bluuebunny/crossref_metadata_2025 huggingface dataset:
Unnatural Amino Acids to Investigate Biological Processes
Authors: Johnson David B.F. , Takimoto Jeffrey K. , Xu Jianfeng , Wang Lei
Published: February 2008

To circumvent the constraint imposed by the 20 canonical amino acids on the study of protein structure and function, various chemical and biosynthetic methods have been developed to incorporate unnatural amino acids into proteins. Unnatural amino acids now can be genetically encoded in living cells in a manner similar to that of common amino acids, which expands site‐directed mutagenesis to diverse novel amino acids. The use of unnatural amino acids grants researchers a multitude of chemical and physical properties that cannot be found in the normal genetic repertoire, which significantly improves their ability to manipulate proteins and protein‐involved biologic processes. Changes have been tailored into proteins to accurately dissect the contribution of hydrogen bonding, hydrophobic packing, cation‐π interaction, and entropy to protein stability, as well as to precisely examine the structural and functional role of crucial residues. Unnatural amino acids also enable the introduction of new chemical reactivities, biophysical probes, mock posttranslational modifications, photoactive groups, and numerous other functionalities for the modification and regulation of protein activities. These studies not only reveal fundamental information of protein structure and function but also explore new means for generating novel protein properties and controlling biologic events.
Taste, Chemical Biology of
Authors: Behrens Maik , Stähler Frauke , Shi Peng , Bufe Bernd , Meyerhof Wolfgang
Published: February 2008

The mammalian sense of taste is crucial for evaluating food palatability and nutritional quality. To achieve the detection of relevant chemicals, evolution has shaped a set of receptor molecules that allow the detection of five basic taste qualities: sweet, umami, bitter, salty, and sour. Each taste modality has unique characteristics and serves a distinct function for an animal's nutrition. Therefore, we address the characteristics, the recent advances, the persisting difficulties, and the future perspectives of the basic taste qualities in separate paragraphs. Enormous progress has been made in the identification and the characterization of taste receptor molecules, and in the growing number of animal genomes accessible from databases, which has inspired us to devote a section of this review to discuss a series of sophisticated evolutionary studies on taste receptor molecules. Finally, evidence is accumulating to show that taste receptor and signal transduction molecules have nongustatory functions as well. The extragustatory expression of such genes and the resulting implications are summarized in the final section.
Plant Terpenoids
Authors: Keeling Christopher I. , Bohlmann Jörg
Published: February 2008

Terpenoids are the largest class of all known natural products. Plants produce a variety of terpenoid compounds that number in the thousands. Some terpenoids are involved in plant growth and development directly (i.e., in primary metabolism), but most plant terpenoids are thought to function in interactions of plants with their biotic and abiotic environment and have traditionally been referred to as secondary metabolites. In addition to the isolation and identification of plant terpenoids, research has concentrated on the biosynthesis, the biological function, and the exploitation of plant terpenoids for human use as biomaterials and pharmaceuticals. Plant terpenoids are biosynthesized from C
5
precursors by the action of prenyl transferases and terpenoid synthases. Often, terpenes are acted on by cytochromes P450 and other enzymes to increase their functionalization. Terpenoid biosynthesis in plants involves several subcellular compartments. The accumulation of terpenoids requires efficient transport systems and specialized anatomical structures. Using isoprene (a hemiterpene), menthol (a monoterpene), artemisinin (a sesquiterpene), and paclitaxel [better known under the registered trademark Taxol (Bristol Myers Squibb, New York)] and diterpene resin acids (diterpenes) as examples, we highlight some strategies, techniques, and results of plant terpenoid research with a strict focus on the low‐molecular‐weight (C
5
–C
20
) terpenoids of specialized plant metabolism.
Transcript Profiling Tools
Authors: Wirta Valtteri , Lundeberg Joakim
Published: February 2008

Regulation of gene expression plays a central role in controlling and shaping the functions of a cell. Tools for quantification of the expression of individual genes have been available for years; but over the past decade, development of the microarray technology and accompanying bioinformatics tools has made it possible to generate comprehensive overviews of the transcriptional events in both diseased and normal cells and tissues. This review covers various approaches for transcript profiling from single genes to more global analyzes approaches. In addition, a detailed description of different microarray‐based technology platforms will be provided, which all enable an essentially genome‐wide characterization of the transcript levels. We also discuss the current challenges and future trends within transcriptional profiling, and we introduce briefly the next generation DNA sequencing technology that will enable a more detailed description of the entire transcriptome, which includes various small RNA species (e.g., microRNA) and other noncoding transcripts.
Transcription‐Based Therapeutics
Authors: Wands Amberlyn M. , Mapp Anna K.
Published: February 2008

