dataesr/scientific-paragraphs-categorization huggingface dataset:
MYC and TWIST1 are both transcription factors, so we evaluated if they epigenetically regulated CCL2 and IL13 expression. We identified MYC and TWIST1 promoter binding upstream of human CCL2 and IL13 protein-coding genes in Gene Transcription Regulation Database (GTRD), a metaanalysis of Chip-seq experiments  (Dreos et al. 2017; Yevshin et al. , 2019)  (Figure  5 -figure supplement 1e). Both MYC and Twist1 demonstrated binding at multiple sites in the promoter regions of CCL2 and IL13 in the ChIP-seq data from several different cancer cell lines (Figure  5 -figure supplement 1e). We also looked for MYC and Twist1 promoter binding sites in mouse Ccl2 and Il13 promoters using motif finding analysis of the public data from JASPAR  (Bryne et al. , 2008)  and Eukaryotic promoter database (EPD)  (Dreos et al.
Small animal imaging in vivo bioluminescent imaging (BLI) was utilized to confirm oncogene activation in transgenic mice beginning one week before, and continuing each week following, Dox removal. BLI was performed on an IVIS Spectrum (Caliper Life Sciences, Hopkinton, MA). Briefly, mice were injected i. p. with the substrate D-Luciferin (150 mg/kg) and then anesthetized with 2. 5% isoflurane delivered by the Xenogen XGI-8 5-port Gas Anesthesia System. Animals were then placed into the IVIS Spectrum, and Living Image Software was used to collect, archive, and analyze photon fluxes and transform them into pseudocolor images. MRI scans were performed using a 7T small animal MRI scanner (Bruker Inc, Billerica, MA, Stanford Small Animal Imaging Facility, CA) equipped with a 40 mm Millipede RF coil (ExtendMR LLC, Milpitas, CA).
Under anesthesia by inhalation of 1-3% isoflurane mixed in with medical-grade oxygen via nose-cone, and acquisitions were gated using the respiratory triggering. For tumor detection, a respiration triggered T2-weighted 3D turbo spin echo sequence was used (TR/TE 3000/205 ms, voxel size (0. 22 mm 3 ). The isotropic voxel size of 0. 22 mm in all directions provides a high in plane and across plane resolution. Thereby, the location of one tumor could be defined in all three orientations using specific landmarks, such as major vessels or other tumors. T2-weighted anatomical imaging was performed approximately once weekly. Anatomical and parametric images were analyzed and tumor volumes were measured using Osirix image processing software (Osirix, UCLA, and Los Angeles, CA).
RNA sequencing RNA sequencing of MYC-HCC and MYC/Twist1-HCC was performed at the Beijing Genomics Institute (BGI) using their BGIseq 500 platform single end 150 bp, 20 million reads per sample. Genes expression level is quantified by a software package called RSEM. We counted the number of identified expressed genes and calculated its proportion to total gene number in database for each sample RNA sequencing data are deposited in Gene Expression Omnibus (GEO). DEseq software was used to perform differential expression analysis. Ingenuity Pathway Analysis (IPA, Qiagen) was used to perform functional pathway analysis. Principal-component analysis (PCA; Qlucore Omics Explorer v. 2. 2) was used to generate a visually interpretable overview of the transcriptional profile of MYC-HCC and MYC/Twist1 HCC.
All assays were performed at least 3 times. Quantitative Real-Time PCR RNA was isolated using RNeasy plus mini kit according to the manufacturer's instructions (Qiagen). cDNA was synthesized using SuperScript III (ThermoFisher). qPCR was performed using specific primers (Key Resources) and SYBR Green (Roche) in an Applied Biosystems Real Time PCR System (Life Technologies). Data were normalized to UBC. A minimum of 3 biological and three technical replicates were used for all qPCR experiments.
Immune cell deconvolution analysis The CIBERSORT gene expression deconvolution package was used to estimate the immune cell composition in the MYC-and MYC/Twist1-HCC. The LM22 signature was used as the immune cell gene signature. We modified it for studying mouse immune subsets by carefully converting the genes in the signature to their respective mouse orthologs. The settings for the run were: 1000 permutations with quantile normalisation disabled. The student T-test was used to infer the statistical significance of the predicted immune cell populations where p<0. 05 was considered significant.
