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At least 1,369 records · Page 76Linked to original sources
PubMed2005

Validation framework of the finite element modeling of liver tissue.

In this work, we aim at validating some soft tissue deformation models using high resolution Micro Computed Tomography (Micro-CT) and medium resolution Cone-Beam CT (CBCT) images. These imaging techniques play a key role in detecting the tissue deformation details in the contact region between the tissue and the surgical tool (probe) even for small force loads, and provide good capabilities for creating accurate 3D models of tissues. Surgical simulations rely on accurate representation of the mechanical response of soft tissues subjected to surgical manipulations. Several finite element (F.E.) models have been suggested to characterize soft tissues. However, validating these models for specific tissues still remains a challenge. For our validation, ex vivo lamb liver is chosen to validate the linear elastic model (LEM), the linear viscoelastic model (LVEM), and the neo-Hooke hyperelastic model (NHM). We found that the LEM is more applicable to lamb liver than the LVEM for small force loads (< 40 g) and that the NHM is closer to reality than the LVEM for this same range of force loads.

Algorithms↗
PubMed2005

Using the fast marching method to extract curves with given global properties.

Curves are often used as anatomical features to match surfaces that represent biological objects, such as the human brain. Automated and semi-automated methods for extracting these curves usually rely on local properties of the surfaces such as the mean surface curvature without considering the global appearance of the curves themselves. These methods may require additional human intervention, and sometimes produce erroneous results. In this paper, we present an algorithm that is based on the fast marching method (FMM) to extract weighted geodesic curves. Instead of directly using the local image properties as a weight function, we use the surface properties, together with the global properties of the curves, to compute a weight function. This weight function is then used by the FMM to extract curves between given points. The general framework can be used to extract curves with different global properties. The resulting curves are guaranteed to be weighted geodesic curves without cusps usually introduced by intermediate points through which the curves are forced to pass. We show some results on both a simulated image and a highly convoluted human brain cortical surface.

Algorithms↗
PubMed2004

Genome-scale models of microbial cells: evaluating the consequences of constraints.

Microbial cells operate under governing constraints that limit their range of possible functions. With the availability of annotated genome sequences, it has become possible to reconstruct genome-scale biochemical reaction networks for microorganisms. The imposition of governing constraints on a reconstructed biochemical network leads to the definition of achievable cellular functions. In recent years, a substantial and growing toolbox of computational analysis methods has been developed to study the characteristics and capabilities of microorganisms using a constraint-based reconstruction and analysis (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.

Bacterial Physiological Phenomena↗
PubMed1977

A computer program for multiple decrement life table analyses.

Life table analysis has traditionally been the tool of choice in analyzing distribution of "survival" times when a parametric form for the survival curve could not be reasonably assumed. Chiang, in two papers [1,2] formalized the theory of life table analyses in a Markov chain framework and derived maximum likelihood estimates of the relevant parameters for the analyses. He also discussed how the techniques could be generalized to consider competing risks and follow-up studies. Although various computer programs exist for doing different types of life table analysis [3] to date, there has not been a generally available, well documented computer program to carry out multiple decrement analyses, either by Chiang's or any other method. This paper describes such a program developed by Research Triangle Institute. A user's manual is available at printing costs which supplements the contents of this paper with a discussion of the formula used in the program listing.

Computers↗
PubMed1998

A computationally efficient alternative for the Liljencrants-Fant model and its perceptual evaluation.

An alternative for the Liljencrants-Fant (LF) glottal-pulse model is presented. This alternative is derived from the Rosenberg model. Therefore, it is called the Rosenberg++ model. In the derivation a general framework is used for glottal-pulse models. The Rosenberg++ model is described by the same set of T or R parameters as the LF model but it has the advantage over the LF model that it is computationally more efficient. It is compared with the LF model in a psychoacoustic experiment, from which it is concluded that in a practical situation it is capable of producing synthetic speech which is perceptually equivalent to speech generated with the LF model.

Auditory Perception↗
PubMed2004

Intensity-based image registration with a guaranteed one-to-one point match.

OBJECTIVES: In this paper, we propose a novel registration technique, which combines the concepts of landmark and automatic, non-rigid intensity-based approaches. A general framework, which might be used for many different registration problems is presented. The novel approach enables the incorporation of different distance measures as well as different smoothers. METHODS: The proposed scheme minimizes a regularized distance measure subject to some interpolation constraints. The desired deformation is computed iteratively using an Euler-scheme for the first variation of the chosen objective functional. RESULTS: A fast and robust numerical scheme for the computation of the wanted minimizer is developed, implemented, and applied to various registration tasks. This includes the registration of pre- and post-intervention images of the human eye. CONCLUSIONS: A novel framework for a parameter-free, non-rigid registration scheme which allows for the additional incorporation of user-defined landmarks is proposed. It enhances the reliability of conventional approaches considerably and thereby their acceptability by practitioners in a clinical environment.

Algorithms↗
PubMedDate not supplied

Medical database security evaluation.

Users of medical information systems need confidence in the security of the system they are using. They also need a method to evaluate and compare its security capabilities. Every system has its own requirements for maintaining confidentiality, integrity and availability. In order to meet these requirements a number of security functions must be specified covering areas such as access control, auditing, error recovery, etc. Appropriate confidence in these functions is also required. The 'trust' in trusted computer systems rests on their ability to prove that their secure mechanisms work as advertised and cannot be disabled or diverted. The general framework and requirements for medical database security and a number of parameters of the evaluation problem are presented and discussed. The problem of database security evaluation is then discussed, and a number of specific proposals are presented, based on a number of existing medical database security systems.

Accreditation↗
PubMed1993

The effect of point of care technology on the quality of patient care.

It is thought that new technologies like computers at the patient's bedside, or point of care technology (PCT) improve nursing productivity, documentation, patient satisfaction and decrease costs. Using the Health Care Technology Assessment (HCTA) framework, (safety, cost, effectiveness, social impact), a descriptive and quasi-experimental study was performed to test the effectiveness and explain the social impact of PCT. A sample of 90 patients from five nursing units in three hospitals were obtained for the study. Half of the patients had computers at their bedside. Data were collected on a hospital pretest/posttest unit and two comparison and experimental units. The main null hypothesis was: There is no difference in the quality of patient care on nursing units with and without PCT. Quality of patient care was measured by patient satisfaction and a nursing care documentation instruments. This hypothesis was rejected. While patients were generally very satisfied with their nursing care on all units, when controlling for time and the presence of the computer, patients who did not have PCT were more satisfied than patients in rooms with PCT. Furthermore, the charts of patients with PCT were less compliant to documentation standards. Conversely, a sub sample of these same patients expressed positive responses to the bedside computer and technologies in their room and this concurred with the current literature. The benefits of the technology were found to outweigh the costs of PCT from the literature review. There was not enough in the literature to draw conclusions about the safety of PCT. In summary, the quality of patient care did not improve with the implementation of PCT in this study.(ABSTRACT TRUNCATED AT 250 WORDS)

Analysis of Variance↗
PubMed2003

Filling the Radon domain in computed tomography by local convex combination.

Radon data interpolation is a necessary procedure in computed tomography (CT), especially for reconstruction from divergent beam scanning. In a polar-grid representation, the Radon data of a fanbeam projection are populated on an arc, rather on a radial line. Collectively, the Radon data generated from a fanbeam CT system are unevenly populated: The population becomes sparser as the polar distance increases. In CT reconstruction, the Fourier central slice theorem requires a radial scanline full of Radon data. Therefore the vacant entries of a scanline must be filled by interpolation. In addition, interpolation is also required in polar-to-Cartesian conversion. In this paper we propose a practical interpolation technique for filling the vacant entries by local convex combination. It is a linear interpolant that generates a value for a grid point from the available data lying in its neighborhood, by a weighted average, with the weights corresponding to the inverse distances. In fact, the linear convex combination serves as a general flat-smoothing operation in filling a vacancy. Specifically, this technique realizes a variety of linear interpolations, including nearest-neighbor replication, two-point collinear, three-point triangulation, and four-point quadrilateral, and local extrapolation, in a unified framework. Algorithms and a simulation demonstration are provided.

Algorithms↗
PubMed2006

PepDist: a new framework for protein-peptide binding prediction based on learning peptide distance functions.

BACKGROUND: Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHC-peptide complexes in the immune system. Developing computational methods for protein-peptide binding prediction is therefore an important task with applications to vaccine and drug design. METHODS: Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose PepDist: a novel approach for predicting binding affinity. Our approach is based on learning peptide-peptide distance functions. Moreover, we suggest to learn a single peptide-peptide distance function over an entire family of proteins (e.g. MHC class I). This distance function can be used to compute the affinity of a novel peptide to any of the proteins in the given family. In order to learn these peptide-peptide distance functions, we formalize the problem as a semi-supervised learning problem with partial information in the form of equivalence constraints. Specifically, we propose to use DistBoost, which is a semi-supervised distance learning algorithm. RESULTS: We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors. One of the major advantages of our novel approach is that it can also learn an affinity function over proteins for which only small amounts of labeled peptides exist. In these cases, our method's performance gain, when compared to other computational methods, is even more pronounced. We have recently uploaded the PepDist webserver which provides binding prediction of peptides to 35 different MHC class I alleles. The webserver which can be found at http://www.pepdist.cs.huji.ac.il is powered by a prediction engine which was trained using the framework presented in this paper. CONCLUSION: The results obtained suggest that learning a single distance function over an entire family of proteins achieves higher prediction accuracy than learning a set of binary classifiers for each of the proteins separately. We also show the importance of obtaining information on experimentally determined non-binders. Learning with real non-binders generalizes better than learning with randomly generated peptides that are assumed to be non-binders. This suggests that information about non-binding peptides should also be published and made publicly available.

Algorithms↗
PubMed2004

Protein structure and fold prediction using tree-augmented naive Bayesian classifier.

For determining the structure class and fold class of Protein Structure, computer-based techniques have became essential considering the large volume of the data. Several techniques based on sequence similarity. Neural Networks, SVMs, etc have been applied. This paper presents a framework using the Tree-Augmented Networks (TAN) based on the theory of learning Bayesian networks but with less restrictive assumptions than the naive Bayesian networks. In order to enhance TAN's performance, pre-processing of data is done by feature discretization and post-processing is done by using Mean Probability Voting (MPV) scheme. The advantage of using Bayesian approach over other learning methods is that the network structure is intuitive. In addition, one can read off the TAN structure probabilities to determine the significance of each feature (say, Hydrophobicity) for each class, which help to further understand the mystery of protein structure. Experimental results and comparison with other works over two databases show the effectiveness of our TAN based framework. The idea is implemented as the BAYESPROT web server and it is available at http://www-appn.comp.nus.edu.sg/-bioinfo/bayesprot/Default.htm.

Artificial Intelligence↗
PubMed2006

Characterizing powder mixing processes utilizing compartment models.

Powder mixing has been the subject of substantial research due to its importance in a variety of industrial sectors, including pharmaceuticals, food, and polymer manufacturing. Although a number of different models have been proposed in the literature, most of them are either empirical or require computationally intensive calculations that make them difficult to implement for realistic systems. The aim of this paper is to develop a simplified framework, based on compartment modeling that efficiently and accurately captures the system behavior. Using the V-blender as a model system, the compartment modeling approach was used to illustrate the effects of vessel loading on mixing as well as the impact of sampling methods on the accuracy of mixing characterization.

Chemistry, Pharmaceutical↗
PubMed2005

A framework for multidimensional modelling of activity and structure of multispecies biofilms.

Concepts from previous biofilm models were integrated to create a framework for the implementation of multidimensional (2D and 3D) multispecies biofilm models. The framework is here described at three levels: (i) mathematical representation of the processes involved in biofilm formation, (ii) numerical implementation into a computer program (freely available from our website http://www.biofilms.bt.tudelft.nl/frameworkMaterial) and (iii) using the program for the creation of biofilm models with multiple bacterial and solute species. An improved version of the individual-based modelling (IbM) that allows structured biomass was used. In this approach biomass composition may be discriminated into any number of particulate species, including extracellular polymeric substances (EPS) for which specific functionality was included. Detachment is also included, described as occurring at the biofilm surface with variable local rates derived from functions of state variables. The application of this modelling framework to a multispecies system with structured biomass is illustrated in a case study where the competition between an organism capable of accumulating polyhydroxybutyrate (PHB, an internal storage compound) and an EPS-producing organism in a two-species biofilm is analysed. Results illustrate that biofilms enriched in PHB-producing organisms may be obtained by supplying substrate intermittently in feast/famine cycles.

Bacteria↗
PubMed2007

Hypothesis testing via integrated computer modeling and digital fluorescence microscopy.

Computational modeling has the potential to add an entirely new approach to hypothesis testing in yeast cell biology. Here, we present a method for seamless integration of computational modeling with quantitative digital fluorescence microscopy. This integration is accomplished by developing computational models based on hypotheses for underlying cellular processes that may give rise to experimentally observed fluorescent protein localization patterns. Simulated fluorescence images are generated from the computational models of underlying cellular processes via a "model-convolution" process. These simulated images can then be directly compared to experimental fluorescence images in order to test the model. This method provides a framework for rigorous hypothesis testing in yeast cell biology via integrated mathematical modeling and digital fluorescence microscopy.

Computational Biology↗
PubMed1995

Evaluation of diffuse technologies: the case of digital imaging networks.

There have been significant developments in recent years in the methodologies and methods for the evaluation of a wide range of health technologies. There remain, though, many technologies which are difficult to evaluate. Often the difficulty stems from the complexity of the technologies themselves, which are in effect hybrids, comprising combinations of several distinct elements. In this paper these are termed 'diffuse' technologies, because the different elements exert different costs and effects, often across several different services. Computer networks are one, increasingly important, example of such technologies in health care. While it is possible to evaluate individual elements of such technologies, it is not clear how to evaluate the technology as a whole, where the whole may be greater (or less) than the sum of the parts. The paper outlines a seven-stage framework for the evaluation of diffuse technologies. The general principles of evaluation are illustrated using the example of picture archiving and communication systems (PACS), which are computer systems designed to capture, store and distribute electronic radiological images within a hospital.

Cost-Benefit Analysis↗
PubMed2006

Bayesian analysis of structural equation models with mixed exponential family and ordered categorical data.

Structural equation models are very popular for studying relationships among observed and latent variables. However, the existing theory and computer packages are developed mainly under the assumption of normality, and hence cannot be satisfactorily applied to non-normal and ordered categorical data that are common in behavioural, social and psychological research. In this paper, we develop a Bayesian approach to the analysis of structural equation models in which the manifest variables are ordered categorical and/or from an exponential family. In this framework, models with a mixture of binomial, ordered categorical and normal variables can be analysed. Bayesian estimates of the unknown parameters are obtained by a computational procedure that combines the Gibbs sampler and the Metropolis-Hastings algorithm. Some goodness-of-fit statistics are proposed to evaluate the fit of the posited model. The methodology is illustrated by results obtained from a simulation study and analysis of a real data set about non-adherence of hypertension patients in a medical treatment scheme.

Bayes Theorem↗
PubMed2005

A fourier theory for cast shadows.

Cast shadows can be significant in many computer vision applications, such as lighting-insensitive recognition and surface reconstruction. Nevertheless, most algorithms neglect them, primarily because they involve nonlocal interactions in nonconvex regions, making formal analysis difficult. However, many real instances map closely to canonical configurations like a wall, a V-groove type structure, or a pitted surface. In particular, we experiment with 3D textures like moss, gravel, and a kitchen sponge, whose surfaces include canonical configurations like V-grooves. This paper takes a first step toward a formal analysis of cast shadows, showing theoretically that many configurations can be mathematically analyzed using convolutions and Fourier basis functions. Our analysis exposes the mathematical convolution structure of cast shadows and shows strong connections to recent signal-processing frameworks for reflection and illumination.

Algorithms↗
PubMed2006

A maximum likelihood approach to density estimation with semidefinite programming.

Density estimation plays an important and fundamental role in pattern recognition, machine learning, and statistics. In this article, we develop a parametric approach to univariate (or low-dimensional) density estimation based on semidefinite programming (SDP). Our density model is expressed as the product of a nonnegative polynomial and a base density such as normal distribution, exponential distribution, and uniform distribution. When the base density is specified, the maximum likelihood estimation of the polynomial is formulated as a variant of SDP that is solved in polynomial time with the interior point methods. Since the base density typically contains just one or two parameters, computation of the maximum likelihood estimate reduces to a one- or two-dimensional easy optimization problem with this use of SDP. Thus, the rigorous maximum likelihood estimate can be computed in our approach. Furthermore, such conditions as symmetry and unimodality of the density function can be easily handled within this framework. AIC is used to choose the best model. Through applications to several instances, we demonstrate flexibility of the model and performance of the proposed procedure. Combination with a mixture approach is also presented. The proposed approach has possible other applications beyond density estimation. This point is clarified through an application to the maximum likelihood estimation of the intensity function of a nonstationary Poisson process.

Data Interpretation, Statistical↗