dataesr/scientific-paragraphs-categorization huggingface dataset:
1% (v/v) FA, over 30 min. TOF MS scans were recorded from 350 to 1,250 m/z, and up to 18 candidate ions per cycle were subjected to fragmentation. Dynamic exclusion was set to 10 s after one occurrence. In MS/MS mode, the fragmentation spectra were acquired within the mass range of 100-1,600 m/z. The quantitative mass spectrometric data files were processed and analysed using MaxQuant (v1. 5. 2. 8)  (Cox & Mann, 2008). The search was performed using a UniProt/Swiss-Prot B. subtilis database (downloaded 17/05/15) with common contaminants included. Enzyme specificity was set to trypsin, with methionine oxidation as a variable modification. Cysteine carbamidomethylation was considered as a fixed modification. The heavy SILAC label was set to K8, the minimal peptide length to 6, and two missed cleavages were ª 2020 The Authors The EMBO Journal 39: e102935 | 2020 allowed.
Proteins extracted from the co-immunoprecipitation eluates were resolved using NuPAGE 4-12% system (Thermo Fisher Scientific), stained with Coomassie blue (R250, Bio-Rad) and in-gel digested using modified trypsin (Promega, sequencing grade) as described, previously  (Casabona et al, 2013). Of note, samples from replicates 1 and 2 were prepared using oxidation and reduction-alkylation procedures as described  (Jaquinod et al, 2012). The peptides were analysed by online NanoLC-MS/MS (UltiMate 3000 and LTQ Orbitrap Velos, Thermo Scientific). In particular, peptides were loaded on a 300 lm × 5 mm PepMap C18 precolumn (Thermo Scientific) and separated on a homemade 75 lm × 150 mm C18 column (Gemini C18, Phenomenex). MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Mascot Distiller (Matrix Science) was used to produce mgf files before identification of peptides and proteins using Mascot (version 2.
6) through concomitant searches against UniProt (B. subtilis strain 168 taxonomy, September 2018 version), GFP-tagged YqgP sequences, classical contaminants database (homemade) and the corresponding reversed databases. The Proline software (http://proline. profiproteomics. fr) was used to filter the results (conservation of rank 1 peptides, peptide identification FDR < 1% as calculated on peptide scores by employing the reverse database strategy, minimum peptide score of 25 and minimum of 1 specific peptide per identified protein group) before performing a compilation, grouping and comparison of the protein groups from the different samples. Proteins were considered as potential partners of the bait if they were identified only in the positive co-IPs with a minimum of three weighted spectral counts or enriched at least five times in positive co-IPs compared with control ones based on weighted spectral counts.
The family of converged structures for YqgP NTD was initially calculated using Dyana 2. 1  (Herrmann et al, 2002). The combined automated NOE assignment and structure determination protocol was used to automatically assign the NOE cross-peaks identified in the NOESY spectra and to produce preliminary structures. In addition, backbone torsion angle constraints, generated from assigned chemical shifts using the TALOS+ program  (Shen et al, 2009) , were included in the calculations. Subsequently, five cycles of simulated annealing combined with redundant dihedral angle constraints were used to produce a set of converged structures with no significant restraint violations (distance and van der Waals violations < 0. 2 A ˚and dihedral angle constraint violation < 5°), which was further refined in explicit solvent using the YASARA software with the YASARA force field  (Harjes et al, 2006).
The structures with the lowest total energy were selected and analysed using the Protein Structure Validation Software suite (www. ne sg. org). Cation-binding experiments were performed in 50 mM Tris-HCl, pH 7. 3, 150 mM NaCl, 5% D 2 O, at 37°C, when the same aliquots of the ligand were added to the protein solution stepwise, after every titration measurement.
Data availability The mass spectrometry (MS) proteomic data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. prote omexchange. org) (Deutsch et al, 2017)  via the PRIDE (Perez-Riverol et al, 2019) partner repository with the dataset identifiers PXD014578 (http://www. ebi. ac. uk/pride/archive/projects/PXD014 578) and PXD014566 (http://www. ebi. ac. uk/pride/archive/projects/ PXD014566). The NMR structural data have been deposited in the Protein Data Bank (PDB) under accession code 6R0J (http://www. rcsb. org/pdb/explore/explore. do?structureId=6R0J) and in the Biological Magnetic Resonance Data Bank (BMRB) under the accession code 34376 (http://www. bmrb. wisc. edu/data_library/summa ry/index. php?bmrbId=34376). Expanded View for this article is available online.