Aberrant transcription patterns are associated with most human diseases. Therefore, enormous interest exists in the development of designer molecules that can be used to regulate directly the transcription of predetermined genes for the ultimate treatment of a wide range of disease states. One emerging strategy is to identify molecules that reconstitute one or more functions of the endogenous proteins that upregulate transcription, transcriptional activators. In doing so, they function as either inhibitors or activators of transcription. Using this approach, a variety of protein‐ and small molecule‐based transcriptional regulators have been developed, and at least one has reached clinical trials.
Methylerythritol Phosphate Pathway for Formation of Isoprene Units
Authors: Rohmer Michel
Published: February 2008

The carbon skeleton of isoprenoids is derived from the branched C5skeleton of isoprene. Isopentenyl diphosphate and dimethylallyl diphosphate represent the biologic equivalents of isoprene. From research on cholesterol biosynthesis in liver tissues and on ergosterol in yeast, mevalonate was accepted as the universal precursor of isoprenoids. However this assertion is inaccurate. Incorporation of labeled acetate and glucose isotopomers into bacterial isoprenoids and into diterpenes of ginkgo embryos indicated fortuitously the existence of an alternative mevalonate‐independent route. Its full elucidation required experiments using13C‐ and2H‐labeled precursors followed by extensive nuclear magnetic resonance analyses as well as a combination of biochemical and molecular biology methods. These additional studies revealed a complete set of novel unsuspected enzymes.
Click Peptides: Design and Applications
Authors: Kiso Yoshiaki , Taniguchi Atsuhiko , Sohma Youhei
Published: February 2008

A “click peptide” is a chemically modified peptide analog used to understand the biologic function of peptides. A click peptide does not exhibit the inherent biologic activity of the original peptide because of a simple chemical modification of the peptide backbone and; by adding an exogenous action (“click”) such as pH‐change or photo‐irradiation, easily affords the native peptide in situ with a quick and easy one‐way conversion via a native amide bond‐forming reaction. For example, the designed click peptides of Alzheimer's amyloid β peptide (Aβ) 1–42, which did not exhibit a self‐assembling nature because of a backbone isomerization from a native Gly
25
‐Ser
26
bond to a β‐ester bond, could migrate to the intact Aβ1‐42 under physiologic conditions by pH‐ or photo‐triggered “click” via an
O
→
N
intramolecular acyl migration reaction. This click peptide overcomes the difficulties in handling Aβ1‐42 in syntheses and biologic experiments, which clarifies the currently unexplained processes of Alzheimer's disease.
Lead Optimization in Drug Discovery
Authors: Lindsley Craig W. , Weaver David , Bridges Thomas M. , Phillip Kennedy J.
Published: February 2008

Lead optimization in drug discovery has changed significantly over the past five years and no longer is fragmented into separate hit‐to‐lead and lead optimization phases. Chemical lead optimization from high‐throughput screening (HTS) to clinical candidate identification is now one seamless process that draws on new technologies for accelerated synthesis, purification, and screening of directed, iterative compound libraries. Advances In high‐throughput screening technologies allow detection of new allosteric modes of target modulation, which provides new chemotypes and target opportunities. With the incorporation of drug metabolism and pharmacokinetics (DMPK) inputs early in the lead optimization work flow, molecules are not optimized solely for target potency and selectivity. Moreover, “closed‐loop” work flows are in place such that synthesis and primary screening operate on a 1‐week turnaround for up to 48 compounds/week with DMPK data cycling every other week to guide compound design, which provides expedited timelines for the development of proof‐of‐concept compounds and clinical candidates with limited human resources.
DNATransposition
Authors: Rousseau Philippe , Chandler Michael
Published: February 2008

DNA transposable elements are a ubiquitous and highly diverse group of mobile genetic elements capable of moving within and between genomes. Despite their diversity, only a limited number of chemical mechanisms, which are catalyzed by enzymes called transposases, are used to promote this movement. DNA transposases can be classified according to these chemistries. We outline present knowledge that concerns the mechanisms adopted by the five different types of transposase identified to date: the DDE‐, Y‐, S‐, Y2‐, and Y1‐transposases. The DDE and Y1 enzymes are perhaps the best characterized, whereas the data available for the Y‐ and S‐transposases suggest that they use similar mechanisms to their closely related cousins, the Y‐ and the S‐ site‐specific recombinases.
Saliva in Health and Disease, Chemical Biology of
Authors: Fábián Tibor K. , Fejérdy Pál , Csermely, Péter
Published: February 2008

