bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extrapolation, Low Dose
Authors: Gaylor David W. 
Published: July 2005

AbstractWhile many substances are toxic to humans at high doses, naturally there is concern about the safety of long‐term exposure to much lower doses of certain materials. Such effects are difficult to observe directly and the usual approach is to expose animals to high doses of the substance, establish a dose–response relation, and use this to extrapolate much lower doses. The use of maximum likelihood to estimate extreme quantiles of the tolerance distribution is explained. Methods for measuring carcinogenic potency, such as the dose causing a tumor in 50% of animals, TD50, and the maximum tolerable dose (MTD) are introduced. For noncancer outcomes, the concepts of acceptable daily intake (ADI) and no observed adverse effect level (NOAEL) are also mentioned.
Immunotoxicology
Authors: Portier Christopher J. , Germolec Dori 
Published: July 2005

Abstract

Immunotoxicology is the study of the response of the immune system to toxicological insults, which are usually induced by xenobiotics. Experiments are carried out
in vitro
or
in vivo
. These can be
host resistance assays
, which concern the ability of the immune system to protect the host from infectious agents and so on or they can be
immune function assays
, which measure the response of the immune system to a stimulus. The former are largely analyzed using methods for categorical data, whereas the latter usually involve very large counts and these are generally analyzed using methods for continuous data. There is current interest in using immunotoxicity data in risk assessment.
Infectivity Titration
Authors: Armitage Peter 
Published: July 2005

AbstractThe virulence of a suspension of particles, typically viruses or bacteria, can be assayed by administering several dilutions of the suspension into host organisms and observing the number of hosts that become infected at each dilution. The usual analysis uses likelihood methods to estimate a parameter that depends on both the density of particles in the suspension and on the chance that each particle will cause an infection. In the simplest model, each particle acts independently of the others and each gives rise to an infection with the same probabilityp. In these circumstances, the probability that a host becomes infected can be found using the Poisson distribution. More elaborate models allowpto vary from host to host according to some distribution. Methods for detecting suchhost variabilityare available. Some discussion is given to the effects of failure of the assumption of independence.
Quality Control in Laboratory Medicine
Authors: Strike P. W. 
Published: July 2005

AbstractAn overview of the principles and practices of Quality Control (QC) in laboratory medicine, encompassing the origins of bias and variability in laboratory measurement, the regulation of same through internal (within‐lab) and external (between‐lab) QC programs, and concluding with an account of the strategies proposed for defining meaningful analytical goals in respect of the “quality” component of QC.
Mutagenicity Study
Authors: Piegorsch Walter W. 
Published: July 2005

AbstractBiometric methods are discussed for analyzing data from bioassays of mutagenic response. Emphasis is placed on the classic microbial system in environmental mutagenesis: the Ames/Salmonellamicrosome assay. The assay produces count data to quantify the mutational response; statistical issues of interest when analyzing such data include testing for extra‐Poisson variability, trend testing for a dose response, and trend testing under a nonmonotone dose response.
Parallel‐Line Assay
Authors: Redmond Carol K. 
Published: July 2005

Abstract
Parallel‐line assays are the most common form of indirect analytical assays. The aim is to estimate the potency of substance under test relative to the potency of a standard. The test and standard preparations are administered at several dose levels. The key assumption is that the expected response for a given dose of the test preparation is the same as that which would be obtained from the standard preparation administered at a dose that is ρ times the given dose: ρ is known as the relative potency. It is further assumed that the expected response is linearly related to the logarithm of the dose. If this obtains, the graphs of expected response against log dose for the test and standard preparations will give parallel lines. The response can be continuous, in which case estimation is through a linear model, or quantal, in which case analysis usually assumes a logistic model. The estimator of ρ is defined in terms of ratios of regression coefficients and interval estimates are often obtained by use of Fieller's theorem.
Radioimmunoassay
Authors: Redmond Carol K. 
Published: July 2005

AbstractRadioimmunoassays (RIA) use radioactive tracers to model biochemical reactions in which specific antigens bind to antibodies in the immune system. The radiation counts of bound labeled to free‐ligand labeled antigen (or antibody) at various fixed dose levels of antigen are utilized to estimate indirectly the unknown concentration of unlabeled antigen (or antibody) in the system. Statistical methods, analogous to those employed in standard quantal bioassay designs, are available to estimate the parameters of the models and the absolute or relative potency of the concentration of antigen (antibody) in the system and its associated confidence intervals.
Serial Dilution Assay
Authors: Ridout Martin 
Published: July 2005

