The application of numerical debris flow modelling for the generation of physical vulnerability curves

For a quantitative assessment of debris flow risk, it is essential to consider not only the hazardous process itself but also to perform an analysis of its consequences. This should include the estimation of the expected monetary losses as the product of the hazard with a given magnitude and the vulnerability of the elements exposed. A quantifiable integrated approach of both hazard and vulnerability is becoming a required practice in risk reduction management. This study aims at developing physical vulnerability curves for debris flows through the use of a dynamic run-out model. Dynamic run-out models for debris flows are able to calculate physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk could suffer an impact. These results can then be applied to consequence analyses and risk calculations. On 13 July 2008, after more than two days of intense rainfall, several debris and mud flows were released in the central part of the Valtellina Valley (Lombardy Region, Northern Italy). One of the largest debris flows events occurred in a village called Selvetta. The debris flow event was reconstructed after extensive field work and interviews with local inhabitants and civil protection teams. The Selvetta event was modelled with the FLO-2D program, an Eulerian formulation with a finite differences numerical scheme that requires the specification of an input hydrograph. The internal stresses are isotropic and the basal shear stresses are calculated using a quadratic model. The behaviour and run-out of the flow was reconstructed. The significance of calculated values of the flow depth, velocity, and pressure were investigated in terms of the resulting damage to the affected buildings. The physical damage was quantified for each affected structure within the context of physical vulnerability, which was calculated as the ratio between the monetary loss and the reconstruction value. Three different empirical vulnerability curves were obtained, which are functions of debris flow depth, impact pressure, and kinematic viscosity, respectively. A quantitative approach to estimate the vulnerability of an exposed element to a debris flow which can be independent of the temporal occurrence of the hazard event is presented

Three “hotspots” important for adenosine A2B receptor activation: a mutational analysis of transmembrane domains 4 and 5 and the second extracellular loop

G protein-coupled receptors (GPCRs) are a major drug target and can be activated by a range of stimuli, from photons to proteins. Despite the progress made in the last decade in molecular and structural biology, their exact activation mechanism is still unknown. Here we describe new insights in specific regions essential in adenosine A2B receptor activation (A2BR), a typical class A GPCR. We applied unbiased random mutagenesis on the middle part of the human adenosine A2BR, consisting of transmembrane domains 4 and 5 (TM4 and TM5) linked by extracellular loop 2 (EL2), and subsequently screened in a medium-throughput manner for gain-of-function and constitutively active mutants. For that purpose, we used a genetically engineered yeast strain (Saccharomyces cerevisiae MMY24) with growth as a read-out parameter. From the random mutagenesis screen, 12 different mutant receptors were identified that form three distinct clusters; at the top of TM4, in a cysteine-rich region in EL2, and at the intracellular side of TM5. All mutant receptors show a vast increase in agonist potency and most also displayed a significant increase in constitutive activity. None of these residues are supposedly involved in ligand binding directly. As a consequence, it appears that disrupting the relatively “silent” configuration of the wild-type receptor in each of the three clusters readily causes spontaneous receptor activity

Recovery for all in the community:Position paper on principles and key elements of community-based mental health care

Backgroud:Service providers throughout Europe have identified the need to define how high-quality community-based mental health care looks to organize their own services and to inform governments, commissioners and funders. In 2016, representatives of mental health care service providers, networks, umbrella organizations and knowledge institutes in Europe came together to establish the European Community Mental Health Services Provider (EUCOMS) Network. This network developed a shared vision on the principles and key elements of community mental health care in different contexts. The result is a comprehensive consensus paper, of which this position paper is an outline. With this paper the network wants to contribute to the discussion on how to improve structures in mental healthcare, and to narrow the gap between evidence, policy and practice in Europe.Main text:The development of the consensus paper started with an expert workshop in April 2016. An assigned writing group representing the workshop participants built upon the outcomes of this meeting and developed the consensus paper with the input from 100 European counterparts through two additional work groups, and two structured feedback rounds via email. High quality community-based mental health care: 1) protects human rights; 2) has a public health focus; 3) supports service users in their recovery journey; 4) makes use of effective interventions based on evidence and client goals; 5) promotes a wide network of support in the community and; 6) makes use of peer expertise in service design and delivery. Each principle is illustrated with good practices from European service providers that are members of the EUCOMS Network.Conclusions:Discussion among EUCOMS network members resulted in a blueprint for a regional model of integrated mental health care based upon six principles.</p

