dataesr/scientific-paragraphs-categorization huggingface dataset:
The graph used in this study contains 529 genes; we can therefore observe the strong connectivity that exists among PID pathways since the total number of genes in the PID is 634. This strong connectivity is important for methods such as ours that are able to reason on the information content of the whole database. We observe, however, that the number of genes connected to cancer pathways in PID is far below the total number of human genes. This underrepresentation of regulatory knowledge is an important limitation of PID. Apart from this fact, PID-NCI includes important modeling information that identifies transcription events. This information allows us to separate gene expression from protein activity. These two parameters are not necessarily correlated, especially in cases involving phosphorylated proteins or complexes such as JUN/FOS.
Key nodes identification. Our analysis of the predictions made by the method allowed us to identify nodes associated with a sign specific to MC compared to NPC datasets. Among these assignments, we found the inhibition of JUN/FOS[n] and FOXM1*[c]. These proteins are known to be involved in cancer in general  52, 53  and in hematological malignancies in particular  43, 44. In the case of FOXM1, we showed that this transcription factor can represent a survival marker when its activity decreases. We can draw a parallel with the bibliography, which identifies the activation of the FOXM1 pathway as a risk factor. For JUN/FOS, our analysis identified this pathway as a potential therapeutic target but not as a survival marker. We observed that inhibition of the associated pathways has been already identified in MM patients  39, 41  and in patients with other cancers.
Using the in silico node perturbation method, we were able to estimate the effect of perturbing a node within a particular dataset (i. e. single patient cancer cell). This method represents a powerful tool for analyzing the consequences of perturbations of oncogenic pathways in a given patient, especially as in-vitro experiments are limited due to the small amount of viable myeloma cells that are obtained after bone marrow aspiration. The results of this in silico analysis show that activation of JUN/FOS[n] had a significant impact on 75% of MC profiles; all of these JUN/FOS[n] = "+" sensible MC profiles had the prediction (JUN/FOS[n], -). In addition, activating FOXM1*[c] had a significant impact on 36. 4% of the profiles; 96. 8% of the FOXM1*[c] = "+" sensible MC profiles had the prediction (FOXM1*[c], -).
Using two approaches of classification, we were able to identify specific assignments for MC datasets compared to NPC datasets. Finally, taking advantage of our modeling framework, we studied the effect of performing single in silico perturbations. One advantage of this method is that it makes it possible to infer information about protein states from transcriptomic data by using the causal nature of the interactions as documented in PID. This can be interesting when constructing biological models and, more specifically, when developing cancer models for which proteomic data are not always available and extractable, whereas transcriptomic data are easier to obtain. Moreover, compared to the previously presented classical pathway analysis methods, we identify not only the specific biological processes that are implicated in cancer profiles but also the mechanisms associated with those phenomena.
that were previously characterized by (JUN/FOS[n], -) and (FOXM1*[c], -). Both the machine-learning and statistical methods identified (JUN/FOS[n], -) and (FOXM1*[c], -) as important markers of MC datasets. In Fig. 2, we show (marked as yellow nodes) how these 5 main proteins or protein complexes appear connected following the PID-NCI representation.
Acknowledgements This study was supported by  Intergroupe Francophone du Myélome  and by a  French Institute National du Cancer  Grant  EVACAMM PROG/09/10  (to H. A. L. , S. M. ), a  National Institutes of Health  Grant  PO1CA155258-01  (to S. M. , H. A. L. , N. C. M. ), and a research grant from  Celgene. B. M. 's PhD scholarship was funded by  GRIOTE  project. We would like to thank  Elise Douillard ,  Magali Devic ,  Emilie Morenton  and  Nathalie Roi  for excellent technical assistance. We thank  Jérémie Bourdon ,  Nathalie Theret  and  Sophia Tsoka  for suggestions and critical reading of the manuscript. We are most grateful to the  bioinformatics core facility of Nantes (BiRD -Biogenouest)  for its technical support.
Acknowledgments: We thank the  Plateforme Imagerie in Vitro (University of Strasbourg ) and  Valérie Demais  for their close assistance and valuable advice regarding electron microscopy experiments; and  Diane Murera  and  Fréderic Gros  for providing Atg5f/f dLck-cre mice. Research in the S. Muller's lab is funded by the  French Centre National de la Recherche Scientifique, Région Alsace , the  Laboratory of Excellence Medalis  ( ANR-10-LABX-0034 ) and the EquipEx program  I2MC  ( ANR-11-EQPX-022 ),  Initiative of Excellence (IdEx), Strasbourg University , the  University of Strasbourg Institute for Advanced Study (USIAS)  and  ImmuPharma France. F. W was a recipient of a post-doctoral fellowship from USIAS; and B. L. was a recipient of a doctoral fellowship from the  USIAS.
