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Contemplating ‘Quality Street’

Een holistisch perspectief op binnenstedelijke herontwikkeling Spatial Planning http://www.uu.nl/agenda/promotie-een-holistisch-perspectief-op-binnenstedelijke-herontwikkeling Promovendus Rien van Stigt onderzoekt waarom het moeilijk is om milieukwaliteit een prominente plaats te geven in de besluitvorming over ruimtelijke plannen. In zijn proefschrift ontwikkelt hij een holistisch perspectief op het complexe proces van compacte binnenstedelijke herontwikkeling. De kwaliteit van de stedelijke leefomgeving is essentieel in duurzame stedelijke ontwikkeling. Die kwaliteit staat met name bij compacte binnenstedelijke herontwikkeling onder druk, en daarom is milieukwaliteit een belangrijke factor in het plannen van zulke ontwikkelingen. Uit de literatuur over de integratie van milieubeleid blijkt dat dit, vooral op lagere bestuurlijke niveaus, niet altijd goed lukt. Er is nog geen overtuigende verklaring waarom dit zo is. Promotor(es): Prof.dr. P.P.J. Driessen en Prof.dr. T.J.M. Spit

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Contemplating ‘Quality Street’

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Discovering operational decisions from data

Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.

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Discovering operational decisions from data

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Quality of care in the intensive care unit from the perspective of patient's relatives

BACKGROUND: The quality standards of the Dutch Society of Intensive Care require monitoring of the satisfaction of patient's relatives with respect to care. Currently, no suitable instrument is available in the Netherlands to measure this. This study describes the development and psychometric evaluation of the questionnaire-based Consumer Quality Index 'Relatives in Intensive Care Unit' (CQI 'R-ICU'). The CQI 'R-ICU' measures the perceived quality of care from the perspective of patients' relatives, and identifies aspects of care that need improvement.METHODS: The CQI 'R-ICU' was developed using a mixed method design. Items were based on quality of care aspects from earlier studies and from focus group interviews with patients' relatives. The time period for the data collection of the psychometric evaluation was from October 2011 until July 2012. Relatives of adult intensive care patients in one university hospital and five general hospitals in the Netherlands were approached to participate. Psychometric evaluation included item analysis, inter-item analysis, and factor analysis.RESULTS: Twelve aspects were noted as being indicators of quality of care, and were subsequently selected for the questionnaire's vocabulary. The response rate of patients' relatives was 81% (n = 455). Quality of care was represented by two clusters, each showing a high reliability: 'Communication' (α = .80) and 'Participation' (α = .84). Relatives ranked the following aspects for quality of care as most important: no conflicting information, information from doctors and nurses is comprehensive, and health professionals take patients' relatives seriously. The least important care aspects were: need for contact with peers, nuisance, and contact with a spiritual counsellor. Aspects that needed the most urgent improvement (highest quality improvement scores) were: information about how relatives can contribute to the care of the patient, information about the use of meal-facilities in the hospital, and involvement in decision-making on the medical treatment of the patient.CONCLUSIONS: The CQI 'R-ICU' evaluates quality of care from the perspective of relatives of intensive care patients and provides practical information for quality assurance and improvement programs. The development and psychometric evaluation of the CQI 'R-ICU' led to a draft questionnaire, sufficient to justify further research into the reliability, validity, and the discriminative power of the questionnaire.

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Quality of care in the intensive care unit from the perspective of patient's relatives

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Projecten 35

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AI-enhanced Data Analysis

Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.

