This is the first episode of Art in Permacrisis, a podcast on the organization of art workers in the face of the ever-growing stack of crises. How can artists make a living without selling their souls? Can we imagine and practice a sustainable art economy beyond precarity? How should we transform the circulation of artworks, the curriculum of art and design academies, the exhibition programs of museums, and the organization of collectives and unions? We invite speakers with combined backgrounds in art, theory, and organizing to share their insights.For this episode, we are welcoming Kuba Szreder. Kuba is a lecturer in art theory at the Academy of Fine Arts in Warsaw and a freelance curator. He co-founded the Free/Slow University of Warsaw and the Office for Postartistic Practices. The main topic of our conversation is Kuba’s book ABC of the Projectariat: Living and Working in a Precarious Art World.
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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 consistent demand for improving products working in a real-time environment is increasing, given the rise in system complexity and urge to constantly optimize the system. One such problem faced by the component supplier is to ensure their product viability under various conditions. Suppliers are at times dependent on the client’s hardware to perform full system level testing and verify own product behaviour under real circumstances. This slows down the development cycle due to dependency on client’s hardware, complexity and safety risks involved with real hardware. Moreover, in the expanding market serving multiple clients with different requirements can be challenging. This is also one of the challenges faced by HyMove, who are the manufacturer of Hydrogen fuel cells module (https://www.hymove.nl/). To match this expectation, it starts with understanding the component behaviour. Hardware in the loop (HIL) is a technique used in development and testing of the real-time systems across various engineering domain. It is a virtual simulation testing method, where a virtual simulation environment, that mimics real-world scenarios, around the physical hardware component is created, allowing for a detailed evaluation of the system’s behaviour. These methods play a vital role in assessing the functionality, robustness and reliability of systems before their deployment. Testing in a controlled environment helps understand system’s behaviour, identify potential issues, reduce risk, refine controls and accelerate the development cycle. The goal is to incorporate the fuel cell system in HIL environment to understand it’s potential in various real-time scenarios for hybrid drivelines and suggest secondary power source sizing, to consolidate appropriate hybridization ratio, along with optimizing the driveline controls. As this is a concept with wider application, this proposal is seen as the starting point for more follow-up research. To this end, a student project is already carried out on steering column as HIL
My research investigates the concept of permacomputing, a blend of the words permaculture and computing, as a potential field of convergence of technology, arts, environmental research and activism, and as a subject of future school curricula in art and design. This concept originated in online subcultures, and is currently restricted to creative coding communities. I study in what way permacomputing principles may be used to redefine how art and design education is taught. More generally, I want to research the potential of permacomputing as a critical, sustainable, and practical alternative to the way digital technology is being taught in art education, where students mostly rely on tools and techniques geared towards maximising productivity and mass consumption. This situation is at odds with goals for sustainable production and consumption. I want to research to what degree the concept of permacomputing can be broadened and applied to critically revised, sustainable ways of making computing part of art and design education and professional practice. This research will be embedded in the design curriculum of Willem de Kooning Academy, focused on redefining the role of artists and designers to contribute to future modes of sustainable organisation and production. It is aligned with Rotterdam University of Applied Sciences sectorplan masters VH, in particular managing and directing sustainable transitions. This research builds upon twenty years of experience in the creative industries. It is an attempt to generalise, consolidate, and structure methods and practices for sustainable art and design production experimented with while I was course director of a master programme at WdKA. Throughout the research I will be exchanging with peers and confirmed interested parties, a.o.: Het Nieuwe Instituut (NL), RUAS Creating 010 kenniscentrum (NL), Bergen Centre for Electronic Arts (NO), Mikrolabs (NO), Varia (NL), Media Arts department at RHU (UK), Media Studies at UvA (NL).
