dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Mass spectrometry was funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation-Projektnummer   INST 152/859-1 FUGG ,  INST 337/15-1 ,  INST 337/16-1 ,  INST 152/837-1 ,  INST 152/947-1 FUGG ). A. I. acknowledges access to the core facilities and laboratories of the  Centre for Structural Systems Biology (CSSB, Hamburg). We also acknowledge technical support from the  SPC facility at EMBL Hamburg. We would like to thank the local contacts for their assistance in using the beamline P13 operated by  EMBL Hamburg  at the  PETRA III  storage ring ( DESY, Hamburg, Germany ). We sincerely thank  Prof. Bernd Kaspers  and his group,  Department of Veterinary Sciences, Ludwig-Maximilians-Universität München, Munich, Germany , for the generation of a chicken anti-CbFic2 antibody.
Data availability Plasmids generated in this study are available upon request. Reasonable requests for the monoclonal anti-AMP antibody  37  as well as the anti-CbFic2 antibody will be fulfilled by the corresponding author, A. I. Further information and requests for resources and reagents should be directed to and will be fulfilled by the corresponding author, A. I. (a. itzen@uke. de). This study did not generate code. Structure factors and model coordinates have been deposited in the RCSB PDB under the accession code 8CIL. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  85  via the PRIDE  86  partner repository with the dataset identifier PXD040330. All data supporting the findings of this study are available within the paper and its Supplementary Information.
The degree of automodification was detected by the specific mass gain of AMPylation of 329 Da. AMPylation was quantified by the proportion of the sum of the signal intensity of all AMPylated signals to the total intensity of all TS-H3 1-36aa signals. Measurements were performed in biological triplicates. Data were processed using GraphPad Prism 8. 0 and represent the mean with standard deviation as error bars. Intact LC-MS analysis of CbFic2 auto-AMPylation: In 100 µl total volume, 0. 2 mg ml -1 (approximately 4. 5 µM) of CbFic2 or CbFi-c2 E66G were incubated in 20 mM HEPES pH 7. 5, 150 mM NaCl, 1 mM MgCl 2 , 1 mM TCEP, 1 mM ATP each in the presence and absence of 5 µM 20 bp dsDNA at 37 °C in the autosampler of LC-MS. Samples were analyzed hourly with maXis II ETD ESI-qTOF LC-MS (Bruker Daltonics) coupled to Elute UHPLC (Bruker) using a ProSwift™ RP-4H 1 ×50 mm column (Thermo Fisher Scientific).
Data were evaluated using DataAnalysis (Version 5. 1, Bruker Daltonics). The degree of automodification was detected by the specific mass gain of AMPylation of 329 Da. AMPylation was quantified by the proportion of the sum of the signal intensity of all AMPylated signals to the total intensity of all CbFic2 signals. Measurements were performed in biological triplicates. Data were processed using GraphPad Prism 8. 0 and represent the mean with standard deviation as error bars.
1 × 1 106 ions; Resolution: 70,000 at 200 m/z; Fill time:240 ms) were analyzed by MS/MS (Higher-energy collisional dissociation (HCD): 25 NES; AGC Target:1 × 105 ions; Resolution: 17,500 at 200 m/z; Fill time: 50 ms; Mass range: 400-1200 m/ z; Dynamic precursor exclusion: 20 s). LC-MS/MS data were searched with the Sequest algorithm integrated in the Proteome Discoverer software (v 2. 41. 15, Thermo Fisher Scientific) against a reviewed human Swissprot database, obtained in April 2020, containing 20365 entries. Carbamidomethylation was set as fixed modification for cysteine residues (+57. 021 Da). The oxidation of methionine (+15. 995 Da), pyro-glutamate formation at glutamine residues and the peptide N-terminus (+17. 027 Da), acetylation of the protein N-terminus (+42. 001 Da) and the AMPylation of serine, tyrosine and threonine residues (+329.
The first STXS measurements combining the H → γγ, ZZ, WW, bb, ττ, and μμ decay modes have been performed by the ATLAS  [17]  and CMS  [16]  Collaborations. First STXS measurements in the H → ττ decay channel alone have been performed by the ATLAS Collaboration  [36, 37]. In this paper, we report three STXS analyses in the H → ττ decay channel performed by the CMS Collaboration. All analyses are based on the LHC data sets of the years 2016-2018, corresponding to an integrated luminosity of 138 fb -1. The events are analyzed in the eμ, eτ h , μτ h , and τ h τ h final states based on the number of electrons, muons, and τ h candidates in the event, where τ h refers to a hadronic τ lepton decay. Results are reported in the form of signal strengths relative to the SM predictions and products of cross sections and branching fraction for the decay into τ leptons.
Event groups 1 and 2 are estimated from data, as will be discussed in the following sections. Event group 3 and the signal are estimated from simulation. Group 1, which still relies on the simulation of the τ decays, group 3, and the signal are subject to simulation-to-data corrections, which have been determined from dedicated control regions, as will be discussed in Sect. 10. 1. For the VH-analysis, the backgrounds generally involve the production of an additional V boson. Details of the background estimation are given in Sect. 9. 4.
Characterization of the classification task The overall success of the NNs in adapting to the given classification task is monitored with the help of confusion matrices. Examples of such confusion matrices for the STXS stage-0 and -1. 2 measurements, evaluated on the full analyzed data set in the μτ h final state, are shown in Fig. 4. For the given representation the matrices have been normalized such that all entries in each column, corresponding to a given true event class, sum to unity. The values on the diagonals in the figure thus represent the sensitivity of the NN to each given true event class. Random association would lead to a uniform distribution across predicted event classes with a weight of one out of seven (twenty) for the stage-0 (-1. 2) training. For the stage-0 classification, sensitivities of 70% and larger can be observed for the qqH, genuine τ, tt, and zll Fig.
[57]. This correction is applied to the simulated Z → , W+jets, and signal events, where a hadronic recoil against a single particle is well defined, and replaces the propagation of jet energy scale corrections to the p miss T. The efficiencies for genuine and misidentified b jets to pass the working points of the b jet identification discriminants, as given in Sect. 3, are determined from data, 123 using t t events for genuine b jets and Z boson production in association with jets for jets originating from light quarks or gluons. Data-to-simulation corrections are obtained for these efficiencies and used to correct the number of b jets in the simulation, as described in Ref. [50]. Data-to-simulation corrections are further applied to Z → ee (μμ) events in the eτ h (μτ h ) and τ h τ h final states, in which an electron (muon) is reconstructed as a τ h candidate, to account for residual differences in the e(μ) → τ h misidentification rate between data and simulation.
Results The model used to extract the signal from the data is defined by an extended binned likelihood of the form  (9)  where i labels all bins of the input distributions for each signal class, with index s, and background class, with index b, defined for each of the analyses as described in Sects. 7-9. Signal and background templates are obtained from the data model as discussed in Sect. 5, with further specifications for the VH-analysis in Sect. 9. The function P(k i | μ s S s ({θ j }) + B b ({θ j })) in Eq. (  9 ) corresponds to the Poisson probability to observe k i events in bin i for a prediction of μ s S s signal and B b background events in that given bin. The scaling parameters μ s of the signal contributions S s with respect to the SM expectation are the parameters of interest (POIs).
The fraction of shared events with respect to the events used by the NN-analysis typically amounts to not more than 20%. Given this small overlap we conclude that both analyses are consistent with each other in their trends and general results. The measurement of the CB-analysis is less precise but closer to the SM expectation. These findings again coincide with the p-values under the assumption of the SM hypothesis, previously reported for the corresponding inclusive measurements. A complete set of the reported measurements is available in the HepData database  [110].
Summary Measurements of Higgs boson production, where the Higgs boson decays into a pair of τ leptons, have been presented. The analyzed data are selected from proton-proton collisions at a center-of-mass energy of 13 TeV collected by the CMS experiment at the LHC from 2016-2018, corresponding to an integrated luminosity of 138 fb -1. Results are presented in the form of signal strengths relative to the standard model predictions and products of cross sections and branching fraction to τ leptons, in up to 16 different kinematic regions, following the simplified template cross section scheme of the LHC Higgs Working Group. For the simultaneous measurements of a neural network based analysis and an analysis targeting vector boson associated Higgs boson production signal strengths are found to be 0.
Participants and procedure Participants were approached in March-April 2021 through multiple channels. First, we invited 800 individuals from a database of volunteers who had registered to participate in experimental research at the Laboratory of Experimental Economics in Montpellier (LEEM, University of Montpellier, France), to complete our online questionnaire. Subsequently, we distributed the survey link through the national database, Risc (Relais d'Information sur les Sciences de la Cognition, https://exp esciences. risc. cnrs. fr), which comprised more than 12,000 volunteers. Finally, we spread the link via social media. Participation was voluntary and non-remunerated. Only participants who were older than 18 and fluent in French were asked to participate. Participants were told that they would be asked to complete a set of questionnaires on topics linked to sickness and infections, and signed a consent before starting the survey.
Ultimately, data from 139 participants were analyzed. The average age of these participants was 38 years (SD = 17 years), ranging from 18 to 85. Among them, 99 (71%) were women, 37 (27%) were men, two self-identified as non-binary, and one preferred not to disclose their gender. As it was mandatory to complete all items of the survey, no data was missing for the included participants. Study data are available at https://osf. io/x69j2/?view_only=82b 9a879e8064edfad8f4a651d9acf9a.
The best model was selected based both on CFA criteria and on the theoretically soundness of the factor solution (i. e. consistent with the existing literature on sickness behavior). Finally, we assessed the correlation of the total score and the scores from both the single-and dual-factor solutions with criteria validity variables. All analyses, apart from CFAs, were conducted using IBM SPSS Statistics 27. As per our internal quality control process, the analyses were repeated by an independent investigator using R 4. 1. 3 (figures from SPSS are reported). CFA analyses were conducted using STATA 17.
, 2017). On the other hand, sickness behavior is typically analyzed in clinical populations and in conjunction with activation of immune processes, and conducting the study in a non-clinical population during a state of constant infection threat can also be appraised as a strength. Future studies should evaluate the validity of the French SicknessQ to measure sickness behavior in French-speaking clinical populations and using experimental investigations of immune activation. Another limitation of this study pertains to the characteristics of the sample. Despite our efforts to advertise the study to various local and national participant databases, the number of respondents was relatively low and not representative of the French general population. Indeed, our sample was predominantly composed of a relatively young population of respondents, with a bias toward women, which hinders the generalizability of the findings, although it is representative of the population of people with functional somatic syndromes (FSS) which tend to have a female predominance  (Narayanan et al.
The analysis of biological and ecological traits has a long history in evolutionary and ecological research. However, trait data are often scattered and standardised terminology that transcends taxonomic and biogeographical context are generally missing. As part of the development of a global trait database of marine species, we collated trait information for European seaweeds and structured the data within the standardised framework of the World Register of Marine Species (WoRMS). We collected 45,175 records for 21 biologically and ecologically relevant traits of seaweeds. This resulted in a trait database for 1,745 European seaweed species of which more than half (56%) of the records were documented at the species level, while the remaining 44% was documented at a higher taxonomic level and subsequently inherited at lower levels.
, 2020)  and call for further development towards a broader trait-based approach that includes non-morphological and other traits  (Mauffrey et al. , 2020). Seaweed trait information remains largely scattered, not widely available, and not semantically standardised, which has hampered the development and application of phycological trait-based approaches over broad taxonomic, spatial, or temporal scales. To overcome such drawbacks and enhance the development of a common ontology favouring the use and comparability of trait-based approaches on seaweeds, we present AlgaeTraits, a seaweed trait database for 1,745 species occurring along the Atlantic and Mediterranean coasts of Europe. We expand the spectrum of commonly used functional traits (i. e. , measurable or quantifiable properties of individuals; https://doi.
org/10. 5194/essd-2022-329 Preprint. Discussion started: 14 October 2022 c Author(s) 2022. CC BY 4. 0 License. McGill et al. , 2006)  to include data on habitat, seasonality, morphology, life cycle, life history, and biogeographical range (Fig. 1 , Table  A1 ). In total, we describe 21 traits linked to taxonomic information, covering nine of the 10 previously prioritised traits in  Costello et al. (2015). These data are available on the AlgaeTraits data portal algaetraits. org, doi: https://doi. org/10. 14284/574 (AlgaeTraits, 2022), which is a subregister of the World Register of Marine species  (WoRMS, 2022; Marine Species Traits, 2022). Here we introduce AlgaeTraits by (i) presenting the methodologies used to collect data, (ii) highlighting trait coverage for the current and first version of the database and (iii) exploring trait variability for a selected subset of traits.
