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At least 559 records · Page 31Linked to original sources
PubMed2005

Nonorthogonal density-matrix perturbation theory.

Recursive density-matrix perturbation theory [A.M.N. Niklasson and M. Challacombe, Phys. Rev. Lett. 92, 193001 (2004)] provides an efficient framework for the linear scaling computation of materials response properties [V. Weber, A.M.N. Niklasson, and M. Challacombe, Phys. Rev. Lett. 92, 193002 (2004)]. In this article, we generalize the density-matrix perturbation theory to include properties computed with a perturbation-dependent nonorthogonal basis. Such properties include analytic derivatives of the energy with respect to nuclear displacement, as well as magnetic response computed with a field-dependent basis. The theory is developed in the context of linear scaling purification methods, which are briefly reviewed.

Journal Article↗
PubMed1999

Stretching single-domain proteins: phase diagram and kinetics of force-induced unfolding.

Single-molecule force spectroscopy reveals unfolding of domains in titin on stretching. We provide a theoretical framework for these experiments by computing the phase diagrams for force-induced unfolding of single-domain proteins using lattice models. The results show that two-state folders (at zero force) unravel cooperatively, whereas stretching of non-two-state folders occurs through intermediates. The stretching rates of individual molecules show great variations reflecting the heterogeneity of force-induced unfolding pathways. The approach to the stretched state occurs in a stepwise "quantized" manner. Unfolding dynamics and forces required to stretch proteins depend sensitively on topology. The unfolding rates increase exponentially with force f till an optimum value, which is determined by the barrier to unfolding when f = 0. A mapping of these results to proteins shows qualitative agreement with force-induced unfolding of Ig-like domains in titin. We show that single-molecule force spectroscopy can be used to map the folding free energy landscape of proteins in the absence of denaturants.

Amino Acid Sequence↗
PubMed2025

Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.

MOTIVATION: Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which the author explores in this essay. RESULTS: The author provides the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then the author considers how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, the author proposes a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task. AVAILABILITY AND IMPLEMENTATION: There are no data or code associated with this article.

Genome-Wide Association Study↗
PubMed2005

Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data.

MOTIVATION: Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. RESULTS: The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm. AVAILABILITY: The CMVE software is available upon request from the authors.

Algorithms↗
PubMed2005

Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited.

Diffuse large-B-cell lymphoma (DLBCL) is an aggressive malignancy of mature B lymphocytes and is the most common type of lymphoma in adults. While treatment advances have been substantial in what was formerly a fatal disease, less than 50% of patients achieve lasting remission. In an effort to predict treatment success and explain disease heterogeneity clinical features have been employed for prognostic purposes, but have yielded only modest predictive performance. This has spawned a series of high-profile microarray-based gene expression studies of DLBCL, in the hope that molecular-level information could be used to refine prognosis. The intent of this paper is to reevaluate these microarray-based prognostic assessments, and extend the statistical methodology that has been used in this context. Methodological challenges arise in using patients' gene expression profiles to predict survival endpoints on account of the large number of genes and their complex interdependence. We initially focus on the Lymphochip data and analysis of Rosenwald et al. (2002). After describing relationships between the analyses performed and gene harvesting (Hastie et al., 2001a), we argue for the utility of penalized approaches, in particular least angle regression-least absolute shrinkage and selection operator (Efron et al., 2004). While these techniques have been extended to the proportional hazards/partial likelihood framework, the resultant algorithms are computationally burdensome. We develop residual-based approximations that eliminate this burden yet perform similarly. Comparisons of predictive accuracy across both methods and studies are effected using time-dependent receiver operating characteristic curves. These indicate that gene expression data, in turn, only delivers modest predictions of posttherapy DLBCL survival. We conclude by outlining possibilities for further work.

Biometry↗
PubMed1999

Distributions of transition matrix elements in classically mixed quantum systems.

The quantitative contributions of a mixed phase space to the mean characterizing the distribution of diagonal transition matrix elements and to the variance characterizing the distributions of nondiagonal transition matrix elements are studied. It is shown that the mean can be expressed as the sum of suitably weighted classical averages along an ergodic trajectory and along the stable periodic orbits. Similarly, it is shown that the values of the variance are well reproduced by the sum of the suitably weighted Fourier transforms of classical autocorrelation functions along an ergodic trajectory and along the stable periodic orbits. The illustrative numerical computations are done in the framework of a hydrogen atom in a strong magnetic field, for three different values of the scaled energy.

Journal Article↗
PubMed2005

Complete description of all self-similar models driven by Lévy stable noise.

A canonical decomposition of H-self-similar Lévy symmetric alpha-stable processes is presented. The resulting components completely described by both deterministic kernels and the corresponding stochastic integral with respect to the Lévy symmetric alpha-stable motion are shown to be related to the dissipative and conservative parts of the dynamics. This result provides stochastic analysis tools for study the anomalous diffusion phenomena in the Langevin equation framework. For example, a simple computer test for testing the origins of self-similarity is implemented for four real empirical time series recorded from different physical systems: an ionic current flow through a single channel in a biological membrane, an energy of solar flares, a seismic electric signal recorded during seismic Earth activity, and foreign exchange rate daily returns.

Journal Article↗
PubMed2004

Nonparametric multiscale energy-based model and its application in some imagery problems.

This paper investigates the use of a nonparametric regularization energy term for devising a example-based rendering and segmentation technique. We have stated this problem in the multiresolution energy minimization framework and exploited the multiscale structure proposed by Wei and Levoy for the texture synthesis problem. In this nonparametric energy minimization framework, we also propose a computationally efficient coarse-to-fine recursive optimization method to minimize the cost function related to this hierarchical model. In this context, the formulation of our example-based regularization term also allows to directly infer an intuitive dissimilarity measure between two contour shapes. This measure is herein exploited to define an efficient shape descriptor for the contour-based shape recognition and indexing problem.

Algorithms↗
PubMed2006

On the integration of physiological mechanisms in the nervous tissue using the MTIP: synaptic plasticity depending on neurons-astrocytes-capillaries interactions.

The objective in this work is twofold: (i) to illustrate the use of the Mathematical Theory of Integrative Physiology (MTIP) [13], that is a general theory and practical method for the systematic and progressive mathematical integration of physiological mechanisms; (ii) to study a complex neurobiological system taken as an example, i.e., the synaptic plasticity depending on brain activity, on astrocytic and neuronal metabolism, and on brain hemodynamics. The functional organization of the nervous tissue is presented in the framework of the MTIP, the ultimate objective being the study of learning and memory by coupling the three networks of neurons, astrocytes and capillaries. Specifically in this paper, we study the influence of the variation of capillaries arterial oxygen on the induction of LTP/LTD by coupling validated mathematical models of AMPA, NMDA, VDCC channels, calcium current in the dendritic spine, vesicular glutamate dynamics in the presynaptic bouton derived from glycolysis and neuronal glucose, mitochondrial respiration, Ca/Na pumps, glycolysis, and calcium dynamics in the astrocytes, hemodynamics of the capillaries. The integration of all these models is discussed by claiming the advantages of using a common framework and a specific dedicated computing system, PhysioMatica.

Astrocytes↗
PubMed2003

Farmer health beliefs about an occupational illness that affects farmworkers: the case of green tobacco sickness.

