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PubMed2001

The influence of personality on nicotine craving: a hierarchical multivariate statistical prediction model.

The present study proposes a hierarchical multivariate statistical prediction model which enables to determine the most prominent variables (physiological, biochemical and personality factors) related to nicotine craving and dopaminergic activation. Based on animal studies reporting a reduction of the rewarding effects of psychotropic drugs after blockade or destruction of the mesolimbic dopamine (DA) system, changes in nicotine craving after pharmacological manipulation by means of a DA agonist (lisuride 0.2 mg) and a DA antagonist (fluphenazine 2 mg) were assessed in 36 healthy male heavy smokers. The major aim was the development of a multivariate prediction model which is applicable in samples lacking variance homogeneity or the prerequisite of a multivariate normal distribution. The model proposed is a combination of multivariate parametric and nonparametric methods taking advantage of their individual merits. Especially personality variables, such as sensation seeking, impulsivity, and neuroticism showed to be important predictors of craving in this responder approach.

Adult↗
PubMed2000

Approaches to estimating daily yield from single milk testing schemes and use of a.m.-p.m. records in test-day model genetic evaluation in dairy cattle.

Statistical models were presented to estimate daily yields from either morning or evening test results. The 64,451 test-day records from 10,392 lactations of 8800 cows were available for analysis from experiments that were designed to investigate the accuracy of an alternate morning and evening four-weekly milk-testing scheme. The experiments were conducted in 152 herds from six German states and covered a span from 1994 to 1998. Milk yield, fat, and protein percentage were recorded for all of the morning and evening milkings. Seven statistical models were fitted to the data to derive formulas for estimating daily yields from morning or evening yields. In general, use of evening milkings less accurately estimated yields than did use of morning milkings. Among the three yield traits the lowest accuracy of estimation of daily yield was found for fat yield. Although the models do not differ much in the correlation between estimated and true daily yields, systematic under- and overestimation of daily yield at the beginning and end of lactation were observed in all models with the exception of model 6, which accounted for heterogeneous variances by parity class, milking interval class, and lactation stage by fitting separate regression formulas within each combination of the three factors. A study to validate the models showed that model 6 is also robust for the analyzed populations. Smoothing model 6 regression formulas across lactation stages caused a systematic pattern of estimation error, although loss in accuracy was minimal by fitting far fewer parameters in the regression formulas. Differences in the accuracy of alternate milking schemes to predict daily yields were found between traits, between morning and evening milkings, and between parity classes. Compared with true daily yields from different lactation stages, variances and correlations of the estimated yields were reduced, which must be accounted for in genetic evaluation. The use of estimated daily yields from morning or evening milkings has a smaller impact on estimated breeding values of bulls than cows. As a result of lower heritability and repeatability of estimated daily yields than true daily yields, the weight on own test-day records for estimating cows' breeding values is lower when cows are in a.m.-p.m. than conventional monthly testing schemes. However, the difference in the weights between estimated and true daily yields decreases as lactation progresses. Use of estimated daily yields is less reliable for estimating breeding value than use of true daily yields.

Animals↗
PubMed2004

Tryptophan-NAD+ pathway metabolites as putative biomarkers and predictors of peroxisome proliferation.

The present study was designed to provide further information about the relevance of raised urinary levels of N-methylnicotinamide (NMN), and/or its metabolites N-methyl-4-pyridone-3-carboxamide (4PY) and N-methyl-2-pyridone-3-carboxamide (2PY), to peroxisome proliferation by dosing rats with known peroxisome proliferator-activated receptor alpha (PPARalpha) ligands [fenofibrate, diethylhexylphthalate (DEHP) and long-chain fatty acids (LCFA)] and other compounds believed to modulate lipid metabolism via PPARalpha-independent mechanisms (simvastatin, hydrazine and chlorpromazine). Urinary NMN was correlated with standard markers of peroxisome proliferation and serum lipid parameters with the aim of establishing whether urinary NMN could be used as a biomarker for peroxisome proliferation in the rat. Data from this study were also used to validate a previously constructed multivariate statistical model of peroxisome proliferation (PP) in the rat. The predictive model, based on 1H nuclear magnetic resonance (NMR) spectroscopy of urine, uses spectral patterns of NMN, 4PY and other endogenous metabolites to predict hepatocellular peroxisome count. Each treatment induced pharmacological (serum lipid) effects characteristic of their class, but only fenofibrate, DEHP and simvastatin increased peroxisome number and raised urinary NMN, 2PY and 4PY, with simvastatin having only a transient effect on the latter. These compounds also reduced mRNA expression for aminocarboxymuconate-semialdehyde decarboxylase (ACMSDase, EC 4.1.1.45), the enzyme believed to be involved in modulating the flux of tryptophan through this pathway, with decreasing order of potency, fenofibrate (-10.39-fold) >DEHP (-3.09-fold) >simvastatin (-1.84-fold). Of the other treatments, only LCFA influenced mRNA expression of ACMSDase (-3.62-fold reduction) and quinolinate phosphoribosyltransferase (QAPRTase, EC 2.4.2.19) (-2.42-fold) without any change in urinary NMN excretion. Although there were no correlations between urinary NMN concentration and serum lipid parameters, NMN did correlate with peroxisome count (r2=0.63) and acyl-CoA oxidase activity (r2=0.61). These correlations were biased by the large response to fenofibrate compared to the other treatments; nevertheless the data do indicate a relationship between the tryptophan-NAD+ pathway and PPARalpha-dependent pathways, making this metabolite a potentially useful biomarker to detect PP. In order to strengthen the observed link between the metabolites associated with the tryptophan-NAD+ pathway and more accurately predict PP, other urinary metabolites were included in a predictive statistical model. This statistical model was found to predict the observed PP in 26/27 instances using a pre-determined threshold of 2-fold mean control peroxisome count. The model also predicted a time-dependent increase in peroxisome count for the fenofibrate group, which is important when considering the use of such modelling to predict the onset and progression of PP prior to its observation in samples taken at autopsy.

