Modeling Healthcare Processes Using Commitments: An Empirical Evaluation

The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7—each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student’s t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel.     

Using Dung to Estimate Gorilla Density: Modeling Dung Production Rate

There is an urgent need for information on western gorilla population sizes and distribution to improve present and plan future conservation actions. Researchers traditionally have estimated gorilla densities on the basis of nest counts despite demonstrated variation in nest production and decay rates. The variation may lead to large biases in estimates of gorilla abundance. We investigated the use of an alternate index of gorilla abundance, via defecation data collected from habituated gorillas at Bai Hokou, Central African Republic. Our sample of 274/370 defecation events/dung piles produced a production rate of ca. 5 dung piles/d: comparable to previous estimates based on much smaller sample sizes. Heuristic models that failed to account for imperfect dung pile detection produced a lower defecation rate estimate than that of a maximum likelihood model that explicitly modeled detection probability. Generalized linear modeling (GLM) showed that dung pile production rate was strongly linked to rainfall, suggesting that failure to correct for seasonal variation in dung pile production rates could lead to substantial biases in gorilla abundance estimates. In our study, failing to distinguish between the number of defecation events and the number of dung piles produced would lead to a ca. 31% overestimate of true gorilla numbers. The use of dung as an index of gorilla abundance shows potential, but more fieldwork and modeling on seasonal variation in dung production rates is required.     

Mining Social Entrepreneurship Strategies Using Topic Modeling

Despite the burgeoning research on social entrepreneurship (SE), SE strategies remain poorly understood. Drawing on extant research on the social activism and social change, empowerment and SE models, we explore, classify and validate the strategies used by 2,334 social entrepreneurs affiliated with the world’s largest SE support organization, Ashoka. The results of the topic modeling of the social entrepreneurs’ strategy profiles reveal that they employed a total of 39 change-making strategies that vary across resources (material versus symbolic strategies), specificity (general versus specific strategies), and mode of participation (mass versus elite participation strategies); they also vary across fields of practice and time. Finally, we identify six meta-SE strategies―a reduction from the 39 strategies―and identify four new meta-SE strategies (i.e., system reform, physical capital development, evidence-based practices, and prototyping) that have been overlooked in prior SE research. Our findings extend and deepen the research into SE strategies and offer a comprehensive model of SE strategies that advances theory, practice and policy making.     

