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PubMed1998

Time scheduling of transit systems with transfer considerations using genetic algorithms.

Scheduling of a bus transit system must be formulated as an optimization problem, if the level of service to passengers is to be maximized within the available resources. In this paper, we present a formulation of a transit system scheduling problem with the objective of minimizing the overall waiting time of transferring and nontransferring passengers while satisfying a number of resource- and service-related constraints. It is observed that the number of variables and constraints for even a simple transit system (a single bus station with three routes) is too large to tackle using classical mixed-integer optimization techniques. The paper shows that genetic algorithms (GAs) are ideal for these problems, mainly because they (i) naturally handle binary variables, thereby taking care of transfer decision variables, which constitute the majority of the decision variables in the transit scheduling problem; and (ii) allow procedure-based declarations, thereby allowing complex algorithmic approaches (involving if then-else conditions) to be handled easily. The paper also shows how easily the same GA procedure with minimal modifications can handle a number of other more pragmatic extensions to the simple transit scheduling problem: buses with limited capacity, buses that do not arrive exactly as per scheduled times, and a multiple-station transit system having common routes among bus stations. Simulation results show the success of GAs in all these problems and suggest the application of GAs in more complex scheduling problems.

Algorithms↗

PubMed1999

Estimation theory and model parameter selection for therapeutic treatment plan optimization.

Treatment optimization is usually formulated as an inverse problem, which starts with a prescribed dose distribution and obtains an optimized solution under the guidance of an objective function. The solution is a compromise between the conflicting requirements of the target and sensitive structures. In this paper, the treatment plan optimization is formulated as an estimation problem of a discrete and possibly nonconvex system. The concept of preference function is introduced. Instead of prescribing a dose to a structure (or a set of voxels), the approach prioritizes the doses with different preference levels and reduces the problem into selecting a solution with a suitable estimator. The preference function provides a foundation for statistical analysis of the system and allows us to apply various techniques developed in statistical analysis to plan optimization. It is shown that an optimization based on a quadratic objective function is a special case of the formalism. A general two-step method for using a computer to determine the values of the model parameters is proposed. The approach provides an efficient way to include prior knowledge into the optimization process. The method is illustrated using a simplified two-pixel system as well as two clinical cases. The generality of the approach, coupled with promising demonstrations, indicates that the method has broad implications for radiotherapy treatment plan optimization.

Algorithms↗

PubMed2006

Exact and approximation algorithms for DNA tag set design.

In this paper, we propose new solution methods for designing tag sets for use in universal DNA arrays. First, we give integer linear programming formulations for two previous formalizations of the tag set design problem. We show that these formulations can be solved to optimality for problem instances of moderate size by using general purpose optimization packages and also give more scalable algorithms based on an approximation scheme for packing linear programs. Second, we note the benefits of periodic tags and establish an interesting connection between the tag design problem and the problem of packing the maximum number of vertex-disjoint directed cycles in a given graph. We show that combining a simple greedy cycle packing algorithm with a previously proposed alphabetic tree search strategy yields an increase of over 40% in the number of tags compared to previous methods.

Algorithms↗

PubMed2002

Evaluation of molecule-microbe interactions with capillary electrophoresis: procedures, utility and restrictions.

Understanding the interactions between molecules and living organisms is of paramount importance for the evaluation of pharmaceutical activity, chemical toxicity and all manner of microbiological studies. The capability of capillary electrophoresis (CE) in the evaluation of molecule-microbe interactions is examined in the present paper. The fundamental chemical concept of the binding or association constant for molecular systems measured in free solution is discussed for biological systems where microorganisms uptake or associate with molecules from their environment. The heterogeneity of the living organisms must be understood and accounted for including differences related to semantics such as concentration units and the nature of the associations between two entities and large differences in the size and number of microorganisms as compared to molecules. Finally, the added complexity and even inhomogeneity of a cell compared to most molecular systems must be considered and possibly controlled. The binding of specific molecules to viruses is discussed. CE can be utilized to quickly determine if a molecule binds very strongly or not at all to a cell (i.e., a binary yes/no answer). This could be useful for initial high-throughput screening purposes when using capillary arrays, for example. CE can be useful for determining unusual (large) molecule/microbe stoichiometries. Finally, CE can sometimes be used to determine the size of binding constants (K(RL)) within certain limits provided experimental conditions can be formulated that minimize problems of biological heterogeneity.

