EXPERTS VERSUS CONSUMERS: A CRITIQUE

In a recent paper Moskowitz (1996) refuted the notion that consumers are incapable of validly rating the sensory aspects of products. An analysis of this paper reveals that references are not presented to support his introduction, there is lack of experimental detail, parameters to compare panels were questionable, the degree of expertise of the expert panel is put to doubt and the model proposed to relate data between panels was not compared to existing models.     

Structure of trichomonads as revealed by scanning electron microscopy.

A scanning electron microscope (SEM) study of Hypotrichomonas acosta (Moskowitz), Trichomonas vaginalis Donné, Pentatrichomonas hominis (Davaine), and Tritrichomonas foetus (Riedmüller) provided new information about the structure of the periflagellar canal; emergence of the flagella from the cell body; structure of the undulating membrane; and position, shape, and size of the pelta. Of special interest were the spatial relationships of the attached part of the recurrent flagellum and the accessory filament in Hypotrichomonas and in the members of Trichomonadinae, i.e. Trichomonas and Pentatrichomonas.     

Structure of Trichomonads as Revealed by Scanning Electron Microscopy*†

SYNOPSIS.
A scanning electron microscope (SEM) study of Hypotrichomonas acosta (Moskowitz), Trichomonas vaginalis Donné. Pentatrichomonas hominis (Davaine), and Tritrichomonas foetus (Riedmüller) provided new information about the structure of the periflagellar canal: emergence of the flagella from the cell body; structure of the undulating membrane; and position, shape, and size of the pelta. Of special interest were the spatial relationships of the attached part of the recurrent flagellum and the accessory filament in Hypotrichomonas and in the members of Trichomonadinae, i.e. Trichomonas and Pentatrichomonas.     

Separation and properties of a red cell sensitizing substance from streptococci

Moskowitz, Merwin (Purdue University, Lafayette, Ind.). Separation and properties of a red cell sensitizing substance from streptococci. J. Bacteriol. 91:2200-2204. 1966.-An antigen that binds onto red cells and causes them to be agglutinated by antiserum was separated from streptococci. Various procedures to extract the antigen from streptococci were investigated, and the greatest amount of antigen was obtained by extraction of cells with a phenol-water mixture. The reaction of the antigen with red cells was shown to be reversible by use of the Ashby mixed agglutination technique. The antigen also combines with a number of different tissues, and it was demonstrated that the antigen could be transferred from red cells to tissues and vice versa. An hypothesis is presented on the basis of these findings which suggests a possible role for this antigen in the etiology of rheumatic fever.     

Stochastic modeling of the neuronal activity in the subthalamic nucleus and model parameter identification from Parkinson patient data

Several stochastic models, with various degrees of complexity, have been proposed to model the neuronal activity from different parts of the human brain. In this article, we use a simple Ornstein–Uhlenbeck process (OUP) to model the spike activity recorded from the subthalamic nucleus of patients suffering from Parkinson’s disease at the time of implantation of the electrodes for deep brain stimulation. From the recorded data, which contains information about the spike times of a single neuron, we identify and extract the model parameters of the OUP. We then use these parameters to numerically simulate the inter-spike intervals and the voltage across the neuron membrane. We finally assess how well the proposed mathematical model fits to the measured data and compare it with other commonly adopted stochastic models. We show an excellent agreement between the computer-generated data according to the OUP model and the measured one, as well as the superiority of the OUP model when compared to the Poisson process model and the random walk model; thus, establishing the validity of the OUP as a simple yet biologically plausible model of the neuronal activity recorded from the subthalamic nucleus of Parkinson’s disease patients.     

