dataesr/scientific-paragraphs-categorization huggingface dataset:
In these MS  1  scans, the ratio between the precursor ion intensity and the other isobaric ions in the precursor ion isolation range is calculated. Then, the purest MS 2 spectrum (highest purity score) is selected as the reference spectrum and pairwise comparison (cosine score) is computed between the purest spectrum and all the other MS 2 spectra for the feature. All MS 2 spectra reaching a cosine score threshold with the reference MS 2 spectra are then merged into one representative MS 2 spectrum. The mass accuracy, isolation width for the ion filtering and cosine score are defined by the user. FBMN after MZmine processing. MZmine  11  is an open-source cross-platform software for MS data processing with an advanced GUI that enables the users to visually optimize parameters and examine the results of each processing step.
FBMN after OpenMS processing. OpenMS is an open-source cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data analysis, including more than 200 tools for common mass spectrometric data-processing tasks  10. Building on our experience with the Optimus development, the integration of OpenMS and FBMN was achieved by creating a GNPSExport tool (TOPP tool) as a part of the OpenMS tool collection (https://github. com/OpenMS/OpenMS). A detailed description of the GNPSExport module and instructions for use with FBMN is available at https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking-with-openms/. Briefly, after running an OpenMS non-targeted metabolomics pipeline, the GNPSExport TOPP tool can be applied to the consensusXML file resulting from FeatureLinkerUnlabeledKD or FeatureLinkerUnlabeledQT tools (alignment step) and the corresponding mzML files.
The two exported files (feature quantification table and MS 2 spectral summary) can be directly used for FBMN analysis on GNPS. The OpenMS-GNPS workflow for metabolomics data processing was implemented as a Python wrapper around OpenMS TOPP tools (https://github. com/Bioinformatic-squad-DorresteinLab/openms-gnps-tools/) and released as a workflow (https://github. com/Bioinformatic-squad-DorresteinLab/ openms-gnps-workflow/) on the GNPS/MassIVE web platform and could be run in OpenMS TOPPAS workflow  31. The OpenMS and GNPS workflow can be accessed and run at https://proteomics2. ucsd. edu/ProteoSAFe/. FBMN after XCMS processing. XCMS (for the most recent version, see https://github. com/sneumann/xcms/) is one of the most widely used software packages for processing of MS-based metabolomics data  27.
The integration of XCMS and FBMN is currently possible using a custom utility function 'formatSpectraForGNPS' to create the MS 2 spectral summary. This function is available on GitHub (https://github. com/jorainer/xcms-gnps-tools/) and is compatible with the CAMERA algorithm for isotopes and adduct annotation  32. Representative XCMS R scripts in Markdown and Jupyter notebook formats are available in GitHub at https://github. com/DorresteinLaboratory/XCMS3_ FeatureBasedMN/. The two exported files (feature quantification table and MS 2 spectral summary) can be directly used for FBMN analysis on GNPS. The detailed documentation is available at https://ccms-ucsd. github. io/GNPSDocumentation/ featurebasedmolecularnetworking-with-xcms3/.
github. io/GNPSDocumentation/ featurebasedmolecularnetworking-with-ms-dial/. FBMN after MetaboScape processing. MetaboScape is a commercial MS metabolomics data-processing software commercialized by Bruker and available on Windows. MetaboScape can perform feature detection, alignment, and annotation of non-targeted LC-MS 2 data acquired on Bruker mass spectrometers. Support for the processing of TIMS coupled to non-targeted LC-MS 2 (LC-TIMS-MS 2 ) was added in MetaboScape 4. 0, which results in LC-TIMS-MS features. FBMN can be performed on LC-MS 2 or LC-TIMS-MS 2 data by exporting the feature quantification table and MS 2 spectral summary from the 'bucket table' using the 'export to GNPS format' function. These files can be uploaded to GNPS for FBMN analysis. Information from MetaboScape, such as the collision cross-section values or other spectral annotations, can be mapped into the molecular networks using Cytoscape  25.
The detailed documentation is available at https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking-with-metaboscape/. FBMN after Progenesis QI processing. Progenesis QI is a commercial feature detection and alignment software developed by Nonlinear Dynamics (Waters) that is compatible with various proprietary and open MS data formats. Progenesis QI can perform feature detection, alignment and annotation of non-targeted LC-MS 2 data acquired either in data-dependent acquisition or data-independent analysis and can also utilize the IMS dimension. FBMN can be performed on any of these data types processed with Progenesis QI (v4. 0), by exporting the feature quantification table (. CSV format) and MS 2 spectral summary (. MSP format). These two files can be exported from the 'identify compounds' submenu by using the functions 'export compound measurement' and 'export fragment database' , respectively.
