dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-seq and data analysis. RNA-seq of nuclear extracts from Ctrl and IPF primary fibroblasts was performed by Single End sequencing using Nextseq500 (Illumina) sequencer with V2 chemistry. RiboGone™ -Mammalian (Clontech) was used to deplete ribosomal RNA from the samples. SMARTer® Universal Low Input RNA Kit for Sequencing (Clontech) was used for pre-amplification before library preparation. Library preparation was performed using the Low Input Libray Prep Kit (Clontech) following the manufacturer's instructions. Raw reads were visualized by FastQC  59  to determine the quality of the sequencing. Trimming was performed using trimmomatic  60  with the following parameters LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:40 CROP:45 HEADCROP:5. High quality reads were mapped to the NONCODE V4. 0 (http://www.
html) with the parameters (analyzeRepeats. pl database. gtf hg19 -count genes -strand both and -rpkm; reads per kilobase per millions mapped). The NONCODE_V. 4. gtf file was used to quantify ncRNAs. KNIME 2. 9. 1 platform (Konstanz, Germany) was used to filter enriched ncRNAs and coding RNAs in the IPF compared with the Ctrl, by using the column filter, numeric binner, and row filter nodes. UCSC genome browser was used to visualize Ctrl and IPF nuclear transcripts. Two scales were used for ncRNA and mRNA visualization. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Roswitha Bender ,  Uta Eule , and  Natascha Wilker  for technical support;  Dr. Yi Qiu ,  College of Medicine, University of Florida , for acetyl-mimic HDAC1-Q and non-acetyl-mimic HDAC1-R constructs;  Dr. Moisés Selman ,  Dr. Annie Pardo , and  Dr. Johannes Graumann  for helpful discussions. Guillermo Barreto  was funded by the  "LOEWE-Initiative der Landesförderung" (Wiesbaden, Germany)  ( III L 4-518/ 15. 004 2009 ); the  "Deutsche Forschungsgemeinschaft" (DFG, Bonn, Germany)  ( BA 4036/4-1 ), and the  Excellence Cluster Cardio Pulmonary System (ECCPS, Giessen, Germany ). K. R. received a doctoral fellowship from  CONACyT-DAAD  ( PKZ91549687 ). T. B. was supported by the  DFG  ( SFB 1213 TP A02  and  KFO 309 TP 08 ,  Z1 ,  Z2 ). This work was done according to the program of competitive growth of the  Kazan Federal University  and the  Russian Government.
Data availability Sequencing data of nuclear RNA in Ctrl and IPF patients have been deposited in NCBI's Gene Expression Omnibus  63  and is accessible through GEO Series with accession number GSE116086. RNA-seq data from total lung homogenates was retrieved through the GEO Series accession number GSE52463. The source data for all the plots and uncropped pictures of all the blots presented in the main and Supplementary Figures are provided as a Source Data file. Please direct any request for further information or reagents to Lead Contact Guillermo Barreto (guillermo. barreto@mpi-bn. mpg. de), LOEWE Research Group Lung Cancer Epigenetic, Max-Planck-Institute for Heart and Lung Research, Parkstrasse 1, 61231 Bad Nauheim, Germany.
Previous studies have documented that the snow-monsoon relationship shows secular changes  (28, 31, 32) , but the mechanisms through which the altered snow cover affects the snow-monsoon relationship have not been identified. Here, we aim to unravel the causes of an evolving relationship between the Eurasian spring snow cover and the subsequent Indian summer monsoon rainfall at the interannual time scale by synthesizing long-term satellite snow cover data, instrumental rainfall records, and reanalysis datasets.
Third, the change in the snow-monsoon relationship was also found when the Climate Research Unit (CRU) precipitation dataset was adopted to represent the Indian summer monsoon rainfall intensity (Fig. 2, C  and D ). To obtain further insight into the shift in the snow-monsoon relationship, we performed linear regression analyses to determine how the spatial pattern of Indian summer monsoon rainfall anomalies covaries with CESCF for the two periods. These analyses show that positive anomalies of spring snow cover over central Eurasia precede a significant decline in summer rainfall especially over northwestern and central India (also known as the core Indian monsoon region) during the period 1967-1990 (Fig. 2C ), while it precedes an increase in summer rainfall after 1990 (Fig. 2D ). Although the shift in R CESCF-AISMR was originally found when using the time series of area-averaged precipitation over India, the shift in summer monsoon rainfall anomalies in response to CESCF between the two study periods is also observed throughout most of the Indian subcontinent, suggesting that the shift could be related to large-scale changes in monsoon circulation.
