dataesr/scientific-paragraphs-categorization huggingface dataset:
F. Inference and Probability computation Overall workflow is summarized in Fig. 1B. VDJ generation models were all inferred using IGoR  [15]. Amino acid P gen distributions were all computed using OLGA  [16]  according to the specified IGoR model parameters. All generated sequences were drawn from the corresponding VDJ generation model using OLGA. Lastly, selection models were all inferred, and evaluated using SONIA. The code for all processes is available on GitHub: IGoR: https://github. com/qmarcou/IGoR OLGA: https://github. com/zsethna/OLGA SONIA: https://github. com/statbiophys/SONIA.
(c) The breadth of supporting annotation databases, permitting to assign a detected signal to an identified chemical with a given confidence level differ in the two areas: whilst extensive databases focused on endogenous markers are widely used in the metabolomics community (e. g. HMDB database  (Wishart et al. , 2018) ), extended and reliable tools to annotate exogenous exposure markers are lacking for exposomics investigations, especially at European level. For instance, there is not US EPA Comptox Dashboard equivalent in Europe to assess the European exposome. (d) Lastly, suspect and non-targeted screening of markers of chemical exposure in human matrices lacks comparable and reproducible methods and procedures, as well as harmonised criteria for documenting method performance and ensure the reliability and comparability of results produced by different laboratories  (Rochat, 2017).
Targeted screening "Targets" are compounds of known chemical name and structure, for which quantitative targeted methods are available, alongside some exposure and risk assessment data  (Smolders et al. , 2009). Highly selective sample preparation is typically undertaken in order to isolate these targeted compounds with maximal removal of matrix interferences  (Yusa et al. , 2012). Detection and quantification is often conducted using low-resolution mass spectrometers (e. g. triple quadrupole -QqQ), usually operated in selected reaction monitoring (SRM) acquisition mode, to provide both high specificity and sensitivity. Identification is supported by comparison with reference data acquired from certified standards (chromatographic retention time, MS and MS/MS spectra) used to validate compound identity prior to analysis.
Post-acquisition processing The post-acquisition data processing step in the context of suspect and non-targeted screening consists of shifting from raw instrumental data to a curated tabulated file (peak list) used for subsequent annotation and statistical analyses. This appears as the main component of non-targeted analyses and represents a substantial effort, since it can be very labour intensive and time-consuming often requiring manual intervention/oversight. This component crucially depends on the availability and performance of bio-informatics tools. A wide range of software is available to perform the extraction of information from the raw data  (Stanstrup et al. , 2019; Hu et al. , 2016). Some of these tools are integrated solutions from MS vendors, e. g. Metaboscape from Bruker, Progenesis QI from Waters, Trace finder, Sieve and Compound Discoverer from Thermo, Mass Profiler Professional from Agilent, MetID from Shimadzu, or XCMSplus from Sciex.
Other options are open source software  (Spicer et al. , 2017b) , many largely implemented in the metabolomics community, e. g. XCMS in the R computational environment/online  (Tautenhahn et al. , 2012) , MZmine 2  (Pluskal et al. , 2010) , Workflow4Metabolomics  (Giacomoni et al. , 2014) , MS-DIAL  (Tsugawa et al. , 2015)  and MetAlign  (Lommen and Kools, 2012). Other in-house developed solutions complete this panel of existing offers, such as HaloSeeker  (Leon et al. , 2019) , an open access tool designed for the specific screening of halogenated markers. These data processing tools aim to detect any signal present in the generated chemical profiles (peak picking), to align common peaks found in the different samples (peak alignment) and report their intensity or area (peak integration).
Compound annotation Importantly, the confidence level associated to the identification of the detected markers depends on the type and extent of structural information collected and available through the implemented analytical workflow. A harmonisation proposal was elaborated in the water analysis and metabolomics communities to clearly distinguish the levels of confidence, from level 5 where only exact mass is available to describe the considered marker, to level 1 where full mass spectrometric pattern (MS/MS data) are available and successfully compared to a analytical standard  (Schymanski et al. , 2014). Intermediate confidence levels are reached through the querying of databases where chromatographic and MS descriptors (so-called "MS ready information") are indexed for a list of a priori defined chemical compounds  (McEachran et al.