Animals↗
PubMed2001

Microbial growth modelling with artificial neural networks.

There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.

Aeromonas hydrophila↗
PubMed2002

Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling.

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution B-spline deformations. This approach presents some advantages over previously published methods: it can treat multiple-part structures and requires less restrictive assumptions on the structure's topology. In this paper, we address the problem of building a 3-D statistical shape model of the left and right ventricle of the heart from 3-D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects.

Algorithms↗
PubMed1997

Misclassification bias in estimates of bereavement effects.

Prospective studies that examine marital status differences in health and mortality frequently fail to update information on marital status in statistical models. The authors illustrate how the resulting misclassification of marital status can produce substantial bias in estimates of bereavement effects associated with widowhood. They use as their main source of data the Longitudinal Study of Aging (LSOA), 1984-1990, a national survey of persons aged 70 years and older. The estimates are based primarily on 3,192 respondents who were married and cohabiting with their spouses at the time of the baseline survey and who could be matched to their spouses' records. Comparisons of widowhood status derived from matched spouse records with reported marital status recorded in LSOA interviews demonstrate that reliance on interview information leads to substantial misclassification of marital status. Results from survival models indicate that estimates of marital status effects are sensitive to whether and how marital status is updated after baseline interviews. For example, updating marital status in hazard models from interview information alone indicates a protective effect of widowhood, whereas classifying widowhood on the basis of spouses' death records reveals a significant bereavement effect (relative mortality risks between 1.3 and 1.4).

Aged↗
PubMed2006

Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE.

MOTIVATION: Regulation of gene expression by a transcription factor requires physical interaction between the factor and the DNA, which can be described by a statistical mechanical model. Based on this model, we developed the MatrixREDUCE algorithm, which uses genome-wide occupancy data for a transcription factor (e.g. ChIP-chip) and associated nucleotide sequences to discover the sequence-specific binding affinity of the transcription factor. Advantages of our approach are that the information for all probes on the microarray is efficiently utilized because there is no need to delineate "bound" and "unbound" sequences, and that, unlike information content-based methods, it does not require a background sequence model. RESULTS: We validated the performance of MatrixREDUCE by inferring the sequence-specific binding affinities for several transcription factors in S. cerevisiae and comparing the results with three other independent sources of transcription factor sequence-specific affinity information: (i) experimental measurement of transcription factor binding affinities for specific oligonucleotides, (ii) reporter gene assays for promoters with systematically mutated binding sites, and (iii) relative binding affinities obtained by modeling transcription factor-DNA interactions based on co-crystal structures of transcription factors bound to DNA substrates. We show that transcription factor binding affinities inferred by MatrixREDUCE are in good agreement with all three validating methods. AVAILABILITY: MatrixREDUCE source code is freely available for non-commercial use at http://www.bussemakerlab.org/. The software runs on Linux, Unix, and Mac OS X.

Algorithms↗
PubMed1990

Mood curves of neurotic-depressive patients undergoing treatment with antidepressants: time-series analyses of experience with HTAKA model.

Statistical time-series analyses of single case data allow a sophisticated person-centered evaluation of therapeutic effects. The possibilities and limitations of a new method of time series analysis are shown by the example of mood curves of neurotic depressive patients. The HTAKA model seems advantageous, because this procedure is less restrictive than other methods as regards the data basis. The cluster-analytical agglomeration of the single-case results indicates a sufficient response to antidepressants in about 40% of the patients who were treated with antidepressants in addition to psychotherapy.

Affect↗
PubMed2004

Statistical mechanics model for the interaction between the neurotransmitter gamma-aminobutyric acid and GABAA receptors.

Interactions between the neurotransmitter gamma-aminobutyric acid (GABA) and GABAA receptor ion channels play an important role in the central nervous system. A statistical mechanics model is proposed for the interaction between GABA and GABAA receptors. The model provides good fits to the electrophysiology data as well as an estimation of receptor activation energies, and predicts the temperature dependence consistent with measurements. In addition, the model provides insights into single channel conductance measurements. This model is also applicable to other ligand-gated ion channels with similar pentameric structures.

Biophysical Phenomena↗
PubMed1980

A statistical mechanical model of the lipid bilayer above its phase transition.

A statistical mechanical model of a bilayer of dipalmitoyl-3-sn-phosphatidylcholine molecules above their phase transition is presented. A molecular field approximation developed in previous work by Marcelja is extended by setting the molecular field at each depth in the bilayer in proportion to the average chain order at that depth. The free energy of the hydrocarbon/water interface and that due to the interaction of the polar headgroups is included in the evaluation of the statistical weights of the chain conformations. The model gives good agreement with several independent experimental results. It resolves the dilemma posed by the experimental evidence that there is (i) a considerable variation in order parameter along the lipid chain, but (ii) no collective tilt in the more ordered region of the chain. The model gives an explanation of how the lipid chains pack under these two constraints. The order parameter profile down the chain does not correspond to the profile across the bilayer.

Calorimetry↗
PubMed2005

An introduction to latent growth models: analysis of repeated measures physical performance data.

The purpose of this paper is to introduce the Latent Growth Model (LGM) to researchers in exercise and sport science. Although the LGM has several merits over traditional analysis techniques in analyzing change and was first introduced almost 20 years ago, it is still underused in exercise and sport science research. This statistical model can be applied to any repeated measures data, but it is most useful when one has an a priori hypothesis about the patterns of change. The strengths of latent growth modeling include: (a) both individual and group levels of change are estimated, (b) either a linear or a curvilinear trajectory can represent individual change, (c) occasions of measurement need not be equally spaced, (d) the statistical model can account for measurement errors, (e) the model can easily include multiple predictors or correlates of change, and (f) as in general structural equation models, statistical models are flexible and allow one to extend the basic idea in several ways, such as comparing changes between groups and examining the change in multivariate latentfactors. In this paper, the basics and an extension of latent growth modeling are explained, and examples with longitudinal physical performance data are presented, along with detailed analysis procedures and considerations.

British Columbia↗
PubMed1995

A statistical mechanical model for hydrogen exchange in globular proteins.

We develop a statistical mechanical theory for the mechanism of hydrogen exchange in globular proteins. Using the HP lattice model, we explore how the solvent accessibilities of chain monomers vary as proteins fluctuate from their stable native conformations. The model explains why hydrogen exchange appears to involve two mechanisms under different conditions of protein stability: (1) a "global unfolding" mechanism by which all protons exchange at a similar rate, approaching that of the denatured protein, and (2) a "stable-state" mechanism by which protons exchange at rates that can differ by many orders of magnitude. There has been some controversy about the stable-state mechanism: does exchange take place inside the protein by solvent penetration, or outside the protein by the local unfolding of a subregion? The present model indicates that the stable-state mechanism of exchange occurs through an ensemble of conformations, some of which may bear very little resemblance to the native structure. Although most fluctuations are small-amplitude motions involving solvent penetration or local unfolding, other fluctuations (the conformational distant relatives) can involve much larger transient excursions to completely different chain folds.

Drug Stability↗
PubMed2004

Is treatment with interferon-alpha effective in all patients with metastatic renal carcinoma? A new approach to the investigation of interactions.

The first analysis of the MRC RE01 trial in metastatic renal carcinoma identified a 28% reduction in the hazard of death for patients treated with interferon-alpha compared with medroxyprogesterone acetate (MPA). No subgroup was identified in which treatment with interferon-alpha was more or less effective than MPA. We used a new approach based on fractional polynomials to investigate the updated data from this trial for the possible interaction of treatment with prognostic factors. In the spirit of hypothesis generation, we considered 10 possible prognostic variables, of which white cell count (WCC) was found to influence the effectiveness of interferon treatment. In patients treated with MPA, there was no prognostic effect of WCC, whereas, in patients treated with interferon, the risk of dying increased significantly with WCC level. We defined subgroups of patients based on WCC levels and estimated a hazard ratio of 0.53 in favour of interferon in patients with WCC <6.5 x 10(9), whereas for patients with WCC >10 x 10(9) the risk appears to be similar between the treatment groups, or even slightly raised in the interferon group. Since our results are derived from flexible statistical models, they may be interpreted as a new hypothesis and require validation in independent data.