Started in the early 2000's, the Web of Data has now become a reality [Bizer 2009]. It keeps on growing through the relentless publication and interlinking of data sets spanning various domains of knowledge. Building upon the Resource Description Framework (RDF), this new layer of the Web implements the Linked Data paradigm [Heath and Bizer 2011] to connect and share pieces of data from disparate data sets. Thereby, it enables the integration of distributed and heterogeneous data sets, spawning an unprecedented worldwide knowledge base. Furthermore, our goal in this talk is not only to present the work achieved, but more importantly to engage in a discussion with the stakeholders of the community, may they be data consumers or producers of sibling classifications concerned with the publication of LOD, about data integration scenarios that may arise from the availability of such a large, distributed, knowledge database.
Taxonomic registers are key tools to help us comprehend the diversity of nature. They are the backbone for integrating independent data sources, and help figure out strategies regarding biodiversity and natural heritage conservation. As such, they naturally stand out as potential contributors to the Web of Data. Several international initiatives on taxonomic thesaurus such as NCBI Organismal Classification [Federhen 2012], AGROVOC Multilingual agricultural thesaurus  [Caracciolo et al. 2013]   inventoried in metropolitan France and overseas territories, in a controlled hierarchy of over 500. 000 scientific names. Our goal is to publish TAXREF on the Web of Data, denoted TAXREF-LD, while adhering to standards and best practices for the publication of Linked Open Data (LOD)  [Farias Lóscio et al.
2017 ]. The publication of TAXREF-LD as LOD required tackling several challenges. Far beyond a sheer automatic translation of the TAXREF database into LOD standards, the key point of the reported endeavor was the design of a model able to account for the two coexisting yet distinct realities underlying TAXREF, namely the nomenclature and the taxonomy. At the nomenclatural level, each scientific name is represented by a concept, expressed in the Simple Knowledge Organization System (SKOS) vocabulary [Miles and Bechhofer 2009], along with an authority and a taxonomic rank. At the taxonomic level, a species is represented by a class in the Web Ontology Language (OWL)  [Schneider et al. 2012]  whose properties are the species traits (habitat, biogeographical status, conservation status. ). Both levels are connected by the links between a species and associated names (the valid name and existing synonyms).
Note that the modelling applies not only to species but also to any other taxonomic rank (genus, family, etc. ). This model has several key advantages. First, it is relevant to biologists as well as computer scientists. Indeed, it agrees with three centuries of thinking on nomenclatural codes  [Ride et al. 1999 , McNeill et al. 2012 ] while, at the same time, it fits in with the philosophy underpinning SKOS and OWL: the nomenclatural level allows circulating through a hierarchy of concepts representing scientific names, and at the taxonomic level, the OWL classes represent the sets of individuals sharing common traits. Second, the model enables drawing links with other data sources published on the Web of Data, that may represent either nomenclatural or taxonomic information. Third, the taxonomy evolves frequently along with newly discovered species and changes in the scientific consensus.
Acknowledgements The working group and this paper were fully funded by the  European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) , a Belgian not-for-profit organisation. ESCEO was responsible for the selection of participants in the preliminary meeting and for the choice of the authors of the manuscript, covering all expenses related to the organisation of the meeting. ESCEO also covered all expenses pertaining to the preparation, writing and submission of the manuscript. ESCEO received unrestricted educational grants from different non-governmental organisations, not-for-profit organisations and commercial partners. None of the partners were involved in the organisation of the ESCEO working group, which prepared this manuscript, and were not part of the writing or review team of the manuscript.
Editorial assistance for the development of this manuscript was provided by  Adelphi Communications Ltd. , UK , and funded by  ESCEO. Compliance with ethical standards Conflicts of interest JAK reports grants from  Amgen ,  Eli Lilly  and  Radius Health ; non-financial support from  Medimaps , and Asahi; and other support from  AgNovos  outside the submitted work. JAK is the architect of FRAX but has no financial interest. CC has received consultancy, lecture fees and honoraria from  Amgen ,  GlaxoSmithKline, Alliance for Better Bone Health ,  Merck Sharp & Dohme ,  Eli Lilly ,  Pfizer ,  Novartis ,  Servier ,  Medtronic  and  Roche. RR has received consulting fees or advisory board fees from  Radius Health, Labatec, Danone and Nestlé. BA has institutional research contracts with  Novartis  and  UCB  outside of the submitted work.
