dataesr/scientific-paragraphs-categorization huggingface dataset:
5 were considered as enriched in comparison with the whole cellular proteome. Bioinformatics analysis. Proteins listed in the Supplementary Table  2  and Supplementary Data 1 were used for analyses of GO (Protein class) classification from the PANTHER (http://pantherdb. org) database. smiFISH analysis. smiFISH was performed as described  35. Briefly, cells were fixed with 4% PFA and permeabilized in 70% ethanol. Cells were then hybridized using two types of probes: (i) 24 unlabelled primary probes containing both a β-actin mRNA targeting sequence and a shared sequence (FLAP); (ii) a secondary probe conjugated to two Cy3 moieties, pre-hybridized in vitro to the primary probes via the FLAP sequence. All probes were purchased from Integrated DNA Technologies. The probe sequences are listed in Supplementary Table  6.
Images correspond to optical slice with a pinhole of 95. 54 µm. To prevent fluorochromes crosstalk contamination, each channel was imaged sequentially using the multitrack recording module before merging. Z-stack pictures were obtained using LAS AF, Leica software. The 3D reconstructions were obtained from Z-slices pictures, by using Leica software (LAS AF, Leica Software, Germany)  4. Correlative microscopy, for performing CLEM of rosettes, we used laser micro-patterned aclar films  45. The aclar films were pre-coated with 0. 5 mg/mL of gelatin in a 24-well plate. NIH-3T3 cells were seeded at 20,000 cells/mL and incubated overnight. The next day, the aclar film was transferred to a 35 mm glass bottom dish (MatTek P35G-1. 0-20-C) for confocal imaging (Leica SP2) using a × 63 objective (NA:1.
Acknowledgements We thank the  Bordeaux Imaging Center (BIC)  for help in fluorescence quantification and  C. Spiegelhalter  from the electron microscopy facility at  IGBMC (Illkirch, France ) for serial sectioning. We thank  Jean-Paul Rameshkoumar  (GREThA  UMR-CNRS 5113 ,  CIS VIA Inno (IdEx Bordeaux) ,  Univ. Bordeaux Pessac, France ) for his help in designing Supplementary Fig. 3. We thank our financial supporters. E. H. is supported by a PhD from the  Ministère de l'Enseignement Supérieur et de la Recherche. Z. E. was supported by a fellowship from  ANR-13-JJC-JSV1-0005. This work has been supported by grants from  ANR-13-JJC-JSV1-0005 ,  SIRIC BRIO ,  La Ligue Nationale contre le Cancer. F. S. and V. M. are supported by fundings from  Equipe Labellisée ,  Ligue Nationale  contre le Cancer 2016, SIRIC BRIO, and  INCA ,  PLBIO15-135  (to F.
Code availability. The in-house developed ImageJ plugin is available from https:// github. com/fabricecordelieres/IJ_PALM_Zeiss_workflow. Data availability. The MS proteomics data were deposited in the Proteo-meXchange Consortium via the PRIDE partner repository with the dataset identifier PXD009390 [https://www. ebi. ac. uk/pride/archive/projects/ PXD009390]. All other data supporting the findings of this study are available from the corresponding author on reasonable request.
Modelio Modelio 3  is a modelling environment developed by Softeam under a dual license, commercial and GPLv3 open-source. Modelio is based on Eclipse Rich Platform (RCP). It is used to create and manage models in various formats and notations. In addition, it provides many software features that compliments its modelling features such as: model transformation, code-to-model reverse engineering, and code generation. The CPSwarm Modelling Tool is built on top of the open source modelling environment (supporting UML2, BPMN2, MARTE, and SysML standards among others) named Modelio. Modelio Architecture is built around a central repository, around which a set of extension are defined cf. Figure  2. Each extension provides some specific facilities, which can be classified in the following categories: • Scoping: this category is composed of Goals, Dictionary, Business Rules and Requirement which allow specifying high-level business models for any IT system; • Modelling: for example, SysML, MARTE and BPMN are included in this category.
