dataesr/scientific-paragraphs-categorization huggingface dataset:
Taking experimentally validated examples, the rules associated with the ligand-based de novo design software DOGS (inSili. com LLC)  [4]  have enabled the production of new chemical entities inhibitors of DAPK3 (death-associated protein kinase 3)  [20] , metabolic rules for promiscuous enzymes have allowed the discovery of novel metabolites in E. coli  [21]  and have also been used to engineer metabolic pathways producing 1,4-butanediol  [9]  and flavonoids  [22]. Going beyond application limited reaction rules, the main contribution of the present paper is to propose a set of transformation rules that enables the generation of any isomer of any given molecule of the chemical space. Precisely, we prove the claim that any isomer of any given molecule of N atoms, can be reached applying at most O(N 2 ) rules.
Isomer enumeration Isomer enumeration is a long-standing problem that is still under scrutiny  [24, 25]. Our intent here is not to provide the fastest enumeration algorithm but to demonstrate how RetroPath2. 0 can perform that job once appropriate reaction rules are provided. However, we provide in Additional file 2: Figure  S1  a comparison of Retropath2. 0's execution time with the OMG and PMG software tools  [25, 26]  specifically dedicated to isomers enumeration. Retropath2. 0 is found faster than OMG but slower than PMG. Thereafter, we outline two approaches making use of RetroPath2. 0. The first is based on the classical canonical augmentation algorithm  [27]  and the second consists of iteratively transforming a given molecule such that all its isomers are produced. We name this latter approach isomer transformation.
Canonical augmentation The principle of canonical augmentation, which is an orderly enumeration algorithm, is to grow a molecular graph by adding one atom at a time and retaining only canonical graphs for the next iteration  [27]. The algorithm first proposed by Brendan McKay has been used to generate the GDB-17 database of small molecules  [29]. The original algorithm has also been modified such that at each step a bond (not an atom) is added to the growing molecules  [25]. In the present implementation we use the original McKay algorithm  [27] , consequently, the number of iterations is the number of atoms one wishes the molecule to have. The algorithm can easily be implemented into RetroPath2. 0 by choosing as a source set a single unbonded atom, and a rule set depicting all possible ways an atom can be added to a molecular graph (see "Methods" section for more information).
Virtual screening in the chemical space In this section we used RetroPath2. 0 to search all molecules that are at predefined distances of a given set of molecules. Such queries are routinely carried out in large chemical databases for drug discovery purposes  [31] , but in the present case we search similar structures in the entire chemical space. To perform search in the chemical space, we used a source set composed of 158 well-known monomers having a molecular weight up to 200 Da. Our rule set included the transformations colored green in Fig. 3  (i. e. transformation rules where double bonds are not transformed into cycles and conversely). For each monomer, RetroPath2. 0 was iterated until no new isomers were generated. Each generated structures at a Tanimoto similarity greater than 0. 5 from its corresponding monomer were retained (Tanimoto was computed using MACCS keys fingerprints  [32] ).
To this end, we selected a dataset of 47 aminoglycosides structures for which antibacterial activities have been measured using a MIC assay  [39]. The dataset is composed of natural aminoglycosides (gentamicin, tobramycin, neomycin, kanamycin A and B, paromomycin, ribostamycin and neamine) to which are added synthetic structures built on a neamine scaffold. This dataset has already been used to build a QSAR model based on CoMFA analysis leading to a Q2 of 0. 6 for a Leave-One-Out (LOO) procedure  [39]. We provide in Additional file 1 a QSAR workflow that makes use of RDKit fingerprints  [19]  and random forest as a learner leading to a higher Q2 (0. 7) for LOO. With that QSAR in hand we run RetroPath2. 0 with a source set composed of the 47 aminoglycosides used in the training set, and two different reaction rules sets.
This need is clearly seen in the Human Metabolome Database (HMDB) where the number of reported masses has recently grown from 20,931 in 2013  [41]  to 74,461 (at the time this manuscript was written), while annotated metabolites in metabolic databases are still in the range of 1847 (HumanCyc). Despite such a growth in databases, a significant amount of spectral peaks remains unassigned. This high fraction of unassigned peaks might be due to several factors including isotope, adduct formation, ion fragmentation, and multimers. Besides such sources of uncertainty in samples, many unassigned peaks should also be due to promiscuous activities of enzymes not yet characterized because of the lack of an appropriate description of the mechanisms of enzyme promiscuity. To gain insights into those mechanisms enabling promiscuity, reaction rules have been shown to be appropriate  [21]  in particular the rules allowing to focus on the center of the reactions.
