Sustainable engineering master module - Insights from three cohorts of European engineering team

Mobility and transnational migration are current social developments among the population of the European Union. These developments in both society-at-large and companies, linked to the challenges of sustainability, lead to new requirements for working in the European Union. Teaching and learning in higher education needs to adapt to these requirements. As a result, new and innovative teaching and learning practices in higher education should provide competencies for transnational teamwork in the curriculum of tomorrow's engineers in order to ensure their competitiveness in the job market. A transnational project-oriented teaching and learning framework, which provides the future key competencies for young engineers was implemented in the course European Engineering Team (EET). Engineering students from four countries participated in a new project-based course that focused on the development of innovative and sustainable products and opportunities. The goal of this paper is to present results and lessons learnt from three cohorts of EET

Integration of Active Pharmaceutical Ingredient production into a pharmaceutical Lean Learning Factory

In the context of the implementation of Lean Production Systems, companies have become significantly more aware of the need for employee qualification and motivation. Due to the high share of practice, Learning Factories have proven to be an effective approach to respond to this circumstance. While the focus of Learning Factories has so far mainly been on discrete manufacturing, applications in the pharmaceutical industry are still comparatively rare. Based on this, a Learning Factory, that takes into account the special requirements and needs of the pharmaceutical industry, was developed and implemented in collaboration with a German pharmaceutical company. So far, Lean culture and tools have been trained on formulation and packaging processes. However, the active pharmaceutical ingredient (API) production, which is characterized by a higher level of automation as well as chemical and process engineering procedures, was initially not displayed, as no sufficient demand was assumed. Due to the increasing need for a holistic consideration of the whole value chain, the API production is moving into the focus of Lean improvements. In this context, many established tools need to be adapted. From feedback of over 120 conducted trainings and a series of interviews, it became clear, that for employees from API production, it is difficult to transfer the necessary knowledge to their work environment, leading to resistance in implementing Lean tools. In order to counter this problem, the Learning Factory was expanded by an additional API learning module. This increased the willingness of employees to participate in the trainings and to implement the contents

Development of high-throughput tools to unravel the complexity of gene expression patterns in the mammalian brain.

Genomes of animals contain between 15 000 (e.g. Drosophila) and 50 000 (human, mouse) genes, many of which encode proteins involved in regulatory processes. The availability of sequence data for many of these genes opens up opportunities to study complex genetic and protein interactions that underlie biological regulation. Many examples demonstrate that an understanding of regulatory networks consisting of multiple components is significantly advanced by a detailed knowledge of the spatiotemporal expression pattern of each of the components. Gene expression patterns can readily be determined by RNA in situ hybridization. The unique challenge emerging from the knowledge of the sequence of entire genomes is that assignment of biological functions to genes needs to be carried out on an appropriately large scale. In terms of gene expression analysis by RNA insitu hybridization, efficient technologies need to be developed that permit determination and representation of expression patterns of thousands of genes within an acceptable time-scale. We set out to determine the spatial expression pattern of several thousand genes encoding putative regulatory proteins. To achieve this goal we have developed high-throughput technologies that allow the determination and visualization of gene expression patterns by RNA in situ hybridization on tissue sections at cellular resolution. In particular, we have invented instrumentation for robotic in situ hybridization capable of carrying out in a fully automated fashion, all steps required for detecting sites of gene expression in tissue sections. In addition, we have put together hardware and software for automated microscopic scanning of gene expression data that are produced by RNA in situ hybridization. The potential and limitations of these techniques ind out efforts to build a Web-based database of gene expression patterns are discussed