dataesr/scientific-paragraphs-categorization huggingface dataset:
Mass Spectrometry -Data-Dependent Acquisition Peptide fractions were analyzed by nanoflow HPLC-MS/MS using an Ultimate 3000 nano RSLC system (Thermo Fisher Scientific) coupled with a Q-Exactive HF mass spectrometer fitted with a nano spray Flex ion source (Thermo Fisher Scientific) and controlled using Xcalibur software (version 4. 3). Approximately 1 µg of each fraction was injected and separated using a 90-minute segmented gradient by preconcentration onto a 20 mm x 75 µm PepMap 100 C18 trapping column then separation on a 250 mm x 75 µm PepMap 100 C18 analytical column at a flow rate of 300 nL/min and held at 45 °C. MS Tune software (version 2. 9) parameters used for data acquisition were: 2. 0 kV spray voltage, S-lens RF level of 60 and heated capillary set to 250 °C. MS1 spectra (390 -1500 m/z) were acquired at a scan resolution of 120,000 followed by MS2 scans using a Top15 DDA method, with 30-second dynamic exclusion of fragmented peptides.
Proteomic Database Search Both DDA-MS and DIA-MS raw files were processed using Spectronaut software (version 13. 12, Biognosys AB). The specific library was generated using the Pulsar search engine to search DDA MS2 spectra against the Sarcophilus harrisii UniProt reference proteome (comprising 22,388 entries, last modified in August 2020). Spectral libraries were generated using all default software (BGS factory) settings, including N-terminal acetylation and methionine oxidation as variable modifications and cysteine carbamidomethylating as a fixed modification, up to two missed cleavages allowed and peptide, protein and PSM thresholds set to 0. 01. For protein identification and relative quantitation between samples, DIA-MS data were processed according to BGS factory settings, with the exception that single-hit proteins were excluded.
Statistical Analysis Spectronaut protein quantification pivot reports, including protein description, gene names and UniProt accession numbers were created for the discovery, validation, and combined datasets. The combined dataset includes the discovery and validation datasets and was used to search for EV sample protein markers suggested by the Minimal information for studies of extracellular vesicles 2018  (30) , and to evaluate the relationship of EV associated proteins with tumor volume. The protein quantitation pivot reports were uploaded into Perseus software (version 1. 6. 10. 50) for further data processing and statistical analysis. Quantitative values were log 2 transformed and proteins filtered according to the number of valid values. The data were filtered in order that a valid value for a given protein was detected in ≥70% of samples in at least one group (i.
e. , discovery: control/diseased; validation: control/latent/early/advanced; combined: captive healthy/wild healthy/latent/early/medium/late). Remaining missing values were imputed with random intensity values for low-abundance proteins based on a normal abundance distribution using default Perseus settings. The filtered proteins in the discovery and validation datasets were considered for differential expression analyses of biomarker candidates, which was determined using two-tailed Student's t-test with a permutation-based false discovery rate (FDR) controlled at 5% and s 0 values set to 0. 1 to exclude proteins with very small differences between means. Significantly upregulated EV associated proteins from the filtered datasets were exported from Perseus and analyzed using R 3. 6. 2 (31).
The utility of each discovery dataset protein as a disease status classifier was investigated by subjecting healthy/ disease cohort sample values of each to receiver operating characteristic (ROC) curve analysis to calculate their area under the curve, sensitivity, specificity, and accuracy with bootstrapped confidence intervals. The classification cut-off values were determined using Youden's index. Discovery dataset proteins with a disease status classification area under the ROC curve greater than 0. 9 were then investigated by ROC curve analyses, if present, in the validation dataset (excluding the latent samples). Proteins with areas under the ROC curve greater than 0. 9 in the discovery and validation dataset were investigated in the latent cohort vs healthy wild controls using protein abundance cut-off values trained to distinguish DFTD infected devils from healthy controls calculated in the validation cohort.
Kendall rank correlation was used to reveal significant correlations between protein abundance from the combined dataset and tumor volumes. Linear models were utilized to search for associations between the level of EV associated proteins and tumor burden, which was calculated by dividing total tumor mass by body weight minus total tumor mass and expressed in percentage. Tumor mass was calculated assuming a tumor density of 1. 1 g per ml of volume as described by Ruiz-Aravena et al. (26).
