dataesr/scientific-paragraphs-categorization huggingface dataset:
These functions are available in the GitHub repository https://github. com/jorainer/xcms-gnpstools. R-markdown documents and python scripts with example analyses and descriptions are available in the documentation. (https://ccms-ucsd. github. io/ GNPSDocumentation/fbmn-iin-xcms/) The files exported by these utility functions can be directly used for IIMN analysis on GNPS. Note that theoretically, it is possible to use RAMClust 10 , CliqueMS  14  , or other packages available for XCMS that perform ion annotation. The results of these packages need to be reformatted to the introduced generic supplementary edges format. The CAMERA integration might serve as a reference and starting point. IIMN with MS-DIAL. MS-DIAL  34  is a polyvalent mass spectrometry data processing software capable of processing various nontargeted LC-MS metabolomics experiments, including ion mobility mass spectrometry (http://prime.
psc. riken. jp/ compms/msdial/main. html). MS-DIAL supports IIMN since version 4. 1. After a standard data processing workflow with MS-DIAL, the alignment results can be exported for IIMN analysis using the GNPS export option. Detailed documentation and representative tutorials are available in the GNPS documentations (https:// ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin-msdial). Dataset processing. All 24 datasets (Supplementary Table  1 ) were processed with the MZmine workflow. As each dataset originates from a different study and was acquired with different LC-MS methods, variable feature detection and alignment parameters were applied, which are summarized in Supplementary Table  4. For all datasets, the same parameters were used for the feature grouping module (meta-Correlate) and the ion identity networking modules, with the only exception that the feature height correlation filter was turned off to group features for the postcolumn salt infusion experiments.
As described previously, this filter should only be applied if the ionization conditions and detection sensitivity are kept constant overall samples. The post-column infusion of different salt solutions for this study promotes the formation of specific ion species in the ionization source. (1) A pair of features were grouped with a retention time tolerance of 0. 1 min, with a minimum overlapping intensity percentage of 50% in at least two samples in the whole dataset (gap-filled features excluded), a feature shape Pearson correlation greater equals 0. 85 with at least five data points and two data points on each edge, and a feature height Pearson correlation greater equals 0. 6 with at least three data points. (2) The initial creation of ion identity networks was performed using the ion identity networking module and a maximum tolerance of 0.
001 m/z or 10 ppm, a comparison where a pair of features and a pair of ion identities only need to match in one sample, and an ion identity library created based on 2 M as the maximum multimers number, a list of adducts ( (a) To add a higher variety of adducts, a maximum multimers number of 2, a list of adducts (   2 , and the different statistical measures and metadata items are described in Supplementary Table  5. One important measure is the identification density, i. e. , all identified nodes and nodes with a maximum distance of n edges to at least one identified compound. Supplementary Figure  5  highlights how the additional edges of ion identity networking increase the identification density in the datasets, measured over a maximum distance of 1-5 edges. The increased density over one edge reflects the new links between unidentified to an identified node by IIN edge.
The identification density is increased for 21 datasets, two datasets with poor identification rates exhibit no change, and one dataset lacks identifications. The maximum identification density increase of +8% over one edge results in a total of 42% of the nodes being either identified or directly linked to an identified compound. The network of the corresponding dataset, i. e. , the post-column salt infusion study, contains a total of 22% identified nodes and 25% nodes with ion identity and MS 2 spectrum in 134 ion identity networks. Ion identity molecular networking decreased the number of unconnected singleton nodes by -12% to a total of 42%. Filtering out nodes with poor MS 2 spectra with less than four signals, which was used as the minimum number of signals for the library matching and FBMN networking, decreases the number of unconnected singleton nodes further to 29%.
Consequently, the network contains many nodes without a match to any library or experimental spectra. Collapsing all nodes with IIN edges into molecular nodes reduces the total network size by -20%, which significantly reduces the overall redundancy and facilitates network visualization and analysis. [M + H] + , [M + Na] + , [M + NH 4 ] + , [M -H + 2Na] + , [M + 2H] 2+ , [M + H] + , [M + Na] + , [M + K] + , [M + NH 4 ] + , [M - H + 2Na] + , [M -H + Ca] + , [M -H + Fe] + , [M + 2H] 2+ , [M + H + Na] 2+ , [M + H + NH 4 ] 2+ , [M + Ca] 2+ , To extract the same statistics on any results from IIMN, download the networking results as a graphml file from a GNPS job page and use the mgf file of that analysis. The special MZmine IIMN build offers two modules in the Tools tab. More information and the latest IIMN enabled MZmine version are available (http://mzmine.
