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PubMed2005

A finite difference thermal model of a cylindrical microwave heating applicator using locally conformal overlapping grids: part I--theoretical formulation.

In this paper, we present a versatile mathematical formulation of a newly developed 3-D locally conformal Finite Difference (FD) thermal algorithm developed specificallyfor coupled electromagnetic (EM) and heat diffusion simulations utilizing Overlapping Grids (OGFD) in the Cartesian and cylindrical coordinate systems. The motivation for this research arises from an attempt to characterize the dominant thermal transport phenomena typically encountered during the process cycle of a high-power, microwave-assisted material processing system employing a geometrically composite cylindrical multimode heating furnace. The cylindrical FD scheme is only applied to the outer shell of the housing cavity whereas the Cartesian FD scheme is used to advance the temperature elsewhere including top and bottom walls, and most of the inner region of the cavity volume. The temperature dependency of the EM constitutive and thermo-physical parameters of the material being processed is readily accommodated into the OGFD update equations. The time increment, which satisfies the stability constraint of the explicit OGFD time-marching scheme, is derived. In a departure from prior work, the salient features of the proposed algorithm are first, the locally conformal discretization scheme accurately describes the diffusion of heat and second, significant heat-loss mechanisms usually encountered in microwave heating problems at the interfacial boundary temperature nodes have been considered. These include convection and radiation between the surface of the workload and air inside the cavity, heat convection and radiation between the inner cavity walls and interior cavity volume, and free cooling of the outermost cavity walls.

Algorithms↗
PubMed1986

The modelling of biological systems in three dimensions using the time domain finite-difference method: I. The implementation of the model.

A computer method has been developed which uses the time domain finite-difference (TDFD) algorithm to calculate the deposition of the electromagnetic (EM) field in three-dimensional biological models. This, the first of two papers, describes the algorithm and the computer programs developed. The method is demonstrated by calculating the penetration of the EM field from a rectangular waveguide radiating into a homogeneous model, the calculation being carried out in two dimensions for simplicity in this paper.

Computer Simulation↗
PubMed1996

A diagnostic algorithm for distinguishing the eosinophilia-myalgia syndrome from fibromyalgia and chronic myofascial pain.

OBJECTIVE: To develop a diagnostic algorithm for the eosinophilia-myalgia syndrome (EMS) that complements the existing case definition. METHODS: We conducted a retrospective study using data on 59 clinical and laboratory variables from a consecutive referral cohort of 91 patients with EMS meeting the Centers for Disease Control and Prevention case definition. Age and sex matched controls included 93 patients with fibromyalgia and 99 patients with chronic myofascial pain. The study period was March 1989 to April 1992. Recursive partitioning was used to create a diagnostic algorithm. RESULTS: In the 283 case patients and controls with disabling myalgias, 4 differentiating variables identified patients with EMS: extremity edema, leukocyte count > 12.5 x 10(9)/l, dyspnea, and absence of arthralgias. These 4 variables form a diagnostic algorithm that has a sensitivity of 95.6%, a specificity of 96.9%, and positive and negative predictive values of 93.5 and 97.9%, respectively. CONCLUSION: This algorithm is practical and can be easily applied in any medical setting. It also readily distinguishes EMS from other common myalgia syndromes.

Adult↗
PubMedDate not supplied

Inference methods for saturated models in longitudinal clinical trials with incomplete binary data.

In the longitudinal studies with binary response, it is often of interest to estimate the percentage of positive responses at each time point and the percentage of having at least one positive response by each time point. When missing data exist, the conventional method based on observed percentages could result in erroneous estimates. This study demonstrates two methods of using expectation-maximization (EM) and data augmentation (DA) algorithms in the estimation of the marginal and cumulative probabilities for incomplete longitudinal binary response data. Both methods provide unbiased estimates when the missingness mechanism is missing at random (MAR) assumption. Sensitivity analyses have been performed for cases when the MAR assumption is in question.

Algorithms↗
PubMed2002

Two-level proportional hazards models.

We extend the proportional hazards model to a two-level model with a random intercept term and random coefficients. The parameters in the multilevel model are estimated by a combination of EM and Newton-Raphson algorithms. Even for samples of 50 groups, this method produces estimators of the fixed effects coefficients that are approximately unbiased and normally distributed. Two different methods, observed information and profile likelihood information, will be used to estimate the standard errors. This work is motivated by the goal of understanding the determinants of contraceptive use among Nepalese women in the Chitwan Valley Family Study (Axinn, Barber, and Ghimire, 1997). We utilize a two-level hazard model to examine how education and access to education for children covary with the initiation of permanent contraceptive use.

Adolescent↗
PubMed1996

Mixed Poisson regression models with covariate dependent rates.

This paper studies a class of Poisson mixture models that includes covariates in rates. This model contains Poisson regression and independent Poisson mixtures as special cases. Estimation methods based on the EM and quasi-Newton algorithms, properties of these estimates, a model selection procedure, residual analysis, and goodness-of-fit test are discussed. A Monte Carlo study investigates implementation and model choice issues. This methodology is used to analyze seizure frequency and Ames salmonella assay data.

Algorithms↗
PubMedDate not supplied

A model protocol for emergency medical services management of asthma exacerbations.

Emergency medical services (EMS) is an important part of the continuum of asthma management. The magnitude of the EMS responsibility is very large, with millions of patients with asthma treated each year by EMS personnel. In response to inconsistencies between the 1997 National Asthma Education and Prevention Program asthma guidelines and a variety of existing EMS protocols on the management of asthma exacerbations, the Centers for Disease Control and Prevention convened a workgroup in 2004 to discuss the various opportunities and challenges ahead. At the meeting, and over the ensuing year, the workgroup created a model protocol that was derived from the National Asthma Education and Prevention Program guidelines. The model protocol is available in both text and algorithm format and offers guidance for EMS systems to develop and implement treatment protocols in their local areas. The workgroup recommendations emphasize flexibility, simplicity, and low-risk practices. By integrating these recommendations into existing protocols, we believe that EMS systems could improve prehospital care for patients with asthma. Demonstration projects are needed to carefully examine the implementation process and the actual impact of the model protocol on various outcomes. The workgroup also encourages more research on EMS management of asthma exacerbations. In the meantime, improved collaboration between EMS and national asthma organizations is an immediate priority and will continue to advance future discussions on how to improve asthma management in the prehospital setting. The workgroup hopes that state and local EMS systems will see the value of the model protocol and encourage its use.

Adrenal Cortex Hormones↗
PubMed2004

Minimum entropy clustering and applications to gene expression analysis.

Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First, we propose the minimum entropy (measured on a posteriori probabilities) criterion, which is the conditional entropy of clusters given the observations. Fano's inequality indicates that it could be a good criterion for clustering. We generalize the criterion by replacing Shannon's entropy with Havrda-Charvat's structural alpha-entropy. Interestingly, the minimum entropy criterion based on structural alpha-entropy is equal to the probability error of the nearest neighbor method when alpha = 2. This is another evidence that the proposed criterion is good for clustering. With a non-parametric approach for estimating a posteriori probabilities, an efficient iterative algorithm is then established to minimize the entropy. The experimental results show that the clustering algorithm performs significantly better than k-means/medians, hierarchical clustering, SOM, and EM in terms of adjusted Rand index. Particularly, our algorithm performs very well even when the correct number of clusters is unknown. In addition, most clustering algorithms produce poor partitions in presence of outliers while our method can correctly reveal the structure of data and effectively identify outliers simultaneously.

Algorithms↗
PubMed1993

Emergency medical services priority dispatch.