Tropical biogeomorphic seagrass landscapes for coastal protection:Persistence and wave attenuation during major storms events

The intensity of major storm events generated within the Atlantic Basin is projected to rise with the warming of the oceans, which is likely to exacerbate coastal erosion. Nature-based flood defence has been proposed as a sustainable and effective solution to protect coastlines. However, the ability of natural ecosystems to withstand major storms like tropical hurricanes has yet to be thoroughly tested. Seagrass meadows both stabilise sediment and attenuate waves, providing effective coastal protection services for sandy beaches. To examine the tolerance of Caribbean seagrass meadows to extreme storm events, and to investigate the extent of protection they deliver to beaches, we employed a combination of field surveys, biomechanical measurements and wave modelling simulations. Field surveys of sea- grass meadows before and after a direct hit by the category 5 Hurricane Irma documented that estab- lished seagrass meadows of Thalassia testudinum re- mained unaltered after the extreme storm event. The flexible leaves and thalli of seagrass and calci- fying macroalgae inhabiting the meadows were shown to sustain the wave forces that they are likely to experience during hurricanes. In addition, the seagrass canopy and the complex biogeomorphic landscape built by the seagrass meadows combine to significantly dissipate extreme wave forces, ensuring that erosion is minimised within sandy beach fore- shores. The persistence of the Caribbean seagrass meadows and their coastal protection services dur- ing extreme storm events ensures that a stable coastal ecosystem and beach foreshore is maintained in tropical regions

Plasma neurofilament light protein correlates with diffusion tensor imaging metrics in frontotemporal dementia

Neurofilaments are structural components of neurons and are particularly abundant in highly myelinated axons. The levels of neurofilament light chain (NfL) in both cerebrospinal fluid (CSF) and plasma have been related to degeneration in several neurodegenerative conditions including frontotemporal dementia (FTD) and NfL is currently considered as the most promising diagnostic and prognostic fluid biomarker in FTD. Although the location and function of filaments in the healthy nervous system suggests a link between increased NfL and white matter degeneration, such a claim has not been fully elucidated in vivo, especially in the context of FTD. The present study provides evidence of an association between the plasma levels of NfL and white matter involvement in behavioral variant FTD (bvFTD) by relating plasma concentration of NfL to diffusion tensor imaging (DTI) metrics in a group of 20 bvFTD patients. The results of both voxel-wise and tract specific analysis showed that increased plasma NfL concentration is associated with a reduction in fractional anisotropy (FA) in a widespread set of white matter tracts including the superior longitudinal fasciculus, the fronto-occipital fasciculus the anterior thalamic radiation and the dorsal cingulum bundle. Plasma NfL concentration also correlated with cortical thinning in a portion of the right medial prefrontal cortex and of the right lateral orbitofrontal cortex. These results support the hypothesis that blood NfL levels reflect the global level of neurodegeneration in bvFTD and help to advance our understanding of the association between this blood biomarker for FTD and the disease process

LiMeS-Lab:An Integrated Laboratory for the Development of Liquid–Metal Shield Technologies for Fusion Reactors

The liquid metal shield laboratory (LiMeS-Lab) will provide the infrastructure to develop, test, and compare liquid metal divertor designs for future fusion reactors. The main research topics of LiMeS-lab will be liquid metal interactions with the substrate material of the divertor, the continuous circulation and capillary refilling of the liquid metal during intense plasma heat loading and the retention of plasma particles in the liquid metal. To facilitate the research, four new devices are in development at the Dutch Institute for Fundamental Energy Research and the Eindhoven University of Technology: LiMeS-AM: a custom metal 3D printer based on powder bed fusion; LiMeS-Wetting, a plasma device to study the wetting of liquid metals on various substrates with different surface treatments; LiMeS-PSI, a linear plasma generator specifically adapted to operate continuous liquid metal loops. Special diagnostic protection will also be implemented to perform measurements in long duration shots without being affected by the liquid metal vapor; LiMeS-TDS, a thermal desorption spectroscopy system to characterize deuterium retention in a metal vapor environment. Each of these devices has specific challenges due to the presence and deposition of metal vapors that need to be addressed in order to function. In this paper, an overview of LiMeS-Lab will be given and the conceptual designs of the last three devices will be presented.</p

QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping

Funder: FP7 People: Marie-Curie Actions; doi: http://dx.doi.org/10.13039/100011264; Grant(s): 238701, 238701Abstract: An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds