This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.
This article investigates the phenomenon of rebound effects in relation to a transition to a Circular Economy (CE) through qualitative inquiry. The aim is to gain insights in manifestations of rebound effects by studying the Dutch textile industry as it transitions to a circular system, and to develop appropriate mitigation strategies that can be applied to ensure an effective transition. The rebound effect, known originally from the energy efficiency literature, occurs when improvements in efficiency or other technological innovations fail to deliver on their environmental promise due to (behavioral) economic mechanisms. The presence of rebound in CE contexts can therefore lead to the structural overstatement of environmental benefits of certain innovations, which can influence reaching emission targets and the preference order of recycling. In this research, the CE rebound effect is investigated in the Dutch textile industry, which is identified as being vulnerable to rebound, yet with a positive potential to avoid it. The main findings include the very low awareness of this effect amongst key stakeholders, and the identification of specific and general instances of rebound effects in the investigated industry. In addition, the relation of these effects to Circular Business Models and CE strategies are investigated, and placed in a larger context in order to gain a more comprehensive understanding about the place and role of this effect in the transition. This concerns the necessity for a new approach to how design has been practiced traditionally, and the need to place transitional developments in a systems perspective. Propositions that serve as theory-building blocks are put forward and include suggestions for further research and recommendations about dealing with rebound effects and shaping an eco-effective transition. Thomas Siderius, Kim Poldner, Reconsidering the Circular Economy Rebound effect: Propositions from a case study of the Dutch Circular Textile Valley, Journal of Cleaner Production, Volume 293, 2021, 125996, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2021.125996.
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.
Doel van het vak Lichamelijke Opvoeding (LO) is dat leerlingen niet alleen beter leren bewegen, maar dat er ook aandacht is voor omgangs- en regelbekwaamheden (bijvoorbeeld fair play, het zelfstandig kunnen spelen van een spel, het organiseren van een activiteit, etc.). Ook het verwerven van (zelf)kennis en inzicht is een doel en daarmee het ontwikkelen van een eigen beweegidentiteit. De huidige praktijk van leerlingevaluatie sluit hier onvoldoende op aan en is soms zelfs demotiverend voor leerlingen. De focus van evalueren ligt daarbij vooral op de (eind)prestatie. Om leerlingen te motiveren en te ondersteunen, zou het leerproces van leerlingen richting de te bereiken doelen meer centraal moeten staan. Leraren LO vinden het echter een uitdaging om leerprocessen te monitoren en formatief te evalueren. Het ontbreekt binnen de LO aan een gebruiksvriendelijk en effectief instrument om dit te doen, passend bij de uiteenlopende doelen van het vak. Onderzoeksliteratuur en beperkte praktijkervaringen stellen een digitaal portfolio voor als mogelijk geschikt instrument. De Sportfolio App is een voorbeeld van zo’n digitaal portfolio, recent ontwikkeld voor LO. Deze app biedt nu nog onvoldoende aansluiting bij de gevarieerde doelen van het vak en is niet gericht op formatieve evaluatie. In het voorgestelde project, vormgegeven volgens de principes van Participatory Design Research, worden ontwerpprincipes en gebruiksrichtlijnen opgeleverd van een digitaal portfolio, waarmee effectief en gebruiksvriendelijk leervorderingen van leerlingen bij het vak LO inzichtelijk gemaakt kunnen worden. Deze principes en richtlijnen zullen toepasbaar zijn op verschillende vormen van (digitale) portfolio’s, waardoor scholen deze kunnen aanpassen aan de eigen mogelijkheden en wensen. Om dit te bereiken wordt er samengewerkt in een netwerk van hbo-onderzoekers, lerarenopleiders, leraren LO uit het voortgezet onderwijs, en ontwikkelaars van de Sportfolio App. De projectopbrengsten worden breed gedissemineerd binnen het werkveld van de LO.
