dataesr/scientific-paragraphs-categorization huggingface dataset:
The emission signal was projected onto an EMCCD (Photometrics, Evolve 512 Delta) with an electron multiplication gain of 250 ADU/photon operating in a frame transfer mode. The instrument was automated using the open-source software micro-manager (https://www. micro-manager. org) and the data displayed using the ImageJ software  75, 76. For image processing, PeakFit 77 was used to localise genomic loci from these images using the filter settings: "shiftFactor":1. 0, "signalStrength":5. 0, "minPhotons":30. 0, "precisionThreshold":40. 0, "minWidthFactor":0. 5, "maxWidthFactor":0. 5, "precisionMethod":"MORTENSEN". Trajectories were then tracked in 2D using custom python code for connecting molecules in subsequent frames if they were within 500 nm (https://github. com/wb104/trajectory-analysis).
Acknowledgements We thank the  EU   FP7 Integrated Project  " 4DCellFate" (277899 ), the  Medical Research Council  ( MR/P019471/1 ) and the  Wellcome Trust  ( 206291/Z/17/Z ) for programme funding. We also thank the  MRC  ( MR/R009759/1  to BDH, and  MR/M010082/1  to EDL), the  Wellcome Trust  ( 106115/Z/14/Z  to IB,  210701/Z/18/Z  to CS), and the  Isaac Newton Trust  ( 17. 24 (aa) to BDH) for project grant funding, and we thank the  Wellcome Trust/MRC  for core funding to the  Cambridge Stem Cell Institute  (including a starter grant  203151/Z/16/Z  to SB). We thank  Tessa Kretschmann  for help in preparing the figures for publication,  Luke Lavis  for providing the JF549 dye,  Joanna Wysocka  for sending published constructs for 2D enhancer tracking,  Andy Riddell  for flow cytometry and  Aelun Crombie  for preliminary analysis of 2D enhancer tracking experiments.
Software and code 1. Data collection (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/) Read alignment 56. Deeptools v2. 5. 0 57. 2D single-molecule peak fitting PeakFit 77 3D single-molecule peak fitting easy-DHPSF software 59 Microscope image acquisition Micro-manager (https://www. micro-manager. org) ImageJ software 75,76 2. Data analysis Hi-C analysis Python NucProcess (https://github. com/tjs23/nuc_processing) NucTools (https://github. com/tjs23/nuc_tools) ChIP-seq analysis R version 3. 6. 0 Molecules were again classified using a Gaussian mixture model (see Single molecule trajectory analysis, above). J).
ATLAS Metadata Interface (AMI) is a generic software ecosystem for metadata aggregation, transformation and cataloguing. Benefiting from about 20 years of feedback in the LHC context, the second major version was released in 2018. This paper describes how to install and administrate AMI version 2. A particular focus is given to the registration of existing databases in AMI, the adding of additional metadata and, finally, the generation of high level HTML 5 search interfaces using a dedicated wizard.
Introduction Originally developed for the ATLAS experiment  [1]  at the CERN Large Hadron Collider (LHC), ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and database storing. Benefiting from about 20 years of feedback  [2, 3] , it provides a wide array of tools (command line tools, lightweight clients) and Web interfaces for searching data by metadata criteria. The second version of AMI, released in 2018, was designed to guarantee scalability, evolutivity and maintainability. It perfectly fits the needs of scientific experiments in big data contexts. The design principles and main features were described in  [4]. A key feature is the AMI Metadata Query Language (MQL)  [5] , a Domain-Specific Language (DSL) for querying databases and permitting to perform queries without (precisely) knowing relations between tables.
In other words, it means that MQL only deals with metadata names while SQL uses a catalog / table / field paradigm. This paper describes how to install and administrate the AMI ecosystem. The following sections present the hardware and software requirements, the deployment and the administration of both the Java backend and the JavaScript frontend of AMI. And finally, a particular focus is given to the registration of existing databases in AMI and to the graphical design of rich HTML 5 search interfaces with a dedicated wizard.
Hardware and software requirements Any machine (x68, x86-64, ARM, …) with at least 4 gigabytes RAM is able to run the AMI ecosystem. It can be deployed on Linux, Mac OSX and Microsoft Windows. Table  1  shows the minimum software requirements for each software of the ecosystem. The AMI ecosystem stores its configuration in a dedicated database: the configuration database (also called "router" database for historical reasons). Table  2  shows the list of supported Database Management System (DBMS).
