dataesr/scientific-paragraphs-categorization huggingface dataset:
74631 cm -1. The output for the ortho energies in the supplementary data, and the extract in Table  6  , all have this magic number added for the ortho principal spectroscopic network. There are a small number (4 para ) of energy levels which are not joined to either of the principal components. If more experimental transitions became available in the future it would be possible to link these to the para principal network. We collated and considered a total of 44 325 transitions from 33 experimental sources (25 474 ortho and 18 851 para ). Of those 254 were found to be inconsistent (could not be validated to within 0. 035 cm -1 ) with others and so removed from the final data set, leaving a total of 44 071 transitions used as input into Marvel (25 293 ortho and 18 778 para ). Fig. 1  gives a visual representation of NumTrans, the number of transitions which link the states of the Marvel ortho -H 32  2 S network, against corresponding upper state energies.
Comparison to other derived energy levels We used a number of sources to compare energy level values against those determined in this work. 94KoJe  [78]  contains a list of energy levels, which are taken or calculated from other sources  [31, 43, 50, 76, 80, 84]  ; see Table  7  for a breakdown by vibrational band. AYT2  [91]  is a theoretical variational linelist for H 2 32 S calculated as part of the ExoMol project  [92, 93]  , a database of theoretical linelists for molecules of astrophysical importance. AYT2 is appropriate up to temperatures of around 20 0 0 K and designed for use in characterising the atmospheres of cool stars and exoplanets. The states file, available from https://www. exomol. com ,.
Assignment We compared the pure rotational levels determined in this work both against those given in 94KoJe  [78]  and the calculated values from the AYT2 linelist  [91]  , up to J = 5. These data are given in Table  8. The pure rotational levels are closer to those from the experimentally determined sources of 94KoJe than to those of variationally calculated AYT2, as would be expected. The pure vibrational levels from this work are also compared with those from 94KoJe  [78]  and AYT2  [91]  , see Table  9. It should be noted here that there are some differences in labelling between these data sources and those used in this work. For example, the pure vibrational levels of 94KoJe  [78]  which are labelled ( v 1 , v 2 , v 3 ) = (2 , 0 , 2) , (1, 0, 2) and (3, 0, 0), match the normal mode labelling of (4, 0, 0), (3, 0, 0) and (1, 0, 2), respectively, Fig.
2. Deviations in cm -1 between energy levels from this work and: 94KoJe  [78]  (see Table  7  ), 95FlGrRaSt  [79]  , 87LeFlCaAr  [76]  , 96UlMaKoAl  [47]. Note the logarithmic vertical-axis. according to the labelling of 01TyTaSc  [82]  and the experimental sources used in this work. We compared all the rovibrational energy levels given in 94KoJe  [78]  , 95FlGrRaSt  [79]  , 96UlMaKoAl  [47]  , and 87LeFlCaAr  [76]  against our set of Marvel energy levels, taking the aforementioned labelling differences into account for 94KoJe  [78]  (see Table  7  ). These comparisons are illustrated in Fig. 2. Most levels are in good agreement, with only a few levels with differences between 0. 1-0. 4 cm -1. These levels with the largest differences are from 87LeFlCaAr  [76]  and 56AlPl  [31]. All the levels which are compared to those of 87LeFlCaAr  [76]  have several transitions which link them to other energy levels in our Marvel dataset, and originate from different sources, which increases their reliability.
This indicates that the lack of accuracy for these particular energies originates from 87LeFlCaAr  [76]  and not the Marvel dataset. The transition data from 56AlPl  [31]  was considered for use in the current study, but the data could not be validated with that from newer sources (see Table  5  ), and so the energy levels are also assumed to be unreliable. As mentioned above, AYT2  [91]  is a theoretical variational linelist for H 32  2 S calculated as part of the ExoMol project  [92, 93]. Highly accurate experimental energy levels provide essential input for testing and improving theoretically calculated line positions such as those within AYT2. The ExoMol data format convention is to have a 'states' and a 'trans' file. The states file gives the calculated rovibrational energy levels, which can be compared against our empirical energy levels, and the trans file contains the Einstein-A coefficients for transitions between these states  [93].
