Effect of temperature on recombinant protein production using the Bm5/Bm5.NPV expression system

A series of experiments have been conducted using a recombinant baculovirus/insect cell expression system (Bm5/Bm5.NPV.CAT) to establish the optimum temperature for both cell growth and virus infection. Bm5 cell growth was found to be limited at temperatures below 22°C and ceased completely at temperatures above 34°C. In the range between 24 and 28°C, final cell densities always reached 96% of the highest achievable viable cell density. The shortest population doubling time was obtained at 28°C. Overall, a consistent increase in metabolism with increasing temperatures was observed. During the infection/viral replication phase, an increase in the temperature from 25 to 31°C resulted in a faster decrease in viable cell density and an earlier production of chloramphenicol acetyltransferase (CAT). Furthermore, protein yield at temperatures above 28°C was significantly reduced. Overall, the best temperature for the infection phase for the Bm5/Bm5.NPV expression system was found to be 25°C when the cells are cultured in serum free media.     

Modelling a circulating fluidized bed riser reactor with gas—solids downflow at the wall

A predictive model was developed for the fully developed zone of a circulating fluidized bed (CFB) riser reactor operating in the fast fluidization regime that overcomes limitations of existing models. The model accounts for the upward flow of gas and solids in the core and downward flow of the two phases in the annulus. Additionally, a numerical solution methodology for the simulation of a riser reactor employing the hydrodynamic model was devised. A simulation was performed using the fast, main Claus reaction to demonstrate the effects of backmixing in the fast fluidization regime. It was found that the molar flow rates of the reactants leaving a fast fluidized CFB riser reactor were significantly higher than those leaving an identical reactor operating in the pneumatic transport regime.     

Kinetics of methane hydrate formation in aqueous electrolyte solutions

Experimental data on the kinetics of methane hydrate formation in aqueous electrolyte solutions are reported. The experiments were carried out in a semi-batch stirred tank reactor in three NaCl and two KCl solutions as well as in a solution containing a mixture of NaCl and KCl at three different nominal temperatures from 270 to 274 K and at pressures ranging from 3.78 to 7.08 MPa. The kinetic model developed by Englezos et al. (1987a) was adapted to predict the growth of hydrates. The model is based on the crystallisation theory coupled with the two-film theory for gas absorption in the liquid phase. The kinetic rate constant which appears in the model was that obtained earlier for methane hydrate formation in pure water. The effect of the electrolytes was taken into account through the computation of the three-phase equilibrium conditions and the corresponding fugacities. Overall, the model predictions match the experimental data very well with the largest prediction error being less than 10%.     

Shadowing Dynamic Scenes with Arbitrary BRDFs

We present a real-time relighting and shadowing method for dynamic scenes with varying lighting, view and BRDFs. Our approach is based on a compact representation of reflectance data that allows for changing the BRDF at run-time and a data-driven method for accurately synthesizing self-shadows on articulated and deformable geometries. Unlike previous self-shadowing approaches, we do not rely on local blocking heuristics. We do not fit a model to the BRDF-weighted visibility, but rather only to the visibility that changes during animation. In this manner, our model is more compact than previous techniques and requires less computation both during fitting and at run-time. Our reflectance product operators can re-integrate arbitrary low-frequency view-dependent BRDF effects on-the-fly and are compatible with all previous dynamic visibility generation techniques as well as our own data-driven visibility model. We apply our reflectance product operators to three different visibility generation models, and our data-driven model can achieve framerates well over 300Hz.     

The effect of specific growth rate and death rate on monoclonal antibody production in hybridoma chemostat cultures

Experimental data collected from bench-scale chemostat cultures of mouse-mouse hybridoma cells have been used for the development of a kinetic model of monoclonal antibody production. The specific antibody productivity was found to be proportional to the specific death rate, indicating a stimulating effect of stress on the ability of the cells to produce antibody. The death rate was found to be an exponential function of the average cell age in the culture. Furthermore, a Generalized Maintenance Energy (GME) model is proposed, to take account of the effects of culture stress on the nutrient uptake rates. The proposed relationships agreed very well with other mouse-mouse hybridoma chemostat culture data in the literature.     

A systematic approach for the efficient estimation of interaction parameters in equations of state using binary vle data

A systematic and computationally efficient approach for the estimation of the interaction parameters in equations of state using binary vapor liquid equilibrium (VLE) data is presented. Initially, the best set of interaction parameters is estimated by implicit least squares. Subsequently, the VLE calculations are performed using these parameters. Based on the quality of the fit it is decided whether these estimates suffice or implicit maximum likelihood estimation should be performed to obtain the statistically best values. Estimation subject to the liquid phase stability constraint is performed when erroneous phase behavior is computed. The approach is illustrated with typical examples.     

Data‐Driven Shape Analysis and Processing

Data‐driven methods serve an increasingly important role in discovering geometric, structural and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data‐driven methods aggregate information from 3D model collections to improve the analysis, modelling and editing of shapes. Data‐driven methods are also able to learn computational models that reason about properties and relationships of shapes without relying on hard‐coded rules or explicitly programmed instructions. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modelling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data‐driven shape analysis and processing.