrating that d' is a measure of sensitivity that is largely independent of the task from which it is determined. However, estimates of proportion correct obtained for each task were shown to be significantly different.     

LABORATORY ERRORS ASSOCIATED WITH THE ESTIMATION OF THE POPULATION DENSITY OF HETERODERA SPECIES IN SOIL

As unexpectedly large sampling errors were obtained in preliminary population studies on Heterodera , an examination was made of the laboratory errors associated with sampling for H. göttingiana and H. schachtii. Soil samples were taken from microplots and appeared to be satisfactory, being without the usual heterogeneity found in sampling from fields. There was little evidence of errors introduced by the technique used for estimating the number of eggs and larvae in a subsample. These errors, when they occurred, were always small, and this standard technique was deemed satisfactory. The logarithmic transformation was suitable for statistical analysis of both cyst and egg counts.
Apart from the residual variation, there were two other important sources of variation when sampling for eggs. There was a large variation between sub-samples, caused by differences in the number of cysts and in the number of eggs per cyst. There was also in some instances a difference between observers counting the same eggs of up to 20%. Where a comparative measure of density is sufficient, this bias is probably unimportant, and to increase accuracy of estimates of eelworm density, most laboratory work should be devoted to the separation of cysts from the soil sample.     

TASTE THRESHOLDS, TASTE SENSITIVITY AND THE EFFECTS OF ADRENALECTOMY IN RATS

A number of behavioral experiments have attempted to measurethe absolute taste thresholds in the rat. Certain aspects ofthe procedures and methods of data analysis used in these experimentsare reviewed with the suggestions that: (1) Although comparisonshave commonly been made between the taste threshold values reportedin different experiments, these are not valid because of theabsence of a common definition of threshold. (2) The high thresholdmodel on which these experiments are based may be inappropriate.(3) It is possible to measure taste sensitivity rather thantaste thresholds, and (4) Had these points been considered inprevious experiments different conclusions might have resultedregarding the effects of adrenalectomy on NaCl sensitivity.     

A THURSTONIAN VARIANT OF THE BETA-BINOMIAL MODEL FOR REPLICATED DIFFERENCE TESTS

The beta-binomial model is combined with a Thurstonian psychometric function to obtain estimates of the parameters of a distribution applicable to replicated difference tests. A method of estimating the variance of d'obtained from these tests is provided. A formula for determining sample size, which is composed of the number of trials (or panelists) and the number of replications, to determine d'is also given.     

The analysis of cancer chemoprevention experiments

This paper is concerned with the analysis of certain cancer chemoprevention experiments that involve Type I censoring. In experiments of this nature, two common response variables are the number of induced cancers and the rate at which they develop. In this study we assume that the number of induced tumors and their times to detection are described by the Poisson and gamma distributions, respectively. Using the method of maximum likelihood, we discuss a procedure for estimating the parameters characterizing these two distributions. We apply standard techniques in order to construct a confidence region and conduct a hypothesis test concerning the parameters of interest. We discuss a method for comparing the effects of two different treatments using the likelihood ratio principle. A technique for isolating group differences in terms of the mean number of promoted tumors and the mean time to detection is described. Using the techniques developed in this paper, we reanalyze an existing data set in the cancer chemoprevention literature and obtain contrasting results.     

Semiparametric estimation of the covariate-specific ROC curve in presence of ignorable verification bias

Covariate-specific receiver operating characteristic (ROC) curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker, when the accuracy of the test is associated with certain covariates. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. In this article, we propose a semiparametric estimation of the covariate-specific ROC curves with a partial missing gold standard. A location-scale model is constructed for the test result to model the covariates' effect, but the residual distributions are left unspecified. Thus the baseline and link functions of the ROC curve both have flexible shapes. With the gold standard missing at random (MAR) assumption, we consider weighted estimating equations for the location-scale parameters, and weighted kernel estimating equations for the residual distributions. Three ROC curve estimators are proposed and compared, namely, imputation-based, inverse probability weighted, and doubly robust estimators. We derive the asymptotic normality of the estimated ROC curve, as well as the analytical form of the standard error estimator. The proposed method is motivated and applied to the data in an Alzheimer's disease research.     

Reaction time in the perceptual processing of taste quality

A series of experiments examined possible strategies human subjectsmight use in tasks requiring rapid detection or recognitionof a taste quality. In a reaction time (RT) paradigm, subjectswere to decide whether each of a series of stimuli flowed overthe tongue contained a previously designated target taste. Severaltasks of varying difficulty were used. The simplest task requiredsubjects to decide whether the target taste or water was presented.The most difficult task required discrimination between twodifferent target tastes in a series of mixtures formed by orthogonallycombining the target taste with two different irrelevant tastes.The speed at which subjects could detect and/or recognize targettastes was related to the RT for the particular taste. However,it was also clear that other variables, including the specificstimuli in the mixtures and the cognitive demands placed onthe subjects, influenced performance. These results suggestthat differences in taste onset time, as indexed by RT, canserve as a cue which subjects use to aid identification of singletastes in a mixture. It is concluded that the ease with whichsubjects can identify single tastes in a mixture is relatedto, among other variables, the differences in taste onset timebetween the tastes.     

