dataesr/scientific-paragraphs-categorization huggingface dataset:
The original classification approach is adapted from 1-h time intervals to 15-min intervals. The DNI variability and the errors of the ASI nowcasts at PSA were studied over a 2-year period (2016 and 2017). Nowcast up to 15 min ahead were created over the same period. For each data sample, the deviation between three reference pyrheliometers and the corresponding DNI values from the DNI maps is calculated and discretized in a DNI variability class, Sun elevation angle, and lead time. For each combination of the DNI variability class, the Sun elevation angle, and lead-time, the deviation with a coverage of 68. 3% is identified and saved into a look-up table describing the basic uncertainty due to the atmospheric variability. As expected, the largest deviations are observed for highly variable conditions.
This innovative method allows a spatial accuracy assessment, which goes far beyond the previously used accuracy metrics of RMSD, MAD, bias, and skill scores for spatially resolved DNI nowcast. The uncertainty increase in the transient zones leads to an increase of the average coverage probability of the uncertainties from 68. 3% to 74%. The coverage probability is reduced to an average coverage probability of roughly 68. 3% by adjusting/reducing the basic uncertainty. For this validation procedure, we used a new data set (year 2018), which is independent from the dataset used for the uncertainty specification. The final nowcasted DNI maps, as well as upper and lower limits, are presented in two example scenarios. These scenarios represent distinct DNI variability classes, cloud heights, cloud speeds, lead times, and Sun positions.
Funding: This research has received funding from the  European Union 's  H2020 program  under Grant Agreement no. 654984  (PreflexMS). Further funding was received by the  German Federal Ministry for the Environment, Nature Conservation, and Nuclear Safety  under Grant Agreement no. 209913807  ( NETRA-CST ) as well as the  German Federal Ministry for Economic Affairs and Energy  within the  WobaS-A project  (Grant Agreement no. 0324307A ).
For all tested parameter combinations, reported values of s eff were computed from 200 independent replicates. All programming was performed in R version 3. 2. 3  75 , and basic codes are freely available on https://github. com/ACouce/NatComm2019. Genome analyses. To conduct the bioinformatic analyses we developed a series of scripts in Python (version 2. 7. 12) (www. python. org). These codes were applied to a panel of 25 bacterial genomes, including relevant pathogens, and chosen to span a wide range of GC compositions. A summary of the main features of these genomes is presented in Supplementary Table  1. For all strains, the predicted coding sequences (CDSs) and their functional classification (COG) were retrieved from the Microscope platform from Genoscope (www. genoscope. cns. fr)  76.
After formatting and parsing, we estimated the average protein-disrupting effect of different mutations for all CDSs across the panel of genomes. We achieved this by computing the Grantham and BLOSUM100 scores for all of the possible substitutions per codon associated with each mutational spectrum. The Grantham and BLOSUM100 matrices were obtained from the AAindex database (www. genome. jp/aaindex)  77 and the NCBI FTP server (ftp. ncbi. nih. gov/blast/matrices), respectively. Codons harbouring incompletely specified bases (e. g. , N, R, Y) were excluded from the analyses. Basic codes are freely available on https://github. com/ACouce/NatComm2019. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
• OOFAT at the centre This model can be visualised as a perfect, or near perfect, nonagon (with a scoring of 3 or higher on each dimension), suggesting that OOFAT is not implemented for one specific purpose, or market, but as an integral part of the institution's overall mission. Overall, ten HEIs follow this approach to OOFAT usage. An example is the OERu, shown in OERu uses open source software, makes all its content available as OER, and offers pathways where students can study their first year of an undergraduate course for free in self-study, and this will then be formally recognised by selected HEIs, allowing transfer into the formal education system. Open practice across all dimensions of the OOFAT model sits at the core of the OERu mission.
During enrolment, the anonymous clinical data and imaging of the eligible patient were forwarded to the other clinical centres. Each patient was only included if no centre was against inclusion and at least two more centres agreed on the inclusion and the treatment. Enrolment of the first four patients was staggered with a minimum interval of two weeks after treatment of the previous patient, to identify any potential early safety problems that may have required terminating the trial. To further standardize the inclusion, patients to be included were those that alternatively would have received bone autograft and/or BMP, thus excluding patients with segmental defects. Of the 30 recruited patients, one was excluded before bone marrow extraction as hepatitis B virus positive serology was confirmed.