Modeling Multiallelic Selection Using a Moran Model

We present a Moran-model approach to modeling general multiallelic selection in a finite population and show how it may be used to develop theoretical models of biological systems of balancing selection such as plant gametophytic self-incompatibility loci. We propose new expressions for the stationary distribution of allele frequencies under selection and use them to show that the continuous-time Markov chain describing allele frequency change with exchangeable selection and Moran-model reproduction is reversible. We then use the reversibility property to derive the expected allele frequency spectrum in a finite population for several general models of multiallelic selection. Using simulations, we show that our approach is valid over a broader range of parameters than previous analyses of balancing selection based on diffusion approximations to the Wright–Fisher model of reproduction. Our results can be applied to any model of multiallelic selection in which fitness is solely a function of allele frequency.NATURAL selection has long been a topic of interest in population genetics, yet the stochastic theory of genes under selection remains underdeveloped compared to the theory of neutral genes. Due to the interplay of stochastic and deterministic forces, models of selection present analytical challenges beyond those of neutral models, although a great deal of progress has been made with models that use diffusion approximations to a Wright–Fisher model of reproduction. Diffusion approximations with selection are, however, sometimes difficult to employ and always require assumptions about population parameters for tractability. These limitations suggest that there may be value in developing new methods of solving the problem of selection in a finite population, and here we do so using a Moran model of reproduction in place of the familiar Wright–Fisher model. Our approach has two major advantages over previous models: general applicability to a wide variety of selection models and accuracy over a broad range of parameter values. In this work, we propose new expressions for the full stationary distributions of allele frequencies under multiallelic selection, as well as expressions for average allele frequency distributions.We restrict our attention to exchangeable models of selection, meaning that relabeling the alleles will not change selective outcomes and thus that selection will be a function of allele frequency rather than allele identity. Many models of selection can be transformed into frequency-dependent forms (Denniston and Crow 1990), and some common models of selection have the desired property of exchangeability. For example, symmetric overdominant selection, in which heterozygotes have a selective advantage over homozygotes but the specific genotype of homozygote or heterozygote has no further selective effect, can be expressed as frequency-dependent selection on individual (exchangeable) alleles, although the direct selection is actually on diploid genotypes. Many other proposed models of multiallelic balancing selection, in which substantial variation is maintained by selection, can be viewed in this way. Such models have been of particular interest because of the potential application to highly multiallelic systems found in nature, such as self-incompatibility (SI) loci in plants and the major histocompatibility complex (MHC) loci in vertebrates, and the desire to analyze these systems is a motivation for the present work. We now review some of the population genetic theory related to these systems.Early in the history of population genetics, Wright (1939) presented a somewhat controversial stochastic model of gametophytic self-incompatibility (GSI) genes, sparking much further theoretical and empirical work. An analytic theory of multiallelic symmetric overdominance was developed along similar lines to this early model (Kimura and Crow 1964; Takahata 1990) and has been used as an approximation to the unknown mode of selection in the MHC (Takahata et al. 1992). Drawing insights from these first two applications, other biological systems where balancing selection was posited, including sex determination in honeybees (Yokoyama and Nei 1979), fungal mating systems (May et al. 1999), and heterokaryon incompatibility in fungi (Muirhead et al. 2002), have also been modeled successfully using closely related approaches. Progress has been made in using these models to address genealogical (Takahata 1990; Vekemans and Slatkin 1994) and demographic (Muirhead 2001) questions, as well as extending the models into more complex modes of selection (Uyenoyama 2003) and reproduction (Vallejo-Marin and Uyenoyama 2008).Models of genetic variation under balancing selection have traditionally been focused on specific systems, such that extensions require entirely new analyses, and have also included a number of simplifying assumptions in the interest of mathematical tractability. For example, the symmetric overdominance model has been strongly criticized as an unrealistic approximation of MHC evolution (Paterson et al. 1998; Hedrick 2002; Penn et al. 2002; Ilmonen et al. 2007; Stoffels and Spencer 2008), and yet it has proved difficult to make finite-population models of any of the more realistic frequency dependence schemes using the same approaches. A constraint on further