The extensions belonging to this category are used to model different specific aspects of a system such as Business Process, component architectures, SOA, or embedded systems; • Code generators: such as C++ or JAVA. These extensions allow users to generate and to reverse the code to/from different programming languages; • Utilities: modules allowing transversal utility facilities like teamwork or import/export functionalities. This architecture allows Modelio modelling environment to be flexible and configurable simply by adding the desired extension and related functionalities. As depicted in Figure  3 , the CPSWarm Modelling tool is composed of Modelio itself, a dedicated CPSwarm extension to provide the functionalities related to CPS swarm design, and also a set of pre-existing extensions to reuses their relevant functionalities in the CPSwarm context.
FREVO FREVO 4  -the FRamework for EVOlutionary design -is a software tool to design self-organizing systems. It is a general purpose framework that optimizes a generic representation of a given problem using evolutionary methods. Such an evolutionary approach requires a testbed that allows extensive and safe testing at low cost, for example a simulation of the target system with a model of the environment, the agents and their sensor/actor interface  (Elmenreich et al. , 2009). As it supports agent-based modelling, it is well suited to evolve the controllers for CPSs, when the controller is implemented as a generic, evolvable representation, e. g. , an artificial neural network (ANN). An iterative heuristic search is applied to find an optimized configuration of the controller for a CPS with respect to a system level optimization measure, called fitness.
The result is a controller that exhibits the local interaction rules to reach the desired global behaviour of the system. The controller can be evaluated on a large scale of parameters under predefined conditions. FREVO uses a modular approach, where the distinct steps of evolutionary design are split into different components. Its graphical user interface (GUI) simplifies the design process and offers statistics and graph generation for easy evaluation of the chosen design. The main purpose of FREVO is to support the optimization process as it guides through the individual steps of the evolutionary design, whereas it requires work by the software developer to implement the modelled problem details. FREVO is implemented in the Java programming language and makes strong use of the object-oriented programming paradigm.
Integration of FREVO to the Architecture FREVO requires configuration and code of a new problem to be implemented via a. java and. xml file. Both files have been already generated in the Modelling Tool, although the implementation part needs to be extended. The main task is to implement the evalu-ateCandidate method, where the given candidate representation needs to be evaluated. This is done by simulation, either by implementing it directly within FREVO or by calling an external simulator. Therefore, a model of the environment and the CPS capabilities, i. e. its sensors and actuator behavior need to be implemented. The sensor input(s) of the CPS are passed to the getOutput method of the candidate representation, which returns the output for the actuator(s). Additionally, a suitable performance measure needs to be implemented that is used to return the fitness value of the simulation run.
After this in vivo labeling, the cells were triton pre-extracted and used for SNAP labeling visualization by microscopy. To quantify fluorescence intensity (TMR signal) in the acquired images we used the ImageJ software  5, 42. We performed three independent experiments and quantified the fluorescence intensity of at least 400 nuclei for each condition. Alternatively, from the same acquired images we counted the number of cells with positive H3. 3 deposition profile. Antibodies. Antibodies were used at the following dilutions: anti-HIRA mouse monoclonal (WC119)  43  western and IF 1:100; anti-CABIN1 rabbit polyclonal (ab3349, or 76600, Abcam) western 1:1000; anti-UBN1 rabbit polyclonal (ab101282, Abcam) western 1:1000; anti-HA epitope rat monoclonal (Roche) western 1:1000; anti-ASF1a rabbit polyclonal (#2990, Cell Signaling) western 1:1000; anti-GFP rabbit polyclonal (platform Curie) western 1:1000 or mouse monoclonal (11 814 460 001, Roche) western 1:1000; anti-XPB (or TFIIH p89) rabbit polyclonal (sc-293, Santa Cruz Biotechnology) IF 1:300; anti-γtubulin mouse monoclonal (T5326, Sigma); anti-p53 rabbit polyclonal (sc-6243, Santa Cruz Biotechnology) western 1:500; anti-DAXX rabbit monoclonal (#4533, Cell Signaling) western 1:500; anti-p150 CAF-1 mouse monoclonal (ab7655, Abcam) western 1:500; anti-p60 CAF-1 rabbit polyclonal western 1:1000  44.
