Predicting industrial-scale cell culture seed trains-A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method.

For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model-assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high-quality data is available, and description of inference uncertainty, providing, apart from a "best fit"-prediction, information about the probable deviation in form of a prediction interval. This contribution illustrates the application of Bayesian parameter estimation and Bayesian updating for seed train prediction to an industrial Chinese hamster ovarian cell culture process, coppled with a mechanistic model. It is shown in which way prior knowledge as well as input uncertainty (e.g., concerning measurements) can be included and be propagated to predictive uncertainty. The impact of available information on prediction accuracy was investigated. It has been shown that through integration of new data by the Bayesian updating method, process variability (i.e., batch-to-batch) could be considered. The implementation was realized using a Markov chain Monte Carlo method.

Spectral complexity reduction of music signals based on frequency-domain reduced-rank approximations: An evaluation with cochlear implant listeners.

Methods for spectral complexity reduction of music signals were evaluated in a listening test with cochlear implant (CI) listeners. To this end, reduced-rank approximations were computed in the constant-Q spectral domain using blind and score-informed dimensionality reduction techniques, which were compared to a procedure using a supervised source separation and remixing scheme. Previous works have shown that timbre and pitch cues are transmitted inaccurately through CIs and thus cause perceptual distortions in CI listeners. Hence, the scope of this evaluation was narrowed down to classical chamber music, which is mainly characterized by timbre and pitch and less by rhythmic cues. Suitable music pieces were selected in accordance to a statistical experimental design, which took musically relevant influential factors into account. In a blind two-alternative forced choice task, 14 CI listeners were asked to indicate a preference either for the original signals or a specific processed variant. The results exhibit a statistically significant preference rate of up to 74% for the reduced-rank approximations, whereas the source separation and remixing scheme did not provide any improvement.