Saliva is a bodily fluid secreted by three pairs of major salivary glands (parotid submandibular and sublingual) and by many of minor salivary glands. Saliva is supplemented with several constituents that originate from blood serum, from intact or destroyed mucosal and immune cells, and from intact or destroyed oral microorganisms that result in a complex mixture of a variety of molecules. Saliva plays an important role in acquired pellicle formation on tooth surfaces, crystal growth homeostasis, bacterial adhesion, plaque formation, and—because of its lubricating effect—in maintaining mucosal integrity of the oral and upper gastrointestinal mucosal surfaces. It also plays an important role in physico‐chemical defense, antimicrobial defense, and wound healing. Many saliva constituents including proteins, carbohydrates, lipids, and ions interact under fine regulation to fulfill these important tasks. Local and systemic disorders may disturb and interrupt these complex balanced functions, which can lead to mucosal and tooth damages. In other cases, systemic disorders induce salivary changes without any significant local effects. Many such changes are of high diagnostic interest because they can be rather specific to the causing conditions and can be used for screening and early diagnosis of several local and systemic disorders.
Platinum Anticancer Drugs, Chemical Biology of
Authors: Zhang Jenny , Whan Renee , Hambley Trevor
Published: February 2008

In the past, studies into the distribution of platinum anti‐cancer agents inside cancer cells were experimentally challenging because of a lack of readily available and sensitive platinum mapping techniques. Recent advancements in the area of platinum mapping in cells have meant that the biologic fate of cisplatin and its derivatives can be studied in several new ways. In particular, electron microscopy, fluorescence microscopy, and synchrotron radiation induced X‐ray emission have made significant contributions in the study of cisplatinin vitro. The applications of these three techniques and the insights they provide into the intracellular distributions and interactions platinum complexes are reviewed here. The strengths and weaknesses of each technique and their potentials for additional use in this field of research have been evaluated.
Signaling Networks in Biology
Authors: Sarkar Anamika , Wist Aislyn , Iyengar Ravi
Published: February 2008

Signal transduction in cells has been an area of intense study for many years. However, until recently, the focus was on interactions between individual components rather than on the global behavior of the cell signaling networks. New experimental technologies, such as protein and DNA microarrays, high‐throughput screening instrumentation, and diverse compound libraries, allow for many interactions to be examined simultaneously. Along with new experimental methods, quantitative models with mathematical methods, such as deterministic, stochastic, and statistical analyses and graph theory, have been useful in the mapping and analysis of intracellular signaling networks. Here we describe current experimental and computational approaches that are used to develop a global understanding of the complex behavior of intracellular signaling networks. We also discuss important applications of signaling network analysis, including the discovery of new drug targets, the identification of the signaling components responsible for the side (off‐target) effects of drugs, and the development of combination therapies.
Aliasing in Fractional Designs
Authors: McGrath Richard N. , Lin Dennis K.J.
Published: February 2008

In this short exposition, we provide an overview of the aliasing (or confounding) among effects that is caused by studying fewer treatment combinations than required in a full factorial design. We show, by example, how to determine the alias structure of the regular two‐level fractional factorial (2

k
−
p

) designs, nonregular two‐level designs, and the regular three‐level fractional factorial (3

k
−
p

) designs.
Blocking
Authors: Gilmour Steven G.
Published: February 2008

When an experiment is designed,
blocking
involves grouping the runs together in sets which are expected to give similar responses. It is used to improve the estimation of the treatment effects of interest. Complete blocks can be used if the number of runs in each block is equal to the number of treatments. Otherwise incomplete blocks are needed. Different methods of choosing incomplete blocks are used depending on whether the treatments are simple, discrete categories or combinations of levels of several factors. Extensions include resolved block designs and row–column designs, such as Latin squares.
Computer Experiments
Authors: Morris Max D.
Published: February 2008

Computer models are often used as substitutes for physical systems in engineering studies. When these studies are organized in advance with particular goals in mind, they are sometimes called
computer experiments
. We briefly describe a few general types of computer experiments and how Gaussian stochastic processes are used as statistical
metamodels
or
surrogates
in some of these settings.
Factorial Experiments
Authors: Kenett Ron S. , Steinberg David M.
Published: February 2008

Proactive experiments are an essential element of the learning process, in all application areas. Statistically designed experiments have been used to accelerate learning since their introduction by R.A. Fisher in the first half of the twentieth century. In this article we review factorial and fractional experiments and provide some examples from the chemical and semiconductors industries marketing and computer intensive simulations, and and briefly discuss the role of factorial experiments in statistical education.
Fractional Factorial Designs
Authors: Voelkel Joseph G.
Published: February 2008

If only a fraction of all the treatment combinations of a full factorial design are selected to be run, the resulting design is called a
fractional factorial design
. By carefully deciding which fraction of the treatment combinations to run, it is possible to construct fractional factorial designs with good properties. We examine these designs in detail for designs in which each factor is studied at two levels. We show how to construct these designs and how to find their properties. We discuss some uses of these designs as well as their advantages and disadvantages. An example is given of a design and its analysis. Extensions are considered to other situations, including three‐level designs, blocked designs, and split‐plot designs.
Interactions
Authors: Reese C. Shane
Published: February 2008

