dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are very grateful to the whole IRD representation of Antananarivo and we deeply thank  Claude-Anne Gauthier ,  Regine Rakotoniaina ,  IRD  drivers  Mohamed-Ali  and  Francois , as well as  Mondesir Cromwel  for their valuable logistical help. No fieldwork would have been doable without  Elahimitogoa Franck Zuma  and  Mahareta Ramindrisoa  who guided us on the field. We also thank chiefs of Lembetabe and Raindrano villages who allowed us to conduct our work in very good conditions. We were hosted at Mamy's place, we deeply thank  Mamy , his wife and their adorable family (Ino,  Sandrina ,  Benandru , Mamal … and all their friends) for their care and the time spent together. All on our way, we were lucky to meet great people and we thank them for their smile, help and their kind curiosity.
Helene Mariot  and  Marion Defrance  are thanked for their careful maintenance of CEREGE's clean laboratory. The project leading to this publication received funding from two  French ANR "Investissement d'avenir" programs  through the  EQUIPEX ASTER-CEREGE  and the  DATCARB  project from the " Excellente initiative" program  of  Aix Marseille University  A*MIDEX. Funding was also provided by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n. 802414  to AR) and by the  Helmholtz-Exzellenznetzwerke  (grant no. ExNet-0001- Phase 2e3 ) "The  Polar System and its Effects on the Ocean Floor (POSY) ". JA acknowledge support from  NSF  grant  OCE-1841888. JWA is supported by a post-doctoral grant of the  French National Research Institute for Sustainable Development (IRD).
Study design and setting We conducted a prospective, observational, longitudinal, real-world, PRO study using a digital data collection platform. A mobile application (app) called MyRealWorld-MG was developed for this study by Vitaccess (London, UK), a digital healthcare consultancy specialised in real-world evidence. Potential participants could download the app from the Google Play or Apple App Store onto their phone or tablet. Participants enter monthly data about their MG, comorbidities, the disease management and the impact of MG on their lives, over a period of approximately 2 years. The period of recruitment for the current interim analysis was from 12 December 2019 to 1 April 2020, and the data used in this publication concern the data entered at baseline only. The rationale and methods of the broader MyRealWorld-MG study have been published elsewhere.
Data collection At study enrolment, participants completed questionnaires in their native language concerning demographics, diagnosis, past treatments, living situation, current medical resource use and sick leave in the past month and several PROMs to assess their HRQoL. Validated translations of all PROMs were used in all countries and remaining study materials were professionally translated. The complete schedule of PROMs that was presented to the participants during the study has been described by Berrih-Aknin et al. 9  In this interim analysis, the baseline results of the PROMs described below are reported.
Demographic and clinical characteristics of the participants At the time of data cut-off for this interim analysis, a total of 834 participants downloaded the app and responded to at least one PROM. The participant flow is presented in figure 1. Self-reported demographic and clinical characteristics of the participants in MyRealWorld-MG are provided in table 1. Of 770 (92. 5%) participants in MyRealWorld-MG with known or self-reported MFGA class,  10  two-thirds suffered from mild generalised MG class II (31%) or moderate generalised MG class III (38. 1%), whereas the proportions of ocular MGFA class I (13. 9%) and severe MGFA class V (1. 6%) disease were small (table  1 ). As the sample of MGFA class V patients was too small to present robust outcomes, these results are not shown but only sporadically mentioned in the text.
Representativeness of the sample The baseline characteristics of the MyRealWorld-MG participants were compared with the populations of large cohorts from the USA, Italy, Germany, Poland, Norway and the Netherlands (table  1 ). 6 28-31 The EXPLORE-MG registry collected data from 232 patients with MG in the USA, including asymptomatic patients and only reported patient's worst MGFA class since diagnosis at baseline. Consequently, the US study reported higher patient numbers at both ends of the spectrum. 28 Compared with all aforementioned cohorts, participants in MyReal-World-MG were younger, and there was a higher proportion of women. 6 28-31 mparison of domains across PROMs The reporting of problems in daily activities, mental health, breathing and vision overlapped across several instruments and although questions were framed differently, results were surprisingly close.
With such selectivity it is possible to discriminate molecules with close chemical structures such as isomers  [2] , conformers  [3]  or isotopomers, each of them having a unique rotational fingerprint. Direct quantification, without the requirement of any calibration standard, can be performed as the integrated absorption is related to the square of the permanent dipole moment of the molecule in question which is, experimentally and/or theoretically, well established  [4]. This is not the case with the transition moments between rovibrational or rovibronic states, respectively in the IR and in the UV domains where absolute quantitative high-resolution spectroscopy is possible only with a small number of compounds listed in the atmospheric databases. At present only a small number of demonstrations of THz quantitative spectroscopy in different chemical mixtures can be found in the literature  [5] [6] [7] [8] [9].