AbstractSerial dilution assays are used to estimate concentrations of bacteria in samples of food, water, soil, and so on. Key assumptions are that the bacteria are distributed randomly throughout the medium, and that the presence or absence of bacteria in subsamples can be ascertained reliably. The concentration of bacteria can then be estimated based on the presence or absence of bacteria in subsamples of different volumes. Essentially, the same method can be used to estimate the proportion of individuals in a population that have some characteristic, for example, in immunology, the proportion of cells that are immunocompetent. The term limiting dilution assay is often used for this type of application. This article considers point and interval estimation of concentration, including bias correction, tests of the underlying assumptions, and issues of experimental design for dilution assays.
Slope–Ratio Assay
Authors: Redmond Carol K. 
Published: July 2005

Abstract
Slope‐ratio assays are similar to parallel‐line assays insofar as both are indirect analytical assays, which aim to estimate the potency of a test preparation relative to that of a standard. Slope‐ratio assays are most often encountered in microbiologic applications. As with a parallel‐line assay, the key assumption is that the expected response to a given dose of the test preparation is the same as that which would be obtained from the standard preparation administered at a dose that is ρ times the given dose. As in parallel‐line assays, the parameter ρ is known as the relative potency. Unlike the parallel‐line assay, the expected response, which is almost always continuous, is linear in the dose, not its logarithm. The graphs of the response against dose for test and standard preparation will be straight lines of different slopes and common intercept, as the assumptions of the assay demand the same outcome for standard and test preparations when the dose is zero. The analysis can be accomplished by a multiple regression, with the estimator of ρ being the ratio of the slope estimators. Interval estimates are often obtained using Fieller's theorem.
Stereology
Authors: Marriott F. H. C. 
Published: July 2005

Abstract
Sampling methods used in stereology are briefly discussed. The fundamental formulae give estimates of volume ratios for the volume of a particular phase, the area of two‐dimensional structures, the length of linear features, and the number of particles. Some inference is also possible about curvature of surfaces. Estimation of the size distribution of spherical particles is described, and the extension to ellipsoidal particles, possible only in restricted cases, is discussed. The properties of tesselations, particularly the cell model and the Johnson–Mehl model for crystal growth, are described. Mathematical morphology, developed by Matheron and Serra as a system of transformations of digitized images, is a valuable technique for revealing relevant features, and stereological indices based on the modified images may be compared.
Up‐and‐Down Method
Authors: Gross Alan J. , McLean David C. 
Published: July 2005

AbstractThe Up‐and‐Down method is employed in dose–response studies where the aim is to model the probability of a binary outcome in terms of covariates, one of which is the dose being administered. The method allows for sequential choice of doses, dependent on the outcome of the previous dose. It is used in toxicology experiments in which the outcome is death: following a death, the next unit receives a lower dose, whereas a survival leads to the dose being increased.
Accident Proneness
Authors: Kemp Adrienne W. , Kemp C. D. 
Published: July 2005

Abstract
The entry reviews five competing models for the occurrence of accidents: (1) pure chance, (2) true contagion, (3) apparent contagion, (4) spells, and (5) liability. The distributions for accident count data that they yield (e.g. Poisson, negative binomial, Neyman type A, short, and generalized Waring) can, however, arise from more than one model. Model identification, in order to modify behavior and hence reduce the number of accidents, has proved largely ineffective. Since 1985, research has moved from model detection to risk evaluation and analysis.
Aging Models
Authors: Müller Hans‐Georg 
Published: July 2005

AbstractModels for aging have come under intensive scrutiny in recent years due to increasing interest in aging, not least due to the ever‐increasing human lifespan. The methods used to model aging are largely similar to those in survival analysis, with unique features such as the use of lifetables and less prominence of incomplete data. An increasingly prominent role is played by event history analysis. Nonparametric, frailty, event history, and extreme value approaches are increasingly prominent due to the uncertainties regarding the shape of population hazard functions, which display remarkable plasticity across sexes, species, and environmental conditions. These newer approaches complement classical likelihood methods associated with the Gompertz and Gompertz–Makeham parametric models and are therefore the methods of choice in the emerging field of biodemography. Recent problems where aging models are relevant concern the influence of genetic variants and signaling pathways on mortality, the modeling of plateaus in the right tail of hazard functions, and the complex interplay between reproduction, diet, and mortality.
Back‐Calculation
Authors: Bacchetti Peter 
Published: July 2005

AbstractBack‐calculation estimates past infection rates of an epidemic disease by working backward from observed incidence. This requires: external knowledge about the distribution of incubation times between infection and disease, possibly including how it is changing over time; incidence data that have been adjusted for reporting delay, underreporting, and possibly seasonal effects; and a parametric model or smoothness assumption that imposes some structure on past infection rates. In the case of human immunodeficiency virus, sensitivity analyses have found that uncertainty in back‐calculation is mainly due to uncertainty in these inputs. Projected incidence is obtained by applying the assumed incubation distribution to the estimated (and extrapolated) infection rates, but direct extrapolation of observed incidence may do as well or better in some circumstances.
Bacterial Growth, Division, and Mutation
Authors: Gusev Y. , Armitage Peter 
Published: July 2005

