dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurements No setting nor calibration is required to take data with a COSMIX case -just to power it. The energy deposited in a bar by a cosmic muon is about 12 MeV, a value much higher than those due to background. Therefore, all analog-triggered signals come from real muons. Two data taking modes, illustrated in Fig. 6 , are possible: the single bar rate is about 1 event/second while the coincidence rate (one bar is movable and can thus be put on top of the other) decreases to about 0. 4 event/second. Moreover, as all high schools in France have digital scopes nowadays, they can use them to trigger on the PIN diode outputs to see the signals associated with single or coincidence detections. Figure  7  shows an example of a COSMIX measurement performed over several hours. The huge rate variation corresponds to a commercial flight, while the decrease at time ∼24000 s can be traced to a trip in the Paris subway.
Smaller but clearly visible rate variations have also been recorded during private plane flights. Many applications are possible for this detector: short introduction to cosmic rays in classrooms, demonstrations at the end of a public lecture or during an exhibit, etc. A collaborative website will allow participating teachers to publish their data while describing how they were acquired. One example: teachers from everywhere in France could be able to study the cosmic ray rates at Rate (particles/minute) Time (s) the different floors of the Eiffel tower, hence reproducing Theodor Wulf's experiment from 1909  [12].
COSMAX The COSMAX ("COSMic ACCelerators") project is software-based and developed by the same CENBG team as the COSMIX case. It is based on a suite of tools installed on a ready-to-use linux virtual machineor directly on linux. It aims at providing access to the very variable gamma ray sky using the Fermi-LAT data. These data are public, readily available, easy to grasp (photon detection time, measured energy and reconstructed incoming direction on the sky) and all-sky. The data analysis tools are the same than those used by the scientific community. They allow the users to produce time-resolved maps or animations and to study many transient phenomena like blazars, gamma ray bursts or pulsars. Figure  8  shows one example of the sky maps which can be produced in the COSMAX framework. Now that the package is fully-functional, the next step will be to improve the user interface to allow users with only a basic knowledge of computing to use this framework and achieve results.
Development of a knowledge management platform Systems medicine involves the large-scale integration of existing knowledge with newly acquired multidimensional data. The MeDALL knowledge management platform was developed to empower all partners with open sharing and access to all the data and information collected, as well as to all the experimental and computational procedures. The MeDALL knowledge base integrates historical and newly collected data from around 44 000 participants on 398 clinical and phenotypic attributes (harmonized from 7495 individual cohort variables) and 160 different follow-ups at 25 different time points between pregnancy and age 20. It also contains information about available blood samples that are stored in the individual biobanks of the different MeDALL partners.
Information on 863 genes involved in allergy (283 from a systematic literature review and 580 from automatic text mining) is integrated with data on protein-protein interactions, transcriptional regulation, miRNA regulation and signalling pathways from public databases. It is directly connected to the omics data generated within MeDALL. Omics data produced or made available within MeDALL include 23 000 historical GWAS, 9500 epigenetics, 2000 proteomics, 750 transcriptomics, IgE microarrays (4000 subjects) and individual estimates of ambient air pollution exposure (10 000 children) using the land use model (ESCAPE)  (7) (8) (9) (10). The newly performed epigenetics, proteomics, transcriptomics and IgE microarray data are integrated into the MeDALL knowledge base which currently includes 3292 IgE chips, 2173 DNA methylations, 1427 biomarkers and 723 transcription experiments.
The availability of longitudinal samples of the same individual is a unique resource. Development of the harmonized MeDALL-Core Questionnaire (in eight languages)  (11)  Numerous birth cohorts have been initiated throughout the world using heterogeneous methods to assess the incidence, course and risk factors for asthma and allergies  (12). One of the major achievements of MeDALL was the development of the harmonized MeDALL-Core Questionnaire (MeDALL-CQ), used historically in 14 cohorts and prospectively in 11  (11). The harmonization of standardized core questions was accomplished in four steps: (i) collection of previous questions from 14 European birth cohorts, (ii) consensus on core questionnaire items, (iii) translation and back-translation of the harmonized English MeDALL-CQ into eight languages and (iv) implementation of the three core questionnaires MeDALL-CQ (two for parents of children aged 4-9 and 14-18 and one for adolescents aged  [14] [15] [16] [17] [18].
