Eva Klein (1925–2025)
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Ultra-sensitive fluorescence-activated droplet single-cell sorting based on Tetramer-HCR-EvaGreen amplification
The current single-cell analysis technologies such as fluorescence-activated cell sorting (FACS) and fluorescence-activated droplet sorting (FADS) could decipher the cellular heterogeneity but were constrained by low sorting performance and cell viability. Here, an ultra-sensitive single-cell sorting platform has been developed by integrating the FADS technology with Tetramer-HCR-EvaGreen (THE) fluorescence signal amplification. The THE system produced much higher fluorescence signal than that of the single Tetramer or Tetramer-HCR signal amplification. Upon application to target MCF-7 cells, the platform exhibited high efficacy and selectivity while maintaining more than 95% cell viability. The THE-FADS achieved sorting efficiencies of 55.5% and 50.3% with purities of 91% and 85% for MCF-7 cells in PBS solutions and simulated serum samples, respectively. The sorted MCF-7 cells showed similar proliferation together with CK19 and EGFR mRNA expression compared with the control cells. The established THE-FADS showed the promising prospects to cellular heterogeneity understanding and personalized medicine.
Building collaborative infrastructures for an interdisciplinary higher education master’s program
This paper examines the practices and importance of building a collaborative infrastructure in interdisciplinary education, using the context of the master’s program developed by the Interdisciplinary Consortium for Applied Research in Ecology and Evolution (ICARE) as a case study. The study focuses on two levels of collaborative infrastructure: The project organization and project practice of the ICARE program and the specific use of CoNavigator, a physical tool for interdisciplinary teaching, learning, and collaboration. The analysis explores the educational aspects of the ICARE program and investigates how the training teams (each consisting of a master’s student, supervisors, and mentors) within the project organized themselves and developed their collaboration methods. By examining the challenges faced by ICARE and the implications for its Trainees and stakeholders, this paper emphasizes the significance of prioritizing and developing robust and explicit collaborative infrastructures both at the program and institutional level, as the challenges identified in ICARE mirror those at higher institutional levels, where interdisciplinary activities are not sustained unless they are fully embedded in the visible and physical structures. The findings provide valuable insights for future interdisciplinary study programs and underscore the necessity of proactive infrastructure planning and implementation to support successful interdisciplinary teaching and learning practices.
Feasibility of meeting future battery demand via domestic cell production in Europe
Batteries are critical to mitigate global warming, with battery electric vehicles as the backbone of low-carbon transport and the main driver of advances and demand for battery technology. However, the future demand and production of batteries remain uncertain, while the ambition to strengthen national capabilities and self-sufficiency is gaining momentum. In this study, leveraging probabilistic modelling, we assessed Europe’s capability to meet its future demand for high-energy batteries via domestic cell production. We found that demand in Europe is likely to exceed 1.0 TWh yr−1 by 2030 and thereby outpace domestic production, with production required to grow at highly ambitious growth rates of 31–68% yr−1. European production is very likely to cover at least 50–60% of the domestic demand by 2030, while 90% self-sufficiency seems feasible but far from certain. Thus, domestic production shortfalls are more likely than not. To support Europe’s battery prospects, stakeholders must accelerate the materialization of production capacities and reckon with demand growth post-2030, with reliable industrial policies supporting Europe’s competitiveness.
Not all who integrate are academics: zooming in on extra-academic integrative expertise
Solving complex problems requires integrating knowledge and skills from various domains. The importance of cross-domain integration has motivated researchers to study integrative expertise: what knowledge and skills help achieve cross-domain integration? Much of the existing research focuses on the integrative expertise of academic researchers who perform inter- and transdisciplinary research. However, academics are not the only ones facilitating integration. In transdisciplinary research, where academics collaborate with professionals, stakeholders, and policymakers, these extra-academic actors can contribute significantly to cross-domain integration. Moreover, many complex problems are addressed entirely outside of universities. This paper contributes to a broader, more inclusive understanding of integrative expertise by drawing attention to the diversity of extra-academic integrative expertise, providing examples of what this expertise looks like in practice, and reflecting on differences with its academic counterpart. The contributions are based on a case study of integrative expertise in Oosterweel Link, a large urban development project in Antwerp, Belgium.
Evolution and impact of the science of science: from theoretical analysis to digital-AI driven research
The Science of Science (SoS) examines the mechanisms driving the development and societal role of science, evolving from its sociological roots into a data-driven discipline. This paper traces the progression of SoS from its early focus on the social functions of science to the current era, characterized by large-scale quantitative analysis and AI-driven methodologies. Scientometrics, a key branch of SoS, has utilized statistical methods and citation analysis to understand scientific growth and knowledge diffusion. With the rise of big data and complex network theory, SoS has transitioned toward more refined analyses, leveraging artificial intelligence (AI) for predictive modeling, sentiment annotation, and entity extraction. This paper explores the application of AI in SoS, highlighting its role as a surrogate, quant, and arbiter in advancing data processing, data analysis and peer review. The integration of AI has ushered in a new paradigm for SoS, enhancing its predictive accuracy and providing deeper insights into the internal dynamics of science and its impact on society.
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