Lopend

project

Beyond applause: A new approach to measuring performing arts impact

Due to their diverse funding sources, theatres are under increasing pressure to demonstrate impact on society. The Raad voor Cultuur (2023) for example advised the secretary of state to include societal impact as an additional evaluation measure next to artistic value. Many theaters, such as the Chassé Theater and Parkstad Limburg Theaters, have reformulated their missions to focus on impact of performances on visitors. This is a profound transformation from merely selling tickets and filling seats, and requires new measurement instruments to monitor, manage, and improve impact. Currently available instruments are insufficient, and effective monitoring is crucial to larger future projects that theaters are currently planning to systematically broaden impacts of performances on their communities. The specific goal of this project is to empower theaters to monitor and improve impact by developing a brief experience impact questionnaire, taking existing data from student projects conducted at the Chassé Theater about performing arts experiences on one hand, and experience impact theory innovations on the other, as starting points. We will develop potential items to measure and benchmark against established measures of valued societal outcomes, such as subjective well-being and quality of life. These will be measured in questionnaires developed with project partners Chassé Theater and Parkstad Limburg Theaters and administered before and after performances across a wide range of genres. The resulting data will enable comparison of new questionnaire items with benchmarked measures of valued societal outcomes. The final product of the project will be a brief impact questionnaire, which within several brief self-report instruments and just a few minutes can effectively be used to quantify the impact of a performing arts experience. A workshop and practice-oriented article will make this questionnaire implementable, thereby mobilizing the key enabling methodology of monitoring and impact measurement in the performing arts sector.

Lopend

project

Beyond applause: a new approach to measuring performing arts impact.

Due to their diverse funding sources, theatres are under increasing pressure to demonstrate impact on society. The Raad voor Cultuur (2023) for example advised the secretary of state to include societal impact as an additional evaluation measure next to artistic value. Many theaters, such as the Chassé Theater and Parkstad Limburg Theaters, have reformulated their missions to focus on impact of performances on visitors. This is a profound transformation from merely selling tickets and filling seats, and requires new measurement instruments to monitor, manage, and improve impact. Currently available instruments are insufficient, and effective monitoring is crucial to larger future projects that theaters are currently planning to systematically broaden impacts of performances on their communities.The specific goal of this project is to empower theaters to monitor and improve impact by developing a brief experience impact questionnaire, taking existing data from student projects conducted at the Chassé Theater about performing arts experiences on one hand, and experience impact theory innovations on the other, as starting points. We will develop potential items to measure and benchmark against established measures of valued societal outcomes, such as subjective well-being and quality of life. These will be measured in questionnaires developed with project partners Chassé Theater and Parkstad Limburg Theaters and administered before and after performances across a wide range of genres. The resulting data will enable comparison of new questionnaire items with benchmarked measures of valued societal outcomes. The final product of the project will be a brief impact questionnaire, which within several brief self-report instruments and just a few minutes can effectively be used to quantify the impact of a performing arts experience. A workshop and practice-oriented article will make this questionnaire implementable, thereby mobilizing the key enabling methodology of monitoring and impact measurement in the performing arts sector.Societal issueThe specific goal of this project is to empower theaters to monitor and improve impact by developing a brief experience impact questionnaire, taking existing data about performing arts experiences on one hand, and experience impact theory innovations on the other, as starting points. Benefit to societyWe will develop potential items to measure and benchmark against established measures of valued societal outcomes, such as subjective well-being and quality of life. Collaborative partnersChassé Theater N.V., Parkstad Limburg Theaters N.V.

Lopend

project

Certification of production process quality through Artificial Intelligence (CERTIF-AI)

Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.