The administration dashboard The ecosystem is centrally administrated via a dedicated dashboard. There are five sections: 1) configuration (JDBC connection pool, command cache, logs, authentication modes, user data protection, …); 2) roles; 3) commands; 4) users; 5) catalogs and three external tools (Schema viewer, Search Modeler and Monitoring). The AMI administration dashboard is available from this URL: https://<domain>:8443/?subapp=AdminDashboard (see Figure  3 ). The "catalogs" session permits registering new databases (aka catalogs). It consists of an editable table to specify: catalog name in AMI, JDBC URL, credentials, … When a new catalog is added, clicking on "Flush Server caches (full)" triggers the extraction of all the catalog, table and field metadata (for instance: field name, type, …), then, it builds a relation graph between tables.
The Search Modeler The AMI Search Modeler is a tool for designing rich, and across catalogs, search interfaces, see Figure  4. For each interface definition, a set of searchable fields is specified so that the end user is able to select data by criterion on these fields, see Figure  5. For each selection, a MQL query (without any join) is generated and the AMI backend automatically generates a more complex SQL query (with joins). 3 ) by "Project", "Dataset name", "Dataset type", "File name" and "File type". 3 ) for searching "Datasets" by "Project", "Dataset name", "Dataset type", "File name" and "File type". If is selected "Project"='AMI' and "Dataset name"='dataset_2', the resulting MQL query will be: SELECT * WHERE [`test`. `PROJECT`. `name` = 'AMI'] and (`test`. `DATASET`. `name` = 'dataset_2').
Conclusion AMI is a well-established and mature metadata ecosystem, proposing services and Web interfaces to more than 2000 active users. The second version of the ecosystem, released in 2018, is very easy to deploy and doesn't require any specialist skills. This paper showed how to install AMI from the last binary packages. A set of HTML 5 interfaces makes totally graphical the administration of AMI (administration dashboard) and both the Schema Viewer and the Search Modeler tools permit creating rich interfaces for searching data by advanced metadata criteria. The AMI Team also provides a ready-to-use Docker  [10]  image. It is planned to publish it on the Docker Hub platform. Additionally, it is seriously considered to deploy the AMI ecosystem with Ansible [11], the new application-deployment tool provided by Red Hat.
Acknowledgements Over the years, we have been helped and supported by many people at  CC-IN2P3  and more recently by the  Rosetta/Philae collaboration , in particular:  Osman Aidel ,  Philippe Cheynet ,  Benoît Delaunay ,  Pierre-Etienne Macchi ,  Mattieu Puel ,  Yves Rogez ,  Mélodie Roudaud  and  Jean-René Rouet.
Simulation To benchmark the sensitivity and specificity of this approach when the true genotypes were known, we extracted high quality data from a subset of HapMap phase III samples (n = 990) processed on 16 chemistry plates and hybridized to Affymetrix 6. 0 chips. A 109 kb region on chr 4 containing 1 SNP and 53 nonpolymorphic markers spans a deletion polymorphism with an allele frequency near 22%. We increased the level of difficulty for genotyping these samples by increasing the variance and/or shifting the location of the probe-level data in a subset of the chemistry plates. For each simulated dataset, we fit both CNVCALL, a mixture model developed for the analysis of the Welcome Trust Case Control Consortium, and CNPBayes  [9, 23]. While we did not provide the true batch labels to either method, CNPBayes estimated the batches from the plate surrogates.
Scalability and software: Hierarchical mixture models were fit to a random sample of 1000 observations from the 7,598 available participants at each CNP region, and to all samples with apparent homozygous deletions. We parallelized our analysis so that all regions were evaluated simultaneously. Bayesian logistic regression models fit independently to each CNV region were also evaluated in parallel. CNPBayes is available from github (https://github. com/scristia/CNPBayes).
1j  and Supplementary Figs. 1a, 2  and 4 ). We achieved this performance using out-of-core processing, a technique used to process data that are too large to fit in a computer's main memory (RAM): MSHub uploads files one at a time into the RAM module; data are then processed and deleted from memory, iteratively. Because only one sample is stored in the memory, the load is constant (Supplementary Fig. 2a-f ). As machine learning approaches gain improved performance with increased volumes of information, including more data into analysis leads to better scores of spectral matches (Fig. 1k ,l and Supplementary Fig. 1b ). The spectral library match scores increase, and their distributions become narrower, indicating better quality of results (Fig. 1p, q ). More files deconvoluted in MSHub leads to fewer chimeric spectra, resulting in higher-quality spectral features, and an increase in the number of annotations with improved scores (Fig.
