dataesr/scientific-paragraphs-categorization huggingface dataset:
Workflow integration One of the advantages of the REST service is that it allows the use of RetroRules in a large variety of web-based applications. Complex queries involving RetroRules can be performed by employing a workflow framework such as KN-IME (17) that allows the interconnection of several web services through well-defined steps, providing reproducibility both for protocols of experimental data analysis and in silico screening. Notably, the RetroRules database can be queried in order to generate a set of reaction rules to be run through the RetroPath2. 0 workflow for retrosynthesis  (4). A REST query can be easily performed and its response converted The tables in white are the parsed meta-information from various datasets and their cross-references to others databases. The reactions and chemical species tables are linked together through the bridge tables reaction products and reaction substrates.
A KNIME workflow providing such facility is available at https://www. myexperiment. org/workflows/5086. In the provided example, RetroRules was queried for reactions in the MetaCyc database. Resulting rules were then successfully used in RetroPath2. 0 in order to identify heterologous biosynthetic routes for resveratrol in E. coli. In yet another application example, RetroRules was integrated with the Selenzyme tool for enzyme sequence selection (5) so that for a given reaction, reaction rules are calculated at different diameters and top candidate sequences are retrieved for each rule. Such application can be considered in the scenario of some target reaction with no annotated sequences. RetroRules allows dissecting the reaction into its associated reaction rules of increasing generalizability by lowering the diameter.
The rules are submitted to Selenzyme, which generates at each diameter the sets of most plausible candidate sequences according to the preselected ranking criteria. Even if the target reaction had no known annotation, a query for one of the reaction rules at some low diameter might deliver sequence candidates of target reaction activity because of enzyme promiscuity. An example of such type of nested queries for those reactions in the database having caffeine as reactant has been implemented using KNIME and is available at https://www. myexperiment. org/workflows/5082.
Reaction rules calculation Reaction rules were generated using the cheminformatics RDKit library http://www. rdkit. org/ in Python. In-depth description and validation of the generation process were described in (4). The procedure is outlined below: 1. Extract reaction information from metabolic databases. Filter out reactions that miss any structure from amongst involved compounds. 2. Remove reactions that do not trigger the modification of substrate (e. g. passive transport), are not balanced, or that involve compounds not fully characterized (e. g. Rgroups). 3. Identify the reaction center (i. e. subpart(s) of substrate(s) that are transformed) based on an atom-atom mapping between substrates atoms and product atoms (AAM). Figure  2  shows reaction 2. 6. 1. 5 with atom mapping, reacting atoms are those labelled 8, 11, 16 and 19.
Stereo reaction rules calculation Computation of the stereo rules involved similar steps than for the non-stereo rules described previously. Additionally, at step 3 of the procedure, tetrahedral atoms changing their R/S chirality through the reaction (creation, inversion, deletion of chirality) as well as atoms involved in E/Z switch around double bonds are added to the reacting atom set  (13). Tetrahedral and E/Z stereochemistry configurations are encoded into SMARTS syntax following the daylight recommendation (http://www. daylight. com/dayhtml/doc/theory/). Release of a comprehensive reaction rule dataset embedding stereochemistry changes was unseen until now to our knowledge. Hence this feature should be regarded as a cutting-edge implementation that is exploratory and still reliant on state-of-the-art stereochem-istry perception by cheminformatics packages such as RD-Kit when used to predict novel products and extend the biochemical space.
Reaction rules on-the-fly calculation The custom rule builder available online (https://retrorules. org/diy) stick to the same procedure that the one outlined for precomputed dataset generation, except in two aspects, which are described hereafter. First, all structures of the input reaction are considered as primary compounds, i. e. no filtering will be attempted to remove cofactors. Importance of each structure involved in a reaction for the resulted rules is therefore up to the user to decide. To help in this, a second difference with the general procedure is that the custom rule builder allows for unbalanced reactions, i. e. reactions where the number of atoms differs between left and righthand sides.
