dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODS ALICE statistical model formulation. The algortihm operates on a dataset of n unique nucleotide TCR sequences (clonotypes) with a given VJ combination. The procedure is then applied to all VJ combinations present in the data. Unique nucleotide sequences have corresponding amino acid sequences. The goal is to find outlying sequences that have an abnormal number of nucleotide variants in the data that differ by at most one amino acid. For each amino acid sequence σ, under the null hypothesis we expect the number of neighbours d to be Poisson distributed: P (d|σ) = e -λ λ d d! , (1) with mean λ = n σ ∼σ QP gen (σ ). The sum is over all possible similar variants σ of σ. Here, similarity σ ∼ σ is defined by having at most one amino acid mismatch, but other measures could be used instead.
For datasets of moderate size, exact computation of P gen of each sequence of interest, e. g. by OLGA  [28] , may be faster. Another option would be to use large number of published datasets  [8, 29] , and treat the number of occurrences of each TCR sequence of interest in these datasets as a proxy for TCR recombination probability, as implemented into VDJtools  [30]. An implementation of the corresponding routine for VDJtools software framework is described at http://vdjtools-doc. readthedocs. io/en/ master/annotate. html#calcdegreestats. Note that VDJtools implementation allows setting an arbitrary Levenstein distance threshold for defining neighboring clonotypes. Forcing clonotypes to have the same VJ/V segments or allowing segment mismatches is also optional. The implementation relies on a pre-compiled control dataset instead of using a generative VDJ rearrangement model, control datasets can be obtained from https://zenodo.
ACKNOWLEDGMENTS This work was supported by the  Russian Science Foundation  grant n. 15-15-00178. M. V. Pogorelyy is supported by  Skoltech Systems biology fellowship. This work was partially supported by the  European Research Council  Consolidator Grant n. 724208. D. M. Chudakov  and  M. Shugay  supported by grant of the  Ministry of Education and Science of the Russian Federation Number   14. W03. 31. 0005  (in part of cancer immunotherapy data analysis). M. Shugay is supported by  Russian Science Foundation  grant n. 17-15-01495.
Differentially expressed genes (n = 3,568; ANOVA) and miRNAs (n = 62; ANOVA) were used for heatmap generation. High and low expression is normalized to the average expression across all of the samples. On the left side, association of the genes in the heatmap with leading edge genes resulting from GSEA analysis on the specified gene sets is identified. (B) Gene set enrichment analysis enrichment plot of amino acid transport dataset showing a negative association between the MSigDb M188 dataset and the LTED gene expression profile. NES, normalized enrichment score. (C) Anti-correlation network of differentially expressed miRNAs and mRNAs. Edge color is proportional to the strength of the anti-correlation (white to red = low to high). Node color is proportional to the LTED versus MCF7 log fold change  (red, downregulated; green, upregulated).
47, log-rank p = 0. 0012, n = 724; Figure  1E ). Similar results were obtained in an independent cohort of ER + breast cancers  (Figures S1B and S1C). A correlation analysis between miR-23b-3p and SLC6A14 was performed on the TCGA patients' dataset selected based on higher (upper quartile) and lower (lower quartile) expression levels of miR-23b-3p (n = 104). The correlation is significant and negative, and, crucially, SLC6A14 expression is completely lost in the high miR-23b-3p-expressing tumors (Figure  S1D ).
Equivalent results were obtained in a dye swap experiment (Figure  S7B ). Crucially, comparable data were obtained by targeting SLC1A2 (Figure  7F  and S7C), indicating that both miR-23b-3p and SLC1A2 are responsible for the increased invasive ability of the ETR cells and that their targeting could reduce the aggressiveness of ETR breast cancer. Finally, we evaluated whether the molecular players differentially expressed in ETR cells versus the parental counterpart and involved in the described reprogramming could have prognostic and predictive value for ET-treated ER + breast cancers. We used a curated dataset that combined $800 ER + breast cancer patients (from independent retrospective studies) who had been treated with adjuvant tamoxifen. Relapse-free survival was used as a surrogate for therapy response.
HR and log-rank Mantel-Cox p values are shown. (I) Correlation of SLC1A2 expression with response to AI in 52 paired ER + breast cancer samples pre-and post-2-week letrozole treatment  (Miller et al. , 2007). A significant decrease in SLC1A2 expression was observed in the responder group, but no difference was found in the non-responder group (Wilcoxon paired t test). ns, not significant. Each dot represents the value derived by a patient's specimen. Red lines connect the mean values pre-and post-letrozole treatment. (J) Correlation of the change in the SLC1A2 and AURKA expression levels following adjuvant fulvestrant treatment (Geo: GSE33658, n = 11; Spearman correlation and p value are indicated in the figure). transporter to compensate for the reduction in amino acid import caused by SLC6A14 impairment.
