dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors would like to thank the anonymous referee for helping to improve the quality of this paper. SK, PMW, and CS received funding through  BMBF Verbundforschung  (grants  05A14BAC ,  05A14MGA , ERASMUS-F, grant  05A08BA1 , and  ELT-MOS , grant  05A15BA1 ). SD acknowledges BMBF support through grant  05A14MGA , and from  DFG  through  DR281/35-1. AMI acknowledges support from the  Spanish MINECO  through project  AYA2015-68217-P. MMR acknowledges fruitful discussions with PER and TJR. The authors are thankful to  A. Vazdekis  for providing the (unpublished) MIUSCAT library. Based on observations made with the  NASA/ESA Hubble Space Telescope , and obtained from the  Hubble Legacy Archive , which is a collaboration between the  Space TelescopevScience Institute (STScI/NASA) , the  Space Telescope European Coordinating Facility  ( ST-ECF/ESA ), and the  Canadian Astronomy Data Centre  ( CADC/NRC/CSA ).
LAMP Primers Design Whole genome reference sequences including those of the thirteen mycobacterial species and subspecies most frequently recovered from respiratory samples in the Institut Hospitalier Universitaire (IHU) Méditerranée Infection microbiology laboratory were obtained from GenBank database (Table  1 ). rpoB gene sequences were extracted and aligned using the Mycobacterium tuberculosis H37Rv genome as template (GenBank accession no AL123456. 3). Hypervariable rpoB regions routinely sequenced for mycobacterial identification to the species level were not considered for the primer design analysis because of higher inter species genetic variability  (14, 15). Rifampin resistance defining regions (RRDRs) known to contain single nucleotide polymorphism (SNP) responsible for drug resistance in M.
tuberculosis were also excluded (Figure  1 )  (16). Inter-species rpoB gene highly conserved regions were analyzed using PrimerExplorer V5 software (Fujitsu, Tokyo Japan). Mutually exclusive sets of forward outer primer F3, backward outward primer B3c, forward inner primer FIP (F2-F1c), and backward inner primer BIP (B2c-B1) were identified. Inter-species nucleotide variability in F1c/B1 5 ′ -terminal, F2/B2c 3 ′ -terminal, and F3/B3c 3 ′ -terminal sites was avoided to ensure optimal stringency in determinant primer annealing sites. When unavoidable such variability was tolerated in the F3/B3c 5 ′ -terminal, F2/B2c 5 ′ -terminal, F1c/B1 3 ′ -terminal, and inner regions  (8). Previously described loop primers and FIP/BIP primer spacers were not used  (9). Individual primers specificity was assessed in silico using NCBI basic local alignment search tool (BLAST) against human and other bacterial, viral, or fungal DNA sequences.
Messenger RNA levels of phenotypic markers of osteogenic differentiation, i. e. transcription factor osterix (Osx), ECM gene osteopontin (Opn) and bone-related collagen 1a2 (Col1a2) were assessed by RT-q-PCR. At day 1, no significant difference could be detected in mRNA expression level of Osx and Opn, whereas Col1a2 was significantly decreased in Smoc2+ cells compared to control cells. The increase in mRNA levels of Osx and Col1a2 over time after 21 days of differentiation was significantly lower in Smoc2+ cells compared to control cells. Smoc2+ cells showed significantly reduced Opn mRNA levels at D21 (Fig  1F ). Altogether, these data support that SMOC2 acts as negative modulator of osteogenic differentiation and ECM mineralization in MC3T3-E1 cells.
Currently, pancreatic progenitors obtained from the differentiation of embryonic stem cell lines are already being transplanted into patients with T1D in a phase 1/2 clinical trial in the USA and Canada (NCT02239354 and NCT03163511). Induced pluripotent stem cells (iPSCs) show the same plasticity of ESC, but can be derived from patient's somatic cells, without ethical issues  [9] [10] [11] [12]. iPSCs are able to differentiate into insulin producing cells, although previous reports adopted different protocols and showed variable efficiency  6, 7, [12] [13] [14]. In addition, previous studies did not perform an accurate quality assessment of b cell derivatives in comparison with human b cell, an issue of particular relevance in light of the current push towards clinical application. We recently published that human iPSCs, reprogrammed from fetal fibroblasts with retroviral vectors, can generate insulin-producing cells, engraft and secrete insulin in vivo  15.