These files can be uploaded to GNPS for FBMN analysis. Information from Progenesis QI, such as the collision cross-section values or other spectral annotations, can be mapped into the molecular networks using Cytoscape  25. The detailed documentation is available at https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking-with-progenesisQI/. Running time and scalability of the FBMN method. While the molecular networking computation part of the FBMN method is performed online on the GNPS web server (runtime of 5 min to several hours depending on the number of features and job parameters), the data-processing part has to be performed with the computational resources available to the researcher (laptop or desktop computer, workstation and cluster/cloud infrastructure). The computational cost of the data-processing part depends on (1) the software employed, (2) the number of samples in the dataset and (3) the parameters set.
For this reason, the computational cost of the method in all scenarios cannot be comprehensively established. Nevertheless, our experience and feedback from the FBMN community with open-source tools such as MZmine or MS-DIAL showed that small datasets (<50 samples) can be processed in 10-60 min with a computer equipped with 8-16 GB RAM. Medium-sized datasets (>100 samples) require the use of a workstation equipped with 16-32 GB RAM, and large datasets (>500 samples) require 32-64 GB RAM. For very large datasets (>1,000 samples), it is currently recommended to use OpenMS or XCMS on a cluster/cloud infrastructure. Integration with other computational mass spectrometry annotation tools. The. MGF file format is accepted by numerous computational MS annotation tools. The use of these annotation tools with the MS 2 spectral summary file enables (1) a reduction in the computation time compared to when using the unprocessed MS file(s) and (2) the subsequent mapping of these annotations to the molecular networks produced by the FBMN method.
Some of these tools are directly available in the GNPS environment, including SIRIUS  15  , DEREPLICATOR  16  , Network Annotation Propagation (NAP)  33  , MS2LDA  17  , MolNetEnhancer  34  and Qemistree  18  (see below for details), as well as other software such as MetWork  35  , CFM-ID 36 and MetFrag  37. SIRIUS. SIRIUS is an advanced software for the computational annotation of small molecules from LC-MS 2 data  15. It is capable of identifying compounds at the molecular formula  38  , and annotating substructural, class  39  and structural levels  40  from the compound MS 2 spectra. The MS 2 spectral summary file (. MGF format) generated for the FBMN is compatible with SIRIUS, either running locally or with the dedicated GNPS workflow (https://ccms-ucsd. github. io/GNPSDocumentation/ sirius/).
DEREPLICATOR tools can be run directly through the FBMN workflow results on GNPS. Alternatively, and for advanced parameterizing, the DEREPLICATOR workflow on GNPS accepts the MS 2 spectral summary file (. MGF format) as input and can directly map into the FBMNs (https://ccms-ucsd. github. io/GNPSDocumentation/dereplicator/). Network annotation propagation. NAP  33  uses MetFrag/MetFusion  37, 42  for the prediction of putative structures and the network topology to rerank structure predictions by propagating the expected structural similarity. NAP is available on GNPS as a dedicated workflow and offers direct support to FBMN (https:// ccms-ucsd. github. io/GNPSDocumentation/nap/). Unsupervised substructure annotation with MS2LDA. MS2LDA uses the latent Dirichlet allocation algorithm to mine for motifs (Mass2Motifs) of co-occurring fragments and neutral losses in MS2 spectra  17, 43.
MS2LDA accepts the outputs of FBMN, allowing the direct mapping between MS2LDA annotations and the molecular networks. MS2LDA can be run on the GNPS web platform (https:// ccms-ucsd. github. io/GNPSDocumentation/ms2lda/) and/or in the MS2LDA web application  43. MolNetEnhancer. MolNetEnhancer 34 combines the outputs from molecular networking, substructure annotation with MS2LDA and other structural annotation tools, including SIRIUS, NAP and DEREPLICATOR, together with automated chemical classification through ClassyFire  44  into a single molecular network  34. MolNetEnhancer accepts input files from classical MN and FBMN. The MolNetEnhancer accepts the MS 2 spectral summary from FBMN and is available through the GNPS web platform (https://ccms-ucsd. github. io/ GNPSDocumentation/molnetenhancer/).
Qemistree. Qemistree is an MS data exploration strategy based on hierarchical organization of structural fingerprints  18  predicted from fragmentation spectra. The fingerprints are predicted with SIRIUS/CSI:FingerID  15, 40  and the tree-based structure obtained allows the application of ecological tools to study the chemical composition. Qemistree is available as a GNPS workflow and directly accepts the outputs of FBMN (https://ccms-ucsd. github. io/GNPSDocumentation/qemistree/). FBMN applications. FBMN makes it possible to resolve isomers in a drug lead discovery effort. The examination of LC-MS 2 data (MSV000080502) from the E. dendroides plant extract showed the presence of numerous chromatographic peaks for ions in the range of m/z 500-900, corresponding to diterpene ester derivatives.