Bacterial Physiological Phenomena↗

PubMed1986

Brainstem terminations of extraocular muscle primary afferent neurons in the monkey.

The central terminations of afferent nerve fibers from the extraocular muscles of the monkey were investigated by means of transganglionic transport of wheat germ agglutinin-conjugated horseradish peroxidase (WGA/HRP). Following injections of selected extraocular muscles with WGA/HRP, terminal labeling was apparent in the ipsilateral trigeminal sensory and cuneate nuclei. The density of trigeminal projections varied markedly from one rostrocaudal level to the next, being heaviest within the ventrolateral portion of pars interpolaris of the spinal trigeminal nucleus. A second extraocular muscle afferent representation was noted in ventrolateral portions of the cuneate nucleus. This projection was restricted to rostral portions of pars triangularis of the cuneate nucleus, partially overlapping the afferent termination from dorsal neck muscles. It is likely that some of the problems encountered in formulating conclusions regarding the functional role of extraocular muscle proprioception are due to a lack of detailed information of the central termination pattern of muscle afferents. Taken together, the present findings should provide a basis for further anatomical and physiological studies designed to elucidate the role played by extraocular muscle proprioceptors in vision and oculomotor control.

Animals↗

PubMed2002

Hierarchical model of the population dynamics of hippocampal dentate granule cells.

A hierarchical modeling approach is used as the basis for a mathematical representation of the population activity of hippocampal dentate granule cells. Using neural field equations, the variation in time and space of dentate granule cell activity is derived from the summed synaptic potential and summed action potential responses of a population of granule cells evoked by monosynaptic excitatory input from entorhinal cortical afferents. In this formulation of the problem, we have considered a two-level hierarchy: the synapses of entorhinal cortical axons define the first level of organization, and dentate granule cells, which include these synapses, define the second, higher level of organization. The model is specified by two state field variables, for membrane potential and for synaptic efficacy, respectively, with both evolving according to different time scales. The two state field variables introduce new parameters, physiological and anatomical, which characterize the dentate from the point of view of neuronal and synaptic populations: (1) a set of geometrical constraints corresponding to the morphological properties of granule cells and anatomical characteristics of entorhinal-dentate connections; and (2) a set of neuronal parameters corresponding to physiological mechanisms. Assuming no interaction between granule cells, i.e., neither ephaptic nor synaptic coupling, the model is shown to be mathematically tractable and allows solution of the field equations leading to the determination of activity. This treatment leads to the definition of two state variables, volume of stimulated synapses and firing time, which describe observed activity. Numerical simulations are used to investigate the populational characterization of the dentate by individual parameters: (1) the relationship between the conditions of stimulation of active perforant path fibers, e.g., stimulating intensity, and activity in the granule cell layer; and (2) the influence of geometry on the generation of activity, i.e., the influence of neuron density and synaptic density-connectivity. As an example application of the model, the granule cell population spike is reconstructed and compared with experimental data.

Animals↗

PubMed2005

Analyzing intrapsychic conflict: compromise formation as an organizing principle.

The author highlights the idea that analysts' recognition of intrapsychic conflict and compromise formation provides them with a most effective way to formulate their patients' problems. A clinical illustration is presented, with attention to the analyst's use of these concepts during the course of the patient's treatment. The author discusses ways in which his thinking about intrapsychic conflict, compromise formation, and unconscious fantasy informs his approach to clinical work. He emphasizes that viewing compromise formation as the organizing principle of much of mental life gives analysts an effective way to understand the underlying structure of the psychic phenomena in which they are interested.

Adult↗

PubMed2004

Cultural relevance and equivalence in the NLAAS instrument: integrating etic and emic in the development of cross-cultural measures for a psychiatric epidemiology and services study of Latinos.

This paper describes the development, translation and adaptation of measures in the National Latino and Asian American Study (NLAAS). We summarize the techniques used to attain cultural relevance; semantic, content and technical equivalency; and internal consistency of the measures across languages and Latino sub-ethnic groups. We also discuss some of the difficulties and thallenges encountered in doing this work. The following three main goals are addressed in this paper: (1) attaining cultural relevance by formulating the research problem with attention to the fundamental cultural and contextual differences of Latinos and Asians as compared to the mainstream population; (2) developing cultural equivalence in the standardized instruments to be used with these populations; and (3) assessing the generalizability of the measures - i.e., that the measures do not fluctuate according to culture or translation. We present details of the processes and steps used to achieve these three goals in developing measures for the Latino population. Additionally, the integration of both the etic and emic perspectives in the instrument adaptation model is presented.

