bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hydropedology and Surface/Subsurface Runoff Processes
Authors: Lin Henry , Brooks Erin , McDaniel Paul , Boll Jan 
Published: October 2008

Abstract
The role of soils has long been recognized as critical to rainfall–runoff processes in watersheds. Hydropedology is an emerging interdisciplinary field that integrates pedology, hydrology, geomorphology, and other related bio‐ and geosciences to study interactive pedologic and hydrologic processes and the landscape–soil–hydrology relationships across space and time. This article presents an overview of hydropedology's contributions to the understanding and modeling of surface/subsurface runoff processes, especially the diagnosis of soil features that can help answer “why‐type” questions in watershed hydrology and the ubiquitous nature of preferential flow and its networks. We highlight two bottlenecks for advancing watershed hydrology and hydropedology: a conceptual bottleneck of modeling subsurface preferential flow networks and a technological bottleneck of nondestructively mapping or imaging subsurface architecture. Quantification of “soil architecture” at various scales and the identification of “hydropedologic functional units” in different landscapes offer promising potentials to advance hydrologic modeling. We present the information of linking surface/subsurface runoff processes to pedologic understanding at three scales of microscopic (macropores and aggregates), mesoscopic (horizons and pedons), and macroscopic (hillslopes and catchments) levels. Various examples from the literature are synthesized to illustrate the key points. Further research needs are suggested in the end.
Purification of Intact Chloroplasts from Arabidopsis and Spinach Leaves by Isopycnic Centrifugation
Authors: Seigneurin‐Berny Daphné , Salvi Daniel , Joyard Jacques , Rolland Norbert 
Published: October 2008

AbstractChloroplasts are plant‐specific organelles. They are the site of photosynthesis but also of many other essential metabolic pathways, such as syntheses of amino acids, vitamins, lipids, and pigments. This unit describes the isolation and purification of chloroplasts from Arabidopsis and spinach leaves. Differential centrifugation is first used to obtain a suspension enriched in chloroplasts (crude chloroplasts extract). In a second step, Percoll density gradient centrifugation is used to recover pure and intact chloroplasts. The Basic Protocol describes the purification of chloroplasts from Arabidopsis leaves. This small flowering plant is now widely used as a model organism in plant biology as it offers important advantages for basic research in genetics and molecular biology. The Alternate Protocol describes the purification of chloroplasts from spinach leaves. Spinach, easily available all through the year, remains a model of choice for the large‐scale preparation of pure chloroplasts with a high degree of intactness. Curr. Protoc. Cell Biol. 40:3.30.1‐3.30.14. © 2008 by John Wiley & Sons, Inc.
The Gene Ontology (GO) Project: Structured Vocabularies for Molecular Biology and Their Application to Genome and Expression Analysis
Authors: Blake Judith A. , Harris Midori A. 
Published: October 2008

AbstractScientists wishing to utilize genomic data have quickly come to realize the benefit of standardizing descriptions of experimental procedures and results for computer‐driven information retrieval systems. The focus of the Gene Ontology project is three‐fold. First, the project goal is to compile the Gene Ontologies: structured vocabularies describing domains of molecular biology. Second, the project supports the use of these structured vocabularies in the annotation of gene products. Third, the gene product‐to‐GO annotation sets are provided by participating groups to the public through open access to the GO database and Web resource. This unit describes the current ontologies and what is beyond the scope of the Gene Ontology project. It addresses the issue of how GO vocabularies are constructed and related to genes and gene products. It concludes with a discussion of how researchers can access, browse, and utilize the GO project in the course of their own research. Curr. Protoc. Bioinform. 23:7.2.1‐7.2.9. © 2008 by John Wiley & Sons, Inc.
Browsing Multidimensional Molecular Networks with the Generic Network Browser (N‐Browse)
Authors: Kao Huey‐Ling , Gunsalus Kristin C. 
Published: October 2008