Proteome of Extracellular Vesicles Derived From DFTD Cultured Cells In Vitro To identify possible signals from DFTD tumors, an EV proteome database derived from cultured DFTD cells was used to identify proteins in serum EVs that may originate from DFTD cells  (32). Proteins upregulated in DFTD EVs relative to healthy fibroblast EVs and in EVs derived from serum samples of DFTD-infected devils relative to healthy controls obtained in the discovery cohort were compared. We have submitted all relevant data of our experiments to the EV-TRACK knowledgebase (EV-TRACK ID: EV220126) (33).
The different clinical stages of the disease were classified according to tumour volumes (see "methods"). Although health status/DFTD stage had a significant effect on the total number of nanoparticles (oneway ANOVA p = 0. 02), no significant pairwise differences between groups were found (Figure  1C ). A proteome dataset comprising combined discovery and validation cohorts (n=87) from the biomarker discovery process was generated by data-independent acquisition mass spectrometry to gain an overview of the serum EV proteome and evaluate the presence of commonly recovered EV protein markers and serum contaminants  (30). Of a total of 345 filtered proteins, 23 established EV markers were identified, including CD9, annexins, heat shock and major histocompatibility complex proteins [Figure  1D ;  (35) ].
CATH3 was the most significantly upregulated protein in serum EV samples derived from DFTD infected devils relative to healthy controls (p < 10e-6) with a 4. 7-fold increase (Figures  2B, D ). CTGF and C5 were significantly upregulated by 5. 7-and 2. 4-fold, respectively, in the DFTD infected devils compared to healthy controls (Figures  2B, D ). To evaluate whether the upregulated proteins present in serum of advanced DFTD-infected devils relative to healthy controls were potentially released by DFTD cells, we used a proteomic database of EVs derived from cultured DFTD cells  (32). We found that of the 96 upregulated EV associated proteins derived from serum of DFTD infected devils relative to healthy controls, 19 of them overlapped with the proteins of EVs derived from DFTD cells that were upregulated relative to EVs derived from healthy fibroblasts (Figure  S2 ).
Six of these 19 proteins found in both cell culture EVs as well as serum proteomic databases yielded an area under the ROC curve greater than 0. 9: F-actin-capping protein subunit alpha (CAPZA) and beta (CAPZB), profilin-1 (PFN1), fructosebisphosphate aldolase A (ALDOA), tyrosine 3-monooxygenase/ tryptophan 5-monooxygenase activation protein zeta (YWHAZ), and ARP3 actin related protein 3 (ACTR3) (Table  S2  and Figure  S2 ). However, none of the three perfect classifiers detected in the discovery cohort (CATH3, CTGF, and C5) were present in the EV cell culture database, suggesting an origin other than DFTD tumors.
9 (Table  S3 ). In agreement with the discovery cohort results, CATH3 and PFN1 were significantly upregulated in different stages of DFTD-infected devils relative to the wild healthy controls by 2. 9and 4. 1-fold, respectively (Figures  3B, C ). ROC curves indicated that CATH3 and PFN1 classified devils with DFTD with 87. 9% and 90. 9% sensitivity and 94. 1% and 88. 2% specificity, respectively (Figure  3D  and Table  S3 ). Unlike CATH3, PFN1 was detected in the cell culture DFTD EV database (Figure  S2 ), suggesting a possible tumor origin. In contrast, other protein candidates identified in the discovery cohort such as CTGF and C5 showed a reduced performance in distinguishing different stages of DFTD from the wild healthy controls, with a sensitivity of 48. 5% and specificity of 88. 2% for CTGF and 84.
MYH10, TGFBI, and CTGF Are Associated With Tumor Burden We used the filtered proteome dataset comprising combined discovery and validation cohorts to evaluate relationship between EV associated proteins abundance and tumor volume. CTFG and C5 were significantly and positively correlated with tumor volume in DFTD infected devils (Figure  5A ), which is consistent with their high predictive power to classify advanced-DFTD stages (large tumor volumes) from healthy individuals in the discovery biomarker phase. Myosin heavy chain 10 (MYH10), transforming growth factor beta induced (TGFBI) and CTGF were the proteins that correlated best with tumor volume (Figure  5A  and Table  S4 ), and their expression levels enhanced as tumor volume increases (Figure  5B ). CATH3 and PFN1 did not demonstrate a significant positive correlation with tumor volume (Figure  5A ) but showed a binary relationship with disease/healthy demonstrated in the discovery and validation cohorts.