Reaction rules scoring Each reaction rule in RetroRules is associated with a score that estimates its degree of biochemical uncertainty. Intuitively, a rule would have less uncertainty if the rule can uniquely be generated from a single enzyme annotation in the database. A penalty score is defined as log 10 (n), where n is the number of distinct non-redundant enzyme sequences that generated the given rule. In order to calculate n, we followed the hierarchical classifications of reaction rules in RetroRules controlled by both the diameter and EC annotation that progressively describe reactions in finer detail (see (4) for details). Such type of penalty score finds application both in order to assess the specificity of a given rule with respect to their annotated sequences and for ranking pathways that were predicted and enumerated through retrosynthesis algorithms.
Reaction rules on-the-fly scoring Penalty scores like the ones described in the previous section are also computed for rules generated from user's custom reactions. First, if the calculated rule already exists within the precomputed dataset, the newly generated rule inherits the precomputed score. In other situations where the newly generated rule does not coincide with any precomputed rule then we used a conservative approach by assigning to that rule the worst score value of the database at the considered diameter. The rationale is that our knowledge about that reaction rule is at least as bad as the worst case in the database.
CONCLUSIONS RetroRules is a database of reaction rules for metabolic pathway discovery and metabolic engineering. Reaction rules are generic descriptions of reactions to be used in retrosynthesis workflows in order to enumerate possible biosynthetic routes connecting target molecules to precursors. The use of such rules is becoming increasingly important in the context of synthetic biology applied to de novo pathway discovery as well as in systems biology to discover underground metabolism and predict new metabolic functions arising from enzyme promiscuity  (18). RetroRules provides a complete set of stereochemistryaware reaction rules spanning >16 000 biochemical transformations, extracted from public databases, and expressed in the community-standard SMARTS format, augmented by a rule representation at different levels of specificity.
FUTURE DEVELOPMENTS The RetroRules database is a public downloadable resource facilitating the task of extracting custom reaction rules subsets for different purposes. In that way, specialized subsets of RetroRules can be anticipated to find application in several fields such as metabolic engineering (central metabolism, plant secondary metabolism etc. ), extended metabolic space (underground metabolism, enzyme promiscuity), drug metabolism, xenobiotics, pollutants degradation, bioremediation, sensing enabling metabolic pathways (as in SensiPath (  19 )), among others. Similarly, the rules can be used to extend metabolic maps of organisms (as in XTMS (  20 )) or to propose new putative metabolites to annotate mass spectrometry spectra using tools such as OpenMS (as in  (21) ). Future will include expanding the scope of our reaction rules with the addition of the BRENDA database  (22)  or other community-driven biochemical knowledge resources.
Figure1. SQL schema used by RetroRules. The tables in white are the parsed meta-information from various datasets and their cross-references to others databases. The reactions and chemical species tables are linked together through the bridge tables reaction products and reaction substrates. In blue are the tables associated with reaction rules. The rules table uniquely identifies a given rule with single reaction id and substrate id at a given diameter. The isStereo parameter determines if the rule describes the stereochemistry of the reaction or not. Each rule is associated with a single SMARTS and SMILES description of that reaction, and to link the two, a smarts smiles table assures that one is able to easily find the corresponding SMILES description of a SMARTS and vice-versa. The rule products table describes the products of a given rule.
Gene expression and microarray analysis Total RNA from SMC was extracted using TRIzol reagent (Invitrogen Life Technologies, Inc. ). For microarray analysis, RNA was hybridized to Agilent human gene expression microarrays (G4851C). Fluorescence values corresponding to raw expression data were extracted. Positive and negative control probes were removed. Non-linear effects such as background or saturation were corrected by Lowess  [20]  against a median profile of all samples  [21]. Values of replicate probes were averaged and the data matrix was filtered to 20,000 probes based on highest median expression values. Clusters of co-expressed genes were identified using K-means (k = 11) on natural-log-transformed and gene-median-centered data with uncentered correlation as a similarity metric in Gene Cluster 3.
0  [22]. The value of k was determined by analyzing the presence of novel phenotype-specific gene clusters after incrementing k (start value k = 10). Hierarchical clustering was performed using Gene Cluster 3. 0 and heatmaps were displayed using Java Treeview. Clusters separating the different arterial beds were selected and a collective p-value was calculated. For each sample, a mean expression value of all genes was calculated. The mean values of different arterial beds were compared. This strategy based on strong correlation of gene expression allowed us to avoid multitesting. Hierarchical clustering of these clusters demonstrated a great homogeneity. Microarray data can be found at: http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?token= ibwbossexboxzmt&acc=GSE84012 Gene Ontology enrichment analysis of the different clusters was performed using GoMiner.
