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Results for “regularized model”

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At least 19 recordsLinked to original sources

PubMed1995

A spatial feature extraction and regularization model for the head-related transfer function.

A functional representation is proposed for complex valued (amplitude and phase) head-related transfer functions (HRTFs), including both frequency and spatial dependence. The frequency variation is spanned by a set of eigentransfer functions (EFs) that are generated using the Karhunen-Loève expansion. Any HRTF is represented as a weighted combination of the EFs where the weights are functions of the HRTFs spatial location and are termed spatial characteristic functions (SCFs). Samples of the SCFs are obtained by projecting the measured HRTFs onto the EFs. A regularization framework is employed to obtain a functional representation for the SCFs by fitting each set of SCF samples with a two-dimensional spline. Acoustic validation of the model's fidelity and predictive capability is provided using 2188 measured HRTFs from a KEMAR manikin and 1816 measured HRTFs from an anesthetized live cat. Errors between measured and modeled HRTFs are generally less than one percent. Larger errors occur in the contralateral regions for KEMAR and lower back regions for the cat as a consequence of the relatively small HRTF amplitudes resulting from head shadowing. Methods for reducing these errors are discussed.

Animals↗

PubMed2004

Skin protection in nursing work: promoting the use of gloves and hand alcohol.

Nursing has been identified as a wet-work occupation, with a high prevalence of occupational irritant contact dermatitis. Reduction of exposure to skin irritants contributes to the prevention of occupational skin disease in nurses. The role of the use of soap and water, hand alcohol and gloves in prevention programmes is discussed. 2 additional measures for reducing exposure to skin irritants are postulated: use of hand alcohol instead of soap and water in disinfection procedures when the hands are not visibly dirty; use of gloves in wet activities such as patient washing to prevent the hands from becoming wet and visibly dirty. We investigated the effectiveness of these recommendations in a model. Mean daily wet-work exposure during nursing work was modelled: regular model. We also modelled exposure to skin irritants in combination with the implementation of these recommendations: prevention model. The hands of healthy volunteers were exposed to the regular or the prevention model over 3 weeks for 5 days a week. The change in transepidermal water loss (TEWL) on the back of the hands was measured after 3 weeks of exposure to these wet-work simulations. An increase in TEWL occurred with the regular model, while mean TEWL decreased in the prevention model. Skin irritation from occlusion by gloves appeared to be more pronounced in the regular model compared to the prevention model. The results of this study justify the conclusion that in nursing work, hand alcohol is the preferred disinfectant. Although the prevention model implies increased occlusive exposure, this has no additional irritant effect, probably because of the absence of soap exposure.

Adult↗

PubMed2000

How do changes to plate thickness, length, and face-connectivity affect femoral cancellous bone's density and surface area? An investigation using regular cellular models.

Models of regular cellular-solids representing femoral head 'medial group' bone were used to (1) compare thickness data for plate-like and beam-like structures at realistic surface areas and densities; (2) test the validity of a standard formula for trabecular thickness (Tb.Th); and (3) study how systematic changes in cancellous bone thicknesses, spacing, and face-connectivity affect relative density and surface area. Models of different face-connectivities, produced by plate removal from the unit cell, were fitted to bone density and surface area data. The medial group bone was anisotropic: the supero-inferior (SI) direction was the principal direction for bone plate alignment and the plane normal to this had the largest number of bone/void intersections per unit line length (P(I)). A comparison of boundary perimeter per unit area data, in planes normal to SI, with surface area data placed the medial group bone between prismatic structures in which walls are parallel to one principal direction and isotropic structures. Selective removal of plates from a closed-cell model produced a similar result. For the same relative density and surface-area, plate-like models had significantly thinner cross-sections than beam-like models. The formula for Tb.Th produced overestimates of model plate thickness by up to 20% at realistic femoral cancellous densities. Trends in data on surface area to volume ratio and density observed on sampled medial group bone could be simulated by plate thickness changes on models of intermediate face-connectivity (approximately 1.5) or by plate removal from models with relatively thick and short (low aspect-ratio) plates. The latter mechanism is unrealistic for it resulted in beam-like structures at low 'medial group' densities, an architecture unlike the predominantly plate-like bone in the sample.