Samples were exposed to secondary antibodies (1:1000, Thermo Scientific, Table  2 ) 1 hour and half in the dark. Nuclei were counterstained with DAPI diluted in PBS (1:10000, Sigma-Aldrich, Saint Louis, MO, USA) and mounted on coverslips with Faramount aqueous mounting solution (DAKO, Agilent). Images were taken with a confocal microscope (Leica SP5, Wetzlar, Germany). For flow cytometry, colonies were dissociated into single cells with Accutase (STEMCELL Technologies), then cells were directly stained with SSEA4, TRA1-60, TRA1-81 (Table  1 ) and results acquired using a FACS Canto flow cytometer and the FACS Diva software. Results were reanalyzed with the FlowJo software version 9 (FlowJo LLC, Ashland, Oregon, USA). To assess iPSC ability to differentiate in the three germinative lineages, iPSC colonies were incubated with Dispase solution (Gibco, Waltham, MA, USA) for 10-15 min at 37 C to promote colony lifting.
Molecular Analysis Total RNA was extracted with the mirVana Isolation Kit (Ambion) and quantified by spectrophotometry (Epoch, Gen5 software; BioTek, Winooski, VT). After DNAse (Invitrogen) treatment, 2 mg of RNA were reverse transcribed with SuperScript III RT (Invitrogen), according to Low-density array clustering analysis was performed with the Morpheus software (https://software. broadinstitute. org/ morpheus). Values of mean difference (MD) between groups were calculated as difference between the log of arithmetic means of the two groups (bH1-Hi-ib v. iPSC or bH1-Hi v. ib): MD ¼ E½jX À Y j ¼ 1 n 2 X n i¼1 X n j¼1 jy i À y j j assuming that random variables X and Y into two groups were independently and identically distributed. Positive or negative signs were then attributed according to up-or down-expression of genes between groups.
Immunocytochemistry For immunocytochemistry iPSC clusters were fixed in PFA 4% (Sigma) and cytospinned for thin-layer cell preparation. Islet clusters were embedded in agarose and paraffin and 3 mm sections were cut with a microtome. Samples were processed routinely for histology. The peroxidaseantiperoxidase immunohistochemistry method (Labvision, Thermo Scientific) was used for detection with the antibodies listed in Table  1. Immunostained slides were acquired using an AperioScanscope (Leica), which allows the scanning and digitalization of the slide by multiple vertical scans at 40Â magnification, and analyzed with the Aperio Image Scope software (Leica).
Cytofluorimetric Analysis Human iPSC and EndoC-bH1 were stained with the Live/ Dead stain (Molecular Probes) to exclude dead cells from the analysis. Intracellular staining required cell permeabilization (Cytofix/Phosflow™ perm buffer III, Becton Dickinson, BD, Franklin Lakes, NJ, USA). Cells were then stained using the following monoclonal antibodies (mAbs): 40/Oct3 Alexa Fluor647 ® anti-OCT3/4; 658A5 Alexa Fluor488 ® anti-PDX-1; R11-560 PE anti-NKX6. 1; T56-706 Alexa Fluor647 ® anti-Insulin (BD). Analysis was carried out on a FACS Canto flow cytometer using the FACS Diva software. Results were analyzed with the FlowJo software version 9 (FlowJo LLC).
Hormone Secretion Hormone levels in iPSC culture supernatants were measured at each differentiation stage using the Bio-Plex Pro™ human diabetes kit (BioRad, Hercules, CA, USA). Samples were assayed according to the manufacturer's instructions, and the plates were read on a Luminex xMAP instrument (BioRad). The acquisition and analysis of the samples were performed with the Bio-Plex Manager 6. 0 software (BioRad).
Funding The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: supported by the  European Commission  ( H2020  grant  681070 ),  Fondazione Diabete Ricerca ONLUS  ( SocietaItaliana di Diabetologia-SID ) and  Eli Lilly Italia  (grant ' Sostegno alla ricerca sul diabete 2017 ').
2°in the widefield mode through an oil immersion 100x Apo TIRF objective with a 1. 49 numerical aperture and alternatively an additional 1. 5x lens. A 2 × 2 binning was used. To perform spatial highresolution acquisition, a fast Z-piezo stage (Piezo Nano Z100) was adapted onto the stage of the microscope. For imaging in the stream mode, images were recorded at the camera frame rate (5 to 6 images per second) for 10 sec. Focus was maintained with the Perfect Focus System (PFS, Nikon). All experiments were performed at 37 °C in an incubation chamber adapted for the microscope (Microscope Temperature Control System, LIS). The set-up was controlled by the MetaMorph software (Molecular Devices). Times of appearance and disappearance of the plasma membrane protrusions around each bacterium were analyzed manually.
