PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “regularized model”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources
PubMed1976

[Experimental latent typhus].

Asymptomatic typhus infection was regularly modelled in guinea pigs with increased resistance to Rickettsia prowazeki; this infection was characterized by the presence of immunological shifts in the blood and immunity to reinfection, persistenc of rickettsia in the organs of animals in the absence of pyretic reaction and periorchites. Modelling in guinea pigs of asymptomatic infection is also possible against the background of passive immunity created by specific immune serum obtained during convalescence. Investigations demonstrated that rickettsiocidal activity (in experiments on chick embryos) and antiinfectious activity (in experiments on guinea pigs) of the sera was caused by 7S-antibodies.

Animals↗
PubMed2004

Learning mixture models with the regularized latent maximum entropy principle.

This paper presents a new approach to estimating mixture models based on a recent inference principle we have proposed: the latent maximum entropy principle (LME). LME is different from Jaynes' maximum entropy principle, standard maximum likelihood, and maximum aposteriori probability estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the expectation maximization (EM) algorithm can be developed. We show that a regularized version of LME (RLME), is effective at estimating mixture models. It generally yields better results than plain LME, which in turn is often better than maximum likelihood and maximum a posterior estimation, particularly when inferring latent variable models from small amounts of data.

Algorithms↗
PubMed1999

Robust QSAR models using Bayesian regularized neural networks.

We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors is illustrated.

Drug Design↗
PubMed1998

Cell-differentiation rules that generate regular mosaic patterns: modelling motivated by cone mosaic formation in fish retina.

We study characteristics of cell-differentiation rules that realize stable formation of regularly arranged checker-board patterns, exemplified by cone "mosaic" zebrafish retina, or the regular arrangement of cone photoreceptor cells. We consider the situation in which cells are arranged on a square lattice and are initially undifferentiated. Later each cell becomes one of the two differentiated states, affected by the state of the neighboring cells. The cells that undergo differentiation form a "morphogenetic cell row" which sweeps from one end to the other end of the lattice through time. This models an outward sweep of the margin of expanding mosaic region of the retina which occurs as undifferentiated photoreceptor cells become differentiated in concentric circles, joining the mosaic. We introduce an index to measure the ability of cell-differentiation rules to generate regular checker-board patterns from irregular initial patterns, and attempt to characterize the successful rules. We first show the importance of six "preservation conditions" which guarantee perfectly regular photoreceptor arrangement for all the rows after a regular row. Then we select an additional six "optimizing conditions" for responses to configuration that are consistently shown by the rules of high average scores. We also examine the effect of interaction between responses to different configurations. Finally we examine the concept of morphogenetic row precedence, i.e. that the successful rules generating a high score tend to treat the consistency with neighbors in the newly differentiated cells (those in the morphogenetic cell row) as more important that the consistency with previously differentiated neighbors.

Animals↗
PubMedDate not supplied

logD7.4 modeling using Bayesian Regularized Neural Networks. Assessment and correction of the errors of prediction.

Bayesian Regularized Neural Networks (BRNNs) employing Automatic Relevance Determination (ARD) are used to construct a predictive model for the distribution coefficient logD7.4 from an in-house data set of 5000 compounds with experimental endpoints. A method for assessing the accuracy of prediction is established based upon a query compound's distance to the training set. logD7.4 predictions are also dynamically corrected with an associated library of compounds of continuously updated, experimentally measured logD7.4 values. A comparison of local models and associated libraries comprising separate ionization class subsets of compounds to compounds of a homogeneous ionization class reveals in this case that local models and libraries have no advantage over global models and libraries.

Algorithms↗
PubMed2004

The heparin-Ca(2+) interaction: the influence of the O-sulfation pattern on binding.

The specific binding of Ca(2+) to synthetic hexasaccharide models of modified heparin has been investigated by NMR and molecular modeling and compared with previous results on a model of regular heparin. These two models represent the regular region of heparin lacking one type of O-sulfate group, either at C-6 of glucosamine or at C-2 of iduronate. The NMR experiments show different responses to the presence of Ca(2+). In the case of the compound lacking O-sulfate groups at C-2, the results are indicative of specific binding similar to that observed for the regular heparin, while the model lacking sulfate groups in position 6 interacts more weakly with Ca(2+). In order to understand the basis of this difference, a molecular modeling study based on a rigid body docking approach of the interaction of these carbohydrates with Ca(2+) and Na(+) was performed. We have found that the results are strongly dependent on the starting orientation of the lateral side chains of the charged groups of the carbohydrate, and that the best agreement with the experimental results is obtained when the starting conformations are taken from previous simulations in the presence of Ca(2+).

