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At least 667 records · Page 37Linked to original sources
PubMed2001

A data analysis algorithm for programmed field-flow fractionation.

An algorithm that employs numerical integration for analysis of field-flow fractionation (FFF) data is presented. The algorithm utilizes detector response, field strength, and channel flow rate data, monitored at discrete time intervals during sample elution to generate a distribution of sample components according to particle size or molecular weight. The field strength and channel flow rate may either be held constant or programmed as functions of time, and it is not necessary for these programs to follow specific mathematical functions. If experimental conditions are monitored during a run, the algorithm can account for any deviation from nominal set conditions. The algorithm also allows calculation of fractionating power for the actual conditions as monitored during the run. The method provides greatly increased flexibility in the application of the FFF family of techniques. It removes the limitations on experimental conditions incurred by adherence to analytically available solutions to FFF theory, allowing ad hoc variation of field strength and other experimental parameters as necessary to increase sensitivity and specificity of the method. An implementation of the algorithm is described that is independent of the FFF technique (i.e., independent of field type) and mode of operation. To reduce computation time, it uses mathematical techniques to reduce the required number of numerical integrations. This is of particular importance when the perturbations to ideal FFF theory, such as those due to the effects of hydrodynamic lift forces, particle-wall or particle-particle interactions, and secondary relaxation, necessitate relatively lengthy numerical calculations.

Algorithms↗
PubMed1997

Physiologic smoothing of blood time-activity curves for PET data analysis.

UNLABELLED: Blood or plasma time-activity curves (TACs) are used as the input function for mathematical models of tracer kinetics in several applications including PET. Uncertainty associated with both the blood data and the PET tissue data can result in uncertainty in the estimates of metabolic rates, blood flow, etc. METHODS: This article presents an approach to reduce the uncertainty in the blood TAC by fitting a model to the curve. The model includes a choice of bolus or infusion input and has three compartments (plasma, interstitial fluid and tissue fluid) with exchange between them. There is a parameter for loss from the plasma compartment. To test the utility of smoothing blood TACs with this approach, a program was set up, using the fluorodeoxyglucose (FDG) model, with simulated noisy blood and tissue TACs. The smoothed blood TAC was compared to a linearly interpolated TAC as the input function with a compartmental model parameter estimation program and with graphical analysis. RESULTS: With a well sampled blood TAC (19 points), the model approach is somewhat more accurate than linear interpolation if the s.d. of noise added to the data exceeded 10%. With sparsely sampled blood TACs (five points) or with a large gap in the blood TAC, the modeled approach was markedly better. For graphical analysis, the model smoothed TAC was also more accurate, although, in general, the results were not as sensitive to the input function. CONCLUSION: This approach, using a physiologically reasonable model to smooth the blood TAC, is a useful aid in PET data analysis, particularly when the data are quite noisy or when there are large gaps in the data.

Computer Simulation↗
PubMed2026

SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization.

Microbiome studies require multiple analytical steps after initial sequence processing. These steps commonly include data harmonization, preprocessing, taxonomic profiling, diversity analysis, differential abundance testing, predictive modeling, network inference, and preparation of publication-ready outputs. Although robust packages are available for many of these tasks, routine use often depends on command-line workflows, repeated data reformatting, and method-specific scripting. These requirements can limit accessibility for experimental researchers and complicate consistent analysis across interdisciplinary teams. We developed SimpleMicrobiome, a web-based R Shiny platform that integrates established microbiome analysis methods into a single interactive downstream workflow. The application accepts standard abundance, taxonomy, and metadata tables, supports interactive preprocessing and sample filtering, and provides modules for taxa profile visualization, alpha and beta diversity analysis, ANCOM-BC2 and MaAsLin2 differential abundance testing, Random Forest modeling with SHAP-based interpretation, microbial association network inference using SparCC and SPIEC-EASI through NetCoMi, correlation heatmaps, and dbRDA/CAP-style association biplots. The platform is implemented as a modular Shiny application so that preprocessing choices are propagated across downstream analyses, results can be exported as figures and tables, and the same application can be run through the public server, source-code installation, or a Docker image. SimpleMicrobiome consolidates major downstream microbiome analysis tasks in an accessible browser-based environment while retaining links to established analytical frameworks. The platform may reduce technical barriers for non-programming users, improve consistency across exploratory and reporting-oriented analyses, and support collaborative microbiome research. The public application is available at https://simplemicrobiome.mglab.org, the source code is available at https://github.com/yjcho2252/SimpleMicrobiome, and a Docker image for local deployment is available at https://hub.docker.com/r/mglab2252/simplemicrobiome.

differential abundance↗
PubMedDate not supplied

Study design and data analysis in clinical and environmental models of nephrotoxicity.