Using yeast to model calcium-related diseases: Example of the Hailey–Hailey disease

Cross-complementation studies offer the possibility to overcome limitations imposed by the inherent complexity of multicellular organisms in the study of human diseases, by taking advantage of simpler model organisms like the budding yeast Saccharomyces cerevisiae. This review deals with, (1) the use of S. cerevisiae as a model organism to study human diseases, (2) yeast-based screening systems for the detection of disease modifiers, (3) Hailey–Hailey as an example of a calcium-related disease, and (4) the presentation of a yeast-based model to search for chemical modifiers of Hailey–Hailey disease. The preliminary experimental data presented and discussed here show that it is possible to use yeast as a model system for Hailey–Hailey disease and suggest that in all likelihood, yeast has the potential to reveal candidate drugs for the treatment of this disorder. This article is part of a Special Issue entitled: Calcium signaling in health and disease. Guest Editors: Geert Bultynck, Jacques Haiech, Claus W. Heizmann, Joachim Krebs, and Marc Moreau.     

Spatial Analysis and Modeling Tool (SAMT): 2. Applications

The Spatial Analysis and Modeling Tool (SAMT) is a GIS-based analytical software for land use study. This paper demonstrates the capabilities of SAMT as applied in studies of land use and landscape change in the catchment area of the River Quillow, North-East Germany. Neural network and fuzzy models are important features that were incorporated in SAMT. The region of study for this application is a catchment area of about 170 km² and is subdivided into 54,000 grid cells for spatial simulations.Three different applications of SAMT are discussed in this article. The first application involves assessment of the potential soil erosion risk on arable land. This user-defined application combines an economic model (LP model), a temperature-driven crop coverage model and a soil erosion risk model (Universal Soil Loss Equation) using data from tables and map information. The second and third applications are winter wheat yield estimates using, respectively, the fuzzy toolbox SAMT_FUZZY in conjunction with expert knowledge, and using the neural network toolbox SAMT_NN based on observations.     

Statistical Analysis of Uniparental Disomy Data Using Hidden Markov Models

Genetic studies of uniparental disomy (UPD) employing many markers have helped geneticists to gain a better understanding of the molecular mechanisms underlying nondisjunction. However, most existing methods cannot simultaneously analyze all genetic markers and consistently incorporate crossover interference; they thus fail to make the most use of genetic information in the data. In the present article, we describe a hidden Markov model for multilocus uniparental disomy data. This method is based on the chi-square model for the crossover process and can simultaneously incorporate all marker information including untyped and uninformative markers. We then apply this novel method to analyze a set of UPD15 data.     

Multiple-Interval Mapping for Quantitative Trait Loci With a Spike in the Trait Distribution

For phenotypic distributions where many individuals share a common value—such as survival time following a pathogenic infection—a spike occurs at that common value. This spike affects quantitative trait loci (QTL) mapping methodologies and causes standard approaches to perform suboptimally. In this article, we develop a multiple-interval mapping (MIM) procedure based on mixture generalized linear models (GLIMs). An extended Bayesian information criterion (EBIC) is used for model selection. To demonstrate its utility, this new approach is compared to single-QTL models that appropriately handle the phenotypic distribution. The method is applied to data from Listeria infection as well as data from simulation studies. Compared to the single-QTL model, the findings demonstrate that the MIM procedure greatly improves the efficiency in terms of positive selection rate and false discovery rate. The method developed has been implemented using functions in R and is freely available to download and use.     

Genome-enabled prediction of genetic values using radial basis function neural networks

The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait-environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.     

Book Reviews

Book reviewed in this article: Brenda Johnson Clay. Unstable Images: Colonial Discourse on New Ireland, Papua New Guinea, 1875–1935 Sanjukta Dasgupta and Malashri Lal (eds). The Indian Family in Transition: Reading Literary and Cultural Texts Sverker Finnström. Living With Bad Surroundings Geoffrey Gray A Cautious Silence: The Politics of Australian Anthropology David K. Jordan, Andrew D. Morris and Marc L. Moskowitz (eds). The Minor Arts of Daily Life: Popular Culture in Taiwan. Honolulu R. H. Mathews (ed. by Martin Thomas). Culture in Translation: The Anthropological Legacy of R. H. Mathews Martin Nakata. Disciplining the Savages, Savaging the Disciplines Andie Diane Palmer. Maps of Experience: The Anchoring of Land to Story in Secwepemc Discourse Antonia Mills (ed.). ‘Hang Onto These Words’: Johnny David's Delgamuukw Evidence Hartmut Lutz (ed. and transl.). The Diary of Abraham Ulrikab: Text and Context Michael W. Scott. The Severed Snake: Matrilineages, Making Place, and a Melanesian Christianity in Southeast Solomon Islands Mark Stevenson. Many Paths: Searching for Old Tibet in New China Pamela J. Stewart and Andrew Strathern (eds). Exchange and Sacrifice Margaret Trawick. Enemy Lines: Warfare, Childhood, and Play in Batticaloa Mark P. Whitaker. Learning Politics from Sivaram: The Life and Death of a Revolutionary Tamil Journalist in Sri Lanka     