Statistics Variables are displayed as mean ± SD. Two investigators performed the morphologic observations independently and blindly, without any indication on the arterial bed or patient group. Data were analyzed by the Kruskal-Wallis one-way analysis of variance and Bonferroni posttest for each factor. Population statistics between ECLAGEN groups was analyzed by Fisher's exact test and global ANOVA for age comparison. Statistical analyses were performed using Graphpad Prism (GraphPad Software, Inc. , La Jolla, CA, USA). A p-value <. 05 was considered statistically significant. All in vitro experiments were performed all least 3 times, with 3 independent samples preparations.
SMC from peripheral arteries express specific transcriptomic signatures Vascular calcification heterogeneity among vascular beds could either derive from local microenvironmental specificities and or reflect molecular singularities between arterial beds. To analyze these differences and identify key molecules involved in this process, we performed a transcriptomic microarray analysis with medial SMCs isolated from the 5 healthy peripheral arterial beds indicated above (from 54 and 64-year-old organ donors) (S1 File). K-means analysis of the microarray data identified several gene clusters with arterial bed(s) specific expression profiles (Fig  5). Incrementing k from 10 to 11 revealed the presence of an FA-specific gene cluster (cluster 10). Further increasing k to 12 or 13 did not reveal any novel phenotype-specific gene clusters.
Funding: This study has been supported by the  Programme Hospitalier de Recherche Clinique  ( PROG/11/58 ) to YG, the  European Commission  ( FP7 People: Marie-Curie Actions , grant  627418 , https://ec. europa. eu/research/mariecurieactions/) to TQ, the  Fondation de l'Avenir pour la Recherche Medicale Appliquee  ( ET4-720 , http://www. fondationdelavenir. org/) to TQ, the  Centre Hospitalier Universitaire de Nantes  to TQ, and the  Agence Nationale de la Recherche  ( ANR-16-CE14-0012-01 , www. agence-nationale-recherche. fr) to BO. Thibaut Quillard 's salary has been provided by the  Fondation Lefoulon Delalande  (http://lefoulondelalande. institut-de-france. fr/), the  European commission  ( FP7 People: Marie-Curie Actions , grant  627418 , https://ec. europa. eu/research/mariecurieactions/), and the  Centre Hospitalier Universitaire de Nantes.
Single Crystal X-ray Diffraction and Structure Analysis A crystal of compound 2b with dimensions of 0. 31 × 0. 13 × 0. 07 mm 3 was mounted on the tip of a glass fiber using epoxy and placed on a Bruker SMART 1000 CCD (Atlanta, GA, USA) sealed tube diffractometer with graphite monochromated MoKα (0. 71073 Å) radiation. Data were measured at 373(2) K using a series of combinations of phi and omega scans. Data collection, indexing and initial cell refinements were all carried out using SMART software  [55]. Frame integration and final cell refinements were done using SAINT software  [56]. The structure was solved using Direct methods and difference Fourier techniques (SHELXTL, V6. 12)  [57]. Hydrogen atoms were placed in their expected chemical positions using the HFIX command and were included in the final cycles of least squares with isotropic Uijs related to the atoms ridden upon.
There is no serious doubt that this field of research will increase significantly in the future, hopefully making another step towards more environmentally friendly and energy/resources saving processes. Supplementary Materials: Supplementary materials are available online. CCDC-1823696 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge via www. ccdc. cam. ac. uk/data_request/cif, or by e-mailing data_request@ccdc. cam. ac. uk, or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; Fax: +44(0)1223-336033.
Metric We use F1-score to measure the classification performance. Due to the imbalanced class distributions of the SemEval dataset, we compute both micro-averaged (large classes dominate) and macro-averaged (small classes dominate) F1scores  (Manning et al. , 2008) , and use their average as the metric, i. e. , F = 1 2 (F micro + F macro ). To evaluate the effectiveness of target adaptation, we use the metric transfer ratio  (Glorot et al. , 2011)  to compare the cross-target and in-target performance of a model: D) , where F (S, D) is the cross-target F1-score of a model trained on the source target S and tested on the destination target D, and F b (D, D) is the in-target F1-score of a baseline model trained and tested on the same target D, which serves as the performance calibration for target adaptation.
