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

MyChEMBL: A Virtual Platform for Distributing Cheminformatics Tools and Open Data

MyChEMBL is an open virtual platform which provides a free, secure, standardised and easy to use chemoinformatics environment for bioactivity data mining, machine learning, application development, learning and teaching. The main technical features of myChEMBL along with its applications and future plans are discussed here.FWN – Publicaties zonder aanstelling Universiteit Leide

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

Molecular insights into disease-associated glutamate transporter (EAAT1 / SLC1A3) variants using in silico and in vitro approaches

Medicinal Chemistr

Data-driven methods to estimate the committor function in conceptual ocean models

In recent years, several climate subsystems have been identified that may undergo a relatively rapid transition compared to the changes in their forcing. Such transitions are rare events in general, and simulating long-enough trajectories in order to gather sufficient data to determine transition statistics would be too expensive. Conversely, rare events algorithms like TAMS (trajectory-adaptive multilevel sampling) encourage the transition while keeping track of the model statistics. However, this algorithm relies on a score function whose choice is crucial to ensure its efficiency. The optimal score function, called the committor function, is in practice very difficult to compute. In this paper, we compare different data-based methods (analog Markov chains, neural networks, reservoir computing, dynamical Galerkin approximation) to estimate the committor from trajectory data. We apply these methods on two models of the Atlantic Ocean circulation featuring very different dynamical behavior. We compare these methods in terms of two measures, evaluating how close the estimate is from the true committor and in terms of the computational time. We find that all methods are able to extract information from the data in order to provide a good estimate of the committor. Analog Markov Chains provide a very reliable estimate of the true committor in simple models but prove not so robust when applied to systems with a more complex phase space. Neural network methods clearly stand out by their relatively low testing time, and their training time scales more favorably with the complexity of the model than the other methods. In particular, feedforward neural networks consistently achieve the best performance when trained with enough data, making this method promising for committor estimation in sophisticated climate models.</p

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

Successive statistical and structure-based modeling to identify chemically novel kinase inhibitors

Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of 50 value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well.Toxicolog

Cancer-associated mutations of the adenosine A2A receptor have diverse influences on ligand binding and receptor functions

The adenosine A(2A) receptor (A(2A)AR) is a class A G-protein-coupled receptor (GPCR). It is an immune checkpoint in the tumor micro-environment and has become an emerging target for cancer treatment. In this study, we aimed to explore the effects of cancer-patient-derived A(2A)AR mutations on ligand binding and receptor functions. The wild-type A(2A)AR and 15 mutants identified by Genomic Data Commons (GDC) in human cancers were expressed in HEK293T cells. Firstly, we found that the binding affinity for agonist NECA was decreased in six mutants but increased for the V275A mutant. Mutations A165V and A265V decreased the binding affinity for antagonist ZM241385. Secondly, we found that the potency of NECA (EC50) in an impedance-based cell-morphology assay was mostly correlated with the binding affinity for the different mutants. Moreover, S132L and H278N were found to shift the A(2A)AR towards the inactive state. Importantly, we found that ZM241385 could not inhibit the activation of V275A and P285L stimulated by NECA. Taken together, the cancer-associated mutations of A(2A)AR modulated ligand binding and receptor functions. This study provides fundamental insights into the structure-activity relationship of the A(2A)AR and provides insights for A(2A)AR-related personalized treatment in cancer.Toxicolog

Chemical genetics strategy to profile kinase target engagement reveals role of FES in neutrophil phagocytosis

Chemical tools to monitor drug-target engagement of endogenously expressed protein kinases are highly desirable for preclinical target validation in drug discovery. Here, we describe a chemical genetics strategy to selectively study target engagement of endogenous kinases. By substituting a serine residue into cysteine at the DFG-1 position in the ATP-binding pocket, we sensitize the non-receptor tyrosine kinase FES towards covalent labeling by a complementary fluorescent chemical probe. This mutation is introduced in the endogenous FES gene of HL-60 cells using CRISPR/Cas9 gene editing. Leveraging the temporal and acute control offered by our strategy, we show that FES activity is dispensable for differentiation of HL-60 cells towards macrophages. Instead, FES plays a key role in neutrophil phagocytosis via SYK kinase activation. This chemical genetics strategy holds promise as a target validation method for kinases.Medicinal Chemistr