Statistical approaches for assessing the effect of a set of explanatory variables on a response variable often involve estimation of joint or interaction effects. This article provides a historical perspective on interaction effects from both an effects model and a cell means model perspective. Factorial designs are presented as an effective means of conducting studies where interaction effects are estimable. The assessment of presence or absence of interaction effects is demonstrated with both statistical hypothesis testing and graphical techniques. An example from the commercial automotive industry provides an illustration of interaction effects.
L
enth's Method for the Analysis of Unreplicated Experiments
Authors: Lenth Russell V.
Published: February 2008

Lenth's method is an objective method for deciding which effects are active in the analysis of unreplicated experiments, when the model is saturated and hence there are no degrees of freedom for estimating the error variance. The article reviews its formulation, computation, and examples, and discusses the choice of critical values and the presentation and interpretation of the results.
Main Effects
Authors: Beder Jay H.
Published: February 2008

This article defines main effects in a factorial experiment and illustrates the concept with several examples. The definition is stated in terms of contrasts in cell means. This article describes a common alternate approach using a “factor effects” parametrization, and explains the relationship between the two approaches. Finally, the article briefly describes how main effects are defined in a fractional factorial experiment.
Minimum Aberration
Authors: Deng Lih‐Yuan
Published: February 2008

The minimum aberration criterion is useful to select good regular two‐level fractional factorial designs. To select good nonregular designs, the generalized minimum aberration criterion can be used. It is a natural extension of the minimum aberration criterion. Other related criteria are also discussed.
P
lackett–
B
urman Designs
Authors: Tyssedal John
Published: February 2008

Plackett–Burman designs are important screening designs. In this article we explain how they can be constructed and how they can be classified according to projectivity of two‐level designs. We give a reasonably exhaustive list of existing methods for analyzing them and illustrate one of these methods by an example. Some techniques useful for constructing designs with good projective properties for various experimental situations are described at the end.
Randomization in Experimental Designs
Authors: Kunert Joachim
Published: February 2008

The article describes the principal ideas of randomization. It points out that randomization can be used to validate a model for analysis.
Factorial Designs, Resolution of
Authors: Romano Daniele
Published: February 2008

After providing the definition of resolution for 2k−pdesigns we present two rationales that justify the practical importance of resolution as a criterion for selecting good fractional designs. Insight is given into designs of resolution III, IV, and V, i.e., the most widespread in practice. Then we describe how to determine the resolution of a design and a simple rule for finding half‐fractions of maximum resolution. Smaller fractions of maximum resolution can be found in specialized books referenced in the text. We also describe some circumstances when the resolution criterion needs to be supplemented or replaced by other user‐relevant criteria.
Sample‐Size Determination in Experimental Designs
Authors: Lenth Russell V.
Published: February 2008

Sample‐size determination is a procedure by which we decide how much data are required in order to achieve stated goals for a statistical study. The article focuses primarily on the two most popular paradigms (confidence interval width and power), and on the use of software to solve sample‐size problems. It discusses what situations are most pertinent with regard to sample‐size planning, the importance of eliciting a relevant effect size, and the use of pilot data. An illustration is given for a complex experiment. There is also information on the related issue of sample‐size allocation, and on sample‐size requirements for pilot studies.
Split‐Plot Designs
Authors: Goos Peter
Published: February 2008

Split‐plot designs are
nested designs
that are frequently used in industrial experimentation and result in correlated observations. A correct analysis of the data from split‐plot designs takes account of the correlation. However, as split‐plot designs are often used inadvertently, the typical split‐plot correlation structure is not recognized in many applications. The resulting statistical analysis is then inappropriate. In this contribution, the agricultural origin of split‐plot experimentation is discussed first. Design aspects are discussed as well as the classical analysis of variance (ANOVA) (
see
Analysis of Variance
) and industrial applications of split‐plot designs. It is explained that a generalized least‐squares analysis can be used for estimation and inference and different approaches to constructing split‐plot designs are discussed.
Uniform Experimental Designs
Authors: Fang Kai‐Tai , Hickernell Fred J.
Published: February 2008

The uniform experimental design is one important kind of space filling design and has been applied in computer experiments, laboratory experiments under model uncertainty, and experiments with mixtures. The uniform design is robust against model misspecification.
Center Points
Authors: Borkowski John J.
Published: February 2008

In a designed experiment, center points can provide an estimate of experimental error, allow for lack‐of‐fit tests and for tests of curvature, or ensure orthogonal blocking and uniform precision. A review of these applications of center points will be given.
Central Composite Designs
Authors: Draper Norman R.
Published: February 2008

Choosing a suitable experimental design is an important first step in investigations of the factors that may influence one or more response variables. Such investigations often lead to the representation of the response variable as a second‐order (quadratic) surface in the predictor variables. Central composite‐type designs are popular, classical, and excellent choices for this purpose. A portion of a design of this type can be performed initially to fit a first‐order model. A second‐order portion can then be added, if it becomes clear that the first‐order fit suggests the need for supplementary information. A three‐predictor variable data set is worked through to illustrate the decisions involved in these types of studies.
Mixture Experiments
Authors: Piepel Greg F.
Published: February 2008