Protein name accession numbers molecular weight (Da) Number of peptides Number of spectra We also performed transmission electron microscopy (TEM) in induced DDμ1 parasites. We found that the parasite morphology was similarly affected, together with parasite organization within the vacuole (  ). We could detect membranous or vesicle-like structures within their lumen and some of our images suggest that they could be formed by internal budding of the limiting membrane (S11 Fig) , as previously observed for the VAC compartment  [54]. However, presently, we cannot conclude whether these vesicles represent dispersed and fragmented VAC-related compartments or enlarged Rab5-positive endosomal structures that we detected by SIM microscopy (Fig  7 ). Collectively, our data suggest that TgAP1 is involved in parasite division by regulating the very late stages of cytokinesis after the budding process has been completed.
Structured illumination microscopy (SIM) SIM was used to obtain high-resolution images using an ElyraPS1 microscope system (Zeiss) with a 100x oil-immersion lens (alpha Plan Apochromat 100x, NA 1. 46, oil immersion) and a resolution of 120 nm along the x-y axis and 500 nm along the z-axis (PSF measured on 100 nm beads; Sampling voxel size: 0,050μm Ã 0,050μm Ã 0,150 μm). Three lasers (405, 488, and 561 nm) were used for excitation. SIM images were acquired with an EMCCD camera (Andor Technology Ltd, UK) and processed with ZEN software, where exposure times varied between 100 and 150 ms. Three-dimensional images were generated using a z-step of 150 nm (total thickness ~5 μm). The acquisition was done sequentially using Zeiss Filter Sets 43HE, 38HE and BP 420-480. 15 frames were acquired to reconstruct one image (5 rotations x 3 phases, with a SIM Grating period of 51μm for the blue channel, 42 μm for the green channel, 34μm for the red channel).
Intracellular growth assay APμ1-KO parasites were allowed to invade HFF monolayers for 3 h and treated with 50nM Rapamycin for 6 h. After 3 washes with warm medium, parasites were allowed to grow for additional 16 h before fixation with 4% PFA. For the DDμ1 strain, parasites were inoculated onto HFF monolayers for 3 h and treated with or without shield-1 (1μM) for 16 h, before fixation with 4% PFA. In both cases, intracellular parasites were counted after staining with anti-GAP45 antibodies. The numbers of parasites per vacuole were counted for more than 200 vacuoles for each condition performed in duplicate. Data are presented as mean values of three independent assays ± SEM.
Egress assay Host cells (HFFs) were seeded in 8-well chambers (Nunc1 Lab-Tek1 II chambered coverglass). 5 Ã 10 4 freshly egressed parasites per well were seeded onto HFF monolayers and allowed to invade for 2 hours. Parasites were then treated with 50nM Rapamycin for six hours. Subsequently medium was changed and parasites were allowed to grow further for 24 hours. The chamber was then placed on an inverted microscope (Axio-observer, Zeiss) equipped with an incubation chamber set at 37˚C, and supplied with 5% CO2. Egress was induced with 2μM calcium ionophore A23187 (Sigma-Aldrich). The movies were captured using a 40X Plan apochromat NA 1. 4 objective. Image acquisition was performed using AxioVision Software (Zeiss) for up to 10 minutes on each well. A total of 50 YFP-positive and 150 YFP-negative vacuoles were monitored.
Proteomic data analysis Raw data collected during nanoLC-MS/MS analyses were processed and converted into Ã. mgf peak list format with Proteome Discoverer 1. 4 (Thermo Fisher Scientific). MS/MS data was interpreted using search engine Mascot (version 2. 4. 0, Matrix Science, London, UK) installed on a local server. Searches were performed with a tolerance on mass measurement of 0. 2 Da for precursor and 0. 2 Da for fragment ions, against a composite targetdecoy database (50620 total entries) built with 3 strains of Toxoplasma gondii ToxoDB. org database (strains ME49, GT1 and VEG, release 12. 0, September 2014, 25264 entries) fused with the sequences of recombinant trypsin and a list of classical contaminants (46 entries). Cysteine carbamidomethylation, methionine oxidation, protein N-terminal acetylation and cysteine propionamidation were searched as variable modifications.