The harmonized MeDALL follow-up leads to more comparable data across different cohorts and offers the possibility to validate the results of former single cohort analyses. Worldwide interest has been expressed in the MeDALL-CQs: future follow-up assessments of the Cincinnati Child- Hypothesis-driven approach: The identification of classical phenotypes was based on experts' criteria following a review of the literature aiming at the definition of IgE-associated allergic diseases. Data-driven approach: To identify the novel phenotypes, the children from the birth cohorts were analysed using hypothesisfree methods by cluster analysis. MeDALL included the birth cohorts with historical databases and harmonized follow-up  (11). They were integrated in a knowledge management system (33) (Table  2 ).
A joint NIH-MeDALL workshop on birth cohorts in allergy and asthma was organized in September 2012 to address a wide research agenda, including potential new study designs and the harmonization of existing birth cohort data  (6). The MeDALL database with its new central database is the starting point for future common and sustainable research initiatives in asthma and allergy. It can be extended with other epidemiologic studies such as birth and patient cohorts.
Development of a bioinformatic model of multimorbidity of allergic diseases An in silico study based on the analysis of the topology of the protein interaction network was performed to characterize the molecular mechanisms of multimorbidity of asthma, Table  2  Pooled MeDALL database AMICS-M: data from 482 integrated participants with up to 12 follow-ups. BAMSE: data from 4089 integrated participants with up to five follow-ups. BiB: data from 13 565 (2594 in MeDALL) integrated participants with up to seven follow-ups. DARC: data from 562 integrated participants with up to nine follow-ups. ECA: data from 3754 integrated participants with up to six follow-ups. EDEN: data from 1140 integrated participants with up to six follow-ups. GINI: data from 5991 integrated participants with up to eight follow-ups.
LISA: data from 3095 integrated participants with up to nine follow-ups. INMA-Gipuzkoa: data from 406 integrated participants with up to four follow-ups. INMA-Sabadell: data from 772 integrated participants with up to seven follow-ups. INMA-Valencia: data from 855 integrated participants with up to six follow-ups. MAS: data from 1314 integrated participants with up to 19 follow-ups. PARIS: data from 1549 integrated participants with up to 10 follow-ups. PIAMA: data from 3963 integrated participants with up to 12 follow-ups. RHEA: data from 1336 integrated participants with up to six follow-ups. ROBBIC-Bologna: data from 434 integrated participants with up to five follow-ups. ROBBIC-Roma: data from 694 integrated participants with up to six follow-ups. eczema and rhinitis. As a first step, proteins associated with either disease were identified using data mining approaches, and their overlap was calculated.
Gender switch during puberty Longitudinal gender-specific analyses of 18 852 children participating in PIAMA, BAMSE, LISAplus, GINIplus, DARC and MAS showed considerable changes in the sex-specific occurrence of asthma and allergic rhinitis at around puberty. The strong male predominance of asthma or rhinitis prevalence in prepubertal childhood declined as the teenagers grew older (manuscripts in preparation). Our hypothesis that by adulthood the gender imbalance in the prevalence is switching to a clear female predominance of asthma and rhinitis will be examined with the next follow-up data of MeDALL cohorts in adulthood.
Omic data for allergy molecular fingerprints and phenotype handprints In the discovery phase of targeted proteomics, levels of a large panel of proteins via multiplexing and ELISAs were measured in four cohorts. In the replication phase, top biomarkers of interest were measured in seven cohorts. The analyses identified a potential novel biomarker of asthma and the role of systemic inflammation in multimorbidities in early childhood. Results were consistent using the classical and novel phenotypes (manuscript in preparation). Following a hypothesis-driven approach, MeDALL data for CC16 (club cell secretory protein) were included in a large international collaborative study. Low CC16 levels at the age of 4 predicted subsequent FEV 1 deficits up to the age of 16 in the BAMSE cohort, and these data were in line with independent results from the CRS (USA) and MAAS (UK) cohorts (  50 ).
