Intergenerational continuity in family behaviors partly results from socialization processes in the parental home. However, socialization is a multidimensional process. This article tests hypotheses about the relative importance of value transmission and modeling in explaining expectations of adolescence concerning the timing of leaving home, and entry into cohabitation, marriage, and parenthood. Structural equation modeling on multiactor data from over 1,000 parent–adolescent child couples in the Netherlands is used to test hypotheses. Results suggest that, in general, both value transmission and modeling are important predictors of adolescents’ expectations concerning the timing of major family events. Moreover, no differences between mothers and fathers and between boys and girls are observed in the strength of the intergenerational relationships studied.
Objective: To explore the nature and extent of possible residual complaints among Dutch hypothyroid patients using thyroid replacement therapy, we initiated a comprehensive study measuring health-related quality of life (QoL), daily functioning, and hypothyroidism-associated symptoms in patients and control persons. Methods: An online survey measuring thyroid-specific QoL (ThyPRO), daily functioning, and hypothyroidismassociated symptoms (ThySHI) was distributed among treated hypothyroid patients and control individuals. The advertising text was formulated in an open-ended manner. Patients also provided their most recent thyroid blood values and their thyroid medication. Results: There was a large-sized impairment of QoL (Cohen’s d = 1.04, +93 % ThyPRO score) in hypothyroid patients on thyroid replacement therapy (n = 1195) as compared to controls (n = 236). Daily functioning was significantly reduced i.e., general health (-38 %), problems with vigorous- (+64 %) and moderate activities (+77 %). Almost 80 % of patients reported having complaints despite thyroid medication and in-range thyroid blood values, with 75 % expressing a desire for improved treatment options for hypothyroidism (total n = 1194). Hypothyroid patients experienced 2.8 times more intense hypothyroidism-associated symptoms than controls (n = 865, n = 203 resp). Patients’ median reported serum concentrations were: TSH 0.90 mU/L, FT4 17.0 pmol/L, and FT3 2.67 pmol/L, with 52 % having low T3 levels (<3.1 pmol/L). The QoL was not found to be related to age, sex, BMI, menopausal status, stress, serum thyroid parameters, the origin and duration of hypothyroidism, the type of thyroid medication, or the LT4 dose used. Conclusions: Our study revealed major reductions in quality of life and daily functioning, and nearly three times more intense hypothyroidism-associated symptoms in treated hypothyroid patients as compared to controls, despite treatment and largely in-range serum TSH/FT4 concentrations. The QoL was not associated with serum thyroid parameters. We recommend future research into the origin of persisting complaints and the development of improved treatment modalities for hypothyroidism.
Purpose The purpose of this research was to explore women’s experiences after breast surgery with scar characteristics and symptoms, and its impact on their health-related quality of life (HRQOL). Material andmethods A qualitative study using semi-structured face-to-face interviewswas conducted among women following prophylactic, oncologic, or reconstructive breast surgery in the Netherlands. A directed content analysis was performed using guiding themes. Themes were “physical and sensory symptoms,” “impact of scar symptoms,” “personal factors,” “impact of scar interventions,” and “change over time.” Results The study population consisted of 26 women after breast surgery. Women experienced a wide range of symptoms like adherence, stiffness, pain, and uncomfortable sensations. Scar characteristics as visibility, location, texture, and size, influenced satisfaction with their appearance. The impact of scar symptoms is reflected in physical, social, emotional, and cognitive functioning, thereby affecting HRQOL. The experienced impact on HRQOL depended on several factors, like personal factors as the degree of acceptance and environmental factors like social support. Conclusion Women can experience a diversity of scar characteristics and symptoms, which play a central role in the perceived impact on HRQOL. Since scarring can have a considerable impact on HRQOL, scarring after prophylactic, oncologic and reconstructive breast surgery should be given more attention in clinical practice and research. Implications for Cancer Survivors Considering scarring as a common late effect after breast surgery and understanding the variety of experiences, which could impact HRQOL of women, can be beneficial in sufficient information provision, expectation management, and informed decision making.
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).
Promoting entrepreneurship is an enabler of smart, sustainable and inclusive growth and it is one objective EU regions have pursued since the EC included it into 2020 Strategy. Entrepreneurship development has economic and social benefits, since it is not only a driving force for job creation, competitiveness and growth; it also contributes to personal fulfillment and to achieve social objectives. That is why the EU encourages entrepreneurial initiatives and to unlock the growth potential of businesses and citizens. However, only a 37% of Europeans (Eurobarometer 2012) would like to be self-employed. The Entrepreneurship Action Plan adopted by the EC in 2013 to reignite Europe’s entrepreneurial spirit includes initiatives for educating young people on entrepreneurship. To ensure that EU economy remains globally competitive, young generations of Europeans need to be inspired to develop their entrepreneurial mindset. EU 2020 Action Plan argues that young people benefitting of a specialised entrepreneurial education are more likely to start-up a business and to better tackle challenges in their professional career and life in general. Hence, there is good reason to ensure better quality of entrepreneurial education. Most approaches in recent years have focused on improving the skills or competences youngsters should obtain only within the education system. However, an integrated approach is needed, where the school, their friends, family and the social environment, shall play each one a relevant role, contributing to generate a more adequate atmosphere to boost their entrepreneurial mindsets, intrapreneurial attitudes and innovation capacities. This project will identify and exchange – through a quadruple helix approach- good practices for creating friendlier entrepreneurial ecosystems and actions to boost entrepreneurship in young people mindsets. The good practices and lessons learnt will be transferred into Action Plans to be included in regional policies.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