ACKNOWLEDGMENTS The authors would like to acknowledge all the members of the devil and wild immunology group for their advice and guidance. We would like to thank  Ginny Ralph  for providing care of captive devils, the  Bonorong Wildlife Sanctuary  for providing access to Tasmanian devils and  Dr Alexandre Kreiss  for collecting the blood and to the Save the  Tasmanian Devil Program  for provision of samples. We are grateful of  Phil  and.
GNSS Velocity Field Combination In order to obtain a velocity field of excellent quality level, the best way to proceed would be to gather for all GNSS stations in Europe, the Receiver INdependent EXchange RInEx data and fully-documented associated metadata, in order to process them together in a self-consistent geodetic solution. The EUREF permanent network (EPN, http://www. epncb. oma. be, a federation of agencies, research institutions and universities) operates geodetic class GNSS stations at the scale of the European continent, and delivers fundamental geodetic products, such as RInEx data and position time series, used to build the global International Terrestrial Reference Frame (ITRF). To complement this effort, the European Plate Observing System (EPOS, http://www. epos-eu. org) aims at facilitating the use of integrated data and products in the field of geosciences.
Selected GNSS Velocity Solutions To generate a combined velocity field solution that covers the entire Europe, we look for previously published velocity solutions, with the requirement that the different databases complement each other. Finally, ten velocity fields are included in the present analysis (Figure  1 ): 1. EPOS-UGA solution in double difference: This GPS velocity solution (doi:https://doi. org/10. 17178/ GNSS. products. Europe) was processed in double difference, using GAMIT software  (Herring et al. , 2018). Velocities have then been estimated through a statistical analysis of the position time-series using MIDAS Software  (Blewitt et al. , 2016). The velocity field is given with respect to the stable Eurasian plate as defined from ITRF2014  (Altamimi et al. , 2016). The solution provides the vertical and horizontal velocities with their respective uncertainties for 1092 GPS stations, most of them located in metropolitan France and Italy, as well as some more sparse information in United Kingdom, Scandinavia, Germany, Greece and the Iberic peninsula.
(Zumberge et al. , 1997)  using GIPSY  (Webb, 1997) , and followed by a common mode estimation. Velocities have been estimated through a statistical analysis of the position time-series using MIDAS Software  (Blewitt et al. , 2016). The velocity field is given with respect to the stable Eurasian plate as defined from ITRF2014  (Altamimi et al. , 2016). This solution contains the vertical and horizontal velocities with their respective uncertainties for 662 GPS stations. Most stations are distributed in the French and Italian Alps. This data set also contains information in metropolitan France, in the Northern Iberic peninsula, as well as more sparse information in the United Kingdom and Germany. 3. NGL Solution for Europe (NGL): This GPS velocity solution  (Blewitt et al. , 2018)  was processed automatically in PPP, using GIPSY  (Webb, 1997).
Velocities have been estimated through a statistical analysis of the position time-series using MIDAS Software  (Blewitt et al. , 2016). The velocity field is given in the IGS14 reference Frame  (Altamimi et al. , 2016). This solution provides the vertical and horizontal velocities with their respective uncertainties for 4095 GPS stations in Europe. This data set shows a high density of stations in France, United Kingdom, Italy, Iberic peninsula, Greece as well as more sparse information in the rest of Europe. 4. Switzerland Solution (ETH): This GPS velocity solution  (Sánchez et al. , 2018a (Sánchez et al. , , 2018b) )  was processed in double-difference with Bernese  (Dach et al. , 2015). The velocities have been estimated using multi-year solutions. The velocity field is given in IGb08 reference frame  (Rebischung, 2012).
This solution provides the vertical and horizontal velocities with their respective uncertainties for 934 GPS stations, with a high density of stations in France, Switzerland, and a lower density in Italy and Spain. 6. Scandinavia Solution (KIER): This GPS velocity solution  (Kierulf et al. , 2021)  was processed in double difference, using GAMIT software  (Herring et al. , 2018). Velocities were estimated using a multiyear geodetic combination and velocity uncertainties are based on time series analysis using Cheetah  (Bos et al. , 2008) , including a combination of white noise and power-law noise. The velocity field is given as defined from ITRF2008  (Altamimi et al. , 2011). This solution provides the vertical and horizontal velocities with their respective uncertainties for 164 GPS stations in Scandinavia.