3B ). These results were found to be similar when using the JRA-55 dataset (fig. S6, A  and B ). The potential mechanism responsible for the shift in snow-related circulations and the resultant snow-air temperature decoupling could be related to the change in the lagged snow-hydrological effect. Numerous observational and modeling studies indicated that the snow-hydrological effect has as a crucial role in determining the land-atmosphere coupling through its effects on the soil moisture  (4, 5, 48). The soil moisture anomalies formed from spring snowmelt may take weeks or months to dissipate, known as the "memory," providing a lagged impact on the summer climate both locally and remotely through changing the surface energy budget  (5, 23). However, as for global warming, changes in snow mass and snow phenology (29) could alter the soil moisture memory, which may disrupt the delayed effect of spring snow on summer climate.
Observational and reanalysis datasets We used AISMR data from the Indian Institute of Tropical Meteorology (IITM) to represent the variability of the Indian summer monsoon rainfall (June to September). This dataset was derived from the area-weighted average of 306 rain-gauge stations that are almost uniformly distributed throughout India  (50). To confirm the reliability of the IITM data, we also used rainfall datasets from the Climatic Research Unit Time Series (0. 5° × 0. 5°;CRU TS v4. 00)  (51)  and the Global Precipitation Climatology Centre (0. 5° × 0. 5°; GPCC v7)  (52). The snow cover data were from the Northern Hemisphere EASE-Grid 2. 0 weekly snow cover and sea ice extent version 4, available at the National Snow and Ice Data Center (NSIDC). The data were stored as binary files in arrays of 720 columns by 720 rows and had a spatial resolution of 25 km.
To make the comparison with other data easier, we converted the EASE-Grid 2. 0 data to a 1° × 1° longitude-latitude grid. The snow cover data were in binary form (snow or no snow), and so we used the SCF, rather than snow cover extent, to represent the variability of snow cover. The monthly SCF was calculated by summing the number of weeks in that month for which snow was present for a pixel and by expressing this number as a fraction of the total number of weeks in that month  (16). In this study, we used only the spring (March, April, and May) snow cover to investigate the snow-monsoon relationship. There are two reasons for this restriction. First, the snow cover has little interannual variability during the winter. Second, the snow-albedo effect was found to be high in the spring because of the rapid retreat of snow cover.
4 region to represent the ENSO, and the average monthly NAO index for January, February, and March to represent the cold season NAO index. Both the ENSO index and the NAO index are also available at the NOAA CPC. We used summer (June to September) Dipole Mode Index (DMI) from the NOAA Earth System Research Laboratory to represent the IOD. The IOD was considered since it exerts an important impact on both the Indian summer monsoon rainfall  (55)  and Eurasian snow cover  (56). The snow cover and precipitation data from 30 earth system models participating in the CMIP5 were also used in our study (table  S1 ). Because of the uncertainties in the climate forcing data for CMIP5 after 2005 (57), we only used the historical simulations data. The CMIP5 models had various spatial resolutions, and so, for convenience, we converted all the CMIP5 model output to a 1° × 1° longitude-latitude grid.
Acknowledgments Funding: This study was supported by the  Strategic Priority Research Program (A)  of the  Chinese Academy of Sciences  ( XDA20100300  and  XDA20050101 ), the  Key Research and Development Programs for Global Change and Adaptation  ( 2017YFA0603604 ), the  International Partnership Program of Chinese Academy of Sciences  ( 131C11KYSB20160061 ), the 13th Five-year  Informatization Plan of Chinese Academy of Sciences  (grant  XXH13505-06 ), and the  Thousand Youth Talents Program in China. We also acknowledge the support of  Kathmandu Center for Research and Education, Chinese Academy of Sciences-Tribhuvan University. Author contributions: T. W. designed the research; T. Z. performed the data analysis; T. W. and T. Z. drafted the paper; all authors contributed to the interpretation of the results and to the text.