This promotes a pro-survival autophagy, without the activation of a pro-apoptotic autophagic flux, which has been reported after the acute targeting of SLC6A14. In addition, in silico analysis of the METABRIC database  (Pereira et al. , 2016)  shows that SLC1A2 and SLC6A14 are not concomitantly overexpressed or amplified in breast cancers and may be mutually exclusive (Figure  S7D ). It is interesting to note that SLC1A2 expression has been reported to play a role in different cancers. SLC1A2 undergoes a genomic breakpoint in its 5 0 region and forms a fusion gene with CD44 in gastric and colorectal cancers  (Tao et al. , 2011; Shinmura et al. , 2015). The CD44-SLC1A2 aberration induces higher glutamate intracellular levels, and SLC1A2 targeting resensitizes tumors to chemotherapy  (Tao et al.
PMT levels were set using control samples. Multicolor images were collected simultaneously in two or three channels. Images were taken using a 20x/NA/air 40x/1. 25 NA oil HCX Plan Apo and 63x, 1. 4-0. 6 NA, oil, HCX Plan APO lens. Images were captured using the Leica LAS-AF image acquisition software. Overlays were generated using LAS-AF software. Photo montages were generated using LAS-AF, but were not further processed. The quantification was performed using FiJi: an outline was drawn around each cell and spots were counted in at least 50 cells derived from three representative 63x images from 3 independent experiments (one per experiment). Real Time RT-PCR (qRT-PCR) and miRNA analysis Total RNA, including small RNAs, was extracted using miRNeasy (QIAGEN #217004), quantified and 500 ng were reverse transcribed using the QuantiTect high capacity cDNA reverse transcription kit  (QIAGEN #205311) , for mRNA analysis, or 10 ng using Taqman MicroRNA Reverse Transcription Kit (Applied Biosystems #43665967), for miRNA analysis.
Gas chromatographic runs were performed with helium as carrier gas at 0. 6 mL/min. The split inlet temperature was set to 250 C and the injection volume of 1 mL. A split ratio of 1:10 was used. The GC oven temperature ramp was from 60 C to 325 C at 10 C/min. The data acquisition rate was 10 Hz. For the Quadrupole, an EI source (70 eV) was used, and full-scan spectra (mass range from 50 to 600) were recorded in the positive ion mode. The ion source and transfer line temperatures were set, respectively, to 250 C and 290 C. The MassHunter data processing tool (Agilent) was used to obtain a global metabolic profiling. Fihen Metabolomics RTL library (Agilent G1676AA) 13 C-tracing experiments using liquid-chromatography-MS (LC-MS) Parental and LTED MCF7 cells were incubated for 48 hours with 0. 15 mM [U-13 C]-aspartic acid (Cambridge Isotope Laboratories) or 0.
Bioinformatic analyses Microarray data were normalized and analyzed using GeneSpring GX v. 14. 8 software (Agilent Technologies). Data transformation was applied to set all the negative raw values at 1. 0, then the quantile normalization was applied. The probes detected in at least one sample were used for statistical analyses. Differentially expressed genes were selected to have a R 2-fold expression difference between groups and an adjusted P-value % 0. 05 at ANOVA test, with Benjamini and Hoechberg correction. Supervised hierarchical clustering was performed for OS samples with GeneSpring clustering tool using the list of differentially expressed genes and Euclidean correlation as a measure of similarity. The mRNA and miRNA anti-correlation network analyses described in Figure  1C  were performed using the R software.
Analysis of human datasets The BRCA TCGA dataset analyzed in Figures  1A, 1B , and 4I is available in The Cancer Genome Atlas (TCGA) data portal (https:// www. cancer. gov/about-nci/organization/ccg/research/structural-genomics/tcga). Gene and miRNA expression data and the corresponding clinical information for ER+ BRCA from TCGA provisional dataset were obtained through Firebrowse (http:// firebrowse. org/) data portal. The normalized reads count for mRNA (RSEM) and mature miRNAs (RPM) were downloaded and log 2 transformed. From the BRCA dataset, we selected samples with miRNA-mRNA and overall survival (OS) data. Survival analysis was performed using Kaplan-Meier curve log-rank testing, using Cutoff finder  (Budczies et al. , 2012)  for best miR-23b-3p/SLCA6A14 high-and low-expression selection.
