dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that there may be more than one entry per compound because some compounds have more than one type of functional group. b Each entry represents a different reference or source for a rate coefficient, but with no more than one recommended for assessments or SAR development. c Where f = 0, compounds contain no substitutions besides C and H, and contain no higher-order bonds; the only compounds that fit this definition are alkanes and cycloalkanes. Halocarbons which may contain many halogen substitutions are treated as one functional group, f = 1, because they tend to behave uniformly, unlike complex multifunctional compounds. ages such as ChemDraw and online services such as the NCI/CADD Online SMILES Translator (National Cancer Institute, 2017). Note that SMILES strings are not strictly unique and may be dependent upon the algorithm used in a given software implementation; therefore all SMILES were provided in their canonical form as output using the opensource Open Babel software program  (O'Boyle et al.
, 2011). Furthermore, other user-specified differences to SMILES output can still occur, even in canonical form, an important example being the representation of nitrogen-oxygen bonds, where we chose to always represent these bonds as dative, solely for consistency. SMILES strings can be easily converted to canonical SMILES using this package, and InChI and InChiKey using this and other open-source/proprietary resources. Less specific identifiers -including molecular formulae, molecular mass, and types of compounds and func-tional groups -are also provided, and these can be used to make broader searches to the database possible.
Results Table  1  is a summary of the number of compounds, reactions, and rate coefficient recommendations in our database, together with the number of non-hydrocarbon functional groups contained within each molecule. As shown above, Fig. 1  provides an overview of the size of the current database in relation to existing compilations of data, and Fig. 4  shows the temperature range covered by our data. From Fig. 4  and  ambient chamber measurements and jacketed flow reactors respectively. It is also notable that the OH radical dataset possesses the largest number of reactions and the largest fraction of temperature-dependent measurements. By contrast, the number of compounds measured for the NO 3 radical is much smaller, and the fraction of temperature-dependent measurements is also much less than for OH.
Regarding the functional form that is used to describe temperature dependences, where obvious curvature can be observed, as shown in Fig. 2  for example, the extended Arrhenius expression is preferred, since this describes the data more faithfully. Ultimately, although most reactions in the OH dataset are expected to be non-Arrhenius, most reactions have yet to be studied over a sufficient temperature range with enough precision to require the third ("n") parameter of the modified Arrhenius expressions to fit the data, so Arrhenius equations constitute the majority of temperaturedependent expressions in the dataset. By contrast, for reactions such as alkene ozonolysis, where quantum tunnelling is not expected to be feasible, any curvature in Arrhenius space is likely to be small, and so far no ozonolysis reactions known to this study have been shown to exhibit non-Arrhenius behaviour.
Discussion As shown in Fig. 1 , the database presented in this work is substantially larger than previous compendia and reflects our attempts to compile all available data concerning gas-phase reactions of organic compounds with selected atmospheric oxidants under atmospheric conditions. The current database provides recommendations for the reactions of VOCs with OH and NO 3 radicals, O 3 , and Cl atoms, the major oxidants that react with organic compounds in the atmosphere. Rate coefficients for the reactions of VOCs with other oxidants can be added in later versions of the dataset if there is sufficient interest. However, the focus of the development of the current database is to support the needs of assessing and modelling the impacts of organic compounds in the atmosphere. For this objective, the ideal is to present rate coefficients for every compound that is emitted into the atmosphere, and for every oxidized organic compound that is formed and reacts in the atmosphere.
Knowledge of rate coefficients of emitted compounds is necessary to assess their atmospheric lifetimes and the impacts of their atmospheric reactions on air quality. A total of ∼ 1700 individual compounds have been identified or estimated to be present in the various chemical categories used in US emissions profiles, of which ∼ 1000 compounds are present in VOC mixtures derived to represent total US, California, and Texas anthropogenic emissions  (Carter, 2015). This database provides rate coefficient assignments for at least the OH reaction for ∼ 90 % of the mass, though only ∼40 % by number of compounds with non-zero emissions. The high coverage in terms of mass emissions is expected, since such compounds are most likely to be a priority for research. However, a very large number of other species are emitted, and, although individually these may be insignificant, they may become important in the aggregate and should be of interest at least to those who use or emit such compounds.