Amixture experimentinvolves combining two or more components in various proportions or amounts and then measuring one or more responses for the resulting end products. Other factors that affect the response(s), such as process variables and/or the total amount of the mixture, may also be studied in the experiment. Amixture experiment designspecifies the combinations of mixture components and other experimental factors (if any) to be studied and the response variable(s) to be measured.Mixture experiment data analysesare then used to achieve the desired goals, which may include (a) understanding the effects of components and other factors on the response(s), (b) identifying components and other factors with significant and nonsignificant effects on the response(s), (c) developing models for predicting the response(s) as functions of the mixture components and any other factors, and (d) developing end products with desired values and uncertainties of the response(s).

Given a mixture experiment problem, a practitioner must consider the possible approaches for designing the experiment and analyzing the data, and then select the approach best suited to the problem. Eight possible approaches are (a) component proportions, (b) mathematically independent variables, (c) slack variable, (d) mixture amount, (e) component amounts, (f) mixture‐process variable, (g) mixture of mixtures, and (h) multifactor mixture. The article provides an overview of the mixture experiment designs, models, and data analyses for these approaches.
Response Surface Methodology
Authors: Steinberg David M. , Kenett Ron S.
Published: February 2008

Response surface methodology is a general strategy for combining designed experiments and regression analysis to explore the relationship between one or more response variables and a set of factors that are thought to affect the responses. Some of the key elements in this strategy include sequential progress, a matching of experimental designs to the complexity of needed regression models and rapid progress to the most interesting parts of the factor space. These methods are often used to seek optimal settings of the factors for reaching specific values of the response, such as a maximum or minimum value. Response surface methods are widely used in research and development and industrial applications.
Rotatable Designs and Rotatability
Authors: Draper Norman R.
Published: February 2008

Choosing a suitable experimental design is an important step in the investigations of the factors that may influence one or more response variables. There are several criteria to keep in mind when the choice is made, one of which is
rotatability
. When a design is
rotatable
, it will produce information that predicts ŷ with the same precision at all points equidistant from the coded origin of the design. Another way of saying this is that the contours of
V
(ŷ), the variance of the predicted value, will be spherical about the design origin. In experimental practice,
exact
rotatability is not essential, but the knowledge of how to obtain it is useful in producing approximate rotatability while perhaps attaining other desirable design characteristics.
Dispersion Effects
Authors: Tort‐Martorell Xavier , Marco Lluis
Published: February 2008

Reduction of variation is a crucial ingredient for quality and productivity improvement. An economical and effective approach is to conduct statistically designed experiments, not only to identify controllable factors that affect the mean level (location) of the response, but also to identify and learn about the ones that affect the dispersion. This article explains how control factors can affect the response variability, how they can be identified and estimated, and gives two well‐documented examples that show how this knowledge can be used to improve products and processes.
Performance Measures for Robust Design
Authors: Murphy Terrence E. , Tsui Kwok‐Leung
Published: February 2008

Despite their widespread use as performance measures in robust design, Taguchi's signal‐to‐noise ratios (SNRs) do not always minimize quadratic loss. The relationship between minimization of expected quadratic loss and Taguchi's SNR was articulated by León
et al.
1. Their performance measure independent of adjustment (PerMIA) is a general performance measure which justifies the use of the Taguchi two‐step procedures as a method of minimizing quadratic loss under certain models. We review the basic conceptual framework of the PerMIA and the literature related to this performance measure.
Signal–Response Systems
Authors: Miller Arden
Published: February 2008

The defining characteristic of a signal–response system is that the function of the system requires a response variable to take different values when the value of a signal factor changes. The performance of the system depends on the relationship between the signal and the response. Improving signal–response systems using designed experiments is usually approached as a special case of parameter design. This article describes several types of signal–response systems, discusses how their performance depends on the signal–response relationship and presents methodology for analyzing experiments aimed at improving their performance.
B
ergman–
H
ynén Method for Dispersion Effects
Authors: Arvidsson Martin , Bergman Bo
Published: February 2008

This paper presents the Bergman‐Hynén method for dispersion effect identification in unreplicated fractional factorials. In contrast with other methods the Bergman‐Hynén test statistic has a known reference distribution which allows use of formal hypothesis testing for the dispersion effect identification.
B
ox–
M
eyer Method for Dispersion Effects
Authors: Meyer R. Daniel
Published: February 2008