Lopend

project

CONNECT: Collective process development for an innovative chemical industry

The SPRONG-collaboration “Collective process development for an innovative chemical industry” (CONNECT) aims to accelerate the chemical industry’s climate/sustainability transition by process development of innovative chemical processes. The CONNECT SPRONG-group integrates the expertise of the research groups “Material Sciences” (Zuyd Hogeschool), “Making Industry Sustainable” (Hogeschool Rotterdam), “Innovative Testing in Life Sciences & Chemistry” and “Circular Water” (both Hogeschool Utrecht) and affiliated knowledge centres (Centres of Expertise CHILL [affiliated to Zuyd] and HRTech, and Utrecht Science Park InnovationLab). The combined CONNECT-expertise generates critical mass to facilitate process development of necessary energy-/material-efficient processes for the 2050 goals of the Knowledge and Innovation Agenda (KIA) Climate and Energy (mission C) using Chemical Key Technologies. CONNECT focuses on process development/chemical engineering. We will collaborate with SPRONG-groups centred on chemistry and other non-SPRONG initiatives. The CONNECT-consortium will generate a Learning Community of the core group (universities of applied science and knowledge centres), companies (high-tech equipment, engineering and chemical end-users), secondary vocational training, universities, sustainability institutes and regional network organizations that will facilitate research, demand articulation and professionalization of students and professionals. In the CONNECT-trajectory, four field labs will be integrated and strengthened with necessary coordination, organisation, expertise and equipment to facilitate chemical innovations to bridge the innovation valley-of-death between feasibility studies and high technology-readiness-level pilot plant infrastructure. The CONNECT-field labs will combine experimental and theoretical approaches to generate high-quality data that can be used for modelling and predict the impact of flow chemical technologies. The CONNECT-trajectory will optimize research quality systems (e.g. PDCA, data management, impact). At the end of the CONNECT-trajectory, the SPRONG-group will have become the process development/chemical engineering SPRONG-group in the Netherlands. We can then meaningfully contribute to further integrate the (inter)national research ecosystem to valorise innovative chemical processes for the KIA Climate and Energy.

Lopend

project

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).

Lopend

project

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.

Afgerond

project

Family Dairy Tech

Family Dairy Tech Sustainable and affordable stable management systems for family dairy farms in India. An example of Dutch technology that is useful to an ?emerging economy?. Summary Problem The demand for dairy products in India is increasing. Small and medium-sized family farmers want to capitalize on this development and the Indian government wants to support them. Dutch companies offer knowledge and a wide range of products and services to improve dairy housing systems and better milk quality, in which India is interested. However, the Dutch technology is sophisticated and expensive. For a successful entry into this market, entrepreneurs have to develop affordable and robust (?frugal?) systems and products adapted to the Indian climate and market conditions. The external question is therefore: ?How can Dutch companies specialised on dairy housing systems adapt their products and offer these on the Indian market to contribute to sustainable and profitable local dairy farming??. Goal Since 2011, VHL University of Applied Sciences (VHL) is collaborating with a college and an agricultural information center Krishi Vigyan Kendra (KVK), Baramati, Pune district, Maharashtra State India. In this region many small-scale dairy farmers are active. Within this project, KVK wants to support farmers to scale up their farm form one or a few cows up to 15 to 100 cows, with a better milk quality. In this innovative project, VHL and Saxion Universities of Applied Sciences, in collaboration with KVK and several Dutch companies want to develop integrated solutions for the growing number of dairy farms in the State of Maharashtra, India. The research questions are: 1. "How can, by smart combinations of existing and new technologies, the cow-varieties and milk- and stable-management systems in Baramati, India, for family farmers be optimized in an affordable and sustainable way?" 2. "What are potential markets in India for Dutch companies in the field of stable management and which innovative business models can support entering this market?" Results The intended results are: 1. A design of an integral stable management system for small and medium-sized dairy farms in India, composed of modified Dutch technologies. 2. A cattle improvement programme for robust cows that are adapted to the conditions of Maharashtra. 3. An advice to Dutch entrepreneurs how to develop their market position in India for their technologies. 4. An advice to Indian family farmers how they can increase their margins in a sustainable way by employing innovative technologies.

Afgerond

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Data Science for Life Science & Health

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Producten 5.302

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Contemplating ‘Quality Street’

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product

Contemplating ‘Quality Street’

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product

Discovering operational decisions from data

MULTIFILE

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Quality of care in the intensive care unit from the perspective of patient's relatives

MULTIFILE

product

Quality of care in the intensive care unit from the perspective of patient's relatives

MULTIFILE

Projecten 35

project

AI-enhanced Data Analysis

Lopend

project

Beyond applause: A new approach to measuring performing arts impact

Lopend

project

Beyond applause: a new approach to measuring performing arts impact.