Safety To clinically confirm the preclinical safety studies that observed absence of toxicity or dissemination or histopathological detection of any human tumour development (GLP preclinical study number 110068 from Oncodesign, Dijon, France), a primary safety endpoint, defined as detection of local and general complications, was fixed for the clinical trial. This end-point was set at any time in the 12 months of follow-up, but specifically required staggering the first four patients by two weeks, adverse event (AE) reporting at three, six, and 12 months, severe adverse event (SAE) and suspected unexpected and serious adverse reactions (SUSAR) reporting at any time, as required by the regulatory frame (to Eudravigilance -European Union Pharmacovigilance database-, to the competent authorities and to the Ethics Committees).
Donation, procurement and testing of the BM were performed in compliance with the European Cells and Tissues Directives; in particular, according to the requirements laid down in Directive 2006/17/EC of 8 February 2006 implementing Directive 2004/23/EC of the European Parliament and of the European Council as regards certain technical requirements for the donation, procurement and testing of human tissues and cells, and applicable national laws. 2005/28/EC of the European Parliament. The authorizations were obtained in all participating countries between January and March 2013. The sponsor of the study was INSERM, Paris, France. The EudraCT final number of the trial was 2011-005441-13 (in France, ID RCB number 2011-A00797-34), and the trial was also incorpo- rated to the database ClinicalTrials.
Data collection, processing and structure determination Data were collected at SOLEIL, PROXIMA1 beamline at cryogenic temperature (100 K), using a Pilatus-6M detector. Low dose data were collected at a wavelength () of 1. 148 at high redundancy and then processed and scaled using the XDS suite  (14). The resulting file was converted into mtz format (XDSCONV program) and then subjected to a first cycle of rigid body refinement in phenix. refine (PHENIX suite,  (15) ). The vacant S. cerevisiae 80S ribosome (PDB ID: 4V88) was used as search model, with each chain refined as a single rigid body. Difference density map (F obs -F calc ) was then manually inspected for the presence of the inhibitor, which was then placed into the density. Electron density for C45 compound was observed only at a single binding site.
The drugs were diluted in human polymix buffer, manually injected into the flow cell and allowed to equilibrate with the ribosome before data acquisition. To ensure that drug binding had equilibrated several movies were recorded over the span of ∼10 min and compared. smFRET data was recorded using a home-built total internal reflection based fluorescence microscope (25) at In the case of C45, there is no interaction with the eukaryotic specific ribosomal protein eL42, as previously reported for CL (PDB: 5TBW). (D) Detailed view of the halogen-interactions occurring between the chlorine groups onto the decalin ring of C45 and the 25S rRNA residues G2793 and G2794. Distance is also reported for the H-bond between the chlorine in position C2 of C45 with C2764. ∼0. 1 kW/cm 2 laser (532 nm) illumination at a time resolution of 40 ms.
Donor and acceptor fluorescence intensities were extracted from the recorded movies and FRET efficiency traces were calculated. FRET traces were selected for further analysis according to the following criteria: a single catastrophic photobleaching event, at least 8:1 signal/background-noise ratio and 6:1 signal/signal-noise ratio, less than four donor-fluorophore blinking events, a correlation coefficient between donor and acceptor <0. 5 and a lifetime of at least 50 frames (2 s at 40 ms time resolution) in any FRET state ≥0. 15. smFRET traces were analyzed using hidden Markov model idealization methods as implemented in the SPAR-TAN software package  (25). In all idealizations, transitions between all states were allowed in a three state model of the human or yeast pre-translocation ribosome (FRET values 0.