For mRNA analysis in Figure  S1C , overall survival (OS) of untreated breast cancer patients belonging to the METABRIC dataset were retrieved using Km-plotter (http://kmplot. com) (Lanczky et al. , 2016). For SLC6A14 (Figure  S1B ), TNFAIP3 (Figure  S1E ) survival analysis, the curated dataset of ER+ breast cancers was created using Km-plotter and included the relapse free survival data of patients belonging to the following datasets: GSE6532  (Loi et al. , 2007) , GSE20711  (Dedeurwaerder et al. , 2011) , GSE7390  (Desmedt et al. , 2007) , GSE21653  (Sabatier et al. , 2011 ), E-MTAB-365 (Reme et al. , 2013), GSE2034  (Wang et al. , 2005) , GSE2990  (Sotiriou et al. , 2006) , GSE17705  (Symmans et al. , 2010) , GSE12093  (Zhang et al. , 2009) , GSE9195  (Loi et al. , 2008) , GSE3494  (Miller et al.
, 2005) , GSE4611  (Karn et al. , 2010) , GSE45255  (Nagalla et al. , 2013) , GSE2603  (Minn et al. , 2005) , GSE16391  (Desmedt et al. , 2009) , GSE42568  (Clarke et al. , 2013) , GSE26971  (Filipits et al. , 2011)  and GSE19615  (Li et al. , 2010). For GATA2 (209710_at; Figure  3I ) overall survival analysis the curated dataset comprises the following datasets: GSE20711, GSE7390, GSE45255, GSE42568, GSE3494. For SLC6A14 (219795_at; Figure  7G ), TNFAIP3 (202643_s_at; Figure  7H ) survival analysis, the curated dataset of ER+ breast cancers that have been treated with adjuvant tamoxifen was created using Km-plotter and included patients that belong to the following datasets GSE6532, GSE2990, GSE17705, GSE12093, GSE9195, GSE45255, GSE16391, GSE3494, GSE26971 and GSE19615. SLC1A2 data in 52 paired ER+ breast cancer samples pre-and post-2-week letrozole treatment (Figure  7I ) were retrieved using GEO (GSE5462)  (Miller et al.
, 2007)  and normalized log2 expression values are shown either pre-or post letrozole treatment for each patient. Correlation data on patients that have been treated with adjuvant fulvestrant (Figure  7J ) were from the GSE33658 dataset  (Massarweh et al. , 2011). SLC1A2 and AURKA log2 normalized expression levels were reported. Changes were obtained by subtracting from the expression value of tumor that have been treated with fulvestrant that of baseline expression. Correlation analysis was performed using Spearman correlation test.
DATA AND CODE AVAILABILITY mRNA and miRNA data derived from microarray analysis are deposited in NCBI Gene Expression Omnibus (GEO) database: accession number GSE120931. Feature Extraction 10. 7. 3. 1 software (Agilent Technologies) was used to obtain the microarray raw-data and data. The R script (mM_Net. R) for the interactor network analysis displayed in Figure  1C  is available and can be downloaded (https:// github. com/matteoramazzotti/TransTools).
ACKNOWLEDGMENTS The work of A. M. was funded by  Fondazione Umberto Veronesi ,  Associazione Italiana Ricerca sul Cancro (AIRC) , and  Fondazione Cassa di Risparmio di Firenze  (grant  19515  to P. C. and A. M. ), and an  AIRC grant  ( 8797 ) to P. C. M. B. is an AIRC fellow. L. -A. M. is supported by  Breast Cancer Now , working in partnership with Walk the Walk. M. M. is supported by  FWO  ( G0D1717N ) and holds an  ERC consolidator grant  ( 773208 ). We thank  Prof. Massimo Negrini  ( University of Ferrara ) and the facility of the  Laboratorio per le Tecnologie delle Terapie Avanzate (LTTA)  for performing the miRNA and gene expression analyses,  Angela Subbiani  for helping with cell culturing, and  Prof. Clare M. Isacke  ( The Institute of Cancer Research ) for critical reading of the manuscript.
