Developing a Constitutively Active Aryl Hydrocarbon Receptor in the Mouse Heart

Abstract not Include

On the certainty framework for causal network discovery with application to tropical cyclone rapid intensification

2022 Spring.Includes bibliographical references.Causal network discovery using information theoretic measures is a powerful tool for studying new physics in the earth sciences. To make this tool even more powerful, the certainty framework introduced by van Leeuwen et al. (2021) adds two features to the existing information theoretic literature. The first feature is a novel measure of relative strength of driving processes created specifically for continuous variables. The second feature consists of three decompositions of mutual information between a process and its drivers. These decompositions are 1) coupled influences from combinations of drivers, 2) information coming from a single driver coupled with a specific number of other drivers (mlinks), and 3) total influence of each driver. To represent all the coupled influences, directed acyclic hypergraphs replace the standard directed acyclic graphs (DAGs). The present work furthers the interpretation of the certainty framework. Measuring relative strength is described thermodynamically. Two-driver coupled influence is interpreted using DAGs, introducing the concept of separability of drivers' effects. Coupled influences are proved to be a type of interaction information. Also, total influence is proved to be nonnegative, meaning the total influences constitute a nonnegative decomposition of mutual information. Furthermore, a new reference distribution for calculating self-certainty is introduced. Finally, the framework is generalized for variables that are continuous with one discrete mode, for which partial Shannon entropy is introduced. The framework was then applied to the rapid intensification of Hurricane Patricia (2015). The hourly change in maximum tangential windspeed was used as the target. The four drivers were outflow layer (OL) maximum radial windspeed (uu), boundary layer (BL) radial windspeed at radius of maximum wind (RMW) (ul), equivalent potential temperature at BL RMW (θe), and the temperature difference between the OL and BL (ΔT). All variables were azimuthally averaged. The drivers explained 45.5% of the certainty. The certainty gain was 35.8% from θe, 24.5% from ΔT, 24.0% from uu, and 15.7% from ul. The total influence of θe came mostly from inseparable effects, while the total influence of uu came mostly from separable effects. Physical mechanisms, both accepted in current literature and suggested from this application, are discussed

THE IMPACT OF INTRA-ARTICULAR HYALURONIC ACID ON THE GAIT OF KNEE OSTEOARTHRITIS PATIENTS

The present study investigated the impact of intra-articular hyaluronic acid (HA) on the gait velocity of knee OA patients, along with patient pain, stiffness, and function. Thirty mild-moderate knee OA patients were randomized to receive three weekly injections of HA (2 ml of 20 mg/ml HA), or placebo (P) (1.2 ml of .001 mg/ml HA). Self-selected (SSGV) and fast (FGV) gait velocity were determined with the GAITRite system; self-reported pain, stiffness, and function were measured with the WOMAC OA Index; and overall patient function was determined with the six minute walk test. Data from one week, three months, and six months post-treatment was analyzed. At six months follow-up, SSGV was significantly improved in both the HA and P groups while only the HA group had significantly improved FGV. The effect of HA injections on knee OA patient gait velocity was not significantly different than P injections

Scaling viral vector production processes into HyPerforma DynaDrive Single-Use Bioreactors

As viral vector gene therapy processes reach the clinical and manufacturing stages, the ability to scale up those processes becomes crucial. In this study we demonstrate how the Thermo Scientific HyPerforma DynaDrive Single-Use Bioreactor (S.U.B.) offers optimal conditions for growth and transfection of Gibco Viral Production Cells (VPC1.0 and VPC2.0 cells), both derivatives of HEK 293F cells, utilizing the Gibco LV-MAX Production System and the Gibco AAV-MAX Production System. We also show scalability of cell growth from flask to HyPerforma DynaDrive S.U.B. in 50L, 500L, and 3000L sizes, offering particular guidance on key scale factors and experience to ensure process success

Asymptotic Preserving Discontinuous Galerkin Methods for a Linear Boltzmann Semiconductor Model

A key property of the linear Boltzmann semiconductor model is that as the collision frequency tends to infinity, the phase space density $f = f(x,v,t)$ converges to an isotropic function $M(v)\rho(x,t)$, called the drift-diffusion limit, where $M$ is a Maxwellian and the physical density $\rho$ satisfies a second-order parabolic PDE known as the drift-diffusion equation. Numerical approximations that mirror this property are said to be asymptotic preserving. In this paper we build two discontinuous Galerkin methods to the semiconductor model: one with the standard upwinding flux and the other with a $\varepsilon$-scaled Lax-Friedrichs flux, where 1/$\varepsilon$ is the scale of the collision frequency. We show that these schemes are uniformly stable in $\varepsilon$ and are asymptotic preserving. In particular, we discuss what properties the discrete Maxwellian must satisfy in order for the schemes to converge in $\varepsilon$ to an accurate $h$-approximation of the drift diffusion limit. Discrete versions of the drift-diffusion equation and error estimates in several norms with respect to $\varepsilon$ and the spacial resolution are also included