Study population and data collection EPIC is a multicenter prospective cohort study designed to investigate the associations between diet, lifestyle, genetic and environmental factors, and the incidence of cancers and other chronic diseases. Over 520,000 participants, aged 25-70 years, were recruited between 1992 and 2000 in 10 European countries (Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the UK). The French cohort included members of a health insurance plan for school and university employees. Participants at the Spanish and Italian centers (except Florence) included blood donors, members of several health insurance programs, employees of several enterprises, civil servants, and people from the general population. In Utrecht and Florence, women mainly participating in mammographic screening programs were recruited for the study.
In Oxford, most of the cohort consisted of "health-conscious" persons from England, Wales, Scotland, and Northern Ireland, many of whom were vegetarians. Participants were recruited from the general population in the other centers. The cohorts of France, Norway, Utrecht, and Naples included only women. All study participants provided written informed consent. Ethical approval for this study was obtained from the International Agency for Research on Cancer review board and local participating centers. Detailed information on the study design, methods, and rationale of the EPIC cohort has been previously reported  [13, 14]. Upon recruitment, participants provided medical, dietary, and lifestyle data during interviews and via questionnaires, including information on alcohol use, smoking status, physical activity, education, reproductive history, breastfeeding, hormone use, and previous illnesses.
Diet over the previous 12 months was assessed using validated country-specific questionnaires  [13] , and individual nutrient intakes were derived from foods included in the dietary questionnaires through the standardized EPIC Nutrient Database  [15]. To correct for systematic under-or overestimation of dietary intake between the study centers, dietary values across centers were scaled using an additive calibration model  [16]. All dietary variables used in this study were calculated by using additive calibration. For the present study, women with prevalent cancers (except non-melanoma skin cancer) at study enrollment (n = 20,477), women who were lost to follow-up (n = 2557), pregnant women at study enrollment (n = 556), and women with missing data on diet, weight or height, physical activity, or breastfeeding were excluded from the analyses (n = 78,245).
Statistical analyses Categorical variables were presented by percentages and continuous variables by arithmetic means and standard deviations (SD) for descriptive purposes. Missing covariate values were sporadic (0. 1-6. 8%, depending on the covariate) and occurred in all participating study centers. We used multiple imputation with a fully conditional specification as provided by PROC MI in SAS  [21] , and missing values of the covariates (highest level of attained education, smoking status, presence of chronic diseases at recruitment, age at menarche, age at first full-term pregnancy, ever use of oral contraceptive pills, and ever use of menopausal hormone therapy) were imputed on the basis of the distribution of age at recruitment, total dietary energy consumption, menopausal status, and all the individual components of the WCRF/AICR lifestyle score and BCIS diagnosis.
≥ 50 years), and menopausal status, we conducted analyses additionally stratified by these factors. Moreover, given the differential associations observed for some risk factors, especially obesity, by menopausal status and use of menopausal hormone therapy  [6] , we looked at the association of the individual components of the WCRF/AICR lifestyle score and BCIS risk, stratifying by menopausal status and menopausal hormone therapy use. Sensitivity analyses were conducted, excluding participants with less than 1 year of follow-up (n = 1794). Analyses were conducted using SAS version 9. 4 (SAS Institute, Cary, NC). All statistical tests were two-sided, and p values < 0. 05 were considered statistically significant.
To this end, our overall analyses were limited by the fact that screening participation was not uniformly assessed in most EPIC cohorts. The lack of a statistically significant association between the individual components of the WCRF/AICR lifestyle score and BCIS risk that we observed in the present study is overall consistent with the findings from studies on invasive breast cancer risk using similar scores  [18, [25] [26] [27] , even though for some individual components (e. g. , alcohol consumption) associations have been detected  [4, 24, 27]. The inverse association between BMI and BCIS risk in premenopausal women in the present study is consistent with recently published data from a pooling project, in which we took part with EPIC  [7]. By contrast, unlike in the NIH-AARP Diet and Health Study  [6] , we did not observe a significant positive association between BMI and postmenopausal BCIS among never users of menopausal hormone therapy, but our results are in accordance with those of the Million Women Study  [8].