AbstractN‐Browse is a graphical network browser for the visualization and navigation of heterogeneous molecular interaction data. N‐Browse runs as a Java applet in a Web browser, providing highly dynamic and interactive on‐demand access to network data available from a remote server. The N‐Browse interface is easy to use and accommodates multiple types of functional linkages with associated information, allowing the exploration of many layers of functional information simultaneously. Although created for applications in biology, N‐Browse uses a generic database schema that can be adapted to network representations in any knowledge domain. The N‐Browse client‐server package is freely available for distribution, providing a convenient way for data producers and providers to distribute and offer interactive visualization of network‐based data. Curr. Protoc. Bioinform. 23:9.11.1‐9.11.21. © 2008 by John Wiley & Sons, Inc.
Superiority Trial
Authors: Sackett David L. 
Published: October 2008

AbstractWhen busy clinicians encounter a new treatment, they ask themselves two questions. First, is itbetterthan (“superior to”) what they are using now? Second, if is not superior, is itas good aswhat they are using now (“noninferior”) and preferable for some other reason (e.g., fewer side effects or more affordable)? In statistical terms, these clinical questions are “one‐sided.” However, traditional statistical concepts call for two‐sided tests of statistical significance and require rejection of the null hypothesis. A solution to both this incompatibility (between one‐sided clinical reasoning and two‐sided statistical testing) and confusion (about the clinical interpretation of statistically nonsignificant results) is now gaining widespread recognition and application. It is encompassed by the concepts of “superiority” and “noninferiority,” and these inextricable concepts are the subjects of this entry.
Intention‐to‐Treat Analysis
Authors: Lachin John M. 
Published: October 2008

AbstractThe intention‐to‐treat principle developed from concerns that analysis of a subset of subjects into randomized a clinical trial may provide a severely biased comparison of the study treatments, and it may not describe the expected outcome in all subjects for whom a treatment might be administered. Theintention‐to‐treat analysisincludes all subjects randomized into the study to provide a pragmatic assessment of the effects of therapy in general use, regardless of other considerations such as drug tolerability or compliance. This approach is contrasted with subset analyses, such as the “efficacy subset” that only includes the subset that tolerated the assigned agent, who were largely compliant and who completed follow‐up.However, a true intention‐to‐treat analysis requires that all those randomized should complete follow‐up and outcome assessments to the extent possible, even if no longer taking the drug, and that their outcome data should be employed in the analysis. This design is termed anintention‐to‐treat design.Many statistical methods provide an unbiased comparison of treatment effects when all missing observations are ignorable, assuming that no bias is introduced. However, this assumption is inherently unverifiable. Thus, the only incontrovertibly unbiased study is one in which all randomized patients are evaluated and included in the analysis, assuming that other features of the study are also unbiased. This design is the essence of the intent‐to‐treat philosophy.
Cardiac Arrhythmia Suppression Trial (CAST)
Authors: Moyé Lemuel A. 
Published: October 2008

AbstractThe development of new antiarrhythmic agents seemed to herald a new era in which the treatment of serious ventricular arrhythmias would be reduced. The arrhythmia suppression hypothesis, for example, the suppression of mild ventricular arrhythmias would reduce the subsequent rate of adverse arrhythmic clinical events, was accepted by many, and FDA approval of these agents preceded a formal proof of the theory.
Integrated Database
Authors: Kenny Susan J. 
Published: October 2008

AbstractIntegrated databases contain information collected from more than one data source. In the process of combining different data sources, individual data elements often need to be transformed or harmonized so that they represent a consistent or standard context. This transformation must be done without the loss of data integrity and interpretation. Combining data from multiple sources yields increased statistical power to identify important relationships between data elements. Without the increased sample size of an integrated database, the relationship between infrequently occurring rare events with some dependent factor, such as treatment regimen, may be difficult to identify. Integrated databases are used by research institutes, pharmaceutical companies, and regulatory agencies during clinical development and during postmarketing surveillance. Integrated databases have been used for many years, but in recent years, the use of integrated databases has expanded beyond the classic application to clinical trial data to include applications to image, genomic, and other digital data. More advances in data warehousing, enterprise software applications, and efforts in standardization of clinical information have influenced the construction and use of integrated databases.
Women's Health Initiative Hormone Therapy Trials
Authors: Bassuk Shari S. , Manson JoAnn E. 
Published: October 2008