The X sign marks residues whose chemical shifts were not calculated due to the lack of resonance signal. D Details of 2D 15 N/ 1 H HSQC spectra of free (green) and Mn-titrated YqgP NTD (10 lM MnCl 2 in red and 40 lM MnCl 2 in blue) for the residues marked in orange in panel (C). As a reference, residues marked in black do not display spectral changes upon titration, e. g. peak intensity does not change for S88N-H upon Mn titration (overlay of green, blue and red contours), but peak intensity is severely depleted for D29N-H upon Mn titration (absence of red contours). Full titration data are available in Dataset EV3. E, F Detection (E) and quantification (F) of steady-state conversions of endogenous MgtE by YqgP variants bearing single-point mutations in putative Mn-binding region (BS184; 187; 196-203, Table EV2), cultivated in modified M9 minimal medium supplemented by low (1 lM) or high (100 lM) MnCl 2.
Acknowledgements We thank  Libor Krásný  for critical reading of the manuscript. TD thanks  David Rudner  and  Eric Cascales  for their support and guidance. We thank  Radko Sou  cek for amino acid analysis and  Stanislava Mat  ejková for elemental analysis. KS acknowledges support from  Czech Science Foundation  (project no. 18-09556S ), and KS and MK acknowledge financial support from the  Ministry of Education, Youth and Sports of the Czech Republic  (project no. LO1302 ),  European Regional Development Fund (ERDF/ESF)  (project No. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000729 ),  Gilead Sciences & IOCB Research Centre , and the  National Subvention for Development of Research Organisations  (RVO:  61388963 ) to the  Institute of Organic Chemistry and Biochemistry. This work was also supported by the  CNRS, Aix-Marseille University (AMU)  and a  Marie-Curie International Reintegration Grant  ( PIRG08-GA-2010-276750 ) to TD.
BC and JD were supported by fellowships from  AMU , and JD also by the  Fondation pour la Recherche Médicale  (FRM, FDT20160435133). Proteomic experiments were partly supported by the  Agence Nationale de la Recherche  ( ProFI  grant  ANR-10-INBS-08-01 ). JB and Jana B rezinová acknowledge support from the  Grant Agency of Charles University  ( GA UK) in Prague  (PhD grant project no. 170214 ).
The conservation planning process relies on three key input datasets (Fig  1 ). These datasets include Grid PUs input, species spatial distribution input, and spatial cost input. Specifically, the PU grid file can be created by ArcGIS extension ET Geowizards (http://www. ian-ko. com/ ). This grid file includes a PU identification number and location information. Species distribution models (SDMs,  [21] ) offer a great potential for marine conservation and planning. SDMs identify relationships between the geographical distribution of species and characteristics of environmental factors  [22]. Such models can predict species' habitat suitability and species' probability of occurrence based on environmental variables. Considered as useful tools that offer a viable compromise between the lack of comprehensive species distribution data and the expense of collecting addition data, SDMs are increasingly utilized in conservation planning as an approximation to species ranges  [23] [24] [25] [26].
A case study in Haizhou Bay, China 2. 3. 1. Study area and target fish. We applied the proposed Grid-DM to small yellow croaker (Larimichthys polyactis) in Haizhou Bay, (119˚11 0 -122˚00 0 E, 34˚00 0 -35˚50 0 N), which is an important fishing ground in China. This stock used to support a major fishery in China and Haizhou Bay. However, the stock has been substantially exploited since the 1970s and is considered overfished  [36]. The small yellow croaker stock in Haizhou Bay experiences two major migrations in its life history: one is the spawning migrations to Haizhou Bay before its spawning season in May and the other is the wintering migrations offshore in November  [37] [38]. We collected biomass data from a bottom trawl survey conducted in September 2011 and 2013-2017, permitted by the Ministry of Agriculture of People's Republic China and Ocean University of China (Supporting information).