Clinical study This study was approved by the institutional review board (IRB) of Stanford University (IRB Number: 28374), and all patients provided informed consent before being enrolled in this study. We prospectively collected blood from patients with HCC or cirrhosis alone. Blood samples were obtained at the time of diagnosis of HCC. Plasma was separated, aliquoted and stored at À80C. Luminex assay to measure cytokine levels was performed as mentioned above. Clinical data was gathered from their medical records.
For a more extensive review of ecosystem processes and anthropogenic sources of COS, the reader is referred to the recently released articles by Whelan et al. who stressed the need for more year-round measurements from a larger number of biomes to provide reliable estimates of terrestrial COS fluxes  [8] , and Zumkehr et al. who provided a global gridded anthropogenic emissions inventory of COS  [9]. Following the approach described by Belviso et al. [10]  now applied on a much longer timeperiod of about four and a half years, continuous atmospheric COS and 222 Radon measurements were collected in a suburban area (Gif-sur-Yvette) in the Greater Paris Area, at two measurement sites. These data are used to quantify non-photosynthetic uptake and ecosystem emission of COS. Nighttime fluxes have been computed from the now widely used Radon-Tracer Method (RTM) as by Yver et al.
The empirical vapor correction is applied systematically to raw data as follows: ½COS� dry ¼ ½COS� humid À ð1 À 2:7½H 2 O� À 4:1½H 2 O� 2 Þ ð1Þ with [COS] and [H 2 O] in ppt and % volume, respectively. The linearity of the mini-QCL was assessed using either ambient air measurement with the mini-QCL next to the GC (S9 Fig), or using calibration tanks with known COS mole fractions (three air compressed cylinders analyzed by GC including the NOAA-ESRL calibration tank used for the LTR assessment of GC measurements). In May 2015, the instruments captured the same episodes of low and high COS levels in the range 400-610 ppt (GC analysis, S9A Fig). GC and laser data, after water vapor correction, were highly correlated (R 2 = 0. 92, n = 252, P � 0. 05, 95% confidence interval of the slope = 0. 88-0.
Multi-year variations of COS mixing ratio and exchange rates The first dataset (August 2014 to July 2019) is made of over 30,500 hourly measurements of atmospheric COS mixing ratio at the GIF site, 7 m above ground level  (Fig 2A). Monthly data generally rely on several hundreds of hourly measurements (n = 527 on average over 56 months) with a few exceptions such as August 2014 (n = 76) and October 2017 (n = 185) due to gaps in the time series in these months (Fig  2B ). The largest peak of COS was recorded in June 2016 with values above 600 ppt (Fig  2B ). COS presents clear seasonal variations with a maximum generally in spring and a minimum in autumn (Fig  2B ). Since the present study aims at identifying the sources and sinks of COS in the footprint area, the question of how these processes impact the shape of the mean seasonal cycle, its amplitude and the sealand gradient will be addressed subsequently (manuscript in preparation).
Anthropogenic emissions of COS from and outside the Paris region The Paris region is frequently subject to large-scale particulate matter (PM) pollution episodes, notably during winter and spring (e. g. [14] ). To give an illustration of the COS atmospheric signature during such pollution episodes, we extracted from the GC and mini-QCL time series four sets of observations to zoom into periods of pollution events lasting several days. A first severe PM pollution episode occurred during February 2015 (Fig 4A ). The PM concentration exceeded the "Alert" threshold defined by AIRPARIF on February 12 because stable nocturnal and diurnal atmospheric conditions prevailed that day, as shown by radon data (Fig  4A ). The enhancements in PM and 222 Rn were associated with COS losses. Surprisingly, during another intense PM pollution episode that took place in March 2015 and was extensively described in  [14] , both time series, especially in March 17 and 18, were more in phase (Fig 4B ).
A similar approach was applied in winter 2016 with both COS instruments running in parallel at a distance of about two kilometers (Fig  5 ). Although there were data gaps in the Dec-Jan time series, very few gaps remained when the GIF and SAC data were combined. In general, there were small spatial variations and stratification in COS across the two sites and the 93 m vertical gradient, at most a few tens of ppt  (Fig 5A). Only one of the many 222 Rn accumulation events in December 2015 and January 2016 coincided with a build-up of COS near the surface. This single event took place on 20 and 21 January 2016 during a medium intensity PM pollution event, i. e. over the "Inform" threshold. The March 2016 survey was also carried out with both COS instruments running in parallel at a small separation (Fig  5B ).
Clinical and Transcriptomic Data of Ovarian Cancer Patients Three cohorts of patients with high-grade serous ovarian cancer were included in this study: Curie, AOCS and TCGA. Curie cohort: Ovarian tumors were obtained from a cohort of 107 patients treated at the Institut Curie between 1989 and 2005. Clinical characteristics of the cohort have already been described in  Mateescu et al. (2011). For each patient, a surgical specimen was taken, prior to any chemotherapeutic treatment, for pathological analysis and tumor tissue cryopreservation. The median patient age was 58 years old (with a range of 31-87 years). Ovarian carcinomas were classified according to the World Health Organization histological classification of gynecological tumors. The Curie transcriptomic dataset is from Affymetrix Human Genome U133 Plus 2.
0 arrays and is freely available in the Gene Expression Omnibus 1  under the accession number, GSE26193. AOCS cohort: Clinical characteristics of the 285 patients included in the AOCS cohort have been previously described in  Tothill et al. (2008) , and transcriptomic data, generated using Affymetrix Human Genome U133 Plus2. 0 arrays, are freely available under the accession number, GSE9899. TCGA cohort: Clinical characteristics of the 557 patients included in the TCGA cohort, as well as transcriptomic data generated using Affymetrix Human Genome U133A arrays, have been previously described in Cancer  Genome and Atlas Research (2011)  and can be downloaded from the NIH Genomic Data Commons (GDC) data portal 2. Most patients treated at Institut Curie are from Caucasian origin and 91% of the patients, for which the ethnicity variable is known in the TCGA cohort, are also from Caucasian origin.
Enrichment of Biological Processes in Transcriptomic Signatures Gene ontology (GO) enrichment analysis was performed using the DAVID bioinformatics resources (Version 6. 7) 3. For each signature tested, the 10 most significant biological processes (based on p-value) were selected. Reduce and Visualize Gene Ontology (REViGO) software  (Supek et al. , 2011 ; accessed January 2017) 4  , with a parameter similarity of 0. 5, was used to summarize information by removing redundant GO terms. Classification of High-Grade Serous Ovarian Cancer From the TCGA Cohort According to Different Transcriptomic Signatures High-grade serous ovarian cancers from the TCGA (Cancer Genome and Atlas Research, 2011) were studied (see Table  1  for cohort description). Genes that comprise the C1-C6  (Tothill et al. , 2008) , Stress/Fibrosis  (Mateescu et al.
, 2011) , and M1-M4  (Bentink et al. , 2012)  signatures were applied to the TCGA transcriptomic data. This allowed us to classify high-grade serous ovarian cancers from the TCGA cohort according to Tothill's, Mateescu's, and Bentink's signatures, and compare them to the Differentiated/Immunoreactive/Mesenchymal/Proliferative (D-I-M-P) classification, initially generated from the TCGA dataset  (Verhaak et al. , 2013). Briefly, we first performed the hierarchical clustering shown in Figure  1A  based on DIMP signature using Euclidean distance and Ward's agglomeration method. To compare this DIMP classification with the others, we next performed similar hierarchical clustering by applying each of the other signatures  (Tothill et al. , 2008; Mateescu et al. , 2011; Bentink et al. , 2012).
Statistical Analysis All statistical analyses were performed in the R environment (Versions 3. 3. 2, 3. 4. 0, and 3. 6. 1) 5. Fisher's exact test was used to determine any association between classes of ovarian cancers and clinical parameters. Overall survival (OS) and disease-free survival (DFS) were investigated using the Cox proportional hazards model and Kaplan-Meier curves through the R packages, survival and survminer. To identify differences between survival curves, p-values were assessed by the logrank test. P-values ≤ 0. 05 were considered to be statistically significant. To take into account multiple testing, p-values were adjusted using the Benjamini-Hochberg procedure using pairwise_survdiff function from R package Survminer. (Tothill et al. , 2008; Mateescu et al. , 2011; Bentink et al.
FUNDING The results presented here are, in part, based upon data generated by the  TCGA Research Network. YK was supported by funding from the  Foundation pour la Recherche Medicale (FRM ,  ING20130526797 ), the  SiRIC-Curie program  ( INCa-DGOS-4654 ), and CB by the  Institut National de la Santé et de la Recherche Médicale  ( Inserm, Poste d'aide à la Recherche Translationnelle en Cancérologie ). The laboratory has also received grants from  Inserm ,  Institut Curie , and the  Ligue Nationale Contre le Cancer (Labelisation) , the  Institut National du Cancer  ( INCa-DGOS-9963 ), the  Foundation ARC  ( PJA 20151203364 ), and the  ICGex  ( ANR-10-EQPX-03 ). We are very grateful to our funders for providing support over the years.
DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. This data can be found here: GSE26193 and GSE9899, Clinical characteristics of the 557 patients included in the TCGA cohort, as well as transcriptomic data generated using Affymetrix Human Genome U133A arrays, have been previously described in Cancer  Genome and Atlas Research (2011)  and can be downloaded from the NIH Genomic Data Commons (GDC) data portal (https: //gdc-portal. nci. nih. gov).
Introduction Published in 2018, the 5C scale is psychometrically validated to assess five psychological antecedents of vaccination (confidence, complacency, constraints, calculation and collective responsibility). The original version offers a validated English and German scale to assess these determinants with a short 5-item scale (1 item per antecedent) and a long 15-item scale (3 items per antecedent). This sample study protocol provides a step-by-step guidance for the process of adapting the 5C scale to another country, language or cultural context. Data obtained from the 5C scale can support developing, implementing and evaluating an intervention and monitoring of general vaccine acceptance and demand. Methods and analysis Phase 1 comprises the adaptation of the 5C scale including the translation and back translation of the antecedents, an expert evaluation of the antecedents and the identification of new antecedents as well as a pretest.
Phase 2 involves the validation of the translated and potentially expanded scale including the assessment of reliability, construct and concurrent validity of all items of the scale. Code for data analysis is provided. Ethics and dissemination The University of Erfurt's institutional review board provided ethical clearance (EV-201900416. 2). The authors suggest and encourage publicly sharing all data obtained from the translated 5C scale (eg, on publication). The materials and the code for data analysis to support the process described in this protocol are available in https:// osf. io/ 2agxe/. Sharing data on vaccine acceptance and demand is in the public and the scientific interest and will facilitate gaining a global overview of its current state and development over time. The authors of the original 5C scale are currently working on an online platform to facilitate publishing the data and to visualise the psychological antecedents across different countries.
Completeness of vaccination status 2 31-33 Number of doses received relative to the total number of recommended doses. A copy of the vaccination card or the provider-recorded data can be retrieved to obtain objective data. Timeliness of vaccination  [34] [35] [36]  Age in days (and reported as whole months) when a child being administered each vaccine dose is calculated by subtracting his/her date of birth from the vaccination date. This age in days is compared with the recommended age ranges based on the official vaccination card. The measurement details are provided in their respective references. survey or CATI); alternatively, several versions with different orders of the items should be created before printing (paper-and-pencil version for a face-to-face survey).
Acknowledgements The authors thank  Laura Goldhahn ,  Aaron Scherer  and the participants of the first workshop in Moscow,  Russian Federation , for their valuable inputs and feedback. The study protocol will be a basis for adapting the 5C antecedents of vaccination to the  Russian Federation (NB, NK, KBH, SD, CB) ,  Denmark (RB, IZ, CB), France (AG-B, EB-N) ,  Canada (ÈD, AG) ,  Poland  (PN, JK),  South Africa (CSW)  and  Finland (JS).
Glacier and climatic data In this study, we used twelve aerial photographs of the n°8 glacier on the Illimani (Fig. 1b ) during the dry seasons  of 1956, 1963, 1975, 1983, and 2009  (available from the Bolivian Servicio Nacional de Aerofotogrametria  [SNA] ). We chose this glacier because it showed the best representation in the oldest available air photographs for this area. It is also a relatively large glacier (1. 95 km 2 in 2009) in Bolivia, and most of its accumulation area is above 5000 m asl. The photographs were digitized with a resolution of 14 µm using a photogrammetric scanner. The internal and external orientation was performed through a digital photogrammetric workstation equipped with a Planar 3D stereoscopic visualization https://doi. org/10. 5194/cp-2020-129 Preprint. Discussion started: 15 October 2020 c Author(s) 2020.
gob. bo/sismet). We used the Extended Reconstructed Sea Surface Temperature (ERSST, version 5,  Huang et al. , 2017)  for the tropical Pacific (Niño 4 region, 5°N-5°S, 160°E-150°W) and tropical North Atlantic (TNA, 20°N-5°N, 60°W-30°W). We also employed the reanalysis dataset from the European Center for Medium Range Weather Forecasts (ECMWF, ERA5). All of the data were obtained from the KMNI Climate Explorer (http://climexp. knmi. nl/getindices) at annual resolution. 3 Results and discussion.