Model‐free feedback design for a mixed cancer therapy

In this article, a model‐free feedback control design is proposed for the drug administration in mixed cancer therapy. This strategy is very attractive because of the important issue of parameter uncertainties unavoidable when dealing with biological models. The proposed feedback scheme use past measurements to update an on‐line simplified model. The control design is then based on model predictive control in which a suitable switching is performed between two different cost functions. The effectiveness of the proposed model‐free control strategy is validated using a recently developed model (unknown to the controller) governing the cancer growth on a cells population level under combined immune and chemotherapy and using real human data. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

A Variational Bayes Approach to the Analysis of Occupancy Models

Detection-nondetection data are often used to investigate species range dynamics using Bayesian occupancy models which rely on the use of Markov chain Monte Carlo (MCMC) methods to sample from the posterior distribution of the parameters of the model. In this article we develop two Variational Bayes (VB) approximations to the posterior distribution of the parameters of a single-season site occupancy model which uses logistic link functions to model the probability of species occurrence at sites and of species detection probabilities. This task is accomplished through the development of iterative algorithms that do not use MCMC methods. Simulations and small practical examples demonstrate the effectiveness of the proposed technique. We specifically show that (under certain circumstances) the variational distributions can provide accurate approximations to the true posterior distributions of the parameters of the model when the number of visits per site (K) are as low as three and that the accuracy of the approximations improves as K increases. We also show that the methodology can be used to obtain the posterior distribution of the predictive distribution of the proportion of sites occupied (PAO).     

Population model-based inter-diplotype similarity measure for accurate diplotype clustering

Classification of the individuals' genotype data is important in various kinds of biomedical research. There are many sophisticated clustering algorithms, but most of them require some appropriate similarity measure between objects to be clustered. Hence, accurate inter-diplotype similarity measures are always required for classification of diplotypes. In this article, we propose a new accurate inter-diplotype similarity measure that we call the population model-based distance (PMD), so that we can cluster individuals with diplotype SNPs data (i.e., unphased-diplotypes) with higher accuracies. For unphased-diplotypes, the allele sharing distance (ASD) has been the standard to measure the genetic distance between the diplotypes of individuals. To achieve higher clustering accuracies, our new measure PMD makes good use of a given appropriate population model which has never been utilized in the ASD. As the population model, we propose to use an hidden Markov model (HMM)-based model. We call the PMD based on the model the HHD (HIT HMM-based Distance). We demonstrate the impact of the HHD on the diplotype classification through comprehensive large-scale experiments over the genome-wide 8930 data sets derived from the HapMap SNPs database. The experiments revealed that the HHD enables significantly more accurate clustering than the ASD.     

Protein-liposome interactions and their relevance to the structure and function of cell membranes

Summary Recent studies on the interactions of soluble proteins, membrane proteins and enzymes with phospholipid model membranes are reviewed. Similarities between the properties of such systems and the behavior of biomembranes, such as alterations in the redox potential of cytochromec after binding to membranes and effects of phospholipid fluidity on (Na + K) ATPase activity, are emphasized. The degree of correspondence between the behavior of model systems and natural membranes encourages the continuing use of model membranes in studies on protein-lipid interactions. However, some of the data on the increase of surface pressure of phospholipid monolayers by proteins and increases in the permeability of liposomes indicate that many soluble proteins also have a capability to interact hydrophobically with phospholipids. Thus a sharp distinction between both peripheral and integral membrane proteins and non-membrane proteins are not seen by these techniques. Cautious use of such studies, however, should lead to greater understanding of the molecular basis of cell membrane structure and function in normal and pathological states. Studies implicating protein-lipid interactions and (Na + K) ATPase activity in membrane alterations in disease states are also briefly discussed.An invited article.     