Aged↗

PubMed2002

On the problem in model selection of neural network regression in overrealizable scenario.

In considering a statistical model selection of neural networks and radial basis functions under an overrealizable case, the problem of unidentifiability emerges. Because the model selection criterion is an unbiased estimator of the generalization error based on the training error, this article analyzes the expected training error and the expected generalization error of neural networks and radial basis functions in overrealizable cases and clarifies the difference from regular models, for which identifiability holds. As a special case of an overrealizable scenario, we assumed a gaussian noise sequence as training data. In the least-squares estimation under this assumption, we first formulated the problem, in which the calculation of the expected errors of unidentifiable networks is reduced to the calculation of the expectation of the supremum of the chi2 process. Under this formulation, we gave an upper bound of the expected training error and a lower bound of the expected generalization error, where the generalization is measured at a set of training inputs. Furthermore, we gave stochastic bounds on the training error and the generalization error. The obtained upper bound of the expected training error is smaller than in regular models, and the lower bound of the expected generalization error is larger than in regular models. The result tells us that the degree of overfitting in neural networks and radial basis functions is higher than in regular models. Correspondingly, it also tells us that the generalization capability is worse than in the case of regular models. The article may be enough to show a difference between neural networks and regular models in the context of the least-squares estimation in a simple situation. This is a first step in constructing a model selection criterion in an overrealizable case. Further important problems in this direction are also included in this article.

Journal Article↗

PubMed1996

Hidden Markov models for sequence analysis: extension and analysis of the basic method.

Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation-maximization training procedure is relatively straightforward. In this paper, we review the mathematical extensions and heuristics that move the method from the theoretical to the practical. We then experimentally analyze the effectiveness of model regularization, dynamic model modification and optimization strategies. Finally it is demonstrated on the SH2 domain how a domain can be found from unaligned sequences using a special model type. The experimental work was completed with the aid of the Sequence Alignment and Modeling software suite.

Algorithms↗

PubMed2005

Specification of the observation model for regularized image up-sampling.

Regularization is one of the most promising methods for image up-sampling, which is an ill-posed inverse problem. A key element of such a regularization approach is the observation model relating the observed lower resolution (LR) image to the desired higher resolution (HR) up-sampled image, used in the data-fidelity term of the regularization cost function. This paper presents an algorithm to determine this observation model based on a model of the physical acquisition process for the LR image, and the ideal acquisition process for the desired HR image, both from the same underlying continuous image. The method is illustrated with typical scenarios corresponding to LR and HR cameras modeled by either Gaussian or rectangular apertures. Experiments with some regularized image up-samplers demonstrate the importance of using the correct, adapted observation model as determined by our algorithm. Index Terms-Camera aperture, data fidelity, image up-sampling, interpolation, multidimensional signal processing, observation model, power spectral density (PSD), super-resolution.

Algorithms↗

PubMed2005

Novel quantitative phenotypes of exercise training in mouse models.

Regular physical exercise has beneficial effects in many human disease states, including cardiovascular diseases, cancer, and depression. Exercise training of genetically modified mouse models may provide insight into the molecular mechanisms that underlie the beneficial effects of exercise. Presently, there is relatively little understanding of the normal physiology of mouse exercise. In this paper, we describe a novel computerized voluntary wheel-running system capable of recording and analyzing individual wheel rotations. Using this system, we demonstrate that C57BL/6 mice run considerable distances during the night in short bouts and at a preferred speed: the cruising speed. We find that the vast majority of running occurs around this cruising speed, which is close to the maximum speed at which the animal can run but is significantly higher than the average speeds recorded by simple digital odometers. We describe how these parameters vary with exercise training and demonstrate marked sex differences in the patterns of voluntary exercise. The results of this study have important implications for the design and interpretation of both voluntary and forced exercise experiments in mouse models. The novel parameters described provide more physiological quantitative measures of voluntary exercise activity and training and will extend the physiological utility of exercise training as a phenotyping tool in genetic mouse models.