We also excluded password protected sites and intranets of corporate or individual organisations since we could not access the precise pages containing the link to an OE publication. Since the application of the European General Data Protection Regulation (GDPR), some servers no longer specify the complete URL of the referrer page. Thus, visits coming from Wikipedia only provide the generic link 'http://wikipedia. fr' regardless of the actual Wikipedia page from the visit was made. Hence, we could not exploit citation contexts coming from Wikipedia. After removal of these domain names, we then extracted the textual contents of the visiting web pages and identified the position of links to publications hosted on OE. This yielded 33,667 citing web pages for the one-year period considered (between 01/01/2017 and 01/01/2018).
org/), "an open source, cross-platform, multilingual lexicon and grammar-based corpus processing suite. " These noun phrases become the input into the further stages of mapping and visualisation of citation contexts. To do this, we generate a co-occurrence matrix for each citation context, the noun phrases (NPs) present in them and the OE resource cited. Using the Gephi software (Bastian, Heymann, & Jacomy, 2009), we generated dynamic graphs with the application sigma. js (Jacomy & Plique, 2017) in order to explore the citation contexts of OE content via NPs and additional data such as the types of citing pages, the citation functions and the time elapsed before the citation took place. The entire data processing procedure is shown in Figure  1. Figure  2  shows an example of a graph generated to explore the citation contexts of OE contents on the web.
While analytical techniques in natural products research massively shifted to liquid chromatographymass spectrometry, lichen chemistry remains reliant on limited analytical methods, Thin Layer Chromatography being the gold standard. To meet the modern standards of metabolomics within lichenochemistry, we announce the publication of an open access MS/MS library with 250 metabolites, coined LDB for Lichen DataBase, providing a comprehensive coverage of lichen chemodiversity. These were donated by the Berlin Garden and Botanical Museum from the collection of Siegfried Huneck to be analyzed by LC-MS/MS. Spectra at individual collision energies were submitted to MetaboLights (https://www. ebi. ac. uk/metabolights/MTBLS999) while merged spectra were uploaded to the GNPS platform (CCMSLIB00004751209 to CCMSLIB00004751517).
new lichen species to report the main identified metabolites  [35] [36] [37] [38]. However, it lacks sensitivity, relies on co-elution with standards, and metabolite identification can be challenging as such approaches are based on comparative identification and not on the generation of proper spectroscopic data. Efforts have been made to update Huneck's and Yoshimura's compendium  [39] [40] [41] [42]  and databases geared towards chemotaxonomy of lichens have arisen, but these are based on a limited number of metabolites, mainly major compounds accessible by TLC detection  [43] [44] [45]. Aside from their traditional use in chemotaxonomy, lichen metabolites, most notably polyketides, have been known for long to be specifically produced by these organisms and to have a variety of bioactive properties  30, [46] [47] [48] [49] [50] [51] [52].
To this effect, a molecular networking dereplication pipeline was developed based on the implementation of an in-house MS/MS database (Fig. 1 ), inspired by the MIADB project spearheaded by Beniddir and co-workers  56. Pure lichen metabolites from Siegfried Huneck's chemical library maintained in Berlin, in addition to an in-house chemical library from the laboratory in Rennes, were used to constitute the MS/MS database labelled LDB: Lichen DataBase. It includes MS/MS spectra for 250 small molecules ionized in ESI -, ESI + and APCI, representing 23% of the alleged 1050 known lichen metabolites. The most common appendages in lichen metabolites are represented, i. e. depsides, depsidones, dibenzofurans, diphenylethers, pulvinic acid derivatives, quinones, xanthones and terpenes (Fig. 1 ). This database sets a new standard of dereplication in lichen chemistry, regarding both detection sensitivity and structure identification reliability.
Sample preparation. Each of the 250 compounds was solubilized in HPLC-grade methanol at 1 mg/mL and placed into 1. 5 mL HPLC vials prior to analysis. Solvents were purchased from Sigma-Aldrich. Data acquisition. Samples were analysed using an Agilent 6530 Accurate-Mass Q-TOF hyphenated with a 1260 Agilent Infinity LC system. The column used was a Waters SunFire C 18 (50 × 4. 6 mm, i. d. 3. 5 µm) with a flow rate of 0. 5 mL/min. Elution solvents used were Milli-Q water +0. 1% FA (A) and HPLC-grade acetonitrile +0. 1% FA (B) and elution gradient was the following: 0 min at 5% B, 7 min at 100% B, 8 min at 100% B, 9 min at 5% B. Most analytes were ionized by electrospray in negative polarity, xanthones and quinones were additionally ionized in positive mode, and terpenes were ionized exclusively with an APCI source.