Therefore, it is reasonable to expect that more of these substances will be studied in the future. Knowledge of the rate coefficients of the oxidized products formed when VOCs react in the atmosphere is necessary for determining the ultimate environmental fates of the emitted compounds and modelling their overall impacts on air quality. However, there are large numbers of possible reactions that many organic compounds and their reactive intermediates can undergo, and the use of automated mechanism generation systems such as GECKO-A  (Aumont et al. , 2005)  is necessary to derive complete mechanisms. Complete coverage of experimental rate coefficients of such a Earth Syst. Sci. Data, 12, 1203 Data, 12, -1216 Data, 12, , 2020  https://doi. org/10. 5194/essd-12-1203-2020 Figure  5. Area-proportional Venn diagram showing the overlap between species formed in GECKO-A for n-octane and α-pinene oxidation, and species present in our database.
To obtain an approximate indication of the types of compounds predicted to be formed by mechanism generation systems, and to assess the coverage of this database concerning their rate coefficients, we used GECKO-A to derive complete mechanisms for the atmospheric reactions of the representative compounds n-octane and α-pinene, which are associated with anthropogenic and biogenic activities respectively, and which are expected to yield distinctly different product distributions. The results of this comparison are shown in Figs. 5  and 6. Figure  5  shows the overlap between the current database and the n-octane and α-pinene products predicted by GECKO-A in terms of individual organic product species  (Valorso et al. , 2011). The area of each curve in this diagram is proportional to the number of species contained within it.
It is clear from this comparison that the number of species that have been studied so far is very small compared with the total number of species produced in the oxidation of these quite structurally simple primary emissions. Furthermore, the overlap between the species studied and the GECKO-A output is vanishingly small. Under the pro-viso that the GECKO-A mechanism is representative of the state of the knowledge in atmospheric chemistry, species that occupy this overlap region can be regarded as the "known knowns" of atmospheric chemistry, i. e. the species that are known to be produced and have known rate coefficients. When known primary emissions are subjected to the rules of atmospheric chemistry known to GECKO-A, the species that do not overlap with our database are considered as "known unknowns".
The area that falls outside these curves is expected to be vast, and it relates to all species that are formed from all primary emissions that do not overlap with the product distribution of α-pinene, n-octane, or our database. We consider this area to represent "unknown unknowns" in atmospheric chemistry. By this definition, the size of this area cannot be known, but it is anticipated that it is very large, especially when all known and unknown primary emissions are included, and when it is acknowledged that there may be many unusual or exceptional product formation pathways that are currently unknown to the GECKO-A model. Beyond these three main groupings, there are several other logical criteria by which species that are not contained within chemical mechanisms may be classified. For example, species that are formed through very minor reaction channels may be excluded by simplification protocols that aim to curb the combinatorial explosion within models and may be considered as "unexplored but potentially known unknowns".
This observation is underscored by the fraction of species for which measurements are available over the complete atmospherically relevant temperature range, the bulk of which will be consumed in the troposphere, which experiences temperatures between 220 and 300 K (see Fig. 4 ). This means that almost all the organic product rate coefficients used by mechanism generation systems like GECKO-A are dependent upon estimation techniques. From Fig. 6 , it is evident that certain functional groups that are relatively uncommon among atmospheric oxidation products of hydrocarbons (e. g. ethers and esters) are well represented in our database, and yet there are many func-Figure  6. Frequency plots comparing the functionalization of compounds within the GECKO-A mechanism of n-octane and α-pinene oxidation, a database of anthropogenic emissions  (Carter, 2015) , and the compounds present in our kinetic database.
