Recent years have shown the emergence of numerous local energy initiatives (prosumer communities) in the Netherlands. Many of them have set the goal to establish a local and sustainable energy provision on a not-for-profit basis. In this study we carried out exploratory case studies on a number of Dutch prosumer communities. The objective is to analyse their development process, to examine the barriers they encounter while organising their initiative, and to find how ICT could be applied to counteract these barriers and support communities in reaching their goals. The study shows that prosumer communities develop along a stepwise, evolutionary growth path, while they are struggling with organising their initiative, because the right expertise is lacking on various issues (such as energy technology, finance and legislation). Participants stated that, depending on the development phase of their initiative, there is a strong need for information and specific expertise. With a foreseeable growing technical complexity they indicated that they wanted to be relieved with the right tools and services at the right moment. Based on these findings we developed a generic solution through the concept of a prosumer community shopping mall. The concept provides an integrated and scalable ICT environment, offering a wide spectrum of energy services that supports prosumer communities in every phase of their evolutionary growth path. As such the mall operates as a broker and clearing house between 2 prosumer communities and service providers, where the service offerings grow and fit with the needs and demands of the communities along their growth path. The shopping mall operates for many prosumer communities, thus providing economies of scale. Each prosumer community is presented its own virtual mall, with specific content and a personalised look-and-feel.
In this article we look at the various paths taken by transnational and domestic entrepreneurs based on their education and work experience. These act as catalysts for skills that allow migrant entrepreneurs to better position themselves in different markets. Differences in migrant entrepreneurs allow us to better understand the strategies employed and the consequences for society and the economy at both domestic and transnational levels. Earlier research has extensively analysed individual characteristics of migrant entrepreneurs and, to a much lesser extent, the geographical nature of their business activities.This article addresses this gap by looking at the geographical orientation of migrant entrepreneurs’ businesses. The research question is as follows: In what ways are transnational or domestic activities of Moroccan migrant entrepreneurs in the Netherlands and Italy influenced by skills acquired in earlier experiences? We provide empirical evidence on the different paths leading to domestic and transnational activities using a micro-level perspective of the experiences collected in the narratives of first-generation Moroccan migrant entrepreneurs who have migrated to Milan or Amsterdam (N=70).Four different paths combining these two life experiences emerged from the interviews: #1 Job-based, #2 Education-driven, #3 Job-education merger, and #4 By chance (neither education nor work experience). The most relevant paths for migrant entrepreneurs seem to be the first (#1) and third (#3) paths. Furthermore, our findings show that transnationally oriented entrepreneurs have an extended business-oriented education and rely on skills learned, in contrast to domestically oriented entrepreneurs who become entrepreneurs ‘by chance’.
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).
