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PubMed2006

Establishing a statistic model for recognition of steroid hormone response elements.

Identification of hormone response elements (HREs) is essential for understanding the mechanism of hormone-regulated gene expression. To date, there has been a lack of effective bioinformatics tools for recognition of specific HRE such as Progesterone Response Elements (PRE). In this paper, a comprehensive survey and comparison of in silico methods is conducted for establishing a more accurate statistic model. Homogeneity of steroid HRE is analyzed and a reliable training dataset is constructed through extensive searching for experimentally validated response elements from more than 150 literature sources. Based on the observation that the verified HREs carry di-nucleotide preservation in comparison with uniform nucleotide distributions, both mono and di-nucleotide Position Weight Matrices are computed to extract the statistic pattern of the positions. It is followed by the sequence transition pattern recognition using a specifically designed profile Hidden Markov Model. Reciprocal combination of the statistic and transition patterns significantly improves the performance of the model in terms of higher sensitivity and specificity. Upon acquisition of the putative response elements in the promoter areas of vertebrate genes, a qualitative scheme is applied to assess the probability for each gene to be a hormone primary target. Using >650 records of experimentally validated steroid hormone response elements, a high sensitivity level of 73% and high specificity level of one prediction per 8.24 kb is reached, allowing this model to be used for further prediction of primary target genes through the analysis of their upstream promoters, for human or other vertebrate genomes of interest. Additional documents, supplementary data and the web-based program developed for response elements prediction are freely available for academic research at . Submission of putative gene promoter regions for recognition of potential regulatory PREs can be as long as 5 kb.

Animals↗
PubMed2005

Statistical modeling of the response characteristics of mechanosensitive stimuli in the human esophagus.

UNLABELLED: It is believed that mechanical stimuli of the human gut activate afferents responding to either noxious or normal, physiologic stimuli. They might be able to sensitize without relation to the contractile state of the smooth muscle. The current study aimed to verify the above characteristics by using a statistical model based on correlation analysis. The esophagus was distended with a bag in 32 healthy subjects by using an inflation rate of 25 mL/min. The luminal cross-sectional areas and sensory ratings were determined during the distentions. The stimuli were repeated after relaxation of the smooth muscle with butylscopolamine and after sensitization with hydrochloride acid. A positive correlation between the sensory responses to distention was found in the non-painful and painful ranges, respectively, but correlations between non-painful and painful ratings were nonsignificant. Relaxation of the smooth muscle did not influence the correlations, and sensitization resulted in inter-individual differences and disappearance of the above clustering into painful and non-painful correlations. In conclusion, afferent nerves encoding high-threshold and low-threshold mechanical stimuli of the human esophagus are not correlated and thus probably represent different populations. The response characteristics have no physiologic relationship to the contractile state of the smooth muscle, and sensitization affects all types of afferents. PERSPECTIVES: The article adds information about sensory processing of mechanical gut stimuli in human beings. This might increase our understanding of visceral pain in health and disease and guide the statistical analysis of experimental data obtained in the gastrointestinal tract.

Action Potentials↗
PubMed1996

A general statistical model for detecting complex-trait loci by using affected relative pairs in a genome search.

Scanning of the human genome by use of affected relative pairs and dense sets of highly polymorphic markers or by emerging techniques such as genomic mismatch scanning. (GMS) is making it possible to identify the genetic etiology of a disease through detection of susceptibility loci. We present a general statistical model and test to detect disease genes, using affected relative pairs and either markers or GMS technologies in a genome search. There are an exact test and large-sample normal approximation that control for the elevated probability of false detection of linkage in a genome search. The approach can be used to determine the sample size needed to obtain a prespecified power to detect a disease gene in the presence of etiologic heterogeneity for a single class or mixture of relative classes, with any number of markers, or clones, markers PIC values, or mapping function. The approach is used to examine differences in performance of markers and GMS technologies in a common statistical framework and to provide practical information for designing studies of complex traits.

Alleles↗
PubMed1998

Spatial and temporal variation of mortality and deprivation 2: statistical modelling.