Structural studies reveal the binding mode of C45 to the eukaryotic 80S ribosome We solved the X-ray crystal structure of the S. cerevisiae 80S ribosome in complex with a synthetic sample of the inhibitor C45 at a maximal resolution of 3. 1 Å (Figure  2A, B , Table  1 ). As shown for its congener, CL (3), C45 binds within the large ribosomal subunit (LSU) E-site pocket (Figure  2 ). The molecular mechanism by which C45 inhibits protein synthesis in eukaryotes is the same as proposed for CL  (3, 8). Its binding impairs the entrance of the CCA-end of the tRNA into the E-site pocket during the elongation phase of protein synthesis. To solve the present structure we used the vacant 80S ribosome (PDB (Protein Data Bank) entry: 4V88 (  13 )) as reference model. The resulting difference density map (F obs -F calc ) (Supplementary Figure  S1 ) allowed us to unambiguously fit C45 within the binding pocket, revealing its mode of binding.
Dispersion interaction calculation confirms the unique structural features of C45 Visual analysis of the crystal structures of C45 and CL (PDB: 5TBW) in complex with the 25S rRNA of the 80S ribosome (C45/RNA and CL/RNA complexes hereafter) revealed that there are at least three different bases (two guanines and one cytosine) of the rRNA pocket that are noncovalently interacting with the A-ring of the lissoclimide compounds. The main mode of interaction between the compounds and guanine bases is through Cl-interactions, while the interaction between the compounds and the cytosine base is a Cl• • • H-N hydrogen-bonding interaction. These involve halogen• • • or halogen• • • H-N interactions as indicated in Figure  3. For CL, we noticed additional O• • • H-N and O• • • H-O hydrogen-bonding interactions with the peptide backbone of residues Pro56 and Phe58 of ribosomal protein eL42.
To further understand these interactions, we considered a reduced model: the rRNA residues of the pocket were minimized to guanines G2793 and G2794, cytosine C2764 (the nearest residues to CL and C45) as well as a small peptide chain consisting of Pro-Val-Phe (from ribosomal protein eL42). Further, the CL and C45 compounds were modeled as substituted decalin rings (Figure  3 ). The structures for these model systems were obtained from the experimentally determined X-ray structures (PDB ID: 5TBW and present work) followed by geometryoptimization of the positions of hydrogen atoms. At the optimized geometries, the binding energies were evaluated using the Becke-Lee-Yang-Parr (BLYP) density functional approximation  (19, 20). Dispersion effects were included via the D3 method  (18). In Table  2 , we report the individual (with C2764, G2793/94 and the peptide Pro-Val-Phe considered as independent entities) interaction energies and the overall (all of the above considered together) net interaction energy of various residues within the E-site involved in binding with C45 and CL.
This would permit engineering of different lissoclimide derivatives with similar binding properties achieved through distinct drug-target interactions. We finally compared our structure with the cryo-EM structure of the Homo sapiens 80S ribosome (32) (PDB ID: 6EK0); superposition of the minimized E-site regions of the 25S and 28S rRNA yielded a root-mean square deviation (RMSD) value of 0. 685 Å, and provided additional ). In (A), the putative hydrophobic interaction between the C10 methyl group on both CL and C45 and the methylated G4371 on the 28S rRNA. By contrast, the 2 -O methylation of G4370 does not interact directly with the drugs but can be considered as target for the design of future compounds with higher potency. PTM modifications are circled for clear identification. CL is represented in yellow, while C45 in magenta.
ACKNOWLEDGEMENTS The Yusupov group is grateful to the staff of PROXIMA 1 beamline at the synchrotron SOLEIL (France) and, in particular, to  Dr Leonard Chavas  and  Dr Pierre Legrand  for providing rapid access and assisting with data collection. We would like to thank  Dr. Iskander Khusainov  ( Yusupov laboratory ) for assistance with yeast ribosomal subunits purification.
FUNDING French National Research Agency  [ ANR-15-CE11-0021-01  to G. Y. ];  La Fondation pour la Recherche Médicale  [DBF20160635745, France to S. P. , G. Y. ];  European Research Council  advanced [294312 to M. M. , M. Y. ];  Russian Government Program of Competitive Growth  of  Kazan Federal University  (to M. Y. );  National Institute of Health  grant [ 1R01GM108889-01  to C. Z. and D. L. M. ];  National Institute of Health  grant [ GM079238  to S. C. B. ];  Swedish Research Council  grant [ 2017-06313  to M. H. ];  National Science Foundation  [ CHE-1800431  to V. K. V. ];  National Institute of Health  grant [ GM129264  to C. D. V. , S. C. B. , M. Y. ]. Funding for open access charge: National Institute of Health.