Examples range from the detailed absorption profiles measurements of small polar molecules with strong absorption signatures  [15]  to the probing weak absorptions of less abundant conformers in flexible molecules  [3]. Once again, the sensitivity of the THz spectrometers used is a critical parameter determining the contours of the experimentally measurable dataset. Like the source power, and the detector noise, the path length of the interaction between the sample and propagating radiation strongly influences the spectrometer sensitivity. High-resolution studies of gases are commonly undertaken using a standard absorption configuration using a sample cell of around 1 m in length. The path length can be extended to a double-pass of even a multiple-pass configuration. Multiple-pass cells with base lengths varying from 50 cm to 5 m have yielded path lengths from around 20 to 200 m with improved sensitivity, they do suffer from a complex baseline and requirement of large sample volumes  [16] [17] [18] [19].
At the output of the cavity the transmitted radiation was focused onto a zero-bias detector with a second polymethylpentene lens (f = 50 mm). The electrical signal was amplified by 60 dB before being sampled by a rapid data acquisition card containing an overhead-free averaging module (Spectrum Instruments). Unlike the infrared, in our case the cavity free spectral range is much larger than the Doppler limited molecular linewidth. For example, FSR/Δν Doppler is around 600 in our case and would fall to 0. 6 for a 1 m cavity operating with a 1 μm wavelength source. Hence to probe the spectral profile of a molecular line, the AMC frequency and cavity mode should be simultaneously scanned over the region of interest. This was achieved by locking the cavity length to the AMC frequency. To facilitate the control loop, a Frequency Modulation (FM), typically of 100 kHz, was applied to the source.
A total of 19 spectra were recorded in the pressure range of 0. 1-3 mbar. For each spectrum the baseline is recorded with an empty cavity under identical conditions. These spectra were all recorded using the average 10 5 ring-down events per data point, with a step size of kHz. They typically contain 180 points requiring an acquisition time of approximately 6 min. The left-hand inset of Fig. 3  shows an example of the measured ring-down times τ with the gas present, and τ 0 without. The quantitative absorption coefficient α is directly obtained without the need to have either the exact length of the cavity or the finesse  [22]. α(υ) = 1 c ( 1 τ(ν) - 1 τ 0 (ν) ) (1) The measured absorption coefficient shows a peak value of 5. 5 × 10 -7 cm -1 at the expected frequency for this transition. The data were fitted to a Voigt profile to account for both the Doppler and collisional line broadening.
A stable molecular signal was obtained, unfortunately at this particularly low pressure we were unable to quantify the flow using a commercially mass flow meter. The J = 7 ← 6 transition of the principal isotope of HCN, with a line strength of 2. 52 × 10 -19 cm -1 / (molecule. cm -  ), was measured at 620. 304 GHz. The trace used was supplied by Messer and contained 640±19 ppb volume of HCN diluted in nitrogen, an example of the spectra obtained is given in Fig. 4. In order to undertake the quantification of the HCN, the total pressure of the gas flow in the cavity should be known. Provided the line broadening coefficient is well know this can be determined using the observed collisional line width  [35]. The data were fitted to a Voigt profile, initially the line centre frequency and Doppler width were fixed to their expected values, the signal to noise of the molecular signal was sufficient to allow these parameters to be freed once the collisional broadening and integrated line intensity were close to the final solution.
Acknowledgements The authors would like to acknowledge the financial support of  SATT Nord and Labex CaPPA (Chemical and Physical Properties of the Atmosphere)  funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissements d'Avenir)  under contract  ANR-11-LABX-005-01. The CIFRE PhD of  Jean Decker  is funded by the  BIOGIE  company in the context of the  TERAWASTE  project ( CPER IRenE , phase 5) dedicated to determine the capabilities of THz spectroscopy to monitor the regulated emissions of a waste recovery centre.
Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gael Mouret reports financial support was provided by SATT Nord and and Labex CaPPA (Chemical and Physical Properties of the Atmosphere) funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissements d'Avenir) under contract ANR-11-LABX-005-01. Gael Mouret has patent #WO/2019/002139 issued to SATT Nord.
Whole exome sequencing analysis revealed a CREBBP de novo missense mutation (c. 5602C>T; p. Arg1868Trp) in exon 31. Arginine at position 1868 is a highly evolutionarily conserved amino acid across species, and in silico prediction tools showed a high pathogenic score of: 28, 0. 99, and 0 for CADD, Polyphen-2, and SIFT, respectively. This variant is absent from gnomAD database and has been previously reported as the cause of Menke-Hennekam syndrome  (Angius et al. , 2019; Banka et al. , 2019; Giles et al. , 1998; Giordano & Avantaggiati, 1999; Janknecht & Hunter, 1996; Menke et al. , 2016; Nishi et al. , 2022; Richards et al. , 2015; Sima et al. , 2022; Vo & Goodman, 2001). It was therefore classified as a pathogenic variation according to ACMG criteria  (Richards et al. , 2015). In light of the severity of the prognosis, termination of pregnancy was demanded by the couple and performed at 32 WG in accordance with French law.