IIMN-based spectral library generation From experimental datasets. To comprehensively cover the fragmentation behavior of a molecule, spectral libraries should contain fragmentation spectra of different ion species acquired with different instrument types and fragmentation methods. IIMN might serve as a solution to expanded spectral libraries. To create new spectral library entries based on IIMN, all 24 datasets were searched for ion identity networks that contain a match to the GNPS spectral libraries with a minimum cosine similarity of 0. 9 and a minimum number of shared fragment ions of 4-6, depending on each dataset's FBMN parameters. For each matching IIN, all contained ion identity features with an MS 2 spectrum and at least three signals above 0. 1% relative intensity were extracted as new library spectra.
The new library entries were constructed based on the highest library match and its attributes, namely, the compound name, structure strings as SMILES and InChI, and the neutral mass, the ion identity provided the ion species information and the precursor m/z, and dataset-specific metadata was added manually. With these strict rules, a total of 538 spectral entries were extracted from all 24 datasets. The new library has a broader and more distributed ion identity coverage when compared to selected representative spectral libraries from MassBank of North America (MoNA) and GNPS. At the same time, it is similar to spectral libraries that were generated with the new MSMS-Chooser library creation workflow in the GNPS ecosystem (Supplementary Fig. 5 ). The new IIMN-based library was made publicly available through the GNPS-library batch submission (Supplementary Tab.
3, https://gnps. ucsd. edu/ProteoSAFe/gnpslibrary. jsp?library=GNPS-IIMN-PROPOGATED). From a natural product compound library. The library creation workflow was repeated and refined on the mass spectrometry data collected for the NIH NPAC ACONN collection of natural products (2179 compounds) provided by Ajit Jadhav (NIH, NCATS). The IIMN workflow was optimized and then applied to two LC-MS datasets collected on mass spectrometers operating in positive ionization mode, the MSV000080492 acquired on a qTOF-MS maXis II (Bruker Daltonics, GmbH) and the MSV000083472 acquired on a Q-Exactive (ThermoFisher Scientific, MA). During feature-based molecular networking, library matching was limited to the manually created GNPS libraries, which were based on the same qTOF-MS dataset (GNPS-NIH-NATURALPRODUCTSLIBRARY_ROUND2_POSITIVE, minimum matched signals = 3, minimum cosine similarity = 0.
6). A new library for both datasets was created with new spectral entries with at least two signals above 0. 1% relative intensity and with ion identities matching to the adduct of the library matches. Furthermore, library matches were filtered by a sample list of compound names contained in LC-MS samples. The IIMN library creation workflow resulted in 805 and 1314 new library entries for the qTOF-MS and the Q-Exactive datasets, respectively. The new IIMN-based library entries were made publicly available through the GNPS-library batch submission and merged into the existing manually created library GNPS-NIH-NATURALPRODUCTSLIBRARY_ROUND2_POSITIVE (Supplementary Table  3 ). In total, we generated 2,657 IIMN-based new spectral library entries. MZmine IIMN workflow for spectral library extraction.
To extract spectral library entries from any IIMN results, download the networking results as a graphml file from a GNPS job page and use the mgf file of that analysis. The special MZmine IIMN build offers the "GNPS results analysis" module in the Tools tab to create library entries based on these two files and provided metadata. The minimum GNPS-library match score sets a threshold for the extraction of library entries. Furthermore, library matches can be filtered to also match the ion identity to the adduct of the library match. A simple comparison between the different reporting formats for adducts was implemented. It removes all spaces, square brackets, and plus symbols (e. g. , harmonizing M + H and [M + H] + ). Filters are available for new library entries with a minimum number of signals above a relative intensity threshold.
The latest information on the IIMN MS 2 library generation workflow in MZmine is available online: http://mzmine. github. io/iin_fbmn Documentation on the GNPS-library batch submission is available at: https://ccms-ucsd. github. io/GNPSDocumentation/batchupload/ Documentation. The documentation of the IIMN workflow is shared in the GNPS documentations on GitHub (https://ccms-ucsd. github. io/GNPSDocumentation/ fbmn-iin/), which also covers FBMN, classical MN, and other related tools. Suggested parameters for FBMN are described elsewhere  7  and the reproducible molecular networking protocol  5  describes MN parameters with step-by-step instructions. MZmine 17 provides help dialogs with parameter descriptions for each module and documentation links on their website (http://mzmine. github. io/ documentation.