, 2018; Oberacher et al. , 2019; Schymanski and Williams, 2017). Spectral properties can be either experimentally determined from analytical standards, or theoretically calculated/modelled through biocomputing tools (e. g. MS/MS spectral similarity networks oft termed "molecular networking" or fragment tree correlations, network propagations via e. g. substructure/motif searching or in silico fragmentation). The same marker' ID confidence scale should be then more largely adopted and harmonised in the exposomic community. Such databases are well developed in the metabolomics community primarily focussed on endogenous compounds acting as markers of effect (e. g. Human Metabolome DataBase (HMDB), and METLIN  (Warth et al. , 2017) ). Although not directly suitable for annotating markers of chemical exposure, these metabolomics-related databases can be useful in the annotation pipeline to reveal and then discard from further processing, endogenous compounds (metabolites of endogenous substances, markers of effect…).
The US-EPA Comptox Dashboard is another existing resource in the field  (McEachran et al. , 2017)  incorporating an environmental compounds component, as well as some particular sub-databases focusing on particular classes of substances, for instance psychoactive substances  (Mardal et al. , 2019; Lung et al. , 2016). Yet, no extended and consolidated MS reference library exists at European scale to annotate markers of chemical exposure (either parent compounds and/or their metabolites) and to accompany the development of the exposomics field. One ambition of the component of the HBM4EU initiative dealing with these new methodological approaches is to build this ambitious and QA/QC consolidated database dedicated to markers of human internal exposure to CECs (Fig. S4 ). It also aims to develop a data processing methodology to prioritise the way to analyse the generated data (peak-picking, pairing, alignment, background subtraction etc.
A set of known markers of exposure covering a broad range of physicochemical properties, selected to be representative for the expected diversity of marker compounds can be used as indicators of the method performances at various levels, covering sample preparation (recovery, matrix effect…), data acquisition (chromatographic and mass spectrometric resolution, mass accu-racy…) and data processing (peak picking and alignment…) steps. A set of relevant test compounds for QA/QC purposes was elaborated in the HBM4EU project and is presented in Table  S1. Global coverage in terms of molecular weight and polarity (illustrated in Fig. S5 ) shows good representativeness of the list of HBM4EU markers of interest. This harmonised QA/QC compound test set remains a matter of further individual adjustments justified by the focus of each study, but facilitates further methodological comparisons, including the implementation of inter-laboratory studies.
Acknowledgements The authors thank the European Union's  Horizon 2020 research and innovation programme HBM4EU  under Grant Agreement No. 733032  for its financial support, as well as the  Région pays de la Loire, France  for its co-funding of the M. P. The authors also thank  Ms. Jane SYKES  for her final reading and editing of the manuscript. PhD grant. E. P. acknowledges support from the  Czech Operational Programme Research, Development and Education -Project Postdoc@MUNI  (No. CZ. 02. 2. 69/ 0. 0/0. 0/16_027/0008360 ).
[18]. One observation is the same pattern of pollutant concentration in the hallway as seen in the clinical dataset. In particular, there is no obvious correlation between the source of smoke in the OR of that simulation and the concentration in the section of the adjacent hallway. In fact, while exposure to surgical smoke in the OR is relatively intense in a short period of time and then vanishes, the pollutant is stagnant in the hallway for a much longer period of time and therefore contributes to long-term exposure. Overall, the delay ∆t 2 in the transmission conditions in (4) and (  5 ) has very little influence on the result and can be neglected. To expand the study, this simplified air quality model was then coupled to the ABM of surgical flow that reproduces the daily activity of a large surgical suite over a long period of time  [30].