A stratified random sampling design was used with a total of 24 sampling sites in 2011 and 18 sites in the following years being selected from five strata per survey  [39]. The bottom trawl was towed at a speed of 2-3 knots for about 1 h, with a trawl net of 12m width and mesh size of 17mm. The two vessels used in the bottom trawl survey have the same size, power and are using the same net and tow speed. At each survey station, a CTD system (XR-420) was used to measure environmental data including depth, bottom temperature and bottom salinity. We divided the study area into 4815 square planning units (PUs) of 9 km 2 (Fig  3 ), based on the scale of the study area, existing MPAs, and historical observations of the species movement  [37] [38]. The species distribution in September 2011, and 2013-2017 was predicted using generalized additive model (GAM)  [40]  and Finite Volume Community Ocean Model (FVCOM)  [41] , and was then interpolated using inverse distance weighting (IDW) tool in ArcGIS 10.
Model parameterization. To reflect the species ontogenetic characteristics, a seasonal influx of spawners before recruitment in May and an outflux of all age-classes stocks in November are included in the biological sub-model, wherein the flux rate was estimated based on Zhong et al. [38]  (Table  1 ). A density-modified SRR  [43]  was developed to describe recruitment of this stock (Table  1 ) (Eq 1). Specifically, the original absolute SSB and recruits were converted into density values by dividing them with the area of the region. The obtained density of SSB and recruits were then used as variables to estimate the modified SRR. Natural mortality and fishing mortality were estimated with length-frequency data for the stock using survey data from 2011 and 2013-2017 (Table  1 )  [44]. While the parameters described above were derived from the survey data in 2011 and 2013-2017, the parameter values of 2012 were assumed to be the average of those in 2011 and 2013 due to the lack of data.
The species distribution layer of 2011 was used as an initial distribution dataset and a baseline for simulation (See Section 2. 3. 1), those of other historical years (species distribution layers of 2013-2017) were used for calibrating criteria as observed data (See Section 2. 3. 1). Specifically, both the observed biomass and simulated biomass in September, 2013-2017 in each PU were transformed to Δ-distribution for comparison  [46] : Linear growth parameter at low stock size, α 0. 14  [43]  Density-dependent parameter, β 6. 674 � 10 -5  [43]  Seasonal influx rate (%) 61. 33  [38]  Seasonal outflux rate (%) 53. 51  [38]  price  standardized.
The present fishing revenue (R) was calculated as: R ¼ P T t¼1 mC t ð1 þ r 12 Þ t ; ð9Þ where C t is the total catch at time step t; μ is the price of small yellow croaker; r is the discount rate; T is the last time step. The present establishment cost (EC) and maintenance cost (MC) were calculated with Eqs (  10 ) and (  11 ) from McCrea-Strub et al. [50] , respectively, which were based on representative data gathered from global MPA literature: EC ¼ 10 4:66 a 0:52 ; ð10Þ MC ¼ 10 5:23 a 0:21 y; ð11Þ where a is the area of MPA and y is the implementation year of MPA. The open source programming language R (version 3. 4. 0) was used in this study  [51]. Visualization of the simulation results was achieved with R package "ggplot2" (version 2. 2. 1)  [52]  and ArcGIS 10. 2  [42].
For example, in our case study of small yellow croaker in Haizhou Bay, we used the data available, including survey biomass and abundance, SRR, and migration patterns of the species, to illustrate how changing management measures may affect the fishery. In this regard, this approach can complement complex ecosystem models, such as Ecospace  [14]  and Atlantis  [16] , to address management problems for species that is data limited but has a high conservation and/or management priority. The proposed model can be used to project temporal and spatial changes of the stock and present socio-ecological performances of management strategies. This is significant because the implementation of spatial management measures is usually associated with high economic and opportunity costs, and financial constraints have been a key challenge for conservation practices globally  [56] ; comprehensive evaluation is, hence, necessary to find cost-effective solutions.
