Protein kinases function as pivotal regulators in biological events, governing essential cellular processes through the transfer of phosphate groups from ATP molecules to substrates. Dysregulation of kinase activity is frequently associated with cancer, ocasionally arising from chromosomal translocation events that relocate genes encoding kinases. Fusion proteins resulting from such events, particularly those involving the proto-oncogene tyrosine-protein kinase ROS (ROS1), manifest as constitutively active kinases, emphasizing their role in oncogenesis. Notably, the chromosomal reallocation of the ros1 gene leads to fusion of proteins with the ROS1 kinase domain, implicated in various cancer types. Despite their prevalence, targeted inhibition of these fusion proteins relies on repurposed kinase inhibitors. This review comprehensively surveys experimentally determined ROS1 structures, emphasizing the pivotal role of X-ray crystallography in providing high-quality insights. We delve into the intricate interactions between ROS1 and kinase inhibitors, shedding light on the structural basis for inhibition. Additionally, we explore point mutations identified in patients, employing molecular modeling to elucidate their structural impact on the ROS1 kinase domain. By integrating structural insights with in vitro and in silico data, this review advances our understanding of ROS1 kinase in cancer, offering potential avenues for targeted therapeutic strategies.
Organizations feel an urgency to develop and implement applications based on foundation models: AI-models that have been trained on large-scale general data and can be finetuned to domain-specific tasks. In this process organizations face many questions, regarding model training and deployment, but also concerning added business value, implementation risks and governance. They express a need for guidance to answer these questions in a suitable and responsible way. We intend to offer such guidance by the question matrix presented in this paper. The question matrix is adjusted from the model card, to match well with development of AIapplications rather than AI-models. First pilots with the question matrix revealed that it elicited discussions among developers and helped developers explicate their choices and intentions during development.
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Education for sustainability scholarship argues that sustainability competence is more than cognitive domain learning that is traditionally (over) focused on reason, knowledge application and testing. Affective domain is missing from the education curricula in general (Sowel, 2005, Dernikos et al, 2020), and in Higher Education in Sustainability (HES) (Shepard, 2008). Yet, “it is possible to construct an argument that the essence of education for sustainability is a quest for affective outcomes” (Shepard, 2008). For example, there is a link between personal values and sustainability performance (Potocan 2021), and emotional intelligence has been seen to be “the foundation of a more cooperative and compassionate [sustainable] society” (Estrada, Rodriguez, Moliner, 2021).
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Students in Higher Music Education (HME) are not facilitated to develop both their artistic and academic musical competences. Conservatoires (professional education, or ‘HBO’) traditionally foster the development of musical craftsmanship, while university musicology departments (academic education, or ‘WO’) promote broader perspectives on music’s place in society. All the while, music professionals are increasingly required to combine musical and scholarly knowledge. Indeed, musicianship is more than performance, and musicology more than reflection—a robust musical practice requires people who are versed in both domains. It’s time our education mirrors this blended profession. This proposal entails collaborative projects between a conservatory and a university in two cities where musical performance and musicology equally thrive: Amsterdam (Conservatory and University of Amsterdam) and Utrecht (HKU Utrechts Conservatorium and Utrecht University). Each project will pilot a joint program of study, combining existing modules with newly developed ones. The feasibility of joint degrees will be explored: a combined bachelor’s degree in Amsterdam; and a combined master’s degree in Utrecht. The full innovation process will be translated to a transferable infrastructural model. For 125 students it will fuse praxis-based musical knowledge and skills, practice-led research and academic training. Beyond this, the partners will also use the Comenius funds as a springboard for collaboration between the two cities to enrich their respective BA and MA programs. In the end, the programme will diversify the educational possibilities for students of music in the Netherlands, and thereby increase their professional opportunities in today’s job market.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
In de automotive sector vindt veel onderzoek en ontwikkeling plaats op het gebied van autonome voertuigtechnologie. Dit resulteert in rijke open source software oplossingen voor besturing van robotvoertuigen. HAN heeft met haar Streetdrone voertuig reeds goede praktijkervaring met dergelijke software. Deze oplossingen richten zich op een Operational Design Domain dat uitgaat van de publieke verkeersinfrastructuur met daarbij de weggebruikers rondom het robotvoertuig. In de sectoren agrifood en smart industry is een groeiende behoefte aan automatisering van mobiele machinerie, versterkt door de actuele coronacrisis. Veel functionaliteit van bovengenoemde automotive software is inzetbaar voor mobiele robotica in deze sectoren. De toepassingen zijn enerzijds minder veeleisend - denk aan de meer gestructureerde omgeving, lagere snelheden en minder of geen ‘overige weggebruikers’ – en anderzijds heel specifiek als het gaat over routeplanning en (indoor) lokalisatie. Vanwege dit specifiek karakter is de bestaande software niet direct inzetbaar in deze sectoren. Het MKB in deze sectoren ervaart daarom een grote uitdaging om dergelijke complexe autonome functionaliteit beschikbaar te maken, zonder dat men kan voorbouwen een open, sectorspecifieke softwareoplossing. In Automotion willen de aangesloten partners vanuit bestaande kennis en ervaring tot een eerste integratie en demonstratie komen van een beschikbare automotive open source softwarebibliotheek, aangepast en specifiek ingezet op rijdende robots voor agrifood en smart industry, met focus ‘pickup and delivery’ scenario’s. Hierbij worden de aanpassingen - nieuwe en herschreven ‘boeken’ in de ‘bibliotheek’ - weer in open source gepubliceerd ter versterking van het MKB en het onderwijs. Parallel hieraan willen de partners ontdekken welke praktijkvragen uit dit proces voortvloeien en welke onderliggende kennislacunes in de toekomst moeten worden ingevuld. Via open workshops met uitnodigingen in diverse netwerken worden vele partijen uitgenodigd om gezamenlijk aan de hand van de opgedane ervaringen van gedachten te wisselen over actuele kennisvragen en mogelijke gezamenlijke toekomstige beantwoording daarvan.