A set of overlapping genes defines molecular fingerprints related to asthma, rhinitis and eczema, blood cell activity, immune and antigen response and receptor activity (Lemonnier et al. , in prepara-tion). Integrative analyses of large-scale transcriptomics and epigenomics datasets with protein expression levels enabled the discovery of an initial allergy phenotype handprint (Ballereau et al. , in preparation). In comprehensive integrative analyses, protein levels were analysed in relation to both genetic variation and methylation profiles in their encoding genes. Multiple CpG sites from 25 genes were found for possible mediation of effects of genetic variation on protein levels. By identifying a set of putatively functional SNPs and CpG methylation sites, these results may provide specific loci to be investigated in association studies.
In studies of multimorbid NCDs (e. g. COPD, cardiovascular diseases, diabetes), the power of population cohorts will be sufficient to assess established diseases. However, cohorts are likely to fail in the identification of early multimorbid diseases, their causality and discovery of biomarkers. Thus, the data of MeDALL are generalizable to multimorbid NCDs across the life cycle. Funding from the EU Structure and Cohesion Funds have been obtained by the R egion Languedoc Roussillon in the frame of MACVIA-LR  (76, 78)  to maintain the database until new funding from the EU or other sources is available for other projects. Despite the successful experience of MeDALL to integrate birth cohorts of asthma and allergy, we really do need to escalate the level of integration. A new methodology should be proposed to combine the strengths and weaknesses of the birth cohorts, possibly enriching them with patient cohorts  (47, 86, 87) , registered data in primary care  (88) , clinical trial databases (89) and/or internet based studies.
Acknowledgments The authors would like to thank the members of the  Scientific and Ethics Advisory Boards  who made invaluable comments during the project duration:  P Burney  ( Imperial College, London, UK ),  SGO Johansson  ( Karolinska Institute, Stockholm, Sweden ),  J Kiley  ( NHLBI, Bethesda, USA ),  P Sterk  ( Amsterdam University, NL ),  M Van Hague  ( Karolinska Institute, Stockholm, Sweden ),  M Yazdanbakhsh  ( Leiden University, NL ),  K Dierickx  ( Leuven, Belgium ),  T Garani-Papadatos  ( Athens, Greece ) and  M Sutrop  (Riga, Estonia).
Circos diagrams of the interactions between ribosomal proteins. Each type of interactions are represented by different colours listed in the inset legend according to the following codes: G-G: interactions between globular domains; G-Ex: interactions between a globular domain and an extension; Ex-Ex: interactions between extensions. This figure is a graphical representation of Table  1  and Supplementary Tables S3-S7. PDB identifiers of ribosome structure used for this analysis are 4v88 (eukarya), 4v8I (eubacteria) and 1s72 (archaea). scale-free and assortative networks containing highly connected hubs  12. Eukaryotic 60S proteins form a single network, with most of the proteins connected to 3 or 4 partners. Through their multiple extensions, hubs such as eL15 and uL4 connect 7 and 8 partners that also belong to the most connected proteins (Fig.
Methods All the structures of eukaryotic, eubacterial and archaeal ribosomes deposited in the protein data bank (PDB)  34  have been systematically analysed in order to characterize their extensions and their inter-proteins contacts. The nomenclature of ribosomal proteins used in the tables and figures follows the a new system for naming ribosomal proteins that has been recently adopted  35  (see also the site of N. Ban laboratory: http://www. bangroup. ethz. ch/research/nomenclature-of-ribosomal-proteins. html). The tables for the conversion between the old and the new system are provided in Supplementary Tables  S13  and S14. The extensions have been defined as elongated protein chains protruding from the globular domain. Segments (seg) are extensions from the N-terminus or the C-terminus ends devoid of secondary structure, mixed extensions (mix) combine helical (H) and unstructured chains (S).
Structured extensions contain α -helices (H), loops (L) or β -hairpins (B). Interactions between extensions or between extensions and globular domain have been systematically detected with the program pymol  36  when inter-molecular distances are less or equal to 4 Å. Network characterisation. Scripts have been written for systematically mapping the protein-protein interactions within the ribosomal subunit for display by the program circos  37. The ribosomal protein networks of the large and small subunits of the three domains have been characterized according to the parameters defined in Barabasi et al. 12. Network motifs detection in non-directed networks has been performed with the program mfinder provided by U. Alon  13. Sequence alignments, interface characterization and structural analysis.
