Explanation of the Master Class programme. You can learn about: the objective, the target groups, the nature of the programme, the topics, the didactics and the duration.Proposal to the Opening Up partners to offer a Master Class in relation to the cluster Development.
Groen zorgt voor een gezonde, leefbare, rendabele, schone en mooie stad, maar is ook economisch interessant voor de stad als vestigingsklimaat van bedrijven. In het Europese project Value (Valuing Attractive Landscapes in the Urban Economy) hebben tien partners van 2008 tot 2012 samengewerkt om bewijs te verzamelen van de economische meerwaarde die groen heeft voor steden. VHL op zijn beurt, onderzocht planningsmethoden over economische meerwaarde van het stadsgroen. In samenwerking met de gemeente Amersfoort is dit via experimenten in praktijk gebracht.
MULTIFILE
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
The SPRONG group, originating from the CoE KennisDC Logistiek, focuses on 'Low Impact in Lastmile Logistics' (LILS). The LILS group conducts practical research with local living labs and learning communities. There is potential for more collaboration and synergy for nationwide scaling of innovations, which is currently underutilized. LILS aims to make urban logistics more sustainable and facilitate necessary societal transitions. This involves expanding the monodisciplinary and regional scope of CoE KennisDC Logistiek to a multidisciplinary and supra-regional approach, incorporating expertise in spatial planning, mobility, data, circularity, AI, behavior, and energy. The research themes are:- Solutions in scarce space aiming for zero impact;- Influencing behavior of purchasers, recipients, and consumers;- Opportunities through digitalization.LILS seeks to increase its impact through research and education beyond its regions. Collaboration between BUas, HAN, HR, and HvA creates more critical mass. LILS activities are structured around four pillars:- Developing a joint research and innovation program in a roadmap;- Further integrating various knowledge domains on the research themes;- Deepening methodological approaches, enhancing collaboration between universities and partners in projects, and innovating education (LILS knowledge hub);- Establishing an organizational excellence program to improve research professionalism and quality.These pillars form the basis for initiating and executing challenging, externally funded multidisciplinary research projects. LILS is well-positioned in regions where innovations are implemented and has a strong national and international network and proven research experience.Societal issue:Last-mile logistics is crucial due to its visibility, small deliveries, high costs, and significant impact on emissions, traffic safety, and labor hours. Lastmile activities are predicted to grow a 20% growth in the next decade. Key drivers for change include climate agreements and energy transitions, urban planning focusing on livability, and evolving retail landscapes and consumer behavior. Solutions involve integrating logistics with spatial planning, influencing purchasing behavior, and leveraging digitalization for better data integration and communication. Digital twins and the Physical Internet concept can enhance efficiency through open systems, data sharing, asset sharing, standardization, collaboration protocols, and modular load units.Key partners: Buas, HR, HAN, HvAPartners: TNO, TU Delft, Gemeente Rotterdam, Hoger Onderwijs Drechtsteden, Significance, Metropolitan Hub System, evofenedex, Provincie Gelderland, Duurzaam Bereikbaar Heijendaal, Gemeente Alphen aan den Rijn, Radboud Universiteit, I&W - DMI, DHL, TLN, Noorderpoort, Fabrications, VUB, Smartwayz, RUG, Groene Metropoolregio.
