dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, human serine palmitoyltransferase exists as four variant complexes (CPX-6663, CPX-6664, CPX-6665, CPX-6681) which share a common E. C. number (2. 3. 1. 50). However, the substrate specificities of these complexes vary, due to the positioning of side chain residues of differing amino acids in the alternate complex components, and this can be fully described by the addition of the appropriate Rhea cross-references. Rhea cross-references are currently available in download files, and will be visualised on the website in the near future. Enzymes are not the only complex components which bind small molecules. Rhea encompasses transporter activity within its concept of a reaction, and cross-references are increasingly being added to complexes involved in trans-portation. The ligands that are bound by receptors are also systematically captured as a 'ligand' annotation topic and are described by both a recommended, human-readable protein, peptide or small molecule name and a UniProtKB or ChEBI accession number, as appropriate.
Defining curation practice for obsoleting complexes and versioning. When the research community's understanding of a complex's existence changes to the extent that we need to delete an existing entry, or we decide to merge a sub-complex into a larger assembly, the original entries remain available in previous release files accessible via our ftp repository (http://ftp. ebi. ac. uk/pub/databases/ intact/complex/). If a complex has been merged into an existing entry, the accession number of the obsoleted complex is added to the complex it has been merged into as a secondary identifier, allowing an external user to still retrieve an entry. Secondary identifiers are available in all download files, while the website currently only displays the primary accession number (e. g. CPX-3042 is now part of Nucleic Acids Research, 2021 5 CPX-2161).
ComplexTab Complexes may contain molecules other than proteins, such as nucleic acids or small molecules, but some users, for example mass spectrometry proteomics scientists, are only interested in the protein components. Protein complexes can be subcomponents of larger assemblies, and users previously needed to parse the files separately to retrieve the participant of these subcomplexes. In response to requests from this user community, we have added a new column to the tab-delimited format, ComplexTab, that contains a list of UniProt accession numbers (including isoform or postprocesses chain extensions) in pipe-separated style with stoichiometry in parentheses.
Data licencing To ensure our data is available for reuse by all interested parties, and in line with EMBL-EBI policy, our licence has been updated to Creative Commons Public Domain (CC0) License (https://creativecommons. org/ publicdomain/zero/1. 0/). This applies to all Complex Portal data, i. e. PSI-MI XML3. 0 (30), MI-JSON and Com-plexTab files  (9) , as well as data directly accessed via web pages/services.
Identification of putative transcription cofactor complexes through the Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) collaboration The GREEKc collaboration was an EC-funded COST action aimed at integrating data and knowledge pertaining to gene regulation, of which the Complex Portal was an active participant throughout. As part of this effort, Velthuijs et al. (31)  have used the Complex Portal as a verification dataset in their analysis of potential transcription cofactors. By combining inferred complexes from hu. MAP 2. 0 (32), curated complexes from CORUM  (33)  and curated physical interaction data from IntAct [this volume NAR paper] and BioGrid  (34)  with a selected set of transcription-related Gene Ontology terms we have identified more than 1500 putative transcription cofactors.
Integration of complexes into Wikidata Wikidata (https://www. wikidata. org/) is part of the infrastructure provided by the Wikimedia Foundation and a sister project of Wikipedia; it initially provided a semantic web infrastructure for encyclopedic knowledge to be used in Wikipedia  (35) , but has since gained traction as a generic linked open resource; over the past years a number of life sciences resources have been aligned to Wikidata, including subsets of UniProt, the Gene Ontology and ChEBI. After we released the first eleven SARS-CoV-2 complexes in April 2020 we joined the COVID19 Virtual Elixir BioHackathon 2020 (https://github. com/ virtual-biohackathons/covid-19-bh20/wiki). During this hackathon we added Wikidata entities for these eleven complexes using a semi-automated curation pipeline using OpenRefine (https://openrefine.
org/) and the Wikidata Integrator Python module  (36). Through this integration into Wikidata, Complex Portal identifiers (https://www. wikidata. org/wiki/Property:P7718) for SARS-CoV-2 complexes as well as selected human complexes, which had already been manually added to Wikidata, were immediately used in the WikiPathways COVID-19 Pathways Collection (http://covid. wikipathways. org/) Lists of putative complexes on our 'watch lists' are available on request (https://www. ebi. ac. uk/support/ complexportal). Wikidata Integrator python module: https://github. com/ SuLab/WikidataIntegrator. Wikidata bot: https://github. com/lubianat/complex bot.