Semi-parametric ROC regression analysis with placement values

Advances in technology provide new diagnostic tests for early detection of disease. Frequently, these tests have continuous outcomes. One popular method to summarize the accuracy of such a test is the Receiver Operating Characteristic (ROC) curve. Methods for estimating ROC curves have long been available. To examine covariate effects, Pepe (1997, 2000) and Alonzo and Pepe (2002) proposed distribution-free approaches based on a parametric regression model for the ROC curve. Cai and Pepe (2002) extended the parametric ROC regression model by allowing an arbitrary non-parametric baseline function. In this paper, while we follow the same semi-parametric setting as in that paper, we highlight a new estimator that offers several improvements over the earlier work: superior efficiency, the ability to estimate the covariate effects without estimating the non-parametric baseline function and easy implementation with standard software. The methodology is applied to a case control dataset where we evaluate the accuracy of the prostate-specific antigen as a biomarker for early detection of prostate cancer. Simulation studies suggest that the new estimator under the semi-parametric model, while always being more robust, has efficiency that is comparable to or better than the Alonzo and Pepe (2002) estimator from the parametric model.     

Role of phosphoinositol 4,5-bisphosphate and diacylglycerol in regulating native TRPC channel proteins in vascular smooth muscle

Stimulation of receptor-operated (ROCs) and store-operated (SOCs) Ca²⁺-permeable cation channels by vasoconstrictors has many important physiological functions in vascular smooth muscle. The present review indicates that ROCs and SOCs with diverse properties in different blood vessels are likely to be explained by composition of different subunits from the canonical transient receptor potential (TRPC) family of cation channel proteins. In addition we illustrate that activation of native TRPC ROCs and SOCs involves different phospholipase-mediated transduction pathways linked to generation of diacylglycerol (DAG). Moreover we describe recent novel data showing that the endogenous phospholipid phosphoinositol 4,5-bisphosphate (PIP₂) has profound and contrasting actions on TRPC ROCs and SOCs. Optimal activation of a native TRPC6 ROC by angiotensin II (Ang II) requires both depletion of PIP₂ and generation of DAG which leads to stimulation of TRPC6 via a PKC-independent mechanism. The data also indicate that PIP₂ has a marked constitutive inhibitory action of TRPC6 and DAG and PIP₂ are physiological antagonists on TRPC6 ROCs. In contrast PIP₂ stimulates TRPC1 SOCs and has an obligatory role in activation of these channels by store-depletion which requires PKC-dependent phosphorylation of TRPC1 proteins. Finally, we conclude that interactions between PIP₂ bound to TRPC proteins at rest, generation of DAG and PKC-dependent phosphorylation of TRPC proteins have a fundamental role in activation mechanisms of ROCs and SOCs in vascular smooth muscle.     

Empirical Bayes estimation of random effects parameters in mixed effects logistic regression models

We extend an approach for estimating random effects parameters under a random intercept and slope logistic regression model to include standard errors, thereby including confidence intervals. The procedure entails numerical integration to yield posterior empirical Bayes (EB) estimates of random effects parameters and their corresponding posterior standard errors. We incorporate an adjustment of the standard error due to Kass and Steffey (KS; 1989, Journal of the American Statistical Association 84, 717-726) to account for the variability in estimating the variance component of the random effects distribution. In assessing health care providers with respect to adult pneumonia mortality, comparisons are made with the penalized quasi-likelihood (PQL) approximation approach of Breslow and Clayton (1993, Journal of the American Statistical Association 88, 9-25) and a Bayesian approach. To make comparisons with an EB method previously reported in the literature, we apply these approaches to crossover trials data previously analyzed with the estimating equations EB approach of Waclawiw and Liang (1994, Statistics in Medicine 13, 541-551). We also perform simulations to compare the proposed KS and PQL approaches. These two approaches lead to EB estimates of random effects parameters with similar asymptotic bias. However, for many clusters with small cluster size, the proposed KS approach does better than the PQL procedures in terms of coverage of nominal 95% confidence intervals for random effects estimates. For large cluster sizes and a few clusters, the PQL approach performs better than the KS adjustment. These simulation results agree somewhat with those of the data analyses.     