Mimicking nature, such enzyme versatility can provide novel ways for biosynthesizing metabolite and even bioproducing non-natural molecule. To that end, the variable diameter method has shown itself to be especially well-suited for modeling the mechanisms of enzyme promiscuity as it has already enabled the experimentally validated discovery of a novel metabolite in E. coli and of the promiscuous enzymes producing it  [21]. In this study, we make use of RetroPath2. 0 to exemplify how variable reaction rule diameters can be used to complete the metabolome of E. coli. More precisely, we used as a source set all the metabolites present in E. coli iJO1366 model  [43]. We first tested the two rule sets aforementioned, a set of about 100 reaction rules part of the BNICE framework  [42]  and a set of 50 reaction rules developed with the Sympheny software  [9].
The reaction rules coded in the form of SMARTS string are provided in Supplementary at MyExperiment. org, along with the EC numbers corresponding to the rules. While the two rule sets were not developed to code only for E. coli reactions, for each EC number there is a corresponding enzyme annotated in E. coli so we kept all rules in the two sets. We then tested reaction rules with variable diameters using the procedure described in the method section to code for all E. coli metabolic reactions extracted from iJO1366 model. Rules were calculated for each reaction with diameters ranging from 2 to 16. Table  2  below provides the number of compound generated running RetroPath2. 0 for one iteration on the metabolites of the iJO1366 model and the rules sets mentioned above (see "Methods" section for additional details).
The column reports the % of peak assigned to at least one compound generated by the rule sets. (  3  a set of rules allowing us to generate any isomer of any given molecule of the chemical space. We also provide examples making use of reaction rules computed from enzymatic reactions. Using rules computed on known reactions has a definite advantage regarding the (bio) synthetic accessibility of the molecule produced, which is not necessarily the case for other techniques producing molecules de novo  [33, 37, 40, [49] [50] [51] [52] [53]. Our method has been implemented into RetroPath2. 0, a workflow running on the KNIME Analytics Platform  [18]. RetroPath2. 0 can easily be used with source molecules and reaction rules different than those presented in the paper. For instance the workflows provided in Additional file 1 can be used with the reaction SMARTS rules and fragment libraries (as source compounds) of the DOGS software (inSili.
This strategy enforces that only one substrate can differ at a time from the substrates of the reference reaction when applying the rule. The following step consisted in computing reactions rules as reaction SMARTS for each component. We did it for diameters 2-16 around the reaction center (panels C and D in Fig. 8 ) by removing from the reaction components all atoms that were not in the spheres around the reaction center atoms. We extracted more than 24,000 reaction components from MetaNetX reactions, each one of those leading to a rule at each diameter (from 2 to 16). We provide in Supplementary at MyExperiment. org a subset of 14,300 rules for E. coli metabolism. The rules were selected based on the MetaNetX binding to external databases and the iJO1366 whole-cell E. coli metabolic model  [43].
Funding This work was supported by the  French National Research Agency  [ ANR-15-CE1-0008 ], the  Biotechnology and Biological Sciences Research Council ,  Centre for synthetic biology of fine and speciality chemicals  [ BB/M017702/1 ]; and  Synthetic Biology Applications for Protective Materials  [ EP/N025504/1 ]. MK acknowledges funding provided by the  DGA  and  École Polytechnique.
The Polysaccharide Utilization Loci (PUL) database was launched in 2015 to present PUL predictions in ∼70 Bacteroidetes species isolated from the human gastrointestinal tract, as well as PULs derived from the experimental data reported in the literature. In 2018 PULDB offers access to 820 genomes, sampled from various environments and covering a much wider taxonomical range. A Krona dynamic chart was set up to facilitate browsing through taxonomy. Literature surveys now allows the presentation of the most recent (i) PUL repertoires deduced from RNAseq large-scale experiments, (ii) PULs that have been subjected to in-depth biochemical analysis and (iii) new Carbohydrate-Active enzyme (CAZyme) families that contributed to the refinement of PUL predictions. To improve PUL visualization and genome browsing, the previous annotation of genes encoding CAZymes, regulators, integrases and SusCD has now been expanded to include functionally relevant protein families whose genes are significantly found in the vicinity of PULs: sulfatases, proteases, ROK repressors, epimerases and ATP-Binding Cassette and Major Facilitator Superfamily transporters.