Calcium↗
PubMedDate not supplied

[A social cultural model of the "regular period" in the sex life of men].

In the experimental-testing examination of 957 patients, statistically significant results of summing up the information relevant to aspects of regular sexual intercourse in male subjects were obtained, which give good grounds for believing that the illicit/secret sexual relationship model prevailing in modern society is indeed that very social and psychological base on which further rise in venereal case rate is recordable among the population of Ukraine. And if we wish some kind of effective measures to be instituted if only to stabilize the level of venereal morbidity in the population we will certainly have to work out a comprehensive long-term state programme aimed to change motivation patterns of the social and culturological model of caring on extramarital sexual life.

Adolescent↗
PubMed2002

A simple model of long-term spike train regularization.

A simple model of spike generation is described that gives rise to negative correlations in the interspike interval (ISI) sequence and leads to long-term spike train regularization. This regularization can be seen by examining the variance of the kth-order interval distribution for large k (the times between spike i and spike i + k). The variance is much smaller than would be expected if successive ISIs were uncorrelated. Such regularizing effects have been observed in the spike trains of electrosensory afferent nerve fibers and can lead to dramatic improvement in the detectability of weak signals encoded in the spike train data (Ratnam & Nelson, 2000). Here, we present a simple neural model in which negative ISI correlations and long-term spike train regularization arise from refractory effects associated with a dynamic spike threshold. Our model is derived from a more detailed model of electrosensory afferent dynamics developed recently by other investigators (Chacron, Longtin, St.-Hilaire, & Maler, 2000;Chacron, Longtin, & Maler, 2001). The core of this model is a dynamic spike threshold that is transiently elevated following a spike and subsequently decays until the next spike is generated. Here, we present a simplified version-the linear adaptive threshold model-that contains a single state variable and three free parameters that control the mean and coefficient of variation of the spontaneous ISI distribution and the frequency characteristics of the driven response. We show that refractory effects associated with the dynamic threshold lead to regularization of the spike train on long timescales. Furthermore, we show that this regularization enhances the detectability of weak signals encoded by the linear adaptive threshold model. Although inspired by properties of electrosensory afferent nerve fibers, such regularizing effects may play an important role in other neural systems where weak signals must be reliably detected in noisy spike trains. When modeling a neuronal system that exhibits this type of ISI correlation structure, the linear adaptive threshold model may provide a more appropriate starting point than conventional renewal process models that lack long-term regularizing effects.

Action Potentials↗
PubMed2006

Titration of mixed micelles containing a pH-sensitive surfactant and conventional (pH-Insensitive) surfactants: a regular solution theory modeling approach.

We present a thermodynamic theory to model the hydrogen-ion titration of mixed micelles containing a pH-sensitive surfactant and any number of conventional (pH-insensitive) surfactants. In particular, a simple expression is derived for the pKm, a parameter analogous to the pKa of simple acids, which describes the deprotonation equilibrium of the micellized pH-sensitive surfactant. The pseudophase approximation and regular solution theory (RST) are used to relate the pKm to (1) the pKa of the surfactant monomers, (2) the critical micelle concentrations (cmc's) of the protonated and deprotonated forms of the pH-sensitive surfactant, (3) the composition of the mixed micelle, and (4) parameters characterizing pairwise interactions between the surfactant molecules in the mixed micelle. Micellar titrations can be used to determine the magnitude of these interaction parameters. Conversely, knowledge of the cmc's and the interaction parameters allows the prediction of the pKm, which can then be used to calculate the micelle composition and surface charge as a function of solution pH. In addition, we have found that, in the context of RST, multicomponent surfactant mixtures are equivalent to a binary surfactant mixture of the pH-sensitive surfactant and a single effective surfactant whose interactions with the pH-sensitive surfactant are an average of those in the multicomponent surfactant mixture. We also discuss the experimental uncertainty in the pKm measurements. To account for the increased uncertainty in the pKm data at extreme micelle compositions, a weighted regression is proposed for the analysis of experimental titration data characterized by widely varying uncertainties. The theory presented here is validated using micellar titration data from the literature for several pH-sensitive surfactants in solutions containing 0.1 M salt. In most cases, the parameters extracted from an analysis of the titration data agree with the cmc and interaction parameters obtained by other means. One notable exception is the surfactant tetradecyldimethylamine oxide (C14DAO), which appears to have concentration-dependent interactions due to extensive growth of cylindrical micelles. Micellar titrations were also conducted on binary surfactant mixtures containing the pH-sensitive surfactant dodecyldimethylamine oxide (C12DAO) and either the cationic surfactant dodecyltrimethylammonium bromide (C12TAB) or the nonionic surfactant dodecyl octa(ethylene oxide) (C12E8). The theory provides a reasonable description of the experimental titration data at all surfactant mixing ratios, although a larger discrepancy is found in the C12DAO/C12E8 system, in which C12E8 interacts preferentially with the protonated, cationic form of C12DAO. Interestingly, C12TAB was also observed to interact preferentially with the protonated, cationic form of C12DAO, although the preference is much weaker than that in the C12DAO/C12E8 system.

