Model predictive control of non-interacting active Brownian particles.

Active matter systems are strongly driven to assume non-equilibrium distributions owing to their self-propulsion, , flocking and clustering. Controlling the active matter systems' spatiotemporal distributions offers exciting applications such as directed assembly, programmable materials, and microfluidic actuation. However, these applications involve environments with coupled dynamics and complex tasks, making intuitive control strategies insufficient.

Experimental and theoretical bulk phase diagram and interfacial tension of ouzo.

Ouzo is a well-known drink in Mediterranean countries, with ingredients water, alcohol and -anethole oil. The oil is insoluble in water, but completely soluble in alcohol, so when water is added to the spirit, the available alcohol is depleted and the mixture exhibits spontaneous emulsification. This process is commonly known as the louche or ouzo effect.

Markedly Improved Catalytic Dehydration of Sorbitol to Isosorbide by Sol-Gel Sulfated Zirconia: A Quantitative Structure-Reactivity Study.

Isosorbide, a bicyclic C6 diol, has considerable value as a precursor for the production of bio-derived polymers. Current production of isosorbide from sorbitol utilizes homogeneous acid, commonly HSO, creating harmful waste and complicating separation. Thus, a heterogeneous acid catalyst capable of producing isosorbide from sorbitol in high yield under mild conditions would be desirable.

Cultural influences on willingness to donate organs among urban native Americans.

Background: The need for organ donation is substantial among Native Americans, driven by the disproportionate burden of ESRD. Due to the dearth of knowledge about willingness to donate (WTD) among urban Native Americans, a group that represents over half of the US Native population, we aimed to examine factors affecting donation.

Methods: We conducted a cross-sectional survey of a convenience sample, using a questionnaire developed specifically for this study using community-based participatory research.

Approximate method for stochastic chemical kinetics with two-time scales by chemical Langevin equations.

The frequently used reduction technique is based on the chemical master equation for stochastic chemical kinetics with two-time scales, which yields the modified stochastic simulation algorithm (SSA). For the chemical reaction processes involving a large number of molecular species and reactions, the collection of slow reactions may still include a large number of molecular species and reactions. Consequently, the SSA is still computationally expensive.

Parameter estimation in stochastic chemical kinetic models using derivative free optimization and bootstrapping.

Recent years have seen increasing popularity of stochastic chemical kinetic models due to their ability to explain and model several critical biological phenomena. Several developments in high resolution fluorescence microscopy have enabled researchers to obtain protein and mRNA data on the single cell level. The availability of these data along with the knowledge that the system is governed by a stochastic chemical kinetic model leads to the problem of parameter estimation.

Comparison of Parameter Estimation Methods in Stochastic Chemical Kinetic Models: Examples in Systems Biology.

Stochastic chemical kinetics has become a staple for mechanistically modeling various phenomena in systems biology. These models, even more so than their deterministic counterparts, pose a challenging problem in the estimation of kinetic parameters from experimental data. As a result of the inherent randomness involved in stochastic chemical kinetic models, the estimation methods tend to be statistical in nature.

Comparison of finite difference based methods to obtain sensitivities of stochastic chemical kinetic models.

Sensitivity analysis is a powerful tool in determining parameters to which the system output is most responsive, in assessing robustness of the system to extreme circumstances or unusual environmental conditions, in identifying rate limiting pathways as a candidate for drug delivery, and in parameter estimation for calculating the Hessian of the objective function. Anderson [SIAM J. Numer.

The stochastic quasi-steady-state assumption: reducing the model but not the noise.

Highly reactive species at small copy numbers play an important role in many biological reaction networks. We have described previously how these species can be removed from reaction networks using stochastic quasi-steady-state singular perturbation analysis (sQSPA). In this paper we apply sQSPA to three published biological models: the pap operon regulation, a biochemical oscillator, and an intracellular viral infection.

Creating a medical home through the 340B Drug Pricing Program.

Objective: This study was designed to track inpatient and outpatient utilization before and after the implementation of the 340B Drug Pricing Program in a public university hospital.

Methods: Before and after design built upon administrative clinical and financial data.

Results: Outpatient and emergency department utilization rates increased with small increments of direct cost.

Racial disparities in age at preventable hospitalization among U.S. Adults.