Therefore, efficient movements by personnel may improve indoor air quality and should be quantified. The architectural design of the OR suite should optimize the circulation of staff and patient movement activities. The next important step in the modeling to address the complementary aspect of biological transmission versus physical transportation is to correlate the database of staff's pulmonary events with the study's findings to recognize these rare events. We can then translate the quantitative model of surgical smoke transport into a risk assessment for staff's health. The model should be surgery-specific: an efficient cyber-physical infrastructure should non-invasively monitor energy usage and smoke presence to instantly deliver awareness on practices that can improve air quality. A factor that has been neglected is the deposition of surgical smoke in the common storage area, see Figure  13 , in which all ORs have personal access to via their back-doors.
HEPfit is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to i) fit the model parameters to a given set of experimental observables; ii) obtain predictions for observables. HEPfit can be used either in Monte Carlo mode, to perform a Bayesian Markov Chain Monte Carlo analysis of a given model, or as a library, to obtain predictions of observables for a given point in the parameter space of the model, allowing HEPfit to be used in any statistical framework. In the present version, around a thousand observables have been implemented in the Standard Model and in several new physics scenarios. In this paper, we describe the general structure of the code as well as models and observables implemented in the current release.
The code is released under the GNU General Public License, so that contributions from users are possible and welcome. In particular, the present version provides Electroweak Precision Observables (EWPO), Higgs signal strengths, and several flavour observables in the SM, in Two-Higgs-Doublet Models (THDM), and in several parameterizations of NP contributions. Furthermore, it also calculates various Lepton Flavour Violating (LFV) observables in the Minimal Supersymmetric Standard Model (MSSM). In the near future, we plan to add many more flavour observables and to enlarge the spectrum of NP models. The paper is organized as follows. In Section 2 we give a brief description of HEPfit including the statistical framework used, the MPI parallelization and some other details. In Section 3 we discuss the models implemented in HEPfit.
In Section 4 we go on discussing some of the observables implemented in HEPfit. In Section 5 we present some physics results obtained using HEPfit in previous publications. Indeed several physics analyses  have been completed using HEPfit and serves as a validation of the code and its use as an open-source computational framework. A detailed description of the installation procedure can be found in Section 6 followed by examples of how to use HEPfit in Section 7. Updated information and detailed online documentation can be found on the HEPfit website  [30].
The HEPfit code HEPfit is a computational tool for the combination of indirect and direct constraints on High Energy Physics models. The code is built in a modular structure so that one can pick and choose which observables to use and what model to analyze. It also provides an interface that can be used to build customized models along with customized observables. This flexible framework allows defining a model of choice and observables that depend on the parameters of this model, thus opening up the possibility of using HEPfit to perform phenomenological analyses in such a model. The tool comes with a built-in statistical framework based on a Bayesian MCMC analysis. However, any statistical framework can be used along with this tool since a library is made available. HEPfit also allows for the incorporation of parametric and experimental correlations and can read likelihood distributions directly from ROOT histograms.
This removes the necessity for setting experimental constraints through parameterized distributions which might require making approximations. Since the statistical core of HEPfit is based on a MCMC, speed of computation is of utmost importance. HEPfit is already massively parallelized to run over large number of CPUs using OpenMPI and scales well to hundreds of processing units. The framework further brings forth the flexibility of defining a model of choice and observables that depend on the parameters of this model, thus opening up the possibility of performing various analyses using HEPfit. The package comes with several examples of how HEPfit can be used and detailed documentation of the code and the physics can be found online on the HEPfit web site. Throughout its development, emphasis has been placed on speed and streamlining error handling.
Statistical framework HEPfit can be used both as a library to compute the values of chosen observables and also as a MCMC based Bayesian analysis framework. While the former approach allows for choosing the statistical framework one wants to use, the latter uses a robust Bayesian MCMC framework implemented in the public code BAT  [1] [2] [3]. In this section we give a brief overview of the Bayesian statistical framework implemented in HEPfit using BAT.