In this work, a Model-Based Systems Engineering approach based on SysML is proposed. This approach is used for the capture and the definition of functional requirements in avionics domain. The motivation of this work is triple: guide the capture of functional requirements, validate these functional requirements through functional simulation, and verify efficiently the consistency of these functional requirements. The proposed approach is decomposed into several steps that are detailed to go from conceptual model of avionics domain to a formal functional model that can be simulated in its operating context. To achieve this work, a subset of SysML has been used as an intermediate modelling language to ensure progressive transformation that can be understood and agreed by system stakeholders.
e. dependencies, induction, and logic, even with support of ontologies. Those approaches do not help much in verification of global consistency. Through the advent of standard modelling languages like OMG UML/SysML, there have been many attempts to use semi-formal modelling approach to capture functional needs and functional constraints on a system of interest. For instance, IBM Harmony SE,  9  or SysMOD. 10 Those approaches provide some interesting results in detection of issues concerning consistency of requirements and missing requirements. But as they rely on semi-formal language with remaining semantic variation points, there can still exist multiple interpretations. Model-Based Systems Engineering approaches have been proposed for avionics systems. In Refs. 11, 12 the authors present a SysML based approach to ensure consistent allocation between functional layer and software architecture.
14. SysML based safety analysis in presented in Ref. 15. It combines MBSE with SysML and modelchecking to ease communication between system engineer and safety engineer. Refs. 14, 15 focus on V&V rather than focusing on how to capture consistently functional requirements while ensuring functional validation using simulation in the context of ARP4754. Using a formal modelling language permits to remove all possible ambiguities related to different possible interpretations. B modelling language, for instance, has been used with success on RATP Meteor subway line in Paris  16  and more recently Event-B to build formal requirements refinement using patterns  17. The use of Petri Nets to formally verify scenarios and use cases has been proposed in Ref. 18. Stimulus tool from Argosim 19 allows expressing system requirements through a formal model.
It conforms to ARP4754A recommendations on traceability and provides support for impact analysis in case of any change in the input functional requirements. The progressive definition of functional chains and their simulation are discussed in Section 4. 3. In the rest of this section, formal definitions of key concepts are proposed. First let us define the basis of the framework with a formal definition of a System from Ref. 26. This system definitions is a refinement of the formal definition of a System initially proposed by Wymore. 27 A System of Interest (SoI) is a 7-tuple defined by: + ¼ T s ; Input; Output; S; q 0 ; F; Q f g ð1Þ where T s is a time scale called the time scale of the system, Input ¼ In; I f gand Output ¼ Out; O f gare input and output datasets, S is a non-empty e-alphabet, called the e-alphabet of states, q 0 is an element of S called the initial state, F is a function called functional behavior such that: F : In Â S Â T s ! Out ð2Þ and Q is a function called states behavior such that: Q : In Â S Â T s ! S ð3Þ An e-alphabet is defined by Ref.
26  to be a set of data (any element that can be exchanged between any real systems) containing the universal blank symbol e. In addition, a dataset is defined to be a pair D = {D, B}, where D is an e-alphabet, and B is a pair {read, write} that enables to implement data behavior (persistent, consumable, accumulative). In the proposed formal framework, + is assumed to be a causal system, i. e. that requires a semantic notion of time to respect an intuitive notion of causality  28  : if one event A causes another event B, then every observer should see A occurring before B. Finally, we consider + to be an open system in the sense of Von Bertalanffy,  29  i. e. a system that interacts with its environment. Interactions can be energy, matter, or information transferred to or from the system boundary.