Adult↗

PubMed2002

Regression-based variable clustering for data reduction.

In many studies it is of interest to cluster states, counties or other small regions in order to obtain improved estimates of disease rates or other summary measures, and a more parsimonious representation of the country as a whole. This may be the case if there are too many to summarize concisely, and/or many regions with a small number of cases. By merging the regions into larger geographic areas, we obtain more cases within each area (and hence lower standard errors for parameter estimates), as well as fewer areas to summarize in terms of disease rates. The resulting clusters should be such that regions within the same cluster are similar in terms of their disease rates. In this paper we present a clustering algorithm which uses data at the subject-specific level in order to cluster the original regions into a reduced set of larger areas. The proposed clustering algorithm expresses the clustering goals in terms of a regression framework. This formulation of the problem allows the regions to be clustered in terms of their association with the response, and confounding variables measured at the subject-specific level may be easily incorporated during the clustering process. Additionally, this framework allows estimation and testing of the association between the areas and the response. The statistical properties and performance of the algorithm were evaluated via simulation studies, and the results are promising. Additional simulations illustrate the importance of controlling for confounding variables during the clustering process, rather than after the clusters are determined. The algorithm is illustrated with data from the Cardiovascular Health Study. Although developed with a specific application in mind, the method is applicable to a wide range of problems.

Aged↗

PubMed2003

Estimating regression models with unknown break-points.

This paper deals with fitting piecewise terms in regression models where one or more break-points are true parameters of the model. For estimation, a simple linearization technique is called for, taking advantage of the linear formulation of the problem. As a result, the method is suitable for any regression model with linear predictor and so current software can be used; threshold modelling as function of explanatory variables is also allowed. Differences between the other procedures available are shown and relative merits discussed. Simulations and two examples are presented to illustrate the method.

Bronchitis↗

PubMed1989

Mixture models for partially unclassified data: a case study of renal venous renin in hypertension.

In many applications of discriminant analysis in medicine, data of known origin which can be reasonably assumed to be a random sample from the entire class may not be available for each of the possible classes. In this paper we note how such situations can be handled by using finite mixture models to formulate the estimation problem. This approach is adopted to model the distribution of the renal venous renin ratio (RVRR) between left and right kidneys in patients with hypertension. This distribution is used in the formation of a probabilistic allocation rule as an aid in the diagnosis of renal artery stenosis, which is potentially curable by surgery.

Chi-Square Distribution↗

PubMedDate not supplied

A multiple decision procedure in clinical trials.

In some multiple treatment arm clinical trials there is an order of preference for the treatments based on secondary considerations like toxicity or cost. In this paper, we consider the case where two or more treatments could have equal prior preference. This formulation includes the problem of comparing several equally preferred experimental treatments to one control, or the comparison of a combination with its components. Our decision procedures will guarantee a high selection probability for the correct treatment(s) when that selection is appropriate. We establish sample size requirements for our decision procedures which can be applied to clinical trials with normal, binomial, or right censored exponential endpoints.

Carcinoma, Squamous Cell↗

PubMedDate not supplied

Marker values at the time of an AIDS diagnosis.

In this paper statistical methods are proposed to estimate the distribution of a CD4 T-cell number at the time of a clinical AIDS endpoint from serial measurements of CD4 T-cell values in a cohort study. The statistical formulation of the problem is that of survival analysis with interval censored data, but in which the endpoints are obtained with measurement error. A measurement error likelihood is developed, assuming normality of the CD4 distribution at AIDS. A maximum likelihood estimation procedure and a Gibbs sampling approach are implemented.

Acquired Immunodeficiency Syndrome↗

PubMed1995

An interactive framework for RNA secondary structure prediction with a dynamical treatment of constraints.

A novel approach aiding in the prediction of RNA secondary structures is presented. Although phylogenetic methods are the most successful at deriving RNA secondary structures, the are not applicable when the number of sequences or the sequence variability is too low. Methods based on energy minimization are therefore of great interest. However, some of the suboptimal RNA secondary structures computed with classic methods are unsaturated structures, i.e. some structures are included into others. Thus, the incorporation of constraints during the process of folding is not possible, while the incorporation of constraints before the process of folding often introduces a bias into the energy function. This paper describes a new procedure which allows for the incorporation of constraints before and during the process of RNA folding. SAPSSARN is an interactive program which offers a framework, both to specify a secondary structure through a set of folding constraints and to compute all the supoptimal saturated RNA secondary structures which satisfy all the folding constraints. At the start, it relies on the computation of the probabilities of pairing of each base with all others according to McCaskill's algorithm. The constraint satisfaction formulation of the problem deals dynamically with a chosen set of folding constraints and, finally, a search algorithm computes all the suboptimal saturated secondary structures which satisfy those folding constraints. Within such a framework, it is possible to test new ideas about RNA folding and secondary structures, including pseudoknots, can be computed. The program is illustrated with RNA sequences on which we obtained results in agreement with known structures by using a protocol which mimics the hierarchical folding of RNA molecules.