Latino migrant and seasonal farmworkers, like all agricultural workers, experience high rates of occupational illness and injury. Interventions to reduce occupational injury among farmworkers must attend to the health beliefs of agricultural employers as well as employees, as employers control many aspects of the work environment. Occupational safety programs for Latino migrant and seasonal farmworkers must also be conceptually based in health behavior change and health disparities theories. We examine health beliefs of tobacco farmers about green tobacco sickness (GTS) to show the importance of delineating employer beliefs in agricultural safety. GTS is a highly prevalent occupational illness among tobacco workers that results from nicotine poisoning through dermal absorption of nicotine during cultivation and harvesting. We use qualitative methods structured by the Explanatory Models of Illness approach to identify farmer beliefs about the etiology, onset, pathophysiology, course, and treatment of GTS. Data were collected through semi-structured in-depth interviews with 15 North Carolina tobacco farmers. A computer-assisted, systematic qualitative analysis framework was applied to the interview transcripts. While tobacco farmers were generally knowledgeable about GTS, their explanatory models for this occupational illness often mis-identified its causes (heat and bending rather than nicotine) and minimized its seriousness. These models included methods of prevention that are not proven (e.g., use of anti-nausea drugs) or are more harmful than GTS (smoking cigarettes). The need for medical treatment was also discounted. Addressing each of these beliefs is important in any program to prevent GTS among farmworkers. Documenting and understanding the beliefs and knowledge of agricultural employers is an important undertaking in our efforts to reduce occupational injury and illness among farmworkers.

Adult↗
PubMed2002

Ecological interface design: progress and challenges.

Ecological interface design (EID) is a theoretical framework for designing human-computer interfaces for complex sociotechnical systems. Its primary aim is to support knowledge workers in adapting to change and novelty. This literature review shows that in situations requiring problem solving, EID improves performance when compared with current design approaches in industry. EID has been applied to industry-scale problems in a broad variety of application domains (e.g., process control, aviation, computer network management, software engineering, medicine, command and control, and information retrieval) and has consistently led to the identification of new information requirements. An experimental evaluation of EID using a full-fidelity simulator with professional workers has yet to be conducted, although some are planned. Several significant challenges remain as obstacles to the confident use of EID in industry. Promising paths for addressing these outstanding issues are identified. Actual or potential applications of this research include improving the safety and productivity of complex sociotechnical systems.

Data Display↗
PubMed2000

Virtual training simulator--designer of EEG signals for tutoring students and doctors to methods of quantitative EEG analysis (qEEG).

Within the framework of the system for computer diagnostics of EEG (qEEG) NeuroResearcher 5.2 is created virtual training simulator--designer of EEG signals and models multidimensional neurodynamic systems of the brain. It is intended for tutoring with minimum engaging of mathematical formulas of doctors and students to comprehension of essence of mathematical methods (classical correlation and spectral analysis and newest methods of multi-dimensional analysis of neurodynamic systems of the brain), which are successfully used for quantitative EEG (qEEG).

Computer Simulation↗
PubMed2006

[Investigation in the dependency of stiffness of cancellous bone on apparent density--based on the combination model of rod-rod structure and perforated plate structure].

The structure of cancellous bone is cellular. There are two basic models of cancellous bone structure namely the model of rod-rod structure and the model of framework of perforated plate. This paper presents several models of structure developed from the model of rod-rod structure to the model of framework of perforated plate. We computed the elastic modulus of cancellous bone based on these modes using homogenization. The relationship between the elastic modulus(E) of cancellous bone and the apparent density(p) was determined to be E = 1.78rho1.88.

Biomechanical Phenomena↗
PubMed2004

Gaussian processes for machine learning.

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.

Algorithms↗
PubMed2005

A novel computational method for real-time preoperative assessment of primary dental implant stability.

A novel methodology which allows for fast and fully automatic structural analysis during preoperative planning for dental implant surgery is presented. This method integrates a fully automatic fast finite element solver within the framework of new concepts in computer-assisted preoperative planning for implant surgery. The planning system including optimized structural planning was validated by experimental results. Nine implants were placed in pig mandibles and mechanically loaded using a testing rig. The resulting displacements were measured and compared with those predicted by numerical analysis during planning. The results show that there were no statistically significant differences (P = 0.65) between the results of the models and the experiments. The results show that fast structural analysis can be integrated with surgical planning software allowing the initial axial implant stability to be predicted in real time during planning. It is believed that such a system could be used to select patients for immediate implant loading and, when further developed, be useful in other areas of preoperative surgical planning.