0, 300 mM NaCl, and 1 mM TCEP. Analytical ultracentrifugation. For centrifugation studies, recombinant HIRA (661-1017), FL CABIN1, and the HIRA/CABIN1 complex were resolved on a Superose 6 10/300 column in 20 mM Tris pH 8. 0, 300 mM NaCl, and 1 mM TCEP. Analytical ultracentrifugation experiments were performed with an XL-I analytical ultracentrifuge (Beckman-Coulter) and a TiAn60 rotor with sixchannel for sedimentation equilibrium (SE) or two-channel for sedimentation velocity (SV) charcoal-filled epon centerpieces and quartz windows. SE data were collected at 4 °C with detection at 280 nm for 2-3 sample concentrations. SE analyses were carried out using global fits to data acquired at multiple speeds for each concentration with strict mass conservation using the program SED-PHAT, or with the program Heteroanalysis using the ideal fitting model.
Error estimates for equilibrium constants derived from SEDPHAT analysis were determined from a 1000-iteration Monte Carlo simulation. See Supplementary Table  1  for a synopsis of loading concentrations, speeds, and model fits. For SV, absorbance profiles were recorded every 5 min over a period of ~15 h at 141,995×g. Data were fit using the c(S) distribution model of the Lamm equation as implemented in SEDFIT. After optimizing meniscus position and fitting limits, sedimentation coefficients (S T,b ) and frictional ratios (f/f o ) were determined by interactive least-squares fitting of the Lamm equation, with all fit RMSDs less than 0. 01. For all analyses, the partial specific volume (υ), solvent density (ρ), and viscosity (η) were derived from chemical composition by SEDNTERP. Hydrodynamic properties calculated from crystal structures were performed using the ZENO extension of the program US-SOMO.
Acknowledgements This work was supported by  la Ligue Nationale contre le Cancer  ( Equipe labellisée Ligue ),  ANR-11-LABX-0044_DEEP  and  ANR-10-IDEX-0001-02   PSL ,  ANR-12-BSV5-0022-02  " CHAPINHIB ",  ANR-14-CE16-0009  " Epicure ",  ANR-14-CE10-0013  " CEL-LECTCHIP ",  EU  project  678563 "EPOCH28", ERC-2015-ADG-694694  " ChromA-DICT ",  ANR-16-CE15-0018  " CHRODYT ",  ANR-16-CE12-0024  " CHIFT " and  ANR-16-CE11-0028  " REPLICAF ", and "Parisian Alliance of Cancer Research Institutes-PACRI ". This work was also supported by the  National Institutes of Health  grants  AG031862  to R. M. and an  American Heart Association predoctoral fellowship   12PRE12030157  to M. D. R. and  JSPS KAKENHI   16K14908  to Y. S. and  Platform for Drug Discovery, Informatics and Structural Life Science, and Basis for Supporting Innovative Drug Discovery and Life Science Research by AMED (Japan)  to T.
It consists of a template of a web-based, simply written document, translatable in all European languages, to be given to each CCS. The SurPass provides a summary of each survivor's clinical history, with detailed information about the original cancer and of treatments received, together with personalised follow-up and screening recommendations based on guidelines published by the International Guidelines Harmonization Group and PanCareSurFup. Results: The SurPass data schema contains a maximum of 168 variables and uses internationally approved nomenclature, except for radiotherapy fields, where a new classification was defined by radiotherapy experts. The survivor-specific screening recommendations are mainly based on treatment received and are automatically suggested, thanks to built-in algorithms.
We report here about the Survivorship Passport (SurPass) application developed as part of the European Unionefunded (FP7-HEALTH-F2-2011 no. 261474) European Network for Cancer research in Children and Adolescents (ENCCA, www. encca. eu) project, the PanCare Childhood and Adolescent Cancer Survivor Care and Follow-up Studies project (PanCareSurFup, www. pancaresurfup. eu e HEALTH 2010 2. 4. 1-7, no. 257505-2), and the European Expert Paediatric Oncology Reference Network for Diagnostics and Treatment (ExPO-r-Net project (Chafea Project Grant Nr: 2013 12 07), aiming to provide each CCS with an electronic treatment summary including individualised recommendations for follow-up as well as respective translations in European languages.