(a) Functional groups such as ethers and esters are overrepresented within this database compared with GECKO-A, whereas other functional groups (e. g. nitrates, peroxy acyl nitrates, and hydroperoxides) are very poorly represented. (b) A mismatch is demonstrated between the number of functional groups per molecule in GECKO-A and that of the compounds found in this database. Better agreement is observed in both cases compared with primary emissions profiles. tional groups that are expected to be commonplace that are very much underrepresented (e. g. nitrates, hydroperoxides, peroxyacids, carboxylic acids, and peroxy acyl nitrates). Furthermore, the number of functional groups contained within a molecule is generally smaller in our database (typically between two and three functional groups per molecule) compared with the molecules produced in GECKO-A, where the modal distribution ranges between approximately three and seven functional groups per molecule.
Although it is anticipated that in-depth analyses of SAR performance will be forthcoming from our expert panel in the future, one well-established method of estimating rate coefficients that arises naturally from the compilation of data presented in our database is that of the correlations exhibited by rate coefficients of VOCs between different oxidants. In Fig. 7 , several such relationships are presented. It is clear that some of these relationships are stronger than others. For example, the correlations of ozone with both hydroxyl and chlorine are relatively high, which has been observed previously in the case of O 3 and OH  (McGillen et al. , 2011). In this example, the mechanism of all reactions in these relationships is electrophilic addition. Conversely, other relationships within this diagram involve a combination of addition and abstraction reactions (e.
g. any correlations between OH, Cl, and NO 3 ). Furthermore, some reactions may be more affected by steric hindrance (e. g. ozonolysis) than others  (McGillen et al. , 2008). Consequently, several trends arise depending on the relative efficiencies with which an oxidant participates in a given mechanism. Therefore, when taken as a whole, such correlations appear surprisingly scattered, although it is noted that individual subsets of these correlations may have good predictive power, as has been observed in the OH-Cl correlations for halocarbons and ethers for example  (Sulbaek Andersen et al. , 2005). Plots showing correlations between the reaction rates of the various oxidants within this database. Since it is possible that a compound may belong to several of the groupings shown in the legend, categorization of these compounds has been prioritized by reactivity (e.
Ongoing work and outlook The work contained in the present database represents clear progress in terms of its comprehensive coverage, availability, and accuracy, and the fact that it can be downloaded and readily searched. However, limitations remain and the following future improvements can be envisioned: 1. There are several oxidants that are of importance to combustion chemistry, and there are some atmospheric or laboratory conditions that are not currently included, such as O( 3 P), O( 1 D), carbonyl oxides, H and Br atoms, and low-temperature OH reactions. 2. Quantitative information on branching ratios for sites of attack is available for certain reactions, which is not yet implemented in the current database. 3. There are at present only a limited amount of metadata based on experimental conditions, but no information on technique/reactor details/pressure/bath gas/reference compounds in relative-rate experiments.
4. There is a wealth of information published on kinetics in the solution phase that is beyond the scope of the current database, which focuses purely on gas-phase reactions. 5. The current approach to extrapolation of rate coefficients using temperature-dependent data outside 298 K is not statistically rigorous. Improvements will re-quire further data analysis such as that outlined in Hites (2017). 6. Similarly, uncertainty estimates that are ≥ 100 % are not physically meaningful. Improvements upon this may require the asymmetrical distribution of errors afforded by the approach of the IUPAC task group. Again, further statistical analyses will be necessary. The timescales over which such improvements can be made is likely to depend on external factors such as funding, the continued participation of members of the expert panel, and the possible participation of other experts.
Conclusions We present a digital, freely available, searchable, and evaluated compilation of chemical kinetic information with a current focus on gas-phase bimolecular reactions. This database responds to a need within the atmospheric chemistry community and elsewhere for an up-to-date, reviewed database https://doi. org/10. 5194/essd-12-1203-2020 Earth Syst. Sci. Data, 12, 1203 Data, 12, -1216 Data, 12, , 2020  that captures the chemical diversity that is found within the kinetics literature. It is intended that this will be a valuable resource for research into SARs, among other applications, where the quality of training sets will impact accuracy and predictiveness directly. Experimentalists will also be able to use this database to compare their measurements with previous data and analogous compounds, and will also be able to easily locate evaluated reference rate coefficients.
Although the current version of this database is the largest database of its kind, there remain many kinetic data that are currently not included in this project, including reactions with several important oxidants, reaction branching ratios, and reactions in other phases besides the gas phase. This, together with the fact that new rate coefficients are published each year, means that further work will be necessary to improve, extend, and maintain this database. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-12-1203-2020-supplement. Author contributions. All authors contributed to the data compilation, reviewing of data, manuscript writing, and ideas behind the work. Furthermore, WPLC assisted in managing the database, writing Excel macros, and managing the project.
To investigate how environmental surveillance could be implemented to complement clinical surveillance and improve public health monitoring and evaluation, we performed a one-year pilot study of municipal wastewater effluents of the city of Clermont-Ferrand, France, to analyse enteric infections  (Bisseux et al. , 2018). In the present study, we analysed wastewater samples by ultra-deep sequencing (UDS) to determine the pattern of EV infections of the whole community and compared the data with those derived from clinical surveillance of the same population and the entire French population. We discuss the relevance and limitations of the present pilot study and the lessons learnt in terms of epidemiologic surveillance and future applications.
Reference databases for EV identification from partial 1D VP1 Two different databases were built for the bio-informatics analysis. The first database, named database I, was built by aligning sequences from all human and non-human EV reference strains downloaded from the NCBI GenBank database. Sequences of Sapelovirus were added as an outgroup. The second database (database II) was built to span intratypic EV diversity. The partial VP1 gene sequences targeted by the PCR primers used in this study were recovered from all nucleotide sequences for each human EV type available in GenBank (as of March 2015). These published sequences were compiled with those determined in our laboratory. All the sequence data sets were constructed with the BioEdit software version 7. 1. 9 (http://www. mbio. ncsu.
edu/bioedit/bioedit. html). Assignment and sequence quality were checked with phylogenetic trees reconstructed by the neighbour joining method implemented in MEGA software v5. 01  (Tamura et al. , 2011). The sequences with assignment error or with suspected sequencing error were discarded. For each type, we selected sequences representing each cluster (defined as sequences with less than 1% of nucleic acid differences). The final database contained 3,256.
Bioinformatic analysis and quality control of sequence data The VSEARCH open source tool for metagenomics (https:// github. com/torognes/vsearch) was used for processing and preparing sequence data. After assembly of paired-end reads, sequences were aligned and those containing ambiguous bases (N) or mismatches in primers were removed. Putative chimaeras were identified and removed with the VSEARCH tool. The remaining sequences that shared >98% nucleotide sequence identity were assigned to a single operational taxonomic unit (OTU). The taxonomic assignment of the sequence representing each OTU was performed by similarity and phylogeny using the two reference databases described above. For each OTU, the closest sequences in database II were selected by similarity for a first assignment. These sequences were then added to the phylogenetic tree reconstructed with FastTree  (Price et al.
, 2010)  from sequences of database I for a second phylogenetic assignment. The consistency of the results provided by the two methods was verified. This process was performed with a modified version of the pipeline PANAM (Phylogenetic Analysis of Next-generation AMplicons https://github. com/ panammeb/)  (Taib et al. , 2013). We checked the data for detecting eventual mistagging effect  (Esling et al. , 2015; Schnell et al. , 2015). By removing sequences representing less 0. 5% of the total abundance, we eliminated the spurious ones (Technical appendix A, supplementary data). Duplicates were then pooled for the final analysis. We normalised the WWTP samples using the vegan package of R software, which consists in a random selection of the same number of sequences in each sample. This number was defined as the number of sequences present in the sample containing the lowest sequence number (16,250 sequences in our run).
Sequencing data Of the merged sequences (assembly of forward and reverse reads), 33% were discarded by the quality control analyses. Of the remaining sequences assigned to an EV type, 0. 4% were removed because of incorrect or inaccurate assignment. The sequences assigned to rhinovirus types (0. 7%) were also excluded, leaving a set of 2,001,179 sequences for further analyses. The cleaning analyses applied to the entire dataset left 1,546,641 sequences in the wastewater samples and 438,750 sequences after the normalisation of data (Table  1 ).
Diversity of EV types in the WWTP samples OTUs were assigned to 3 taxonomic species EV-A, B and C (Table  1 ). No sequences were allocated to the EV-D species. A total of 48 EV types were detected in the wastewater samples. Among them, 15 types (CV-A1, A11, A13, A19, A20, and A22, EV-A76, A89, A90, A119, EV-B78, EV-C99, C105, C109, and C116) are rarely reported in the clinical setting at a national level. Some, such as EV-A119 and EV-A76, were detected sporadically in the wastewater samples and others, such as CV-A11 and CV-A22, were present over time in several consecutive samples. A set of 16 sequences clustered in a single OTU and shared 99. 4% nucleotide identity with the Sabin PV-1 vaccine sequence (accession number V01150. 1) and accordingly were classified as Sabin-like PV-1. After cleaning and normalisation, sequences were clustered into 180 OTUs, which were assigned to 33 EV types (Table  1 ).
g. CV-A11). Most viral sequences (54%) recorded in patients clustered with wastewater OTUs, evidence of close genetic relationships between viruses within the community and those detected in patients. Marked differences were observed among EV types in the genetic clustering rate of clinical sequences with wastewater OTUs. This rate ranged from 0% (CV-A2, EÀ6, and EÀ25) to 75% (CV-A6) and 100% (EÀ11). The CV-B5 sequences  genetically distant from all other sequences and was identified as CV-B5 genogroup A by phylogenetic analysis (data not shown). - - - - - - Sabin-like-PV-1 16 0,0 1 - - - - - - EV-C105 4 0,0 1 - - - - - - CV-A19 3 0,0 1 - - - - - - total EV-C 294.
Strains detected in wastewater are in green and clinical strains in red. Networks were built with Bionumerics software and maximum parsimony methods. Strains with less than 2% nucleotide differences were grouped into a single sphere with a diameter proportional to the number of strains inside. For CV-B5, the two genogroups A and B, as defined in a previous study  (Henquell et al. , 2013) , are indicated. EV-A CV-A4 4 CV-A6 4 CV-A2 3 CV-A5 1 CV-A8 1 EV-A71 1 EV-A120 1 EV-B CV-B5 30 EÀ25 8 EÀ18 7 EÀ5 6 EÀ6 6 EÀ7 6 EÀ11 6 EÀ16 4 CV-B3 3 EÀ9 3 EÀ13 2 EÀ21 2 CV-A9 1 CV-B1 1 CV-B4 1 EV-C EV-C109 2 CV-A13 1 EV-C99 1 EV-D EV-D68 17 NT 8 population under study were also detected in wastewater monitoring. There were close phylogenetic relationships between virus sequences detected in patients and wastewater.
CV-A16 is an example in this respect. Conversely, while CV-A6 accounted for 0. 03% reads in wastewater, the virus was detected in four patients reported with meningitis, myocarditis, and HFMD (including a case of atypical HFMD clinical presentation). Our data are consistent with the emergence of CV-A6 as a cause of worldwide disease concern  (Bian et al. , 2015). Environmental and clinical surveillance revealed the unexpected epidemiologic pattern of the EV-C types: large circulation in the urban community and few disease cases  (Apostol et al. , 2012; Benschop et al. , 2017; Harvala et al. , 2014). All the three EV-C types identified in patients were traced back to wastewater. EV-C109 was detected in two patients referred for respiratory diseases and clustered close to viruses in wastewater.