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Every patient is different - his/her genomes, environment, disease history and exposure to drugs. Tumours, in particular, are often heterogeneous in their genetic make-up and their response to drugs, both within and between samples. Classic clinical trials basically ignore this complexity or, as in stratified medicine, attempt to reduce it to an analysis of a small number of still enormously heterogeneous patient groups. Medicine, however, is not the only area in which we are faced with such complex 'n = 1' (every individual case is different) situations. The weather we experience today, characterised by tens of terabytes of measurement data, has never occurred before and will never occur again. Similar to the situation in medicine, we cannot predict the development of today's weather by looking for identical weather conditions in the past, and we cannot, in real life, test drugs for every individual patient in clinical trials with large numbers of biologically identical patient replicas. We can, however, do it with the help of models duplicating the 'n = 1' situation on the computer, an approach which will also have to be used in both patient treatment and prevention as well as drug development in the future if we do not want to continue to make dangerous and expensive mistakes in real life.

<b><i>Background:</i></b> Knowledge in the era of Omics and Big Data has been increasingly conceptualized as a public good. Sharing of de-identified patient data has been advocated as a means to increase confidence and public trust in the results of clinical trials. On the other hand, research has shown that the current research and development model of the biopharmaceutical industry has reached its innovation capacity. In response to that, the biopharmaceutical industry has adopted open innovation practices, with sharing of clinical trial data being among the most interesting ones. However, due to the free rider problem, clinical trial data sharing among biopharmaceutical companies could undermine their innovativeness. <b><i>Method:</i></b> Based on the theory of public goods, we have developed a commons arrangement and devised a model, which enables secure and fair clinical trial data sharing over a Virtual Knowledge Bank based on a web platform. Our model uses data as a virtual currency and treats knowledge as a club good. <b><i>Results:</i></b> Fair sharing of clinical trial data over the Virtual Knowledge Bank has positive effects on the innovation capacity of the biopharmaceutical industry without compromising the intellectual rights, proprietary interests and competitiveness of the latter. <b><i>Conclusion:</i></b> The Virtual Knowledge Bank is a sustainable and self-expanding model for secure and fair clinical trial data sharing that allows for sharing of clinical trial data, while at the same time it increases the innovation capacity of the biopharmaceutical industry.

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i. e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software. (c) 2018 S. Karger AG, Basel

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software.

Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies.

"-Omics" research is in transition with the recent rise of multi-omics technology platforms. Integration of "-omics" and multi-omics research is of high priority in sepsis, a heterogeneous syndrome that is widely recognized as a global health burden and a priority biomedical funding field. We report here an original study on bibliometric trends in the use of "-omics" technologies, and multi-omics approaches in particular, in sepsis research in three (supra)national settings, the United States, the European Union 28 Member States (EU-28), and China. Using a 5-year longitudinal bibliometric study design from 2011 to 2015, we analyzed the sepsis-related research articles in English language that included at least one or multi-omics technologies in publicly available form in Medline (free full texts). We found that the United States has had the lead (almost one-third of publications) in the inclusion of an "-omics" or multi-omics technology in sepsis within the study period. However, both China and the EU-28 displayed a significant increase in the number of publications that employed one or more types of "-omics" research (p < 0.005), while the EU-28 displayed a significant increase especially in multi-omics research articles in sepsis (p < 0.05). Notably, more than half of the multi-omics research studies in the sepsis knowledge domain had a university or government/state funding source. Among the multi-omics research publications in sepsis, the combination of genomics and transcriptomics was the most frequent (40.5%), followed by genomics and proteomics (20.4%). We suggest that the lead of the United States in the field of "-omics" and multi-omics research in sepsis is likely at stake, with both the EU-28 and China rapidly increasing their research capacity. Moreover, "triple omics" that combine genomics, proteomics, and metabolomics analyses appear to be uncommon in sepsis, and yet much needed for triangulation of systems science data. These observations have implications for "-omics" technology policy and global research funding strategic foresight.