"Building on the tabular analyses exemplified in our first paper and widely used in the medical literature, we use generalised linear models to provide a formal, statistical approach to the analysis of mortality and deprivation relationships, and their change over time. Three types of fixed effects model are specified and estimated with the same ward-level data sets for Wales examined in our first paper. They are: Poisson models for analysing mortality and deprivation at a single cross section in time; repeated-measures Poisson models for analysing mortality-deprivation relations, not only at cross sections in time, but also their changes over time; and logit models focusing on temporal changes in mortality-deprivation relationships. Nonlinear effects of deprivation on mortality have been explored by using dummy variables representing deprivation categories to establish the connection between formal statistical models and the tabular approach"

Demography↗
PubMed2003

Statistical model for assessing the impact of targeted, school-based dental sealant programs on sealant prevalence among third graders in Ohio.

OBJECTIVES: Since Ohio school-based dental sealant programs target economically disadvantaged groups, simple comparison of sealant prevalence between schools with sealant programs and those without is problematic due to underlying disparities between the two in sealant prevalence. The goal of our analysis was to estimate the impact of sealant programs on sealant prevalence among third graders in Ohio by applying a statistical model to data from a 1998-99 Ohio oral health screening survey of schoolchildren to control for differences in background characteristics. METHODS: Included in the analysis were 9,747 third graders at randomly selected schools in Ohio. Chi-square statistics and survey logistic regression were used to analyze the association of sealant presence with school sealant program participation, dental care payment method, sex, race, and school lunch program eligibility. RESULTS: The unadjusted odds ratio for dental sealant presence was 3.4 (95% confidence interval [CI]=2.6, 4.4; P<.01). Adjusting for race and income, the odds of having dental sealants among children in schools with dental sealant programs increased to 4.8 (95% CI=3.5, 6.5; P<.01). CONCLUSIONS: Not controlling for confounders can result in underestimation of the impact of targeted school sealant programs.

Chi-Square Distribution↗
PubMed2005

Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2.

BACKGROUND: It has been shown that the classical receptive fields of simple and complex cells in the primary visual cortex emerge from the statistical properties of natural images by forcing the cell responses to be maximally sparse or independent. We investigate how to learn features beyond the primary visual cortex from the statistical properties of modelled complex-cell outputs. In previous work, we showed that a new model, non-negative sparse coding, led to the emergence of features which code for contours of a given spatial frequency band. RESULTS: We applied ordinary independent component analysis to modelled outputs of complex cells that span different frequency bands. The analysis led to the emergence of features which pool spatially coherent across-frequency activity in the modelled primary visual cortex. Thus, the statistically optimal way of processing complex-cell outputs abandons separate frequency channels, while preserving and even enhancing orientation tuning and spatial localization. As a technical aside, we found that the non-negativity constraint is not necessary: ordinary independent component analysis produces essentially the same results as our previous work. CONCLUSION: We propose that the pooling that emerges allows the features to code for realistic low-level image features related to step edges. Further, the results prove the viability of statistical modelling of natural images as a framework that produces quantitative predictions of visual processing.

Models, Statistical↗
PubMed2004

Statistical modeling of sequencing errors in SAGE libraries.

MOTIVATION: Sequencing errors may bias the gene expression measurements made by Serial Analysis of Gene Expression (SAGE). They may introduce non-existent tags at low abundance and decrease the real abundance of other tags. These effects are increased in the longer tags generated in LongSAGE libraries. Current sequencing technology generates quite accurate estimates of sequencing error rates. Here we make use of the sequence neighborhood of SAGE tags and error estimates from the base-calling software to correct for such errors. RESULTS: We introduce a statistical model for the propagation of sequencing errors in SAGE and suggest an Expectation-Maximization (EM) algorithm to correct for them given observed sequences in a library and base-calling error estimates. We tested our method using simulated and experimental SAGE libraries. When comparing SAGE libraries, we found that sequencing errors can introduce considerable bias. High abundance tags may be falsely called as significantly differentially expressed, especially when comparing libraries with different levels of sequencing errors and/or of different size. Truly, differentially expressed tags have decreased significance as 'true'-tag counts are generally underestimated. This may alter if tags near the threshold of differential expression are called significant. Moreover, the number of different transcripts present in a library is overestimated as false tags are introduced at low abundance. Our correction method adjusts the tag counts to be closer to the true counts and is able to partly correct for biases introduced by sequencing errors. AVAILABILITY: An implementation using R is distributed as an R package. An online version is available at http://tagcalling.mbgproject.org

Algorithms↗
PubMedDate not supplied

Capacitance in ant cuticle is frequency dependent: a statistical model.

The capacitance and the electric resistance of the cuticle of the ant Cataglyphis bicolor nigra Andr é (Hymenoptera, Formicinae ) were measured. The measurements were done at the frequencies 100 Hz and 1000 Hz and at a temperature range of 27.5-45 degrees C. Inverse correlation was observed between the capacitance and the frequency, so that at the higher frequency the capacitance was lower. Thus, in some instances, at 1000 Hz the capacitance ranged between 0.49 and 2. 16nF , while at 100 Hz it ranged between 5.74 and 19. 39nF at the measured temperature range. A similar inverse correlation was detected also between the resistance and the frequency. At 1000 Hz, the resistance values in some specimens ranged between 0.166 and 0.278 M omega whereas at 100 Hz they varied between 0.342 and 0.883 M omega. At both frequencies measured there was a temperature-dependence of the capacitance and of the electric resistance. With increase in temperature there was increase in the capacitance and a decrease in the resistance. Invariably, the trend of cuticular behavior was similar under cooling as under warming, but the values differed, creating a gap (hysteresis). Under cooling the resistance values were higher and the reverse was true for the capacitance. A statistical model is offered which graphically describes the behavior of the ant cuticle (resistance and capacitance) under changes in temperature and frequency. X-ray analysis of the cuticle revealed the presence of Ca as the most prominent element. Additional elements, but less prominent were P, S and K. It also contains Fe and Zn. The finding of a correlation between the capacitance and the frequency might lead to the following conclusions: that the measured system contains polarized substances; that the measured value in each case represents a resultant of values obtained from the measurement of more than one electrical circle (and probably more than one network of electrical circles); and that possibly a combination of (a) and (b) prevails. Presumably these changes in the capacitance and resistance at different temperatures and frequencies indicate that the ant cuticle is capable of responding to changes in the physical ambience , thus promoting proper ant spatial orientation and the pursuant behavior.

Animals↗
PubMedDate not supplied

Re-examination of the ED01 study. Review of statistics: the need for realistic statistical models for risk assessment.

The statistical analyses of the ED01 study leave us with the following conclusions: 1) To be realistic, risk assessment using chronic animal studies should be based on time-to-response data and models. 2) Simple standards such as a virtual safe dose and simple models such as the one-hit model or linear extrapolation seem ill-suited as scientific means for risk assessment for such extensive data sets as the ED01 study. 3) The development of new methods for analysis, modeling and risk assessment should be encouraged if future decisions are to be realistically based on chronic animal studies.

2-Acetylaminofluorene↗
PubMedDate not supplied

A statistical model to optimize enzyme-linked immunosorbent assay parameters for detection of Mycoplasma gallisepticum and M. synoviae antibodies in egg yolk.

An enzyme-linked immunosorbent assay (ELISA) was developed to quantify Mycoplasma gallisepticum (MG) and M. synoviae (MS) antibodies in egg-yolk extract. Various parameters of ELISA were evaluated and optimized. A statistical model was developed to study the relationship between ELISA absorbance (A) and antigen concentration, antibody concentration, and time of reading of the test. These factors explained 62% of the variability in A for the MG antigen and 74% of the variability in A for the MS antigen. The optimum concentration of MG antigen was 4 micrograms protein/ml, and that for MS antigen was 3 micrograms protein/ml. A similar model was developed to study the relation between A and conjugate concentration, antibody concentration, and time of reading of the test. The optimum concentration of the conjugate was found to be 1:4000. The within-run and between-run coefficients of variation for MG were 8% and 9%, respectively, and those for MS were 9% and 7%, respectively, indicating a high degree of reproducibility of these tests. There was a high correlation between ELISA and hemagglutination-inhibition test results.