Acknowledgements. The 1999 drilling campaign at  Nevado Illimani  was organized and covered by a joint  French (IGE and IRD)  and  Swiss (PSI  and  University of Bern ) project. The drilling campaign in 2017 was organized in the frame of the  ICE MEMORY project  (www. icememory. org)supported by  IRD ,  CNRS ,  UGA Foundation and local Bolivian institutions  ( Universidad Mayor San Andres -La Paz ). Drilling equipment was provided by  IGE  and  CNRS/INSU/C2FN   CLIMCOR  project ( ANR-11-EQPX-0009-CLIMCOR ). We thanks all participants taking part of the field campaigns. This study is part of the investigations of the  Brazilian National Institute of Science and Technology of the Cryosphere (Brazilian National Council for Scientific and Technological Development -CNPq  Process  465680/2014 ).
Pathway Enrichment Analyses We used the ExomeChip-Data-driven Expression-Prioritized Integration for Complex Traits (EC-DEPICT)  (18, 19)  gene set enrichment analysis method to evaluate nonsynonymous index variants (strongest nonsynonymous variant within 61 Mb boundary) with P , 5 3 10 24 for association with either 1) leptin unadjusted for BMI or 2) leptin adjusted for BMI. EC-DEPICT's primary innovation is the use of "reconstituted" gene sets, which consist of gene sets downloaded from several databases that have been extended based on publicly available large-scale coexpression data  (18). Two analyses were performed: 1) all coding variants (N 5 93 loci for leptin unadjusted for BMI and N 5 91 loci for leptin adjusted for BMI) and 2) coding variants with MAF ,5% only (N 5 77 loci for leptin unadjusted for BMI and N 5 65 loci for leptin adjusted for BMI).
Impact of Val94Met Variant in LEP on Leptin Protein Stability We used UCSF Chimera 1. 13. 1 to model the threedimensional (3D) protein structure and valine-to-methionine substitution in the leptin protein  (28). The Rotamers tool and the Dunbrack Rotamer Library were used to view and evaluate amino acid sidechain rotamers. The displayed orientation of methionine was chosen based on the clashes and contacts observed in the protein and hydrogen bonds  (29). To predict protein stability, we used SDM  (30) , iStable  (31) , CUPSAT  (32) , and I-Mutant 2. 0  (33). All analyses applied the 3D structure for leptin (identifier 1AX8) from the RSCB Protein Data Bank as the reference data set.
Acknowledgments. The authors thank all investigators, study members, and study participants for their contributions to the participating studies. The acknowledgments for all participating studies are provided in the Supplementary Material. This research has been conducted using the UK Biobank resource and carried out under UK Biobank project 9072. Details on patient and public involvement in the UK Biobank are available online at www. ukbiobank. ac. uk/ about-biobank-uk and www. ukbiobank. ac. uk/wp-content/uploads/2011/07/ Summary-EGF-consultation. pdf?phpMyAdmin5trmKQlYdjjnQIgJ%2CfAzikMhEnx6. The ongoing research by V. S. in collaboration with Bayer Ltd. is unrelated to the current study.
and Duality of Interest. The funding information for all participating studies are provided in the Supplementary Material. D. M. -K. is a part-time clinical research consultant for Metabolon, Inc. M. A. N. 's participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, National Institutes of Health. V. S. has served on advisory boards for Novo Nordisk and Sanofi and received honoraria from these companies. V. S. also has ongoing research collaboration with Bayer Ltd. B. M. P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. J. R. K. reports stock ownership in Bristol-Myers Squibb, Johnson & Johnson, Merck, and Pfizer. R. S. F. is currently an employee of Vertex Pharmaceuticals Incorporated, though all work for this article was done prior to Vertex affiliation.
Funding and Duality of Interest. The funding information for all participating studies are provided in the Supplementary Material. D. M. -K. is a part-time clinical research consultant for Metabolon, Inc. M. A. N. 's participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, National Institutes of Health. V. S. has served on advisory boards for Novo Nordisk and Sanofi and received honoraria from these companies. V. S. also has ongoing research collaboration with Bayer Ltd. B. M. P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. J. R. K. reports stock ownership in Bristol-Myers Squibb, Johnson & Johnson, Merck, and Pfizer. R. S. F. is currently an employee of Vertex Pharmaceuticals Incorporated, though all work for this article was done prior to Vertex affiliation.