, 2017)  is a prominent example of such a strategy. The CO2M concept relies on a constellation of sun-synchronous satellites with XCO 2 spectral imagers to be deployed from 2025 by the European Commission and the European Space Agency (ESA). It will be based on passive radiance measurements in the shortwave infrared (SWIR), a part of the spectrum that is sensitive to CO 2 and CH 4 concentrations throughout the troposphere including the boundary layer, like almost all of the space missions that have been dedicated to greenhouse gas (GHG) monitoring so far  (Crisp, 2018). Much remains to be understood and to be developed in order to ensure that such a constellation provides information about emissions with enough detail to be relevant for policy makers. In this context, observing system simulation experiments (OSSEs) of atmospheric inversions with synthetic images of XCO 2 data have supported the design of the space missions that will monitor the anthropogenic emissions  (Buchwitz et al.
XCO 2 pseudo-data sampling and error As detailed in Sect. 2. 3, the OSSE framework of the inversions requires the location and time of the individual XCO 2 data, and the associated error statistics, but not the explicit values of the synthetic observations themselves. In this study, we consider pseudo-satellites with a low Earth orbit (LEO) whose altitude and inclination parameters are similar to those of the A-Train (705 km and 98. 2 • , respectively;  Parkinson et al. , 2006). The satellite observations are assumed to occur at 11:00 LT in the morning. Successive tracks of a single satel-lite on this orbit are separated by ∼ 25 •. However, we do not study the potential of a specific satellite nor that of a constellation of such LEO satellites depending on their number. Furthermore, this study focuses on results at the scale of 6 h.
, 2013a; Broquet et al. , 2018) , in particular any "systematic error". Thus, the errors on the XCO 2 data at the spatial resolution of the measurements are assumed to be Gaussian, unbiased and uncorrelated in space or time. The distribution of the standard deviation (SD) for these errors is also assumed to be uniform; therefore, these errors are summarized by a single value of SD (denoted as the data precision hereafter). A large number of scenarios are tested for the observation specifications: the precision on the individual XCO 2 data varies between 0. 3 and 2 ppm, and the spatial resolution of the ground pixels can take the following values: 2 km × 2 km (longitude × latitude), 2 km × 3 km, 3 km × 3 km, 3 km × 4 km and 4 km × 4 km. The reference is a precision of 0. 6 ppm and a spatial resolution of 2 × 2 km 2.
Maps and time series of anthropogenic emissions and natural fluxes High-resolution maps of anthropogenic emissions are needed to define appropriate point and area sources to be controlled by the inversion. High-resolution maps of anthropogenic and biogenic fluxes are also needed to distribute the controlled local to regional budgets of these fluxes on the spatial grid of the CHIMERE model. Finally, such maps are needed to provide insights into the typical budgets of fluxes at the control resolution and, thus, to quantify the prior uncertainty in these budgets with a suitable order of magnitude. The anthropogenic CO 2 emissions are extracted from several datasets compiled by IER  (Pregger et al. , 2007; Thiruchittampalam et al. , 2012). These datasets provide maps of the annual budgets per sector of anthropogenic activity over different domains and at different spatial resolutions.
The land surface natural fluxes are derived from 8 km resolution simulations made with the Vegetation Photosynthesis and Respiration Model (VPRM) for the year 2016. This prognostic model delivers hourly values of net ecosystem exchange (NEE) by assimilating satellite and meteorological data  (Mahadevan et al. , 2008). These values of NEE are interpolated over the CHIMERE area at the hourly timescale. Natural ocean fluxes are ignored.
