This conference paper deals with various organizations and pilot initiatives regarding the theme of sustainability.
The paper explores the process of early growth of entrepreneurial science-based firms. Drawing on case studies of British and Dutch biopharmaceutical R&D firms, we conceptualize the speed of early growth of science-based firms as the time it takes for the assembly (or combined development) of three types of critical resources - a functionally-diverse management team, early fundraising and development of technology. The development of these resources is an unfolding and interrelated process, the causal direction of which is highly ambiguous. We show the variety of paths used by science-based firms to access and develop these critical resources. The picture that emerges is that the various combinations of what we call "assisted" and "unassisted" paths combine to influence the speed of firm growth. We show how a wide range of manifestations of technology development act as signaling devices to attract funding and management, affecting the speed of firm development. We also show how the variety of paths and the speed of development are influenced by the national institutional setting.
Background: Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results: We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion: MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.
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