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At least 19 recordsLinked to original sources

Testing hypotheses about determinants of protein structure with high-precision, high-throughput stability measurements and statistical modeling.

Statistical modeling provides the mathematics to use data from large numbers of mutant proteins to generate information about hypotheses concerning protein structure not easily obtained from anecdotal studies on small numbers of mutants. Here we use the unfolding free energies of 303 unique eglin c mutant proteins obtained from high-precision, high-throughput chemical denaturation measurements to assess models concerning helix stability. A model with helix propensity as the sole determinant of stability accounts for 83% of the mutant-to-mutant variation in stability for 99% of the mutant proteins (three outliers). When position effects and side chain-side chain interactions are added to the model, the fraction of variation explained increases to 92%. The propensity parameters in this model are identical to helix propensity values derived from other approaches. Measurement error accounts for another 1% of the mutant-to-mutant variation in stability. While the data support terms for several of the expected stabilizing/destabilizing effects, it does not support terms for several others, including i, i + 3 effects in the center of the helix and helix-dipole effects. In addition, the model does better with terms for several stabilizing/destabilizing effects for which we cannot identify the physical basis. The precision of our unfolding stability measurements (+/-0.087 kcal/mol) allows us to conclude that the 7% of variation in stabilities of the mutant proteins not accounted for by the model or by measurement variation is both real and large with respect to the nonpropensity terms in the model. The analysis also shows that the common practice of using C(m)m(av) instead of C(m)m(mut) to calculate DeltaG(HOH,N-D) values for each mutant protein results in a loss of information. We see no correlation between the residuals derived from the full model and m(mut) - m(wt), and hence it is unlikely our m(mut) values reflect mutant-to-mutant differences in the denatured state.

Cloning, Molecular↗

Predicting intradialytic hypotension from experience, statistical models and artificial neural networks.

BACKGROUND: Symptomatic intradialytic hypotension (IDH) associated with increased mortality in hemodialysis patients is difficult to predict and hence prevent. Artificial Neural Networks (ANNs) are promising tools to solve multidimensional non-linear problems. The aim of the study was to verify in which way mathematical models, statistics or knowledge of patients influence the ability of the nephrologists to predict IDH. METHODS: The performance of ANNs was compared with that of independent nephrologists supported by a logistic regression giving odds ratio for each studied variable (NEPHiS) or of nephrologists in charge of the patients without (NEPHc) or with statistical support as for NEPHiS (NEPHcS). Data from 98 hemodialysis patients were analysed in order to select patients with frequent IDH (>10% of the dialysis sessions). Complete data on 1979 dialysis sessions from 7 patients were retrieved. The ability to predict the occurrence of hypotension episodes was compared (ROC curves) between ANNs, NEPHc/S (N=7) in Switzerland and NEPHiS from independent dialysis centers in Western Australia (N=10). RESULTS: ANN gave the most accurate correlation between estimated and observed IHD episodes compared to NEPHc (p<0.001), but a similar performance was attained by NEPHcS (p<0.001). NEPHiS were superior to NEPHc (P<0.05), but inferior to ANN (P<0.01). For a sensitivity of 80%, specificity was 44% for ANNs, 33% for NEPHcS and 20% for NEPHc. CONCLUSIONS: ANNs are superior to nephrologists in predicting IDH episodes; however when supported by a statistical analysis, nephrologists reach ANNs in their prediction ability. IDH still remains difficult to predict even with mathematical models.

Aged↗

Predicting the patient at low risk for lymph node metastasis with localized prostate cancer: an analysis of four statistical models.

PURPOSE: Statistical models using preoperative Prostate-Specific Antigen, Gleason primary grade or score of the biopsy specimen, and clinical stage have been developed to predict those patients with clinically localized prostate cancer at low risk for lymph node metastasis. It has been recommended that these patients do not require pelvic lymph node dissections. Four such models were evaluated to assess their accuracy in identifying this subgroup of patients. METHODS AND MATERIALS: We reviewed the records of 214 patients with clinically localized prostate cancer who underwent pelvic lymph node dissections. Data from these patients were entered into the four models. RESULTS: Lymph node metastasis was detected in 14% of patients. The results showed the following for each of the proposed models respectively: 78, 50, 76, and 42% of the patients were identified as low risk and, hence, would be spared pelvic lymph node dissections. The false negative rates are 13 (7.8%), 5 (4.6%), 14 (8.6%), and 1 (1.1%). Sensitivities are 56.7, 83.3, 53.3, and 96.7%. CONCLUSIONS: While the pelvic lymph node dissection is the most accurate method of detecting occult nodal metastasis, statistical models can identify a cohort of low risk patients that may be spared lymphadenectomy.

Adult↗

Prediction of dimethyl disulfide levels from biosolids using statistical modeling.

Two statistical models were used to predict the concentration of dimethyl disulfide (DMDS) released from biosolids produced by an advanced wastewater treatment plant (WWTP) located in Washington, DC, USA. The plant concentrates sludge from primary sedimentation basins in gravity thickeners (GT) and sludge from secondary sedimentation basins in dissolved air flotation (DAF) thickeners. The thickened sludge is pumped into blending tanks and then fed into centrifuges for dewatering. The dewatered sludge is then conditioned with lime before trucking out from the plant. DMDS, along with other volatile sulfur and nitrogen-containing chemicals, is known to contribute to biosolids odors. These models identified oxidation/reduction potential (ORP) values of a GT and DAF, the amount of sludge dewatered by centrifuges, and the blend ratio between GT thickened sludge and DAF thickened sludge in blending tanks as control variables. The accuracy of the developed regression models was evaluated by checking the adjusted R2 of the regression as well as the signs of coefficients associated with each variable. In general, both models explained observed DMDS levels in sludge headspace samples. The adjusted R2 value of the regression models 1 and 2 were 0.79 and 0.77, respectively. Coefficients for each regression model also had the correct sign. Using the developed models, plant operators can adjust the controllable variables to proactively decrease this odorant. Therefore, these models are a useful tool in biosolids management at WWTPs.

Air Pollutants↗

Generalized image models and their application as statistical models of images.

A generalized image model (GIM) is presented. Images are represented as sets of four-dimensional (4D) sites combining position and intensity information, as well as their associated uncertainty and joint variation. This model seamlessly allows for the representation of both images and statistical models (such as those used for classification of normal/abnormal anatomy and for inter-patient registration), as well as other representations such as landmarks or meshes. A GIM-based registration method aimed at the construction and application of statistical models of images is proposed. A procedure based on the iterative closest point (ICP) algorithm is modified to deal with features other than position and to integrate statistical information. Furthermore, we modify the ICP framework by using a Kalman filter to efficiently compute the transformation. The initialization and update of the statistical model are also described. Preliminary results show the feasibility of the approach and its potentialities.

Algorithms↗

Analysis of longitudinal data: the integration of theoretical model, temporal design, and statistical model.

This article argues that ideal longitudinal research is characterized by the seamless integration of three elements: (a) a well-articulated theoretical model of change observed using (b) a temporal design that affords a clear and detailed view of the process, with the resulting data analyzed by means of (c) a statistical model that is an operationalization of the theoretical model. Two general varieties of theoretical models are considered: models in which the time-related change of primary interest is continuous, and those in which it is characterized by movement between discrete states. In addition, two general types of temporal designs are considered: the longitudinal panel design and the intensive longitudinal design. For each general category of theoretical models, some of the analytic possibilities available for longitudinal panel designs and for intensive longitudinal designs are discussed. The article concludes with brief discussions of two issues particularly relevant to longitudinal research--missing data and measurement--and a few words about exploratory research.

Humans↗

[Selection of a statistical model for the evaluation of the reliability of the results of toxicological analyses. II. Selection of our statistical model for the evaluation].

Part II presents a statistical model devised by the authors for evaluating toxicological analyses results. The model includes: 1. Establishment of a reference value, basing on our own measurements taken by two independent analytical methods. 2. Selection of laboratories -- basing on the deviation of the obtained values from reference ones. 3. On consideration of variance analysis, t-student's test and differences test, subsequent quality controls and particular laboratories have been evaluated.

Analysis of Variance↗

[Selection of a statistical model for evaluation of the reliability of the results of toxicological analyses. I. Discussion on selected statistical models for evaluation of the systems of control of the results of toxicological analyses].

2 statistical models for evaluation of toxicological studies results have been presented. Model I. after R. Hoschek and H. J. Schittke (2) involves: 1. Elimination of the values deviating from most results-by Grubbs' method (2). 2. Analysis of the differences between the results obtained by the participants of the action and tentatively assumed value. 3. Evaluation of significant differences between the reference value and average value for a given series of measurements. 4. Thorough evaluation of laboratories based on evaluation coefficient fx. Model II after Keppler et al. As a criterion for evaluating the results the authors assumed the median. Individual evaluation of laboratories was performed on the basis of: 1. Adjusted test "t" 2. Linear regression test.

Models, Theoretical↗

Statistical modeling of atherosclerotic plaque in carotid B mode images--a feasibility study.

A feasibility study undertaken to model atherosclerotic plaque in carotid B-mode images is presented. The study is based on 33 regions-of-interest collected from arterial images obtained from four patients. A bimodal gamma distribution with five parameters is proposed to model the statistics of the pixels in the gray-level images. The parameters of the distribution are evaluated for regions containing plaque using curve-fitting techniques. This bimodal distribution appears to be a reasonable fit to the statistics of the pixels. This statistical model may aid in the objective classification of arterial plaque through the use of its parameters.

Algorithms↗

Eutrophication risk assessment in coastal embayments using simple statistical models.

A statistical methodology is proposed for assessing the risk of eutrophication in marine coastal embayments. The procedure followed was the development of regression models relating the levels of chlorophyll a (Chl) with the concentration of the limiting nutrient--usually nitrogen--and the renewal rate of the systems. The method was applied in the Gulf of Gera, Island of Lesvos, Aegean Sea and a surrogate for renewal rate was created using the Canberra metric as a measure of the resemblance between the Gulf and the oligotrophic waters of the open sea in terms of their physical, chemical and biological properties. The Chl-total dissolved nitrogen-renewal rate regression model was the most significant, accounting for 60% of the variation observed in Chl. Predicted distributions of Chl for various combinations of the independent variables, based on Bayesian analysis of the models, enabled comparison of the outcomes of specific scenarios of interest as well as further analysis of the system dynamics. The present statistical approach can be used as a methodological tool for testing the resilience of coastal ecosystems under alternative managerial schemes and levels of exogenous nutrient loading.