Animals↗

PubMed1992

Mammography usage and the health belief model.

Regular screening mammograms for asymptomatic women are the most effective method for early detection of breast cancer. This study assessed the relative influence of Health Belief Model (HBM) constructs on prior mammography usage and the intention to obtain mammograms with data from a sample of 1,057 women over the age of 35 years residing in an urban community in the United States. Covariance structure analysis with latent variables was used initially to perform a confirmatory factor analysis of indicators of Socioeconomic Status (SES), Perceived Susceptibility, Perceived Barriers, Perceived Benefits, Cues to Action, Prior Mammography, and Future Intentions. Once a plausible factor structure was confirmed, a predictive path model was tested with Future Intentions and Prior Mammography as the outcome variables. Cues to Action, operationalized as a physician influence variable, particularly impacted Prior Mammography, and Perceived Susceptibility was the most powerful predictor of Future Intentions. SES only related significantly to Perceived Barriers, and Cues to Action, and did not directly influence Prior Mammography and Future Intentions. HBM predictor variables alone accounted for the relationship between previous mammography experience and intentions to obtain mammograms in the future. Health education implications and an applied outreach program are discussed.

Adult↗

PubMed2001

Locally regularized spatiotemporal modeling and model comparison for functional MRI.

In this work we treat fMRI data analysis as a spatiotemporal system identification problem and address issues of model formulation, estimation, and model comparison. We present a new model that includes a physiologically based hemodynamic response and an empirically derived low-frequency noise model. We introduce an estimation method employing spatial regularization that improves the precision of spatially varying noise estimates. We call the algorithm locally regularized spatiotemporal (LRST) modeling. We develop a new model selection criterion and compare our model to the SPM-GLM method. Our findings suggest that our method offers a better approach to identifying appropriate statistical models for fMRI studies.

Arousal↗

PubMed2003

Adherence to structured physical exercise in overweight and obese subjects: a review of psychological models.

Regular physical exercise is a widely accepted means of reducing mortality and improving a number of health outcomes, which is also efficacious in the treatment of obesity. Unfortunately, despite the evidence of favourable short- and long-term effects, the results seem to be affected by a lack of adherence, particularly over the long term. It is very important to evaluate and predict good adherence in order to be able to assess the real efficacy of physical activity in the treatment of various diseases. In this paper, we describe most of the psychological models used to evaluate such adherence, and investigate the physiological and psychological bases of physical activity, the most suitable type of exercise for obese patients and some useful strategies for ensuring long-term adherence.

Exercise↗

PubMedDate not supplied

Carbohydrate-deficient transferrin in the ethanol-consuming rat model.

Regular ethanol consumption leads to the appearance of carbohydrate-deficient transferrin in the plasma of human subjects. The mechanism for this finding remains uncertain. We have exposed female Wistar rats to ethanol from 3 weeks of age to 22 weeks and have studied transferrin in these animals. Isoelectric focusing (IEF) of serum identified two additional transferrin forms in the ethanol-exposed rats. Routine staining and immunofixation demonstrated transferrin focused at pH 5.45 and 5.65. Neuraminidase-treated normal rat serum focused in 2 bands at pH 5.85 and 6.15 corresponding to the additional bands in ethanol-treated rats. Human transferrin exposed to neuraminidase focused at pH 5.7 as described by others. The results suggest that the rat may be used as an animal model to study the complex effects of ethanol on transferrin and iron metabolism.