Adult↗
PubMed2004

Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images.

We describe a new 3-D statistical shape model of the heart consisting of atria, ventricles and epicardium. The model was constructed by combining information on standard short- and long-axis cardiac MR images. In the model, the variability of the shape was modeled with PCA- and ICA-based shape models as well as with non-parametric landmark probability distributions and a probabilistic atlas. The statistical atlas was built from 25 healthy subjects. The shape model was evaluated by applying it to image segmentation. The probabilistic atlas was found to be superior to the other shape models (P < 0.001) in this study.

Adult↗
PubMed2000

A statistical mechanical model for predicting B-DNA curvature and flexibility.

A statistical mechanical model taking into account the symmetric twisting, tilting, sliding fluctuations and asymmetric rolling fluctuations has been proposed to predict the macroscopic curvature and flexibility of B-DNA. Based on the statistical data of structural parameters of double helix in nucleic acid database and the related theoretical analysis, the equilibrium angular parameters (Omega, rho and tau) describing the orientation of successive base-pair planes, the translation parameters (D(y)) along the long axis of neighboring base-pair step and the corresponding force constants are arranged for ten dimers appropriately. Under the assumption of independent angular parameters, independent base-pair steps and a simple energy function, we can calculate the macroscopic curvature and the flexibility of DNA sequences through the transformation matrix and the Boltzmann ensemble average. The predictions on curvature and flexibility of DNA have been compared with the corresponding experimental data. The agreement is remarkably good. It is demonstrated that the lowering of the temperature does increase the DNA curvature.

Animals↗
PubMed1991

The impact of breakthrough clinical trials on survival in population based tumor registries.

Three statistical models are developed to study the impact that two breakthrough clinical trials (MOPP for Hodgkin's disease and PVB for disseminated testicular cancer) had on survival in the Connecticut tumor registry and the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) registry program. A segmented regression model is used in conjunction with the Cox semi-parametric proportional hazards model, as well as the parametric Weibull and exponential cure models. These models allow us to determine approximately when survival first began to improve dramatically, indicating that improved treatments had become available, and how long it took for survival to level off again indicating that the full population survival impact had been realized. In addition, the degree to which the parametric models fit allows us to determine if the survival improvements occur within a parametric family. Results of the modelling indicate that dissemination took approximately 11 years in Hodgkin's disease while only 3 years in disseminated testicular cancer. In both disease sites survival first broke with prior trends between the time that the breakthrough trial started and its publication, indicating that earlier moderately successful 'precursor' trials with combination chemotherapy may have initiated the improved population survival trends. Reasons for the difference in dissemination time in the two cancer sites are examined in order to understand what factors may be responsible for the speed of dissemination and effective utilization of new therapies.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2004

Comparison of prosthetic valve hydrodynamic function: objective testing using statistical multilevel modeling.

BACKGROUND AND AIM OF THE STUDY: The performance of novel prosthetic heart valves is assessed using in-vitro hydrodynamic function tests. The study aim was to examine the problem of objective discrimination of hydrodynamic performance to determine significant differences between valve designs, and illustrate proposed methodology using data collected from five different polyurethane tri-leaflet valve designs. METHODS: Two engineering designs were manufactured with leaflets of the same polyurethane (GE, LE); design L was manufactured using three further leaflet materials of differing material modulus (LL, L4, L5). Six valves were made in each design, each tested at five flow rates in a standard hydrodynamic test rig, with five test replications for each valve. The data were analyzed using multilevel statistical modeling methods, allowing simultaneous comparison of multiple regression lines describing valve performance. The multilevel model is hierarchical in structure, in this case with two levels of data, describing individual valves at level 2 and test replicates at level 1. In all cases, the multilevel model uses the hydrodynamic function measure of interest, e.g. mean pressure gradient or leakage, with logarithmic transformation as required as the dependent variable, Y. The independent variable, X, is, in all cases, the natural logarithm of the RMS flow measured through the valve. RESULTS: The two-design multilevel model enabled quantitative discrimination of designs GE and LE, showing that design GE had significantly better hydrodynamic function overall than design LE in this case (mean pressure gradient was estimated as 0.93 mmHg lower at low cardiac output, 14.74 mmHg lower at 9.6 l/min). The five-design multilevel model showed clearly the relatively poor hydrodynamic performance of designs L4 and L5 compared with others. The procedure was straightforward, and produced a statistical comparison among valve designs that is not easily achieved by other means. CONCLUSION: This methodology provides a useful means of objective assessment of valve function for valve developers. Variance estimates provided by the analysis also provide a basis for quality control of valve production and testing.

Equipment Failure Analysis↗