5 min by MS-Cluster (Fig. 2b  and Supplementary Fig. 9 ). Close examination of the clustered spectra revealed that, while all MS 2 spectra for the precursor m/z 589. 31 present fragment ions m/z 501. 26, 423. 21, 335. 16 and 295. 17, three distinct spectral types could be established based on the relative intensities of the ions (Supplementary Fig. 10 ). FBMN of the dataset with MZmine processing (see the GNPS job) enabled the differentiation of the MS 2 spectra of seven isomers (Fig. 2b  and Supplementary Fig. 11 ; see the molecular network view). A detailed discussion of the differences observed between the two methods can be found in the Supplementary Note 1 and Supplementary Table  1. Interestingly, in the original study  7  , OpenMS was used for FBMN and resulted in the observation of three different positional isomers instead of seven, which shows that different processing methods and/or parameters can lead to different results with FBMN.
The benefit of using FBMN can be illustrated with the metal chelating agent EDTA, widely used in beauty products, food and scientific protocols. A search for its occurrences in public spectral datasets with the MS search tool (MASST)  20  showed that it is frequently observed in plasma samples where it is used during the sample preparation. Using classical MN, we analyzed a public dataset of human plasma where EDTA was observed (MSV00008263; see Supplementary Note 2 for protocol and MS parameters); the results showed that the EDTA ions are found in two molecular networks: one network consisting of [M+H] + spectra and the other of [M+Na] + spectra. Interestingly, each of these networks have one node with a large number of clustered spectra (node 91,205 for 4,655 spectra and node 116,470 for 571 spectra), yet EDTA ions are represented by multiple nodes, although these nodes have the same precursor ion mass and retention time.
Detailed analyses showed that while the median pairwise cosine values between EDTA spectra were high (median values of 0. 93 and 0. 94), the spectra were not clustering into a single node. Examination of the multiple fragmentation spectra for EDTA ions showed that (1) some are chimeric spectra that are 'contaminated' by fragment ions produced by coeluting isobaric ions and (2) other spectra were dominated by low-intensity fragment ions resulting from MS 2 spectra acquired at low intensity. The method of FBMN was applied on that same dataset using the OpenMS-GNPS workflow (see the job), and the results showed that it efficiently reduced the appearance of these redundant node patterns from the same molecule (see the FBMN job; Fig. 2f ), both for the molecular networks containing the [M+H] + and [M+Na] + spectra.
2h , Supplementary Note 3 and Supplementary Figs. 13-19). The sample preparation and MS methods are described in Supplementary Note 3. The files along with the parameters for MZmine are available on the following MassIVE repository (MSV000084092). The OLS analysis was performed with Python 2 (v2. 7. 15) using the linear regression function of the sklearn package (v0. 20. 1)  33. The analysis is available as a Jupyter notebook at https://github. com/lfnothias/ FeatureBasedMolecularNetworking_RelativeQuantEval/. The molecular networking jobs and parameters can be accessed at https://gnps. ucsd. edu/ProteoSAFe/status. jsp ?task=daf3f0d7cec94104b2c9001739964c31 for classical MN, https://gnps. ucsd. edu/ ProteoSAFe/status. jsp?task=f443cad083be4979aedd2af0f97b9fe9 for FBMN with MZmine, https://gnps.
ucsd. edu/ProteoSAFe/status. jsp?task=d6a430cc6da2458f8135 ae76126eb763 for GNPS-OpenMS and https://gnps. ucsd. edu/ProteoSAFe/status. jsp? task=53bcfa39fa674c749b4da0b613df1b8d for FBMN with OpenMS. FBMN enables molecular networking with IMS. The sample NIST 1950 serum  46  was analyzed using a timsTOF Pro (Bruker Daltonics) in data-dependent acquisition mode using PASEF  47. The data were then processed with MetaboScape (v5. 0), and the results were exported for FBMN analysis on GNPS. The MS acquisition method, data and parameters used for the processing were deposited on MassIVE (MSV000084402). Classical MN data were annotated with the GNPS  4  , NIST17 and LipidBlast 48 spectral libraries (https://gnps. ucsd. edu/ProteoSAFe/ status. jsp?task=f2adc2cf33c646548798d0e285197a96). Lipid annotation in MetaboScape was performed using SimLipid (v6.