Abstract
The Women's Health Initiative (WHI) postmenopausal hormone therapy (HT) trials were designed to determine the balance of benefits and risks of HT taken for chronic disease prevention by initially healthy postmenopausal women (age range, 50—79 years; mean age, 63 years). Women with an intact uterus were randomized to estrogen–progestin or placebo for a mean of 5.6 years; women with hysterectomy were randomized to estrogen alone or placebo for a mean of 7.1 years. Compared with their respective placebo‐group counterparts, women in the estrogen–progestin group were significantly more likely to experience an adverse health event, whereas women in the estrogen‐alone group were as likely to do so. Specifically, estrogen–progestin was associated with increased risks of coronary heart disease (CHD), stroke, venous thromboembolism, and breast cancer, as well as reduced risks of hip fracture and colorectal cancer. Estrogen alone was associated with an increased risk of stroke and a reduced risk of hip fracture; it had no effect on CHD risk. The results do not support the use of HT for chronic disease prevention in postmenopausal women as a whole. However, because few WHI participants were within 5 years of menopause, these trials cannot conclusively determine the risk–benefit balance of HT in recently menopausal women. Accumulating data suggest that the timing of HT initiation in relation to age or menopause onset may critically influence the relationship between such therapy and risk of CHD as well as its overall risk–benefit balance, with younger or recently menopausal women experiencing more favorable effects than older women or women further from menopause.
TNT
Trial
Authors: C. LaRosa John , M. Grundy Scott , D. Waters David 
Published: October 2008

Abstract

In the Treating to New Targets (TNT) study, 10,001 patients with stable CHD and LDL‐C levels <130 mg/dL (3.4 mmol/L) were randomized to double‐blind therapy with either atorvastatin 10 or 80 mg/day and were followed for a median of 4.9 years. The primary endpoint was an occurrence of a first major cardiovascular event, defined as CHD death, nonfatal, non‐procedure‐related myocardial infarction, resuscitated cardiac arrest, and fatal or nonfatal stroke. Mean LDL‐C levels achieved during treatment were 77 mg/dL (2.0 mmol/L) with atorvastatin 80 mg and 101 mg/dL (2.6 mmol/L) with atorvastatin 10 mg. A primary event occurred in 434 patients (8.7%) receiving atorvastatin 80 mg and 548 patients (10.9%) receiving atorvastatin 10 mg, representing a 2.2% absolute reduction in risk and a 22% reduction in risk of major cardiovascular events (HR = 0.78; CI = 0.69, 0.89;
P
< 0.001) in the high‐dose group. In conclusion, intensive lipid‐lowering therapy with atorvastatin 80 mg in patients with stable CHD provided significant additional clinical benefit beyond therapy with atorvastatin 10 mg.
Dose Escalation Guided by Graded Toxicities
Authors: O'Quigley John , Wages Nolan 
Published: October 2008

AbstractIn most cancer dose‐finding studies, the toxicities are graded typically from 0 (the absence of any side effects) to 4 based on the presence of serious and potentially life‐threatening toxicity. Usually, these grades are subsequently recoded whereby all toxicity grades between 0 and 3 are given the value 0 and are considered to be non‐dose‐limiting toxicities (DLT), and grade 4 (a dose‐limiting toxicity) is coded as 1. We describe here how greater use can be made of this information, which potentially leads to more efficient and effective dose‐finding studies. Our focus is on two‐stage continual reassessment method (CRM) designs. During this design, an initial escalation stage requires no use of a model until the first toxicity is encountered, at which time the second stage, based on a model, kicks in. In this context, two possible ways can be used to make use of information on toxicity grades. We can make use of this information in either one of these two stages in different ways. In the first stage, the ideas are usually straightforward that appeal mostly to common‐sense arguments. Nonetheless, it can be observed that use of graded toxicity information in the first stage alone can make an important contribution to increased efficiency. Use of graded toxicity information in the second stage requires an additional model to demonstrate the rate of toxicities. This method is described as well as the potential improvements in performance, which can be achieved by the use of such models.
Repository
Authors: Winkler Cheryl A , Groover Kathleen H. 
Published: October 2008