ESI conditions were set with the capillary temperature at 320 °C, source voltage at 3. 5 kV, and a sheath gas flow rate of 10 L/min. Regarding APCI analyses, the corona current was set to 4 µA, the nebulizer pressure was 35 psig and 10 L/min nitrogen flow heated at 350 °C was used for desolvation. Capillary, fragmentor and skimmer voltages were set to 3500 V, 175 V and 65 V respectively. There were four scan events: negative or positive MS, window from m/z 100-1200, then Database constitution. 309 files in the d agilent format were thus generated and converted in an mzXML format using the MSConvert 60 module from Proteowizard  61, 62. Raw converted data were then treated using a custom script in the R 3. 6. 0 language  63  with the MSnBase package  64  to isolate MS/MS spectra at each collision energy for the given metabolite and save them as a one scan mzXML file.
Additionally, a merged MS/MS spectrum was generated as a one scan mzXML file by averaging previously generated spectra for a given compound. Merged mzXML files were assembled in a single mgf file using a private online workflow provided by the GNPS platform and uploaded with a metadata file as an open access MS/MS database (available at https://gnps. ucsd. edu/ ProteoSAFe/gnpslibrary. jsp?library=LDB_POSITIVE for the positive mode spectra, and https://gnps. ucsd. edu/ ProteoSAFe/gnpslibrary. jsp?library=LDB_NEGATIVE for the negative spectra). Each spectrum was manually curated to assess the quality of the fragmentation and the identity of fragmented metabolites. 2a, b ) and can therefore be expected to provide a valuable information for lichen chemical profiling given the high degree of structural recurrence within these organisms.
A further criterion for database convenience assessment deals with the ability to provide general data acquisition that encompass as wide as possible a diversity of the metabolites it comprises. The occurrence of acidic functions within a vast majority of lichen metabolites was incentive for their analyses in negative-ion mode, consistently with former reports  25. While the main analysis parameters (viz. 10/25/40 eV collisions energies) afforded convenient tandem mass spectra for most structures investigated herein, two specific cases required further acquisition settings to be applied. At first, depsides underwent an extensive fragmentation under the aforementioned conditions. To remedy, further analyses using lower collision energies were carried out (i. e. 2. 5/5/7. 5 eV) on this specific structural class, which afforded overall better results than the former parameters.
At last, terpenes and steroids were analyzed using an APCI source which provides better results than ESI for low to medium-polarity compounds  67  , although Atmospheric Pressure Photo Ionization would probably shift more again towards non-polar compounds. The database is available on GNPS for molecular networking applications. Fig. 2c, d  show two molecular networks generated with the spectra from the LDB as input and each node (or molecule) was colored according to the chemical scaffolds they represent, as described by Huneck and Yoshimura  24  (parameters in Table  S1 ). Generally speaking, the negative-ion mode generated molecular network is bigger due to the much higher number of compounds from the LDB analyzed in this mode, which should prevail in lichenochemistry. The molecular network obtained from the positive-ion mode revealed a more limited clustering, which may be related to the subset of compounds analyzed in this ionization mode also being among the most scattered in negative polarity analyses.
2c ), that somehow came to exhibit close-enough fragmentation processes to belong to a same cluster. Such "erratic" structures are represented by quinones (Fig. 2c, d , in red) which produce few fragments in MS/ MS and are found mostly as self-looped nodes or related to compounds having similar appendages. Each molecular network presented in this article is available on the GNPS platform: https://gnps. ucsd. edu/ProteoSAFe/status. jsp?task=cc4925fa2ccd43b790b708576f47e7b5 for the negative mode (Fig. 2c ) and https://gnps. ucsd. edu/ ProteoSAFe/status. jsp?task=c79d748b515b4357a515bcec1435e6a1 for the positive mode (Fig. 2d ). Each cluster can be found with the same numbering as in the figure, as well as each node along with their spectra and identifications. To the best of our knowledge, this is the first sizeable MS/MS database of lichen compounds.
Data Records MS/MS data of the LDB can be found on the GNPS (merged spectra) and on Metabolights (merged and individual collision energy spectra). It can be accessed through GNPS in the library webpage (https://gnps. ucsd. edu/ProteoSAFe/libraries. jsp), each spectrum having its own accession number, CCMSLIB00004751209 to CCMSLIB00004751434 for negative spectra, CCMSLIB00004751435 to CCMSLIB00004751517 for positive spectra. The spectral collection is also available for download from MetaboLights under the identifier: MTBLS999  59. S2 ). Acetone extracts of the lichens were analysed in negative polarity LC-MS and produced files were subjected to an MZmine-GNPS workflow (parameters in Tables  S3  and S4 ). A molecular network was thus produced and dereplication was carried out by three different methods: (i) using only the LDB, (ii) using all GNPS spectral libraries including the LDB, (iii) using all GNPS spectral libraries excluding the LDB.