Download and visualize the results in a network analysis software (e. g. , Cytoscape The option Direct Cytoscape Preview/Download provides the IIMN network and its collapsed version as Cytoscape projects with various style presets Refer to the documentation on how to run FBMN within GNPS and multiple mass spectrometry data processing tools. https://ccms-ucsd. github. io/GNPSDocumentation/featurebasedmolecularnetworking/ForIIMN,refer to the related part of the GNPS documentation. https://ccms-ucsd. github. io/GNPSDocumentation/fbmn-iin/Generation of collapsed ion identity networks. One result of the GNPS-IIMN workflow is the combined networks with IIN collapsed into single nodes. For this, all ion nodes with the same IIN ID are merged into a representative node based on the feature with the highest library match score, if available, or otherwise the feature with the maximum abundance.
and [M + Fe] 2+ ), and an empty list of modifications were used. (b) To add a greater variety of modifications and larger multimers, a maximum multimers number of 5, a list of adducts ([M + H] + , [M + NH 4 ] + , and [M + 2H] 2+ ), and a list of modifications ([M -H 2 O], [M -2H 2 O], [M -3H 2 O], [M -4H 2 O], [M -HFA], and [M -ACN]) were used. Dataset statistics. Ion identity molecular networking statistics on all datasets were extracted with a new MZmine module and exported to a comma-separated file (csv) for evaluation in Microsoft Excel. The module is included in the special IIMN build of MZmine. All available statistics were based on the spectral input file (mgf) and the resulting network file (graphml), which was downloaded from the dataset's corresponding GNPS results page. The graphml file contains all ion identity molecular networking results, namely, the nodes representing individual features and the edges between nodes.
The mgf spectral summary file contains the corresponding MS 2 spectrum for each feature node. While classical MN and FBMN depend on MS 2 data for each node, IIN creates new MS 1 -based edges that might include nodes without an MS 2 spectrum in the resulting network. For a comparison between FBMN and IIMN, only nodes present within both networks (with an MS 2 spectrum) are considered. A statistical summary and in-depth statistics on each dataset are provided in a supplementary Microsoft Excel workbook (Supplementary Data 1). Excerpts are summarized in Supplementary Table.
Acknowledgements We thank the  German Chemical Industry Fund  ( FCI, Fonds der Chemischen Industrie ) for a Ph. D. scholarship and travel support to R. S. P. C. D. was supported by the  Gordon and Betty Moore Foundation  ( GBMF7622 ), the  U. S. National Institutes of Health  ( P41 GM103484 ,  R03 CA211211 ,  R01 GM107550 ). We thank the  Deutsche Forschungsgemeinschaft  for support to  D. P. ( PE 2600/1-1 ), to  H. U. H. ( HU 730/9-3 ), and to S. B. and  K. D. ( BO 1910/20 ). L. F. N. was supported by the  US National Institutes of Health  ( R01 GM107550 ), and the  European Union 's  Horizon 2020 program  ( MSCA-GF, 704786 ). A. M. C. R. and P. C. D. were supported by the  National Sciences Foundation  grant  IOS-1656481. L. I. A. was supported by the  National Science Foundation  grant  OCE-1736656.
T. P. was supported by the  Czech Science Foundation  Grant  21-11563  M. M. R. is supported by  Public Health Service  Grants  AI126277 ,  AI114625 ,  AI145325 , by the  Chiba University-UCSD Center for Mucosal Immunology, Allergy , and  Vaccines  and an Investigator in the  Pathogenesis of Infectious Disease Award  from the  Burroughs Wellcome Fund. D. P. , M. A. P. , and K. I. P. were supported by the  National Science Foundation's Center for Aerosol Impacts on the Chemistry of the Environment (CAICE)  under grant number  CHE1801971. K. L. M. was supported by the  Gordon and Betty Moore Foundation  ( GBMF6920 ) and the  US National Institutes of Health  ( R01 GM132649 ). A. B. thanks  FAPESP  fellowship ( 2018/24865-4 ). C. O. G. thanks  FAPESP  scholarship ( 2019/06061-8 ). R. T. was supported by the  US National Institutes of Health  ( NCCIH T32AT010131 ).
