CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

A low-cost 3D-printed sample-holder for stirring-based decellularization of biological tissues

An innovative, low-cost, 3D-printed sample-holder is proposed for reproducible and effective stirring-based decellularization of biological tissues. The sample-holder was designed to be low-cost, easy to use with conventional laboratory equipment, and manufacturable through 3D printing. During stirring-based decellularization, the sample holder exposes the samples to convective flow, enhancing the reagent transport while protecting the samples from disruptive forces. Computational fluid dynamics analyses were carried out to elucidate the developing hydrodynamics. Explanatory tests, performed on human cardiac tissue samples, demonstrated the effectiveness of the presented device

A low-cost scalable 3D-printed sample-holder for agitation-based decellularization of biological tissues

Decellularized extracellular matrix is one of the most promising biological scaffold supporting in vitro tissue growth and in vivo tissue regeneration in both preclinical research and clinical practice. In case of thick tissues or even organs, conventional static decellularization methods based on chemical or enzymatic treatments are not effective in removing the native cellular material without affecting the extracellular matrix. To overcome this limitation, dynamic decellularization methods, mostly based on perfusion and agitation, have been proposed. In this study, we developed a low-cost scalable 3D-printed sample-holder for agitation-based decellularization purposes, designed for treating multiple specimens simultaneously and for improving efficiency, homogeneity and reproducibility of the decellularization treatment with respect to conventional agitation-based approaches. In detail, the proposed sample-holder is able to house up to four specimens and, immersed in the decellularizing solution within a beaker placed on a magnetic stirrer, to expose them to convective flow, enhancing the solution transport through the specimens while protecting them. Computational fluid dynamics analyses were performed to investigate the fluid phenomena establishing within the beaker and to support the sample-holder design. Exploratory biological tests performed on human skin specimens demonstrated that the sample-holder reduces process duration and increases treatment homogeneity and reproducibility

BioDynaMo: a modular platform for high-performance agent-based simulation

MotivationAgent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design.ResultsWe present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research.Availability and implementationBioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information.Supplementary informationAvailable at https://doi.org/10.5281/zenodo.5121618

Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models which are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future. Having the IBM 100 x 100 challenge in mind, where possible, we also provide resource estimates for the examples given using error mitigated quantum computing

Axial spondyloarthritis in patients with recurrent fever attacks: data from the AIDA network registry for undifferentiated autoInflammatory diseases (USAIDs)

BeckgroundDespite the recent advances in the field of autoinflammatory diseases, most patients with recurrent fever episodes do not have any defined diagnosis. The present study aims at describing a cohort of patients suffering from apparently unexplained recurrent fever, in whom non-radiographic axial spondylarthritis (SpA) represented the unique diagnosis identified after a complete clinical and radiologic assessment.Materials and methodsPatients’ data were obtained from the international registry on Undifferentiated Systemic AutoInflammatory Diseases (USAIDs) developed by the AutoInflammatory Disease Alliance (AIDA) network.ResultsA total of 54 patients with recurrent fever episodes were also affected by non-radiographic axial SpA according to the international classification criteria. SpA was diagnosed after the start of fever episodes in all cases; the mean age at the diagnosis of axial SpA was 39.9 ± 14.8 years with a diagnostic delay of 9.3 years. The highest body temperature reached during flares was 42°C, with a mean temperature of 38.8 ± 1.1°C. The most frequent manifestations associated to fever were: arthralgia in 33 (61.1%) cases, myalgia in 24 (44.4%) cases, arthritis in 22 (40.7%) cases, headache in 15 (27.8%) cases, diarrhea in 14 (25.9%) cases, abdominal pain in 13 (24.1%) cases, and skin rash in 12 (22.1%) cases. Twenty-four (44.4%) patients have taken daily or on-demand non-steroidal anti-inflammatory drugs (NSAIDs) and 31 (57.4%) patients have been treated with daily or on demand oral glucocorticoids. Colchicine was used in 28 (51.8%) patients, while other conventional disease modifying anti-rheumatic drugs (cDMARDs) were employed in 28 (51.8%) patients. Forty (74.1%) patients underwent anti-tumor necrosis factor (TNF) agents and 11 (20.4%) were treated with interleukin (IL)-1 inhibitors. The response to TNF inhibitors on recurrent fever episodes appeared more effective than that observed with anti-IL-1 agents; colchicine and other cDMARDs were more useful when combined with biotechnological agents.ConclusionSigns and symptoms referring to axial SpA should be inquired in patients with apparently unexplained recurrent fever episodes. The specific treatment for axial SpA may lead to a remarkable improvement in the severity and/or frequency of fever episodes in patients with unexplained fevers and concomitant axial SpA

Towards a European Health Research and Innovation Cloud (HRIC)

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans