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At least 1,099 records · Page 61Linked to original sources
PubMed2002

A critical analysis of the role of growth hormone and IGF-1 in aging and lifespan.

Studies in Caenorhabditis elegans demonstrate that disruption of the daf-2 signaling pathways extends lifespan. Similarities among the daf-2 pathway, insulin-like signaling in flies and yeast, and the mammalian insulin-like growth factor 1 (IGF-1) signaling cascade raise the possibility that modifications to IGF-1 signaling could also extend lifespan in mammals. In fact, growth hormone (GH)/IGF-1-deficient dwarf mice do live significantly longer than their wild-type counterparts. However, multiple endocrine deficiencies and developmental anomalies inherent in these models confound this interpretation. Here, we critique the current mammalian models of GH/IGF-1 deficiency and discuss the actions of GH/IGF-1 on biological aging and lifespan.

Aging↗
PubMed1963

Discrimination of successiveness: a test of a model of attention.

Interpreting attention as a periodic phenomenon, we show its relevance to discriminating the successiveness of signals presented to separate sense modalities. Experiments confirm the expected linear relation between the probability of discriminating pairs of successive from pairs of simultaneous signals and make it possible to infer the period of attention.

Attention↗
PubMed2003

A latent class model for individual differences in the interpretation of conditionals.

We investigated the hypothesis that there are three levels of performance associated with conditional reasoning: (1) Unsophisticated reasoners solve a modus tollens by accepting the invited inferences, treating the conditional as if it were a biconditional. (2) Reasoners of an intermediate level can resist the invited inferences, but cannot find the line of reasoning needed to endorse modus tollens. (3) Sophisticated reasoners do not draw the invited inferences either, but they do master the strategy to solve a modus tollens. On a first set of six problems, solved by 214 adolescents, an unrestricted latent class analysis revealed the existence of a large subgroup of reasoners with a biconditional interpretation of the conditional, and a smaller subgroup with a conditional interpretation. On a second set of 24 problems, solved by the same participants, a restricted latent class model corroborated the existence of a large subgroup of unsophisticated reasoners and a smaller subgroup of reasoners of an intermediate level. No evidence was found for the existence of a subgroup of sophisticated reasoners. As expected, the class of biconditional reasoners was associated with the class of unsophisticated reasoners, and the class of conditional reasoners was associated with the class of reasoners of an intermediate level. Furthermore, the former showed a biconditonal response pattern on truth table tasks, whereas the latter showed a conditional response pattern.

Adolescent↗
PubMed2001

Comparative application of multiple receptor methods to identify aerosol sources in northern Vermont.

This study applies and compares results of four receptor modeling techniques to a common set of speciated fine particle measurement data collected ata remote site in northwestern Vermont between 1988 and 1995. Two multivariate mathematical models, positive matrix factorization and UNMIX, were applied to the measurement data and identified seven "common" sources that had similar compositions and similar fine mass contributions in both models. Two ensemble backward trajectory techniques, potential source contribution function and residence-time analysis, were also applied to evaluate and interpret the mathematical model results. The trajectory techniques indicate a strong regional character to the upwind locations associated with aerosol contributions from most of the sources identified independently by the mathematical models and help in the interpretation of those results. The process of model comparison provides insights on the strengths and limitations of the individual and combined source attribution techniques. Convergent results among the multiple methods provide a degree of confidence that each of the receptor methods may represent useful tools for future air quality management. Divergent or inconsistent results among the models can help identify limitations of the individual models and of the underlying aerosol and meteorological data sets.

Aerosols↗
PubMed1999

Population HIV-1 dynamics in vivo: applicable models and inferential tools for virological data from AIDS clinical trials.

In this paper, we introduce a novel application of hierarchical nonlinear mixed-effect models to HIV dynamics. We show that a simple model with a sum of exponentials can give a good fit to the observed clinical data of HIV-1 dynamics (HIV-1 RNA copies) after initiation of potent antiviral treatments and can also be justified by a biological compartment model for the interaction between HIV and its host cells. This kind of model enjoys both biological interpretability and mathematical simplicity after reparameterization and simplification. A model simplification procedure is proposed and illustrated through examples. We interpret and justify various simplified models based on clinical data taken during different phases of viral dynamics during antiviral treatments. We suggest the hierarchical nonlinear mixed-effect model approach for parameter estimation and other statistical inferences. In the context of an AIDS clinical trial involving patients treated with a combination of potent antiviral agents, we show how the models may be used to draw biologically relevant interpretations from repeated HIV-1 RNA measurements and demonstrate the potential use of the models in clinical decision-making.

Acquired Immunodeficiency Syndrome↗
PubMed1997

Teaching cardiac rhythm strip interpretation within a cross-cultural context: using Nicaraguan nurses as a model.

Critical care nurses have been interpreting rhythm strips in the United States for years, and recently this concept has become a new responsibility for critical care nurses in Nicaragua. The focus of this article is on the unique challenges encountered by a nurse educator when teaching critical care concepts to non-English-speaking critical care nurses. Using Nicaraguan nurses as a model, the author shares experiences of teaching advanced concepts to non-English-speaking nurses. The specific concept taught was rhythm strip interpretation and the potential value in nursing care practice.

Critical Care↗
PubMed1992

Pharmacokinetic-pharmacodynamic modeling: time-dependent protein binding--an alternative interpretation of clockwise and counterclockwise hysteresis.

Development of effect compartment model theory has greatly enhanced our understanding of the relationship between pharmacokinetics and pharmacodynamics. When effect versus concentration in serum (usually total concentration) is plotted and counterclockwise hysteresis is observed, an initial disequilibrium between receptor(s) and serum is generally presumed and an effect compartment model is used; alternatively, clockwise hysteresis may infer tolerance, which may be characterized by an adaptation model. In this simulation study, the influence of time-dependent binding to serum protein on the relationship between effect and concentration in serum was investigated. In these simulations, time-dependent protein binding occurred as a result of an increase in protein concentration in serum or displacement by a metabolite. When concentration of free drug in serum was responsible for the pharmacological response, and response versus total drug concentration in serum was plotted, counterclockwise hysteresis, consistent with an effect compartment, occurred with a time-dependent decrease in binding to serum protein. Clockwise hysteresis, consistent with tolerance, occurred with a time-dependent increase in binding to serum protein. For both sets of simulations, no hysteresis was observed when response was plotted against concentration of free drug in serum. These results indicate that, when response is related to concentration of free drug, measurement of concentration of free drug may allow a clearer interpretation of the pharmacokinetic-pharmacodynamic relationship.

Blood Proteins↗
PubMedDate not supplied

The impact of centering first-level predictors on individual and contextual effects in multilevel data analysis.

BACKGROUND: Multilevel data analysis is a powerful analytical tool. Properly applying the models and correctly interpreting the findings are two interrelated general issues in using multilevel modeling (MLM). There are two specific issues when using MLM: (a) separating the individual-level effects of a predictor variable from its contextual effects and (b) centering first-level predictor variables. This can have major implications for interpreting the results at higher levels, and its impact on second-level interpretation is not always apparent. OBJECTIVES: The major purposes of this article are to show how to separate organizational-level effects from individual-level effects and to show how first-level centering decisions affect the interpretation of second-level coefficients. METHODS: The hierarchical linear models (HLM) are used to analyze a hypothetical data set with 385 patients nested within 10 hospitals, using uncentered, group-mean-centered, and grand-mean-centered versions of the predictor variable. RESULTS: Uncentered and grand-mean-centered models are equivalent, but group-mean-centered models are not equivalent to the other two. For the grand-mean-centered and uncentered models, second-level coefficients provide correct estimates of the individual effect and the contextual effect when the contextual predictor variable is included in the second-level model. The group-mean-centered model leads to a second-level coefficient where individual-level effects are confounded with contextual-level effects. DISCUSSION: There is no single best answer to the question of whether to use group-mean centering or grand-mean centering. The theory and specific questions to be answered should be the researcher's guide to selecting which centering approach to use. Understanding the implications of first-level centering is essential to interpreting second-level coefficients correctly.