1r, s ). MSHub performs as well or better as other deconvolution tools (Fig. 1t, u  and Supplementary Figs. 3 4 5 ). Linear scaling for MSHub makes it the only tool amenable to repository-scale operation in its present form (Supplementary Table  2 ). GNPS saves deconvoluted data as a summary file, so the deconvolution step does not need to be re-performed for any future analyses. Once the summary file is generated by GNPS-MSHub or imported from another deconvolution tool, the spectra can be searched against public, private or commercial libraries. Matches are narrowed down based on user-defined filtering criteria, such as number of matched ions, Kovats index, balance score, cosine score and abundance. We provide freely available reference data of 19,808 spectra for 19,708 standards, a ~29% increase of free public libraries.
Methods Tutorials and general note. The tools are accessible through http://gnps. ucsd. edu. The documentation to use the GC-MS interface can be found at https://ccms-ucsd. github. io/GNPSDocumentation/gcanalysis/. The tutorials for the deconvolution can be accessed at https://ccms-ucsd. github. io/GNPSDocumentation/gc-ms-deconvolution/, and the library search and molecular networking instructions can be found at https://ccms-ucsd. github. io/ GNPSDocumentation/gc-ms-library-molecular-network/. The tutorial for spectral libraries upload can be found at https://ccms-ucsd. github. io/GNPSDocumentation/batchupload/. The GNPS workflows can be launched with recommended default settings or adjusted according to user needs. The ranges and effect of settings are described in the tutorial. The results can be inspected and quality filters applied according to user criteria.
There are no other GC-MS deconvolution and annotation infrastructures that also work with the data in a repository. The scalability, reproducibility, capture of knowledge and ability to efficiently reuse data in the public domain make the GC-MS infrastructure in GNPS unique compared to other existing open or commercial resources. GNPS promotes FAIR use practices for mass spectrometry data  20. The community infrastructure can be accessed at https://gnps. ucsd. edu under the header 'GC-MS EI Data Analysis'. Deconvolution. Currently, 1D EI GC-MS data are amenable. We recommend using a minimum of ten files in the data set for deconvolution with MSHub. If the user only has fewer than ten files, spectral deconvolution and alignment should be performed using alternative methods (for example, MZmine, OpenChrom, AMDIS, MZmine/ADAP, MS-DIAL, BinBase, XCMS/XCMS Online, MetAlign, SpecAlign, SpectConnect, PARAFAC2, MeltDB or eRah).
After using one of those tools, molecular networking can be performed in the same fashion as for MSHub (a detailed description is given in the Supplementary Notes), as the library search GNPS workflow accepts input from other tools into the GNPS/MassIVE environment. GNPS directly supports deconvolution output from MZmine/ADAP and MS-DIAL. The quantitative table of the deconvolution output can be used for statistical analysis with external tools. Library search. Once the. mgf file is generated by GNPS-MSHub or imported from another deconvolution tool, the spectral features can be searched against public libraries (currently GNPS has Fiehn, HMDB, MoNA and VocBinBase) or the user's own private or commercial libraries (such as NIST and Wiley) and the freely available reference data of 19,808 spectra for 19,708 standards released with this manuscript.
github. io/GNPSDocumentation/gcanalysis/). The chemical distributors were then visualized using 'ili  19. The chemical annotations of features have been cross-referenced from the library search jobs as described in the tutorial. Using balance filters at 50% and >0. 9 cosine, we arrived at annotations that, once visualized, revealed the distributions of skin volatiles (Fig. 2f-i ). For example, squalene was found on all locations but less on the feet. Hexanoic acid was most abundant on the chest and armpits. Globulol, a perfume ingredient that this individual used on the chest, was most intense on the chest, whereas phenylene dibenzoate, a skincare ingredient, was found on the face and hands. The three-dimensional model, the feature table used for mapping and the snapshots shown in Fig. 2f -i are available at https://github.
com/aaksenov1/ Human-volatilome-3D-mapping-. Generation of molecular networks. The data were collected across multiple studies as described in the Supplementary Notes. All of the data sets (Supplementary Table  1 ) were processed on the GNPS MSHub deconvolution workflow as described in the tutorial. The figures were generated as described in the Supplementary Notes. Testing and validation. All modules were tested and validated individually to determine possible fail points and the results validated by manually reviewing the annotations that are obtained. The full pipeline was also tested for a variety of data sets, including those collected for this study (the 'GC-MS analysis for validation studies' section of the Supplementary Notes) and data from several previously published studies and unpublished public data.