Climate Change is Concern (CC), Feminist Movement (FM), Hillary Clinton (HC), Legalization of Abortion (LA), and Donald Trump (DT). The class labels are favor, against, and neither, and their distributions are shown in Table2. Tweets on an Australian mining project (AM): the second is our collection of tweets on a mining project in Australia obtained using Twitter API. It includes 220,067 tweets posted from January 2016 to June 2017 that contain the project name in the text. We remove all URL-only tweets and duplicate tweets, and obtain a set of 40,852 (unlabeled) tweets. Due to the lack of annotation, this dataset is only used for our qualitative evaluation.
Climate Change is Concern (CC), Feminist Movement (FM), Hillary Clinton (HC), Legalization of Abortion (LA), and Donald Trump (DT). The class labels are favor, against, and neither, and their distributions are shown in Table2. Tweets on an Australian mining project (AM): the second is our collection of tweets on a mining project in Australia obtained using Twitter API. It includes 220,067 tweets posted from January 2016 to June 2017 that contain the project name in the text. We remove all URL-only tweets and duplicate tweets, and obtain a set of 40,852 (unlabeled) tweets. Due to the lack of annotation, this dataset is only used for our qualitative evaluation. Target %Favor %Against %Neither #Total CC FM HC LA DT 59. 4 28. 2 16. 6 17. 9 20. 9 4. 6 53. 8 57. 4 58. 3 42. 3 36. 0 18. 0 26. 0 23.
8 36. 8 564 949 984 933 707 Table 2: SemEval-2016 Task 6 Tweet Stance De- tection dataset used in our evaluation. 3. 1 Datasets SemEval-2016: the first dataset is from SemEval-2016 Task 6 on Twitter stance detection, which contains stance-bearing tweets on different targets. We use the following five targets for our experi- ments:.
Antibody Array Data Analysis Detected values of Cy3 intensity were normalized to an internal median background level on each slide. Analytes of interest were defined as those where the median value of the preterm group was outside the interquartile range of the controls. For analyses designed to generate hypotheses about group differences for gene expression studies, the distribution of values according to gestation category (preterm versus term) was investigated using independent samples Mann-Whitney U test, individual test p-values are reported, and a threshold of <0. 05 was used to select proteins for microglia gene expression analysis. Analyses were performed using SPSS 21. 0 (SPSS Inc. , Chicago, IL, United States).
Microarray Analysis As previously published, Miltenyi Biotec (France) performed microarrays (Mouse Agilent Whole Mouse Genome Oligo Microarrays, 8 × 60K) on 6 samples per time point per group for CD11b enriched cell samples from P1, P5, and P10 mice exposed to IL-1β or PBS; a total of 24 samples  (Krishnan et al. , 2017). Preparation of samples for array analysis has been previously described  (Husson et al. , 2005; Chhor et al. , 2013; Krishnan et al. , 2017). The Agilent feature extraction software was used to process microarray image files. We only included signal intensities above background. Signal intensity values were background subtracted and uploaded following instructions by Miltenyi Biotec GmbH (Stefan Tomiuk) and PerkinElmer (Matt Hudson) into GeneSifter Analysis Edition v4. 0 1  for further analysis as previously described  (Gustavsson et al.
Mapping of Dynamic Regulation of Inflammatory and Myelination-Associated Proteins During Experimental Developmental Brain Injury To better understand how the proteins elevated in preterm infant CSF are regulated after injury, we mined an existing dataset in which dynamic changes in microglia gene expression were measured in an experimental model of developmental white matter injury. In this model, damage was induced by intraperitoneal injections of recombinant IL-1β (10 µg/ml) prior to the onset of myelination (twice daily from postnatal day [P]1 to P4 and one injection at P5) (Figure  1A ). This paradigm mimics the pathophysiology of human perinatal brain injury by chronically impairing oligodendrocyte differentiation and myelination, as evidenced by immunostaining of myelin proteins, the oligodendrocyte lineage and electron microscopy  (Favrais et al.