04; Premier Biosoft) and mapped to the FBMN (https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=0d89db67b097 4939a91cb7d5bfe87072). The molecular networks were visualized with Cytoscape (v3. 7. 1; ref. 25  ), and the results are presented in Supplementary Fig. 1. Large dataset processing with OpenMS and XCMS. The processing of very large metabolomics datasets (>1,000 samples) is limited by the scalability of existing LC-MS feature detection tools, especially those based on a GUI (such as MZmine and MS-DIAL). We showed that, with specific peak-picking parameters, the use of XCMS or OpenMS enables the processing of large metabolomics studies for FBMN (MSV000080030; approximately 2,000 samples; Supplementary Note 4, Supplementary Table  2  and Supplementary Fig. 19 ). Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We gratefully acknowledge financial support from the  U. S. National Institutes of Health (NIH)  for the  Center for Computational Mass Spectrometry grant  ( P41 GM103484 ), the reuse of metabolomics data ( R03 CA211211 ) and the tools for rapid and accurate structure elucidation of natural products ( R01 GM107550  and  U19 AG063744 01 ) to P. C. D. ; the  NIH  grants  R24GM127667  and  1R01LM013115  and a  National Science Foundation (NSF)  award ( ABI 1759980 ) to N. B. ; the  European Union's Horizon 2020  grants  704786  (MSCA-GF to L. -F. N. ), 634402 and 777222 (T. A. and I. P. ) and a  European Research Council   Consolidator grant METACELL  (T. A. ). L. -F. N. was supported by the  Center for Microbiome Innovation  from the  University of California San Diego  (support program award).
M. L. was supported by the  Deutsche Forschungsgemeinschaft  ( BO 1910/20-1 ). J. J. J. v. d. H. was supported by an  Accelerating Scientific Discoveries Grant  funded by the  Netherlands eScience Center (NLeSC ; no. ASDI. 2017. 030 ). S. N. acknowledges funding from  BMBF  (grant no. 031L0107 ) and the  European Commission  ( PhenoMeNal grant   EC654241 ). F. V. was funded by the  Department of Navy, Office of Naval Research Multidisciplinary University Research Initiative (MURI)  award ( N00014-15-1-2809 ). V. V. P. acknowledges support from the  ALSAM Foundation  ( Therapeutic Innovation Award and L. S. Skaggs Professorship ) and the  NIH  ( R35 GM128690 ). T. P. is a  Simons Foundation Fellow of the Helen Hay Whitney Foundation. Z. K. was supported by the project  International Mobility of Researchers  ( CZ.
Data availability The LC-MS 2 data for the E. dendroides dataset, along with the MZmine project and parameters used, can be accessed on the MassIVE submission (MSV000080502; Creative Commons CC0 1. 0 Universal license). The classical MN and FBMN jobs can be accessed via the GNPS website at https://gnps. ucsd. edu/ProteoSAFe/ status. jsp?task=189e8bf16af145758b0a900f1c44ff4a and https://gnps. ucsd. edu/ ProteoSAFe/status. jsp?task=672d0a5372384cff8c47297c2048d789, respectively. LC-MS 2 data for the AGP were downloaded from MassIVE (MSV000080186; Creative Commons CC0 1. 0 Universal license) and processed with MZmine (v2. 37). The MZmine project along with parameters and export files were deposited (MSV000084095; Creative Commons CC0 1. 0 Universal license). The classical MN and FBMN jobs can be accessed at https://gnps.
ucsd. edu/ProteoSAFe/ status. jsp?task=3c27e43d908c4044bace405cc394cd25 and https://gnps. ucsd. edu/ ProteoSAFe/status. jsp?task=0a8432b5891a48d7ad8459ba4a89969f, respectively. The LC-MS 2 data for the EDTA case are available on the MassIVE submission (MSV00008263; Creative Commons CC0 1. 0 Universal license). The classical MN job can be accessed at https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=fbac1a50 61ba4ad683a284ef55d45df6. The OpenMS and FBMN jobs are available at https:// proteomics2. ucsd. edu/ProteoSAFe/status. jsp?task=83a0a417a49b4b76b61e9a8191 a6ea2d at https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=8f40420c11694cf9ab0 6fdf7a5a4c53b, respectively. The MS acquisition method, data and parameters used for the processing of the serum analysis with the timsTOF mass spectrometer were deposited (MSV000084402).
Code availability The FBMN workflow is available as a web interface on the GNPS web platform (https://gnps-quickstart. ucsd. edu/featurebasednetworking/). The workflow code is open source and available on GitHub (https://github. com/CCMS-UCSD/GNPS_ Workflows/tree/master/feature-based-molecular-networking/). It is released under the license of The Regents of the University of California San Diego and free for non-profit research (https://github. com/CCMS-UCSD/GNPS_Workflows/blob/ master/LICENSE/). The workflow was written in Python (v3. 7) and deployed with the ProteoSAFE workflow manager used by GNPS (https://proteomics. ucsd. edu/Software/ProteoSAFe/). We also provide documentation, support, example files and additional information on the GNPS documentation website (https:// ccms-ucsd. github.
io/GNPSDocumentation/featurebasedmolecularnetworking/). The source code of the GNPSExport module in MZmine is available at https:// github. com/mzmine/mzmine2/ under the GNU General Public License. The source code of the GNPSExport tool in OpenMS is available at https://github. com/ Bioinformatic-squad-DorresteinLab/OpenMS/under the BSD license. The source code for the GNPSExport custom function for XCMS is available at https://github. com/jorainer/xcms-gnps-tools/ under the GNU General Public License.
LIFR Is Underexpressed in GC Tissue and Low LIF/LIFR is Associated to Poor Survival in Diffuse Type GC The need for new reliable biomarkers in cancer is more and more important for proper diagnosis and to predict prognosis of patients, especially in GC, where relapse is high. Since LIF supplementation and activation of LIF/LIFR signalling in the GC context seems to be beneficial in terms of CSC targetting, its capacity as a GC cancer biomarker was pondered. Consequently, the Oncomine database was queried for LIFR expression in human GC tissues compared with associated non-tumorous gastric mucosa. Both the Cui and Cho datasets show that LIFR is significantly under-expressed in GC tissues versus associated non-tumorous tissues (Figure  7A(i) ). Further analysis of LIFR according to the different GC subtypes showed that it is under-expressed in all subtypes without significant difference between diffuse, intestinal and mixed GC histological subtypes (Figure  7A (ii)).
LIFR is Underexpressed in GC Tissue and Low LIF/LIFR is Associated to Poor Survival in Diffuse Type GC The need for new reliable biomarkers in cancer is more and more important for proper diagnosis and to predict prognosis of patients, especially in GC, where relapse is high. Since LIF supplementation and activation of LIF/LIFR signalling in the GC context seems to be beneficial in terms of CSC targetting, its capacity as a GC cancer biomarker was pondered. Consequently, the Oncomine database was queried for LIFR expression in human GC tissues compared with associated non-tumorous gastric mucosa. Both the Cui and Cho datasets show that LIFR is significantly underexpressed in GC tissues versus associated non-tumorous tissues (Figure  7A(i) ). Further analysis of LIFR according to the different GC subtypes showed that it is under-expressed in all subtypes without significant difference between diffuse, intestinal and mixed GC histological subtypes (Figure  7A (ii)).
Interestingly, analysis of GC patients' overall survival probability using the KMplot database showed that in diffuse type GC, low LIF and LIFR expression are associated with low patient survival compared with high LIF and LIFR expression, which lead to a better prognosis of patients (Figure  7B ). In intestinal type GC, this tendency remained the same for LIFR expression but is inversed for LIF since low LIF expression is correlated to high survival of patients and high expression to low survival (Figure  7B ). This difference in prognosis of GC patients again shows how LIF is pleiotropic and can lead to different fates depending on the type of cancer. In addition, overall patients' survival analysis according to LIFR and LIF combined expression showed that high expression of both in diffuse type GC leads to better prognosis than low expression, and the same trend is observed for intestinal type GC, though not significant.
LIFR cytometry preparation was carried out as above with anti-human CD118 (LIFR)-PE antibody (12D3 clone, BD Biosciences, Le Pont de Claix, France). Cytometry analysis was performed using a BD FACSCanto II instrument and the DIVA software (BD Biosciences, Le Pont de Claix, France). Dead cells were excluded based on side scatter characteristics and 7-AAD positivity.
Primary antibodies used were 1:100 rabbit anti-YAP and 1:100 mouse anti-TAZ E5P2N (cat. 4912 and cat. 71192, respectively; Cell Signalling Technology, Saint-Cyr-L'École, France). Images were taken using an Eclipse 50i epi-fluorescence microscope (Nikon, Champigny sur Marne, France) with the NIS-BR acquisition software and a ×40 (numerical aperture, 1. 3) oil immersion objective. Nuclear YAP/TAZ count and mean grey value measurements were carried out using the ImageJ 1. 52p software (National Institutes of Health, Rockville Pike, Bethesda, MA, USA)  [44].