Acknowledgments We thank  A. Bongiovanni  for his help in image analysis,  P. J. Sloves  and  M. Cossa  for their help in performing experiments and  Eik Hoffmann  for critical reading of the manuscript. We are grateful to  M. Meissner  for providing the DD overexpression and CreLOX plasmids and the DiCre parasite strain. We thank  J. F. Dubremetz ,  V. Carruthers ,  D. Soldati-Favre ,  D. S. Roos ,  M. Gissot ,  M. J. Gubbels  and  G. E. Ward  for providing us with antibodies and plasmids.
Funding: KV, FS, MAH, GL, and SM are supported by the  Laboratoire d'Excellence (LabEx)   ParaFrap  from the  National Agency for Research   ANR-11-LABX-0024  and  ANR-14-CE14-0002-01. MAH is supported by the  European Research Council  ( ERC Consolidator  grant no. 614880 ). SM is supported by a  Chaire d'Excellence UniversiteLille Nord de ROP ). France /Centre National de la Recherche Scientifique  , or preparation of the manuscript.
number of unique peptides / spectra Protein name Accession numbers Molecular weight (Da) IP-1 IP-2 μ1-HA μ1-HA mu1 adaptin TGGT1_289770 48 918 11/55 8/16 gamma adaptin TGGT1_313670 107 036 20/53 24/77 beta adaptin TGGT1_240870 101 920 18/47 19/76 sigma1 adaptin TGGT1_270370 19 679 2/4 1/2 clathrin heavy chain TGGT1_290950 194 480 4/6 1/1 ENTH-domain containing protein TGGT1_214180 65 903 1/1 1/1 https://doi. org/10. 1371/journal. ppat. 1006331. t001.
The cross-sectional area of 1062 ± 309 fibers per muscle was quantified using ImageJ software (http:// rsb. info. nih. gov/ij/). For the quantification of macrophages in skeletal muscle, cryosections were labeled with antibodies against F4/80 (#ab6640, Abcam) and laminin (#L9393, Sigma Aldrich) overnight at 4 °C and labeling using the second antibody was performed for 2 hours at 37 °C. Secondary antibodies were coupled to Cy3 and FITC (Jackson Immunoresearch Inc), respectively. Fluorescent immunolabelings were recorded with a Zeiss Axio Observer A1 microscope connected to a CoolSNAP HQ 2 camera at 20X magnification. For each experiment, at least 5 randomly chosen fields were counted in a blinded manner (range: 5-14 fields, mean: 9 ± 3 fields). The number of labeled macrophages (i. e. , F4/80 positive cells) was calculated using Image J software (http://rsb.
info. nih. gov/ij/) and normalized to the analyzed muscle area. RNA isolation, cDNA synthesis and real time quantitative polymerase chain reaction. Total RNA was collected from gastrocnemius muscle by using a phenol/chloroform extraction (Qiazol, Qiagen) followed by column purification (NucleoSpinRNA, Macherey Nagel). RNA (200 ng) was reverse transcribed using the iScript cDNA synthesis kit (Bio-Rad). The selected forward and reverse primer sequences are listed in Supplementary Table  1. Real-time PCR was performed in a 20 µl final volume using the SsoFast EvaGreen Super mix (Bio-Rad). Fluorescence intensity was recorded using a CFX96 Real-Time PCR Detection System (Bio-Rad). Data were analyzed using the ∆∆CT method of analysis. Reference genes (hypoxanthine guanine phosphoribosyl transferase, ribosomal protein large P0 and α-tubulin) were used to normalize the expression levels of genes of interest  65.
Antibody against LC3b (1:500) was from Sigma-Aldrich. Incubation with horseradish peroxidase-conjugated secondary antibody (Dako) allowed the chemiluminescent detection of immunocomplexes (GE Healthcare Life Sciences). α-tubulin immunoblots were used to check for equal protein loading. Labeled membranes were quantified using ImageJ software (http://rsb. info. nih. gov/ij/).
Acknowledgements Priscilla Orlando  and  Ghislaine Roux  are gratefully acknowledged for their help in animal care. The authors thank  Rémi Mounier  ( Institut NeuroMyoGène ,  Université de Lyon, France ) for his helpful comments. We also thank  Pauline Vacher  for her contribution to immunohistomorphometry analysis. We also acknowledge  H. Hamzeh-Cognasse  and  T. Bourlet  ( GIMAP, Université de Lyon, France ) for the use of cryostat and microscope.