AbstractRepositories for the acquisition, storage/archival, and distribution of biosamples are essential to the success of clinical trials and biomedical research. In the current era of genomics and proteomics, the banking of human biosamples and associated phenotypes for the discovery of genetic factors underpinning rare and common diseases has expanded the role and impact of repositories and biobanks. Today's repositories are banking an unprecedented number of biosamples and data for molecular‐based research. This article provides an overview of repository models, the core function and management of repositories, and issues that pertain to the protection of donor rights and confidentiality.
Generalized Estimating Equations
Authors: Hardin James W. , Hilbe Joseph M. 
Published: October 2008

AbstractCorrelated datasets develop when multiple observations are collected from a sampling unit (e.g., repeated measures of a bank over time, or hormone levels in a breast cancer patient over time), or from clustered data where observations are grouped based on a shared characteristic (e.g., observations on different banks grouped by zip code, or on cancer patients from a specific clinic). The generalized linear model framework for independent data is extended to model correlated data via the introduction of second‐order variance components directly into the independent data model's estimating equation. This generalization of the estimating equation from the independence model is thus referred to as a Generalized Estimating Equation (GEE). This article discusses the foundation of GEEs as well as how user‐specified correlation structures are accommodated in the model‐building process. This article also discusses the relationship and similarity to the underlying generalized linear model framework and we point out alternative approaches to GEEs for modeling correlated data such as fixed‐effects models and random‐effects models.
Disease Trials in Reproductive Diseases
Authors: Johnson Julia V. 
Published: October 2008

AbstractHistorically, studies in reproductive medicine have been of limited quality mainly because of the challenging group of subjects, the limited effective study design, and the lack of standards advised for clinical trials. In addition, reproductive medicine has classically received limited funding for the National Institute of Child Health and Human Development (NICHD) at the National Institutes of Health (NIH). Because of the importance of reproductive health issues, the NIH has developed programs to increase translational and clinical research. In addition, research has encouraged standards for reproductive research to assure high‐quality clinical trials. This article will review the classic limitations of studies in reproductive medicine, the standards that have improved the quality of research, and it will describe NIH funded programs to promote knowledge and optimize patient care.
Imaging Science in Medicine
Authors: Hendee William R. 
Published: October 2008

Abstract
The use of imaging technologies has expanded tremendously in medicine over the past two decades. This expansion reflects the evolution of new imaging tools that reveal human anatomy and physiology in ever‐increasing detail, as well as the development of digital data acquisition and processing techniques that extract information from human tissues in ever‐expanding degrees of subtlety. A further factor that is stimulating growth in imaging is the expansion of knowledge about biological systems at the cellular, molecular, and genetic levels. This factor is transforming medical imaging from a “technology‐push” discipline into one where fundamental biological questions, as well as important clinical issues, are the force driving development of new imaging technologies. This transformation demands enhanced knowledge about the interface between biological properties of tissues and the characteristics of imaging systems. Exploration of the existing state of knowledge about the interface, and what is needed for the immediate future, is the subject of this article.
Biological Assay, Overview
Authors: Sen Pranab K. 
Published: October 2008

