More and more information labels appear on the front of food packages, increasing the complexity of consumer decision-making and enhancing consumer scepticism toward food labels. It is important to evaluate the efficacy of information communicated to consumers. The experimental study among 209 Dutch consumers compared the effect of health and hedonic labels on consumer scepticism toward the labels and consumer responses to food products (apple juice and a chocolate cookie) under three presentation conditions (visual, visual-tactile and multisensory). The results demonstrated that consumers were more sceptical toward the hedonic label than toward the health label. The influence of consumer scepticism on product experience, product evaluation and purchase intention varied for different product categories. For a hedonic product (a chocolate cookie), the hedonic label had a more positive effect on consumer responses compared to the health label. The results also showed that the multisensory presentation reduced scepticism and enhanced product evaluation for the hedonic product compared to the visual and tactile presentations. The results suggest that multisensory experience may alter consumer scepticism toward food labels and thus product evaluation and consumer choice. Our findings can be useful for food manufacturers and policy makers in evaluating the efficacy of food labels and information presented on food packages.
LINK
Human activity recognition system is of great importance in robot-care scenarios. Typically, training such a system requires activity labels to be both completely and accurately annotated. In this paper, we go beyond such restriction and propose a learning method that allow labels to be incomplete and uncertain. We introduce the idea of soft labels which allows annotators to assign multiple, and weighted labels to data segments. This is very useful in many situations, e.g., when the labels are uncertain, when part of the labels are missing, or when multiple annotators assign inconsistent labels. We formulate the activity recognition task as a sequential labeling problem. Latent variables are embedded in the model in order to exploit sub-level semantics for better estimation. We propose a max-margin framework which incorporate soft labels for learning the model parameters. The model is evaluated on two challenging datasets. To simulate the uncertainty in data annotation, we randomly change the labels for transition segments. The results show significant improvement over the state-of-the-art approach.
DOCUMENT
This article analyses four of the most prominent city discourses and introduces the lens of urban vitalism as an overarching interdisciplinary concept of cities as places of transformation and change. We demonstrate the value of using urban vitalism as a lens to conceptualize and critically discuss different notions on smart, inclusive, resilient and sustainable just cities. Urban vitalism offers a process-based lens which enables us to understand cities as places of transformation and change, with people and other living beings at its core. The aim of the article is to explore how the lens of vitalism can help us understand and connect ongoing interdisciplinary academic debates about urban development and vice versa, and how these ongoing debates inform our understanding of urban vitalism.
DOCUMENT
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).
In het project ’Duurzaam vlees, natuurlijk!’ werken veehouders, keurmerken, regionale en landelijke branche- en ketenorganisaties, consumentenorganisaties en WUR samen met de vier Groene Hogescholen (Aeres, HAS, Inholland, VHL) aan een roadmap voor het meten en communiceren van duurzaamheid in de veehouderij vanuit een integrale benadering. In de verduurzaming van de veehouderij nemen klimaateffecten een belangrijke plaats in met de Carbon Footprint als leidend criterium. Het vastleggen en toerekenen van emissies is lastig en een lage Carbon Footprint staat vaak op spanning met andere duurzaamheidscriteria zoals biodiversiteit en extensief weiden. Er zijn ook andere thema’s van maatschappelijk belang, zoals de relatie burger-boer, dierenwelzijn, landschap, natuur, biodiversiteit en cultuurhistorie. De diverse aspecten van duurzaamheid zijn terug te vinden in de verschillende afzonderlijke keurmerken die ontwikkeld zijn. Dit project heeft tot doel een integraal overzicht te vormen van keurmerken, meetmethoden en duurzaamheidscriteria voor de veehouderij, percepties van consumenten en het inzichtelijk maken van de spanningsvelden daartussen. Vanuit het overzicht wordt een roadmap ontworpen voor doorontwikkeling van bestaande keurmerken t.a.v. criteria, methodologie, allocatie, om aansluiting te vinden bij de behoeften van verschillende doelgroepen, waaronder consumenten en zakelijk afnemers. Daarbij worden alle sectoren binnen de vlees-producerende veehouderij in ogenschouw genomen, waarbij er in het bijzonder aandacht is voor duurzame productie van vlees van rundvee.
Food hubs in Noord-Nederland zien een mogelijkheid tot substantiële verbreding van hun dienstverlening door regionale voedselproducten niet alleen aan eindconsumenten te verkopen, maar ook aan grotere instellingen zoals zorgaanbieders. Van de laatste hebben de ziekenhuizen de intentie vastgelegd om maatschappelijk verantwoord in te kopen. De eerste stappen in de voedsellevering van de hubs aan de ziekenhuizen worden voorzichtig gezet, maar worden bemoeilijkt door inkoopvoorwaarden met betrekking tot leveringsgemak, hoeveelheden, kosten en leveringsgaranties. Bovendien zijn food hubs relatief kleine ondernemingen tegenover professionele inkopers van de ziekenhuizen. Er zijn echter ook goede kansen voor de food hubs door onderling samen te werken en door in te spelen op dieetwensen van patiënten. De centrale vraag van het onderzoeksproject van de Hanzehogeschool Groningen, waarin 9 food hubs en 2 ziekenhuizen deelnemen, is met welk businessmodel de food hubs aan de eisen en wensen van de ziekenhuizen kunnen voldoen. Het gaat dan om een businessmodel waarin de wijze van samenwerken tussen de food hubs, boeren en eventuele verwerkers, het productaanbod en prijs, alsook de wijze van communiceren met de ziekenhuizen geïntegreerd zijn. Er wordt gebruik gemaakt van design science. Op basis van de wensen, eisen en mogelijkheden van de food hubs en ziekenhuizen wordt eerst in co-creatie een voorlopig businessmodel ontwikkeld. Dit model wordt in een pilot in de praktijk gebracht en getest. De ervaringen van de pilot worden geanalyseerd en doorontwikkeld tot een definitief businessmodel en een handleiding om zover te komen. Het uiteindelijke businessmodel kan bijdragen tot een versteviging van de economische positie van de lokale food hubs en de aangesloten boeren, een duurzame voedselinkoop van de ziekenhuizen, en meer mogelijkheden om patiënten gezonde en aantrekkelijke diëten te bieden. De projectresultaten zullen wordt verbreed naar een businessplan voor de levering van lokaal voedsel aan (semi-)publieke en private organisaties, dat breder uitgerold kan worden.
