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PubMed1998

Inferential statistical method for analysis of nonsinusoidal hybrid time series with unequidistant observations.

Most variables of interest in laboratory medicine show predictable changes with several frequencies in the span of time investigated. The waveform of such nonsinusoidal rhythms can be well described by the use of multiple components rhythmometry, a method that allows fitting a linear model with several cosine functions. The method, originally described for analysis of longitudinal time series, is here extended to allow analysis of hybrid data (time series sampled from a group of subjects, each represented by an individual series). Given k individual series, we can fit the same linear model with m different frequencies (harmonics or not from one fundamental period) to each series. This fit will provide estimations for 2m + 1 parameters, namely, the amplitude and acrophase of each component, as well as the rhythm-adjusted mean. Assuming that the set of parameters obtained for each individual is a random sample from a multivariate normal population, the corresponding population parameter estimates can be based on the means of estimates obtained from individuals in the sample. Their confidence intervals depend on the variability among individual parameter estimates. The variance-covariance matrix can then be estimated on the basis of the sample covariances. Confidence intervals for the rhythm-adjusted mean, as well as for the amplitude-acrophase pair, of each component can then be computed using the estimated covariance matrix. The p-values for testing the zero-amplitude assumption for each component, as well as for the global model, can finally be derived using those confidence intervals and the t and F distributions. The method, validated by a simulation study and illustrated by an example of modeling the circadian variation of heart rate, represents a new step in the development of statistical procedures in chronobiology.

Biometry↗

PubMed2005

Modeling the electrophoretic mobility of analytes in binary solvent electrolyte systems in capillary electrophoresis using an artificial neural network.

An artificial neural network (ANN) methodology was used to model the electrophoretic mobility of basic analytes in binary solvent electrolyte systems. The electrophoretic mobilities in pure solvent electrolytes, and the volume fractions of the solvents in mixtures were used as input. The electrophoretic mobilities in mixed solvent buffers were employed as the output of the network. The optimized topology of the network was 3-3-1. 32 experimental mobility data sets collected from the literature were employed to test the correlation ability and prediction capability of the proposed method. The mean percentage deviation (MPD) between the experimental and calculated values was used as an accuracy criterion. The MPDs obtained for different numerical analyses varied between 0.21% and 13.74%. The results were also compared with similar calculated mobilities which were derived from the best multiple linear model from the literature. From these results it was found that the ANN methodology is superior to the multiple linear model.

Adrenergic beta-Antagonists↗

PubMed2002

[Risk factors in women with gestational diabetes mellitus].

OBJECTIVE: To identify risk factors for gestational diabetes mellitus (GDM) in women and to study the contribution of family history of type-2 diabetes to the risk for DDM. METHODS: A case-control study was performed in 85 women with GDM and 177 cases controls. Univariate and multivariate logistic regression and log-linear model were used to identify risk factors of GDM. RESULTS: Multivariate logistic regression showed that obesity before pregnancy, family history of type-2 diabetes, birth weight of pregnant women, age, fasting plasma level of triglyceride, physical inactivity, etc. all were risk factors for GDM. Analysis with log-linear model showed that parents' (father's or mother's) history of type-2 diabetes associated with GDM, with P-values of 0.012 and 0.017, respectively. Prevalence of diabetes in the mothers of proband with GDM was 9.41%, as compared with that in the fathers of proband with GDM 8.24%, with no statistical significance. CONCLUSIONS: Obesity before gestation, family history of type-2 diabetes, low birth weight of mother, age, increased fasting plasma level of triglyceride, as well as parents' history of type-2 diabetes, all were risk factors for GDM. Physical exercise was found to be a protective factor for GDM. Mother's history of type-2 diabetes did not differ from father's in contributing to the onset of GDM in their offspring.

Adult↗

PubMed2001

Fast prediction of hydration free energies from molecular interaction fields.

A novel empirical model is presented that allows the fast computation of hydration free energies with high accuracy. The linear model is based upon the separation of the free energy of hydration into a cavity and an interaction term. The cavity contribution is modeled as a linear combination of molecular volume and surface terms. The interaction part is derived from the statistical three-dimensional (3D) free energy density and is modeled approximately as a molecular interaction field using the program GRID. A compression scheme is employed to represent this 3D information on the molecular surface by means of a linear combination of surface functions. A set of 81 small organic molecules with known experimental hydration free energies is used to determine the coefficients of the linear model by least squares regression. The fit is statistically significant yielding a correlation coefficient of 0.99, a root mean square error of 0.27 kcal/mol for the 81 molecules belonging to the training set, and 0.63 kcal/mol for an independent test set of 10 molecules.

Computer Simulation↗

PubMed1992

A non-linear haemodynamic model for the arterial pulsatile component of the intracranial pulse wave.

An indication that pressure pulses in cerebral arteries may play a role in the configuration of intracranial pressure pulsations is given by the observation that vasospasm of cerebral arteries narrows the amplitude of the intracranial pressure wave. The present work develops a mathematical model for the transmission of arterial pressure pulses across the compliant arterial wall to the surrounding intracranial space. Compliance of both the arterial segment and the intracranial space are considered. So as to retain accuracy at higher values of the mean intracranial pressure, a physiological range in which pulse transmission is enhanced due to lower pressure gradients but intracranial compliance is not necessarily decreased, a logistic fit is used to model the intracranial pressure-volume relationship. A sequence of approximations (with error bounds) is obtained for the induced intracranial pressure pulse amplitude as a function of arterial pulse amplitude, mean transmural pressure, and mean intracranial pressure. It is found that at higher mean intracranial pressures, where the usual exponential assumption for the intracranial pressure-volume curve loses validity, the amplitude of the transmitted arterial pressure pulse depends non-linearly on the mean intracranial pressure.

Animals↗

PubMed2003

Frequency-specific interaural level difference tuning predicts spatial response patterns of space-specific neurons in the barn owl inferior colliculus.

Space-specific neurons in the barn owl's inferior colliculus have spatial receptive fields (RFs) because of sensitivity to interaural time difference and frequency-specific interaural level difference (ILD). These neurons are assumed to be tuned to the frequency-specific ILDs occurring at their spatial RFs, but attempts to assess this tuning with traditional narrowband stimuli have had limited success. Indeed, tuning assessed in this manner, when processed via a linear model of spectral integration, typically explains only approximately half the variance in spatial response patterns. Here we report our findings that frequency-specific ILD tuning of space-specific neurons, when assessed from responses to broadband stimuli, predicted nearly 75% of the variance in spatial responses, using a linear model of spectral integration (p < 0.0001; n = 97 neurons). Furthermore, when we tested neurons using only those frequencies we found to be spatially relevant, we saw that their responses were similar to those elicited by broadband stimuli. When we used frequencies not identified as spatially relevant, such similarity was lacking. Furthermore, spectral components that elicited high firing rates when presented as narrowband stimuli were found in several cases to be irrelevant for or detrimental to the definition of spatial RFs. Thus, neurons achieved sharp spatial tuning by selecting for ILDs of a subset of spectral components in noise, some of which were not identified using narrowband stimuli.

Acoustic Stimulation↗

PubMed2007

Linear and curvilinear effects of inbreeding on production traits for walloon Holstein cows.

The nonlinear effects of inbreeding were studied by comparing linear and curvilinear regression models of phenotypic performances on inbreeding coefficients for production traits (milk, fat, and protein yields) of Holstein cows in their first lactation. Three different regression models (linear, quadratic, and cubic) were introduced separately into a single-trait, single-lactation, random regression test-day model. The significance of the different regression coefficients was studied based on a t-test after estimation of error variances and covariances associated with the different regression coefficients. All of the tested regression coefficients were significantly different from 0. The traditional regression coefficients of milk, fat, and protein yields on inbreeding were, respectively, -22.10, -1.10, and -0.72 kg for Holstein cows in their first lactation. However, the estimates of 305-d production losses for various classes of animals based on inbreeding coefficients showed that the effect of inbreeding was not a linear function of the percentage of inbreeding. The 305-d milk yield loss profiles attributable to inbreeding, obtained by the various regression models, were different. However, for inbreeding coefficients between 0 and 10%, these differences were small.

