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31P-magnetic resonance spectroscopy studies of nucleated and non-nucleated erythrocytes; time domain data analysis (VARPRO) incorporating prior knowledge can give information on the binding of ADP.

Human erythrocytes have no nucleus, mitochondria or endoplasmic reticulum, whereas chicken erythrocytes have a nucleus and mitochondria and are closer in internal morphology, to cells such as the hepatocyte. Erythrocytes were used to test the hypothesis that 31P-MRS invisibility of ADP is associated with the presence of intracellular organelles. Simple frequency domain spectral analysis methods showed that all the acid extractable ADP (and ATP) was MR-visible in human erythrocytes. However, such methods gave variable estimates for 31P-NMR spectra of fresh chicken erythrocytes from which no conclusions could be drawn about the MR-visibility of ADP. Only when the data were fitted by a method incorporating prior knowledge of the ATP and ADP peak structure, using the time domain VARPRO method, was it possible to conclude that in fresh chicken erythrocytes, similar to other nucleated cells (liver, muscle), all the acid extractable ADP appeared to be MRS invisible, indicating binding or sequestration by intracellular organelles.

Adenosine Diphosphate↗

Design of experiment and data analysis by JMP (SAS institute) in analytical method validation.

Validation of an analytical method through a series of experiments demonstrates that the method is suitable for its intended purpose. Due to multi-parameters to be examined and a large number of experiments involved in validation, it is important to design the experiments scientifically so that appropriate validation parameters can be examined simultaneously to provide a sound, overall knowledge of the capabilities of the analytical method. A statistical method through design of experiment (DOE) was applied to the validation of a HPLC analytical method for the quantitation of a small molecule in drug product in terms of intermediate precision and robustness study. The data were analyzed in JMP (SAS institute) software using analyses of variance method. Confidence intervals for outcomes and control limits for individual parameters were determined. It was demonstrated that the experimental design and statistical analysis used in this study provided an efficient and systematic approach to evaluating intermediate precision and robustness for a HPLC analytical method for small molecule quantitation.

Chromatography, High Pressure Liquid↗

Application of reliability coefficients in cDNA microarray data analysis.

Gene expression microarray technology has been widely used in areas such as human cancer research to identify molecular characteristics of sample specimens. The microarray study, however, is a very complicated procedure which involves numerous sources of variability that may be either systematic or random. Systematic variation is often eliminated by applying normalization procedures, but at present there are no standard criteria available to evaluate the performance of a particular normalization approach. In this paper, we propose a reliability-type coefficient as a criterion to assess the effectiveness of normalization procedures in eliminating systematic variation. Simulation studies show that this criterion performs reasonably well in a range of settings. The proposed method is illustrated using a subset of an ongoing microarray study of soft-tissue sarcoma.

Computer Simulation↗

[Drug therapy of acute manias. A retrospective data analysis of inpatients from 1997 to 1999].

The aim of the present retrospective chart analysis was to compare published treatment algorithms with the treatment patterns of 90 consecutive inpatients suffering from acute mania or hypomania at the Department of General Psychiatry, University of Vienna, from 1997 to 1999. Treatment strategies during the first 14 days and on discharge as well as sociodemographic and illness related data were evaluated. The results of our study reflect that international guidelines were not included in daily practice from 1997 to 1999 with regard to the usage pattern of atypical antipsychotics versus typical neuroleptics. Also, recommendations have not been taken into account about monotherapy with a mood stabilizer as first-line treatment for acute mania (polypharmacia was the predominant treatment scheme) and the advice to taper off benzodiazepines (at discharge).

Acute Disease↗

HTS quality control and data analysis: a process to maximize information from a high-throughput screen.

Changes in all aspects of HTS from compound management through to evaluation of hits and leads, strengthened by infrastructure improvements, in both automation and informatics, have made possible increased analysis and implementation of process and quality control throughout HTS. This paper focuses on the process of HTS with an emphasis on quality control, reducing the variability of all the processes that have an impact on the final result, and argue that by increasing the quality of the entire process that data mining of primary screening data is in fact possible and will reduce cycle times to medicinal chemistry.

Automation↗

Kinetics of T cell proliferation: a mathematical model and data analysis.

A mathematical model of in vitro T cell proliferation controlled by IL-2 internalization is presented. The model describes the T cell transition from G1 to S+G2+M stages of the cell cycle and introduces "molecular" equations for the G1-S phase control. These equations consider current knowledge of the biochemical mechanisms of receptor synthesis, ligand-receptor binding, and internalization of ligand-receptor complexes. The model describes the kinetic data for in vitro T cell proliferation at various IL-2 concentrations (50-500 pM) for various exposure time (6-26 h). The kinetic parameters were calculated based on the model. The results obtained suggest that increase in the IL-2 concentration and exposures decrease the critical ligand-receptor concentration in the cells which control proliferation. The time tau, which characterizes the delay in the G1-S transition, was constant at various IL-2 concentrations.

Animals↗

Multivariate data analysis using D-optimal designs, partial least squares, and response surface modeling: A directional approach for the analysis of farnesyltransferase inhibitors.

We have investigated the combined use of partial least squares (PLS) and statistical design principles in principal property space (PP-space), derived from principal component analysis (PCA), to analyze farnesyltransferase inhibitors in order to identify "activity trends" (an approach we call a "directional" approach) and quantitative structure-activity relationships (QSAR) for a congeneric series of inhibitors: the benzo[f]perhydroisoindole (BPHI) series. Trends observed in the PCA showed that the descriptors used were relevant to describe our structural data set by clearly identifying two well-defined structural subclasses of inhibitors. D-Optimal design techniques allowed us to define a training set for PLS study in PP-space. Models were derived for each biological assay under evaluation: the in vitro Ki-Ras and cellular HCT116 tests. Each of these assay-based sets was subdivided once more into two subsets according to two structural classes in this BPHI series as revealed by the PCA model. The response surface modeling (RSM) methodology was used for each subset, and the corresponding RSM plots helped us identify "activity trends" exploited to guide further analogue design. For more precise activity predictions more refined PLS models on constrained PP-spaces were developed for each subset. This approach was validated with predicted sets and demonstrates that useful information can be extracted from just a few very informative and representative compounds. Finally, we also showed the potential use of such a strategy at an early stage of an optimization process to extract the first "activity trends" that might support decision making and guide medicinal chemists in the initial design of new analogues and/or lead followup libraries.

Alkyl and Aryl Transferases↗

Shrunken p-values for assessing differential expression with applications to genomic data analysis.

In many scientific problems involving high-throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p-values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision-theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.

Biometry↗

Estimating equations for removal data analysis.

We consider the problem of estimating a population size from successive catches taken during a removal experiment and propose two estimating functions approaches, the traditional quasi-likelihood (TQL) approach for dependent observations and the conditional quasi-likelihood (CQL) approach using the conditional mean and conditional variance of the catch given previous catches. Asymptotic covariance of the estimates and the relationship between the two methods are derived. Simulation results and application to the catch data from smallmouth bass show that the proposed estimating functions perform better than other existing methods, especially in the presence of overdispersion.

Animals↗

Functional multi-layer perceptron: a non-linear tool for functional data analysis.

In this paper, we study a natural extension of multi-layer perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results, which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.

Algorithms↗

Neuroimaging of emotion: empirical effects of proportional global signal scaling in fMRI data analysis.

Global variations of BOLD-fMRI signal are often considered as nuisance effects. This unwanted source of variance is commonly eliminated using proportional global signal scaling (PGSS). However, application of PGSS relies on the assumption that global variations of BOLD signal and experimental conditions are uncorrelated. It has been shown for cognitive tasks that the unjustified application of PGSS might greatly distort statistical results. The present study examined this issue in the domain of emotion research. Specifically, fMRI data were obtained in a block-design, while 21 subjects passively viewed high and low emotionally arousing pleasant, unpleasant, and neutral pictures. Violations of the orthogonality assumption were found for analyses of emotional pictures high in arousal, causing dramatically different outcomes when compared to analyses performed without PGSS. Application of PGSS was associated with attenuated emotional activation in visual cortical areas, insensitivity to emotional activations in limbic and paralimbic regions, and widely distributed artificial deactivations. In contrast, the orthogonality assumption was not violated for low arousing emotional materials. Thus, the validity of using PGSS varied as a function of the emotional arousal of the stimuli. Taken together, the unwarranted use of PGSS might contribute to conflicting results in affective neuroscience fMRI studies, in particular with respect to limbic and paralimbic structures.

Cerebral Cortex↗

Computerized data analysis of amino acids in physiologic fluids.

A Fortran IV computer program is described which identifies, computes, and statistically evaluates amino acid concentrations determined by an automatic amino acid analyzer in physiologic fluids. The program accepts retention time and intergrated peak areas from two calibration standards for identification and computational reference. Two internal standards are used to compensate for variations in injector performance and ninhydrin decay. Calibration standard responses are statistically treated to assist in detection of equipment malfunction. The statistical data base is automatically explanded through inclusion of data from normal patients. Amino acid concentrations are printed with appropriate mean and standard deviation values in a format acceptable as a final report.

Amino Acids↗

Sample size calculation for multiple testing in microarray data analysis.

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation method for designing microarray studies. Through simulations and data analyses, we find that the proposed methods for testing and sample size calculation are computationally fast and control error and power precisely.

Computer Simulation↗

Oxidative metabolism of rabbit and rat intestine with short chain fatty acids and glucose: an evaluation of data analysis.

1. Glucose sustained VO2 of rabbit ileum, caecum, and distal colon better than SCFAs. 2. In rabbit proximal colon, while VO2 was stimulated in the presence of butyrate it was not sustained. 3. Rat caecum utilized glucose but it was not necessarily the best substrate for either the ileum or colon of this species and SCFAs appeared to stimulate VO2 of rat ileum and inhibit VO2 in rat caecum and colon. 4. It was concluded from the comparison of the two methods of data analyses that curve-fitting the data by a negative exponential equation provides for a more clear and in depth interpretation of such studies.