The simulation of y based on a given estimate of x is given by the linear observation operator H: x → y = Hx, which chains three operators. The first operator H distr distributes the controlled hourly budgets of emissions in space within the controlled areas and https://doi. org/10. 5194/amt-14-403-2021 Atmos. Meas. Tech. , 14, 403-433, 2021 provides the spatial and temporal mapping of the boundary conditions whose scaling factor is controlled by the inversion. The second operator H transp is the atmospheric transport from the emissions and the boundary conditions to the full CO 2 and XCO 2 fields. Finally, the third operator H sample performs the XCO 2 sampling at the location of the XCO 2 data (Sect. 2. 1. 2). Differences between Hx and observed values for y arise due to (1) uncertainties in x and (2) the combination of errors in the observation operator and in the observation data that are together referred to as "observation errors".
Practical implementation of the OSSEs While, in principle, R should characterize both the errors in XCO 2 data and the errors from the observation operator H, this study focuses on the impact of the observation sampling and errors only. It ignores the errors from the observation operator. Moreover, the observation errors are restricted to the measurement noise, which is uncorrelated in space and time as detailed in Sect. 2. 1. 2. Thus, the different R matrices used for the OSSEs (depending on the observation sampling and noise) are all diagonal. The errors on the individual pseudoobservations are described by a uniform precision (σ XCO 2 ) depending on the chosen satellite configuration (Sect. 2. 1. 2). However, the observation vector y is defined by the transport model grid rather than by the precise location and coverage of the data.
A performance simulation tool The analytical formulation of the inversion makes it easy to test different scenarios of observation systems that monitor anthropogenic emissions; however, in this study, the OSSEs with the inversion framework have been performed in order to investigate the potential of the satellite imagery of XCO 2 from helio-synchronous orbits only, depending on instrumental configurations or other factors such as the wind or the amplitude of the emissions. Such spaceborne imagery similar to CO2M may become the critical component of operational atmospheric inversion systems for the monitoring of CO 2 anthropogenic emissions. These operational inversion systems will likely have to jointly assimilate data from many of the existing satellite missions, from ground-based networks and from the planned spectro-imagers.
However, efforts have been made to limit the amplitude of systematic errors in the concept of the new CO2M mission. Our new inversion framework allows for a realistic simulation of the observation sampling and errors to be accounted for. Nevertheless, generating simulations of the systematic errors from the retrieval of XCO 2 data that are suitable for the purpose of our study would have been difficult. Systematic errors are not described in the uncertainties computed by existing retrieval schemes. Furthermore, they depend on specific measurement configurations and on the evolving skill of radiative transfer inverse models and of empirical biascorrection systems, so that their characterization based on diagnostics with existing missions may hardly apply to future missions. Simulating realistic patterns of cloud cover consistent with the meteorology for the different test cases investigated would have also been challenging.
Future studies will integrate more realistic simulations of observation sampling and errors from different concepts of spaceborne imagery, based on radiative transfer inverse modeling applied to realistic fields of surface and atmospheric conditions and instrumental specifications. A last significant simplification of the general problem of the inversion of the anthropogenic emissions based on XCO 2 data has been stressed by  Ciais et al. (2020). Anthropogenic emissions of CO 2 bear a major share of emissions from biofuel combustion which can hardly be separated spatially from the fossil fuel combustion component. Furthermore, the emissions of CO 2 by human respiration represent a significant portion of the total CO 2 emitted from cities. The XCO 2 data and the atmospheric inversion approaches can hardly be used to distinguish between these different components if they cannot rely on complementary data.
In the synthetic framework of our experiments, we simulate satellite overpasses over the area of interest at 11:00 LT, and we conduct inversions with windows covering the 6 h before these overpasses (05:00-11:00 LT), which have been identified as the periods that might be directly constrained by the satellite observations. Focusing on the instrumental noise, the analysis of the sensitivity to the errors on XCO 2 data ignores systematic errors from the retrieval process. Moreover, all images are assumed to be taken under clear-sky conditions with the swath centered on the targeted sources. Another critical point is that the transport modeling errors are ignored. Finally, (i) the optimal spatial coverage of the targeted plumes which are seen entirely except during the tests of sensitivity to the swath width, (ii) the lack of systematic and transport modeling errors, and (iii) the assumption that the inversion system is perfectly informed about the statistics of the errors in the prior estimates of the emissions, biogenic fluxes and transport model boundary conditions, raise optimistic scores of URs.
Acknowledgements. The authors are grateful to the TAS partners, in particular Sandrine Mathieu; the LMD partners, in particular  Vincent Cassé ,  Cyril Crevoisier  and  Olivier Chomette ; colleagues from  LSCE , including  Yilong Wang  and  Elise Potier ; and the Chaire TRACE team for fruitful discussions. This work was performed using HPC resources from  GENCI-TGCC  (grant no. 2019-A0070102201 ). Financial support. This research has been supported by  Chaire Industrielle TRACE  (grant no. ANR-17-CHIN-0004-01 ), which is cofunded by the  French National Research Agency (ANR) ,  TAS ,  Suez Environnement  and  Total Raffinage Chimie. Review statement. This paper was edited by  Andre Butz  and reviewed by three anonymous referees.
OBSERVATIONS We obtained the data via ESO Time Observation run (0102. D-0271) to image R136 using the "classical imaging" mode of IRDIS  Langlois et al. (2014)  in H-and K-bandpass filters. For our purpose we used the same spectral band split into the two IRDIS channels to correct for residual detector artefacts such as hot pixels and uncorrelated detector noise among other instrumental effects. We maximized the image sharpness of the total exposure by discarding the single frames with poorer Strehl ratio and by correcting "a posteriori" the residual tip-tilt image motion on each short exposure before combining them. Observations were performed in October/November (K/H) 2018, achieving high dynamical range and high angular resolution imaging in both the H and K bandpass, over a FOV of 10. 8" × 12.
1" (2. 7pc × 3. 0pc), centered on the core of the cluster. The natural seeing was 0. 69 ± 0. 10 arcseconds in K and 0. 58 ± 0. 05 in H during the observations. The night was rated as "Clear" meaning that less than 10% of the sky (above 30 degrees elevation) was covered by clouds, and the transparency variations were less than 10% during the exposures. Our data consists of 1088/544 frames of 2. 0/4. 0s exposures taken with the IRDIS broad-band H and K filters (BB-H, BB-K). and the filters information. Figure  1  shows the final reduced images in the H (left) and K (right). These images are the deepest and sharpest images taken from the core of R136 so far. The Wolf-Rayet star R136a1 was used for guiding the AO loop of SPHERE confirming the high level of performances even for faint guide stars which surpass both NACO and MAD performances.
PHOTOMETRY To analyse the final images we applied the same method/tools as on first epoch data  (Khorrami et al. 2017). For the photometry we used the Starfinder package implemented in IDL  (Diolaiti et al. 2000). Starfinder is designed for the analysis of AO images of crowded fields, like the Galactic Center, for instance, as in  Pugliese et al. (2002). This method determines the empirical local Point Spread Function (PSF) from several isolated sources in the image and uses this PSF to extract other stellar sources across the FOV. For the present study, four and seven well isolated stars (within 0. 47" × 0. 47") were used to extract the PSF in the K and H, respec-   tively. The FOV in K is more crowded than in H (see Figure  1 ), so finding the bright isolated stars (within 0. 47" × 0. 47") in K is harder than in H.
08 and 0. 11 mag, compare to their Kmag in the first epoch (11. 07 mag and 11. 32 mag). The brightness of R136a3 and R136b in K (11. 45 mag and 11. 67 mag) are consistent with the first epoch K data  (11. 44 mag and 11. 66 mag). R136c in the K (11. 31 mag) is 0. 17 mag brighter than in 2015 (11. 48 mag), making this source the second brightest in our K catalogue. In previous studies R136c shows indications of binarity based on its strong X-ray emission (Portegies  Zwart, Pooley, & Lewin 2002; Townsley et al. 2006; Guerrero & Chu 2008)  and possible lowamplitude RV variations  (Schnurr et al. 2009). Comparing the brightness of common detected sources within two epochs, the K magnitude of 56% of the sources have changed more than twice their photometric errors 4. To make sure that this is not due to Starfinder photometry analysis (which is based on the reference input PSF), we have used DAOPHOT algorithm for this analysis as well.