The T-cell protein tyrosine phosphatase (TCPTP) pathway consists of signaling events mediated by TCPTP. Mutations and genetic variants of some genes in the TCPTP pathway are associated with lung cancer risk and survival. In the present study, we first investigated associations of 5,162 single nucleotide polymorphisms (SNPs) in 43 genes of this TCPTP pathway with lung cancer risk by using summary data of six published genome-wide association studies (GWAS) of 12,160 cases and 16,838 controls. We identified 11 independent SNPs in eight genes after correction for multiple comparisons by a false discovery rate <0. 20. Then, we performed in silico functional analyses for these 11 SNPs by eQTL analysis, two of which, PTPN2 SNPs rs2847297 and rs2847282, were chosen as tagSNPs. We further included two additional GWAS datasets of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls), and the overall effects of these two SNPs among all eight GWAS studies remained significant (OR = 0.
Results Analysis of six GWAS datasets. Overall, 5162 SNPs from 43 TCPTP pathway genes in the six GWAS datasets from the Transdisciplinary Research in Cancer of the Lung and The International Lung Cancer Consortium (TRICL-ILCCO) Consortium were identified, and their associations with lung cancer risk are shown in the Manhattan plot (Fig. 1A ). After multiple-testing correction, 112 SNPs in eight genes (ATR, EGFR, MET, PIK3R1, PIK3R3, PTPN2, STAT3, and STAT5A) remained significantly associated with lung cancer risk with FDR <0. 20. The results of associations with lung cancer risk are summarized in Supplementary Table  S2. Based on LD analysis (r 2 > 0. 30) and online functional prediction analyses by using SNPinfo, RegulomeDB, and HaploReg, we selected to perform additional analyses for 11 SNPs: rs11707731 in ATR; rs845553, rs1140762 and rs17172432 in EGFR; rs34280975 in MET; rs706714 in PIK3R1; rs7538978 in PIK3R3; rs2847297 and rs2847282 in PTPN2; rs3744483 in STAT3; rs1135669 in STAT5A for further study (Supplementary Figure  S1  and Supplementary Table  S3 ).
Functional validation by eQTL analysis 21. We assessed associations between the 11 SNPs and mRNA expression levels by using the genotyping and expression data available from the lymphoblastoid cell lines derived from 373 individuals of European descent (http://www. 1000genomes. org/), and we found that only rs2847297 and rs2847282 were associated with expression levels of PTPN2 in additive, dominant and recessive models   1. Summary of the functional prediction and eQTL analysis results of the 11 selected SNPs in the TCPTP pathways in silico. a Reference allele/effect allele. b P value of eQTL analysis results TFBS = transcription factor binding site. (Table  1 ). Regional association plots for rs2847297 and rs2847282 in 500 kb up-and downstream region were shown in Fig. 1B. The SNP rs2847297 was in a low LD with rs2847282 (Fig.
Proliferation and cell cycle assays demonstrated that overexpression of PTPN2 would decrease serum requirement, increase formation of larger colonies in soft agar, alter morphology, and rapidly progress through G1 and S phases and the rate of cell division  25, 26. Another study showed that the proliferation rate would reduce in TCPTP (-/-), compared to TCPTP (+/+), lymphocytes  27. We found that PTPN2 mRNA expression levels in matched lung cancer tissues were increased compared to adjacent normal tissues from the TCGA database, some other studies also demonstrated that PTPN2 expression levels were higher in lung AD  28, 29  and SQ  30, 31  than in normal lung tissues. These findings provided oncogenic evidence of PTPN2 and were consistent with our results that the two susceptibility loci of PTPN2 were associated with a decreased lung cancer risk as a result of a decreased mRNA expression level of the gene.
Cigarette smoke is the major risk factor for lung cancer, especially for SQ. Study showed that smoking led to an increased expression of Nkx2  34  , which is the transcription factor (TF) of PTPN2. Therefore, it is likely that the locus has the possibility of influencing lung cancer risk of ever smokers through changing the expression of PTPN2. Our study has some limitations. First, genes in the TCPTP pathway were identified mainly from the Molecular Signatures Database and Genecards. Although we did search some relative articles to complete the list of genes in the pathway, some newly discovered genes in the pathway might have been missed. Second, although we demonstrated the association of thetwo novel potentially functional loci in PTPN2 with lung cancer risk with functional evidence from eQTL analyses, the exact biochemical and molecular mechanisms are still unclear.