The expected behavior is represented by a SysML execution specification (thin green rectangle on the lifelines). Let u a functional scenario on a System + to be the 3-tuple defined by: u ¼ l 0 ; C l ; } È É ð6Þ where l 0 is a triggering incoming functional message, C l is a set of functional messages, and } is a function called occurs at that associates a message to a time instant: } : C l ! R þ : ð7Þ A functional message is emitted by a source lifeline and received by a target lifeline. In the proposed framework, three kind of message are defined: incoming, outgoing and reflexive. Incoming and outgoing messages carry functional data (data, material or energy) while reflexive messages specify internal functions. Let l in, l out 2 C l to be a functional incoming and outgoing message defined by the 3-tuple: l in ¼ l out ¼ source; target; data f g ð8Þ f t l in: source ð Þ2C A ^ft l in: target ð Þ¼R ð9Þ f t l out: source ð Þ¼R ^ft l out: target ð Þ2C A ð10Þ where, source and target are respectively the source and target lifelines in C L , and data is a dataset.
Therefore, a functional chain consumes system's inputs to produce system's outputs through the activation of nested function calls. Let n, a functional chain to be a 4-tuple defined by: Fig. 3  Black-box operational scenario examples with only one functional scenario. n ¼ Input; Output; G; f sp È É ð14Þ where Input and Output are datasets (see Definition (  1 )), G is a nested graph of function calls, and f ps is a function called system functional execution such that: f sp : Input Â G ! Output ð15Þ There is no strict consensus on ''function" definition between the considered domains: the ARP4754A gives its own definition, while the INCOSE Systems Engineering Hand-Book and INCOSE SEBoK glossary  32  propose slightly different definitions. In the proposed framework, a system function is defined to be the intended behavior of a system that consumes functional flows and produces functional flows regardless of implementation.
This definition is a combination of the definitions given by the ARP4754A and the SE HandBook. The concepts of function definition and function call are separated. This separation is motivated by the need of reusable functions. In that way the same function can be called in several architectures, or several models. With that separation, the behavior of a given function definition can be expressed by a nested directed graph G of nodes V representing lower-level functions calls. Let w, a function definition to be a 5-tuple defined by: w ¼ w in ; w out ; G; f p ; f w È É ð16Þ where w in and w out are function input and output datasets, G = {V, E} is a nested graph where V is a set of function calls and E is a set of functional flows, f p is a functional called function data production such that: f p : w in Â G ! w out ð17Þ and f w is a function called function definition instantiation that associates a typing function call to its definition: f w : V ! C w ð18Þ The meta model of the Fig.
Experiments and results During requirement capture, 8 main use cases and 4 actors were identified for the OMS. Among those use cases, the three depicted on the Fig. 7  were used for experiments. Those use cases were chosen to cover important functions and communication aspects of the OMS. ( (3) Upload data to target member system: it provides important functionality with regards to aircraft software equipment configuration and maintenance. From those 3 use cases 17 operational black-box scenarios were derived, containing 108 functional messages between actors and the OMS. After semantics unification, functional messages were reduced to 71 messages. Then, using the semantics mapping of Table  1 , the scenarios were automatically combined to create executable top-level functional chains. An excerpt of functional chains is depicted on the Fig.
Acknowledgements This article results from an  MBSE Integrated Project Team (IPT ). The work was done within  COMAC Beijing Aeronautical Science and Technology Research Institute. We are warmly grateful to  Jiang XIN  for his support to this project. We also kindly thank  Jie DAI ,  Xie MIN ,  Cary WANG , and  Wang CHEN , for the construction of the case study and for their valuable comments.
To examine these research questions, the DASH score, used to assess maternal dietary quality, needs to be derived in a harmonised way for the seven ALPHABET cohorts to reduce heterogeneity. The harmonisation is important because even though it is common to assess dietary intake through food frequency questionnaires (FFQ) in epidemiological studies, these questionnaires can differ in structure and length from one cohort to the other. Some studies have successfully achieved post hoc standardisation of dietary data from diverse sources and cohorts  [20, 21]. Furthermore, several variants of DASH index derivation methods have been published, with notable differences in the food components included and the scoring criteria  [15, 16]. No consensus exists but the most commonly used DASH score method is the one proposed by Fung et al.