, 2020). Velocities and their uncertainties were derived from filtered daily position time series, using MIDAS Software  (Blewitt et al. , 2016)  and spatially varying robust estimation of the common modes  (Kreemer & Blewitt, 2021). The velocity field is given in IGS14 reference frame which is compatible with ITRF2014  (Altamimi et al. , 2016). The solution provides vertical and horizontal velocities and their uncertainties for 2383 GPS stations, located in North-West Europe between latitudes 38°N and 64°N. 9. Balkans solutions (BALK): This horizontal GPS velocity solution  (D'Agostino et al. , 2020)  was processed in PPP  (Zumberge et al. , 1997)  using GIPSY  (Webb, 1997) , followed by a common mode estimation. Velocities have been estimated through a statistical analysis of the position time-series using MIDAS Software  (Blewitt et al.
, 2016). The velocity field is given with respect to the stable Eurasian plate as defined from IGS14  (Altamimi et al. , 2016). 10. Dense solution of the EUREF Permanent Network (EPND): This multiyear GPS velocity solution  (Kenyeres et al. , 2019)  was obtained by combining weekly solutions of 27 analysis centers with CATREF software  (Altamimi et al. , 2007). The velocity field is given in the ITRF2014  (Altamimi et al. , 2016). This solution provides the vertical and horizontal velocities with their respective uncertainties for 2264 GPS stations in Europe.
Transformation Into a Common Reference Frame The solutions considered do not refer to the same reference frames, or the reference frame realization may slightly differ from one solution to the other. To compare and combine them, it is necessary to project all velocity fields into a common reference frame. We use EPND solution  (Kenyeres et al. , 2019)  as reference because it is based on the EPN multiyear solution (e. g. ,  Legrand & Bruyninx, 2009; Legrand et al. , 2012) , that is a local densification of the ITRF2014  (Altamimi et al. , 2016). In addition this solution includes a large number of stations both in western and eastern Europe, therefore offering a good coverage and a large number of tying point for all other solutions. We align all other solutions to EPND solution in ITRF2014 by applying a 6-parameter Helmert Transformation using the velrot module of GAMIT/GLOBK package  (Herring et al.
Hence there is no systematic difference between the solutions at common stations once they are projected in the same reference frame. The largest dispersions are observed on datasets that have a limited number of common stations, or on datasets mostly centered on regions with large velocities (i. e. , tectonically active areas) or uncertainties, such as CEUR or BALK datasets that host a large number of stations in Italy or in the Balkans. The standard deviation of the CEUR solution, is larger than the one of other solutions, maybe reflecting a larger uncertainty in the velocities associated with this solution. Also, for each transformed solution, the residual with respect to ITRF2014 velocities at each IGS station is also computed and summarized in Figure  S1  in Supporting Information S1.
On the other hand, time series analysis usually provides larger uncertainties associated with the velocities, since time-correlated noise affects the velocity estimate. Finally, statistical analysis of the position time series (with MIDAS software for example  (Blewitt et al. , 2016) ) usually leads to even larger uncertainties associated with the velocities, that are computed from the standard error of the distribution histogram. This situation complicates the combination of different velocity field solutions, associated with multiple values for common stations that are not fully compatible between each other. To overcome this problem, a procedure must be established for harmonizing the uncertainties. The uncertainties of the velocities, for the ten data sets used in this work, follow a log-normal distribution (Figure  S2  in Supporting Information S1).
Removal of Stations With Largest Uncertainty The stations that exhibit the largest uncertainties have been removed from each data set. Given that the East and North uncertainty components display similar distributions for most solutions (Figure  S2  in Supporting Information S1), East and North uncertainties were merged into a unique horizontal uncertainty: 𝛿𝛿ℎ𝑜𝑜𝑜𝑜 = √ 𝛿𝛿 2 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 + 𝛿𝛿 2 𝑁𝑁𝑜𝑜𝑜𝑜𝐸𝐸ℎ For each solution, a new lognormal model was fitted to the distribution of horizontal velocity uncertainties. The stations exhibiting uncertainties larger than the 99th percentile were removed from the datasets. Stations with largest uncertainties on the vertical velocity were removed with a similar procedure.
It is possible to see that the smaller the radius considered for outlier identification, the larger is the number of outliers detected. Whereas for the smoothing criterion, the larger the radius of the area considered, the larger is the number of stations smoothed. Finally, it is important to note that using a large threshold value for detecting outliers or for the smoothing implies that less velocities are removed from the database. To choose the outlier detection and smoothing radius for our preferred solution, we analyze three quantitative metrics in addition to our sensitive study: (a) the total number of stations N i kept in each filtered solution i, (b) the number of velocities smoothed Nv i , and (c) the variance reduction between each filtered solution i and the raw solution. The raw solution (all class stations, with no outliers removed and no velocities smoothed) has 4,863 stations.