10-FOLD INCREASE IN CAZy-ANALYZED SPECIES In order to achieve a >10-fold increase in PULDB, we analyzed 820 complete genome sequences (∼3 million genes) mostly of the Bacteroidetes phylum downloaded from JGI (http://genome. jgi. doe. gov/) and NCBI (https://www. ncbi. nlm. nih. gov/nuccore) servers. Our PUL prediction procedure relies on genomic data but also requires the semimanual expert annotation of CAZymes  (5). We identified 153 202 CAZyme modules in the 820 genomes, mostly glycoside hydrolases (53%) and glycosyltransferases (31%), classified according to the sequence-based families that are described in the CAZy database. Then the 820 genomes were subjected to the PUL predictions as described earlier. Compared to the 2015 PULDB dataset (3), the new genome sampling expands far beyond the human gastrointestinal tract (now represented by ∼80 species), and notably includes 64 rumen gut species, as well as many bacterial species from soil or marine environments.
The coverage of Bacteroidetes taxonomical diversity also drastically increased. The 2015 dataset almost exclusively consisted of species from the Bacteroidales order (70% belonging to the Bacteroides genus). In the current dataset, Bacteroidales only represents 40% (only half being from the Bacteroides genus), a proportion comparable to the Flavobacteriales order while three additional orders (Cytophagales, Sphingobacteriales and Chitinophagales) are now also presented. Moreover, the presence of the PUL fundamental susCD gene tandem now allows the prediction of PULs beyond the Bacteroidetes phylum, namely in the Gemmatimonadetes and Ignavibacteriae phyla (which group with Bacteroidetes in the FCB group), and also in the Balneolaeota phylum. To facilitate navigation across the various taxonomical levels, and to identify species of interest, we implemented a new browsing tool in PULDB.
We adapted the Krona multilayered pie-chart, introduced for metagenomics analysis  (6) , to represent the hierarchical aspects of the taxonomy (Figure  1 ). Implemented using the latest HTML5 and JavaScript interactive technology, Krona allows zooming in and out very efficiently and can be easily customized by the user for the desired taxonomic depth or font size, allowing the production of high-quality publication-ready pictures. It also offers text searches and improved navigation. We also added a color scale indicative of the number of PULs per genome (estimated for ancestral taxa by a simple arithmetic mean) which immediately offers an overview of the PUL diversity at the different taxonomical levels. Finally, in the upright part, where Krona provides statistics about genome for each taxa, we added several hyperlinks to the species list,   (35)  of the region before the creation of families GH136-GH143  (16).
To facilitate the retrieval of characterized PULs by their cognate substrate, a new field appears in the PULDB homepage, to search for a given character substring within the PUL substrate labels. Finally, the recent RGII publication notably reported the biochemical characterization of seven new glycoside hydrolase families that were immediately added to the CAZy database, designated GH137 to GH143. Similarly, other publications led to the creation of new CAZyme families: GH136, GH144, GH145, PL24 to PL27  (28) (29) (30) (31) (32) (33) (34). All new CAZy families have also been added to PULDB. As a consequence, the PUL predictions are improved by these new families, which allow refinement of both PUL boundaries and prediction confidence, as illustrated with the Jbrowse view  (35)  of the homologous RGII-PUL in Terrimonas ferruginea DSM 30193 (Figure  2 ).
ADDITIONAL DISPLAY OF SULFATASES, PRO-TEASES, EPIMERASES, ROKs AND TRANS-PORTERS In PULDB, simplified representations of PULs are proposed as trains whose wagons, the constitutive proteins, are colored/tagged if their protein function is relevant in the PUL context. We initially focused on SusC outermembrane transporter (purple), SusD outer-membrane binding proteins (orange), several regulators (light blue), integrases which sometimes join adjacent PULs (dark gray) and CAZyme families (mainly glycoside hydrolases in light pink, polysaccharide lyases in dark pink, carbohydratebinding modules in green, carbohydrate esterases in brown). All other proteins remained tagged as 'unknown' (light gray). To increase readability of these PUL representations, we searched additional protein families with relevant function in PULs, based on (i) the literature, (ii) over-representation in PUL contexts and (iii) reliability of Pfam domain annotation  (36).
THE PUL ALIGNER A new tool is presented in this PULDB release to allow a user to search and identify PULs that are similar to a PUL of interest, and is accessible in the web pages dedicated to each PUL. This tool is a PUL aligner which allows retrieval conserved modular organizations. Inspired from the RADS modular alignment method for proteins  (46) , this tool produces local alignments of a query PUL (or CAZyme cluster) against all PULs (and CAZyme clusters) in PULDB. However, instead of aligning concatenated amino-acid sequences of proteins, it treats each protein relevant to PUL function as one character. Implementing the classical Needleman-Wunsch algorithm  (47) , it requires a substitution-scoring matrix between modules, as well as gap costs. A simple scheme based on the most relevant features of PULs was empirically designed.
During the TRAQA campaign, 20 June-13 July 2012, meteorological conditions mainly favoured continental outflow to the Mediterranean Basin from different source regions  (Di Biagio et al. , 2015). The data collected during this airborne campaign were radiative properties of aerosols (absorption and scattering coefficients), particle number concentration and particle composition. Two intensive campaigns, ADRIMED and SAFMED, were conducted in 2013. The first one, ADRIMED (Aerosol Direct Radiative impact on the regional climate in the MEDiterranean region) took place between 11 June and 5 July 2013  (Mallet et al. , 2016). The first part of this campaign is characterized by changes in the synoptic flux: easterly (16 June), southerly (19 June) and north-westerly (29 June) for example. The ADRIMED measurement strategy was mainly composed of two in situ super-sites (Ersa and Lampedusa) and aircraft and balloon measurements.
MOCAGE (Modele de Chimie Atmospherique à Grande Echelle) is an offline global chemistry transport model with grid-nesting capability used for research at Météo-France in a wide range of scientific studies on tropospheric and strato-spheric chemistry, at various spatial and temporal scales. MOCAGE has been used, for example, for studying the impact of climate on air composition  (Teyssèdre et al. , 2007; Lacressonnière et al. , 2012; Lamarque et al. , 2013)  or tropospheric-stratospheric exchanges using data assimilation  (Barré et al. , 2014). MOCAGE is also used for daily operational air quality forecasts in the framework of the French platform Prev'Air  (Rouil et al. , 2009, http ://www2. prevair. org/, last access: 4 April 2018) and in the European CAMS project (Copernicus Atmospheric Monitoring Service).
In CAMS, the MOCAGE model is one of the seven models contributing to the regional ensemble forecasting system over Europe  (Marécal et al. , 2015 , http://macc-raq-op. meteo. fr/ index. php, last access: 4 April 2018). The version of MOCAGE used in this study is fully detailed in  Sič et al. (2015)  and  Guth et al. (2016). Two chemical schemes are implemented in order to represent both the tropospheric and the stratospheric air composition in MOCAGE. The Regional Atmospheric Chemistry Mechanism (RACM)  (Stockwell et al. , 1997)  is used in the troposphere. For the stratosphere, it is the REPROBUS scheme (REactive Processes Ruling the Ozone BUdget in the Stratosphere) which is implemented  (Lefèvre et al. , 1994). Regarding aerosols, the version of the model used in the present study includes desert dust, sea salt, primary organic aerosols, black carbon and secondary inorganic aerosols (SIA) managed through the ISORROPIA module  (Fountoukis and Nenes, 2007) , including the interactions with sea salt aerosols.