Flavour physics The list of observables already implemented in HEPfit includes several leptonic and semileptonic weak decays of flavoured mesons, meson-antimeson oscillations, and lepton flavour and universality violations. All these observables have been also implemented in models beyond the SM. HEPfit has a dedicated flavour program in which several ∆F = 2, ∆F = 1  [6, 8, 9, 11, 12, 14, 21, [112] [113] [114]  observables have been implemented to state-of-the-art precision in the SM and beyond. HEPfit also includes observables that require lepton flavour violation. In Table  6  we list some of the processes that have either been fully implemented ( ) or are currently under development (•). We also list out the models in which they have been implemented. H eff refers here both to NP in the weak effective Hamiltonian as well as to NP in the SMEFT.
The bottom plot, taken from ref. [28] , gives a pictorial representation of constraints on several effective couplings, including correlations, for different future lepton colliders. The HEPfit code was also used to obtain most of the results presented in the future collider comparison study in Ref. [27]. These are summarized in the Electroweak Physics chapter of the Physics Briefing Book  [121] , prepared as input for the Update of the European Strategy for Particle Physics 2020. 6   -15 - Figure  2. Results of a fit for the LHCb results on the angular variable P 5 in two different theoretical scenarios: assuming the validity of an extrapolation of the QCD sum rules calculation of ref. [126]  at maximum hadronic recoil to the full kinematic range (left), or allowing for sizable long-distance contributions to be present for q 2 closer to 4m 2 c (right).
You can specify the path to root-config using the option -DROOT CONFIG DIR=<path to root-config>. BOOST: CMake also checks for BOOST headers availability in the system and fails if BOOST headers are not installed. You can specify the path to the BOOST include files with -DBOOST INCLUDE DIR=<boost custom include path>/boost/. The recommended installation flags for a locally installed HEPfit with full MPI and MCMC support is: $ cmake. -DLOCAL_INSTALL_ALL = ON -DMPIBAT = ON This will enable easy portability of all codes and easy upgrading to future version as nothing will be installed system wide. Also, this is useful if you do not have root access and cannot install software in system folders. After successful CMake run, execute the build commands: $ make $ make install to compile and install HEPfit, where the command make VERBOSE=1 enables verbose output and make -j allows for parallel compilation.
Post installation After the completion fo the installation with make install the following three files can be found in the installation location. The file libHEPfit. h is a combined header file corresponding to the library libHEPfit. a. Using hepfit-config: A hepfit-config script can be found in the <CMAKE INSTALL PREFIX>/bin/ directory, which can be invoked with the following options: • --cflags to obtain the include path needed for compilation against the HEPfit library. • --libs to obtain the flags needed for linking against the HEPfit library.
Acknowledgments The HEPfit developers would like to thank several physicists who have contributed indirectly to its development with suggestions, bug reporting and usage of the code. The developers would also like to thank the authors of flavio [132], in particular  David Straub , since a large fraction of the flavour physics code in HEPfit has been tested against it. At various stages of its development, we have compared HEPfit with other publicly available codes, namely, FeynHiggs [133,134], SuperISO [135], SuSeFLAV [136], Gfitter [137], RunDec [138], and  ZFITTER [139 ]. The developers also acknowledge the use of Madgraph [140] and its allied frameworks. L. R. has been supported by the  U. S. Department of Energy  under grant  DE-SC0010102. D. C. has been supported by the  (Indo-French) CEFIPRA/IFCPAR  Project No.
5404-2. Several of the developers of HEPfit were supported by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement n. 279972 "NPFlavour". This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement n o 772369). M. C. is associated to the  Dipartimento di Matematica e Fisica, Università di Roma Tre , and E. F. and L. S. are associated to the  Dipartimento di Fisica, Università di Roma "La Sapienza ". V. M. and  A. Pe. are supported by the  Spanish Government and ERDF  funds from the  EU Commission  [Grant  FPA2017-84445-P] , the  Generalitat Valenciana  [Grant  Prometeo/2017/053 ], the  Spanish Centro de Excelencia Severo Ochoa Programme  [Grant  SEV-2014-0398 ] and funded by  Ministerio de Ciencia, Innovación y Universidades, Spain  [Grant  FPU16/01911 ] and [Grant  FPU15/05103 ] respectively.