Data availability All raw (. raw) and centroided (. mzXML or. mzML) mass spectrometry data as well as processed data (. mgf and. csv) and ion identity molecular networks are available through the MassIVE repository (massive. ucsd. edu). Individual MassIVE dataset identifiers are listed in Supplementary Table  1. Dataset metadata and MZmine processing parameters are available in Supplementary Table  4. Links to IIMN job pages for each dataset are listed in Supplementary Table 6 with options for downloading or online analysis of results. Job cloning provides access to all parameter values and to reproducible data reanalysis. The statistical results on all 24 datasets are available in Supplementary Data 1. The IIMN-based MS 2 spectral libraries of propagated spectral entries can be used within GNPS or downloaded for free.
The IIMN-based MS 2 spectral library from experimental datasets is available on GNPS (https://gnps. ucsd. edu/ProteoSAFe/gnpslibrary. jsp?library =GNPS-IIMN-PROPOGATED). The IIMN-based MS 2 spectral libraries from 2 datasets generated for the NIH Natural Products Library (NIH NPAC ACONN) were merged into the existing manually created GNPS library (NIH Natural Products Library Round 2), available on GNPS (https://gnps. ucsd. edu/ProteoSAFe/gnpslibrary. jsp?library=GNPS -NIH-NATURALPRODUCTSLIBRARY_ROUND2_POSITIVE). Individual download links for the two libraries are https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=c39d7 88d30f9408a9e53e68ec84868c6 (Q-Exactive dataset) and https://gnps. ucsd. edu/Proteo SAFe/status. jsp?task=904e6d42b5024c5cacef6dd86f02b714 (Q-TOF-MS dataset). The datasets are available with their accession IDs in the MassIVE repository: MSV000082081 [https://massive.
ucsd. edu/ProteoSAFe/dataset. jsp?task=f65bfac6208a436fab483cd284f52 a33], MSV000084116 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=81538c459 d0447ef972d267d2fb0111d], MSV000084008 [https://massive. ucsd. edu/ProteoSAFe/ dataset. jsp?task=93f45e7eba2e456083a35a92610fff52], MSV000084099 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=6ab3caf2593e4310a6516357f0657aeb], MSV0000 84119 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=fb0ca514a168427d817a29 ba90c4b9f2], MSV000084024 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=ce 840c3053d04c9b8ef1d6daf7068a98], MSV000082045 [https://massive. ucsd. edu/Proteo SAFe/dataset. jsp?task=64e3aacbbbfd4b8681e7e788cb6b16fa], MSV000084101 [https:// massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=3b3d495b93c047adb2d8d25bf6205dc9], MSV000083729 [https://massive.
ucsd. edu/ProteoSAFe/dataset. jsp?task=552dca562b1e4 dd8a2ddbeb5e162d290], MSV000083772 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=eaf6dfcfddc94612a4aa85e9c2e308db], MSV000083601 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=48dc7250e98e45ffb988cfd353d53a3d], MSV0000840 45 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=43e2ce9cd85c47678bf3250db b9e047b], MSV000084112 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=a0363 9b5a08d42d283b714b64146087c], MSV000082630 [https://massive. ucsd. edu/ProteoSA Fe/dataset. jsp?task=90cefc55f6464e20a873e471c5b962e1], MSV000084007 [https:// massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=c809f27dd91445f68c8cc522936119f4], MSV000084056 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=bea6f8e8a5f140 74bd884ee6dd659ab9], MSV000084107 [https://massive.
ucsd. edu/ProteoSAFe/dataset. jsp?task=469779645cd94f159280a88e07c9cf7a], MSV000084063 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=9d478e5f428443ff829f3decbd5759d6], MSV000081832 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=3720644eead2496f8b48f6c09b8d 4790], MSV000084134 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=1ce9d22 90ad04fc4bb4acf143ffe0b92], MSV000084148 [https://massive. ucsd. edu/ProteoSAFe/ dataset. jsp?task=af4486f029a546d09e1dd572b66ddac5], MSV000084158 [https:// massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=1fedc205b6104024a901297a9c0ef151], MSV000084170 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=17709af0ba294 e1387192091a1c541e7], MSV000084118 [https://massive. ucsd. edu/ProteoSAFe/dataset. jsp?task=a13ac7b7be10421c8a168176752cc586].
Code availability The IIMN workflow is available as an interface on the GNPS web platform (https://gnpsquickstart. ucsd. edu/featurebasednetworking). The workflow code is open source and available on GitHub (https://github. com/CCMS-UCSD/GNPS_Workflows). It is released under the license of The Regents of the University of California and free for nonprofit research (https:// github. com/CCMS-UCSD/GNPS_Workflows/blob/master/LICENSE). The workflow was written in Python (ver. 3. 7) and deployed with the ProteoSAFE workflow manager employed by GNPS (http://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/), and we invite everyone to contribute to the documentation on GitHub.