AbstractModels of cell growth and division in bacteria were considered more than 60 years ago. The evolution of models of bacterial growth was closely followed by the development of models of the cell cycle in budding and fission yeast, cell cultures, and mammalian cells. In some of these theoretical models, new mechanisms have been hypothesized, which later have been confirmed in experiments on spontaneous and induced mutations, signal transduction pathways, cell cycle regulation, and programmed cell death. Thus, for many years, modeling of the bacterial cell cycle has been a “proving ground” for refinement of theoretical models in cell biology. The major questions addressed in bacterial growth models have included: stochastic and deterministic models of cell population dynamics; different models of the cell cycle in individual cells, such as growth control, random transition, and mitotic clocks; generational dependence models; unequal cell‐division models; spontaneous mutation models and fluctuation tests.
Branching Processes
Authors: Jagers Peter 
Published: July 2005

Abstract
Branching processes are stochastic population models based on explicit descriptions of individual life and reproduction. The basic assumption is that individuals live and multiply independently. This can, however, be relaxed. Individuals may be of various types and have varying life spans. In the simple branching processes, there is only one type of individual and life span is determistic (one year or season) or exponentially distributed, yielding Markov processes, and as simple special cases, birth‐and‐death processes. But more general branching processes allow complex reproduction patterns as well as arbitrary life distributions.
The main results are that no finite population can persist forever. It either dies out or it grows, usually at the exponential rate postulated by Euler and Malthus. However, recent results show that if population size can influence individual reproduction, other growth modes, like linear, can also occur. In exponential growth, the age‐distribution, and population composition generally, stabilize.
Brownian Motion and Diffusion Processes
Authors: Chiang Chin Long 
Published: July 2005

AbstractA brief review of the one‐dimensional Brownian motion in terms of three perspectives. (1) As the limiting case of a random walk, the total displacement has a normal distribution. (2) The mathematical model leads to a partial differential equation, which is known as Fokker–Planck diffusion equation. And (3) as a stochastic process, both the time element and the displacement are continuous variables. The solution of the partial differential equation for the transition density function also has the normal distribution. Wiener–Levy process is a special case.The Ornstein–Uhlenbeck process, which describes the velocity of the movement of a particle in a Brownian motion is also mentioned.
Chain Binomial Model
Authors: Longini Ira M. 
Published: July 2005

Abstract
The chain binomial model arises from chains of binomial probability mass functions, where each function is conditioned on previous function. Products of these probability mass functions give the probabilities of particular sequences of binomial realizations. This chain structure allows for detailed analysis of physical phenomena that can be represented by chain binomials. The simple chain structure allows for statistical inference based on likelihood theory.

The chain binomial model forms the statistical underpinnings of the life table. In addition, the chain binomial model has been important in the analysis of infectious disease spread in small groups such as households. For example, the Reed–Frost model is a chain binomial model where the number of new infectives at some time
t
depends on the number of infectives and susceptibles at time
t
− 1. This model has been used to estimate epidemiological parameters for the spread of such diseases as measles, smallpox, and influenza.
Chronic Disease Models
Authors: Manton Kenneth G. 
Published: July 2005

AbstractWe discuss the need for multidimensional stochastic models of physiological processes to describe the evolving risk of chronic disease over a lifetime. Modifications of that type of model needed to describe different types of risk factor and health effects collected in different longitudinal studies are described. How health processes are “captured” in different longitudinal study designs is discussed, as is the description of the intra‐individual dynamics of degenerative processes. The concept of hormesis is introduced as are more standard types of hazard models (Weibull and Gompertz) drawn from reliability theory. Measurements that may be made in molecular epidemiological studies are discussed and issues that they raise for statistical modeling of disease processes reviewed. Methods for analyzing the genetic contributions to chronic disease risks are presented and discussed.
Clinical Signals
Authors: Campbell Michael J. 
Published: July 2005

AbstractClinical signals are obtained when human activity is monitored, usually electronically. Typical examples are brain activity via the EEG, and heart activity from the ECG. Other signals include blood pressure and measures of lung function. The signals can be sampled at short time intervals, such as 0.1 s for measuring autonomic function, or a longer periods such as every 2 hrs for measuring circadian activity. Techniques of analysis include spectral analysis, and time‐varying spectral analysis. Examples are given for heart rate and blood pressure variability, the EEG, and human luteinizing hormone levels.
Epidemic Curve
Authors: Rosenberg Philip S. 
Published: July 2005

