KKF-Model Platform Coupling : summary report KKF01b

Nederland bereidt zich voor op een sneller stijgende zeespiegel en een veranderend klimaat. Hiervoor is het Deltaprogramma gestart. Dit deltaprogramma voorziet een serie beslissingen die grote gevolgen zullen hebben voor het beheer van het water in Nederland. Om deze beslissingen zorgvuldig te nemen is informatie nodig over hoe het klimaat en de stijgende zeespiegel dit waterbeheer zullen beïnvloeden. De modellen die de gevolgen van klimaatverandering berekenen zullen daarom met dezelfde klimaat forcering en gekoppeld aan elkaar moeten worden gebruikt. In dit onderzoek is gekeken naar het linken van hydrologische en hydrodynamische modellen – en daaraan gekoppelde modellen die de ontwikkelingen in natuur en landgebruik modelleren -- die het gebied van de Alpen tot en met de Noordzee inclusief Nederland beschrijven

The endogenous caspase-8 inhibitor c-FLIPL regulates ER morphology and crosstalk with mitochondria

Components of the death receptors-mediated pathways like caspase-8 have been identified in complexes at intracellular membranes to spatially restrict the processing of local targets. In this study, we report that the long isoform of the cellular FLICE-inhibitory protein (c-FLIPL), a well- known inhibitor of the extrinsic cell death initiator caspase-8, localizes at the endoplasmic reticulum (ER) and mitochondria-associated membranes (MAMs). ER morphology was disrupted and ER Ca2+-release as well as ER-mitochondria tethering were decreased in c-FLIP-/- mouse embryonic fibroblasts (MEFs). Mechanistically, c-FLIP ablation resulted in enhanced basal caspase-8 activation and in caspase-mediated processing of the ER-shaping protein reticulon-4 (RTN4) that was corrected by re-introduction of c-FLIPL and caspase inhibition, resulting in the recovery of a normal ER morphology and ER-mitochondria juxtaposition. Thus, the caspase-8 inhibitor c-FLIPL emerges as a component of the MAMs signaling platforms, where caspases appear to regulate ER morphology and ER-mitochondria crosstalk by impinging on ER-shaping proteins like the RTN4

Measurement of S-phase duration of adult stem cells in the flatworm Macrostomum lignano by double replication labelling and quantitative colocalization analysis

Platyhelminthes are highly attractive models for addressing fundamental aspects of stem cell biology in vivo. These organisms possess a unique stem cell system comprised of neoblasts that are the only proliferating cells during adulthood. We have investigated T-s (S-phase duration) of neoblasts during homoeostasis and regeneration in the flatworm, Macrostomum lignano. A double immunohistochemical technique was used, performing sequential pulses with the thymidine analogues CldU (chlorodeoxyuridine) and IdU (iododeoxyuridine), separated by variable chase times in the presence of colchicine. Owing to the localized nature of the fluorescent signals (cell nuclei) and variable levels of autofluorescence, standard intensity-based colocalization analyses could not be applied to accurately determine the colocalization. Therefore, an object-based colocalization approach was devised to score the relative number of double-positive cells. Using this approach, T-s (S-phase duration) in the main population of neoblasts was similar to 13 h. During early regeneration, no significant change in T-s was observed

The small GTPase Rab29 is a common regulator of immune synapse assembly and ciliogenesis

Acknowledgements We wish to thank Jorge Galán, Gregory Pazour, Derek Toomre, Giuliano Callaini, Joel Rosenbaum, Alessandra Boletta and Francesco Blasi for generously providing reagents and for productive discussions, and Sonia Grassini for technical assistance. The work was carried out with the financial support of Telethon (GGP11021) and AIRC.Peer reviewedPostprin

Nutrient stress alters the glycosylation status of LGR5 resulting in reduced protein stability and membrane localisation in colorectal tumour cells: implications for targeting cancer stem cells

BACKGROUND LGR5 is an important marker of intestinal stem cells and performs its vital functions at the cell membrane. Despite the importance of LGR5 to both normal and cancer stem cell biology, it is not known how microenvironmental stress affects the expression and subcellular distribution of the protein. METHODS Nutrient stress was induced through glucose starvation. Glycosylation status was assessed using endoglycosidase or tunicamycin treatment. Flow cytometry and confocal microscopy were used to assess subcellular distribution of LGR5. RESULTS Glucose deprivation altered the glycosylation status of LGR5 resulting in reduced protein stability and cell surface expression. Furthermore, inhibiting LGR5 glycosylation resulted in depleted surface expression and reduced localisation in the cis-Golgi network. CONCLUSIONS Nutrient stress within a tumour microenvironment has the capacity to alter LGR5 protein stability and membrane localisation through modulation of LGR5 glycosylation status. As LGR5 surface localisation is required for enhanced Wnt signalling, this is the first report to show a mechanism by which the microenvironment could affect LGR5 function

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

© 2016. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Ioannis Kioutsioukis, et al, ‘Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data’, Atmospheric Chemistry and Physics, Vol 16(24): 15629-15652, published 20 December 2016, the version of record is available at doi:10.5194/acp-16-15629-2016 Published by Copernicus Publications on behalf of the European Geosciences Union.Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.Peer reviewedFinal Published versio

SREBP-2/PNPLA8 axis improves non-alcoholic fatty liver disease through activation of autophagy

Dysregulated autophagy is associated with steatosis and non-alcoholic fatty liver disease (NAFLD), however the mechanisms connecting them remain poorly understand. Here, we show that co-administration of lovastatin and ezetimibe (L/E) significantly reverses hepatic triglyceride accumulation concomitant with an increase in SREBP-2 driven autophagy in mice fed a high-fat diet (HFD). We further show that the statin mediated increase in SREBP-2 directly activates expression of patatin-like phospholipase domain-containing enzyme 8 (PNPLA8) gene, and PNPLA8 associates with autophagosomes and is associated with a decrease in cellular triglyceride. Moreover, we show that over-expression of PNPLA8 dramatically decreases hepatic steatosis through increased autophagy in hepatocytes of HFD-fed mice. Live-cell imaging analyses also reveal that PNPLA8 dynamically interacts with LC3 and we suggest that the SREBP-2/PNPLA8 axis represents a novel regulatory mechanism for lipid homeostasis. These data provide a possible mechanism for the reported beneficial effects of statins for decreasing hepatic triglyceride levels in NAFLD patients.ope

ARTEMIN Promotes De Novo Angiogenesis in ER Negative Mammary Carcinoma through Activation of TWIST1-VEGF-A Signalling

10.1371/journal.pone.0050098PLoS ONE711

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance