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CureQ Predict, Delay & Cure polyglutamine(Q) caused neurodegeneration

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).

Detecting volatiles that pose a chronic health risk using a highly deployable and novel biosensor based around structural colour

Structural colour (SC) is created by light interacting with regular nanostructures in angle-dependent ways resulting in vivid hues. This form of intense colouration offers commercial and industrial benefits over dyes and other pigments. Advantages include durability, efficient use of light, anti-fade properties and the potential to be created from low cost materials (e.g. cellulose fibres). SC is widely found in nature, examples include butterflies, squid, beetles, plants and even bacteria. Flavobacterium IR1 is a Gram-negative, gliding bacterium isolated from Rotterdam harbour. IR1 is able to rapidly self-assemble into a 2D photonic crystal (a form of SC) on hydrated surfaces. Colonies of IR1 are able to display intense, angle-dependent colours when illuminated with white light. The process of assembly from a disordered structure to intense hues, that reflect the ordering of the cells, is possible within 10-20 minutes. This bacterium can be stored long-term by freeze drying and then rapidly activated by hydration. We see these properties as suiting a cellular reporter system quite distinct from those on the market, SC is intended to be “the new Green Fluorescent Protein”. The ability to understand the genomics and genetics of SC is the unique selling point to be exploited in product development. We propose exploiting SC in IR1 to create microbial biosensors to detect, in the first instance, volatile compounds that are damaging to health and the environment over the long term. Examples include petroleum or plastic derivatives that cause cancer, birth defects and allergies, indicate explosives or other insidious hazards. Hoekmine, working with staff and students within the Hogeschool Utrecht and iLab, has developed the tools to do these tasks. We intend to create a freeze-dried disposable product (disposables) that, when rehydrated, allow IR1 strains to sense and report multiple hazardous vapours alerting industries and individuals to threats. The data, visible as brightly coloured patches of bacteria, will be captured and quantified by mobile phone creating a system that can be used in any location by any user without prior training. Access to advice, assay results and other information will be via a custom designed APP. This work will be performed in parallel with the creation of a business plan and market/IP investigation to prepare the ground for seed investment. The vision is to make a widely usable series of tests to allow robust environmental monitoring for all to improve the quality of life. In the future, this technology will be applied to other areas of diagnostics.

European Bachelor in Physical Activity and Lifestyle Counselling (co-financed by the European Union Project Number: 510029-LLP-1-NL-ERASMUS-ECDSP Programme: Lifelong learning programme Sub-programme: Erasmus)

Human Centered Smart Factories: design for wellbeing for future manufacturing

Manual labour is an important cornerstone in manufacturing and considering human factors and ergonomics is a crucial field of action from both social and economic perspective. Diverse approaches are available in research and practice, ranging from guidelines, ergonomic assessment sheets over to digitally supported workplace design or hardware oriented support technologies like exoskeletons. However, in the end those technologies, methods and tools put the working task in focus and just aim to make manufacturing “less bad” with reducing ergonomic loads as much as possible. The proposed project “Human Centered Smart Factories: design for wellbeing for future manufacturing” wants to overcome this conventional paradigm and considers a more proactive and future oriented perspective. The underlying vision of the project is a workplace design for wellbeing that makes labor intensive manufacturing not just less bad but aims to provide positive contributions to physiological and mental health of workers. This shall be achieved through a human centered technology approach and utilizing advanced opportunities of smart industry technologies and methods within a cyber physical system setup. Finally, the goal is to develop smart, shape-changing workstations that self-adapt to the unique and personal, physical and cognitive needs of a worker. The workstations are responsive, they interact in real time, and promote dynamic activities and varying physical exertion through understanding the context of work. Consequently, the project follows a clear interdisciplinary approach and brings together disciplines like production engineering, human interaction design, creative design techniques and social impact assessment. Developments take place in an industrial scale test bed at the University of Twente but also within an industrial manufacturing factory. Through the human centered design of adaptive workplaces, the project contributes to a more inclusive and healthier society. This has also positive effects from both national (e.g. relieve of health system) as well as individual company perspective (e.g. less costs due to worker illness, higher motivation and productivity). Even more, the proposal offers new business opportunities through selling products and/or services related to the developed approach. To tap those potentials, an appropriate utilization of the results is a key concern . The involved manufacturing company van Raam will be the prototypical implementation partner and serve as critical proof of concept partner. Given their openness, connections and broad range of processes they are also an ideal role model for further manufacturing companies. ErgoS and Ergo Design are involved as methodological/technological partners that deal with industrial engineering and ergonomic design of workplace on a daily base. Thus, they are crucial to critically reflect wider applicability and innovativeness of the developed solutions. Both companies also serve as multiplicator while utilizing promising technologies and methods in their work. Universities and universities of applied sciences utilize results through scientific publications and as base for further research. They also ensure the transfer to education as an important leverage to inspire and train future engineers towards wellbeing design of workplaces.