A Dynamic Model for Determining the Temporal Distribution of Environmental Burden

Abstract: In an article on the role of temporal information in life-cycle assessment in this journal, Field and colleagues argued that frequently it is not the single product but the "fleet" (or cohort) of products that "is the appropriate unit of analysis," and that in focusing on the fleet one "explicitly introduces the notion of time as a critical element of comparative life-cycle assessments. …" Major transitions, such as replacement of one fleet of products by an alternative fleet, correspond to a system in a transient rather than steady state, and explicit consideration of time is central to transient analysis.
One tool increasingly used as part of life-cycle assessment, economic input-output (EIO) analysis, at best deals with time in an implicit fashion. This article illustrates how the sequential interindustry model (SIM), a formulation of the EIOmodel that explicitly represents time, might be utilized in life-cycle assessment. SIM introduces this temporal component by explicitly accounting for the time required by production activities and the resulting sequencing of the inputs. This can be thought of as engineering rather than accounting information. The data demands of such a model are not likely to be met at present or at any time in the near future. Even so, simulation methods and the use of so-called synthetic data have a history of productive use in a number of fields, including the social sciences.
SIM also utilizes the contribution of Joshi on the application of the EIO model to environmental impact and the inclusion of the use as well as the production phases of a product in EIO analysis. The possibility of accounting for discounting of future events, with its impact on decision making, is also briefly discussed.     

Analysis of Matched Case–Control Data in Presence of Nonignorable Missing Exposure

Summary.   The present article deals with informative missing (IM) exposure data in matched case–control studies. When the missingness mechanism depends on the unobserved exposure values, modeling the missing data mechanism is inevitable. Therefore, a full likelihood-based approach for handling IM data has been proposed by positing a model for selection probability, and a parametric model for the partially missing exposure variable among the control population along with a disease risk model. We develop an EM algorithm to estimate the model parameters. Three special cases: (a) binary exposure variable, (b) normally distributed exposure variable, and (c) lognormally distributed exposure variable are discussed in detail. The method is illustrated by analyzing a real matched case–control data with missing exposure variable. The performance of the proposed method is evaluated through simulation studies, and the robustness of the proposed method for violation of different types of model assumptions has been considered.     

Regulatory Considerations for the Classification of Video Monitoring in Dissolution Testing

The goal of this article is to discuss the classification of video recordings and images when applied to dissolution testing in USP apparatus 1 and 2. Three use cases are presented. The first case presents the use and classification of video as RBE (review by exception) data under GAMP 5. The second case presents the use of video in formulation development in a research and development environment. The third case presents a feasibility study using readily available computer vision software to recognize and measure objects in the dissolution vessel, setting the groundwork for the use of image analysis as a quantitative tool. The classification of video as “electronic data”, requiring 21 CFR part 11 compliance, versus its classification as a RBE data under GAMP 5, likely depends upon its use case. Another goal of this article is to establish a position on the use of video monitoring technology as a tool for dissolution testing that is fit for purpose and compliant with regulations regarding video data management and information.Key words: computer vision, dissolution, GAMP, USP, video monitoring     

Population genetic inference from resequencing data

This article is concerned with statistical modeling of shotgun resequencing data and the use of such data for population genetic inference. We model data produced by sequencing-by-synthesis technologies such as the Solexa, 454, and polymerase colony (polony) systems, whose use is becoming increasingly widespread. We show how such data can be used to estimate evolutionary parameters (mutation and recombination rates), despite the fact that the data do not necessarily provide complete or aligned sequence information. We also present two refinements of our methods: one that is more robust to sequencing errors and another that can be used when no reference genome is available.