Background: Similar to the well-documented racial inequities in health status, disease burden, healthcare access, and hospitalization, studies have generally found higher rates of hospitalization resulting from ambulatory care-sensitive conditions for blacks compared to whites. Beyond identifying disparity in rates of disease or risks of hospitalization, identifying disparity in age at hospitalization may provide deeper insight into the social and economic effects of disparities on individuals, families, and communities.

Purpose: The objective of this paper is to evaluate potential racial disparities in age of preventable hospitalizations as measured by ambulatory care-sensitive conditions.

Stochastic kinetic modeling of vesicular stomatitis virus intracellular growth.

By building kinetic models of biological networks one may advance the development of new modeling approaches while gaining insights into the biology. We focus here on building a stochastic kinetic model for the intracellular growth of vesicular stomatitis virus (VSV), a well-studied virus that encodes five genes. The essential network of VSV reactions creates challenges to stochastic simulation owing to (i) delayed reactions associated with transcription and genome replication, (ii) production of large numbers of intermediate proteins by translation, and (iii) the presence of highly reactive intermediates that rapidly fluctuate in their intracellular levels.

Implications of decoupling the intracellular and extracellular levels in multi-level models of virus growth.

Virus infections are characterized by two distinct levels of detail: the intracellular level describing how viruses hijack the host machinery to replicate, and the extracellular level describing how populations of virus and host cells interact. Deterministic, population balance models for viral infections permit incorporation of both the intracellular and extracellular levels of information. In this work, we identify assumptions that lead to exact, selective decoupling of the interaction between the intracellular and extracellular levels, effectively permitting solution of first the intracellular level, and subsequently the extracellular level.

Image-guided modeling of virus growth and spread.

Although many tools of cellular and molecular biology have been used to characterize single intracellular cycles of virus growth, few culture methods exist to study the dynamics of spatially spreading viruses over multiple generations. We have previously developed a method that addresses this need by tracking the spread of focal infections using immunocytochemical labeling and digital imaging. Here, we build reaction-diffusion models to account for spatio-temporal patterns formed by the spreading viral infection front as well as data from a single cycle of virus growth (one-step growth).

Two classes of quasi-steady-state model reductions for stochastic kinetics.

The quasi-steady-state approximation (QSSA) is a model reduction technique used to remove highly reactive species from deterministic models of reaction mechanisms. In many reaction networks the highly reactive intermediates (QSSA species) have populations small enough to require a stochastic representation. In this work we apply singular perturbation analysis to remove the QSSA species from the chemical master equation for two classes of problems.

Stochastic simulation of catalytic surface reactions in the fast diffusion limit.

The master equation of a lattice gas reaction tracks the probability of visiting all spatial configurations. The large number of unique spatial configurations on a lattice renders master equation simulations infeasible for even small lattices. In this work, a reduced master equation is derived for the probability distribution of the coverages in the infinite diffusion limit.

State estimation approach for determining composition and growth rate of Si1-xGex chemical vapor deposition utilizing real-time ellipsometric measurements.

We present an algorithm that simultaneously deduces from real-time ellipsometric measurements both the growth rate and the composition of Si1-xGex films deposited via chemical vapor deposition. The heart of the algorithm is a dynamic, first-principles model of the deposition system and the ellipsometric sensor. The model predicts the ellipsometric parameters psi and Delta during film growth.

On the origins of approximations for stochastic chemical kinetics.

This paper considers the derivation of approximations for stochastic chemical kinetics governed by the discrete master equation. Here, the concepts of (1) partitioning on the basis of fast and slow reactions as opposed to fast and slow species and (2) conditional probability densities are used to derive approximate, partitioned master equations, which are Markovian in nature, from the original master equation. Under different conditions dictated by relaxation time arguments, such approximations give rise to both the equilibrium and hybrid (deterministic or Langevin equations coupled with discrete stochastic simulation) approximations previously reported.

Intensive intervention improves primary care follow-up for uninsured emergency department patients.

Objectives: To test an intervention designed to improve primary care use and decrease emergency department (ED) utilization for uninsured patients using the ED.

Methods: Using a randomized design, an intensive case-management intervention was tested with patients identified at a Level 1 urban trauma center from April 2002 through July 2002. Following assessment in the ED, six-month follow-up data were gathered from four primary care sites (two Federally Qualified Health Centers, two hospital outpatient clinics) and two area hospitals.