Data Interpretation, Statistical↗
PubMed2002

Megavariate analysis of hierarchical QSAR data.

Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and makes the modelling efforts less reliable. Model interpretation may be misleading and predictive power may deteriorate. A better alternative is usually to partition the variables into blocks of logically related variables and apply hierarchical data analysis. Such blocked data may be analyzed by PCA and PLS. This modelling forms the base-level of the hierarchical modelling set-up. On the base-level in-depth information is extracted for the different blocks. The score vectors formed on the base-level, here called 'super variables', may be linked together in new matrices on the top-level. On the top-level superficial relationships between the X- and the Y-data are investigated. In this paper the basic principles of hierarchical modelling by means of PCA and PLS are reviewed. One objective of the paper is to disseminate this concept to a broader QSAR audience. The hierarchical methods are used to analyze a set of 10 haloalkanes for which K = 30 chemical descriptors and M = 255 biological responses have been gathered. Due to the complexity of the biological data, they are sub-divided in four blocks. All the modelling steps on the base-level and the top-level are reported and the final QSAR model is interpreted thoroughly.

Alkanes↗
PubMed2003

Modeling flow and sediment transport in a river system using an artificial neural network.

A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance and interpretability while at the same time making the model more understandable to the engineering community.

China↗
PubMed1985

A simulation model for studies of intestine cell dynamics.

A dynamic simulation model for the epithelial cell structures of the intestine has been developed. The model is based on hypotheses found in the literature. It is shown that basic cell dynamics can be reproduced by simulation experiments. The simulation program is designed so that model output comparable with experimental findings can be obtained. The use of a dynamic simulation model can be interpreted as a simulated experiment in vivo. The model therefore allows analysis of dynamic behaviour that cannot be performed by normal experimental work. This means that the model can be used as a tool for verification/falsification of hypotheses about dynamics in both normal and abnormal cell structures.

Animals↗
PubMed1986

Identification and analysis of the regulation of a prestalk cell-surface antigen of Dictyostelium discoideum.

The properties of two differentiation antigens, rsa 4.2 and rsa 3.1, were examined. Both appear on the cell surface early in differentiation, but they differ with respect to their cell-type specificity. rsa 4.2 appears 1-2 h after differentiation has begun and is present on all cells during all stages of differentiation. In contrast, rsa 3.1 appears after 1-2 h on all aggregating amebae and later becomes restricted to prestalk cells. The pattern of regulation of rsa 3.1 indicates that this prestalk antigen appears on all cells early in differentiation but disappears in cells that differentiate along the prespore pathway. As a result, only cells in the anterior of migrating slugs carry this antigen. Predictions of two competing models of Dictyostelium pattern formation, i.e., position-dependent differentiation and random, position-independent differentiation, were tested by flow cytometry and immunochemical staining of sections of cells at the mound and mound-with-tip stages. Our results do not rule out either model, although they are incompatible with the simplest interpretation of the model for position-independent differentiation. The results clearly indicate that cells that ultimately differentiate along the spore pathway pass through an earlier cell state that includes the presence of a prestalk cell-surface antigen identified as rsa 3.1.

Antibodies, Monoclonal↗
PubMedDate not supplied

Regression modeling strategies: an illustrative case study from medical rehabilitation outcomes research.

The practice of outcomes research is growing in all segments of the health care industry, yet few practitioners and researchers are prepared to deal with the completion of statistical analyses that characterize the new focus on results. This article discusses basic model formulation and interpretation. It also encourages the use of statistical models that study the simultaneous effects of many variables on an outcome and gives examples of relationships among variables that are not simple and linear. The methods are illustrated with a dataset consisting of stroke rehabilitation inpatients discharged during a 3-year period with an admission date that is within 1 year after stroke.

Forecasting↗
PubMed1991

A statistical model for in-vitro assessment of patient sensitivity to cytotoxic drugs.