730900. This work has been performed in the frame of Rénag and EPOS (European Plate Observing System). Rénag is member of the Research Infrastructure (RI) Résif-Epos, managed by CNRS-Insu. Résif-Epos benefits from the support of the Ministry of Ecological Transition and RESIF-CORE (Convention No ANR-11-EQPX-0040). ORPHEON GNSS data were provided to the authors for a scientific use in the framework of the GEODATA-INSU-CNRS convention.
Data Availability Statement All individual Global Navigation Satellite System (GNSS) velocity field solutions are online available: EPOS-UGA solution is available at https://www. isterre. fr/english/research-observation/research-projects/european-projects/article/epos-gnss-products. html, NGL solution is available at http://geodesy. unr. edu, ETH solution is available at https://doi. pangaea. de/10. 1594/PANGAEA. 886889, MONT solution is available at https:// data. oreme. org/doi/view/2cdc72ec-1066-486c-aef7-9da36662f46d, KIER solution is available at https://doi. org/10. 1016/j. jog. 2021. 101845, KIER solution is available as supplementary material at https://www. sciencedirect. com/science/article/pii/S0264370721000314?via%3Dihub, CEUR solution is available at http://cegrn. cisas. unipd.
it/CEGRN/network/CEG_tableVEL. htm, KREE solution is available at https://dataverse. harvard. edu/dataset. xhtml?persistentId=doi:10. 7910/DVN/ONATFP, BALK solution is available as supplementary material at https://www. sciencedirect. com/science/article/pii/S0012821X20301898#se0090, EPND solution is available at https://epnd. sgo-penc. hu/downloads/ and INGV solution that has been provided through personal communication by the author. The code used to combined and transform the GNSS velocity fields is rotvel, a package distributed with the GAMIT/GLOBK suite.
ZEB1 ectopic expression in melanoma cells impairs CD8 + T cell recruitment in syngeneic mouse models, resulting in tumor immune evasion and resistance to immune checkpoint blockade. Mechanistically, we demonstrate that ZEB1 directly represses the secretion of T cell-attracting chemokines, including CXCL10. Finally, Zeb1 knock-out, by promoting CD8 + T cell infiltration, synergizes with anti-PD-1 antibody therapy in promoting tumor regression. Conclusions We identify the ZEB1 transcription factor as a key determinant of melanoma immune escape, highlighting a previously unknown therapeutic target to increase efficacy of immunotherapy in melanoma. Trial registration number NCT02828202.
RNA sequencing analyses RNA was extracted from FFPE sections with the RNeasy FFPE Kit (Qiagen). Libraries were prepared from 100 ng RNA with the TruSeq RNA exome kit (Illumina), and sequencing was performed on the CLB genomic platform, on Illumina NextSeq or Novaseq machines with a paired-end protocol (2×75 bp, 64Mp reads). Raw sequencing reads were aligned on the human genome (GRCh38) with STAR (V. 2. 7. 3a), with the annotation of known genes from gencode V. 33. Gene expression was quantified using Salmon (V. 1. 1. 0) and the annotation of protein coding genes from gencode V. 33. RNA-seq quality controls and analyses were performed on the Gilles Thomas Bioinformatics platform as detailed in online supplemental material and methods. ZEB1 mRNA expression (TPM) was also analyzed in the melanoma single cell RNA-Seq data set from Jerby-Arnon et al.
26  Seven-color immunofluorescence multiplex stainings and digital image analyses Three micrometer tissue sections were cut from formalinfixed paraffin-embedded human melanoma specimens. The sections underwent immunofluorescence staining using the OPAL technology (Akoya Biosciences) on a Leica Bond RX. A seven-color panel (online supplemental table  2 ) was designed. DAPI was used for nuclei detection. Sections were digitalized with a Vectra Polaris scanner (Perkin Elmer, USA). An autofluorescence treatment of images was carried out using the Inform software (Perkin Elmer). A blinded evaluation of the staining was carried using the HALO Image Analysis Software (Indica Labs). Tumors were phenotyped based on SOX10, CD8, and CD4 expression using the HighPlex module in respectively four phenotypes: melanoma cells expressing SOX10, CD8 + cells, CD4 + cells and other cells expressing none of the SOX10, CD8, or CD4 marker.