Remodeling of Fibrous Extracellular Matrices by Contractile Cells: Predictions from Discrete Fiber Network Simulations

Contractile forces exerted on the surrounding extracellular matrix (ECM) lead to the alignment and stretching of constituent fibers within the vicinity of cells. As a consequence, the matrix reorganizes to form thick bundles of aligned fibers that enable force transmission over distances larger than the size of the cells. Contractile force-mediated remodeling of ECM fibers has bearing on a number of physiologic and pathophysiologic phenomena. In this work, we present a computational model to capture cell-mediated remodeling within fibrous matrices using finite element based discrete fiber network simulations. The model is shown to accurately capture collagen alignment, heterogeneous deformations, and long-range force transmission observed experimentally. The zone of mechanical influence surrounding a single contractile cell and the interaction between two cells are predicted from the strain-induced alignment of fibers. Through parametric studies, the effect of cell contractility and cell shape anisotropy on matrix remodeling and force transmission are quantified and summarized in a phase diagram. For highly contractile and elongated cells, we find a sensing distance that is ten times the cell size, in agreement with experimental observations.Comment: Accepted for publication in the Biophysical Journa

Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model

Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches

Control variates for stochastic gradient MCMC

It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC (SGMCMC). These methods use a noisy estimate of the gradient of the log-posterior, which reduces the per iteration computational cost of the algorithm. Despite this, there are a number of results suggesting that stochastic gradient Langevin dynamics (SGLD), probably the most popular of these methods, still has computational cost proportional to the dataset size. We suggest an alternative log-posterior gradient estimate for stochastic gradient MCMC which uses control variates to reduce the variance. We analyse SGLD using this gradient estimate, and show that, under log-concavity assumptions on the target distribution, the computational cost required for a given level of accuracy is independent of the dataset size. Next we show that a different control variate technique, known as zero variance control variates, can be applied to SGMCMC algorithms for free. This post-processing step improves the inference of the algorithm by reducing the variance of the MCMC output. Zero variance control variates rely on the gradient of the log-posterior; we explore how the variance reduction is affected by replacing this with the noisy gradient estimate calculated by SGMCMC

Modeling human ad hoc coordination

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-Agent collaboration

Extending the multi-level method for the simulation of stochastic biological systems

The multi-level method for discrete state systems, first introduced by Anderson and Higham [Multiscale Model. Simul. 10:146--179, 2012], is a highly efficient simulation technique that can be used to elucidate statistical characteristics of biochemical reaction networks. A single point estimator is produced in a cost-effective manner by combining a number of estimators of differing accuracy in a telescoping sum, and, as such, the method has the potential to revolutionise the field of stochastic simulation. The first term in the sum is calculated using an approximate simulation algorithm, and can be calculated quickly but is of significant bias. Subsequent terms successively correct this bias by combining estimators from approximate stochastic simulations algorithms of increasing accuracy, until a desired level of accuracy is reached. In this paper we present several refinements of the multi-level method which render it easier to understand and implement, and also more efficient. Given the substantial and complex nature of the multi-level method, the first part of this work (Sections 2 - 5) is written as a tutorial, with the aim of providing a practical guide to its use. The second part (Sections 6 - 8) takes on a form akin to a research article, thereby providing the means for a deft implementation of the technique, and concludes with a discussion of a number of open problems.Comment: 38 page

A radiologic analysis: Relationships of the thoracic spine to aid palpation of the thoracic transverse processes

Introduction: In the field of osteopathic manipulative medicine, palpation is an important skill used by physicians to localize structures to diagnose and treat patients with somatic dysfunction throughout the musculoskeletal system. Specifically, in the thoracic spine, physicians can use the more superficial spinous process of each vertebra to assist in locating the deeper, more challenging to palpate, transverse process of the vertebra. Historically, the “rule of threes”, proposed by Mitchell et al in 1979, describing the relationships between spinous processes and transverse processes of the thoracic spine has been taught in osteopathic medical schools. However, another model was more recently proposed by Geelhoed et al in 2006. To our knowledge, these models have never been analyzed radiologically using computed tomography in patients. Objectives: To evaluate the accuracy of several proposed models, including the rule of 3’s and Geelhoed’s rule, which aid in palpation of the transverse processes of the thoracic spine based on their relation to the spinous processes. Furthermore, the study aims to analyze the intervertebral and intravertebral relationships of the transverse processes and spinous processes of the thoracic spine. Methods: This was an observational study with retrospective analysis of high-resolution computed tomography of the chest in the prone position. Four measurements were taken per thoracic vertebra in the coronal plane during inspiration. Based on the measurements, it was determined whether each individual vertebra followed the models or not. The measurements were further analyzed to define additional intervertebral and intravertebral relationships between the transverse and spinous processes of the thoracic spine. Results: We predict that the high-resolution computed tomography will reveal the intervertebral and intravertebral relationships as well as the aid in defining the accuracy of the relationships between the spinous processes proposed in osteopathic manipulative models. Conclusion: We conclude that retrospective radiological studies using state of the art images has the ability to confirm and/or expand our ability to utilize osteopathic principles and models in diagnosing and treating patients

A novel approach to assessing the ecosystem-wide impacts of reintroductions

Reintroducing a species to an ecosystem can have significant impacts on the recipient ecological community. Although reintroductions can have striking and positive outcomes, they also carry risks; many well intentioned conservation actions have had surprising and unsatisfactory outcomes. A range of network-based mathematical methods have been developed to make quantitative predictions of how communities will respond to management interventions. These methods are based on the limited knowledge of which species interact with each other and in what way. However, expert knowledge isn’t perfect and can only take models so far. Fortunately, other types of data, such as abundance time-series, is often available, but, to date, no quantitative method exists to integrate these various data types into these models, allowing more precise ecosystem-wide predictions. In this paper, we develop mathematical methods that combine time-series data of multiple species with knowledge of species interactions and we apply it to proposed reintroductions at Booderee National Park in Australia. There have been large fluctuations in species abundances at Booderee National Park in recent history, following intense feral fox (Vulpes vulpes) control – including the local extinction of the greater glider (Petauroides volans). These fluctuations can provide information about the system isn’t readily obtained from a stable system, and we use them to inform models that we then use to predict potential outcomes of eastern quoll (Dasyurus viverrinus) and long-nosed potoroo (Potorous tridactylus) reintroductions. One of the key species of conservation concern in the park is the eastern bristlebird (Dasyornis brachypterus), and we find that long-nosed potoroo introduction would have very little impact on the eastern bristlebird population, while the eastern quoll introduction increased the likelihood of eastern bristlebird decline, although that depends on the strength and form of any possible interaction.We thank the ARC Centre of Excellence for Environmental Decisions, The National Environmental Research Project Decisions Hub and an ARC Linkage Project (LP160100496) for funding. CB is the recipient of a John Stocker Postdoctoral Fellowship from the Science and Industry Endowment Fund. MB is supported by an ARC Future Fellowship (FT170100274). EMM is a current ARC Future Fellowship (FT170100140) and was supported by an ARC DECRA Fellowship for the majority of this work

Prediction of recurrent Clostridium difficile infection using comprehensive electronic medical records in an integrated healthcare delivery system

BACKGROUNDPredicting recurrentClostridium difficileinfection (rCDI) remains difficult. METHODS. We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007–2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model.RESULTSDespite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591–0.605), had good calibration, or had good explanatory power.CONCLUSIONSOur ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power.Infect Control Hosp Epidemiol2017;38:1196–1203</jats:sec