Renewing agricultural grasslands for improved yields and forage quality generally involves eliminating standing vegetation with herbicides, ploughing and reseeding. However, grassland renewal may negatively affect soil quality and related ecosystem services. On clay soil in the north of the Netherlands, we measured grass productivity and soil chemical parameters of ‘young’ (5–15 years since last grassland renewal) and ‘old’ (>20 years since last grassland renewal) permanent grasslands, located as pairs at 10 different dairy farms. We found no significant difference with old permanent grassland in herbage dry matter yield and fertilizer nitrogen (N) response, whereas herbage N yield was lower in young permanent grassland. Moreover, the young grassland soil contained less soil organic matter (SOM), soil organic carbon (C) and soil organic N compared to the old grassland soil. Grass productivity was positively correlated with SOM and related parameters such as soil organic C, soil organic N and potentially mineralizable N. We conclude that on clay soils with 70% desirable grasses (i.e., Lolium perenne and Phleum pratense) or more, the presumed yield benefit of grassland renewal is offset by a loss of soil quality (SOM and N-total). The current practice of renewing grassland after 10 years without considering the botanical composition, is counter-productive and not sustainable.
MULTIFILE
Renewing agricultural grasslands for improved yields and forage quality generally involves eliminating standing vegetation with herbicides, ploughing and reseeding. However, grassland renewal may negatively affect soil quality and related ecosystem services. On clay soil in the north of the Netherlands, we measured grass productivity and soil chemical parameters of ‘young’ (5–15 years since last grassland renewal) and ‘old’ (>20 years since last grassland renewal) permanent grasslands, located as pairs at 10 different dairy farms. We found no significant difference with old permanent grassland in herbage dry matter yield and fertilizer nitrogen (N) response, whereas herbage N yield was lower in young permanent grassland. Moreover, the young grassland soil contained less soil organic matter (SOM), soil organic carbon (C) and soil organic N compared to the old grassland soil. Grass productivity was positively correlated with SOM and related parameters such as soil organic C, soil organic N and potentially mineralizable N. We conclude that on clay soils with 70% desirable grasses (i.e., Lolium perenne and Phleum pratense) or more, the presumed yield benefit of grassland renewal is offset by a loss of soil quality (SOM and N-total). The current practice of renewing grassland after 10 years without considering the botanical composition, is counter-productive and not sustainable.
MULTIFILE
Children with Marfan (MFS) and Loeys-Dietz syndrome (LDS) report limitations in physical activities, sports, school, leisure, and work participation in daily life. This observational, cross-sectional, multicenter study explores associations between physical fitness and cardiovascular parameters, systemic manifestations, fatigue, and pain in children with MFS and LDS. Forty-two participants, aged 6–18 years (mean (SD) 11.5(3.7)), diagnosed with MFS (n = 36) or LDS (n = 6), were enrolled. Physical fitness was evaluated using the Fitkids Treadmill Test’s time to exhaustion (TTE) outcome measure. Cardiovascular parameters (e.g., echocardiographic parameters, aortic surgery, cardiovascular medication) and systemic manifestations (systemic score of the revised Ghent criteria) were collected. Pain was obtained by visual analog scale. Fatigue was evaluated by PROMIS® Fatigue-10a-Pediatric-v2.0-short-form and PROMIS® Fatigue-10a-Parent-Proxy-v2.0-short-form. Multivariate linear regression analyses explored associations between physical fitness (dependent variable) and independent variables that emerged from the univariate linear regression analyses (criterion p <.05). The total group (MFS and LDS) and the MFS subgroup scored below norms on physical fitness TTE Z-score (mean (SD) −3.1 (2.9); −3.0 (3.0), respectively). Univariate analyses showed associations between TTE Z-score aortic surgery, fatigue, and pain (criterion p <.05). Multivariate analyses showed an association between physical fitness and pediatric self-reported fatigue that explained 48%; 49%, respectively, of TTE Z-score variance (F (1,18) = 18.6, p ≤.001, r2 =.48; F (1,15) = 16,3, p =.01, r2 =.49, respectively). Conclusions: Physical fitness is low in children with MFS or LDS and associated with self-reported fatigue. Our findings emphasize the potential of standardized and tailored exercise programs to improve physical fitness and reduce fatigue, ultimately enhancing the physical activity and sports, school, leisure, and work participation of children with MFS and LDS. (Table presented.)
Cell-based production processes in bioreactors and fermenters need to be carefully monitored due to the complexity of the biological systems and the growth processes of the cells. Critical parameters are identified and monitored over time to guarantee product quality and consistency and to minimize over-processing and batch rejections. Sensors are already available for monitoring parameters such as temperature, glucose, pH, and CO2, but not yet for low-concentration substances like proteins and nucleic acids (DNA). An interesting critical parameter to monitor is host cell DNA (HCD), as it is considered an impurity in the final product (downstream process) and its concentration indicates the cell status (upstream process). The Molecular Biosensing group at the Eindhoven University of Technology and Helia Biomonitoring are developing a sensor for continuous biomarker monitoring, based on Biosensing by Particle Motion. With this consortium, we want to explore whether the sensor is suitable for the continuous measurement of HCD. Therefore, we need to set-up a joint laboratory infrastructure to develop HCD assays. Knowledge of how cells respond to environmental changes and how this is reflected in the DNA concentration profile in the cell medium needs to be explored. This KIEM study will enable us to set the first steps towards continuous HCD sensing from cell culture conditions controlling cell production processes. It eventually generates input for machine learning to be able to automate processes in bioreactors and fermenters e.g. for the production of biopharmaceuticals. The project entails collaboration with new partners and will set a strong basis for subsequent research projects leading to scientific and economic growth, and will also contribute to the human capital agenda.
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).
Wheelchair users with a spinal cord injury (SCI) or amputation generally lead an inactive lifestyle, associated with reduced fitness and health. Digital interventions and sport and lifestyle applications (E-platforms) may be helpful in achieving a healthy lifestyle. Despite the potential positive effects of E-platforms in the general population, no studies are known investigating the effects for wheelchair users and existing E-platforms can not be used to the same extent and in the same manner by this population due to differences in physiology, body composition, exercise forms and responses, and risk injury. It is, therefore, our aim to adapt an existing E-platform (Virtuagym) within this project by using existing data collections and new data to be collected within the project. To reach this aim we intend to make several relevant databases from our network available for analysis, combine and reanalyze these existing databases to adapt the existing E-platform enabling wheelchair users to use it, evaluate and improve the use of the adapted E-platform, evaluate changes in healthy active lifestyle parameters, fitness, health and quality of life in users of the E-platform (both wheelchair users and general population) and identify determinants of these changes, identify factors affecting transitions from an inactive lifestyle, through an intermediate level, to an athlete level, comparing wheelchair users with the general population, and comparing Dutch with Brazilian individuals. The analysis of large datasets of exercise and fitness data from various types of individuals with and without disabilities, collected over the last years both in the Netherlands and Brazil, is an innovative and potentially fruitful approach. It is expected that the comparison of e.g. wheelchair users in Amsterdam vs. Sao Paulo or recreative athletes vs. elite athletes provides new insight in the factors determining a healthy and active lifestyle.