Animals↗
PubMed1993

Statistical model for fetal death, fetal weight, and malformation in developmental toxicity studies.

The purpose of this paper is to present a statistical model for analyzing the joint effects of exposure on fetal death, fetal weight, and malformation in a developmental toxicity study. In addition to allowing for the usual litter effect, the model allows for correlations between different outcomes measured on the same fetus. Fitting the model requires first focusing on non-live outcomes by modeling the probability of fetal death or resorption as a function of dose. Then outcomes among live fetuses are modeled using a two-stage regression approach. The first stage models fetal weight as a function of dose and the second stage models fetal malformation as a function of dose, as well as residuals from the weight model. The regression coefficients from the malformation model have intuitive interpretations in terms of correlations between littermates and between different outcomes measured within the same fetus. Not only does the approach provide a useful way to investigate the relationship between adverse fetal outcomes, it also yields a natural framework for conducting quantitative risk assessment. A procedure is proposed for quantifying overall risk by incorporating the three outcomes in order to estimate safe dose levels and corresponding lower confidence limits. The method is illustrated using data from an experiment in mice conducted through the National Toxicology Program.

Abnormalities, Drug-Induced↗
PubMed2004

Support for Barker hypothesis upheld in rat model of maternal undernutrition during the preimplantation period: application of integrated 'random effects' statistical model.

In response to a recent paper published in Reproductive BioMedicine Online by Walters and Edwards (2003), this study reports the application of a random effects regression analysis for evaluation of integrated data involving maternal and embryo/offspring components. Using this method, it is possible to confirm the conclusions of an earlier study that rat maternal undernutrition during the preimplantation period results in blastocyst cell number reduction and post-natal outcomes, including altered growth rates and elevated blood pressure.

Animals↗
PubMed2004

An experimentally confirmed statistical model on arm movement.

The purpose of this research work was to develop a methodology to model arm movement in normal subjects and neurologically impaired individuals through the application of a statistical modelling method. Thirteen subjects with Parkinson's disease and 29 normal controls were recruited to participate in an arm motor task. An infrared optoelectronic kinematic movement analysis system was employed to record arm movement at 50 times per second. This study identified the modified extended Freundlich model as one that could be used to describe this task. Results showed that this model fit the data well and that it has a good correspondence between the observed and the predicted data. However, verification of the model showed that the residuals contained a sizeable autocorrelation factor. The Cochrane and Orcutt method was applied to remove this factor, which improved the fit of the model. Results showed that Parkinson's disease subjects had a higher autocorrelation coefficient than the normal subjects for this task. A positive correlation (r(s) = 0.72, p < 0.001) was found between the Langton-Hewer stage and the autocorrelation coefficient of PD subjects. This finding suggests that if autocorrelation is positively correlated with disease progression, clinicians in their clinical practice might use the autocorrelation value as a useful indicator to quantify the progression of a subjects' disease. Significant differences in model parameters were seen between normal and Parkinson's disease subjects. The use of such a model to represent and quantify movement patterns provides an important base for future study.

Aged↗
PubMed1978

A statistical model for predicting response of breast cancer patients to cytotoxic chemotherapy.

A binary logistic model is used for predicting response to cytotoxic chemotherapy for a breast cancer patient on the basis of her tumor enzyme activity profile. The enzymes used in the model are lactate dehydrogenase, nicotinamide adenine dinucleotide phosphate-isocitrate dehydrogenase, and phosphoglucomutase, all of which were measured on primary tumor specimens from each patient. The statistical model provides an estimate of the probability that an individual will respond to treatment. Chemotherapeutic treatment consisting of combination cytotoxic drugs and subsequent evaluation of patient response followed cooperative group protocol guidelines, including outside review to confirm the patient evaluation. The model based on this study, which represents 5 years of patient follow-up, correctly predicts clinical outcome in 32 of the 37 cases available.