AbstractBiological essay (or “bioassay”), in its classical form, is concerned with the measurement of the concentration of an active ingredient in a test preparation, by estimation of its potency relative to that of a standard preparation. Assays, usually by animal experimentation, form an essential tool of biological standardization. Standard designs and methods of analysis are described here, including stochastic approximation, radioimmunoassays, semiparametric and nonparametric methods, and bioequivalence studies. In a broader sense, Bioassay refers to experiments, with biological units, to detect possible dose–response (or toxicity‐adverse effect) relationships. Underlying toxicodynamics (and/or pharmacodynamics) and toxicokinetics (and/or pharmacokinetics), possibly in conjunction with toxicogenomics (and/or pharmacogenomics) may render such studies statistically quite different from conventional biometric studies. Some relevant approaches are considered here.
Logistic Regression
Authors: Lemeshow Stanley , Hosmer David W. 
Published: October 2008

Abstract
This article deals with the use of logistic regression to model data, where the response variable is binary and there are potentially numerous explanatory or predictor variables. Methods of estimating the coefficients in the model and procedures for hypothesis testing are considered. The correspondence of the coefficients of the logistic regression model to the odds ratio is noted along with methods for computing point estimates and confidence intervals for the odds ratio. Interpretation of coefficients for dichotomous, polytomous, and continuous independent variables is carefully developed. Confounding and effect modification are easily dealt with through the use of this modeling technique. All these concepts are illustrated through the analysis of low birth weight data.
Outliers
Authors: Hawkins Douglas M. 
Published: October 2008

Abstract
Outliers are observations highly discordant from the rest of the data, either in a univariate distribution or in relation to a model with explanatory variables. They may arise through contamination, errors in data gathering, or misspecification of the model. Approaches to detection include “high‐breakdown” methods involving partitions of the data into acceptable and discordant sets; and sequential identification of individual outliers by forward selection or, preferably, backward elimination. Likelihood ratios or multiple comparison methods may be used. Special cases are considered and the topic is illustrated by an example using data on body weight and brain weight of animals and dinosaurs.
Overdispersion
Authors: Dean Charmaine B. 
Published: October 2008

AbstractCount data analyzed under a Poisson assumption or data in the form of proportions analyzed under a binomial assumption often exhibit overdispersion, where the empirical variance in the data is greater than that predicted by the model. This entry illustrates how overdispersion may arise and discusses the consequences of ignoring it, in particular, the underestimation of standard errors of covariate effects. Straightforward methods for ascertaining whether overdispersion is evident are presented. Simple methods for incorporating overdispersion within the framework of quasi‐likelihood estimation are reviewed as well as generalized linear mixed models, which form a broad scheme for model‐based analysis when overdispersion is present.
Response Surface Methodology
Authors: Raghavarao D. , Altan S. 
Published: October 2008

AbstractResponse Surface Methodology (RSM) is used to determine the optimal setting givenkquantitative experimental factors. A preliminary first‐order experiment based on a Plackett–Berman design identifies important factors with a first pass estimate of the optimal point by means of the method of steepest ascent. This is followed by a confirmatory second‐order experiment using a suitable fractional factorial, Central Composite or Box–Behnken design. RSM identifies the optimal setting by estimating the coefficients of a polynomial equation describing the responses from thenexperimental runs, each run corresponding to a prescribed choice of factor combinations. The relationship betweennandk, the points in the experimental design and their properties in relation to orthogonality, rotatability, and uniform precision constitute the major part of the RSM.
Risk Assessment
Authors: Armitage Peter 
Published: October 2008

AbstractThe “risk” of a potentially dangerous hazard may be measured either by the probability of its occurrence (perhaps per unit time or unit of exposure), or by combining this with a measure of its severity. Risk assessment may involve estimation of these quantities, evaluation of their significance for individuals and communities, the extent to which individuals may differ in their perceptions of risks, and the measures taken by individuals and communities to reduce the impact of risks. These aspects are considered in turn.
Wilcoxon Signed‐Rank Test
Authors: Woolson R. F. 
Published: October 2008

Abstract
This is a nonparametric test procedure for the analysis of matched‐pair data, based on differences, or for a single sample. The null hypothesis is that the differences, or individual observations in the single‐sample case, have a distribution centered about zero. The absolute values are ranked. The test statistic is the sum of the ranks for either the positive or the negative values. Examples and details, including large‐sample properties, are given.