Multiplication noise in the human visual system at threshold: 2. Probit estimation of parameters

A mathematical technique is described that relates detection model parameters to stimulus magnitude and experimental probability of detection. The normalizing transform is used to make the response statistics approximately Gaussian. Conventional probit analysis is then applied. From measurements at M stimulus levels, a system of M equations is solved and estimates of M unknown parameters of the detection model are obtained. The technique is applied to a threshold vision model based on additive and multiplicative Poisson noise. Results are obtained for the parameter estimates for individual subjects, and for the standard deviation of the estimates, for various values of the stimulus energy and number of trials. A frequency-of-seeing experiment is performed using a point-source stimulus that randomly assumes 3 energy levels with 200 trials per level. With a central efficiency of 50%, the estimated ocular quantum efficiency for our four subjects lies between 12% and 23%, the average dark count at the retina lies between 8 and 36 counts, and the threshold count for our (low falsereport rate) data lies between 11 and 32. The theoretical results reduce to those obtained by Barlow (J. Physiol. London 160, 155–168, 1962), in the absence of dark light and multiplication noise.This work was supported by the National Science Foundation     

THURSTONIAN MODELS AND VARIANCE II: EXPERIMENTAL CONFIRMATION OF THE EFFECTS OF VARIANCE ON THURSTONIAN MODELS OF SCALING

Subjects rated taste intensities using category scales under a variety of experimental protocols, which induced differences in computed values of d′. These were explained in a Thurstonian/signal detection context, by variation in the effects of adaptation changing perceived intensities (perceptual variance), by the effects of forgetting (memory variance) and by differences in the idiosyncratic ways that subjects use category‐rating scales (boundary variance).     

Taste performance on the anterior human tongue varles with fungiform taste bud density

This study demonstrated that individual diversities of tastesensitivity on the anterior tongue are due, in part, to variationsin fungiform taste bud density. Citric acid solutions were deliveredto a closed, spatially-matched (each test site was 43 mm²) flowchamber attached to the surface of the anterior tongue in 84subjects. A two-alternative forced choice, modified staircaseprocedure was used to derive a detection threshold value forcitric acid. The same session also included a visual analograting procedure to scale the taste intensity judgement of fiveconcentrations of citric acid. The taste buds within the chamberwere distinguished by methylene blue stain and recorded by videomicroscopy.The sip-and-spit method was used to contrast the spatially-matchedcondition with whole mouth stimulation. We found that detectionthreshold values were inversely related to the number of fungiformtaste buds, independent of gender or age. Whole-mouth thresholdvalues determined for each subject were always well below thespatially-matched threshold values. In addition, the data demonstratedthat subjects were able to scale the dynamic range of citricacid solutions. However, y-intercept approached zero intensityas the number of fungiform papillae decreased. The observeddifferences in citric acid sensitivity and fungiform taste buddensity indicated that taste performance on the anterior tonguein humans varies, in part, with the number of taste buds.     

Role of 4-hydroxylated estradiol in reducing Ca2+ uptake of uterine arterial smooth muscle cells through potential-sensitive channels

Entry of ionic Ca2+ into the vascular smooth muscle cell for contraction is thought to be mediated by two major membrane channels. The first are designated as potential-sensitive channels (PSCs), which are opened by membrane depolarization, and the second, as receptor-operated channels (ROCs), which are activated by alpha 1-receptor-ligand interactions. This study was designed to determine the presence of these 2 distinct populations of Ca2+ entry channels in smooth muscle cells of the uterine arteries in pigs. This was studied by measuring the baseline tone and contractile properties of uterine arteries in in vitro perfusion studies, as well as their specific Ca2+ uptakes. These parameters showed markedly different sensitivities towards two smooth muscle inhibitors used in this study: D-600 and amrinone. D-600 specifically inhibits uptake of extracellular Ca2+ through PSCs, while amrinone specifically inhibits Ca2+ uptake through ROCs. By choosing an appropriate concentration of D-600 or amrinone, Ca2+ uptake and contractions of uterine arterial segments induced by high-K+ (PSC activator) and phenylephrine (ROC activator) could be selectively inhibited. Furthermore, it was demonstrated that the blockade of Ca2+ uptake by D-600 and amrinone was additive, excluding the interpretation of a common Ca2+ pathway with two separate mechanisms for opening it. It was also determined that 4-hydroxylated estradiol (4OH-E2), a compound known to increase uterine blood flow in pigs, decreased Ca2+ uptake through the PSCs and exhibited no effect on ROCs. The presence of separate Ca2+ pathways that can be activated independently by agonists may indicate a refined system for controlling uterine blood flow.     

用BP神经网络基于氨基酸特性预测非同源蛋白质二级结构含量

依据蛋白质氨基酸特性,以氨基酸组成和有偏自协方差函数为特征矢量,用BP神经网络提出了一种预测非同源蛋白质中α螺旋和β折叠二级结构含量的计算方法。采用相互独立的非同源蛋白质数据库对该方法进行了检验。用Ponnuswamy值时,对二级结构α螺旋和β折叠含量的预测结果是;自检验平均绝对误差分别为0.069和0.065,相应标准偏差分别为0.044和0.047;他检验平均绝对误差分别为0.077和0.070,相应标准偏差分别为0.051和0.049。与仅以氨基酸组成为特征矢量的BP神经网络方法比较,相应的他检验平均绝对误差分别减小了0.024和0.016,标准偏差分别减小了0.031和0.018;与改进的多元线性回归方法比较,相应的他检验平均绝对误差分别减小了0.018和0.011,准偏差分别减小了0.020和0.012。表明：基于氨基酸组成和有偏自协方差函数为特征矢量的BP神经网络预测蛋白质二级结构含量的方法可有效提高预测精度。