Smart Vision for UAVs

Binnen het RAAK-project Smart Vision for UAVs is kennis ontwikkeld om UAVs autonoom te laten vliegen. Deze kennis is verankerd in de Twirre-architectuur voor UAVs. Vanaf het begin is afgesproken dat alle ontwikkelingen (zoals de architectuur, software en beschrijving van hardware) van Twirre openbaar zijn. De Twirre-architectuur is getest met meerdere prototypes. Met de Top-up wordt het mogelijk op korte termijn de Twirre-architectuur te documenteren en open source te maken. Daardoor wordt de verworven kennis openbaar toegankelijk en de verdere ontwikkeling daarvan versneld. Een unique selling point van Twirre is dat deze architectuur niet ontworpen is voor een specifieke UAV of voor UAVs van een specifieke leverancier. Twirre simuleert in feite de stick-commando’s van de grondpiloot en communiceert met de rest van de hardware van de UAV als of het een ontvanger is die radiografische de stick-commando’s ontvangt. Omdat de communicatie van de ontvangers met de rest van de UAV-hardware is gestandaardiseerd, is Twirre UAV-leverancier agnostisch. De Twirre- architectuur is goed schaalbaar van kleine tot grote UAVs. Het gebruik van low-cost componenten maakt Twirre niet alleen geschikt voor het bedrijfsleven, maar ook voor onderwijs- en hobbyprojecten. Twirre voegt aan een commodity UAV een Local Position System (LPS) toe met een breed scala aan sensoren, zoals (stereo) camera’s, ultrasoon sonars, LIDAR’s, gyroscopen, acceleratiemeters, magnetisch kompas en RTK GPS. Alle noodzakelijke berekeningen om met de UAV geautomatiseerd te kunnen vliegen, worden op een processorbord op de UAV zelf uitgevoerd. Verder zijn er software missie-bouwstenen ontwikkeld die herbruikbaar zijn, zodat sneller en eenvoudiger nieuwe missies ontwikkeld kunnen worden. Over Twirre zijn twee peer-reviewed wetenschappelijke artikelen gepubliceerd. Het Twirre-concept wordt op dit moment verder doorontwikkeld en geëvalueerd in projecten op verschillende toepassingsgebieden: • het TKI Wind op Zee project Inspection with automated UAVs using Computer Vision; • het KIEM SI project Autonoom navigeren met drones in de glastuinbouw; • binnen het Region of Smart Factory project Smart Sailing wordt door de NHL een spin-off van Twirre gebruikt; • er is een KIEM SI aangevraagd voor het project Autonoom navigeren met drones in magazijnen. De verwachting is dat er in de nabije toekomst veel vraag zal zijn naar nieuwe slimme toepassingen van autonome UAVs. De Twirre architectuur kan hieraan een belangrijke bijdrage leveren. Door Twirre open source en public domain te maken, worden de ontwikkeling en het laagdrempelig gebruik van Twirre gestimuleerd. Hiermee komt de kennis van Twirre tevens beschikbaar voor derden: bedrijven, instellingen en personen die niet bij het RAAK- project betrokken waren. Door een online-Twirre-community op te zetten, wordt de verdere ontwikkeling van Twirre nog meer versneld en wordt het gebruik ervan opgeschaald. In de community worden bijdragen van derden aan de Twirre-architectuur toegevoegd en beschikbaar gesteld aan iedereen.