Antineoplastic Agents↗
PubMed2002

On Wiener filtering and the physics behind statistical modeling.

The closed-form solution of the so-called statistical multivariate calibration model is given in terms of the pure component spectral signal, the spectral noise, and the signal and noise of the reference method. The "statistical" calibration model is shown to be as much grounded on the physics of the pure component spectra as any of the "physical" models. There are no fundamental differences between the two approaches since both are merely different attempts to realize the same basic idea, viz., the spectrometric Wiener filter. The concept of the application-specific signal-to-noise ratio (SNR) is introduced, which is a combination of the two SNRs from the reference and the spectral data. Both are defined and the central importance of the latter for the assessment and development of spectroscopic instruments and methods is explained. Other statistics like the correlation coefficient, prediction error, slope deficiency, etc., are functions of the SNR. Spurious correlations and other practically important issues are discussed in quantitative terms. Most important, it is shown how to use a priori information about the pure component spectra and the spectral noise in an optimal way, thereby making the distinction between statistical and physical calibrations obsolete and combining the best of both worlds. Companies and research groups can use this article to realize significant savings in cost and time for development efforts.

Algorithms↗
PubMed2002

Development of a statistical model for estimating spatial and temporal ambient ozone patterns in the Sierra Nevada, California.

Statistical approaches for modeling spatially and temporally explicit data are discussed for 79 passive sampler sites and 9 active monitors distributed across the Sierra Nevada, California. A generalized additive regression model was used to estimate spatial patterns and relationships between predicted ozone exposure and explanatory variables, and to predict exposure at nonmonitored sites. The fitted model was also used to estimate probability maps for season average ozone levels exceeding critical (or subcritical) levels in the Sierra Nevada region. The explanatory variables--elevation, maximum daily temperature, and precipitation and ozone level at closest active monitor--were significant in the model. There was also a significant mostly east-west spatial trend. The between-site variability had the same magnitude as the error variability. This seems to indicate that there still exist important site features not captured by the variables used in the analysis and that may improve the accuracy of the predictive model in future studies. The fitted model using robust techniques had an overall R2 value of 0.58. The mean standard deviation for a predicted value was 6.68 ppb.

Altitude↗
PubMed2004

Statistical model for assessing the portion of fine particulate matter transported regionally and long range to urban air.

OBJECTIVES: This study attempted to develop a simple statistical model for assessing the contribution of aerosols transported regionally and those transported long range to the concentrations of fine particulate matter (PM2.5) in urban air in Helsinki. METHODS: The construction and testing of the linear regression model was based on PM2.5 measurement data from two locations in the City of Helsinki (Vallila & Kallio) and on ion concentration data obtained from the three nearest monitoring stations of The Co-operative Programme for Monitoring and Evaluating of the Long-range Transmission of Air Pollutants in Europe (EMEP). The "ion sum" was calculated on the basis of the following daily measured EMEP parameters in 1998--2000: (i) sulfate (SO4(2-)), (ii) the sum of nitrate (NO3-) and nitrogen acid (HNO3), and (iii) the sum of ammonium (NH4+) and ammonia (NH3). The ion sum was compared with sulfate as the proxy variable for PM2.5 transported long range. RESULTS: The correlation of the daily average PM2.5 concentration with the ion sum (R2=0.59-0.61) was higher than that with sulfate (R2 = 0.48-0.50). The regression estimates showed relatively small year-to-year variation. The contribution of long-range transport to the measured PM2.5 concentration in urban air in Helsinki was estimated to be 64-76%. CONCLUSIONS: The results showed a strong association between the ion sum interpolated from the EMEP data and the PM2.5 concentration measured at urban sites in Helsinki. This association can be utilized in local dispersion modeling of the PM2.5 concentration in urban air.

Air Pollutants↗