Alcohol Drinking↗

PubMed1995

A model of regular geriatric follow-up by home visits to selected patients discharged from a geriatric ward: a randomized controlled trial.

The aim of this randomized controlled trial was to describe and evaluate a model of regular follow-up by home visits to selected elderly patients discharged from a geriatric ward. Ninety-seven patients were randomized to receive regular follow-up visits by a geriatric team at 1, 3, 8 and 16 weeks after discharge, and 96 patients to receive standard care. Based on the geriatric evaluation, medical and social adjustment was carried out, if indicated. Six months after discharge, significantly more patients in the intervention group were allocated to home help (p < 0.05), but only 42 (44%) were readmitted to a hospital, vs 62 (64%) in the control group (p < 0.005). Differences between the groups in mortality and nursing-home placement were not statistically significant. Regular follow-up home visits by a geriatric team after in-patient geriatric evaluation and management reduce the risk of hospital readmissions among highly selected frail geriatric patients.

Aged↗

PubMed2005

Application of perceptual difference model on regularization techniques of parallel MR imaging.

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or to improve image SNR. The quality of reconstructed images is limited by the inaccurate estimation of the sensitivity map, noise in the acquired k-space data and the ill-conditioned nature of the coefficient matrix. Tikhonov regularization is a popular method to reduce or eliminate the ill-conditioned nature of the problem. In this approach, selection of the regularization map and the regularization parameter is very important. Perceptual difference model (PDM) is a quantitative image quality evaluation tool that has been successfully applied to varieties of MR applications. High correlation between the human rating and PDM score shows that PDM should be suitable to evaluate image quality in parallel MR imaging. By applying PDM, we compared four methods of selecting the regularization map and four methods of selecting the regularization parameter. We found that a regularization map obtained using generalized series (GS) together with a spatially adaptive regularization parameter gave the best reconstructions. PDM was also used as an objective function for optimizing two important parameters in the spatially adaptive method. We conclude that PDM enables one to do comprehensive experiments and that it is an effective tool for designing and optimizing reconstruction methods in parallel MR imaging.

Brain Mapping↗

PubMed2006

A regularized discriminative model for the prediction of protein-peptide interactions.

MOTIVATION: Short well-defined domains known as peptide recognition modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes and biochemical pathways. Since high-throughput experiments like yeast two-hybrid and phage display are expensive and intrinsically noisy, it would be desirable to more specifically target or partially bypass them with complementary in silico approaches. In the present paper, we present a probabilistic discriminative approach to predicting PRM-mediated protein-protein interactions from sequence data. The model is motivated by the discriminative model of Segal and Sharan as an alternative to the generative approach of Reiss and Schwikowski. In our evaluation, we focus on predicting the interaction network. As proposed by Williams, we overcome the problem of susceptibility to over-fitting by adopting a Bayesian a posteriori approach based on a Laplacian prior in parameter space. RESULTS: The proposed method was tested on two datasets of protein-protein interactions involving 28 SH3 domain proteins in Saccharmomyces cerevisiae, where the datasets were obtained with different experimental techniques. The predictions were evaluated with out-of-sample receiver operator characteristic (ROC) curves. In both cases, Laplacian regularization turned out to be crucial for achieving a reasonable generalization performance. The Laplacian-regularized discriminative model outperformed the generative model of Reiss and Schwikowski in terms of the area under the ROC curve on both datasets. The performance was further improved with a hybrid approach, in which our model was initialized with the motifs obtained with the method of Reiss and Schwikowski. AVAILABILITY: Software and supplementary material is available from http://lehrach.com/wolfgang/dmf.

Algorithms↗

PubMed2001

Applying the transtheoretical model to regular moderate exercise in an overweight population: validation of a stages of change measure.

BACKGROUND: The overweight population may benefit from Transtheoretical Model-based interventions focusing on regular moderate exercise. Current stages of change measures assessing regular moderate exercise specific to an overweight population (BMI > or =25) are lacking. This study examined the validity of a staging algorithm for moderate exercise for the purposes of healthy weight management. METHODS: A sample of 670 healthy adults (mean age 50.9 +/- 15.0; mean BMI 30.6 +/- 5.5; 53% female; 93% Caucasian) completed a questionnaire that included demographics, self-reported levels of exercise, and constructs from the Transtheoretical Model (TTM). Analyses of variance and follow-up tests were used to assess the concurrent and construct validity of the staging algorithm. RESULTS: The staging algorithm discriminated those in the action stages from those in the preaction stages for the moderate- and strenuous-intensity categories (P < 0.001). The constructs of pros and cons (P < 0.001) and confidence (P < 0.001) differed across the stages. CONCLUSIONS: In addition to demonstrating good concurrent and construct validity for the stages of change measure, the patterns found across the stages of change were consistent with the theoretical predictions of the TTM and replicated the patterns observed in previous studies.

Adult↗

PubMed2006

Fractional calculus in biomechanics: a 3D viscoelastic model using regularized fractional derivative kernels with application to the human calcaneal fat pad.

A viscoelastic model of the K-BKZ (Kaye, Technical Report 134, College of Aeronautics, Cranfield 1962; Bernstein et al., Trans Soc Rheol 7: 391-410, 1963) type is developed for isotropic biological tissues and applied to the fat pad of the human heel. To facilitate this pursuit, a class of elastic solids is introduced through a novel strain-energy function whose elements possess strong ellipticity, and therefore lead to stable material models. This elastic potential - via the K-BKZ hypothesis - also produces the tensorial structure of the viscoelastic model. Candidate sets of functions are proposed for the elastic and viscoelastic material functions present in the model, including two functions whose origins lie in the fractional calculus. The Akaike information criterion is used to perform multi-model inference, enabling an objective selection to be made as to the best material function from within a candidate set.

Adipose Tissue↗

PubMed1983

Specious causal attributions in the social sciences: the reformulated stepping-stone theory of heroin use as exemplar.

The claims based on causal models employing either statistical or experimental controls are examined and found to be excessive when applied to social or behavioral science data. An exemplary case, in which strong causal claims are made on the basis of a weak version of the regularity model of cause, is critiqued. O'Donnell and Clayton claim that in order to establish that marijuana use is a cause of heroin use (their "reformulated stepping-stone" hypothesis), it is necessary and sufficient to demonstrate that marijuana use precedes heroin use and that the statistically significant association between the two does not vanish when the effects of other variables deemed to be prior to both of them are removed. I argue that O'Donnell and Clayton's version of the regularity model is not sufficient to establish cause and that the planning of social interventions both presumes and requires a generative rather than a regularity causal model. Causal modeling using statistical controls is of value when it compels the investigator to make explicit and to justify a causal explanation but not when it is offered as a substitute for a generative analysis of causal connection.

Heroin Dependence↗

PubMed1998

Two-dimensional statistical model for regularized backprojection in SPECT.

In SPECT, both the noise affecting the data and the discretization of the inverse Radon transform are responsible for the ill-posed nature of the reconstruction. To constrain the problem, we propose a regularized backprojection method (RBP) which takes advantage of the relationships existing between the continuity properties of the projections and those of the reconstructed object. The RBP method involves two stages: first, a statistical model (the fixed-effect model) is used to estimate the noise-free part of the projections. Then, the filtered projections are reconstructed using a backprojection algorithm (spline filtered backprojection) which ensures that the reconstructed object belongs to a space consistent with that containing the projections. The method is illustrated using analytical simulations, and the RBP approach is compared to the conventional filtered backprojection. The effect on the reconstructed slices of the parameters involved in RBP is studied in terms of spatial resolution, homogeneity in uniform regions and quantification. It is shown that appropriate combinations of these parameters yield a better compromise between homogeneity and spatial resolution than conventional FBP, with similar quantification performances.

Algorithms↗