Trait collection Trait data collection started with an extensive literature review in 2013, supported by the Biology project of the European Marine Observation and Data Network (EMODnet). In 2015, the data were further refined with expert opinions. In total, more than 200 references were used and 33 experts (all co-authors on this article) contributed by filling out a survey. For specific traits where interspecific variation is known to be minimal, trait information was documented in the World Register of Marine Species (WoRMS, 2022) at the genus level. The database behind WoRMS (Aphia, see further) is built in such a way that the information at the genus level is then automatically inherited to all species within this genus, if no information at the species level was provided or available.
Trait data We included 21 traits for marine seaweeds, of which eight ecological traits, four morphological traits, six life cycle traits, and three life history related traits (Fig. 1 , Table  A1 ). Ecological traits relate to the habitat of the seaweed with respect to 'Environment' (Marine, Freshwater, Brackish), tidal 'Zonation', various degrees of 'Wave exposure', 'Environmental position' (substrate), 'Tolerance to organic pollutants', 'Vertical space use', and 'Seasonality'. In the few instances where seaweed species are known for their potential to form blooms this has been indicated as well (Fig. 1 , Table  A1 ). Morphological traits describe the thallus based on 'Body Shape', 'Body Size', 'Calcification', and 'Cytomorphology' (Fig. 1 , Table  A1 ). 'Body size' is the only quantitative trait included in AlgaeTraits and can be documented as a mean, maximum, or minimum value to describe the thallus length or diameter.
Life history related traits describe patterns that influence demography and population dynamics, while life cycle traits relate to the various stages an organism undergoes from one stage in its development (e. g. fertilsation) to the same stage in the next generation  (Albecker et al. 2021 ) (Fig. 1 , Table  A1 ). In addition to these 21 traits, species distributions were documented as ecoregions defined by  Spalding et al. (2007)  and can be used to subset the database according to geographic region. Moreover, WoRMS also displays species' occurrences of the Ocean Biodiversity Information System (OBIS; www. obis. org) (Fig. 1 ). Contrary to the other traits, 'Environment' and 'Distribution' were not uploaded as traits (i. e. , attributes) in the database system (Aphia, see further) but as environment flags and distributions.
Taxonomy The total number of described seaweeds is estimated at 9,250 species on a global scale  (Appeltans et al. , 2012; Guiry 2012) , of which at least 1,800 species occur in Europe  (Costello et al. , 2022). The taxonomic classification of these species is not always straightforward and is continuously updated. To provide the best possible taxonomic accuracy, the trait data were implemented in WoRMS. WoRMS aims to provide a complete taxonomic authoritative list of all currently published names of marine species and is synchronised with AlgaeBase, the most complete database on global algal taxonomic information  (Guiry and Guiry, 2022).
Database structure and management The AlgaeTraits database is a thematic subregister of WoRMS, part of the Aphia platform  (Vandepitte et al. , 2015; 2018). The Aphia platform is an MS SQL (Microsoft Structured Query Language) database specifically built to include taxonomic data and related information such as biological traits. In total, Aphia contains more than 400 data fields, which are maintained by more than 500 experts under guidance of the WoRMS steering committee, which takes the lead on setting priorities and future directions, and coordination of the experts  (Vandepitte et al. , 2015; 2018). Both editors and database users are supported by a data management team that includes technical and scientific staff hosted at the Flanders Marine Institute (VLIZ) and can be contacted through info@marinespecies.