Animals↗
PubMedDate not supplied

Characterizing the use of health care services delivered via computer networks.

OBJECTIVE: Evaluators must develop methods to characterize the use of the rapidly proliferating electronic networks that link patients with health services. In this article the 4-S framework is proposed for characterizing the use of health services delivered via computer networks. The utility of the 4-S framework is illustrated using data derived from a completed, randomized field experiment in which 47 caregivers of persons who had Alzheimer's disease accessed ComputerLink, a special computer network providing information, communication, and decision support to homebound caregivers of persons who have Alzheimer's disease. DESIGN: Human-computer interaction theories characterize the use of health services delivered via computer networks in behavioral terms. The 4-S framework incorporates perspectives based on user (subject) behavior: access to and use of the total system, use of specific services, behavior within single sessions, and enduring behavioral characteristics. The 4-S framework was tested in a secondary analysis of data from over 3,800 uses of ComputerLink. MEASUREMENT: The 4-S framework was instantiated using data obtained from the ComputerLink evaluation. Three types of secondary data were obtained. A passive monitor of access to the computer network provided quantitative information, such as time of day when access occurred, duration of access, and sequence of services used. Full-text messages were available from the public message postings. Subjective appraisal of use was obtained from self-reporting by users at the end of the experiment. RESULTS: The components of the 4-S framework were suitable to characterize operational aspects of ComputerLink use by Alzheimer's disease caregivers. Through application of the 4-S framework, an understanding of both quantitative use and qualitative use emerged (e.g., insight was gained into the differential use of specific services). CONCLUSIONS: The 4-S framework provided a mechanism for combining various measures of use into a coherent whole. The framework promotes a precise characterization of use and thereby facilitates evaluation of health services delivered via computer networks. It is suitable for evaluation of user satisfaction, measurement of needs resolution, and ascertainment of selected clinical outcomes.

Alzheimer Disease↗
PubMed2003

From sarcomere to cell: an efficient algorithm for linking mathematical models of muscle contraction.

Two classes of mathematical framework have previously been developed to model active tension generation in contracting muscle. Cross-bridge models of muscle are biophysically based but computationally expensive to solve, and thus unsuitable for embedding in spatially distributed continuum representations. Fading memory models are computationally efficient but provide limited biophysical insight. In this study a novel computational method is proposed for coupling these two frameworks such that biophysical events can be determined and computational tractability maintained. Within the cross-bridge model, the functional forms of the distribution of cross-bridges, as a function of strain in each state, are approximated using the distribution moment approach. Using the variables of area, mean and standard deviation of each distribution, analytic expressions are developed to calculate the temporal dynamics of stiffness, tension and energy. A root finding method is employed to adjust the variables such that the temporal dynamics of the cross-bridge model match those of an equivalent fading memory model. The method is demonstrated for sinusoidal perturbations in length at two frequencies, with an approximate 30-fold increase in computational efficiency over a conventional technique for finding a solution to the cross-bridge model.

Adenosine Triphosphate↗
PubMedDate not supplied

[Effect of the computer-assisted diagnostic system [ADM] on the success and cost of diagnosis (author's transl)].

Taking into account the interest we have in helping physicians arrive at a diagnosis and in choosing the procedures utilized in the computer-assisted diagnostic system (ADM) already described, a study was devised to estimate the efficiency of the system in relation to costs. From 300 patient records, 80 presenting a true diagnostic problem within the framework of the diseases already in computer memory were selected. A comparison was made between the normal procedure, with and without computer help, keeping in mind the diagnoses themselves as well as the performance and the cost. Criteria and evaluation methods were evaluated. The results, even though preliminary, are encouraging and justify the continued development of the system.

Cost-Benefit Analysis↗