M. F. acknowledge the financial support from  MINECO  grant  FPA2016-76005-C2-1-P ,  Maria de Maetzu  program grant  MDM-2014-0367  of  ICCUB  and  2017 SGR 929. G. G. d. C. is supported by the  National Science Centre, Poland , under research grant no. 2017/26/D/ST2/00225. The work of S. M. has been supported by  JSPS KAKENHI  Grant No. 17K05429. M. V. is supported by the  NSF  Grant No. PHY-1915005. N. Y. is supported by  JSPS KAKENHI  Grant Numbers  JP15H05889 ,  JP15K21733 , and  JP17H02875. A. M. C. acknowledges support by the  Swiss National Science Foundation (SNF)  under contract  200021 178967. We would also like to thank the developers of BAT, in particular  F. Beaujean ,  K. Kröninger  and  D. Greenwald  for support during various stages of integration of BAT with HEPfit.
Resulting raw sequences (7,354,164 read pairs) were trimmed using fastq_quality_trimmer from the FASTX Toolkit (quality threshold 30; minimum read length 200) and assembled into 6,804,772 contigs using PandaSeq v2. 11  (Masella et al. , 2012). Data were analyzed with Mothur (V. 1. 40. 3) according to the MiSeq Standard Operating Procedures  (Kozich et al. , 2013). Contigs were pre-clustered (allowing for four mismatches), and aligned to the Silva_SEED 132b database for sequence classification. Chimeric sequences were removed (Vsearch) and the remaining sequences classified taxonomically  (Wang et al. , 2007). Non-bacterial sequences were removed and the remaining sequences were then clustered into operational taxonomic units (OTUs) at a 97% identity level and each OTU was classified to the genus level where possible  (Wang et al.
One possibility in the future would be to use axenization protocols based on the movement of gametes, as has been done for Ulva mutabilis  (Spoerner et al. , 2012) ; at least some strains of Ectocarpus have previously been shown to produce phototactic gametes  (Kawai et al. , 1990). A second alternative is the continuous use of antibiotics throughout the experiment, and working with antibiotic-resistant bacterial strains. In this context a better understanding of the metabolic requirements of the algae will help to durably maintain axenic cultures. Despite these challenges, the present study constitutes an important proof of concept for the use of metabolic complementarity to study simplified system of mutualistic host-symbiont interactions. We anticipate that, in the long run, this concept can be applied not only to controlled co-culture experiments, but that it will also prove useful for the interpretation of more complex datasets such as metatranscriptomic or metagenomic data.
and CNRS UMR8030), the France Génomique and French Bioinformatics Institute national infrastructures (funded as part of Investissement d'Avenir program managed by Agence Nationale pour la Recherche, contracts ANR-10-INBS-09 and ANR-11-INBS-0013) are acknowledged for support within the MicroScope annotation platform. This manuscript has been released as a Pre-Print at BioRXiv  (Burgunter-Delamare et al. , 2019).
ACKNOWLEDGMENTS We thank  Laurence Dartevelle  for advice on algal culturing;  Sylvie Rousvoal  for extractions of bacterial DNA;  Gwenn Tanguy  and  Erwan Legeay  from the  GENOMER platform  ( FR2424 ,  Station Biologique de Roscoff ) for access to the sequencing platform and support during library preparation;  François Thomas ,  Angélique Gobet , and  Maeva Brunet  for helpful discussions and for sharing their mock community;  Dominique Marie  and  Christian Jeanthon  for granting us access to their flow cytometer; and the  ABIMS platform  and the  GenOuest bioinformatics core facility  (https://www. genouest. org)for providing the computing infrastructure for amplicon analyses and community predictions. The LABGeM (CEA/Genoscope.
FUNDING This work was funded partially by the  CNRS Momentum call , the  ANR  project  IDEALG  ( ANR-10-BTBR-04 ) " Investissements d'Avenir, Biotechnologies-Bioressources , " the  European Union 's  Horizon 2020 research and innovation Programme  under the  Marie Sklodowska-Curie  grant agreement number  624575  (ALFF), and joint Ph. D. scholarship from the Brittany region (Project  HOSALA ) and the  Sorbonne University  ( ED227 ).