For almost fifteen years, the availability and regulatory acceptance of new approach methodologies (NAMs) to assess the absorption, distribution, metabolism and excretion (ADME/biokinetics) in chemical risk evaluations are a bottleneck. To enhance the field, a team of 24 experts from science, industry, and regulatory bodies, including new generation toxicologists, met at the Lorentz Centre in Leiden, The Netherlands. A range of possibilities for the use of NAMs for biokinetics in risk evaluations were formulated (for example to define species differences and human variation or to perform quantitative in vitro-in vivo extrapolations). To increase the regulatory use and acceptance of NAMs for biokinetics for these ADME considerations within risk evaluations, the development of test guidelines (protocols) and of overarching guidance documents is considered a critical step. To this end, a need for an expert group on biokinetics within the Organisation of Economic Cooperation and Development (OECD) to supervise this process was formulated. The workshop discussions revealed that method development is still required, particularly to adequately capture transporter mediated processes as well as to obtain cell models that reflect the physiology and kinetic characteristics of relevant organs. Developments in the fields of stem cells, organoids and organ-on-a-chip models provide promising tools to meet these research needs in the future.
MULTIFILE
Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N=101) in comparison to students with other health conditions (N=2465) and students with no health conditions (N=25,077). We applied propensity score weighting to balance outcomes. The research showed that autistic students’ academic success was predictable, and these predictions were more accurate than predictions of their peers’ success. For first-year success, study choice issues were the most important predictors (parallel program and application timing). Issues with participation in pre-education (missingness of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors for the second-year success and delays in the second and final year of their bachelor’s program. In addition, academic performance (average grades) was the strongest predictor for degree completion in 3 years. These insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.
Since the European Union wants to reduce the oil dependence of the transportation system, the uptake of alternative vehicle technologies are stimulated in the member states. In the Netherlands, stimulation is already largely implemented in the form of a comprehensive charging infrastructure. This infrastructure is widely used by the electric vehicle drivers and thus there may occur a form of competition for the charging points. In this paper we address this problem by predicting the short-term availability of charging points at a given location and time by using the historical charging data in a space-time series model. The model shows better accuracy with respect to a naive method for short term predictions up to one day. This will allow charging point operators to provide customers with the service of looking up estimated charging point availability in the nearby future.
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).
Direct Air Capture (DAC) technology is necessary to help achieve the EU's 2050 climate goals, since it allows for net-negative emissions. This will be needed to offset historic emissions while working alongside with other CCU technologies. To make DAC technology truly effective, the carbon footprint of the process itself should be as low as possible. This project describes research plans to minimize the DAC carbon footprint (as well as cost per ton of CO2) by developing technology to maximize DAC filter lifetimes. The project outlines a strategic partnership between Skytree, a Dutch DAC start-up, and Dr. Baumgarter’s research group at the University of Amsterdam. Based on Life Cycle Analyses (LCA) performed by Skytree, they have identified that extending the lifetime of DAC filters can lower the overall carbon footprint by 35%. Similarly, Techno-Economic Assessment indicated that this increased lifetime could lower the cost per ton of CO2 by 10%. To achieve this, both parties will develop an indicator technique to accurately describe filter lifetime to allow for data-driven optimized filter maintenance. The indicator development will expand on a patented technology developed by Skytree. The current technology uses a colorimetric dye to qualitatively assess filter capacity. By gaining access to advanced analytical methods built at UvA, this technology can be enhanced to allow for quantitative sorbent capacity and thus lifetime predictions. Since Dr. Baumgartner’s group specializes in building innovative spectroscopic technique that can monitor functional materials during gas sorption processes, the proposed studies will be able to directly and accurately link sorbent capture performance (using IR spectroscopy) with indicator dye intensity (using UV-Vis spectroscopy). This will allow for the fast development of a calibrated filter lifetime indicator. This makes the foreseen research highly practical and impactful, as the results will directly be implemented in commercial DAC/CCU technology.
Nature-based coastal management is mainstream in the Netherlands. About 12 Mm3 of sand is added annually to the coast to compensate coastal erosion and maintain high safety levels against flooding. This amount will likely increase to compensate for accelerated sea level rise. (Mega-)Nourishments may also strengthen and support biodiversity and recreational values of the coastal zone and associated wetland areas. However, the ecological and societal impacts of mega-nourishments on open coasts are not well established, hampering comparison of pros and cons of different nourishment strategies. This knowledge gap is largely due to the lack of suitable methods to monitor and predict the spreading of nourishment sand along the coast and into tidal basins. Ameland Inlet provides us with a unique opportunity to develop and test novel approaches to fill this knowledge gap in close collaboration with our consortium and stakeholders. In 2018 the first tidal inlet mega-nourishment (5 Mm3) was placed in the Ameland Inlet ebb-tidal delta, and geomorphic and biotic responses nearby are closely monitored in the Kustgenese 2.0 and SEAWAD programmes. Our research builds on the insights gained, will gather new data to investigate off-site effects (linked with SIBES/SIBUS sampling), and build a common knowledge-base with stakeholders. We will develop novel luminescence-based methods to monitor the temporal and spatial dispersal of nourishment sand. These insights will be combined with an inventory of off-site biotic responses to nourishment and the role biota play in the mixing of nourishment sand with natural sediments. Combined results will be used to develop and validate models to trace transport paths of individual grains and improve morphodynamic predictions. Throughout the project, we will collaborate and interact intensely with coastal managers and (local) stakeholders to address concerns and exchange insights, creating a platform for co-assessment and optimization of nourishment designs and strategies.