Funding The work reported in this manuscript was undertaken while  Ms. Karavasiloglou  was funded under grant  KFS-4114-02-2017. The coordination of the  European Prospective Investigation into Cancer and Nutrition (EPIC)  is supported financially by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer. The national cohorts are supported by the  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (France) ;  German Cancer Aid ,  German Cancer Research Center  ( German Cancer Research Center) ,  Federal Ministry of Education and Research (Federal Ministry of Education and Research) ,  Deutsche Krebshilfe, Deutsches Krebsforschungszentrum , and  Federal Ministry of Education and Research (Germany) ;  Hellenic Health Foundation (Greece) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (Ministry of Health, Welfare and Sport) ,  Netherlands Cancer Registry (Netherlands Cancer Registry), LK Research Funds, Dutch Prevention Funds, Dutch Zorg Onderzoek Nederland ,  World Cancer Research Fund, Statistics Netherlands  (the  Netherlands) ;  Nordic Centre of Excellence Programme on Food, Nutrition and Health (Norway) ;  Health Research Fund (FIS) -Instituto de Salud Carlos III (ISCIII) , the  Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra , and the  Catalan Institute of Oncology -ICO ;  Swedish Cancer Society, Swedish Research Council , and  County Councils of Skåne and Västerbotten (Sweden) ;  Cancer Research UK  ( 14136  to  EPIC-Norfolk ,  C570/A16491  and  C8221/A19170  to  EPIC-Oxford) , and the  Medical Research Council  ( 1000143  to  EPIC-Norfolk ,  MR/M012190/1  to  EPIC-Oxford) (UK).
g. using datasets from  Rignot et al. , 2013b; Depoorter et al. , 2013). The oversimplicity of the depth dependence not only makes the initial pattern very different from the observed pattern, but also leads to a significant overestimation of the grounding-line retreat compared to ocean-ice-sheet coupled models  (Seroussi et al. , 2017; Jordan et al. , 2018; De Rydt and Gudmundsson, 2016). The second approach parameterises the melt rates as a function of ocean temperature and salinity profiles. The simplest parameterisations are mere functions of the difference between the temperature and the melting-freezing point at the ice-ocean boundary, the thermal forcing, using a linear (e. g. Beckmann and Goosse, 2003; Favier et al. , 2016)  or a quadratic dependency (e. g. DeConto and Pollard, 2016).
Acknowledgements. The simulations were performed using HPC resources from  GENCI-CINES  (Grant  2018 , project  A0040106066 ). We thank two anonymous reviewers for their valuable and constructive comments, which helped to improve the clarity of the paper. Financial support. This work was funded by the  French National Research Agency (ANR)  through the  TROIS-AS  ( ANR-15-CE01-0005-01 ) and the  ANR-15-IDEX-02  projects. Review statement. This paper was edited by  Alexander Robel  and reviewed by two anonymous referees.
Code availability. We used Elmer/Ice Version 8. 3 at revision 6be9699, which is available at https://github. com/ ElmerCSC/elmerfem (last access: 18 April 2019), and NEMO-3. 6 at revision 6402. The experimental protocol is composed of the coupling framework version 1. 1, available at https://doi. org/10. 5281/zenodo. 2562838 (Jourdain and Favier, 2019) ; the NEMO set-up version Feb-2019, available at https://doi. org/10. 5281/zenodo. 2562731 (Jourdain,  2019a); and the Elmer/Ice set-up version 1. 2, available at https://doi. org/10. 5281/zenodo. 2563156 (Jourdain, 2019b).
Despite its normal morphology, Runx1 KO newborn ovaries exhibited aberrant transcriptomic profile: expression of 317 genes was altered significantly compared to the control (Fig. 4c ; Dataset S1). The transcriptomic changes of Runx1 KO ovary were reminiscent of the ovary lacking Foxl2, a conserved gene involved in ovarian differentiation/maintenance in vertebrates. In the mouse, loss of Foxl2 results in normal ovarian morphogenesis at birth despite aberrant ovarian transcriptome  5. When comparing the genes changed significantly in the Runx1 KO (317 genes) with those affected by the loss of Foxl2 (749 genes) in newborn ovary, we found that 41% of the genes differentially expressed in Runx1 KO (129/317) were also misregulated in the absence of Foxl2 (Fig. 4c ). The large majority of these 129 genes (93%; 120 genes) were similarly changed by the loss of either Runx1 or Foxl2: 69% were downregulated in both KOs (89 genes) and 24% were upregulated in both KOs (31 genes; Dataset S1).