Methods The SurPass concept aimed to define a common template of a document to be given to the individual patient after the elective end of treatment therapies. The document had to fulfil the following set of requirements: i) be available on paper and digitally; ii) be written in a simple way, containing cancer history and therapy information according to a common coding scheme whenever possible; iii) provide advice and guidance on patient-specific LTFU of possible late effects according to internationally accepted standard guidelines for follow-up and care, as available and iv) be translatable to all EU languages. Major developments included the following: first, relevant stakeholders of various European institutions and communities actively participated, and paediatric oncology late effect experts (see acknowledgements), parents' and survivors' organisations (Childhood Cancer International (CCI) and the Pan-European Network for Care of Survivors after Childhood and Adolescent Cancer (PanCare)) were actively involved.
The variables summarising the details about the original cancer and associated treatments were defined for the summary section of the SurPass document. When available, internationally approved nomenclature and coding schemes were adapted for each variable. Otherwise, a specific coding system was developed. Second, Cineca IT experts designed the electronic infrastructure for the SurPass data collection and insertion, taking into consideration security and privacy issues, the possibility of data transfer from already available clinical datasets and multilanguage support. The resulting electronic platform had to be fully compliant with the latest national and international data privacy regulations. Third, the screening recommendations for possible late complications of treatment were developed by PanCareSurFup in collaboration with the International Guideline Harmonization Group (IGHG) (www.
ighg. org/international-guideline-harmonization-group/ methods/process)  [16]. Finally, these components were integrated to form the SurPass structure and ready-to-use tool. During the phase of prototype development, institutions interested in the SurPass were given a personal password to test remote data entry and built-in algorithms for defining personal recommendations. Over 50 accounts were activated, and feedback from users was then considered for further implementation and improvements. Furthermore, the possibility of automatic download from databases of the clinical trial in which the survivor was previously enrolled was also tested. When the first SurPass prototype had been developed, a test phase was carried out at one institution (Istituto Giannina Gaslini, Italy) after ethics committee approval was granted.
Treatment summary template The SurPass data schema (Fig. 1 ) contains a total of 168 variables, divided into five sections: i) demographic data; ii) tumour description and other concurrent diseases, either cancer predisposition syndromes (e. g. ataxia telangiectasia) or other clinical conditions not cancer associated (e. g. diabetes) if any; iii) front-line treatment for the primary main tumour and salvage treatment in case of progression/relapse before the first elective end of treatment, with details on iiia) chemotherapy, iiib) stem cell transplantation, iiic) radiation therapy, iiid) major surgery, iiie) other relevant clinical events that occurred during treatment and iiif) medical prescriptions at the time of elective end of treatment; iv) screening recommendations and v) follow-up. Variables were translated from English into several European languages, including Croatian, Czech, Dutch, French, German, Italian, Lithuanian and Spanish, within the scope of the European project ExPO-r-Net (www.
For survivors having undergone stem cell transplantation, information on the date of the procedure, source of cells, conditioning regimen (to be included in the cumulative dose calculation of chemo-immuneradio-therapy), GVHD grade and type is included in the SurPass. In the absence of a radiotherapy coding system, radiotherapy experts found it necessary to adapt the radiotherapy classification scheme as in use in North America (www. survivorshipguidelines. org), which is based on body regions rather than radiation fields. Because the level of detail on the treatment field varied greatly across decades, and continues to change, we drafted a three-tier scheme, where level one addresses large anatomical areas, level two whole organs, and level three defines parts of those organs. A special code was also generated for specific, commonly used large radiotherapy fields that include multiple anatomical sites (e.
g. family history) and local circumstances. For each guideline, an organ-specific brochure has been prepared by paediatric oncologists of the PanCare network after approval by the respective guideline group (in paper and electronic format) to summarise the personal recommendations in lay language with a questionand-answer format (examples in online Figs. 1e4). A general description of the pathophysiology of the organ at risk is also included. The SurPass platform automatically suggests the recommendations to be delivered to the survivor according to the treatment received. The treating physician approves the personalised recommendations and issues the personalised SurPass, bearing the survivor's name and SurPass number. In addition, the organ-specific brochure with the survivor name and SurPass number can be printed.
