Search

The circumgalactic medium (CGM) plays a crucial role in galaxy evolution as it fuels star formation, retains metals ejected from the galaxies, and hosts gas flows in and out of galaxies. For Milky Way–type and more-massive galaxies, the bulk of the CGM is in hot phases best accessible at X-ray wavelengths. However, our understanding of the CGM remains largely unconstrained due to its tenuous nature. A promising way to probe the CGM is via X-ray absorption studies. Traditional absorption studies utilize bright background quasars, but this method probes the CGM in a pencil beam, and, due to the rarity of bright quasars, the galaxy population available for study is limited. Large-area, high spectral resolution X-ray microcalorimeters offer a new approach to exploring the CGM in emission and absorption. Here, we demonstrate that the cumulative X-ray emission from cosmic X-ray background sources can probe the CGM in absorption. We construct column density maps of major X-ray ions from the Magneticum simulation and build realistic mock images of nine galaxies to explore the detectability of X-ray absorption lines arising from the large-scale CGM. We conclude that the O VII absorption line is detectable around individual massive galaxies at the 3 σ –6 σ confidence level. For Milky Way–type galaxies, the O VII and O VIII absorption lines are detectable at the ∼ 6 σ and ∼ 3 σ levels even beyond the virial radius when coadding data from multiple galaxies. This approach complements emission studies, does not require additional exposures, and will allow for probing the baryon budget and the CGM at the largest scales.

Abstract Background Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis. Results The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.

We have simulated the formation of a galaxy cluster in a Λ cold dark matter universe using 13 different codes modelling only gravity and non-radiative hydrodynamics (RAMSES, ART, AREPO, HYDRA and nine incarnations of GADGET). This range of codes includes particle-based, moving and fixed mesh codes as well as both Eulerian and Lagrangian fluid schemes. The various GADGET implementations span classic and modern smoothed particle hydrodynamics (SPH) schemes. The goal of this comparison is to assess the reliability of cosmological hydrodynamical simulations of clusters in the simplest astrophysically relevant case, that in which the gas is assumed to be non-radiative. We compare images of the cluster at z = 0, global properties such as mass and radial profiles of various dynamical and thermodynamical quantities. The underlying gravitational framework can be aligned very accurately for all the codes allowing a detailed investigation of the differences that develop due to the various gas physics implementations employed. As expected, the mesh-based codes RAMSES, ART and AREPO form extended entropy cores in the gas with rising central gas temperatures. Those codes employing classic SPH schemes show falling entropy profiles all the way into the very centre with correspondingly rising density profiles and central temperature inversions. We show that methods with modern SPH schemes that allow entropy mixing span the range between these two extremes and the latest SPH variants produce gas entropy profiles that are essentially indistinguishable from those obtained with grid-based methods. [ABSTRACT FROM AUTHOR]

In East Africa, wetlands are steadily converted to agriculture for food security reasons. This study analyzed high spatial resolution panchromatic and color photographs in the Anyiko wetland in Kenya to reveal wetland conversions between 1966 and 2018. In addition, socio-economic determinants of land use/cover change are assessed in the Anyiko wetland. Socio-economic data was collected through a questionnaire survey of 226 households. A Chi-squared Automatic Interaction Detector (CHAID) decision tree approach is utilized to assess determinants of wetlands conversion. The results showed that between 1966 and 2018, the wetland area reduced by 55%, mostly attributed to agricultural development. Households were more likely to cultivate the wetland if they did not harvest papyrus for artisanal products, were male-headed and lacked alternative sources of income. The perceptions that wetland is “wasteland” and conversion to agriculture provides higher net monetary benefit did not influence wetland cultivation. Hence, the conversion of the wetland was determined by the socio-economic status of the households rather than perceptions on its value.

Abstract Background Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. Contribution In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as ‘low-scoring’ ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling. Results We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (https://github.com/laeeq80/spark-cpvs) and can be run on high-performance computers as well as on cloud resources.

Particle-in-Cell (PIC) methods are widely used computational tools for fluid and kinetic plasma modeling. While both the fluid and kinetic PIC approaches have been successfully used to target either kinetic or fluid simulations, little was done to combine fluid and kinetic particles under the same PIC framework. This work addresses this issue by proposing a new PIC method, PolyPIC, that uses polymorphic computational particles. In this numerical scheme, particles can be either kinetic or fluid, and fluid particles can become kinetic when necessary, e.g., particles undergoing a strong acceleration. We design and implement the PolyPIC method, and test it against the Landau damping of Langmuir and ion acoustic waves, two stream instability and sheath formation. We unify the fluid and kinetic PIC methods under one common framework comprising both fluid and kinetic particles, providing a tool for adaptive fluid-kinetic coupling in plasma simulations.

A general-purpose Global Research Data Infrastructure (GRDI) for all sciences and research purposes is not conceivable for the next decade as there are too many discipline-specific modalities that currently prevail for such generalisation efforts to be effective. On the other hand, a more pragmatic approach is to start from what currently exists, identify best practices and key issues, and promote effective inter-domain collaboration among different components forming an ecosystem. This will promote interoperability, data exchange, data preservation, and distributed access (among others). This ecosystem of interoperable research data infrastructures will be composed of regional, disciplinary, and multidisciplinary components, such as libraries, archives, and data centres, offering data services for both primary datasets and publications. The ecosystem will support data-intensive science and research and stimulate the interaction among all its elements, thus promoting multidisciplinary and interdisciplinary science. This special issue includes a set of independent papers from renowned experts on organisational and technological issues related to GRDIs. These documents feed into and compliment the GRDI2020 roadmap, which supports a Global Research Data Infrastructure ecosystem.

In the vision of Global Research Data Infrastructures (GRDIs), data storage and management plays a crucial role. A successful GRDI will require a common globally interoperable distributed data system, formed out of data centres, that incorporates emerging technologies and new scientific data activities. The main challenge is to define common certification and auditing frameworks that will allow storage providers and data communities to build a viable partnership based on trust. To achieve this, it is necessary to find a long-term commitment model that will give financial, legal, and organisational guarantees of digital information preservation. In this article we discuss the state of the art in data storage and management for GRDIs and point out future research directions that need to be tackled to implement GRDIs.