Animals↗

Crystallization and X-ray diffraction data analysis of leukotriene A4 hydrolase from Saccharomyces cerevisiae.

The Saccharomyces cerevisiae leukotriene A4 (LTA4) hydrolase (scLTA4 hydrolase) has been crystallized in order to study the two activities of LTA4 hydrolase in an evolutionary perspective. Single well diffracting crystals are obtained after switching from the hanging-drop method to liquid-liquid diffusion in capillaries using PEG 8000 as precipitant. These crystals belong to space group P2(1)2(1)2(1), with unit-cell parameters a = 70.8, b = 98.1, c = 99.2 A. Intensity data to 2.3 A resolution were collected from a native scLTA4 hydrolase crystal using synchrotron radiation. A molecular-replacement solution was obtained using the human LTA4 hydrolase structure and the program BEAST.

Crystallization↗

The influence of experimental design and data analysis on the determination of recovery kinetics of radiation damage between acute dose-rate treatments in vivo.

Current interest in determining the rate of recovery of damage between radiation doses in fractionated treatments has resulted in the development of several experimental designs and methods of analysis to address this. One approach is where two or more fractions are given with a varying interval. Isoeffect doses are then determined from the dose-response curves for each interval, and these are plotted on a logarithmic axis against time on a linear scale. An estimate of the rate of dose recovery can then be made if the data show monoexponential or well-defined multiexponential kinetics. However, three problems can be identified in this simple protocol. First, most repair models (e.g. Thames' IR and Curtis' LPL) assume that between two doses loge (cell survival), i.e. underlying effect, not dose itself, recovers exponentially with time. Experimental data support this assumption. Since underlying effect and dose are not linearly related, recovery measured from the change in isoeffect dose can appear substantially slower (depending on dose per fraction) than the true underlying recovery rate of damage. This artifact is avoided by converting dose increments into changes in underlying effect (with the linear-quadratic model) or by measuring underlying effect more directly in 'top-up' experiments. The use of (neutron) top-up experiments is preferred, as it enables recovery between constant X-ray doses per fraction to be studied, and makes no prior assumptions regarding either the shape of the X-ray dose-response curve or how recovery takes place, although the shape of the neutron dose-response curve must be known. Second, plotting log (unrecovered damage) against time can overestimate recovery half-times, because such plots cannot handle negative values and therefore become naturally weighted in favour of the data from the longer time intervals where the difference from complete recovery is smallest. This problem is managed by using nonlinear regression to fit the values of unrecovered damage expressed on a linear scale against interval. Third, experiments using three or more evenly spaced fractions, 'concertina'-style, permit interaction between non-adjacent fractions. If this is not taken account of, then recovery appears to be initially faster and multiexponential, even though the underlying recovery may be actually monoexponential. Thus concertina experiments are poor at resolving the precise shape of recovery-kinetics profiles and are less suited for measuring any dependence of recovery rate on dose per fraction compared with approaches using either just two fractions, or two fractions per day.

DNA Repair↗

Research in physical medicine and rehabilitation. IX. Primary data analysis.

The primary statistical analysis is approached from the standpoint of what is required to publish in medical research journals. Descriptive and bivariate statistics cover the majority of medical research articles now published. Statistical guidelines for review of manuscripts are used to develop guidelines for analysis, including specifying the objective, the source of subjects and response rate, differences detectable with the expected sample size, appropriateness of statistics for one and two variables, method of presentation of results and conclusions and calculation of confidence intervals. Common mistakes to avoid include use of standard error of the mean instead of standard deviation, use of standard deviation with skewed data, failure to describe the statistical test used, multiple comparisons and failure to use special forms of t test and chi 2.

Cerebrovascular Disorders↗

Proposed modification to data analysis for statistical motor unit number estimate.

Motor unit number estimation (MUNE) is an important electrophysiological technique for quantitative measurement of motor neuron loss. Although commonly used, there is no consensus concerning the optimal procedure for statistical MUNE, particularly regarding several operator-dependent variables. To assess the variables, we analyzed 500 sequential, submaximal compound muscle action potential (CMAP) responses at three or four stimulus intensities in 10 controls and 10 patients with amyotrophic lateral sclerosis (ALS). In both controls and ALS patients, we found that posttest filtering data based on 20% or 25% windows or 2, 2.5, or 3 SD excludes <5% of data. Windows of 10% or 15% excluded <5% of data in controls but not in ALS patients. Excluding data based on +/-2 SD, the coefficient of variation for final MUNE was 12% in controls and 6% in ALS patients. Group sizes of 30 or 50 and sample sizes of 300 to 500 sequential CMAP responses per run yielded the lowest coefficient of variation. We propose that statistical MUNE data should be analyzed based on excluding data >2 SD from the mean, because this is operator independent, includes the majority of data, effectively excludes clearly outlying data, such as fasciculations or movement artifact, and has a reasonable coefficient of variation.

Adult↗