Acknowledgements The conversion of the data from different repositories was supported by grant  R03 CA211211  on reuse of metabolomics data, to build enabling chemical analysis tools for the ocean symbiosis program, and the development of a user-friendly interface for GC-MS analysis was supported by the  Gordon and Betty Moore Foundation  through grant  GBMF7622. The  University of California, San Diego Center for Microbiome Innovation  supported the campus-wide seed grant awards for data collection that enabled the development of some of this infrastructure. P. C. D. was supported by the  National Science Foundation  (grant no. IOS-1656475 ) and the  National Institutes of Health (NIH)  (grant nos. U19 AG063744 01 ,  P41 GM103484 ,  R03 CA211211  and  R01 GM107550 ). K. V. and I. L. are very grateful for the support of the  Vodafone Foundation  as part of the  DRUGS/DreamLab  project.
The work of R. L. and P. L. B. on the data set 30 was supported by  Metaboscope , part of the ' Platform 3A ' , funded by the  European Regional Development Fund , the  French Ministry of Research, Higher Education and Innovation , the  Provence-Alpes-Côte d' Azur region , the  Departmental Council of Vaucluse  and the  Urban Community of Avignon. S. A. and A. R. F. acknowledge the  PlantaSYST  project by the  European Union 's  Horizon 2020 Research and Innovation Programme (SGA-CSA  nos. 664621  and  739582  under FPA no. 664620 ). V. V. acknowledges support from the  National Institute on Alcohol Abuse and Alcoholism  award  R24AA022057. M. Guma  and R. C. acknowledge the support of the  Krupp Endowed Fund grant. A portion of mass spectra in the public reference library was produced within the framework of the  State Task for the Topchiev Institute of Petrochemical Synthesis RAS  and with the support of the RUDN University Program 5-100.
R. S. B. acknowledges support of the  State Task for the Topchiev Institute of Petrochemical Synthesis RAS. L. N. K. acknowledges support of the  RUDN University Program 5-100. I. M. acknowledges support of the  Israel Science Foundation  (project no. 1947/19 ) and  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Programme  (project no. 640384 ). J. S. has been supported by  NIH/NIAMS   R03AR072182 , the  Colton Center for Autoimmunity , the  Rheumatology Research Foundation , the  Riley Family Foundation  and the  Snyder Family Foundation. J. Manasson  acknowledges support from the  2017 Group for Research and Assessment of Psoriasis  and  Psoriatic Arthritis Pilot Research Grant  and  NIH/NIAMS   T32AR069515. R. G. is grateful to the  Azrieli Foundation  for the award of an  Azrieli Fellowship.
M. Guma  and R. C. acknowledge the support of the  Krupp Endowed Fund  grant. R. C. was also funded by  T32AR064194-07. The authors are grateful to  R. da Silva  for his contribution to developing the first prototype of the  EI data network  and his continuous assistance with further development and testing of the infrastructure. The authors are also grateful to  M. Vance  and  D. Farmer , who organized the sampling for HomeChem Indoor Chemistry Project (https://indoorchem. org/projects/homechem/) that allowed the collection of samples for the MSV000083598 data set. B. Ross  has assisted with collecting data for the MSV000084348 data set. GC-MS analyses for collection of the MSV000084211 and MSV000084212 data sets were supported by  N757   Doctorados Nacionales  and project  EXT-2016-69-1713  from the  Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS) , the seed project  INV-2019-67-1747  and the  FAPA  project of  Chiara Carazzone  from the  Faculty of Science at Universidad de los Andes  and the grant  FP80740-064-2016  of  COLCIENCIAS.
Code availability The source code of the MSHub software, including low-and high-resolution data processing versions, is available online at Github (version used in GNPS) (https://github. com/CCMS-UCSD/GNPS_Workflows/tree/master/mshub-gc/ tools/mshub-gc/proc) and at BitBucket (standalone version in MSHub developers' repository, both high and low resolution: https://bitbucket. org/iAnalytica/ mshub_process/src/master/). Scripts used to parse, filter, organize data and generate the plots in the manuscript are available online at Github (https://github. com/bittremieux/GNPS_GC_fig). Script for merging individual. mgf files into a single file for creating global network is available at Github (https://github. com/ bittremieux/GNPS_GC/blob/master/src/merge_mgf. py). The three-dimensional model, the feature table with coordinates used for the mapping and the snapshots shown in Fig.