The sequence alignments of all the eubacterial ribosomal proteins have been kindly provided by U. Wolf and N. Yutin  15. They can be downloaded from the site: ftp://ftp. ncbi. nih. gov/pub/wolf/_suppl/ribo. The aligned sequences have been visualized and analysed with the program Jalview  38. Consensus sequences and conserved residues of the ribosomal proteins at the interface area have been represented in Supplementary Fig. S5 , with the "zappo" colouring scheme and in Supplementary Table  S12. Conserved residues have been integrated in pymol scripts for their three-dimensional representation and zappo colouring (Figs  5  and 6 ).
Structural analysis of the interfaces. The structures of each interacting protein pairs have been analysed in all the available PDB crystallographic structures of three species (T. thermophilus, D. radiodurans and E. coli) 50S and 70S subunits whose resolution is greater than 3. 5 Å (Supplementary Table  S11 ). Pymol scripts have been (c) View of the network in which proteins are coloured according to the functional sites: blue cartoons: tRNA site "sensory-proteins"; green cartoons: the first layer of proteins interacting with tRNA sensory-proteins; cyan cartoons: peptide tunnel "sensory-proteins"; magenta cartoons: the first layer of proteins that interact with peptide tunnel "sensory-proteins"; yellow cartoons: inter-proteins. (d) Schematic representation of the network with the colour code used in (c).
The coloured arrows represent the possible transmission pathways between the sensory-proteins and the processing inter-proteins. developed to systematically superimpose together all the equivalent protein models in order to compare them at the interface regions. Although homologous 50S proteins superimpose extremely well, twelve interfaces have been found to differ from one structure to the next (Supplementary Table  S15  and Fig. S10 ). Knowing that in the resolution range (2. 9-3. 5 Å) misinterpretation of electron density maps in unclear regions are possible, we have carefully checked the reliability of these different models by the systematic analysis of their electron density maps. The program Phenix 39 has been automated to compute 2fo-fc and difference maps of all the models listed in Supplementary Tables  S11  and S15 , from their structure factors deposited into the PDB.
Pymol scripts have been developed to systematically compare the maps at the sites where structural difference have been observed. Based on the high quality of electron density maps of recent high resolution structures (4v8i, 4y4o, 4ybb, 4z8c, 4w2h, 4w2f, 4y4p) and the careful comparison of the density maps, our analysis has revealed that in most cases, the conformation "x" (coloured in red in Supplementary Table  S15  and reported Supplementary Fig. S10 ) corresponds to misinterpretations of poorly resolved maps in these regions (Supplementary Fig. S11 ). For example, our analysis reveals that the residues built in the alternative conformation "x" have been in fact, fitted into density peaks corresponding to neighbouring residues (see legend of Supplementary Fig. S11 ). We deduced that the all the of the 50S subunit interfaces display a unique conformation whatever the species, the crystallization conditions, the translation factor and antibiotic added or the translation step in which the ribosome has been crystallized.
Tot nb of interactions 81 57 30 21 21 Tot nb of interactions/100nt rRNA 2. 2 3. 2 1 1. 3 0. 7 Table 1. Statistics of extension numbers, types and interactions observed in the large and small ribosomal subunits of the three domains (ribosomal subunit PDB identifiers for eukarya: 4v88, eubacteria: 4v8i, archaea: 1s72).
As shown in Table  1 , the PDL1 groups were not associated with patient's age and sex, and pathological features such as pathological type, tumor size, lymph node status and tumor grade. Finally, a recent study published a transcriptional classification of pancreatic ductal carcinoma identifying four tumor subtypes (squamous, pancreatic progenitor; immunogenic; and aberrantly differentiated endocrine exocrine) with different molecular pathways and prognosis  [42]. We applied this classification to our dataset and found a significant correlation with the PDL1 status: 56% of PDL1-up tumors were in the squamous subtype (Table  1 ), the one with the worst prognosis. This was confirmed by the fact that PDL1 mRNA was up-regulated in the squamous subtype vs other subtypes in the Bailey's data set  [42]  and in our own data set (data not shown).