With regards to genes regulating myelination, although pro-survival/myelination genes Tgfb1 and Spp1 were slightly upregulated early and late in injury, respectively, genes whose products are predicted to impair white matter health were upregulated at all time points (Figure  1G ). This data suggest that the proteins enriched in human preterm CSF are dynamically expressed by microglia following developing white matter insult, with an initial and sustained pro-inflammatory response followed by a delayed anti-inflammatory response, and a continuous expression of genes predicted to inhibit healthy myelination.
Protein expression The expression of cGAS, STING, and β-actin proteins was assessed by Western blot as described  (30)  using two polyclonal rabbit anti-cGAS antibodies (HPA031700, Sigma & NBP1-86761, Novus Biologicals, see Supporting Information), a polyclonal rabbit anti-STING antibody (19851-1-AP, Proteintech), and monoclonal anti-β-actin antibody (mAbcam8226, Abcam). Protein expression was quantified using ImageJ software.
The 36 genes were considered as an artificial gene set termed innate antiviral response (IAR) gene set. Its perturbation by infection, transfection or gain-/loss-of-function studies was assessed through Gene Set Enrichment Analysis (GSEA  (34) ). GSEA determines whether an a priori defined set of genes shows statistically significant differences between two biological states. False discovery rate (FDR) < 0. 05 was considered statistically significant. Heatmaps illustrating the induction (red) or repression (blue) of the genes of the IAR compared to control were illustrated using Morpheus software (Broad Institute of MIT and Harvard, Cambridge, MA, USA). The heatmap illustrating the induction (red) or repression (blue) of individual genes in chimeric mouse livers were designed using GenePattern (Broad Institute of MIT and Harvard, Cambridge, MA, USA).
FISH analyses Fluorescence in situ hybridization (FISH) analyses were performed as described  (28, 35). Briefly, liver samples were collected from mice and then immediately embedded into optimal cutting temperature compound (OCT). OCT-embedded liver sections were cryosectioned (10 μm) using a cryostat (Leica). Upon fixation with 4% formaldehyde at 4°C, washing, and dehydration in ethanol, tissue sections were boiled at 90-95°C for 1 min in a pretreatment solution (Affymetrix-Panomics), followed by a 10 min digestion in protease QF (Affymetrix-Panomics) at 40°C. Sections were then hybridized using specific probe sets targeting HBV (target region nucleotides 483-1473 of HBV [Genotype D, GenBank V01460]) and human MD21D1 (VA1-3013492-VC, Affymetrix-Panomics). Preamplification, amplification and detection of bound probes were performed according to the manufacturer's instructions.
Acknowledgments We thank  D. Garcin  ( University of Geneva, Switzerland ) for providing the SeV inoculum;  D. Calabrese ,  S. Wieland  and  M. Heim  ( University of Basel, Switzerland ) for the FISH analyses and helpful discussions;  F. Zhang  ( Broad Institute of MIT and Harvard, Cambridge, MA, USA ) for lentiCas9-Blast and lentiGuide-Puro plasmids,  C. Thumann ,  N. Brignon , and  M. Oudot  ( Inserm U1110 ) for excellent technical support, and  J. M. Rousée  from  Laboratoire Schuh-groupement Bio 67 (Strasbourg ) for the quantification of HBV loads in sera of humanized mice. Financial support: This work was supported by the  European Union  ( ERC-AdG-2014-671231-HEPCIR ,  EU H2020-667273-HEPCAR ,  EU-InfectEra HepBccc) , the  National Institute of Health  ( NIAID 1R03AI131066-01A1 ,  NCI R21 CA209940  and  NIAID U19AI123862 ), the  Agence Nationale de Recherches sur le Sida et les Hepatites Virales  ( ANRS ,  2015/1099 ), the  Fondation ARC pour la Recherche sur le Cancer  ( TheraHCC IHUARC IHU201301187 ),  GACR   17-15422S , and a  PhD fellowship of the Région Alsace, France.
Statistics Quantitative variables are expressed as means and standard deviation (SD). Qualitative variables are expressed as frequency and percentage. The statistical analysis were carried out using STATISTICA software (data analysis software system), version 12 (StatSoft, Inc. , Tulsa, OK, USA). Student's t test for independent samples was used for comparison in means between two groups while a one-way ANOVA with NIR post hoc test was performed for comparison involving three or more groups. Comparison in proportions between groups was performed with chi-square (or Fisher exact test when required). All analyses were performed two-tailed and the limit of significance was set at p < 0. 05.