6 ). Our results demonstrate that a combined loss of Runx1 and Foxl2 induces partial masculinization of the supporting cells during fetal development of the ovary. To further characterize the impacts of the combined loss of Runx1 and Foxl2 on ovarian differentiation, we compared the transcriptome of Runx1/Foxl2 DKO newborn ovaries with the transcriptomes of control, Runx1, or Foxl2 single KO ovaries (Fig. 7  and Dataset S2). Between Runx1/Foxl2 DKO and control ovaries, 918 genes were differentially expressed, with 499 genes downregulated and 419 genes upregulated in the DKO ovary (fold-change >1. 5; p<0. 05; Dataset S3). The heat-map for the 918 differentially expressed genes in Runx1/Foxl2 DKO ovaries demonstrated allele-specific impacts with a mild and often non-significant effect in the absence of Runx1, an intermediate/strong effect in the absence of Foxl2, and a strongest effect in the absence of both Runx1 and Foxl2 (Fig.
7a ). Gene ontology (GO) analysis revealed that the downregulated genes were associated with "ovarian follicle development" and "female gonad development" whereas "male sex determination" was the most significantly enriched process for the upregulated genes (Fig. S4 ). To determine the contribution of Runx1 and Foxl2 to the transcriptomic changes, the genes significantly downregulated (Fig. 7b, d  & e ; Dataset S4) or upregulated (Fig. 7c, f,  & g ; Dataset S4) were compared among Runx1/Foxl2 single and double knockouts. Conforming to the hierarchical clustering (Fig. 7a ), Foxl2 was the main contributor to the transcriptional changes observed in Runx1/Foxl2 DKO: 61% of the genes downregulated in DKO were also downregulated in Foxl2 KO (304/499 genes in the overlapping region between purple and red circle in Fig.
To determine how the loss of Runx1 contributed to the early masculinization of Runx1/Foxl2 DKO ovaries, we identified the potential RUNX1-regulated genes by comparing the transcriptomes of Runx1/Foxl2 DKO and Foxl2 single KO ovaries. A total of 218 genes were differentially expressed between Runx1/Foxl2 DKO and Foxl2 KO ovaries, with 114 genes significantly upregulated, and 104 genes significantly downregulated in the DKO (fold-change >1. 5; p<0. 05; Fig. 7h  and Dataset S5). Expression of most of these genes was already altered in Foxl2 single KO ovaries; however, the additional loss of Runx1 exacerbated their mis-expression. For instance, the pro-testis gene Dmrt1 and Nr5a1 were significantly upregulated whereas the granulosa-cell enriched transcripts Fst and Ryr2 were further downregulated at birth (Fig.
8a ). Among them, RUNX DNA motif was the second most significantly co-enriched motif. The other motifs were for CTCF, a factor involved in transcriptional regulation, enhancer insulation and chromatin architecture  42  , and for the DNA motif recognized by multiple members of the nuclear receptor family including liver receptor homolog-1 (LRH1 encoded by Nr5a2) and SF1 (encoded by Nr5a1), a known co-factor of FOXL2  43, 44. DNA motifs for TEAD transcription factors of the Hippo pathway, ETS, NFYA and GATA4 were also significantly enriched. The enrichment of RUNX motif with FOXL2 binding motif suggests that RUNX1, the only RUNX enriched in pre-granulosa cells, could bind similar genomic regions to FOXL2 in the fetal ovary. To confirm this hypothesis, we performed ChIP-seq for RUNX1 in E14. 5 ovaries (Dataset S6), the same stage the FOXL2 ChIP-seq data were obtained  41.
7 ). To identify potential direct target genes of RUNX1 or/and FOXL2, we focused on the 918 genes differentially expressed in Runx1/Foxl2 DKO ovaries, and determined which genes were nearest to RUNX1 or/and FOXL2 binding peaks (Fig. 9a  and Dataset S7). More than 50% of these genes (492/918; Fig. 9a ) were the closest gene to RUNX1 or/and FOXL2 peak. Some of these genes were nearest to only FOXL2 peaks (116 genes in Fig. 9a ). For example, Pla2r1, a transcript enriched in pre-granulosa cells  39  and similarly downregulated in both Foxl2 KO and Runx1/Foxl2 DKO fetal ovaries (Fig. 9b ), contained two FOXL2-specific peaks, one in the promoter and one in the first intron. On the other hand, 102 genes (Fig. 9a ) had RUNX1 specific peaks near their genomic locations. For instance, Ryr2, another transcript enriched in pre-granulosa cells  39  , was strongly downregulated in both Runx1 KO and Runx1/Foxl2 DKO fetal and newborn ovaries (Fig.