Emissions At the global scale, the anthropogenic emissions used are the MACCity emissions representative for 2013 given at a 0. 5 • × 0. 5 • resolution (van der  Werf et al. , 2006; Lamarque et al. , 2010; Granier et al. , 2011; Diehl et al. , 2012). The biogenic emissions are based on  Sindelarova et al. (2014)  for the volatile organic compounds. They are at a 0. 5 • × 0. 5 • resolution, monthly and representative for 2010. NO x emissions from the soil come from the GEIA dataset  (Yienger and Levy, 1995)  while nitrogen oxides from lightning are taken into account following  Price et al. (1997). GFAS emissions (Global Fire Assimilation System,  Kaiser et al. , 2012)  giving daily biomass burning emissions based on satellite data are used here. The natural aerosols emissions are dynamically computed using  Marticorena and Bergametti (1995)  and  Kok (2011)  for mineral dust and  Gong (2003)  for sea salt.
Comparison with MODIS aerosol optical depth MODIS daily mean AODs were used to evaluate the model simulations. For this purpose, we select both the daily MODIS and Deep Blue data level 3 (L3, collection 6) for the year 2012 and 2013 and perform an additional quality control and screening as presented in  Sič et al. (2015)  and  Guth et al. (2016). AODs in MOCAGE are calculated at 550 nm using Mie theory with refractive indices taken from the Global Aerosol Data Set  (Köpke et al. , 1997)  and extinction efficiencies derived with Wiscombe's Mie scattering code for homogeneous spherical particles  (Wiscombe, 1980). Figure  1  presents the maps of the annual MNMB (modified normalized mean bias, dimensionless) of the AOD simulated with the MOCAGE model against the MODIS and the Deep Blue AODs for the years 2012 and 2013.
Comparison with European air quality monitoring stations from the AQeR databases The description of the data used in this section is available in Appendix B. Table  5  presents the statistics for the comparison between the MOCAGE simulations and Air Quality e-Reporting (AQeR) hourly data for the year 2012 and 2013, and for PM 10 and PM 2. 5. This table shows a similar behaviour for 2012 and 2013. Aerosol concentrations are underestimated, and MNMBs for PM 10 are -0. 64 and -0. 58, respectively, for 2012 and 2013, with correlations of 0. 62 and 0. 59. PM 2. 5 concentrations are well represented with lower MNMB (-0. 19 and -0. 27) and higher correlations (0. 71 and 0. 68). The aerosol underestimation can be explained at least partly by the lack of secondary organic aerosols in the model MOCAGE, but also by uncertainties in the anthropogenic emission inventories, particularly in the eastern part of the Mediterranean region.
Comparison with the EMEP database In order to characterize the model behaviour against aerosol composition, we use the EMEP programme measurements (downloaded from the website http://ebas. nilu. no/, last access: 4 April 2018). From this set of measurements, we use measurements of concentrations of secondary inorganic aerosols, carbonaceous aerosols and sodium, and wet deposition of secondary inorganic aerosols and sodium, in order to check the behaviour of the model regarding these simulated components. We do not present chloride comparisons because the chemistry of HCl with volatile organic compounds is not represented in the model. Gaseous HCl is evaporated when nitrate is condensed on sea salt aerosols. We only give the results for the year 2013 since there are too few stations available in 2012 to be statistically significant.
Secondly, we compared the simulations to in situ surface observations. The comparison to the AQeR database in terms of particulate matter shows a larger bias in summer related to the lack of SOA in the MOCAGE model. This is consistent with the negative bias when comparing the MOCAGE simulation to organic aerosol measurements from the EMEP database. Finally we compared MOCAGE simulation to wet deposition measurements from the EMEP database. We showed a good agreement in terms of bias. From this evaluation, we show that the MOCAGE simulation give realistic results compared to observations. Nevertheless, there are large regions, especially in northern Africa, where we do not have in situ measurements available to evaluate the model.
Firstly, we compared the simulation to observations in order to do an evaluation of the simulation. We showed the model was able to well represent the aerosol optical depth on the Mediterranean Basin using MODIS, Deep Blue and AERONET data. Secondly, we compared the model simulations to in situ concentration data from the AQeR and EMEP database. This comparison shows that the model represents well the secondary inorganic aerosols while the lack of secondary organic aerosols is clearly apparent both in seasonal particulate matter observations and in aerosol composition observations. Also, this comparison highlights a lack of observations in the southern part of the domain, and especially in northern Africa, to fully evaluate the model using in situ surface measurements. Secondly we use the two year-long simulations to compute the aerosol budget over the Mediterranean Basin on a annual and monthly basis.