SURF

Environmental variables such as air pollution, noise, floods, green space and conflict , shape human health and disease. An important concept is the human exposome, the totality of an individual’s exposure to the environment over their lifetime. The exposome can explain a large proportion of our health, yet quantification across the world population remains surprisingly limited. As we are facing global climate and population change, it becomes increasingly important to understand and quantify the exposome. However, existing studies are often small-scale, do not integrate human mobility affecting personal exposure or focus on narrow sets of variables, thereby failing to capture the full range of socioeconomic and physical variables and values. Harmonized global scale quantitative assessments of the entire exposomes of the world population remain limited. We address this by collecting data sets on environmental variables for a wide range of geo-domains at high resolution (

Abstract Computational astrochemical models are essential for helping us interpret and understand the observations of different astrophysical environments. In the age of high-resolution telescopes such as JWST and ALMA, the substructure of many objects can be resolved, raising the need for astrochemical modeling at these smaller scales, meaning that the simulations of these objects need to include both the physics and chemistry to accurately model the observations. The computational cost of the simulations coupling both the three-dimensional hydrodynamics and chemistry is enormous, creating an opportunity for surrogate models that can effectively substitute the chemical solver. In this work we present surrogate models that can replace the original chemical code, namely Latent Augmented Neural Ordinary Differential Equations. We train these surrogate architectures on three datasets of increasing physical complexity, with the last dataset derived directly from a three-dimensional simulation of a molecular cloud using a photodissociation region (PDR) code, 3D-PDR. We show that these surrogate models can provide speedup and reproduce the original observable column density maps of the dataset. This enables the rapid inference of the chemistry (on the GPU), allowing for the faster statistical inference of observations or increasing the resolution in hydrodynamical simulations of astrophysical environments.

We numerically study the distribution of entanglement between the Dutch cities of Delft and Eindhoven realized with a processing-node quantum repeater and determine minimal hardware requirements for verifiable blind quantum computation using color centers and trapped ions. Our results are obtained considering restrictions imposed by a real-world fiber grid and using detailed hardware-specific models. By comparing our results to those we would obtain in idealized settings, we show that simplifications lead to a distorted picture of hardware demands, particularly on memory coherence and photon collection. We develop general machinery suitable for studying arbitrary processing-node repeater chains using NetSquid, a discrete-event simulator for quantum networks. This enables us to include time-dependent noise models and simulate repeater protocols with cut-offs, including the required classical control communication. We find minimal hardware requirements by solving an optimization problem using genetic algorithms on a high-performance-computing cluster. Our work provides guidance for further experimental progress, and showcases limitations of studying quantum-repeater requirements in idealized situations.

Interleukin-4 (IL-4) is a hematopoietic cytokine composed by a four-helix bundle stabilized by an antiparallel beta-sheet and three disulfide bonds: Cys3-Cys127, Cys24-Cys65, and Cys46-Cys99. IL-4 is involved in several immune responses associated to infection, allergy, autoimmunity, and cancer. Besides its physiological relevance, IL-4 is often used as a "model" for protein design and engineering. Hence, to understand the role of each disulfide in the structure and dynamics of IL-4, we carried out several spectroscopic analyses (circular dichroism [CD], fluorescence, nuclear magnetic resonance [NMR]), and molecular dynamics (MD) simulations on wild-type IL-4 and four IL-4 disulfide mutants. All disulfide mutants showed loss of structure, altered interhelical angles, and looser core packings, showing that all disulfides are relevant for maintaining the overall fold and stability of the four-helix bundle motif, even at very low pH. In the absence of the disulfide connecting both protein termini Cys3-Cys127, C3T-IL4 showed a less packed protein core, loss of secondary structure (~9%) and fast motions on the sub-nanosecond time scale (lower S2 order parameters and larger τc correlation time), especially at the two protein termini, loops, beginning of helix A and end of helix D. In the absence of Cys24-Cys65, C24T-IL4 presented shorter alpha-helices (14% loss in helical content), altered interhelical angles, less propensity to form the small anti-parallel beta-sheet and increased dynamics. Simultaneously deprived of two disulfides (Cys3-Cys127 and Cys24-Cys65), IL-4 formed a partially folded "molten globule" with high 8-anilino-1-naphtalenesulphonic acid-binding affinity and considerable loss of secondary structure (~50%decrease), as shown by the far UV-CD, NMR, and MD data.