Animals↗

PubMed1984

Multiple-dose nonlinear regression analysis program for the microcomputer.

An adaptation of a previously published program for nonlinear regression analysis of serum drug concentrations which can utilize data obtained during multiple-dose administration is described. Specific programs have been developed for aminoglycosides, digoxin, and theophylline. The programs provide initial parameter estimates (for a one-compartment linear model) for each drug based on mean population parameters and refined estimates based on observed concentration data. Clinical examples which demonstrate the flexibility of these programs are provided. The programs may also be employed for fitting data for any drug that can be adequately described by a one-compartment linear model.

Adult↗

PubMed2004

[Study on the application of artificial neural network in analysing the risk factors of diabetes mellitus].

OBJECTIVE: To study the use of neural network in determining the risk factors of diseases. METHODS: With back-propagation neural network (BP network) as fitting model based upon data gathered from an epidemiological survey on diabetes mellitus and under the network structure of 22-6-1, the mean impact value (MIV) for each input variables and sequencing the factors according to their absolute MIVs were calculated. The results from BP network with multiple logistic regression analysis and log-linear model for united actions between factors were compared with optimizing Levenberg-Marquardt algorithm. RESULTS: By BP network analysis, the sequence of importance for the risk factors of diabetes mellitus became: faster pulse, diabetes mellitus family history, living longer in the investigated area, with medical record of nephropathy, having higher ratio for waist-to-hip, being male, with medical records of diseases as hyperlipoproteinmia, coronary heart disease, hypertension, high diastolic pressure, higher income, do no drink alcohol, age, higher systolic pressure, less educated, body mass index, with medical records of other diseases, physical exercise related to jobs smoking, occupation, with medical record for cerebrovascular disease, with medical record for liver disease etc. However, only 7 factors were statistically significant in multiple logistic regression analysis. The sequence of their importance appeared as: pulse, diabetes mellitus family history, the medical record of nephropathy, waist-to-hip ratio, the medical record of hypertension, work-place related exercise and age. The sequences of importance were almost the same between the two while the difference could partly be explained by the interaction among risk factors through log-linear model. CONCLUSION: Neural network could be used to analyze the risk factors of diseases and could assimilate more complicated relationships (main effects and interactions) between inputs and outputs, better than using the traditional methods.

Adult↗

PubMed2003

Mortality and temperature in Sofia and London.

STUDY OBJECTIVE: Heat and cold have been associated with increased mortality, independently of seasonal trends, but details are little known. This study explores associations between mortality and temperature in two European capitals-Sofia and London-using four years of daily deaths, air pollution, and weather data. DESIGN: Generalised additive models were used to permit non-linear modelling of confounders such as season and humidity, and to show the shape of mortality-temperature relations-using both two day and two week average temperatures separately. Models with linear terms for heat and cold were used to estimate lags of effect, linear effects, and attributable fractions. PARTICIPANTS: 44701 all age all cause deaths in Sofia (1996-1999) and 256464 in London (1993-1996). MAIN RESULTS: In London, for each degree of extreme cold (below the 10th centile of the two week mean temperature), mortality increased by 4.2% (95% CI 3.4 to 5.1), and in Sofia by 1.8% (0.6 to 3.9). For each degree rise above the 95th centile of the two day mean, mortality increased by 1.9% (1.4 to 2.4) in London, and 3.5% (2.2 to 4.8) in Sofia. Cold effects appeared after lags of around three days and lasted-particularly in London-at least two weeks. Main heat effects occurred more promptly. There were inverse associations at later lags for heat and cold in Sofia. CONCLUSIONS: Average temperatures over short periods do not adequately model cold, and may be inadequate for heat if they ignore harvesting effects. Cold temperatures in London, particularly, seem to harm the general population and the effects are not concentrated among persons close to death.

Bulgaria↗

PubMed1996

Using the EM-algorithm for survival data with incomplete categorical covariates.

Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With generalized linear models, when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights proposed in Ibrahim (1990). In this article, we extend the EM by the method of weights to survival outcomes whose distributions may not fall in the class of generalized linear models. This method requires the estimation of the parameters of the distribution of the covariates. We present a clinical trials example with five covariates, four of which have some missing values.

Algorithms↗

PubMed1999

Analysis of a model of membrane potential for a skin receptor.

A linear model for the membrane potential of a nerve ending associated with a sense-of-touch skin receptor is formulated and analyzed. Because the nerve has cytoplasmic extensions and is stimulated through a membrane strain mechanism, the model is a generalized cable equation with linear oblique boundary conditions (i.e. boundary conditions involving both time and space derivatives). The model is stimulated through a certain conductance parameter. We establish solutions for the problem and we consider a particular physiological question, both analytically and computationally, of determining conditions on parameters and stimulus which give threshold level potential values at a particular boundary point.

Linear Models↗

PubMed2004

Caregiving stress, endogenous sex steroid hormone levels, and breast cancer incidence.

Stress is hypothesized to be a risk factor for breast cancer. The authors examined associations of hours of, and self-reported levels of stress from, informal caregiving with prospective breast cancer incidence. Cross-sectional analyses of caregiving and endogenous sex steroid hormones were also conducted. In 1992 or 1996, 69,886 US women from the Nurses' Health Study, aged 46-71 years at baseline, answered questions on informal caregiving; 1,700 incident breast cancer cases accrued over follow-up to 2000. A subset of 665 postmenopausal women not taking exogenous hormones returned a blood sample in 1990. Numbers of hours of care provided to an ill adult or to a child were each summed and analyzed as 0 (reference), 1-14, and >/=15 per week. Cox proportional hazards models were used in prospective analyses and linear models in cross-sectional analyses. High numbers of caregiving hours and self-reported stress did not predict a higher incidence of breast cancer. However, compared with women providing no adult care, women providing >/=15 hours of adult care (median, 54) had significantly lower levels of estradiol (geometric mean, 9.21 pg/ml vs. 7.46 pg/ml (95% confidence interval: 6.36, 8.76)) and bioavailable estradiol (geometric mean, 1.86 pg/ml vs. 1.35 pg/ml (95% confidence interval: 1.00, 1.82)). Stress from caregiving did not appear to increase breast cancer risk.

Aged↗

PubMed1998

Neural network based control schemes for flexible-link manipulators: simulations and experiments.

This paper presents simulation and experimental results on the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The controllers are designed by utilizing the modified output re-definition approach. The modified output re-definition approach requires only a priori knowledge about the linear model of the system and no a priori knowledge about the payload mass. Four different neural network schemes are proposed. The first two schemes are developed by using a modified version of the 'feedback-error-learning' approach to learn the inverse dynamics of the flexible manipulator. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network (referred to as the 'output neural network') is responsible for specifying an appropriate output for ensuring minimum phase behavior of the system. The second neural network is responsible for implementing an inverse dynamics controller. The performance of the four proposed neural network controllers is illustrated by simulation results for a two-link planar flexible manipulator and by experimental results for a single flexible-link test-bed. The networks are all trained and employed as online controllers and no off-line training is required.

Journal Article↗

PubMed2003

Genetic associations between frozen shoulder and tennis elbow: a female twin study.

OBJECTIVES: To estimate the heritability of frozen shoulder (FS) and tennis elbow (TE) and to examine the two disorders for possible genetic or environmental associations. METHODS: Self-reporting questionnaire data on risk factors for, and a physician diagnosis of, FS or TE were obtained from 865 monozygotic (MZ) and 963 dizygotic (DZ) unselected female twin pairs aged between 20 and 76 yr registered with the St Thomas' UK Adult Twin Registry. The heritability of each disorder was estimated in a classic twin study. The association between FS and TE was then explored by log-linear modelling comparing MZ with DZ individuals and twin pairs for the presence of both disorders. RESULTS: The prevalence of FS and TE were 11.6 and 16.7%, respectively. A heritability of 42% was estimated for FS and 40% for TE after adjusting for age. There was no confounding by environmental risk factors. Log-linear modelling demonstrated FS and TE, independently, to be associated within members of a twin pair and confirmed a stronger association in MZ than DZ pairs. In addition the two disorders occurred together 2-3 times more frequently in individuals than would be expected by chance. However, there was no association between FS and TE across members of a twin pair, implying no evidence for a shared genetic component to the two disorders. CONCLUSIONS: Genetic factors are implicated in the aetiology of both frozen shoulder and tennis elbow but are independent of each other. The two disorders occur together 2-3 times more frequently than by chance in individuals. However, the association is most likely mediated by individual-specific environmental factors common to the two conditions and not by a common genetic susceptibility.