, 1997) to detect type IV pili. Cells were incubated with 10 µg. mL -1 secondary ARTICLE NATURE COMMUNICATIONS | DOI: 10. 1038/s41467-018-06948-x goat anti-rabbit and goat anti-mouse polyclonal antibodies coupled to AlexaFluor-488 and -568 (Invitrogen) for 1 h. DNA was detected with 0,5 µg. mL -1 DAPI. All antibodies and DAPI were diluted in PBSG. Coverslips were mounted in Vectashield mounting medium (Vector Laboratories) and sealed with clear nail polish. SIM was performed on the ELYRA system controlled by the Zen software (Carl Zeiss) equipped with diode lasers at 405 nm, 488 nm and 561 nm. Images were acquired with an oil immersion 100x Plan-Apochromat M27 objective with a 1. 46 numerical aperture and an additional 1. 6x lens. Nominal pixel size was 0. 049 and 0. 084 µm in the XY and XZ planes, respectively, using a 1 × 1 binning.
Imaging was performed on a LaB6 microscope operated at 200 kV (Technai G2, FEI) and equipped with a 4 KX 4K CMOS camera (F416, TVIPS). Automated data collection for 2D imaging were carried out with the EMTools software suite. Cell culture on anodized aluminum oxide (AAO) membranes (or Anodiscs). 13 mm Whatman Anodiscs with 100 nm pores (GE Healthcare) were coated for 30 min with 0,1 µg. mL -1 APP3 in 10 mM HEPES pH 7. 4, rinsed three times in 0,1 M HEPES and once with distilled water, and then coated for 30 min with 100 µM BCN-RGD in PBS and rinsed three times in PBS  33. 1,5. 10 5 HUVEC were seeded per disc and cultivated overnight. The next day, cell culture medium was renewed with or without 100 nm cytochalasin D for 20 min at 37 °C and cells were fixed in 2. 5% glutaraldehyde (final concentration).
Acknowledgements This work was supported by the  French ministry for research and higher education (ACO) ; the  Integrative Biology of Emerging Infectious Diseases (IBEID) laboratory of excellence (GD)  and the  VIP European Research Council  starting grant (GD). We gratefully acknowledge the  Imagopole-Citech of Institut Pasteur (Paris, France ) as well as the  France-BioImaging infrastructure network  supported by the  French National Research Agency  ( ANR-10-INSB-04 ; Investments for the Future), and the  Région Ilede-France  (program  Domaine d'Intérêt Majeur-Malinf)  for the use of the Zeiss LSM 780 Elyra PS1 microscope and the Zeiss Auriga scanning electron microscope. The authors greatly acknowledge the Cell and  Tissue Imaging  ( PICT-IBiSA ),  Institut Curie , member.
Despite the common use of BTS in clinical decision-making, there is a relative lack of data on both defining tumor burden and evaluating the impact of tumor burden on outcome with therapy. The purpose of this study was to retrospectively assess the impact of BTS on clinical outcomes in patients with metastatic melanoma treated with the anti-programmed death 1 (PD-1) antibody pembrolizumab in the KEYNOTE-001 trial (ClinicalTrials. gov identifier, ). Specifically, we assessed the relationship between BTS and several traditional clinical prognostic factors specific to melanoma (eg, LDH and M-stage) as well as other baseline characteristics such as age, gender, ECOG PS, BRAF status, previous treatments, tumor expression of programmed death ligand 1 (PD-L1), and site of metastases. In addition, we assessed the association of BTS with the clinical outcomes of objective response rate (ORR) and overall survival (OS).
Univariate factors with P < 0. 10 were then analyzed using a multivariate logistic regression to test independence in a stepwise procedure with alpha-to-enter 0. 025 and alpha-to-remove 0. 05. The association of baseline clinical factors with OS was estimated with a univariate Cox proportional hazard analysis applying the Efron method for handling ties. Statistical analyses were done using SAS (version 9. 3). The data cutoff date for this post hoc analysis was September 18, 2015.
The design of the study included up to 10 lesions instead of the traditional 5 in RECIST v1. 1 and, for the purposes of this manuscript, we included all 10 lesions as captured in the database. Therefore, our assessment of BTS does not include all lesions present in the patient and does include up to 5 more lesions than would be counted in RECIST v1. 1. Another limitation of the current study is that we did not explore the difference between having multiple small tumors and having one large tumor. We believe this work is important and should be a part of future of analyses in melanoma and other tumor types, along with analysis of the number of involved metastatic sites. In summary, BTS is strongly associated with several baseline clinical factors and clinical outcomes in patients with metastatic melanoma treated with pembrolizumab.