Since the first general-purpose computing machines came up in the middle of the twentieth century, computer science's popularity has been growing steadily until our time. The first computers represented a significant leap forward in automating calculations, so that several theoretical methods could be taken from paper into practice. The continuous need for increased computing capacity made computers evolve and become more and more powerful. Nowadays, high-performance computing (HPC) is a crucial component of scientific and technological advancement. This book chapter introduces the field of HPC, covering key concepts and essential terminology to understand this complex and rapidly evolving area. The chapter begins with an overview of what HPC is and how it differs from conventional computing. It then explores the various components and configurations of supercomputers, including shared memory systems, distributed memory systems, and hybrid systems and the different programming models used in HPC, including message passing, shared memory, and data parallelism. Finally, the chapter discusses significant challenges and future directions in supercomputing. Overall, this chapter provides a comprehensive introduction to the world of HPC and is an essential resource for anyone interested in this fascinating field.

The European Commission and the educational sector are simultaneously working on innovative concepts that involve credentials and wallets. The EU has high ambitions to create a digital identity ecosystem, while educational institutions are exploring ways to use credentials to support flexible and life-long learning. Both initiatives share similar terminology, technology and standards. Also, common use cases can be defined, but that does not make the initiatives identical from a business and governance perspective. When comparing the use cases and roles the educational sector has with the proposed EU digital identity framework (EUDI), this becomes clear. Diplomas are not issued by an identity framework. The educational sector should take ownership of credentials that are part of its core process. At the same time, interoperability with the EUDI ecosystem must be kept. The sector should explore additional ways to influence the EUDI development in architecture, standards and governance.

Abstract MotivationWe are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals. Hence, most biomedical data is kept in secure and protected silos. Therefore, it remains a challenge to reuse these data without infringing the privacy of the individuals from which the data were derived. Federated analysis of Findable, Accessible, Interoperable, and Reusable (FAIR) data is a privacy-preserving solution to make optimal use of these multi-omics data and transform them into actionable knowledge.ResultsThe Netherlands X-omics Initiative is a National Roadmap Large-Scale Research Infrastructure aiming for efficient integration of data generated within X-omics and external datasets. To facilitate this, we developed the FAIR Data Cube (FDCube), which adopts and applies the FAIR principles and helps researchers to create FAIR data and metadata, to facilitate re-use of their data, and to make their data analysis workflows transparent, and in the meantime ensure data security and privacy.

Traditional datacenter analysis is based on high-level, coarse-grained metrics. This obscures our vision of datacenter behavior, as we do not observe the full picture nor subtleties that might make up these high-level, coarse metrics. There is room for operational improvement based on fine-grained temporal and spatial, low-level metric data. We leverage in this work one of the (rare) public datasets providing fine-grained information on datacenter operations, with over 60 billion measurements captured in 15-second intervals. We show evidence that fine-grained information reveals new operational aspects, that the different metrics cannot be derived from one another (and thus need to be captured), and that many low-level metrics, gathered frequently are key to understanding datacenter operations. We propose a holistic analysis for datacenter operations, providing statistical characterization of node and workload aspects. Our analysis reveals both generic and machine learning-specific aspects, summarized in over 30 observations, providing deep insight into this dataset and the originating cluster. We give actionable insights, surprising findings, and exemplify how our observations support performance-engineering tasks such as workload prediction and long-term datacenter design.

The second FAIRCORE4EOSC Briefing summarises the latest technical updates on the components development, the progress on case studies and the major achievements. Furthermore, it includes an overview of FAIRCORE4EOSC participation in events and forged synergies with partners and other projects.

FAIRCORE4EOSC is envisioned to work in close collaboration with other actors in the EOSC ecosystem, including the EOSC Association itself, the H2020 project EOSC-Future, as well as the Horizon Europe project FAIR-IMPACT, with which we have also established a Technical Bridging Team. FAIRCORE4EOSC Briefings are designed to provide periodic updates to the EOSC Partnership on a quarterly basis. They provide a quick overview on project progress, fostering uptake in EOSC of main technical results. Extra! Extra! Read all about it! FAIRCORE4EOSC First Briefing!