STUDY OBJECTIVE: To test the ability of a locally designed priority dispatch system to safely exclude the need for advanced life support (ALS). DESIGN: Retrospective review of emergency medical services (EMS) incident records to determine how often the lone dispatch of basic life support (BLS) units, staffed with basic emergency medical technicians, subsequently required or involved ALS care. SETTING: A large centralized municipal EMS system with a tiered ALS/BLS ambulance response. All BLS units carry automated defibrillators. MEASUREMENTS: Consecutive EMS records (35,075) were reviewed by computerized search for ALS procedures. Records indicating ALS procedures were tabulated and then manually reviewed for the nature of and probable indication for the ALS intervention. INTERVENTION: Brief sequences of computer-stored questions that help dispatchers identify (or exclude) signs and symptoms indicating the need for ALS. RESULTS: The dispatch triage system spared ALS units from initial dispatch in 14,100 of the EMS incidents (40.2%), increasing their availability and use for more serious calls. Among these 14,100 cases, only 41 patients (0.3%) later received drugs such as nitroglycerin and naloxone; another 27 patients (0.2%) received resuscitative interventions such as epinephrine or defibrillation. Furthermore, on closer analysis, the immediate presence of a paramedic might have provided a true potential for advantage in outcome for only five or six patients (less than 0.04 of the 14,100 BLS dispatches). Meanwhile, many important operational, fiscal, and cost-effective patient care benefits were realized with this system. CONCLUSION: A computer-aided dispatch triage algorithm can facilitate improvements in both EMS system operations and prehospital patient care by safely and reliably identifying EMS incidents requiring only BLS.

Algorithms↗
PubMed1998

A mixture model approach to the mapping of quantitative trait loci in complex populations with an application to multiple cattle families.

A mixture model approach is presented for the mapping of one or more quantitative trait loci (QTLs) in complex populations. In order to exploit the full power of complete linkage maps the simultaneous likelihood of phenotype and a multilocus (all markers and putative QTLs) genotype is computed. Maximum likelihood estimation in our mixture models is implemented via an Expectation-Maximization algorithm: exact, stochastic or Monte Carlo EM by using a simple and flexible Gibbs sampler. Parameters include allele frequencies of markers and QTLs, discrete or normal effects of biallelic or multiallelic QTLs, and homogeneous or heterogeneous residual variances. As an illustration a dairy cattle data set consisting of twenty half-sib families has been reanalyzed. We discuss the potential which our and other approaches have for realistic multiple-QTL analyses in complex populations.

Animals↗
PubMed2006

On weighting clustering.

Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via weights on the instance points, somehow making clustering move toward the hardest points to cluster. The motivations come principally from an analogy with powerful supervised classification methods known as boosting algorithms. However, interest in this analogy has so far been mainly borne out from experimental studies only. This paper is, to the best of our knowledge, the first attempt at its formalization. More precisely, we handle clustering as a constrained minimization of a Bregman divergence. Weight modifications rely on the local variations of the expected complete log-likelihoods. Theoretical results show benefits resembling those of boosting algorithms and bring modified (weighted) versions of clustering algorithms such as k-means, fuzzy c-means, Expectation Maximization (EM), and k-harmonic means. Experiments are provided for all these algorithms, with a readily available code. They display the advantages that subtle data reweighting may bring to clustering.

Algorithms↗
PubMed2005

EM in high-dimensional spaces.

This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this algorithm does not try to compress the data to fit low-dimensional models. Instead, it models Gaussian distributions in the (N - 1)-dimensional space spanned by the N data samples. We are able to show that this algorithm converges on data sets where low-dimensional techniques do not.

Algorithms↗
PubMed2005

YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities.

BACKGROUND: A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. RESULTS: YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at http://yana.bioapps.biozentrum.uni-wuerzburg.de. CONCLUSION: A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts.

Algorithms↗
PubMed1998

Multi-level modelling of conception in artificial insemination by donor.

Data on insemination with donor's sperm have a crossed hierarchical structure due to the coexistence of female factors (ovulatory cycles within pregnancy attempt within women) and male factors (inseminations within donations within donors). A crossed random multi-level logistic model, taking account of this structure, permits an improved estimation of the fixed effects and provides insights into their influence at each level in the hierarchy. We present an efficient algorithm for fitting such models using alternating EM steps. We further discuss the inclusion of compositional covariates to determine what information the quality of a donation conveys regarding the donor basal fecundability and on the specific sperm donation.

Adult↗
PubMed2004

An expectation/maximization nuclear vector replacement algorithm for automated NMR resonance assignments.

We report an automated procedure for high-throughput NMR resonance assignment for a protein of known structure, or of an homologous structure. Our algorithm performs Nuclear Vector Replacement (NVR) by Expectation/Maximization (EM) to compute assignments. NVR correlates experimentally-measured NH residual dipolar couplings (RDCs) and chemical shifts to a given a priori whole-protein 3D structural model. The algorithm requires only uniform (15)N-labelling of the protein, and processes unassigned H(N)-(15)N HSQC spectra, H(N)-(15)N RDCs, and sparse H(N)-H(N) NOE's (d(NN)s). NVR runs in minutes and efficiently assigns the (H(N),(15)N) backbone resonances as well as the sparse d(NN)s from the 3D (15)N-NOESY spectrum, in O (n(3)) time. The algorithm is demonstrated on NMR data from a 76-residue protein, human ubiquitin, matched to four structures, including one mutant (homolog), determined either by X-ray crystallography or by different NMR experiments (without RDCs). NVR achieves an average assignment accuracy of over 99%. We further demonstrate the feasibility of our algorithm for different and larger proteins, using different combinations of real and simulated NMR data for hen lysozyme (129 residues) and streptococcal protein G (56 residues), matched to a variety of 3D structural models.

Algorithms↗
PubMed2003

A method for estimating the CTF in electron microscopy based on ARMA models and parameter adjustment.

In this work, a powerful parametric spectral estimation technique, 2D-auto regressive moving average modeling (ARMA), has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as auto regressive (AR) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier transform of the complete image or parts of it and performing some average (periodogram averaging). Previous works revealed that AR models can be used to improve CTF estimation and the detection of its zeros. ARMA models reduce the model order and the computing time, and more interestingly, achieve increased accuracy. ARMA models are generated from electron microscopy (EM) images, and then a stepwise search algorithm is used to fit all the parameters of a theoretical CTF model in the ARMA model previously calculated. Furthermore, this adjustment is truly two-dimensional, allowing astigmatic images to be properly treated. Finally, an individual CTF can be assigned to every point of the micrograph, by means of an interpolation at the functional level, provided that a CTF has been estimated in each one of a set of local areas. The user need only know a few a priori parameters of the experimental conditions of his micrographs, for turning this technique into an automatic and very powerful tool for CTF determination, prior to CTF correction in 3D-EM. The programs developed for the above tasks have been integrated into the X-Windows-based Microscopy Image Processing Package (Xmipp) software package, and are fully accessible at www.biocomp.cnb.uam.es.

Algorithms↗
PubMed2005

The difference between observed and expected prevalence of MCAD deficiency in The Netherlands: a genetic epidemiological study.

Medium chain acyl coenzyme A dehydrogenase (MCAD) deficiency is assumed to be the most common inherited disorder of mitochondrial fatty acid oxidation. Few reports mention the difference between the expected and observed prevalence of MCAD deficiency on the basis of the carrier frequency in the population. We performed a population-wide retrospective analysis of all known MCAD-deficient patients in The Netherlands. In this study, the observed prevalence of MCAD deficiency in The Netherlands was 1/27 400 (95% confidence interval (CI) 1/23 000-1/33 900), significantly different from the expected prevalence of 1/12 100 (95% CI 1/8450-1/18 500). The observed prevalence of MCAD deficiency showed a remarkable north-south trend within the country. From the patients in this cohort, it can be observed that underdiagnosis contributes to a larger extent to the difference between the expected and observed prevalences of MCAD deficiency in our country, than reduced penetrance. We determined estimates of the segregation proportion in a cohort of 73 families under the assumption of complete ascertainment (p(LM) = 0.41, 95% CI 0.31-0.51) and single ascertainment (p(D) = 0.28, 95% CI 0.19-0.37). With the expectation-maximization algorithm, a third estimate was obtained (p(EM) = 0.28, 95% CI 0.20-0.37). The agreement between the latter two estimates supports incomplete selection and the segregation proportions were in agreement with normal mendelian autosomal recessive inheritance.

Acyl-CoA Dehydrogenase↗