Many database programs are developing in archaeology  (Herzlinger & Grosman, 2018; LeFebvre et al. , 2019)  and physical anthropology, either at the initiative of personal development or collaborative  (Eve & Hunt, 2008; Feugère, 2011; Gailledrat, 2016) , or still institutional  (Cuy et al. , 2017; Meghini et al. , 2017). At the same time, targeted tools are also being developed to meet specific needs: sex determination  (Brŭžek et al. , 2017) , stature calculation  (Chan, 2013) , construction of stratigraphic diagrams  (Desachy, 2008)  or for 3D visualizations of skeletons  (Kepple et al. , 1998; Proko, 2014; Sachau-Carcel, 2012). Thematic database programs also make it possible to compile a variety of data for medical purposes, such as forensics, to assess human variability  (Jantz and Moore-Jansen, 2000).
These databases are bibliographic  (Adams, 2004; McEntyre & Lipman, 2001) , pedagogical  (Ackerman, 1998; Kappelman & Keane, 2011)  or documentary  (Bureau, 2019; Lewis & Griffin, 2011). Despite this important selection, we have not found an efficient and simple answer to the recording of human bones in an archaeological context. As part of this continuity, computer scientists and an anthropologist have developed an application open source called HumanOS (freely downloadable at http://www. humanos. cnrs. fr/). The goal is to optimize the recording of human bones from archaeological sites, in the field and the laboratory. Initial incomplete versions were presented in 2016  (Colleter et al. , 2015)  and 2017  (Colleter & Barreau, 2017). These prototypes served as a basis for discussion with various anthropologists who encouraged us to develop a stable final version presented in this paper.
Genesis Following the rescue excavation of the whole of a convent in Rennes (France)  (Colleter et al. , 2019 (Colleter et al. , , 2016;; Le Cloirec, 2017) , a local database has been developed in Python and PostgreSQL to facilitate the registration and management of information. More than 800 burials had been excavated. On this occasion, interactive skeletons were developed to provide simplified information about the presence and the state of conservation of the bones. Information concerning funerary archaeology (position of bodies, containers and decomposition spaces) and the biology of subjects (sex, age at death, etc. ) were added. A catalogue of tombs could thus be generated from the collected data. Although efficient, this application was not intuitive enough, nor ergonomic and required some prerequisites in computer science.
This database did not allow for direct field recording. The archaeologists had to re-encode their data at the beginning of the laboratory study. Also, the development of HumanOS is the porting of this database in a simpler environment, for dissemination and easier use to ensure the sustainability of data accessibility, and field recording. So, several constraints have imposed themselves on the project. We wanted to create a modular tool according to the problematics of researchers, a tool which is ergonomic, portable, without the need for an internet connection, a tool which stores the recorded information under several formats, royalty-free and open source, in a sustainable computer language for the development of complementary modules. The application is downloadable from http://www. humanos.
cnrs. fr to install it, simply unzip the folder. The archive consists of a folder sources, containing the structure of the database, and a file HumanOS. html which allows the opening of the application in any web environment (Chrome, Firefox, Safari, etc. ). Creating a shortcut from this file allows to open the application from the desktop of the computer. For better identification, the icon of the shortcut can be modified with HumanOS-icone. ico located in the folder sources.
The bones are recorded in the form of tables, 166 bones for the adults, 327 for the children and 229 for the babies. The continuous (metric) and discontinuous information (discrete) can be recorded according to the user's needs. The rarest bones in archaeology are not coded in the database (such as the inner ear bones, the sesamoids, the hyoid bone, etc. ). If the user wishes, they can be noted inbox "commentaries". The 16 principle articulations are coded according to the same technique, the metric information (i. e. levels, weight, length, etc. ) or discrete (i. e. presence or absence of pathologies, discrete traits, etc. ) can easily be associated in the discontinuous information. The recorded data concern sites, burials, skeletons and all related information. The only solution for working offline with a full web application is web storage, this is a mechanism for storing data in the Web browser  (Takasu et al.