One aspect of product quality is variability in finished product. Taguchi showed the world that variability is a consequence of product design and not just manufacturing performance. He also showed us that variability can be studied in engineering optimization experiments. Product variables that affect variability are said to have dispersion effects (
see
Dispersion Effects
). Using the Box‐Meyer method, dispersion effects can be estimated simply from the same unreplicated two‐level designs used to study effects on the mean performance.
Ridge Analysis in Experimental Design
Authors: Draper Norman R.
Published: February 2008

Ridge analysis is a valuable tool for exploring second‐order surfaces that have been fitted to experimental data. Paths of absolute maxima (ridge of maxima), absolute minima (ridge of minima), or of intermediate turning values (ridges that are neither overall maxima nor minima but are best locally) can be located and followed. The paths can start from
any
selected location in the predictor space; often the coded origin of the experimental space would be chosen.
P
oisson Processes
Authors: Boxma Onno J. , Yechiali Uri
Published: February 2008

The Poisson process is a stochastic counting process that arises naturally in a large variety of daily life situations. We present a few definitions of the Poisson process and discuss several properties as well as relations to some well‐known probability distributions. We further briefly discuss the compound Poisson process.
L
évy Processes
Authors: Kella Offer
Published: February 2008

This entry contains an overview of Lévy processes and their multidimensional extensions, which are processes having stationary, independent increments that are continuous in probability thereby having versions with almost surely right continuous sample paths. Special cases are Poisson processes, Brownian motions and compound Poisson processes. Infinite divisibility, which is closely related to Lévy processes, is defined and discussed as well as the reflected version of these processes and associated martingales, both in the one‐ and multidimensional cases. Markov additive processes, which are extensions of Lévy process, as well as their reflected versions and associated martingales are introduced as well.
M
arkov Processes
Authors: Stadje Wolfgang
Published: February 2008

This article gives a survey of the basic results on Markov processes with countable or continuous state space and with discrete or continuous time.
Availability Models
Authors: Aven Terje
Published: February 2008

Consider a system which is repaired or replaced at failures. The problem is to analyze the availability of the system at a fixed point in time or for an interval [u,v]. This article presents models and methods for analyzing the availability, both for one‐unit systems and for more complex systems comprising several components (monotone systems). Exact formulae as well as approximation approaches based on asymptotic results are covered.
Physical Degradation Models
Authors: Wang Xiao
Published: February 2008

Many physical systems degrade over time. Recent advances in sensing and measurement technologies are making it feasible to collect extensive amounts of data on physical degradation. Degradation data are a rich source of reliability information and offer many advantages over time‐to‐failure data. We present some common physical degradation models and describe their applications for reliability inference.
Degradation and Failure
Authors: Nikulin Mikhail
Published: February 2008

Degradation and failure time data are usually used in reliability, bioreliability, biology, and medical statistics to estimate the survival or the intensity functions. The degradation data are often a rich source of reliability information and offer many advantages over failure time data, when the aging process due to cumulated wear and tiredness is observed. Some statistical approaches to model the relationship between degradation and failure data are considered here.
Accelerated Life Models
Authors: Nikulin Mikhail
Published: February 2008

Statistical models describing dependence of the lifetime distribution on the explanatory variables are considered. Such models are used in reliability and survival analysis to study the reliability of complex systems.
Competing Risks
Authors: Lindqvist Bo Henry
Published: February 2008

Consider a unit which can experience any one ofkcompeting failure types, and suppose that for each unit we observe the time to failure,T, and the type of failure,C ∈ {1, 2, …,k}. The case of observing the pair (T,C) is termedcompeting risksin statistical literature. After considering some examples we review basic notation and theory of competing risks. In particular we consider the latent failure time approach to competing risks in which thekrisks are represented by potential failure timesT1, …,Tkwhere only the smallest,T = minjTj, is observed together with its indexC = arg minjTj. In reliability studies, the marginal distributions of theTjare often of primary interest, but are unfortunately nonidentifiable in general. Additional, though nontestable, assumptions to obtain identifiability are considered, as are bounds for the marginal distributions given in terms of observable functions. The likelihood function of right censored competing risks data is given and its consequences for both parametric and nonparametric estimation are explained. Extensions of the classical theory of competing risks to more general Markov models and to repairable systems are briefly discussed.
Subjective Life Models
Authors: Mendel Max
Published: February 2008

This chapter deals with several subjective life models that are derived from first principles. First, the ingredients of these models including the hazard and its differential, maintenance policies, aging rates, and failure rates are rigorously defined from a decision‐theoretic perspective. Then the concepts of no‐aging and aging are defined and the corresponding statistical models are derived. Also, the circumstances under which these models converge to form commonly used statistical models are shown.
Precedence Tests
Authors: Balakrishnan Narayanaswamy , Tony Ng Hon Keung
Published: February 2008

