Notes

Synchronization of repeated neurological networks using unbounded delays along with time-varying coefficients through many times differential inequalities.
Preterm birth is a global health concern and continues to contribute to substantial neonatal morbidity and mortality despite advances in obstetric and neonatal care. The underlying aetiology is multi-factorial and remains incompletely understood. In this review, the complex interplay between the vaginal microbiome in pregnancy and its association with preterm birth is discussed in depth. Advances in the study of bacteriology and an improved understanding of the human microbiome have seen an improved awareness of the vaginal microbiota in both health and in disease.In recent years, 3D printing has had a huge impact on the field of biotechnology from 3D-printed pharmaceuticals to tissue engineering and microfluidic chips. Microfluidic chips are of particular interest and importance for the field of biotechnology, since they allow for the analysis and screening of a wide range of biomolecules - including single cells, proteins, and DNA. The fabrication of microfluidic chips has historically been time-consuming, however, and is typically limited to 2.5 dimensional structures and a restricted palette of well-known materials. Due to the high surface-to-volume ratios in microfluidic chips, the nature of the chip material is of paramount importance to the final system behavior. With the emergence of 3D printing, however, a wide range of microfluidic systems are now being printed for the first time in a manner that facilitates flexibility while minimizing time and cost. Nevertheless, resolution and material choices still remain challenges and in the focus of current research, aiming for (1) 3D printing with high resolutions in the range of tens of micrometers and (2) a wider range of available materials for these high-resolution prints. The first part of this chapter highlights recent emerging technologies in the field of high-resolution printing via stereolithography (SL) and 2-photon polymerization (2PP) and seeks to identify particularly interesting emerging technologies which could have a major impact on the field in the near future. The second part of this chapter highlights current developments in the field of materials that are used for these high-resolution 3D printing technologies.Digital twins (DTs) are expected to render process development and life-cycle management much more cost-effective and time-efficient. A DT definition, a brief retrospect on their history and expectations for their deployment in today's business environment, and a detailed financial assessment of their attractive economic benefits are provided in this chapter. The argument that restrictive guidelines set forth by regulatory agencies would hinder the adoption of DTs in the (bio)pharmaceutical industry is revisited, concluding that those companies who collaborate with the agencies to further their technical capabilities will gain significant competitive advantage. The analyzed process development examples show high methodological readiness levels but low systematic adoption of technology. Given the technical feasibilities, financial opportunities, and regulatory encouragement, concerns regarding intellectual property and data sharing, though required to be taken into account, will at best delay an industry-wide adoption of DTs. In conclusion, it is expected that a strategic investment in DTs now will gain an advantage over competition that will be difficult to overcome by late adopters.Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development and process steps. Ipilimumab purchase One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities in cell behavior, and influences of changes in cultivation conditions necessitate adequate digital solutions to provide information about the current and near future process state to derive correct process decisions.For this purpose digital seed train twins have proved to be efficient, which digitally display the time-dependent behavior of important process variables based on mathematical models, strategies, and adaption procedures.This chapter will outline the needs for digitalization of seed trains, the construction of a digital seed train twin, the role of parameter estimation, and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed train for seed train digitalization. This chapter outlines the needs for digitalization of cell proliferation processes (seed trains), the construction of a digital seed train twin as well as the role of parameter estimation and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed, as an example for seed train digitalization. It has been shown in which way prior knowledge and input uncertainty can be considered and be propagated to predictive uncertainty.Rising demands for biopharmaceuticals and the need to reduce manufacturing costs increase the pressure to develop productive and efficient bioprocesses. Among others, a major hurdle during process development and optimization studies is the huge experimental effort in conventional design of experiments (DoE) methods. As being an explorative approach, DoE requires extensive expert knowledge about the investigated factors and their boundary values and often leads to multiple rounds of time-consuming and costly experiments. The combination of DoE with a virtual representation of the bioprocess, called digital twin, in model-assisted DoE (mDoE) can be used as an alternative to decrease the number of experiments significantly. mDoE enables a knowledge-driven bioprocess development including the definition of a mathematical process model in the early development stages. In this chapter, digital twins and their role in mDoE are discussed. First, statistical DoE methods are introduced as the basis of mDoE. Second, the combination of a mathematical process model and DoE into mDoE is examined.
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