Data availability All of the data used in the preparation of this manuscript are publicly available at the MassIVE repository at the University of California, San Diego Center for Computational Mass Spectrometry website (https://massive. ucsd. edu). The data set accession numbers are: #1 (MSV000084033), #2 (MSV000085136), #3 (MSV000084034), #4 (MSV000084036), #5 (MSV000084032), #6 (MSV000084038), #7 (MSV000084042), #8 (MSV000084039), #9 (MSV000084040), #10 (MSV000084037), #11 (MSV000084211), #12 (MSV000083598), #13 (MSV000080892), #14 (MSV000080892), #15 (MSV000080892), #16 (MSV000084337), #17 (MSV000083658), #18 (MSV000083743), #19 (MSV000084226), #20 (MSV000083859), #21 (MSV000083294), #22 (MSV000084349), #23 (MSV000081340), #24 (MSV000084348), #25 (MSV000084378), #26 (MSV000084338), #27 (MSV000084339), #28 (MSV000081161), #29 (MSV000084350), #30 (MSV000084377), #31 (MSV000084145), #32 (MSV000084144), #33 (MSV000084146), #34 (MSV000084379), #35 (MSV000084380), #36 (MSV000084276), #37 (MSV000084277) and #38 (MSV000084212).
Proteomes of BM-Ads and SC-Ads Differentiate Their Lipid Metabolic Functions To better understand the metabolic specificities of BM-Ads, we conducted proteomic analyses on paired SC-Ads and BM-Ads. The general data analysis strategy is described in Figure  S2A. After quality control, 3,259 proteins were robustly detected. Interestingly, when we searched for proteins known to be secreted by adipocytes, termed adipokines  (Fasshauer and Bl€ uher, 2015) , our dataset did not highlight significant differences between the two types of adipocytes. Furthermore, k-means clustering of adipokine expression did not partition samples according to their anatomical location (Figure  S2B ). As shown in Table  S1 , BM-Ads and SC-Ads expressed similar levels of both the adipocyte-specific adipokine adiponectin (ADIPOQ), as well as leptin (LEP), a hormone predominantly produced by adipocytes  (Fasshauer and Bl€ uher, 2015).
We found similar cAMP responses in both BM-AT and SC-AT (Figure  4E ). Unlike SC-AT, direct activation of adenylate cyclase by forskolin failed to stimulate glycerol and FFA (C) Glycerol release was measured from isolated SC-Ads and BM-Ads as a readout for complete lipolysis under basal conditions or after stimulation with isoprenaline (iso). The data are the means of 7 independent experiments and normalized to the total lipid quantity. The histograms represent mean ± SEM; ***p < 0. 001 according to two-way ANOVA followed by Bonferroni post-test. (D) Free fatty acids (FFAs) released from isolated SC-Ads and BM-Ads as a readout of lipolysis under basal conditions or after stimulation with isoprenaline. Data are the means of 7 independent experiments and are normalized to the total lipid quantity.
The histograms represent mean ± SEM; **p < 0. 01 according to twoway ANOVA followed by Bonferroni post-test. (E) cAMP production in SC-AT and BM-AT after stimulation with isoprenaline for 30 min. Data are the mean of 8 independent experiments, normalized to protein content. Histograms represent the mean ± SEM; *p < 0. 05 according to two-way ANOVA followed by Bonferroni post-test. (F) TG content measured in cell lysates from 3T3F442A (3T3) and OP9 cell lines or human BM-MSCs (MSC) before and after adipogenic differentiation (d, differentiated; nd, non-differentiated). Data are the mean of at least 4 independent experiments (4 independent donors were used for human BM-MSCs) and (legend continued on next page) Cell Reports 30, 949-958, January 28, 2020 955 release in BM-AT, showing that lipolytic defect is downstream of adenylate cyclase (Figures  S4B  and S4C ).
ACKNOWLEDGMENTS This work benefited from the assistance of Stephanie Balor and Vanessa Soldan from the Multiscale Electron Imaging platform (METi) of the Centre de Biologie Integrative (Toulouse, France). Lipidomic analysis was performed at the Mass Spectrometry Facility of the Beth Israel Deaconess Medical Center (Boston, USA). This work was supported by the ''Fondation de France'' (contract no. 171352) for running costs and a 2-year post-doctoral fellowship for C. A. D. E. received a post-doctoral fellowship from the Fondation pour la Recherche Medicale (SPF201809007124). This work also benefited from the Toulouse Reseau Imagerie (TRI)-RIO Optical Imaging Platform at the Institute of were normalized to the quantity of total protein. The histograms represent mean ± SEM; *p < 0. 05; **p < 0.