A nonparametric two‐sample life test based on the order of early Failures–precedence test–is discussed. First, the basic idea of precedence‐type life‐testing procedures and some related works in the literature are reviewed. Then, some efficient alternatives for the precedence test are discussed which include the maximal precedence test, Wilcoxon‐type rank‐sum precedence tests, and the weighted precedence and maximal precedence tests with an extension to progressively censored data. Exact powers of these test procedures under the Lehmann alternative are discussed and evaluated numerically. Finally, applications of precedence‐type statistics to multisample selection problems are briefly mentioned.
Change‐Point Models
Authors: Jensen Uwe , Lütkebohmert Constanze
Published: February 2008

Change‐point models describe formally the problem of deciding whether a stochastic process is homogeneous in some sense. We review and classify various change‐point models with a focus on the more recent ones. In particular, we consider change‐point detection models in which the decision is based on sequentially observed data. Both, discrete and continuous models are presented. Finally, we give an overview of some parametric and nonparametric regression and hazard rate models with change points.
Statistical Process Control,Bayesian
Authors: Tsiamyrtzis Panagiotis , Hawkins Douglas M.
Published: February 2008

Statistical process control (SPC) is a collection of statistical methods which can be used to maintain quality of an outcome of a process. The SPC tools originally developed within a purely frequentist framework required independent identically distributed observations and known process parameters. Before actual process monitoring can begin (phase II) they require a phase I data gathering exercise for chart calibration. The Bayesian SPC approach avoids the need for a large phase I calibration exercise, and provides a consistent well defined framework for use of prior information which is almost always available but is hard to accommodate and so is ignored by the traditional frequentist SPC methods. Furthermore, in the Bayesian SPC we can transform the inferential procedure to an equivalent decision theory problem. The ability to provide (relatively easy) forecasting is another great advantage of the Bayesian perspective. Although Bayesian methods may require substantially more computing effort than classical control charts, their benefits increasingly outweigh this cost in the modern setting of accessible computer power.
Bayesian Design of Reliability Experiments
Authors: Meeker William Q. , Zhang Yao
Published: February 2008

This article describes Bayesian methods for planning reliability experiments. A Bayesian reliability experiment is a reliability experiment in which the experimenters intend to use prior information and for which Bayesian methods will be used in making inferences from the resulting data. Within the framework of a general model for reliability involving unknown parameters, we describe the basic ideas of Bayesian experimental design. We then describe a large‐sample normal approximation that provides an easy to interpret yet useful simplification to the problem of Bayesian evaluation of reliability experiments and discuss aspects of finding optimum experiments within this framework. We point to several examples of the methodology that are available in the statistical literature.
Masked Failure Data:Bayesian Modeling
Authors: Kuo Lynn
Published: February 2008

In the analysis for masked system lifetime data, the exact component that causes the system's failure is often unknown. For each series system at test, we observe its system's failure time and a set of components that includes the component actually causing the system to fail. The objective is to make inferences for the reliability of the components and their diagnostic probabilities. Basic Bayesian models are reviewed. Typically, both Expectation and Maximization (EM) and Markov Chain Monte Carlo (MCMC) algorithms are employed for inferences.

Both exponential distributions and Weibull distributions for the component lifetimes and some masking probability models are used here to illustrate these techniques.
Bayesian Networks
Authors: Ben‐Gal Irad
Published: February 2008

Bayesian networks are probabilistic models based on direct acyclic graphs. These models enable a direct representation of causal relations between variables. Their structure is ideal for combining prior knowledge, which often comes in causal form, and observed data. This article gives a short description of the concepts of this important class of models that have become extremely popular in recent years.
Life Cycle Costs and Reliability Engineering
Authors: Elsayed Elsayed A.
Published: February 2008

This chapter presents the life cycle cost (LCC) and relates it to product or system reliability. It discusses LCC through the product life phases beginning with the design phase and ending with warranty and field operation phase. It demonstrates how decisions in every phase directly or indirectly affect the LCC over the product life. Determinations of optimal process parameters or decision that minimize the LCC are demonstrated through examples in different phases of the product life cycle.
Warranty Modeling
Authors: Thomas Marlin U.
Published: February 2008

Manufacturer warranty costs vary with product quality, particularly reliability and failure characteristics that relate to warranty claims. These costs represent a significant portion of the total cost and are therefore important in product planning and manufacturing control decisions. In this article, models are presented for assisting manufacturers in analyzing and making economic decisions for product warranties. Methods are developed for predicting costs, determining the amount of reserve funds for covering warranty expenses, and determining optimum periods of warranty coverage.
Total Time on Test Plots
Authors: Bergman Bo , Klefsjö B.
Published: February 2008

In the 1970s the total time on test (TTT) plot, and its theoretical counterpart, the scaled TTT transform, was presented by R. E. Barlowet al. as a tool for model identification based on data representing lives of nonrepairable equipment. It has been used when analyzing different aging properties, optimising replacement policies and studying test statistics. Generalisations of the TTT plot have also been used for analysis of data from repairable units. It has also been suggested as a tool for the selection of active factors in design of experiments. In this paper we give an overview of TTT plotting and its practical and theoretical applications. A wide range of references are suggested for further reading.
Stationary Replacement Strategies
Authors: Bergman Bo
Published: February 2008