ACKNOWLEDGMENTS The authors thank the patients and their families and caregivers, and all investigators and site personnel, for participating in the study;  Roger Dansey, MD  ( Merck & Co. , Inc. , Kenilworth, NJ ), for critical manuscript review; and  QualTek Molecular Laboratories  ( Goleta, CA ) for PD-L1 immunohistochemistry assay testing. Medical writing and editorial assistance, funded by  Merck & Co. , Inc. , Kenilworth, NJ, were provided by  Tricia Brown, MS,  and  Payal Gandhi, PhD , of the ApotheCom pembrolizumab team (Yardley, PA). This study was funded by  Merck & Co. , Inc. , Kenilworth,  NJ. DISCLOSURE OF POTENTIAL CONFLICTS OF INTEREST R. W. Joseph  has a consulting or advisory role for  Merck & Co. , Inc. , Kenilworth, NJ,  Bristol-Myers Squibb ,  Novartis , and Exelixis; and received research funding to his institution from  Merck & Co.
was performed by sequencing real-time PCR products as previously described  [12]. Genotyping of D. fragilis isolates was carried out by nested PCR and sequencing a fragment of the gene encoding for the small ribosomal subunit  [14] , followed a second PCR using D. fragilis-specific primers pair DF1/DF4  [15]. The sequences obtained were submitted to the NCBI database using the nucleotide basic local alignment search tool (http:// www. ncbi. nlm. nih. gov/BLAST/) and genotypes were identified by determining the exact match or closest similarity against genotype 1 or genotype 2 (strain Bi/Pa)  [21]. Blastocystis sp. subtypes (STs) were identified by determining the exact match or closest similarity against all known STs, according to the updated classification of Alfellani et al. [22]. Specific primers were designed to amplify a 260 bp fragment length of the small ribosomal subunit of Simplicimonas similis.
Statistical analyses and figures Statistical analyses were performed using RStudio and QtiPlot softwares. The tests were twosided, with a type I error set at α = 0. 05. Quantitative data was presented as means ± standard deviation. The categorical data was presented as frequency and associated proportions. The differences across groups were compared using Student's t-test and the chi-squared test or Fisher's exact test for categorical parameters. Figures were elaborated using LibreOffice Calc, QtiPlot and TheGIMP softwares. Cartography base were obtained using OpenStreetMap (OpenStreetMap contributors), kindly provided by Geofabrik under CC-BY 4. 0.
Helminths were present in 7. 9% of all the subjects, without any difference between asymptomatic and symptomatic groups. The most prevalent helminth was Hymenolepis nana (Table  3 ), followed by Schistosoma mansoni, Necator americanus (GenBank accession numbers MH053422-MH053424), Ancylostoma ceylanicum (GenBank accession number MH053421), Trichiuris trichiura and Ascaris lumbricoides. The overall prevalence of protozoa was 75. 9% (Table  3 ), Blastocystis sp. being the most prevalent with 69. 8%. Taken together, protozoans were not more prevalent in the asymptomatic group (73. 7%) or in the symptomatic group (62. 7%). The overall prevalence of Entamoeba sp. was 34. 0%, Entamoeba coli being the second most prevalent protozoa of our study (26. 4%). The prevalence of E. dispar and E. histolytica (identified by PCR) were respectively 4.
Simplicimonas similis Since we found one sample bearing S. similis DNA, we developed a specific real-time PCR assay and screened all included subjects in order to increase the detectability of S. similis. PCR products of each positive sample were subsequently sequenced to confirm the presence of the parasite. With this technique, 3 additional positive subjects were identified. Two of the four subjects, three men and one woman, aged between 21 and 49 years, complained about symptoms, and all were infected with other parasites. Three subjects lived in low sanitary level neighborhoods (2 in area 2 and 1 in area 4) and 1 lived in the highest sanitary level quarter of area 1. The four isolates (GenBank accession numbers MH053425-MH053428) presented a high homology with each other and with the S.
similis strain CH394 (Fig  5 ). One of the carriers of S. similis was re-sampled one year later as well as two members of his family and his zebu (Bos taurus indicus). The first subject was positive again, as well as one of the two family members (GenBank accession number MH057079) and the zebu (GenBank accession number MH057080). All isolates presented a high homology with the CH394 strain (Fig  5 ).