Adult↗

PubMed1997

Assessing the effects of interventions using longitudinal data with samples subject to selection.

Consider an uncontrolled study in which subjects are selected to start a new intervention because one or more previous measurements of a variable are within a particular range. Our aim is to use the repeated values obtained prior and subsequent to the start of the intervention to assess the effects of its introduction. However, because selection is based on the same variable as is being used to assess efficacy, regression to the mean will confound the interpretation of the results. In this paper, we present a likelihood-based method for evaluating the effects of the intervention using the repeated measurements while adjusting for the effects of the selection. The method uses a linear model to describe each subject's pattern of responses before and after the start of the new intervention. Additionally, it assumes that the effect of starting the new intervention on an individual's intercept and slope is the same for all subjects. However, no distributional assumption is made about the pattern of the linear model across subjects, thus making it particularly appropriate for phase I/II studies when patient histories on those not selected for the study are not available.

Acquired Immunodeficiency Syndrome↗

PubMed1993

Development of linear and nonlinear hand-arm vibration models using optimization and linearization techniques.

Hand-arm vibration (HAV) models serve as an effective tool to assess the vibration characteristics of the hand-tool system and to evaluate the attenuation performance of vibration isolation mechanisms. This paper describes a methodology to identify the parameters of HAV models, whether linear or nonlinear, using mechanical impedance data and a nonlinear programming based optimization technique. Three- and four-degrees-of-freedom (DOF) linear, piecewise linear and nonlinear HAV models are formulated and analyzed to yield impedance characteristics in the 5-1000 Hz frequency range. A local equivalent linearization algorithm, based upon the principle of energy similarity, is implemented to simulate the nonlinear HAV models. Optimization methods are employed to identify the model parameters, such that the magnitude and phase errors between the computed and measured impedance characteristics are minimum in the entire frequency range. The effectiveness of the proposed method is demonstrated through derivations of models that correlate with the measured X-axis impedance characteristics of the hand-arm system, proposed by ISO. The results of the study show that a linear model cannot predict the impedance characteristics in the entire frequency range, while a piecewise linear model yields an accurate estimation.

Arm↗

PubMed2004

Investigation of possible neural architectures underlying information-geometric measures.

A novel analytical method based on information geometry was recently proposed, and this method may provide useful insights into the statistical interactions within neural groups. The link between informationgeometric measures and the structure of neural interactions has not yet been elucidated, however, because of the ill-posed nature of the problem. Here, possible neural architectures underlying information-geometric measures are investigated using an isolated pair and an isolated triplet of model neurons. By assuming the existence of equilibrium states, we derive analytically the relationship between the information-geometric parameters and these simple neural architectures. For symmetric networks, the first- and second-order information-geometric parameters represent, respectively, the external input and the underlying connections between the neurons provided that the number of neurons used in the parameter estimation in the log-linear model and the number of neurons in the network are the same. For asymmetric networks, however, these parameters are dependent on both the intrinsic connections and the external inputs to each neuron. In addition, we derive the relation between the information-geometric parameter corresponding to the two-neuron interaction and a conventional cross-correlation measure. We also show that the information-geometric parameters vary depending on the number of neurons assumed for parameter estimation in the log-linear model. This finding suggests a need to examine the information-geometric method carefully. A possible criterion for choosing an appropriate orthogonal coordinate is also discussed. This article points out the importance of a model-based approach and sheds light on the possible neural structure underlying the application of information geometry to neural network analysis.

Action Potentials↗