One of the main causes of numerous health problemsis a lack of physical activity. To promote a more active lifestyle,the Hanze University started a health promotion program. Participants were motivated to reach their daily goal of physical activityby means of an activity tracker in combination with two-weeklycoaching sessions. Employing the data of the experiment, weinvestigated the manners in which the predictability of physicalactivity of a participant during the day can be improved. Thecollected step count data was used to construct personalisedmachine learning models, by taking into account the differencebetween physical activities during weekdays on the one handand weekends on the other hand. The training of algorithmsper participant in combination with the time-slices weekdays,weekend and the whole week improves the accuracy of theprediction model. The performance of the models improveseven further when the individualised time-sliced models arecombined. More contextual data, like free time and workinghours, might even extend the accuracy. The use of personalisedprediction models, based on machine learning and time slices,could become an addition in preventive personalized eHealthsystems and mobile activity monitoring. For instance, this canconstitute as a viable addition to a virtual coaching system to helpthe participants to reach their daily goal. As the individualisedmodels allow for predictions of the progression of the physicalactivity during the day, they enable the virtual coaching systemto intervene at the appropriate moment in time.
The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
OBJECTIVE: To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo2peak) and peak power output (Wpeak).DESIGN: Cross-sectional study.SETTING: Multicenter.PARTICIPANTS: Cancer survivors (N=283) in 2 randomized controlled exercise trials.INTERVENTIONS: Not applicable.MAIN OUTCOME MEASURES: Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions.RESULTS: ICCs of SRT-predicted Vo2peak and Wpeak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for Vo2peak and ±59W with a bias of 5W for Wpeak. Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of Vo2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of Wpeak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for Wpeak.CONCLUSIONS: Predictions of Vo2peak and Wpeak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for Wpeak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription.
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.