progress is the fact that the standard model of stochastic population genetics, the Wright–Fisher model, is in fact quite difficult to analyze.The Wright–Fisher model of reproduction employs nonoverlapping generations, so that for a diploid population of size N, all 2N allele copies are chosen simultaneously when forming a new generation of individuals. While it is straightforward to describe this reproduction scheme mathematically as a discrete-time Markov chain, that chain unfortunately appears intractable even in simple cases (Ewens 2004). Traditionally, then, diffusion approximations have been used to obtain quantities of interest, such as the equilibrium expected number of alleles, allele frequency spectra, and fixation probabilities and times. Diffusion approximations are derived in the limit , but are applicable to problems of finite N, provided that the strengths of other forces such as mutation and selection can be assumed to be weak, of O(N⁻¹) (Ewens 2004). Watterson (1977) derived such a diffusion approximation for multiallelic symmetric overdominance using these assumptions. More recently, as interest in population genetics has turned to problems of inference, Grote and Speed (2002) considered sampling probabilities under the diffusion approximation for symmetric overdominance, while Donnelly et al. (2001) and Stephens and Donnelly (2003) proposed computational methods for some asymmetric models.Although strong selection can be modeled using diffusion approximations by making the product of the population size and the selection coefficient (Ns) large, the assumption of weak selection is not in fact appropriate for the canonical biological systems of balancing selection. Specifically, selection coefficients are defined by the differences in fitness (the expected number of offspring) among individuals in the population at a given time. These differences may be large in systems such as GSI, where the fitness of a very common allele may be very small while the fitness of other alleles may be greater than one.In an attempt to deal with the extremely strong selection of gametophytic self-incompatibility, Wright''s (1939) original model focused attention on the dynamics of a single representative allele. He collapsed the influence of all other alleles into a single summary statistic: the homozygosity, F, which is a function of the frequencies of all alleles, and which Wright (1939) assumed to be constant. The analysis is essentially that of a two-allele system, using a one-dimensional diffusion analysis. This approach, while shown by simulation to be very effective in the appropriate parameter range (Ewens and Ewens 1966), received substantial criticism on mathematical grounds (Fisher 1958; Moran 1962; Ewens 1964b). Ewens (1964b), in particular, objected to the use of diffusion theory for GSI, pointing out that strong frequency-dependent selection violates the diffusion requirement that both the mean and the variance of the change in allele frequencies be small and of O(N⁻¹). Ewens (1964a) then applied Wright''s basic one-dimensional diffusion approach to modeling symmetric overdominance, but assumed that selection was weak and of O(N⁻¹) to stay within the strict limits of the diffusion approximation.Kimura and Crow (1964) and Wright (1966), on the other hand, presented alternative one-dimensional diffusion approximations to symmetric overdominance, closer in spirit to Wright''s original model of GSI, that did not make the weak-selection assumption. Watterson (1977) was concerned about both the inconsistencies of the approximations used in these models and the treatment of F as a constant rather than as a random variable dependent upon allele frequencies. Using his own multiallelic diffusion approximation for symmetric overdominance (Watterson 1977), he derived an alternative (small-Ns) approximation to the frequency of a single representative allele. We consider this approximation, as well as the best-known one-dimensional symmetric overdominance diffusion, the strong-selection approximation of Kimura and Crow (1964), in comparison with our alternative approach to deriving allele frequency spectra under general multiallelic selection with exchangeable alleles.To avoid the approximations required to employ Wright–Fisher/diffusion-based methods, we turn to an alternative model of reproduction in a finite population: the overlapping-generations model of Moran (1962). In the Moran model, a single allele copy dies and another reproduces in each time step, rather than all 2N allele copies simultaneously being replaced by offspring each generation. As in the Wright–Fisher model, this reproduction scheme is represented mathematically by a Markov chain. Unlike the Wright–Fisher model, however, the Moran model can sometimes yield tractable, exact solutions to the underlying Markov chain, without the need to resort to diffusion approximations. We exploit this trait to develop a new stochastic theory of multiallelic selection with minimal dependence on assumptions about population parameter values. Our method has the additional benefit of being flexible: it can accommodate any exchangeable model of multiallelic selection and either of two general models of parent-independent mutation, the infinite-alleles and k-allele models of mutation. Our Moran-model predictions agree well with the results of Wright–Fisher simulations.     