Protist abundances and traits To disentangle potential effects of protist presence and abundance on microbial communities, we estimated protist population sizes through fluid imaging of 3 mL subsamples out of four microcosms from each treatment using a FlowCam (Fluid Imaging Technologies, Scarborough, ME, USA). Fluid imaging also yields high-resolution measurements of multiple cell traits like cellular volume and shape, and optical properties of the cells  (Gibert et al. , 2017). We used ensuing trait data to assess potential responses of the protists to imposed experimental conditions as well as potential responses of the microbial communities to both protist traits and densities (detailed below). We focused on nine different phenotypic characteristics: five measurements of shape and size (length, area, volume, circularity, and aspect ratio) and four measurements of optical properties of the cells (sigma-intensity and three components of hue: Red/Green, Red/Blue and Blue/Green ratios).
Amplicon Sequence Data Processing and Microbial Community Structure Analysis We used 16S rRNA amplicon sequencing to examine the impacts of temperature, time, and protists, on microbial community structure. After the incubation period (12 or 24 days), we collected the microbial communities by filtering 200mL from each microcosm onto gammairradiated 0. 2µm nitrocellulose membranes (Advantec, Taipei, Taiwan) and stored the filters at -20 o C until DNA extraction. Total genomic DNA was extracted from each filter using DNeasy PowerWater DNA Extraction Kits (Qiagen, Hilden, Germany), modified with a heating step (60 o C) before the initial vortexing step to maximize lysis across different microbial cell types. We fluorometrically quantified the genomic DNA concentrations with Qubit (Thermo Fisher, Waltham, MA, USA), and sent an equimolar set of genomic DNA samples to the Research Technology Support Facility (RTSF) at Michigan State University for amplicon prep and sequencing.
We targeted the V4 hypervariable region of the 16S rRNA gene using the standard 515F/806R universal primers with 12 bp Golay barcodes  (Caporaso et al. , 2011). RTSF sequenced our samples with Illumina MiSeq (PE 250 bp, V2 chemistry), and returned 9,069,268 raw reads (average/sample: 62,981 reads), publicly available at EMBL-ENA under project accession number: PRJEB44142 (ERS6200133 -ERS6200276). We processed the raw fastq sequence data through Dada2 (v1. 16. 0;  (Callahan et al. , 2016)  in R (v4. 0. 2; R Core Team, 2013), to trim and filter low quality sequence reads and calculate error rates for denoising and merging the pair-ends into 3801 non-chimeric representative amplicon sequence variants, or ASVs. These representative ASVs were further curated with Lulu to reduce artificially inflated diversity due to amplification and sequencing errors, resulting in 1423 representative ASVs  (Frøslev et al.
5-6;  (Oksanen et al. , 2019). We tested for individual and interactive treatment effects with a perMANOVA using the adonis() function in vegan R (v2. 5-6;  (Oksanen et al. , 2019). We performed multilevel pairwise comparisons of the community data using the pairwise. adonis() function in the pairwiseAdonis R package (v0. 0. 1;  (Martinez Arbizu, 2017). We also examined differences among group community variation -or spread in ordination space-using the betadisper() function (PERMDISP2;  (Anderson et al. , 2006)  in vegan R (v2. 5-6;  (Oksanen et al. , 2019) , which uses multivariate homogeneity of group dispersions, or the multivariate form of a Levene's test  (Anderson, 2006; O'Neill & Mathews, 2000). For treatments imposing significant changes in microbial community structure, we also identified potential positive or negatively responding microbes (ASVs).
RESPONSE 11: Thank you! We apologize for the confusion. Three different people conducted analyses on this article and two use R and one does not use R. This is the reason for the ANOVA being conducted with a different program. The ANOVA analysis was performed in JASP, free and open source software that acts as GUI for R. We have now included the. jasp file for this analysis in the data package at KNB (GracklecausalJASP. jasp), which can be downloaded and opened by anyone who has also downloaded JASP. This also allows one to review and recreate our analysis. We also apologize for including Tabe 1, which may have caused some confusion as "residuals" are not typically reported for a repeated measures ANOVA. We have revised how those data are reported. With that said, the model remains the same, a 2 (Audio Cue: Tone vs Noise) x 2 (Cue Type: Observe vs Intervene) repeated measures ANOVA.