AbstractAn epidemic curve is a plot of time trends in the occurrence of a disease or other health‐related event for a defined population and time period. The event rate that is plotted may measure the number of cases per unit time, or it may express the numbers of events relative to the number in the study population (incidence rates). In infectious disease epidemiology, the epidemic curve is used to document the scope and duration of an epidemic, to help determine the source of the infection and the modes of transmission or exposure, and to glean information about the incubation period of the disease. In other settings, including behavioral and chronic disease epidemiology, epidemic curves are used to document the scope of public health problems.
Epidemic Models, Deterministic
Authors: Heesterbeek J. A. P. , Roberts M. G. 
Published: July 2005

AbstractIn this article, we briefly review some of the approaches to deterministic modeling of epidemic phenomena. We have chosen to structure the approaches by specific biological examples that exemplify a certain approach and that were, in addition, historically important in shaping the development of the deterministic theory. These examples are measles, malaria, helminth infections, and HIV. For types of models, we briefly discuss ODE models and PDE age‐structured models of compartmental SIR‐type (both for microparasitic agents), ODE models for helminth parasites, formulated in terms of average parasite load, and the more general microparasite models in terms of integro‐differential equations. In the latter, there is flexibility in defining the infectivity of individuals as a function of infection‐age, which does not exist in the compartmental models. We also discuss the way to calculate the basic reproduction ratioR0for each class of models.
Epidemic Models, Inference
Authors: Britton Tom 
Published: July 2005

AbstractThis article reviews inference procedures for the general epidemic model assuming a homogeneous community, and for a model for a community partitioned into households. The data may be complete, with the epidemic observed continuously, or may record only the final epidemic size. Methods for estimation of the contact rate, recovery rate, and reproduction number are described, using maximum likelihood, Martingale theory, and Markov chain Monte Carlo.
Epidemic Models, Recurrent
Authors: Nåsell Ingemar 
Published: July 2005

AbstractDeterministic and Stochastic epidemic models are described and various aspects of long‐term behavior are contrasted. The threshold predicted by the deterministic model is a poor approximation to the stochastic analogue. Damped oscillations given by the deterministic model are seen when stochastic models are averaged over realizations, but not for individual realizations. Extinction is seen only in stochastic models, with a probability dependent on population size.
Epidemic Models, Spatial
Authors: Renshaw Eric 
Published: July 2005

AbstractFor a proper understanding of the dispersal and control of infectious diseases, it is often necessary to consider spatial models in which individuals do not mix homogeneously. A birth, death, and migration/infection process for separate colonies, with migration/infection to nearest neighboring colonies, is considered from both deterministic and stochastic viewpoint. Topics covered include Monte Carlo simulation and threshold levels, with particular reference to the transmission of HIV/AIDS and rabies.
Epidemic Models, Stochastic
Authors: Gani J. 
Published: July 2005

AbstractThis article describes the most commonly used stochastic epidemic models. For discrete time, the Greenwood and Reed–Frost chain binomials are outlined, with examples. In continuous time, the simple SI, the general SIR and the carrier‐borne CSR models are discussed, and Whittle's Threshold Theorem stated. The article concludes with some references to recent literature.
Epidemic Models, Structured Population
Authors: O'Neill P. D. 
Published: July 2005

AbstractReal‐life epidemics invariably occur within populations composed of individuals who do not mix with each other entirely at random. Instead, there is frequently a nonhomogeneous mixing structure, so that individuals are more likely to spend time with those they live or work with, or who are geographically close, for example. Here we review a number of stochastic epidemic models, which attempt to incorporate population structure, providing descriptions of the models and highlighting associated results of interest. Aspects of related statistical inference are also considered. Such models offer a more realistic alternative to those that assume homogeneous mixing of either the entire population, or large subpopulations.
Image Analysis and Tomography
Authors: Glasbey C. A. , Berman M. , Hudson H. M. 
Published: July 2005

AbstractImage analysis is the use of computers to extract quantitative information from images, such as those that arise from microscopes, medical scanners, electrophoresis gels, and illuminated objects. We give examples of such images, an overview of the different types of digital image, and the eclectic collection of methodologies that have been proposed to analyse them. We then consider the tomographic reconstruction of images from projections, and the three major components of image analysis: enhancement, segmentation, and taking measurements. We illustrate the methods using the examples.
Infectious Disease Models
Authors: Becker Niels G. 
Published: July 2005

Abstract
Infectious disease models are used to gain insights into strategies for the control of infectious diseases. Stochastic versions are also used in the analysis of infectious disease data, where they help to focus the analysis on epidemiologically meaningful parameters, take care of dependence in the data, and compensate for the fact that only part of the infection process is observable. Different characteristics of disease transmission and progression have promoted a variety of models, including transmission chain models that keep track of infected individuals in “generations” and models that monitor the transmission dynamics continuously over time. Models formulated to describe outbreaks in households are particularly useful, because households are a convenient unit for empirical studies.