This paper gives a general overview of the idea of stationary replacement strategies and a general formulation of the objective function to be minimized is given. Some special cases are discussed. Finally, a comment critical to the usage of stationary replacement policies is given.
Maintenance Optimization in Random Environments
Authors: Özekici Süleyman
Published: February 2008

We consider reliability models that operate in a randomly changing environment. The random environment represents important factors that affect the stochastic and deterministic parameters of the reliability model under consideration. We focus, in particular, on the maintenance problem involving complex systems. We do not aim to report new results on recent ongoing research. Rather, the objective is to provide an overview of the models and results on reliability models in random environment. This will include mainly previous work done by the author.
Replacement Strategies
Authors: Popova Elmira , Popova Ivilina
Published: February 2008

We define what is a replacement policy for a system that fails randomly in time and its main characteristics. There are several parameters that drive the structure of the optimal policy. We provide a detailed discussion of the time horizon, objective functions, and failure time distributions as part of any replacement policy.
Imperfect Repair
Authors: Hollander Myles , Samaniego Francisco J. , Sethuraman Jayaram
Published: February 2008

The notion of “imperfect repair”, as introduced by Brown and Proschan, is reviewed, and generalizations by Block, Borges and Savits and by Kijima are discussed. Basic properties of these models are highlighted, including the proportional hazards property of the time until first perfect repair and a number of preservation properties enjoyed by these models. The authors summarize the published work on statistical inference based on data assumed to follow an imperfect repair model (i.e., given failure times plus mode of repair indicators), including constrained and unconstrained nonparametric maximum likelihood estimation and estimation
via
nonparametric Bayes methods.
Load‐Sharing Models
Authors: Kvam Paul H. , Lu Jye‐Chyi
Published: February 2008

Load sharing describes how the failure probabilities of components within a system depend on the working status of the other components. For example, if a component fails in the system, the other components may experience an increase in failure rate by inheriting an extra “load” from the failed one. Load sharing has its roots in stress‐strength models for fiber bundles, but extends to many time‐to‐failure test environments, including software reliability and materials testing. Load‐share rules characterize how the load changes after a component within the system fails, and can be estimated if unknown.
Maintenance Optimization
Authors: Mazzuchi Thomas A. , Van Noortwijk Jan M. , Kallen Maarten‐Jan
Published: February 2008

Maintenance optimization is the problem of determining cost‐optimal maintenance decisions for a system or structure to ensure a safe and economic operation. An important concept in maintenance optimization is that of life‐cycle costing, where the total costs of design, building, maintenance, and demolition are considered over the entire life span of the system or structure in question. In optimizing maintenance, the uncertainties in the time to failure and/or the deteriorating condition should be taken into account. Proper stochastic deterioration models are Markov processes with independent increments (e.g., gamma processes) and Markov decision processes. This chapter reviews mathematical decision models to optimize time‐based maintenance (e.g., in terms of age‐ and block‐replacement intervals) and condition‐based maintenance (e.g., in terms of inspection intervals). Using renewal theory, optimal maintenance decisions under uncertain deterioration can be determined for which the expected nondiscounted cost per unit time or the expected discounted cost over an unbounded time horizon is minimal. Using Bayesian statistics, these optimal maintenance decisions can be adapted on the basis of observations.
Expert Opinion in Reliability
Authors: Sims Benjamin H. , Koehler Wiedlea Andrew , Wilson Gregory D.
Published: February 2008

Expert opinion can be a useful source of information for assessing reliability, provided it is obtained in a manner that minimizes common sources of bias and ambiguity. The term for obtaining expert opinion in this manner iselicitation. In reliability assessment, elicitation is most often directed at two distinct elements of expert opinion: expertjudgmentabout the value of a specific quantity or probability of interest, or expertknowledgeabout the dependency structure of a system.
System Availability
Authors: Gaver Donald P.
Published: February 2008

Availability
quantifies the propensity of a system to be functionally operative upon demand. It increases if operating times between failures (“uptimes”) are long, and decreases if, following failure or anticipatory removal, logistics delays and repair (“downtimes”) are protracted. This chapter summarizes the general availability concept and discusses the limitation of
operational availability
suggesting that
mission availability
is often more useful and appropriate.
Multivariate Imperfect Repair Models
Authors: Ebrahimi Nader
Published: February 2008

Brown and Proschan introduced univariate imperfect repair models, in which a system, upon failure, is replaced with a new one with probability α (perfect repair), and is minimally repaired with probability 1 − α (imperfect repair). This article deals with multivariate versions of imperfect repair models for systems of
k
components with dependent lifetimes.