RetroRules is a database of reaction rules for metabolic engineering (https://retrorules. org). Reaction rules are generic descriptions of chemical reactions that can be used in retrosynthesis workflows in order to enumerate all possible biosynthetic routes connecting a target molecule to its precursors. The use of such rules is becoming increasingly important in the context of synthetic biology applied to de novo pathway discovery and in systems biology to discover underground metabolism due to enzyme promiscuity. Here, we provide for the first time a complete set containing >400 000 stereochemistry-aware reaction rules extracted from public databases and expressed in the communitystandard SMARTS (SMIRKS) format, augmented by a rule representation at different levels of specificity (the atomic environment around the reaction center).
Current resources for reaction rules present some drawbacks, some focus on specific applications or have restricted access. For instance BNICE. ch (8) , which is only available under a proprietary license; Chematica, that consists of reaction rules extracted from Reaxys for organic synthesis available under commercial license; reaction rules from MetRxn database (9) calculated through code available on GitHub (database of rules non available); MINE, a database that uses the BNICE reaction rules in order to generate new chemical structures for metabolomics data analysis,  (10)  but the reaction rules are not provided; and Transform-MinER, a reaction search tool based on KEGG reaction rules not available as a database  (11). Moreover, another limitation is that traditional reaction rule-based systems do not take stereochemistry into account  (12).
Database content RetroRules provides a complete set of reaction rules spanning >16 000 biochemical transformations expressed at different levels of promiscuity. The number of distinct reaction rules depending of diameter considered is summarized in Table  1. Even at the most permissive level (diameter = 2), the number of generated unique reaction rules exceed the number of biochemical reactions. This is due to the generation process in use considering that (i) reaction rules are generated for both directions of every reaction--which doubles the number of rules--and (ii) when a reaction involves multiple reactants that are not cofactors, a separate rule is generated for each substrate (see Materials and Methods section). Interestingly, while expressing stereochemistry in reaction rules opens up a wider range of possible transformations, such increase in reaction rules mainly occurs on EC classes that are typically involved in stereochemistry changes such as EC 5.
1 and EC 5. 2 (racemases and epimerases and cis-trans-isomerases, respectively), for which the number of distinct rules increases from 75 to 1171. RetroRules is freely available to download as an SQLite database containing 15 tables and around 6 million entries that interrelate reactions, rules, metabolites, sequences, and a rule score based on biochemical diversity (Figure  1 ). At the center of the schema is the rules table that contains the information to uniquely describe a reaction rule from a mono-component reaction. A rule is uniquely associated with a given substrate, from a given reaction at a given diameter and, to determine if a given rule expresses stereochemistry of a reaction, the Boolean-type column is-Stereo. Because a reaction rule can have multiple products, the rule products table includes all the unique identifiers of a rule (i.
e. reaction id, substrate id, diameter and isSetereo) combined with the columns product id and stoichiometry that describe a chemical species product for a rule and the number of occurrences of that product in the rule, respectively. The rule products table thus contains a one-to-many relationship with the rules table. Since a given SMARTS or SMILES reaction rule description can apply to multiple reactions, substrates and diameters, both can be found in the smarts and smiles tables respectively. The remaining tables contain meta-information extracted from source databases. All protein sequences, chemical structures and parent reactions data are available in their respective public databases.
Web server The RetroRules database and related web services runs on our infrastructure. It is written in the Python 3 language using the Flask framework. The database content can be accessed through the website (Figure  3 , https:// retrorules. org), or through a REST API service (https:// retrorules. org/api/v0. 7/). In both cases the user may make ranked searches based on EC numbers, reaction IDs from metabolic databases (MetaNetX, Brenda, BiGG, KEGG, MetaCyc, Reactome, Rhea, SABIO-RK and The SEED) according to cross-references provided by MNXref version 3. 0  (14) , substrate names, or chemical structures in InChI format. Results of searches are returned as a sortable table that can be browsed online, exported into JSON or CSV, or directly accessed using the REST API. Providing a REST API facilitates the access to the RetroRules on-the-fly service or to the database using analytic pipelines and other bioinformatics tools.
The underlying chemical transformation with its associated atom-atom mapping for each individual rule can be inspected in detail through the RetroRules web service and a drawing of the reaction rule may be exported as SVG. The single reaction rule display gathers additional information not included in the query result table such as the sets of related mono-component reactions, substrate and reactions through MetaNetX identifiers, and the associated sequences through UniProt identifiers. Lastly, in the circumstance that the user does not find the desired reaction rule in the precomputed database, RetroRules provides the user with the ability to generate custom rules using the Do It Yourself web service (https: //retrorules. org/diy).