A review has been completed for a verification and validation (V&V) of the (Excel) BioGas simulator or EBS model. The EBS model calculates the environmental impact of biogas production pathways using Material and Energy Flow Analysis, time dependent dynamics, geographic information, and Life Cycle analysis. Within this article a V&V method is researched, selected and applied to validate the EBS model. Through the use of the method described within this article: mistakes in the model are resolved, the strengths and weaknesses of the model are found, and the concept of the model is tested and strengthened. The validation process does not only improve the model but also helps the modelers in widening their focus and scope. This article can, therefore, also be used in the validation process of similar models. The main result from the V&V process indicates that the EBS model is valid; however, it should be considered as an expert model and should only be used by expert users.
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Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.
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The aim of this study was to develop a valid instrument to measure student nurses’ perceptions of community care (SCOPE). DeVellis’ staged model for instrument development and validation was used. Scale construction of SCOPE was based on existing literature. Evaluation of its psychometric properties included exploratory factor analysis and reliability analysis. After pilot-testing, 1062 bachelor nursing students from six institutions in the Netherlands (response rate 81%) took part in the study. SCOPE is a 35-item scale containing: background variables, 11 measuring the affective component, 5 measuring community care perception as a placement, 17 as a future profession, and 2 on the reasons underlying student preference. Principal axis factoring yielded two factors in the affective component scale reflecting ‘enjoyment’ and ‘utility’, two in the placement scale reflecting ‘learning possibilities’ and ‘personal satisfaction’, and four in the profession scale: ‘professional development’, ‘collaboration’, ‘caregiving’, and ‘complexity and workload’. Cronbach’s α of the complete scale was .892 and of the subscales .862, .696, and .810 respectively. SCOPE is a psychometrically sound instrument for measuring students’ perceptions of community care. By determining these perceptions, it becomes possible to positively influence them with targeted curriculum redesign, eventually contributing to decreasing the workforce shortage in community nursing.
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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).
Mycelium biocomposites (MBCs) are a fairly new group of materials. MBCs are non-toxic and carbon-neutral cutting-edge circular materials obtained from agricultural residues and fungal mycelium, the vegetative part of fungi. Growing within days without complex processes, they offer versatile and effective solutions for diverse applications thanks to their customizable textures and characteristics achieved through controlled environmental conditions. This project involves a collaboration between MNEXT and First Circular Insulation (FC-I) to tackle challenges in MBC manufacturing, particularly the extended time and energy-intensive nature of the fungal incubation and drying phases. FC-I proposes an innovative deactivation method involving electrical discharges to expedite these processes, currently awaiting patent approval. However, a critical gap in scientific validation prompts the partnership with MNEXT, leveraging their expertise in mycelium research and MBCs. The research project centers on evaluating the efficacy of the innovative mycelium growth deactivation strategy proposed by FC-I. This one-year endeavor permits a thorough investigation, implementation, and validation of potential solutions, specifically targeting issues related to fungal regrowth and the preservation of sustained material properties. The collaboration synergizes academic and industrial expertise, with the dual purpose of achieving immediate project objectives and establishing a foundation for future advancements in mycelium materials.
The Water Framework Directive imposes challenges regarding the environmental risk of plastic pollution. The quantification, qualification, monitoring, and risk assessment of nanoplastics and small microplastic (<20 µm) is crucial. Environmental nano- and micro-plastics (NMPs) are highly diverse, accounting for this diversity poses a big challenge in developing a comprehensive understanding of NMPs detection, quantification, fate, and risks. Two major issues currently limit progress within this field: (a) validation and broadening the current analytical tools (b) uncertainty with respect to NMPs occurrence and behaviour at small scales (< 20 micron). Tracking NMPs in environmental systems is currently limited to micron size plastics due to the size detection limit of the available analytical techniques. There are currently no methods that can detect nanoplastics in real environmental systems. A major bottleneck is the incompatibility between commercially available NMPs and those generated from plastic fragments degradation in the environment. To track nanoplastics in environmental and biological systems, some research groups synthesized metal-doped nanoplastics, often limited to one polymer type and using high concentrations of surfactants, rendering these synthesized nanoplastics to not be representative of nanoplatics found in real environment. NanoManu proposes using Electrohydrodynamic Atomization to generate metal doped NMPs of different polymers types, sizes, and shapes, which will be representative of the real environmental nanoplastics. The synthesized nanoplastics will be used as model particles in environmental studies. The synthesized nanoplastics will be characterized and tested using different analytical methods, e.g., SEM-EDX, TEX, GCpyrMS, FFF, µFTIR and SP-ICP-MS. NanoManu is a first and critical step towards generating a comprehensive state-of-the-art analytical and environmental knowledge on the environmental fate and risks of nanoplastics. This knowledge impacts current risk assessment tools, efficient interventions to limit emissions and adequate regulations related to NMPs.