3 <   < 2. 1. Figure  9  shows the evolutionary models (isochrones) can not be fitted for these scattered sources. We prefer to consider a (larger) error of 0. 5 mag on the extinction, to estimate the stellar mass range for each star at a given age. We estimated the stellar masses just for common sources between H and K data (1499 sources) using both their H and K magnitudes fitted to PARSEC isochrones at three different ages: 1, 1. 5, and 2 Myr. The mass function (MF), is plotted in Figure  10  considering a Gaussian distribution for each stellar mass. We convert the photometric/extinction uncertainty into a mass (gaussian) distribution. This Gaussian uncertainty in the mass of each star is accounted for, when constructing the MF. Each star fainter than K=17 (about 13. 4 M at 2 Myr) is corrected for completeness, according to its brightness and its location in the FOV (see Section 4 for more information).
This can be explained by completeness since the Γ  and Γ   are not compatible. But for the mass range of 10 -300 M where the data are complete (see Figure  5  and Figure  A1 ) and the MFs are not affected by completeness (compare Γ  and Γ   in table 3), the slope values in the inner region is slightly flatter than the outer region, and consequently than the whole FOV. Comparing the stellar population in the inner region with the outer in CMD (Figure  9 ), detected sources are Table  3. Γ CC is the slope of the completeness corrected MF and Γ NC is the not-corrected one. The stellar masses are estimated using parsec isochrones with DM=18. 49, A  = 0. 35 and E(H-K)=0. 1, at different ages (first column) and mass ranges (second column), for the whole FOV (top), and central r < 3" region (middle), and outer r > 3" region (bottom).
JHK COLOURS Among 818 sources detected in J and K in 2015 data, and 1499 sources detected in H and K in 2018, we could detect 790 sources common in total (J, H and K). 30% of the newly detected sources in 2018 had enough SNR to be detected in the K data in 2015 (they are brighter than the faintest star in the 2015 K catalogue) but these sources were not listed in the J since the AO correction for J data is not as good as in K. These sources were either located in the central part of the cluster where AO halo (uncorrected part of the stellar signal with the FWHM of seeing) was large leading to decreasing their SNR, or in the outer part of the cluster were the PSF were distorted (see photometric selection criteria in  Khorrami et al. (2017) ) due to anisoplanatism. For these 790 common sources between two epochs we have plotted colour-colour diagram in (J -K) vs (H -K) for 413 stars in the central region  < 3" (Figure  13  top) and 377 sources in the outer region  > 3" (Figure  13 -bottom).
DATA AVAILABILITY All data are incorporated into the article and its online supplementary material. A2  for their IRIDS (J, H, K) and HST (U, V, I) magnitudes. Left: whole FOV, Right: same as left but zoomed in the core to avoid confusion. Background image is the SPHERE/IRDIS/K in 2018. Table  A1 : List of brightest spectroscopically known stars in the core of R136 studied in optical. T eff , log L/L , and log g, and spectral types are taken from  Bestenlehner et al. 2020   Crowther et al. (2010 ), bestenlehner et al. (2014 ), and Crowther et al. (2016) , respectively. HSH95 WB85, and ID  are the stars identification in  Hunter et al. (1995) ,  Weigelt & Baier (1985) , and SPHERE/IRDIS/K catalogue in 2018 (this work), respectively. K and H are the SPHERE IRDIS magnitudes and J 15 is the SPHERE IRDIS magnitude in our first epoch data  (Khorrami et al.
ACKNOWLEDGEMENTS The  Star Form Mapper  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  687528. ZK acknowledges the support of a  STFC Consolidated Grant  ( ST/K00926/1 ). This work has made use of the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble), PYTHEAS/LAM/CeSAM (Marseille) ,  OCA/Lagrange (Nice) ,  Observatoire de Paris/LESIA (Paris) , and  Observatoire de Lyon  ( OSUL/CRAL ). This work is supported by the  French National 12 Z. Khorrami et al. Programms (PNPS). AB is funded by the  European Research Council   H2020-EU. 1. 1ICYBOB  project (Grant No. 818940 ). JP and RW acknowledge support by the  Czech Science Foundation  project no. 19-15008S  and by the institutional project RVO: 67985815.