, 2015). Unlike cookies, the storage limit is far larger and information is not automatically transmitted to a server. In this way, the JSON data are stored in local storage, when the user clicks the "Save" button. Automatic recording of information in the local storage is fast, but with lots of data, it requires settings in the browser preferences. Indeed, according to web browsers, the storage limit in the cache is not the same. This limit can be a hindrance with the recording of too much data. Also, to allow more data to be stored in the HumanOS application, a procedure allows you to modify the characteristics, for the moment in Firefox. It is accessible in the "sources" folder called "increase_the_storage_limit_in_Firefox. pdf". Optimization of this recording is under study. A "sample.
json" file is in the "HumanOS/sample" folder to test the application and its functionality. We wanted to develop an application easy to modulate by the user. The list of choices can be freely created according to the problematic of the study of one or the others (field area, chronology, form and profile of the pits, funeral architecture, metric, discrete characters, etc. ). The data format used by the application is JavaScript Object Notation (JSON). Whether stored in the local storage of the browser, imported or exported, their structure remains the same. As for many archaeological databases, one site contains elements, "here burials", which contain other elements, "here skeletons", etc. Each element is also associated with a set of parameters, explained in detail in the section concerning the application's behaviour.
The export Beyond the only ergonomic input of data, the HumanOS application allows then to export the files under different formats for (i) file sharing; (ii) the creation of an automatic report of the skeletons for archiving; (iii) statistical use in the environmental program adapted to the user (XLS, R, QGIS, etc. ) and vectored images of the skeletons (Fig. 5 ). The durability of the recording, thanks to the different formats used (JSON, CSV, SVG and HTML) makes it possible to ensure a cross-search of data, available in several databases, and a follow-up of the development over time. Data exports in. JSON or. CSV make the tool interoperable, and so they are compatible with many operating systems like R for statistics, or QGIS for spatialized queries. The multiplication of the exporting formats proposed, allows for great suppleness in the use of the data.
The numerous "commentary" boxes made it possible to specify their contents, but without an adequate keyboard in the field, only the checkboxes and the selection from the drop-down lists were recorded. The generation of the sheets and their export allowed the printing progressively of the burials to archive the data before structures were removed. The printouts were used to note details that were reported on days of bad weather and at the beginning of the study in the database. The automatic writing of a report is a real time-saver for storing all the primary information of the burial. In addition to the application, photographs of each grave were also printed in the field to check their quality and directly annotate cross-checks, taphonomics, constraints, and other miscellaneous information.
At this stage, the use of a printer seemed indispensable. In the laboratory, with HumanOS software, the complete re-entry phase of field data was dispensed. Indeed, the information was directly recorded in the database from the archaeological excavation operation. This time was used in the study to complete the data and refine the descriptions. The first inventories were useful for sharing basic information with the various specialists mobilized for the operation (ceramologists and archaeologists). After washing and packaging the skeletons, the presence of bones was specified according to their conservation. The dental diagrams could be completed with precision. A count of the bone samples was made according to the maturation of the skeletons to calculate a minimum number of individuals (Fig.
Importance of application and benefits According to the problematics posed by the anthropologist, the recording systems are different. The goal of creating databases is to impose and homogenize the entry of information while facilitating their management by sorting and searching according to multiple criteria. None of the databases is perfect but we have tried to give the freedom to the user to create their own listings. The lack of data is also recorded, as crucial information to obtain common denominators for information processing. The Database Management System allows to store, save, edit, sort and query information. The management of this database in open license environments (GNU GPL v3) makes it possible to ensure the durability of data accessibility, to couple them with tools, in particular, statistical computations and sometimes even automatic generation of documents (catalogue of tombs, skeleton conservation diagrams,.
). It is the user interface, as ergonomic as possible, that allows the use of these tools, without code or complex computer manipulation. Data recording is fast, flexible and easy to use. HumanOS is designed as a minimal database for human remains. The application can both serve the fields of archaeology, physical anthropology and biological anthropology (Fig. 6 ). The primary inventory of the data makes it possible to dissociate the description (inventory of bones, biology, the position of the body, etc. ), of the interpretation (the type of container, presence of a shroud, clothing, shoes, etc. ). By being able to work on tactile support, the addition of information is intuitive and practical from the field to the laboratory. The installation is simple: just unzip the file and click on "HumanOS.