Bioinformatics analysis was performed by the Bioinformatics platform of the University of Paris Cité and Imagine Institute. Briefly, after demultiplexing, sequence reads were aligned to the human genome (NCBI build37/hg19) using BWA software. Downstream processing was carried out with the Genome analysis toolkit GATK (Haplotypecaller, Unifigenotyper, Samtools, and Freebayes). All variants with a read coverage ≤5× and a Phred-scaled quality of ≤20 were filtered out. Search for deletions and exonic duplications is performed by comparison of reading depths after normalization. Variants were annotated and filtered using a local interface (PolyWeb) developed by the Bioinformatics platform according to the current HGVS nomenclature, assisted by tools for databases (gnomAD, HGMD-Pro, Clinvar) and prediction software (Polyphen-2 HumanVar (PPN2), SIFT, CADD, Mutation Taster, MaxEntScan, SSF-like, NNSplice, and Gene Splicer).
The UFO representation of the particle physics model has been chosen to rely on Python objects defined through a set of attributes that encode physical properties so that the model could be straightforwardly accessed and parsed by any highenergy physics tool. One of the advantages of the design choices made is that the UFO is modular. Additionally the format is easily expandable to include new pieces of information not originally considered. These design choices allowed later developments that permitted the inclusion of decay width information  [3] , modifications of the propagators associated with any given field  [4] , renormalisation group running effects impacting some of the model's parameters and masses  [5] , and ingredients relevant to higher-order perturbative calculations in quantum field theories  [6].
The evolution of the UFO format The aim of this section is to provide a general overview of the UFO 2. 0 format for new physics models, that we propose to call the Universal Feynman Output (UFO) format in order to distinguish it from the initial version  [2]  released a decade ago. In the following, we emphasise the philosophy behind the UFO format, as well as its general structure. The content of the different files included in a UFO model and the associated syn-tax are discussed in more detail in dedicated subsequent sections. A UFO model consists of a set of Python files that can be used with a large class of publicly available computer packages relevant for high-energy physics calculations. The UFO format has been built around the philosophy that a model implementation should be independent of the software tool that uses it.
In the latter case, the documentation has never been collected in a single document, despite being at the heart of the frameworks introduced in  [6, 20, 21] ). In particular, UFO models suitable for NLO calculations have become standard in high-energy phenomenology during the last decade, a large variety of NLO-compatible UFO models being now available (especially from the Feyn-Rules model database 1 ). Although all extensions mentioned above are already being used by several codes, there is no official documentation of the structure of the UFO format beyond the original proposal. The main purpose of this document is therefore to provide an update of the UFO documentation, which contains all the features relevant for computations beyond LO accuracy. Before starting to discuss the general outline of a UFO model implementation, let us first make a comment about the name.
General file structure of the UFO In the remainder of this paper we discuss in detail the structure of the files contained in a UFO model. All the files, collected in a single directory, must be valid Python files, therefore with a file extension. py. Whereas most files are model-specific and contain the definition of the objects relevant to each model (e. g. particles and parameters), some of the files are modelindependent and contain Python-code objects defining, for instance, the Python classes used in a UFO model. The following model-specific files are mandatory in any valid UFO model directory, particles. py parameters. py vertices. py lorentz. py couplings. py -coupling_orders. py -function_library. py These files contain the basic definitions related to a model. If a model is to be used for computations beyond LO accuracy, three extra files are mandatory and must be included, -CT_vertices.
py -CT_couplings. py 1 See the webpage http://feynrules. irmp. ucl. ac. be/wiki/ NLOModels. -CT_parameters. py Moreover, in the specific case of electroweak corrections in the high-energy (Sudakov) approximation, this list must be complemented by an additional file that is described in this document for the first time, -CT_ewcasimirs. py Finally, every UFO directory may contain certain optional files, which specify additional model information, -form_factors. py decays. py propagators. py running. py The content of these files is described in detail in section 4, so that we limit ourselves here to highlighting some features that are common to all of them. Each file defines a list of objects. The classes that can be used are predefined and included in the mandatory file object_library. py (see below), and only standard Python syntax is allowed.