Bayesian Nonparametric Modeling Using Mixtures of Triangular Distributions

Nonparametric modeling is an indispensable tool in many applications and its formulation in an hierarchical Bayesian context, using the entire posterior distribution rather than particular expectations, increases its flexibility. In this article, the focus is on nonparametric estimation through a mixture of triangular distributions. The optimality of this methodology is addressed and bounds on the accuracy of this approximation are derived. Although our approach is more widely applicable, we focus for simplicity on estimation of a monotone nondecreasing regression on [0, 1] with additive error, effectively approximating the function of interest by a function having a piecewise linear derivative. Computationally accessible methods of estimation are described through an amalgamation of existing Markov chain Monte Carlo algorithms. Simulations and examples illustrate the approach.     

Modeling the Impact of Alternative Immunization Strategies: Using Matrices as Memory Lanes

Existing modeling approaches are divided between a focus on the constitutive (micro) elements of systems or on higher (macro) organization levels. Micro-level models enable consideration of individual histories and interactions, but can be unstable and subject to cumulative errors. Macro-level models focus on average population properties, but may hide relevant heterogeneity at the micro-scale. We present a framework that integrates both approaches through the use of temporally structured matrices that can take large numbers of variables into account. Matrices are composed of several bidimensional (time×age) grids, each representing a state (e.g. physiological, immunological, socio-demographic). Time and age are primary indices linking grids. These matrices preserve the entire history of all population strata and enable the use of historical events, parameters and states dynamically in the modeling process. This framework is applicable across fields, but particularly suitable to simulate the impact of alternative immunization policies. We demonstrate the framework by examining alternative strategies to accelerate measles elimination in 15 developing countries. The model recaptured long-endorsed policies in measles control, showing that where a single routine measles-containing vaccine is employed with low coverage, any improvement in coverage is more effective than a second dose. It also identified an opportunity to save thousands of lives in India at attractively low costs through the implementation of supplementary immunization campaigns. The flexibility of the approach presented enables estimating the effectiveness of different immunization policies in highly complex contexts involving multiple and historical influences from different hierarchical levels.     

Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry

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Modeling Complex Phenotypes: Generalized Linear Models Using Spectrogram Predictors of Animal Communication Signals

Summary A major goal of evolutionary biology is to understand the dynamics of natural selection within populations. The strength and direction of selection can be described by regressing relative fitness measurements on organismal traits of ecological significance. However, many important evolutionary characteristics of organisms are complex, and have correspondingly complex relationships to fitness. Secondary sexual characteristics such as mating displays are prime examples of complex traits with important consequences for reproductive success. Typically, researchers atomize sexual traits such as mating signals into a set of measurements including pitch and duration, in order to include them in a statistical analysis. However, these researcher‐defined measurements are unlikely to capture all of the relevant phenotypic variation, especially when the sources of selection are incompletely known. In order to accommodate this complexity we propose a Bayesian dimension‐reduced spectrogram generalized linear model that directly incorporates representations of the entire phenotype (one‐dimensional acoustic signal) into the model as a predictor while accounting for multiple sources of uncertainty. The first stage of dimension reduction is achieved by treating the spectrogram as an “image” and finding its corresponding empirical orthogonal functions. Subsequently, further dimension reduction is accomplished through model selection using stochastic search variable selection. Thus, the model we develop characterizes key aspects of the acoustic signal that influence sexual selection while alleviating the need to extract higher‐level signal traits a priori. This facet of our approach is fundamental and has the potential to provide additional biological insight, as is illustrated in our analysis.     

Macromolecular Symmetric Assembly Prediction Using Swarm Intelligence Dynamic Modeling

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Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology.     

Modeling of Neonatal Skull Development Using Computed Tomography Images

Neonatal skull anatomy and its evolution have received less attention with respect to the brain anatomy in neuroscience and neuroanatomy studies. Meanwhile, their influence on normal brain development and their impact on the results of functional brain studies have been demonstrated by several researches. Such disesteem is due to the weak appearance of the cranial bones, fontanels and sutures in images acquired by MRI which presents actually the only available aperture for observing the neonatal head volume in details. This paper presents an unprecedented retrospective CT-based study on modeling the neonatal skull and its development during the first weeks of life in a standard space defined by the available neonatal MRI model. We create two neonatal head atlases for the age ranges of 39-40 and 41-42 week's gestational age using symmetric group-wise normalization method. The created atlases allow direct observation of ossification patterns and precise three-dimensional measurement of anatomical features from neonatal skull during development. Development of the neonatal skull has been examined here using nineteen CT scans of neonates with two-week gestational age ranges of 39 to 40 and 41 to 42. Deformation-based morphometry method is applied with the use of Jacobian determinant maps to identify growth patterns and observe ossification during specified time interval. Precise three-dimensional measurements of anterior fontanel size, scalp eliminated head circumference and the area corresponding to the fontanel-sutures were performed by extracting fontanels and sutures.