Journal Article↗
PubMed2001

Algebraic analysis for nonidentifiable learning machines.

This article clarifies the relation between the learning curve and the algebraic geometrical structure of an unidentifiable learning machine such as a multilayer neural network whose true parameter set is an analytic set with singular points. By using a concept in algebraic analysis, we rigorously prove that the Bayesian stochastic complexity or the free energy is asymptotically equal to lambda(1) log n - (m(1) - 1) log log n + constant, where n is the number of training samples and lambda(1) and m(1) are the rational number and the natural number, which are determined as the birational invariant values of the singularities in the parameter space. Also we show an algorithm to calculate lambda(1) and m(1) based on the resolution of singularities in algebraic geometry. In regular statistical models, 2lambda(1) is equal to the number of parameters and m(1) = 1, whereas in nonregular models, such as multilayer networks, 2lambda(1) is not larger than the number of parameters and m(1) > or = 1. Since the increase of the stochastic complexity is equal to the learning curve or the generalization error, the nonidentifiable learning machines are better models than the regular ones if Bayesian ensemble learning is applied.

Algorithms↗
PubMed1995

A minimal single-channel model for the regularity of beating in the sinoatrial node.

It has been suggested that the normal irregular beating of the heart is a manifestation of deterministically chaotic dynamics. Evidence proffered in support of this hypothesis includes a 1/f-like power spectrum, a small noninteger correlation dimension, and self-similarity of the time series. The major cause of the normal fluctuations in heart rate is the impingement of several neural and hormonal control systems upon the sinoatrial node, the natural pacemaker of the heart. However, intrinsic fluctuations of beat rate can be seen in the isolated node, devoid of all neural and hormonal inputs, and even in a single cell isolated from the node. The electrical activity in such a single cell is generated by ions flowing through discrete channels in the cell membrane.We decided to test the hypothesis that the fluctuations in beat rate in a single cell might be due to the fluctuations in the activity of this population of single channels. We thus assemble a model consisting of 6000 channels and probe its dynamics. Each channel has one or more gates, all of which must be open to allow current to flow through the channel. Since these gates are thought to open and close in a random manner, we model each gate by a Markov process, assigning a pseudorandom number to each gate every time that it changes state from open to closed or vice versa. This number, in conjunction with the classical voltage-dependent Hodgkin-Huxley-like rate constants that control the speed with which a gate will open or close, then determines when that gate will next change state. We also employ a second method that is much more efficient computationally, in which one computes the lifetime of the ensemble of 6000 channels. We show that the Monte Carlo model has behavior consistent with the hypothesis that the irregular beating seen experimentally in single nodal cells is due to the (pseudo)random opening and closing of single channels. However, since the pseudorandom number generator used in the simulations is deterministic, one cannot state that the activity in the model is random (or stochastic). Thus, it would be premature to claim that the irregularity of beating in a single nodal cell is accounted for by the stochastic behavior of a population of a few thousand single channels lying in the membrane of the cell. Finally, we consider some implications of our work for the naturally occurring in situ fluctuations in heart rate ("heart rate variability"). (c) 1995 American Institute of Physics.

Journal Article↗
PubMed1998

Blunt impact causes changes in bone and cartilage in a regularly exercised animal model.

The goal of this study was to document the effect of blunt-impact trauma on the knee in a small animal model that incorporated a known level of physical exercise after impact. We hypothesized that a single blunt impact to the patellofemoral joint, of a magnitude comparable with our earlier studies, would result in degenerative changes to cartilage and to subchondral bone of the patella. Blunt impacts were delivered to rabbit patellofemoral joints without producing bone fracture, and biomechanical and histological analyses were performed on joint tissues at various times. At 12 months, the subchondral bone plate was thicker on the impacted side than on the unimpacted side and than that of the controls at comparable locations (near where surface fissures were found on the impacted side). The instantaneous modulus of cartilage was significantly less on the impacted side than that of controls at 3, 6, and 12 months after impact. The relaxed modulus of cartilage on the unimpacted side increased with time after impact and was significantly greater than that of controls at 12 months. These facts suggest that in this exercise model, the contralateral limb should not be considered a control. The retropatellar cartilage on the impacted side was significantly less thick than that of controls at 12 months, and histological analyses of the cartilage and bone indicated early-stage osteoarthrosis in the impacted joint. Thus, in this animal model a single subfracture blunt impact produced degeneration of joint tissues.

Animals↗
PubMed2001

Chaos and regular dynamics in model multi-habitat plankton-fish communities.

This work is focused on the role of diffusive interaction between separate habitats in a patchy environment in plankton pattern formation. We demonstrate that conceptual reaction-diffusion mathematical models constitute an appropriate tool for searching and understanding basic mechanisms of plankton pattern formation and complex spatio-temporal plankton dynamics

Animals↗
PubMed2002

A free-surface lattice Boltzmann method for modelling the filling of expanding cavities by Bingham fluids.

The filling process of viscoplastic metal alloys and plastics in expanding cavities is modelled using the lattice Boltzmann method in two and three dimensions. These models combine the regularized Bingham model for viscoplastic fluids with a free-interface algorithm. The latter is based on a modified immiscible lattice Boltzmann model in which one species is the fluid and the other one is considered to be a vacuum. The boundary conditions at the curved liquid-vacuum interface are met without any geometrical front reconstruction from a first-order Chapman-Enskog expansion. The numerical results obtained with these models are found in good agreement with available theoretical and numerical analysis.

Colloids↗
PubMed2004

[Mathematical protocol for radiofrequency ablation of liver tumors and its clinical application].

OBJECTIVE: To develop a preoperative protocol for ultrasonography-guided percutaneous radiofrequency ablation (RFA) on liver tumors larger than 3.5 cm in diameter based on mathematical models and clinical experience, and to evaluate its ablated effect compared to the previous non-math method. METHODS: One hundred and twenty-five patients with 80 primary and 55 secondary liver tumors (4.7 +/- 0.9 cm in diameter, ranged from 3.6 - 7.0 cm) were enrolled in this study, of which the first 22 patients (23 tumors) had been treated empirically before the mathematical model was set up and were referred as the non-math group. The rest 103 patients (112 tumors) were treated based on the mathematical model and referred as the math group. Based on principle of overlapping spheres, a mathematical analysis was performed to investigate how multiple ablation spheres could overlap and cover larger tumors most efficiently. Some mathematical models such as regular prism and regular polyhedron model were chosen to estimate the mathematical protocol which included least ablation (sphere) number and optimal overlapping mode required to adequately ablate a large and spherical target lesion. The target volume consisted of the tumor plus a 0.5 - 1 cm tumor-free margin. The operation method for electrode placement was also described. RESULTS: The procedure success rate for the math group was 88.4% (99/112), local recurrence rate and estimated mean time until local recurrence were 25.9% (29/112) and 17.5 months, respectively. While for the non-math group the results were 52.2% (12/23) (P < 0.01), 56.5% (13/23) (P < 0.05) and 11.9 months (P < 0.05), respectively. The therapy results in the math group were much better than in the non-math group. CONCLUSION: This study provides theoretic basis and clinical guidance for RFA therapy for liver tumors larger than 3.5 cm. These results could be used to reduce local recurrence rate and improve treatment response.

Adult↗