No abstract

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical "quality by testing" to rational, knowledge-based "quality by design" approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real-time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed-batch production processes with varying cultivation conditions employing a comprehensive on- and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two-dimensional multi-wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.

BACKGROUND: Climate warming is considered to affect the characteristics of heat waves by increasing their duration, frequency and intensity, which can have dramatic consequences for ectothermic arthropods. However, arthropods may respond to heat waves via plastic modifications, which could differently affect a predator and its prey. We examined this assumption using prominent counterparts in biological control, the predatory mite Phytoseiulus persimilis and its prey, the spider mite Tetranychus urticae. Individuals of both species were separately exposed to mild and extreme heat waves during their juvenile development.RESULTS: Both species developed faster during extreme heat waves, but the proportional increase of the developmental rates was higher in the prey. Independent of sex, P. persimilis reached smaller size at maturity under extreme heat waves, whereas the body size modifications were sex-dependent in T. urticae: males became smaller, but females were able to maintain their size.CONCLUSIONS: An accelerated development may result in the reduction of the exposure time of susceptible juvenile stages to heat waves and prey stages to predators. Plastic size adjustments caused a shift in the female predator-prey body size ratio in favor of the prey, which may lead to higher heat resistance and reduced predation risk for prey females under extreme heat waves. In conclusion, our findings indicate that species-specific shifts in age and size at maturity may result in lower suppression efficacy of the predator P. persimilis against its prey T. urticae with severe consequences for biological control of spider mites, if global warming continues.

A common situation in filtering where classical Kalman filtering does not perform particularly well is tracking in the presence of propagating outliers. This calls for robustness understood in a distributional sense, i.e.; we enlarge the distribution assumptions made in the ideal model by suitable neighborhoods. Based on optimality results for distributional-robust Kalman filtering from Ruckdeschel (Ansätze zur Robustifizierung des Kalman-Filters, vol 64, 2001; Optimally (distributional-)robust Kalman filtering, arXiv: 1004.3393, 2010a), we propose new robust recursive filters and smoothers designed for this purpose as well as specialized versions for non-propagating outliers. We apply these procedures in the context of a GPS problem arising in the car industry. To better understand these filters, we study their behavior at stylized outlier patterns (for which they are not designed) and compare them to other approaches for the tracking problem. Finally, in a simulation study we discuss efficiency of our procedures in comparison to competitors