IT infrastructure development The web-based platform provides a secure online database that enables the collection and storage of all the data and images in a standard format, allowing the selfgeneration of the SurPass document (Fig. 2 ). The web-based platform can be delivered from the Italian Cineca-certified data centre in SaaS (Software as a Service) mode or can be installed at any local data center that can guarantee compliance to security and quality standards if it is requested according to the countryrelated needs. In addition, personalisation can be done to activate the platform in a dedicated environment. Fig. 3  summarises the SurPass data flow and access. Patient data can be either automatically imported from already existing electronic health records, using interoperability standards and data linkage procedures, or inserted and completed through remote data input.
Personal data are encrypted, and the platform is compliant with the highest security standards (using HTTP and SSL encryption standards) and data quality procedures, according to ISO 270001 certification (https://www. cineca. it/ en/content/certifications). Privacy is enforced with rolebased user security (survivor, health professional and data manager), authentication, identification and authorisation mechanisms to share and store data. Data ownership is regulated in compliance with the European Directive 2016/679, which went into effect on 25 May 2018 (GDPR): individual centres/survivors retain data ownership; the software provider must be  nominated as data processor and guarantees the correct management of the survivors' data. When the treating physician issues the personalised SurPass and recommendations to the survivor or their parents, it is possible to activate a personal account to give the survivor access to view and/or print the electronic documents (summary treatment, brochures and any other documents uploaded on the platform such as radiotherapy files) in any of the available languages.
Implementation and impact The SurPass is being integrated in several paediatric cancer centres as well as in some EU Member States' National Cancer Plans. In 2015, it was included in the 5year Austrian cancer plan for paediatrics. In 2017, the Italian Association of Paediatric Haematology and Oncology (AIEOP) approved the SurPass for use in all the AIEOP centres. Belgium, Croatia, Germany, Lithuania, Portugal and Spain are considering its implementation. For each survivor, the treatment summary can be uploaded either manually by retrieving data from hardcopies of their clinical record or electronically by downloading data from databases used during the treatment period (e. g. electronic clinical records, databases of clinical trials in which the former patient was enrolled or cancer registries).
The electronic download of data was tested in Italy, using the AIEOP-I-BFM ALL 2009 protocol. After mapping of the variables in common between the two databases, it was possible to download 61% of the requested data (72% if only mandatory fields were considered), thus reducing the average time of 1. 5 h to just 30 min for preparing the SurPass for a standard risk leukemic patient. A user manual is available either online or in paper format. The treating physicians and/or authorised personnel access the secured website (http://www. survivorshippassport. org): and login with their personal credentials. The first step is to insert the personal data of the survivor to create his or her personal record. Then, treatment data should be inserted following the predefined forms of the web-based platform.
While the PFC and SurPass are quite similar initiatives, they differ in that the SurPass has been developed to meet the needs of all European CCS and has therefore been made available in many European languages. The SurPass' strong link with the guidelines developed by the IGHG make it ideally suited for use in multiple countries. In addition, the SurPass has a built-in follow-up form, which may allow for the collection of information about the results of the screening tests during follow-up in a standardised format. This information could eventually provide statistics about the prevalence and/or incidence of chronic conditions or second malignant neoplasms in the long-term survivor population and could be used at the institutional level or shared with other national or international groups through cooperative projects.
We believe that careful strategies to improve the communication of health risks are needed to avoid an unnecessary psychological burden on survivors. We plan to further build on the questionnaire and continue to evaluate the use and impact of the SurPass in each centre which will be issuing it. The SurPass should ideally not stand on its own but be an integrated part of a system of LTFU thereby assuring a point-of-contact for CCS in need of support. Communications should be tailored to each CCS taking into account their cultural, emotional, cognitive and psychological background  [11]. While there is a need to communicate sensitive clinical aspects to a survivor of childhood cancer without generating unnecessary anxiety, there is also a need for clear and accurate medical language which can be understood by any healthcare professional who needs to access this information.