dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, three papers  [35, 36, 124]  appeared in 2013 documenting the electron-noble gas scattering cross sections available at that time on LXCat, along with an article about computational techniques for calculating cross sections in atomic systems. [125] Intercomparisons of cross section data and comparisons of experimental and calculated swarm parameters using these data were made. While not going so far as to recommend data, these papers provide guidance as to which data users should select for a given situation. A second series of papers documenting data in H 2 /D 2 , in N 2 , and in O 2 is in progress. The LXCat team is pursuing further work in this direction and efforts on the part of other people in the community to critically review data on LXCat are welcome. The second guideline À open access À should not mean that downloaded data become anonymous.
Proper referencing of data downloaded from the site is essential for the survival of LXCat, and publications making use of data downloaded from LXCat should reference the site as well as the publications, if any, listed in each contributor's database. The format for referencing data retrieved from the site is: [database name], www. lxcat. net, [retrieved date]. It is hoped that members of the scientific community remain vigilant about the need for proper referencing of downloaded data. LXCat does not grant authorization to third parties (commercial interests, in particular) to download and distribute data from the LXCat site. Third parties are requested to contact LXCat for an explicit policy statement on this point. Thirdly, the databases on LXCat are dynamic; that is, rather than assign a DOI to each database (which would become immutable), contributors are allowed to upgrade their database as new data become available or when corrections are needed.
The history of changes in each database is recorded on the site, and data as they existed at any date after November 2013 can be recovered on-line. Versions predating November 2013 are available upon request. All downloaded files include the retrieved date in the header text, and, for traceability of data used in calculations, it is important to include this date in all references to data downloaded from the site. The format of data downloaded from LXCat is compatible with an increasing number of codes, both freeware and commercial, and links to some are listed on the website. In addition, LXCat is set up to act as a node for VAMDC, the Virtual Atomic and Molecular Data Center, which is a portal through which a number of databases can be accessed. [126] hile many of the data available through VAMDC are relevant to needs for astrophysics, VAMDC is not limited to this topical area and all LXCat database contributors have the option of having their data indexed for VAMDC.
However, only those data flagged with ''VAMDC'' can be seen through this portal. LXCat welcomes opportunities for linking to or collaborating with other projects. It is worth mentioning at this point that discussions have been initiated in the low temperature plasma community about developing databases and service similar to LXCat but for low and high pressure plasma chemistries. Such datasets usually consist of rate coefficients for two and three body processes. LXCat is being extended to accommodate this data type for electrons and ions. Both for LXCat and such new databases, discussions remain open about the strategies and requirements for validating complete datasets of cross sections/rate coefficients for given plasma systems. It is clear that the wish of the community is going toward ''validated'' datasets of cross sections (þrate coefficients) to be readily available for plasma modeling.
However as stressed in the introduction and again several times in the description of data available in databases, the completeness of a dataset cannot be defined generally. This is a specific issue related to the nature itself of low temperature plasmas where the properties of the system cannot be easily parameterized as a function of a few parameters like pressure, temperature and gas composition like in thermal systems. For going beyond the ''cold plasma limit'' (i. e. , negligible densities of excited state densities who play then no role into electron and ions dynamics), it is necessary to take into account the population densities of excited states explicitly and include their individual contributions. To do so, it is then necessary to go for state-to-state excitation kinetics. Many efforts are still needed into that field but the aim of LXCat is gathering available data for electrons and ions and to provide tools to users to assess such data.
For the validation of given datasets, discussions are currently ongoing in the plasma community for the strategies and methods to be developed and adopted to attain such a goal. Since the beginning of the project, more than 40 people representing institutions in 15 different countries have devoted time and effort to the LXCat project. The contributors include modeling groups who are willing to make their collections of data available publically as well as experimental or computational groups who generate data. The data contributors and the other people who have contributed to the infrastructure of the site itself constitute the ''LXCat team,'' most of whom are co-authors on this paper. Usage statistics indicate a steady increase in the number of visitors from around the world, and a glance at the titles of papers referring to LXCat show that researchers from many different scientific and technical areas make use of the data available on this site.
The website has undergone a complete transformation since the first public version in 2010  [127]  ; ion and electron data have been merged into a common structure that can be upgraded for the foreseeable future. Regular backups of the databases are made and so the data themselves are quite secure. Nevertheless, survivability of the overall project has been a concern because there has been no well-defined structure and very little funding. To these ends, the non-profit organization ''Data for Modeling Plasmas'' was set up in 2015 which defines a structure and provides a means for continuity and for accepting donations, annual dues, or grants from individuals or institutional partners. The objective of this organization as stated in its formal by-laws is ''to facilitate the exchange of numerical tools and data for modeling the physics and chemistry of plasmas.
(K 0 ), (binary or ternary) ion-molecule reaction rates and electron detachment (d/N). This tabulated set of swarm data comprises electron and ion interactions with the noble gases (He, Ne, Ar, Xe), atmospheric gases (O 2 , N 2 , H 2 O, N 2 O, CO 2 ), hydrocarbons (CH 4 ), fluororocarbons (CF 4 , C 2 F 6 , CHF 3 , CF 3 I, C 4 F 8 and C 2 H 2 F 4 ), and SF 6. Recent research on molecules of biological interest such as Tetrahydrofuran (THF, C 4 H 8 O) and Tetrahydrofurfuil Alcohol, (THFA, C 5 H 10 O 2 ), as well as electron and ion swarm data in binary mixtures of H 2 O with other gases will soon be available in this database.
transcribed to the TRINITI database on LXCat are those for O 2. Considerably, more data are available through ''EEDF. '' These include complete sets for the following species: He, Ne, Ne_reduced, Ar, Kr, Xe, Xe_reduced, Cs, Na, O, H 2 , N 2 , O 2 , CO, F 2 , HCl, CO 2 , H 2 O, BCl 3 , NF 3 , CF 3 I, CF 4 , CH 4 , C 2 H 4 , SF 6.
Acknowledgments: The LXCat master server in Toulouse, France was purchased by the  CNRS R eseau des Plasmas Froids , and since 2013 the  Eindhoven University of Technology  has maintained a mirror site. Excellent technical assistance for the project was provided Sanchita Chowdhury during her stay at LAPLACE. LCP,  MCB , and GJMH would like to acknowledge LAPLACE ( CNRS and University of Toulouse ) for providing some funds for travel and exchange visits. The work of LLA and VG was partially supported by the  Portuguese FCT-FundaS  c ão para a Ciência e a Tecnologia under Project  UID/FIS/50010/2013. The work of OZ and KB was supported by the  United States National Science Foundation  under grants  PHY-1403245  and  PHY-1520970 , as well as the XSEDE allocation  PHY-090031. The  Natural Sciences and Engineering Research Council of Canada  is acknowledged for having supported the work of  CEB  and the work of ADS.
MJB and LC thank the  Australian Research Council  for funding. EC would like to thank the A. von Humboldt foundation for a postdoctoral fellowship. The work of BC was partially supported by the  STAE RTRA in Toulouse. RS and LS are thankful for support from  IAEA Vienna, DAE-BRNS  and the  Government of India. JdeU acknowledges PAPIIT IN111014. ETHZ is financially supported by  ALSTOM Grid GmbH ,  Pfiffner AG  and  ABB Switzerland. The work at  UCL  was supported by the  UK Science and Technology Facilities Council (STFC)  under grants  ST/F011687 ,  ST/I000151 , and  ST/K004069 ).
Bacteria with two cell membranes (diderms) have evolved complex systems for protein secretion. These systems were extensively studied in some model bacteria, but the characterisation of their diversity has lagged behind due to lack of standard annotation tools. We built online and standalone computational tools to accurately predict protein secretion systems and related appendages in bacteria with LPScontaining outer membranes. They consist of models describing the systems' components and genetic organization to be used with MacSyFinder to search for T1SS-T6SS, T9SS, flagella, Type IV pili and Tad pili. We identified ~10,000 candidate systems in bacterial genomes, where T1SS and T5SS were by far the most abundant and widespread. All these data are made available in a public database. The recently described T6SS iii and T9SS were restricted to Bacteroidetes, and T6SS ii to Francisella.
of system, complicating the assessment of their genetic diversity. On the other hand, secretion systems are often encoded in one or a few neighbouring operons. This information can facilitate the identification of genes encoding secretion systems in genome data  22, 23. Several programs were previously made available to identify components of some, but not all, protein secretion systems (Table  1 ). These programs are very useful to the biologist interested in browsing the known systems or in annotating a small set of sequences. However, they are web-based, and thus poorly adapted for the analysis of very large datasets. Few of these programs categorise systems as complete or incomplete, and none allows the definition of these parameters. These programs do not identify systems scattered in the chromosome, they only predict components or in some case clusters of components.
Some of these parameters can also be modified in the webserver version. The accuracy of the models was quantified against an independent dataset of experimentally validated systems. Importantly, we provide models to search for an unparalleled number of protein secretion systems (and some partly homologous systems): T1SS, T2SS (Tad and T4P), T3SS (flagellum), T4SS (conjugation system), T5SS, T6SS i-iii , and T9SS. We used the models to search for protein secretion systems in a large panel of bacterial genomes. Previous surveys, mostly dating from a time when few genomes were available, analysed the distribution of some specific protein secretion systems in genomes or metagenomes  17, 20, 24, [26] [27] [28] [29] [30] [31]. Due to space limitations, we will not attempt at re-assessing all these works.
Results and Discussion Overview of the approach. We defined 22 customisable models for the protein secretion systems and related appendages (File S1, Figs  1 2 3 4 5 6 ). This was done in four steps (see Materials and Methods for details): identification of reference and validation datasets, definition of the models, model validation, use of the models to identify the systems in bacterial genomes. Firstly, we searched the primary literature, reviews and books for references of well-studied systems  9, 16, 20, 26, 28, [32] [33] [34] [35] [36] [37] [38] [39] [40]. We used them to define two independent datasets (reference and validation) of experimentally studied secretion systems (Tables  S1  and S2 ). Secondly, the reference dataset was used to define the model and protein profiles for each type of system.
The model includes information on the number of components that are mandatory (necessarily present in a system), accessory (not necessarily present in the system), and forbidden (never present in the system). The occurrences of the components were searched using specific hidden Markov model (HMM) protein profiles with HMMER  41. Protein profiles are more sensitive and specific than Blast-based approaches  42. Our models used 204 protein profiles (included in the package, File S1), of which 194 were built in our laboratory and the rest taken from public databases (Tables  S4  and S5 ). We decided to build and use our own profiles instead of using those present in public databases, because they showed better specificity and sensitivity for our purpose: predicting and discriminating accurately secretion systems and related appendages.
To quantify these trends we searched for profiles with sequence similarity to our profiles in TIGRFAM (the database providing the most specific profiles in our analyses). Only 102 of the 199 profiles not taken from TIGRFAM had significant hits in that database and nearly half of them (48) had multiple hits for the same profile. A table with TIGRFAM profiles matching ours is available (Table  S6 ). The model indicates which genes are co-localised (at less than a given distance relative to contiguous genes in the cluster), and which genes might be encoded elsewhere in the genome (designated loners). Thirdly, the models were validated both in the reference and in the independent validation (that was not used to design the models and protein profiles) datasets using MacSyFinder (Table  2 )  25.
We also compared our results Table  1. Public webservers (Web) and downloadable applications (App) to identify components (C), clusters of components (CC), or complete (eventually scattered, S) bacterial protein secretion systems. with those of T346Hunter 43 for T3SS and T6SS (see Supplementary TextS1 and Table  S7 ). We could not make a direct comparison of our results and those of the remaining programs in Table  1  because they do not provide tables with results from the analysis of complete genomes (as T346Hunter does) and they cannot be used locally to analyze large datasets. Finally, the models were used with MacSyFinder to identify occurrences of each system in 1,528 complete genomes of diderm-LPS species. This procedure retrieved automatically all validly predicted secretion systems (Table  S3 , and see http://macsydb.
The model is described in Fig. 1a , and its use resulted in the correct identification of all T1SS in the reference and in the validation datasets. Overall, 20,847 proteins matched the protein profiles of the T1SS components in the bacterial genomes (Fig. 1b ). The vast majority of these were not part of T1SS because they did not fit the T1SS model. We found 1,637 occurrences of the T1SS model in 821 genomes (Fig. 1b ). The remaining proteins were probably associated with the numerous other systems carrying components homologous to those of the T1SS. We found T1SS in more than half of the genomes of diderm bacteria (54%). Some genomes contained many systems; e. g. , Bradyrhizobium oligotrophicum S58 and Nostoc sp. PCC 7524 encoded a record number of 9 systems (Table  S3 ). ABC and MFP were encoded together and OMF apart in more than half (57%) of the T1SS (Fig.
1c ). We found 95 loci encoding ABC and MFP in replicons lacking OMF. Many of these systems may be functional, since 94 of these loci were found in genomes encoding at least one OMF in another replicon. Multi-replicon functional T1SS have been previously reported  51. T2SS, T4P and Tad pili. T2SS are encoded by 12 to 16 genes, many of which are homologous to components of the T4P and the Tad pilus  17, 52, 53  (Fig. 2 ). We used the protein families conserved in the reference dataset to build 13 protein profiles for T2SS, 11 for type IV pili, and 10 for Tad pili (Materials and Methods, Tables  S4  and S5 ). We did not build profiles for GspA and GspB because they were rarely identified in T2SS and their alignments were unreliable due to low sequence similarity. The most frequent components in the reference dataset were defined as mandatory in the models.
The least conserved components were defined as accessory. Some profiles built for one type of system produced matches to (homologous) components of other types of systems. Discrimination between systems was facilitated by the definition of some specific components as forbidden (e. g. , GspC was declared forbidden in Tad and T4P). We identified all of the T2SS and Tad systems of the reference dataset using our models for these systems (Table  S1 ). In the validation dataset we missed some components of four of the 18 T2SS (Table  2 , Table  S2 ), therefore failing to pass the threshold for a complete system in these four cases. For example, we missed the very atypical T2SS of Legionella pneumophila because it failed the co-localisation criterion (unusually, it is encoded in five distant loci, Table  S2 )  54.
In two-partner systems (T5bSS), the translocator and passenger are encoded in two separate (typically contiguous) genes. T5SS rely on the Sec machinery for inner-membrane translocation and require other cellular functions for biogenesis. Many of these functions are ubiquitous in diderm-LPS bacteria and do not facilitate the identification of T5SS. Hence, our models only included information on the conserved, mandatory translocator domain of T5SS (Fig. 4 , Fig. S3 ). Two recently proposed families of T5SS -T5dSS and T5eSS 72,73 -were not matched by the T5SS profiles. We will build specific profiles for the detection of these sub-types when enough experimentally validated examples become available. Our models were able to identify all T5SS in the reference and validation datasets, with the exception of an atypical T5bSS of Pseudomonas aeruginosa consisting of a translocator domain fused with a component of the chaperone usher pathway  74.
We found 3,829 T5aSS in the genomes of diderm bacteria, which makes them by far the most abundant secretion system in our dataset. Certain Chlamydiae genomes contain up to 21 T5aSS. We found 1,125 T5bSS (0-8 per genome) and 849 T5cSS (0-24 per genome). T5SS were encoded in 62% of the genomes of diderm bacteria. T6SS. T6SS secrete effectors to bacterial or eukaryotic cells. They were recently divided in three sub-types  40  , among which T6SS i is by far the most studied  [75] [76] [77] [78] [79] [80] [81]. This sub-type has more than a dozen components  78, 82. We built profiles for 14 conserved protein families (Fig. 5a , Materials and Methods, Tables S1, S4 and S5), of which 13 were previously described as the most conserved components of the T6SS i 20. The remaining profile corresponds to the PAAR-repeat-containing EvpJ protein family of the spike complex  83  , present in eight out of the nine T6SS i in the reference dataset.
Using this model we identified all T6SS i of the reference and validation datasets. We only found an inaccuracy in Escherichia coli O42 where two systems adjacent in the genome were identified as a single system. Part of the T6SS i machinery is structurally homologous to the puncturing device of phages, from which it may have originated  84. Yet, our model did not identify a T6SS i in any of the 998 phages present in GenBank, showing that it does not mistake puncturing devices for components of the T6SS. We identified 652 T6SS i in 409 bacterial genomes, with up to six T6SS i per genome in some Burkholderia pseudomallei strains. Around 9% of the T6SS i were encoded in multiple loci in the genome. Interestingly, 35% of the replicons encoding a T6SS i encoded TssI (VgrG) away from the main loci, with a PAAR-containing component (EvpJ) and/or the chaperone TssD (Hcp) (Fig.
Conclusion We were able to identify nearly all protein secretion systems in both the reference and the validation datasets. The few missed systems were either very atypical (such as the scattered T2SS of Legionella) or included components very divergent in sequence (several T2SS). In the latter case, the relaxation of the parameters of the T2SS model allowed their identification. We emphasize that our models are publicly available and can be modified by the user to increase their sensitivity. Relaxing the parameters for the detection of the components (HMMER i-evalue and profile coverage), or for the genetic organization (required quorum of components, co-localisation criterion) often allowed retrieving more putative systems. We emphasize that we have not modified the default models in function of the validation procedure because that would have made our validation procedure inaccurate.
We display the number of occurrences of systems occurring rarely in our dataset on top of the bars. Clades with less than 4 genomes and/or with unreported phylogenetic position are not shown (i. e. , Chrysiogenetes, Gemmatimonadetes, Nitrospirae and Thermodesulfobacteria). This sketch tree was drawn from the compilation of different published phylogenetic analyses  [115] [116] [117] [118]. user is free to take the default models and make them less strict. Nevertheless, this might lead to an increased number of false positives. Complementary analyses can also facilitate the identification of systems. For example, when multiple profiles match a given protein, the one of the system usually provides the highest score (Fig. 2b, c ). This is one of the advantages of using specifically designed protein profiles, instead of generic profiles as can be found in PFAM: the system-specific profiles distinguish between homologs components in different types of molecular systems.
We may have under-estimated the presence of protein secretion systems in poorly sampled phyla because of the rapid evolution of extracellular components and the paucity of experimental data. Yet, several pieces of evidence suggest that we may have identified most systems. 1) We identified almost all known systems in the reference and validation datasets. 2) We identified at least one type of secretion system in almost all clades of diderm bacteria. 3) We identified components of T4P and Tad (homologous to T2SS), F-T3SS (homologous to NF-T3SS), and cT4SS (homologous to pT4SS) with profiles for the protein secretion systems in many clades, including monoderms (Table  S3 ). Most of these systems are monophyletic. If our protein profiles match homologs in outgroup systems, then they probably match all occurrences of the system.
Materials and Methods Data. The genomes of bacteria (2,484) and archaea (159) were downloaded from NCBI RefSeq (ftp://ftp. ncbi. nih. gov/genomes/, November 2013). We took from this dataset the 1,528 genomes of bacteria classed as diderm in the literature  [103] [104] [105]  (Table  S3 ). A total of 998 genomes of phages were downloaded from Genbank (last access, February 2013). The sequences of the reference protein secretion systems were retrieved from Genbank or from complete genomes (Tables  S1  and S2 ). Systems definition and identification. We built a dataset of experimentally studied secretion systems (T1SS-T6SS, T9SS) and related appendages (Tad, T4P and the bacterial flagellum) from the analysis of published data. We selected these systems in order to maximise sequence diversity.