Acknowledgements We would like to thank  Jon Barker ,  Fawad Sheikh , and  Sheila Smith  from  Highams Park School  for continued support and enthusiasm, along with Highams Park students  Jack Franklin  and  Samuel Sheppard  for their assistance in this project. We are grateful to  Laura McKemmish  for helpful discussions and advice,  Clara Sousa-Silva  for setting up the ORBYTS education project, and to  Anita Heward ,  Will Dunn ,  Marcell Tessenyi , and the rest of the Twinkle team for their ongoing support. We give thanks to  Shuiming Hu  for contributions to the data used in this work. This work was supported by  STFC  Project  ST/J002925 , the  RSF  (grant  17-12-01204 ), the  ERC  under Advanced Investigator Project  267219 ,  NK-FIH  (grant  K119658 ), and the  COST action MOLIM: Molecules in Motion  ( CM1405 ).
Acknowledgements The authors would like to thank  Patrick Lamielle  for designing gaz control chamber, and  Simon Gree  for helpfull discussion regarding the Raman microspectroscopy. This work was supported by funding from  Agence Nationale pour la Recherche  (Project  PHOTOMOC ,  ANR-14-CE26-0039 & Project 2PhotonInsight ,  ANR-16-CE08-0020 ),  Région Grand Est  ( MIPPI 4D ) and  Ministry of Science and Technology, Taiwan  (Project  104-2923-E-009-001-MY3 ).
Materials and Methods Collection of dataset and gene clustering with OrthoMCL. To collect a comprehensive dataset, we considered the largest bacterial genome repertoire at NCBI Genebank  34  (ftp://ftp. ncbi. nlm. nih. gov/genomes/genbank/bacteria/) and retrieved 109 completely sequenced Mycobacterium proteomes (Supplementary Table  S1 ). Proteins sharing minimum 30% of amino acid identity over at least 50% of their length were clustered together with the OrthoMCL algorithm using default parameters  35. OrthoMCL is an all-against-all BLAST search program that cluster homologous sequences (orthologs and recent paralogs) based on Markov gene clustering method  35. Inflation index is an important parameter that determines the size of the cluster  35. A lower inflation value (stringent clustering i.
e. fewer clusters with more proteins) may place true orthologs in separate clusters, while higher inflation value (lenient clustering i. e. more clusters with fewer proteins) may cluster sequences of different functions together  35. Here, we considered inflation index value of 1. 5 which was suggested to balance sensitivity and specificity while including the maximum number of sequences in the clusters  35. OrthoMCL resulted in 17,215 families of orthologous proteins (Cluster of Orthologous Groups or COGs) of which 523 groups of short proteins (less than 50 amino acids) and 252 probable species-specific groups containing members from same species (paralogs) were discarded. Remaining 16,440 groups were considered for further analysis. Ortholog identification and tree construction. In order to identify probable orthologs outside of Mycobacterium genus, the longest member of each remaining COG (16,440) was searched against the NCBI non-redundant (NR) protein database with the BlastP (v-2.
Amino acid sequences of significant hits were retrieved from NCBI non-redundant (NR) database using Blastdbcmd utility and their full taxonomic lineages were obtained from NCBI taxonomy database using ETE toolkit  36  with the help of their NCBI Gene Identifier (GI) numbers. For each group, multiple sequence alignment was generated by Muscle (v-3. 6) multiple sequence alignment tool under default settings  37. Phylogenetic trees were constructed in two phases. First, we constructed maximum likelihood trees from each such alignments using double-precision version of FastTree  38  algorithm with parameters -spr 4, -mlacc 2, -slownni for slower and more exhaustive search andgamma option which accounts for the uncertainties in rates of evolution at different sites  38. For each group, the best fitting amino acid substitution model optimized for maximum likelihood trees was detected by ProtTest3  39  and implemented in FastTree.
Here, the ancestral states were reconstructed through unordered states assumption of Wagner Parsimony model using various gain penalties (1, 3, 5 etc. ). For Mycobacterium species tree needed for this analysis, we first listed all the Mycobacterium genomes involved in gene transfer (receiver clade of trees where we found HGT). A representative phylogenetic tree (Supplementary Fig. S1 ) was constructed for these genomes based on the alignment of 16S rRNA gene sequences retrieved from SILVA (release 128)  43  , a high-quality ribosomal RNA database. Reliable 16S rRNA gene sequences could not be found for few genomes and were not included in the tree. For each probable HGT, a gene presence/absence matrix is generated by mapping the species in receiver groups with the species in the tree and used as the character matrix for ancestral state reconstruction.
For reliable statistics, functional enrichment analysis was conducted for the GO terms/Pfam domains which appear at least 10 times considering both the dataset (Supplementary File 2). It is noteworthy that since we searched through representative protein, the number of entries in HGT category is based on HGT events rather than genes. Mapping of horizontally transferred genes on KEGG pathways. Genes involved in HGT were mapped to the KEGG  45  (Kyoto Encyclopedia of Genes and Genomes) functional categories using BlastKOALA 46 genome annotation tool. BlastKOALA assigns KEGG Orthology functional categories (designated by "K" numbers) based upon homology to precompiled databases. Here, the representative sequence (longest) of each HGT COG was searched against "species prokaryotes" database of BlastKOALA using default settings.
Antibiotic resistance and Mycobacterium virulence. Candidate HGT proteins were annotated with antibiotic resistance information based upon their sequence similarity with known antibiotic resistance genes. Representative sequences from the candidate HGT COGs were searched against the following databases with BlastP algorithm (i) ARDB-Antibiotic Resistance Genes Database (v-1. 1)  48 , (ii) The Comprehensive Antibiotic Resistance Database (CARD) (v-1. 2. 1) 49 , (iii) Antibiotic Resistance Gene-ANNOTation database (v-3)  50  , and (iv) MEGARes: an Antimicrobial Database for High-Throughput Sequencing (v-1. 0. 1)  51. These databases are repositories of putative antibiotic resistance genes and related information collected from various resources. Currently, these databases contain 7828, 2311, 1808, and 3824 putative antibiotic resistance gene/protein sequences respectively.
To identify the sequences with significant similarity we considered cut-off values of 50% identity over half (50%) of the protein length and e-value less than 10 -5. HGT proteins were classed according to their putative antibiotic resistance potentiality based upon the functional description of their significant Blast hits. For virulence information we retrieved the protein coding sequences of bacterial virulence factor determinants from the Virulence Factors of Bacterial Pathogens database  52  and treated in a similar manner. Currently (last updated 24 th October 2017), this database contains sequences of 2,595 experimentally verified and 26,524 known and predicted virulence factors. Here, our query sequences were annotated against the experimentally verified dataset.
When we compared their distribution among these two datasets, most of the GO terms (>90%) were found to occur with similar but at a low frequency. However, we found significant (P < 0. 05) difference for several functional classes (Supplementary File 2). For instance, biological processes such as methylation (GO:0006306), different types of transport (GO:0006810, GO:0006814, GO:0015833, GO:0055085, GO:0003333), different metabolic processes (GO:0006751, GO:1901135, GO:0006725, GO:0005975, GO:0008152)), etc. were found to be significantly (P < 0. 05) over-represented among the candidate HGTs and DNA replication (GO:0006260), translation (GO:0006412), transposition (GO:0006313) etc. were found to be significantly (P < 0. 05) under-represented (Fig. 3B ). Among molecular function ontologies, hydrolase activity (GO:0016813, GO:0019120), ionotropic glutamate receptor activity (GO:0004970), N-methyltransferase activity (GO:0008170) etc.
were found to be over-represented and terms such as phosphopantetheine binding (GO:0031177), transposase activity (GO:0004803), etc. were found to be underrepresented (P < 0. 05; Fig. 3A ). Similarly, phosphopyruvate hydratase complex (GO:0000015), membrane (GO:0016020) etc. cellular component ontologies were found to be significantly (P < 0. 05) over-represented among candidate HGTs with underrepresentation of ribosome (GO:0005840) (Fig. 3D ). The vast enrichment of enzymatic activities and transport may be considered as an indication that genes involved in these processes are more frequently exchanged than the genes related to the central machinery of a cell such as DNA replication, translation, etc. When we assessed the Pfam domains, we found total 230 unique Pfam domains in candidate HGT dataset and 568 unique Pfam domains in the reference dataset, however only a few of them showed a significant difference in their distribution between these two datasets (Fig.
3C  and Supplementary File 2). Some of the Pfam domains that are more likely to undergone gene exchange are DeoR C-terminal sensor domain (PF00455), DeoR-like-helix-turn-helix domain (PF08220), methyltransferase domain (PF08242), sis domain (PF01380), etc. Overall these results highlighted the functional characteristics of candidate HGT genes which would help to understand their biological significance. Horizontally transferred genes in Mycobacterium metabolism. Previously, a great research endeavor was given to understand whether horizontally acquired genes had played any role in the acquisition of new metabolic traits  [11] [12] [13]. Considering the general enrichment of several metabolic and enzymatic processes among the candidate HGTs here we decided to explore the issue in more details.
Metabolic pathways and enzyme commission numbers associated with the candidate HGT proteins were obtained following 'K' numbers annotations retrieved from KEGG database. Here, we could retrieve functional annotations for 942 proteins in our dataset. pathogens  4, [6] [7] [8] [22] [23] [24] [25]. Here,  e tested whether the candidate HGT genes had played any role in the origin of antibiotic resistance among Mycobacterium species. Considering homology with pre-compiled antibiotic resistance genes from several databases, we found evidence of putative antibiotic resistance like properties in 11 groups out of 1,683 groups with detected gene transfer events (Table  1  and Supplementary File 2). These groups showed homology with genes conferring resistance against drugs as such chloramphenicol, viomycin, aminoglycosides including streptomycin, fluoroquinolones and erythromycin (Table  1 ).
While their primary purpose is to actively transport different types of metabolites, several types of Mycobacterium transporters are implicated in resistance to antibiotics and are used as potential drug targets  59, 60. Although essential for both extra and intracellular lifestyle, Mycobacterium genomes encode a comparatively lesser number of transporters than other bacteria such as Escherichia coli or Bacillus subtilis 60. To date, there is no clear understanding about their origin, however, the role of HGT in shaping their evolution is suggested by previous studies showing that the exportin Rv0986-Rv0987 (important for the host association of M. tuberculosis) including several others are acquired foreign genes  22, 23. Considering the general enrichment of different transporters in our HGT dataset here we hypothesized that a significant fraction of Mycobacterium transporters have been acquired through HGT.
55st of candidate HGT genes with putative antibiotic resistance and virulence properties. Antibiotic and virulence properties are annotated based on homology with known antibiotic resistance genes from various databases (details in main text). haveprovidedstrong evidence for extensive HGTs in and between different Mycobacterium species including M. tuberculosis which raised question about the validity of the clonal paradigm55. Considering phylogenetic signals Becq et al. and Rosas-Magallanes et al. independently speculated that extent of HGTs among Mycobacterium species is comparable to any other species.
Acknowledgements The authors thank  Mr. Olivier Chabrol  for technical supports. The authors acknowledge  Olga Cusack  for her expert assistance in editing the manuscript. This study was supported by  IHU Méditerranée Infection, Marseille, France  and by the  French Government  under the ≪Investissements d'avenir≫ (Investments for the Future) program managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d' Azur and European funding FEDER PRIMI. A. Panda  benefits a PhD grant from  Fondation Méditerranée Infection, Marseille, France.
Introduction One of the key isotopes for present and future energy applications is the fissile nucleus 239 Pu. As such it occurs frequently in the NEA Nuclear Data High Priority Request List (HPRL). In conventional Light Water Reactors (LWR), up to one third of the energy released is from the fission of 239 Pu. 239 Pu is produced through neutron capture reaction on 238 U, followed by two beta decays. For many of the Generation IV reactors or current LWR using MOX fuel, fission of plutonium dominates the energy production. Gamma-ray emission in nuclear fission is one of the least known fission observables and for this reason measurement requests of PFGS for 239 Pu in the thermal and fast neutron region are in the NEA e-mail: wilson@ipno. in2p3. fr Nuclear Data HPRL. Recent results on PFG emission in thermal neutron induced fission of 239 Pu have been published in Ref  [1].
LICORNE The LICORNE neutron source generates forward focused neutron cones by exploiting neutronproducing reactions in inverse kinematics. The major advantage is that detectors can be placed around the sample to be irradiated without the need for shielding from neutrons emitted by the source. A further consequence is a very low thermal neutron background in the experimental room. This feature of LICORNE makes the study of fissile isotopes in the fast neutron region possible. In the experiment reported here, PFGS characteristics of 239 Pu at an incident neutron energy of 1. 81±0. 52 MeV were studied. In order to estimate the neutron flux in the space after the LICORNE gas cell, a GEANT4  [4]  code was developed to simulate the physical processes in LICORNE (see Fig. 1 ). The program incorporates user-defined physical processes, which are p( 7 Li,n) 7 Be and p( 11 B,n) 11 C reactions, into the standard physics list provided by GEANT4.
Study design This prospective multicenter observational study was performed in 116 worldwide surgical departments from 44 countries over a 6-month period (April 1, 2016-September 30, 2016). All consecutive patients admitted to surgical departments with clinical diagnosis of AA were included in the study. Patient demographics included the following: age, sex, previous episodes of suspected appendicitis, comorbidities (immunosuppression, severe cardiovascular disease, Charlson Comorbidity Index (CCI))  [39] , previous antimicrobial therapy, clinical data (axillary temperature, diffuse tenderness, right lower quadrant pain, right lower quadrant tenderness, vomiting) and laboratory findings at admission (white blood count (WBC) and C-reactive protein (CRP)), radiological diagnosis (ultrasound (US) and computer tomography (CT) findings), Alvarado Score, Andersson's Score  [4, 5] , type of surgical treatment and adequate source control, WSES Grading System  [38] , type and duration of antimicrobial therapy, collection of peritoneal swab, microorganisms isolated, admission to intensive care unit (ICU), duration of hospitalization, re-operation, management of postoperative complications at days 7 and 30, Clavien-Dindo Score  [40] , histopathological findings, and mortality.
All patients were monitored until they were discharged or transferred to another ward. The center coordinator of each participating medical institution collected and compiled clinical data in an online case report database. Differences in local surgical practice of each center were respected, and no changes were impinged on local management strategies. Each center followed its own ethical standards and local rules. The study was monitored by a coordinating center, which processed and verified any missing or unclear data submitted to the central database. The study did not attempt to change or modify the clinical practice of the participating physicians: neither informed consent nor formal approval by a local ethics committee was required because of the purely observational nature of the study.
Statistical analysis Data were analyzed in absolute frequency and percentage, in the case of qualitative variables. Quantitative variables were analyzed as medians and interquartile range (IQR). Univariate analyses were performed to study the association between risk factors and in-hospital mortality using a chi-square test, or a Fisher's exact test, if the expected value of a cell was < 5. All tests were two-sided, and p values of 0. 05 were considered statistically significant. To investigate factors associated with death, we constructed a logistic regression model, including variables with p < 0. 05 in the univariate analysis. All statistical analyses were performed using Stata 11 software package (StataCorp, College Station, TX, USA).
S2-A,S2-B ,   C, D ) and ACE index (E,F). Dotted line indicates the cutoff point after which the differences to the 100% treatment stop being significant (see Table  1 ). Diversity measurements were performed on transformed data. Data represents 96% OTUs. for non-metazoan eukaryotes and metazoan NMDS, and Supplement Fig. S3-A,S3 -B for PCoA ordination, respectively). PERMANOVA analysis showed a significant effect of sample volume in both non-metazoan eukaryotic and metazoan community beta-diversity (Table  1 ). PERMDISP revealed significant overall differences among treatments. Non-metazoan community composition of the 100% treatment (28. 2 g) was different from all sample volumes below 43% of the total sampled area (12 g) but not significantly different from any of the sample volumes equal or larger than this value (Table  1 ).
However, the same authors warn that artefacts due to varying sampling depths can persist even when using relative OTU abundance  48. Since we cumulated reads numbers from 0. 2-10 g volumes to represent up to 100% of the cores, it is possible that there was an over-inflation of rare taxa in the larger sample volumes that would affect our diversity metrics. However, as discussed above and shown in Figs  4  and 5  and Figs S7 and S8 in Supplementary Information, combining reads of different sample volumes had no significant effects on beta-diversity assessment. Furthermore, an analysis with a dataset trimmed to contain only the 30 most abundant non-metazoan eukaryotic taxa in each sample volume, produced similar results to the whole data set in terms of the effect of sample size on beta-diversity measurements (PERMANOVA, F = 8.