Body size Seaweed 'Body size' was described in several dimensions (thallus length, diameter, width, thickness, height) and varied from a few micrometres in thickness or width (e. g. , filamentous, turf forming species such as Hapalospongidion macrocarpum) to several metres long (e. g. , kelp such as Saccharina latissima). Compared to other traits, 'Body size' had a relatively low data coverage of 28% (490 species) for European accepted seaweed species (Fig. 3 , Fig. 5 6 7 ).
Macroalgal blooming 'Macroalgal blooming' indicates the demonstrated capacity of a species to produce blooms. This trait had relatively low data coverage of 13% (233 species) on a European level (Fig. 3 , Fig. 5 6 7 ). Notorious examples of blooming species include Ulva prolifera, Cladophora glomerata, Caulerpa cylindracea, holopelagic Sargassum and Rugulopteryx okamurae, and events have been often linked to eutrophication  (Charlier et al. 2008; Smetacek and Zingone 2013). The trait, however, shows considerable variation with geographic location. This is exemplified by several non-native species, which bloom in the invaded region while showing non such behaviour in their regions of origin. This variation was incorporated in the database by linking entries for 'Macroalgal blooming' to geographic localities.
Discussion AlgaeTraits includes 21 traits, 1,742 European seaweed species, and is structured within a general framework and ontology aiming to describe all marine species  (Costello et al. , 2015). With a mean coverage of ~60% per trait, this database will be a solid tool for a variety of biological research and related fields, including marine conservation, nature-based solutions, and aquaculture. For example, traits can be used to monitor community and ecosystem changes  (McGill et al. , 2006; Vélez-Rubio et al. , 2021)  or to identify conservation priorities (Albouy   et al. , 2017; Cardeccia et al. , 2018; Esmaeili et al. , 2022). In addition, they can be incorporated into predictive modelling to assess eco-evolutionary consequences of climate change  (Schleuning et al. , 2020) , can contribute to predicting the invasiveness of species  (Nyberg and Wallentinus, 2005; Quell et al.
2021 ), be used in research aiming to better understand the driving forces of evolutionary trait history  (Heesch et al. , 2021) , or the mechanisms of community assembly  (Weiss and Ray, 2019) , and can even help to assess ecosystem services vulnerability  (Díaz et al. , 2013; Stevenson, 2014). The AlgaeTraits database complements recent efforts to collect and publicly provide well-structured and organised seaweed trait data. Just as  MarLIN (2006)  and SeaTraIn (2022) we focussed on biologically important traits. But contrary to these other initiatives, AlgaeTraits includes all European seaweed species, and presents a different, yet overlapping, set of traits. MarLIN (2006)  covers over 40 traits, of which 12 overlap with AlgaeTraits, but is limited to only 30 seaweed species. SeaTrain (2022) covers around 10 traits, of which two traits overlap with AlgaeTraits, and includes 96 seaweed species.
As to taxonomic coverage (1,742 species) and inclusion of functional important traits as prioritised by  Costello et al. (2015) , AlgaeTraits is the most extensive seaweed trait database published so far. In this first version of AlgaeTraits, not all traits are available for all species yet, and the current list of traits is not yet complete. For example, other key eco-evolutionary traits that might be considered to be included in the database are 'Tolerance to light', 'Tolerance to temperature', 'Tolerance to grazing', 'Tolerance to sedimentation' or 'Tolerance to epiphytism'. There is currently no possibility to describe parasitic algal life forms in the database, ignoring a relatively diverse group of red algae. Completion and further refining and expanding the database will be an ongoing effort of the AlgaeTraits editor community.
AlgaeTraits currently has five thematic editors, who actively update the trait information on a voluntary basis. During an upcoming workshop (December 2022), more experts will be trained to contribute to AlgaeTraits through the online editing interface, and expansion of this European database to a global level will be initiated. The AlgaeTraits editorial community will also need to report to the WoRMS steering committee. As the database expands, adding new traits or trait values can be considered under thorough consideration and discussion with the WoRMS steering committee and data management team. For now, traits are included at the species level in AlgaeTraits. However, multiple traits can exhibit substantial intraspecific variation  (Kattge et al. , 2011). For example, morphological seaweed traits can differ among populations depending on underlying genetic patterns  (Serisawa et al.