Bayes Theorem↗

Statistical models for human fecundability.

Statistical models for fertility studies play a critical role in understanding the biology for conception and in identifying potential environmental factors that may negatively affect human reproduction. Aided with improved computing capability, statistical modeling for fertility data has made a remarkable progress in the past decade. The author provides an overview of the historic and current developments for fecundability models.

Fertility↗

A statistical model for predicting the outcome in breast cancer malpractice lawsuits.

OBJECTIVE: To analyze specific medical, legal, and cost factors that predict the probability of successfully defending lawsuits filed because of failure to diagnose breast cancer. METHODS: Seventy-six malpractice cases handled by the Massachusetts Medical Professional Insurance Association between June 29, 1983 and December 30, 1993 were abstracted and analyzed using univariate analysis, multivariate stepwise logistic and least-square regression analysis, and the Cox proportional hazards model to identify statistically significant associations between clinical factors and medicolegal outcomes. RESULTS: Obstetrician-gynecologists were defendants in the largest number of cases (38) and incurred the highest total indemnity ($7,629,570). The probability of defending a suit successfully increased with smaller tumor size and younger patients (less than 40 years of age). The failure to perform a biopsy was associated with a decreased probability of successful defense. Variables predicting high case cost included younger patient age, an increased length of delay in diagnosis, and the failure to perform a biopsy. The presence of metastasis at diagnosis was associated with an increased interval from diagnosis to the initiation of a suit. CONCLUSION: Statistical models that use medicolegal and cost factors can predict both the probability of a successful defense and the total cost of a breast cancer malpractice case.

Adult↗

Uses and abuses of statistical models for evaluating trauma care.

OBJECTIVE: A statistical model is a powerful tool for evaluating trauma care. With accident and emergency clinicians becoming more aware of the importance of monitoring their department's performance, there is a danger that such models may be inappropriately applied to new sets of trauma cases, resulting in misleading evaluations. The primary objective is to present a method for assessing whether an existing survival probability model can be correctly applied to a different data set. METHODS: Correct and incorrect utilization of statistical models for evaluating the mortality of trauma patients are presented. Circumstances that may lead to the modification of an existing model, and the use of statistical models for evaluating different aspects of trauma care, are suggested.

England↗

Statistical modeling of positron emission tomography images in wavelet space.

A new method is introduced for the analysis of multiple studies measured with emission tomography. Traditional models of statistical analysis (ANOVA, ANCOVA and other linear models) are applied not directly on images but on their correspondent wavelet transforms. Maps of model effects estimated from these models are filtered using a thresholding procedure based on a simple Bonferroni correction and then reconstructed. This procedure inherently represents a complete modeling approach and therefore obtains estimates of the effects of interest (condition effect, difference between conditions, covariate of interest, and so on) under the specified statistical risk. By performing the statistical modeling step in wavelet space. the procedure allows the direct estimation of the error for each wavelet coefficient; hence, the local noise characteristics are accounted for in the subsequent filtering. The method was validated by use of a null dataset and then applied to typical examples of neuroimaging studies to highlight conceptual and practical differences from existing statistical parametric mapping approaches.

Artifacts↗

Statistical models in assessing fold change of gene expression in real-time RT-PCR experiments.

Real-time RT-PCR has been frequently used in quantitative research in molecular biology and bioinformatics. It provides remarkably useful technology to assess expression of genes. Although mathematical models for gene amplification process have been studied, statistical models and methods for data analysis in real-time RT-PCR have received little attention. In this paper, we briefly introduce current mathematical models, and study statistical models for real-time RT-PCR data. We propose a generalized estimation equations (GEE) model that properly reflects the structure of repeated data in RT-PCR experiments for both cross-sectional and longitudinal data. The GEE model takes the correlation between observations within the same subjects into consideration, and prevents from producing false positives or false negatives. We further demonstrate with a set of actual real-time RT-PCR data that different statistical models yield different estimations of fold change and confidence interval. The SAS program for data analysis using the GEE model is provided to facilitate easy computation for non-statistical professionals.

Adipose Tissue↗

Annoyance from multiple transportation noise: statistical models and outlier detection.

OBJECTIVE: Statistical models for the annoyance from multiple transportation noise are needed to understand and predict the annoyance resulting from specific noise exposures. METHODS: Models from the class of generalized linear models are suggested and discussed. Observations which are not well explained by the considered model are regarded as outliers. Outlier detection methods are applied to the data modelled by robust estimates using different link functions. RESULTS: The discussed methods are applied to data from a laboratory experiment using generalized linear models. While considering outliers, a generalized linear model with a complementary log-log link is found to be a good choice in modelling the exposure-response relationship between noise levels and annoyance.

Humans↗