Protocols for clinical studies of nephrotoxicity may include several elements. They include background information, study objectives, study design, data handling and analysis, organization and administration, and methods and definitions. Response variables used to indicate the development of clinically apparent renal disease should be clearly defined. Susceptibility factors such as diabetes, hypertension, cardiovascular disease, obesity, smoking history, and genetic factors may influence the development of renal disease and other health outcomes. These factors may also affect the pattern of abnormal biomarkers that appear during the development of renal disease. Some individuals who are normal by standard clinical criteria will be in various stages of disease development and will have abnormal biomarker levels. With all approaches, an adequate baseline assessment of biomarker values is critically important. Consistent findings among studies reinforce conclusions.

Biomarkers↗
PubMed2004

Applications of Bayesian statistical methods in microarray data analysis.

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

Bayes Theorem↗
PubMed2003

The effect of activity-based financing on hospital efficiency: a panel data analysis of DEA efficiency scores 1992-2000.

Activity-based financing (ABF) was implemented in the Norwegian hospital sector from 1 July 1997. A fraction of the block grant from the state to the county councils has been replaced by a matching grant depending upon the number and composition of hospital treatments. As a result of the reform, the majority of county councils have introduced activity-based contracts with their hospitals. This paper studies the effect of activity-based funding on hospital efficiency. We predict that hospital efficiency will increase because the benefit from cost-reducing efforts in terms of number of treated patients is increased under ABF as compared with global budgets. The prediction is tested using a panel data set from the period 1992-2000. Efficiency indicators are estimated by means of data envelopment analysis (DEA) with multiple inputs and outputs. Using a variety of econometric methods, we find that the introduction of ABF has improved efficiency when measured as technical efficiency according to DEA analysis. The result is less uniform with respect to the effect on cost-efficiency.

Cost Allocation↗
PubMed2002

Extension of variance components approach to incorporate temporal trends and longitudinal pedigree data analysis.

Here we present a method that permits one to evaluate genetic effects and to detect genetic linkages by using serial observations of quantitative traits in pedigrees. We developed a statistical method that incorporates longitudinal family data and genetic marker information into an estimating equations framework. With this approach, we can study changes in components over time that measure polygenic and major genetic variances as well as shared and individual-specific environmental effects. Our method provides a measure of heritability from analysis of longitudinal data. Results using longitudinal family data from the Center for Preventive Medicine (Nancy, France) are presented. The results of our analysis show that the apolipoprotein E locus has no effect on interindividual variability in systolic blood pressure. We found that the longitudinal measure of heritability of systolic blood pressure is 0.32.

Apolipoproteins E↗
PubMed2004

Kernel-based self-organized maps trained with supervised bias for gene expression data analysis.

Self-Organized Maps (SOMs) are a popular approach for analyzing genome-wide expression data. However, most SOM based approaches ignore prior knowledge about functional gene categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in a kernel space. Gaussian kernels are used and their mean and variance components are adapted in order to optimize the fitness to the input density. The KSDG-SOM also grows dynamically up to a size defined with statistical criteria. It is capable of incorporating a priori information for the known functional characteristics of genes. This information forms a supervised bias at the cluster formation and the model owns the potentiality of revising incorrect functional labels. The new method overcomes the main drawbacks of most of the existing clustering methods that lack a mechanism for dynamical extension on the basis of a balance between unsupervised and supervised drives.

Algorithms↗
PubMed2003

[Basic Documentation in Child and Adolescent Psychiatry: Preliminary Data Analysis and Practicability]

A lightly modified version of the basic documentation of the Child and Adolescent Psychiatric Associations was introduced in three different clinics. Relevant items were analysed for about 5300 patients. The data of the 3 clinics were compared. There was a fair amount of agreement in the age structure and the distribution of the diagnoses between the clinics. A regular and detailed comparative analysis of the data will be developed as a measure of quality. The results of these analyses will be discussed in the 3 clinics and implemented in the daily routines.

Journal Article↗
PubMed1998

The determinants of health expenditure in the OECD countries: a pooled data analysis.

This paper uses international health expenditure and the latest OECD data to investigate the determinants of aggregate health expenditure. The study differs from most previous studies in two principal ways. First, it uses a somewhat larger sample for estimation, with pooled time-series, cross-section data for 22 OECD countries for a 20-year period. Most previous work has used a purely cross-section approach: in this case, the small sample size reduced the statistical reliability of results and limited the number of hypotheses that can be tested simultaneously. Second, and following from this, a more extensive range of hypotheses is tested, with particular emphasis on those relating to the contractual relations between payers, providers and patients. The findings show, for example, that the use of primary care "gatekeepers" seems to result in lower health expenditure and also that the way of remunerating physicians in the ambulatory care sector appears to influence health expenditure; capitation systems tend to lead to lower expenditure than fee-for-service systems.

Cross-Cultural Comparison↗
PubMed1975

Multivariate data analysis based on a computerized patient monitoring system.

Multivariate time series data in post-operative patients (respiratory and cardiovascular) are compared to reference groups. Using this technique under the program control of a computerized patient monitoring system (IBM 1800) various classes in the respiratory and cardiovascular spectrum can define the co-ordinate system in hyperspace. The patient in crisis is recognised by his deviation from normal rates of change of the variable set, as well as by the time trajectories of recovery in the hyperspace.

Computers↗