Formation of colonies in semisolid medium is an assay used for the study of stem cell characteristics in hematopoietic and solid tumors. Previous experience with leukemia patients failed to show an association between the reduction in colony formation observed when patient blast cells were exposed to increased concentrations of an anticancer agent, and the subsequent patient response to the agent. By introducing a model that takes into account the possibility of a resistant subpopulation of clonogenic cells, the paper demonstrates that the null result was due to an inadequate summarization of the dose-response curve, and in fact a statistically and biologically significant association exists between one of the parameters of the model and patient response. The properties, implementation, and interpretation of the model are discussed.

Biometry↗
PubMed1996

[Paradigmatic shifts in clinical practice in the the last generation].

Physicians have always used theoretical models (paradigms) to interpret clinical reality, and have changed the prevailing model only when it could no longer satisfy clinical needs. The purpose of this essay is to review some of the paradigmatic changes in clinical reasoning that have occurred since my undergraduate medial education. My training in the 50's was along the bio-medical model that reduced all diseases to structural or biochemical dysfunctions. Within this framework, causes were perceived as leading inevitably rather than probabilistically to their consequences, and chance and ambiguity had a very small role in explication of pathophysiologic mechanisms and in diagnostic reasoning. The doctor-patient relationship was paternalistic and the orientation to extending survival rejected notions of quality of life and involved parsimonious utilization of health care resources. Today however, clinical reasoning has shifted from deductive and deterministic to inductive (evidence-based) and probabilistic. Disease is believed to result from multiple factors rather than from single causes, and there is increasing acceptance of psycho-social factors of disease. Awareness of the confounding effects of false-positive and false-negative tests has changed the attitude to diagnostic evaluation. Terms, such as risk indicators of disease, predictive value of tests and risk-benefit ratio are increasingly used in discussing clinical decisions. We respect the patient's autonomy more than we did in the past, and consider his/her preferences and quality of life in clinical decision-making. Fair distribution of medical resources is considered as an ethical principle. Finally, clinical guidelines are no longer viewed as counter-intuitive, but rather as effective means to reduce the disturbingly high rates of medical error.

Clinical Medicine↗
PubMed2005

The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products.

The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for describing multiple, independent phenomena in the ongoing background activity. Whereas the single KP model can be interpreted by assuming that background activity is generated by randomly distributed dipoles with certain spatial and temporal characteristics, the sum model can be physiologically interpreted by assuming a composite of such processes. Taking enough terms into account, the spatiotemporal sample covariance matrix can be described exactly by this extended model. In the estimation of the sum of KP model, it appears that the sum of the first 2 KP describes between 67% and 93%. Moreover, these first two terms describe two physiological processes in the background activity: focal, frequency-specific alpha activity, and more widespread non-frequency-specific activity. Furthermore, temporal nonstationarities due to trial-to-trial variations are not clearly visible in the first two terms, and, hence, play only a minor role in the sample covariance matrix in terms of matrix power. Considering the dipole localization, the single KP model appears to describe around 80% of the noise and seems therefore adequate. The emphasis of further improvement of localization accuracy should be on improving the source model rather than the covariance model.

Algorithms↗
PubMed2005

Sources of uncertainty in model predictions: lessons learned from the IAEA Forest and Fruit Working Group model intercomparisons.

The International Atomic Energy Agency (IAEA), through the BIOMASS program, has provided a unique international forum for assessing the relative contribution of different sources of uncertainty associated with environmental modeling. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte-Carlo modeling are often recommended. The issue of model uncertainty is still rarely addressed in practical applications and the use of several alternative models to derive a range of model outputs (similar to what was done in IAEA model intercomparisons) is one of a few available techniques. This paper addresses the often overlooked issue of what we call 'modeler uncertainty,' i.e., differences in problem formulation, model implementation and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit and Forest Working Groups created under the BIOMASS program (BIOsphere Modeling and ASSessment). The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that the parameter uncertainty (as evaluated by a probabilistic Monte-Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in fate and transport modeling and risk characterization.

Fruit↗