Due to their diverse funding sources, theatres are under increasing pressure to demonstrate impact on society. The Raad voor Cultuur (2023) for example advised the secretary of state to include societal impact as an additional evaluation measure next to artistic value. Many theaters, such as the Chassé Theater and Parkstad Limburg Theaters, have reformulated their missions to focus on impact of performances on visitors. This is a profound transformation from merely selling tickets and filling seats, and requires new measurement instruments to monitor, manage, and improve impact. Currently available instruments are insufficient, and effective monitoring is crucial to larger future projects that theaters are currently planning to systematically broaden impacts of performances on their communities.The specific goal of this project is to empower theaters to monitor and improve impact by developing a brief experience impact questionnaire, taking existing data from student projects conducted at the Chassé Theater about performing arts experiences on one hand, and experience impact theory innovations on the other, as starting points. We will develop potential items to measure and benchmark against established measures of valued societal outcomes, such as subjective well-being and quality of life. These will be measured in questionnaires developed with project partners Chassé Theater and Parkstad Limburg Theaters and administered before and after performances across a wide range of genres. The resulting data will enable comparison of new questionnaire items with benchmarked measures of valued societal outcomes. The final product of the project will be a brief impact questionnaire, which within several brief self-report instruments and just a few minutes can effectively be used to quantify the impact of a performing arts experience. A workshop and practice-oriented article will make this questionnaire implementable, thereby mobilizing the key enabling methodology of monitoring and impact measurement in the performing arts sector.Societal issueThe specific goal of this project is to empower theaters to monitor and improve impact by developing a brief experience impact questionnaire, taking existing data about performing arts experiences on one hand, and experience impact theory innovations on the other, as starting points. Benefit to societyWe will develop potential items to measure and benchmark against established measures of valued societal outcomes, such as subjective well-being and quality of life. Collaborative partnersChassé Theater N.V., Parkstad Limburg Theaters N.V.

Lopend

project

Certification of production process quality through Artificial Intelligence (CERTIF-AI)

Lopend

project

CONNECT: Collective process development for an innovative chemical industry

Lopend

project

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

Lopend

project

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

Afgerond

project

Family Dairy Tech

Afgerond

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Data Science for Life Science & Health

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Producten 5.302

Contemplating ‘Quality Street’

Contemplating ‘Quality Street’

Discovering operational decisions from data

Impacts of floating urbanization on water quality and aquatic ecosystems:

Impacts of floating urbanization on water quality and aquatic ecosystems:

Quality of care in the intensive care unit from the perspective of patient's relatives

Quality of care in the intensive care unit from the perspective of patient's relatives

The mediatisation of statistics : data-driven journalism and the controversy on "quality statistics"

The mediatisation of statistics : data-driven journalism and the controversy on "quality statistics"

Projecten 35

AI-enhanced Data Analysis

Beyond applause: A new approach to measuring performing arts impact

Beyond applause: a new approach to measuring performing arts impact.

Bio-inspired drones for sustainable horticulture

Certification of production process quality through Artificial Intelligence (CERTIF-AI)

CONNECT: Collective process development for an innovative chemical industry

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

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

Family Dairy Tech

Partijen 1

Data Science for Life Science & Health

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Producten 5.302

Contemplating ‘Quality Street’

Contemplating ‘Quality Street’

Discovering operational decisions from data

Impacts of floating urbanization on water quality and aquatic ecosystems:

Impacts of floating urbanization on water quality and aquatic ecosystems:

Quality of care in the intensive care unit from the perspective of patient's relatives

Quality of care in the intensive care unit from the perspective of patient's relatives

The mediatisation of statistics : data-driven journalism and the controversy on "quality statistics"

The mediatisation of statistics : data-driven journalism and the controversy on "quality statistics"

Projecten 35

AI-enhanced Data Analysis

Beyond applause: A new approach to measuring performing arts impact

Beyond applause: a new approach to measuring performing arts impact.

Bio-inspired drones for sustainable horticulture

Certification of production process quality through Artificial Intelligence (CERTIF-AI)

CONNECT: Collective process development for an innovative chemical industry

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

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

Family Dairy Tech

Partijen 1

Data Science for Life Science & Health