Several of the files define analytic expressions for interaction vertices or coupling constants in the theory. All standard arithmetic operations in Python can be used to write such analytic expressions in the UFO format, augmented by some special symbols whose meaning is described in subsequent sections together with the precise syntax. Besides these model-specific files which are at the heart of every UFO implementation, there are a couple of mandatory model-independent files that need to be included in every valid UFO directory, -object_library. py -__init__. py together with the optional file -write_param_card. py that has a specific practical use. As already mentioned, the file object_library. py contains the definition of all classes used in a UFO model. It includes several lists providing easy access to the full content of the model within the code.
In addition, the Python file object_library. py also includes a list all_functions whose role is to gather all Function objects instantiated in the model (see section  3. 4). The content of the files function_library. py and write_param_card. py is detailed in sections 3 and 4. We only focus here on the file __init__. py. This file identifies the content of a UFO directory as a valid Python module that can be loaded with the standard command import, and it may contain any valid Python command that should be evaluated when the model is loaded. In particular, the file __init__. py imports all other Python files relevant to the model, and it additionally allows users to add general information about the model, as shown in the following example: __author__ = "H. Solo, C. Bacca" __date__ = "06.
While we suggest to update existing UFO models so that they become Python 3 compatible, it is up to the code using UFOs to make sure that Python version compatibility is addressed properly and internally. For instance, GoSam, Herwig 7 and MadGraph5 aMC@NLO convert UFO models compatible with Python 2 to their Python 3 equivalent in order to use them. Finally, users can include information on the gauges available for the model implementation. This is achieved through the variable gauge that contains a list of integers, as for instance in gauge = [0,1] The value 0 refers to the unitarity gauge, whereas the value 1 stands for the Feynman gauge. Other integer values are allowed, provided that they are consistently defined in the UFO model, in particular through appropriate definitions in the files parameters.
The tp symbol that appears on the left-hand side of the equality represents a unique Python identifier that is further used internally within the model to refer to that particle. It has thus to follow Python requirements for names of variables. However, this identifier will not appear within any of the lists introduced in the file object_library. py, that include instead the objects themselves. All Particle objects instantiated within a UFO model must therefore have unique name attribute values, as this is how they should be referred to within any code using UFOs, in addition to unique Python identifiers. In addition, such a constraint holds for all the other classes of objects introduced below: two instances of a given class must have different name attributes 2. In the UFO conventions, the spin representation has to be provided in the 2s + 1 form where s denotes the particle spin.
Consequently, the only possibility to modify the value of a constant parameter is to edit directly the file parameters. py. A typical declaration of an external parameter would be tb = Parameter( name = 'tb', nature = 'external', type = 'real', value = 10. , texname = '\\text{tb}', lhablock = 'HMIX', lhacode =  [ 2 ]  ) In this example, we considered the parameter tan β that is defined as the ratio of the vacuum expectation values of the neutral Higgs fields in two-Higgs-doublet models, and that is often taken as one of the external parameters describing the Higgs sector of the model. The above expression declares an instance of the Parameter class called tb (the value of the name attribute being tb). The nature of this parameter is external, as indicated by the value of the nature attribute (that has been set to external).
This is indicated by setting the type attribute to the value complex. In the case of internal parameters, the attribute value is fixed to a valid algebraic Python expression represented by a string. This formula can depend on any external, constant or internal parameter already declared in the file parameters. py (i. e. on any parameter appearing before in the file). For constant parameters, a numerical value has to be provided instead. As an illustrative example, we show how to define the cosine of the β angle. It can be derived from tan β (defined as an external parameter earlier), and can be declared in a UFO model as cbeta = Parameter( name = 'cbeta', nature = 'internal', type = 'real', value = 'math. cos(math. atan(tb))', texname = '\\cos\\beta' ) after having properly imported the math module.
Positive integers are used to associate a colour index with one of the particles incoming to the vertex, the exact value referring to the position of the particle in the list provided through the attribute particles of the Vertex class. Negative integers must appear exactly twice in a monomial, and they correspond to contracted (i. e. summed over) indices. In the UFO conventions, the position of the first particle in the list particles corresponds to 1, in contrast to standard Python arrays. Moreover, it is up to users to verify the consistency between the colour structures appearing in a vertex definition and the representations of the particles entering this vertex, as programs processing UFO models may reject models in which the.
the attribute spins of the object VVSS1), and the structure of its interactions relates the two bosons through the Minkowski metric (cf. the attribute structure of the object VVSS1). The corresponding UFO implementations for the structure of the Lorentz objects SSSS1 and VVSS1 are then 1 '1' η µ1µ2 'Metric(1,2)' The last attribute of the Vertex class is related to the coordinates G ij of the vertex in the colour ⊗ spin basis. They are provided in the form of a Python dictionary through the couplings attribute of the Vertex class. This dictionary relates the coordinate (i, j), where i and j refer to a specific colour and spin basis element respectively, to the value of the corresponding coupling strength given as a Coupling object. The list of all Coupling objects necessary for the implementation of a given model is declared in the file couplings.