PDL1 expression and survival We assessed the prognostic value of PDL1 expression in terms of disease-free survival (DFS) and overall survival (OS). DFS and OS data were available for 254 patients non-metastatic at diagnosis and treated with surgery. Regarding DFS, the median follow-up was 7. 5 months (range, 1-84) months, 163 patients displayed a DFS event, and the 2-year DFS was 32% (95%CI,  [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]. As shown in Figure  1 , PDL1 expression influenced DFS with 14% 2-year DFS (95%CI, 7-31) in the "PDL1-up" group versus 37% (95%CI,  in the "PDL1-not-up" group (p = 0. 00032, log-rank test). The HR for DFS event was 1. 90 (95%CI, 1. 33-2. 70) in the "PDL1-up" group versus "PDL1-not-up" group (p = 0. 0004, Wald test). The median DFS was 6 months (range, 1 to 63) in "PDL1-up" group versus 10.
Gene expression data sets We gathered clinicopathological and gene expression data of clinical pancreatic carcinoma samples from nine publicly available data sets  [65] [66] [67] [68] [69] [70] [71] [72]  (https:// tcga-data. nci. nih. gov/tcga/) comprising at least one probe set representing PDL1. Data were collected from the National Center for Biotechnology Information (NCBI)/ Genbank GEO, ArrayExpress, and TCGA databases. The nine data sets are described in Supplementary Table  7. Samples were profiled using whole-genome DNA microarrays (Affymetrix, Agilent, or Illumina) and RNASeq (Illumina). The pooled data set contained 565 samples, including 453 primary cancer samples and 112 normal pancreatic samples. The study was approved by our institutional board.
Gene expression data analysis Data analysis required pre-analytic processing. First, we normalized each DNA microarray-based data set separately, by using quantile normalization for the available processed data from non-Affymetrix-based sets (Agilent, Illumina), and Robust Multichip Average (RMA) with the non-parametric quantile algorithm for the raw Affymetrix data sets. Normalization was done in R using Bioconductor and associated packages. Then, we mapped hybridization probes across the different technological platforms present. We used SOURCE (http://smd. stanford. edu/cgi-bin/source/sourceSearch) and EntrezGene (Homo sapiens gene information db, release from 09/12/2008, ftp://ftp. ncbi. nlm. nih. gov/gene/) to retrieve and update the non-Affymetrix gene chips annotations, and NetAffx Annotation files (www.
affymetrix. com; release from 01/12/2008) for the Affymetrix annotations. The probes were then mapped according to their EntrezGeneID. When multiple probes represented the same GeneID, we retained the one with the highest variance in a particular dataset. PDL1 (CD274) tumour expression was measured by analysing different probe sets whose identity and specificity were verified using the NCBI program BLASTN 2. 2. 31+ (Supplementary Table  8 ). For the TCGA data, we used the available normalized RNASeq data that we log 2 -transformed. Next, we corrected the nine studies for batch effects using z-score normalization. Briefly, for each gene expression value in each study separately, all values were transformed by subtracting the mean of the gene in that dataset divided by its standard deviation, mean and standard deviation being measured on primary cancer samples only.
Global data set was obtained by concatenation of the nine normalized sets by matching their EntrezGeneID. We performed principal component analysis (PCA) using the top 2,000 most variable genes extracted from the nine data sets, before and after normalization, to verify the accuracy of the normalization in removing the set-specific variation in gene expression (Supplementary Figure  3 ). PDL1 expression, before and after normalization, is shown for each dataset in Supplementary Figure  4. PDL1 expression in tumors (T) was measured as discrete value after comparison with mean expression in the 112 normal pancreatic samples (NP): upregulation, thereafter designated "up" was defined by a T/NP ratio ≥2 and no upregulation ("not-up") by a T/ NP ratio < 2. Because of the involvement of PDL1 in immunity, we analyzed gene expression signatures linked to immune response in cancers.