Algorithms↗

PubMed1998

An Experimental Investigation of the Incentives to Form Agricultural Marketing Pools.

This paper presents theoretical extensions and laboratory tests of Hoffman and Libecap's (1994) model of individual firms' incentives to form agricultural marketing pools. The key incentives are lower variance in output prices and economies in scale in marketing. This paper extends the model by formulating the pooling problem as a game of incomplete information in which firms have heterogeneous and private risk attitudes. An experiment is conducted to test the theoretical implications of this model. Statistical analysis of the experimental data supports the model predictions of pooling rates, but also reveals that subjects form systematic probability biases and do not behave as strategically as the model suggests. Copyright 1998 Academic Press.

Journal Article↗

PubMed2003

Nonlinear estimation and modeling of fMRI data using spatio-temporal support vector regression.

This paper presents a new and general nonlinear framework for fMRI data analysis based on statistical learning methodology: support vector machines. Unlike most current methods which assume a linear model for simplicity, the estimation and analysis of fMRI signal within the proposed framework is nonlinear, which matches recent findings on the dynamics underlying neural activity and hemodynamic physiology. The approach utilizes spatio-temporal support vector regression (SVR), within which the intrinsic spatio-temporal autocorrelations in fMRI data are reflected. The novel formulation of the problem allows merging model-driven with data-driven methods, and therefore unifies these two currently separate modes of fMRI analysis. In addition, multiresolution signal analysis is achieved and developed. Other advantages of the approach are: avoidance of interpolation after motion estimation, embedded removal of low-frequency noise components, and easy incorporation of multi-run, multi-subject, and multi-task studies into the framework.

Algorithms↗

PubMed1980

Comparative pharmacokinetics of chlorambucil and melphalan in man.

We have studied the pharmacokinetics of orally administered chlorambucil and melphalan in patients with hematologic malignancies and solid tumors. With a standard oral dose of 0.6 mg/kg, chlorambucil showed much more rapid systemic appearance than did melphalan and had a mean peak plasma concentration and area under the plasma disappearance curve which was 3-4 times greater than that observed in patients receiving melphalan. Melphalan had extremely variable systemic availability which was not observed with chlorambucil, and was not related to problems in tablet formulation. Chlorambucil undergoes extensive active metabolism to phenylacetic acid mustard, whereas melphalan undergoes rapid chemical degradation and has little, if any, active metabolism. On a pharmacokinetic basis, chlorambucil's greater in vitro stability, its more rapid and predictable systemic availability after oral dosing, and its extremely low urinary excretion make it a more predictable alkylating agent for clinical use than melphalan, especially for patients with reduced renal function.

Biotransformation↗

PubMed1994

A nonlinear regularization approach to early vision.

We propose a new class of approaches to smooth visual data while preserving significant transitions of these data as clues for segmentation. Formally, the given visual data are represented as a noisy (image) function g, and we present a class of continuously formulated global minimization problems to smooth g. The resulting function u can be characterized as the minimizer of a specific nonquadratic functional or, equivalently, as the result of an associated nonlinear diffusion process. Our approach generalizes the well-known quadratic regularization principle while retaining its attractive properties: For any given g, the solution u to the proposed minimization problem is unique and depends continuously on the data g. Furthermore, convergence of approximate solutions obtained by finite element discretization holds true. We show that the nodal variables of any chosen finite element subspace can be interpreted as computational units whose activation dynamics due to the nonlinear smoothing process evolve like a globally asymptotically stable network. A corresponding analogue implementation is thus feasible and would provide a real time processing stage for the transition preserving smoothing of visual data. Using artificial as well as real data we illustrate our approach by numerical examples. We demonstrate that solutions to our approach improve those obtained by quadratic minimization and show the influence of global parameters which allow for a continuous, scale-dependent, and selective control of the smoothing process.

Computer Simulation↗