This report presents the conceptual design of a new European research infrastructure EuPRAXIA. The concept has been established over the last four years in a unique collaboration of 41 laboratories within a Horizon 2020 design study funded by the European Union. EuPRAXIA is the first European project that develops a dedicated particle accelerator research infrastructure based on novel plasma acceleration concepts and laser technology. It focuses on the development of electron accelerators and underlying technologies, their user communities, and the exploitation of existing accelerator infrastructures in Europe. EuPRAXIA has involved, amongst others, the international laser community and industry to build links and bridges with accelerator science — through realising synergies, identifying disruptive ideas, innovating, and fostering knowledge exchange. The Eu-PRAXIA project aims at the construction of an innovative electron accelerator using laser- and electron-beam-driven plasma wakefield acceleration that offers a significant reduction in size and possible savings in cost over current state-of-the-art radiofrequency-based accelerators. The foreseen electron energy range of one to five gigaelectronvolts (GeV) and its performance goals will enable versatile applications in various domains, e.g. as a compact free-electron laser (FEL), compact sources for medical imaging and positron generation, table-top test beams for particle detectors, as well as deeply penetrating X-ray and gamma-ray sources for material testing. EuPRAXIA is designed to be the required stepping stone to possible future plasma-based facilities, such as linear colliders at the high-energy physics (HEP) energy frontier. Consistent with a high-confidence approach, the project includes measures to retire risk by establishing scaled technology demonstrators. This report includes preliminary models for project implementation, cost and schedule that would allow operation of the full Eu-PRAXIA facility within 8—10 years.

This report presents the conceptual design of a new European research infrastructure EuPRAXIA. The concept has been established over the last four years in a unique collaboration of 41 laboratories within a Horizon 2020 design study funded by the European Union. EuPRAXIA is the first European project that develops a dedicated particle accelerator research infrastructure based on novel plasma acceleration concepts and laser technology. It focuses on the development of electron accelerators and underlying technologies, their user communities, and the exploitation of existing accelerator infrastructures in Europe. EuPRAXIA has involved, amongst others, the international laser community and industry to build links and bridges with accelerator science — through realising synergies, identifying disruptive ideas, innovating, and fostering knowledge exchange. The Eu-PRAXIA project aims at the construction of an innovative electron accelerator using laser- and electron-beam-driven plasma wakefield acceleration that offers a significant reduction in size and possible savings in cost over current state-of-the-art radiofrequency-based accelerators. The foreseen electron energy range of one to five gigaelectronvolts (GeV) and its performance goals will enable versatile applications in various domains, e.g. as a compact free-electron laser (FEL), compact sources for medical imaging and positron generation, table-top test beams for particle detectors, as well as deeply penetrating X-ray and gamma-ray sources for material testing. EuPRAXIA is designed to be the required stepping stone to possible future plasma-based facilities, such as linear colliders at the high-energy physics (HEP) energy frontier. Consistent with a high-confidence approach, the project includes measures to retire risk by establishing scaled technology demonstrators. This report includes preliminary models for project implementation, cost and schedule that would allow operation of the full Eu-PRAXIA facility within 8—10 years.

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14$^{+}$ monocytes, CD16$^{+}$ neutrophils, and naive CD4$^{+}$ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of $\textit{cis}$-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk. ; This work was predominantly funded by the EU FP7 High Impact Project BLUEPRINT (HEALTH-F5-2011-282510) and the Canadian Institutes of Health Research (CIHR EP1-120608). The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 282510 (BLUEPRINT), the European Molecular Biology Laboratory, the Max Planck society, the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017', SEV-2012-0208 and Spanish National Bioinformatics Institute (INB-ISCIII) PT13/0001/0021 co-funded by FEDER ""Una Manera de hacer Europa". D.G. is supported by a "la Caixa"-Severo Ochoa pre-doctoral fellowship, M.F. was supported by the BHF Cambridge Centre of Excellence [RE/13/6/30180], K.D. is funded as a HSST trainee by NHS Health Education England, S.E. is supported by a fellowship from La Caixa, V.P. is supported by a FEBS long-term fellowship and N.S.'s research is supported by the Wellcome Trust (Grant Codes WT098051 and WT091310), the EU FP7 (EPIGENESYS Grant Code 257082 and BLUEPRINT Grant Code HEALTH-F5-2011-282510) and the NIHR BRC. The Blood and Transplant Unit (BTRU) in Donor Health and Genomics is part of and funded by the National Institute for Health Research (NIHR) and is a partnership between the University of Cambridge and NHS Blood and Transplant (NHSBT) in collaboration with the University of Oxford and the Wellcome Trust Sanger Institute. The T-cell data was produced by the McGill Epigenomics Mapping Centre (EMC McGill). It is funded under the Canadian Epigenetics, Environment, and Health Research Consortium (CEEHRC) by the Canadian Institutes of Health Research and by Genome Quebec (CIHR EP1-120608), with additional support from Genome Canada and FRSQ. T.P. holds a Canada Research Chair.