dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the family members for their participation in this study. We thank  Emmanuel Thévenot ,  Nicolas Vitale ,  Kerstin Kutsche , and  Célio Pouponnot  for providing us with PAK3-PIM, β-PIX, α-Pix, and replicative lentiviral plasmids, respectively. pHAGE TRE dCas9-VP64 was a gift from  Rene Maehr & Scot Wolfe  (Addgene plasmid #  50916 ). We also want to thank the  Fondation Jérôme Lejeune  for supporting us (JVB) financially and the  Fondation pour la Recherche Médicale  for supporting JC.
Classical Risk Assessment. Basic PRM principles for risk assessment are the use of qualitative or quantitative methods, depending on the context, the data availability, the client's requirement, and the available time. Classical parameters are probability (or likelihood) and impact (or gravity). In the tramway project, existing scales were 10-level discrete scales, for probability and impact. Knowing that 12 additional risks had been considered, the complete 56-risk list with initial qualitative assessment is given in Table  3. Classically, this step finishes here and is directly followed by analysis. Here, a complementary CST-based assessment is made, in order to assess risk interdependencies.
These uncertainties in the forcing fields can have a major role in the fidelity of OGCM simulations on intra-seasonal, seasonal, interannual and longer time-scales  (McWilliams, 1996). Hence, accurate near-surface atmospheric fields are essential for realistic simulations in a forced ocean model. Here, we offer a brief overview of how the CORE-II atmospheric state compares to observational based measures. However, a full assessment of these impacts requires comparisons to simulations run with other atmospheric products such as Japanese 55year atmospheric reanalysis (JRA-55) based surface dataset for driving ocean-sea-ice models (JRA55-do:  Tsujino et al. , 2018)  and DRAKKAR  (Brodeau et al. , 2010)  forcing. The analysis presented in this paper provides a necessary starting point for that assessment.
Section 2 describes the CORE-II simulations and the main goal of the study. Section 3 describes models and observational datasets. Section 4 contains main results and discussions organized as: (i) the evaluation of CORE-II wind speed with in-situ observation (Section 4. 1); (ii) the time mean features of SST and its seasonal cycle over the key regions of the Indian Ocean, including the AS, BoB, Eastern Equatorial Indian Ocean (EEIO) and Thermocline Ridge (TR) region (Section 4. 2); (iii) surface salinity and BL (Section 4. 3); (iv) subsurface features of temperature and salinity over AS, south eastern AS (SEAS), BoB, EEIO ( Section 4. 4); (v) surface and subsurface equatorial currents in CORE-II models and observations (Section 4. 5); (vi) Indian Ocean meridional overturning circulation (Section 4.
Models We provide a summary of 16 global ocean/sea-ice model configurations in Table  1 , with further details provided in the CORE-II papers of  Danabasoglu et al. (2014)  and  Farneti et al. (2015). We focus on the 5th CORE-II forcing cycle and use model years corresponding to years of available observations for 1982-2007. The surface fluxes of heat, freshwater/salt, and momentum are determined using the CORE-II inter-annual forcing (IAF) atmospheric datasets, the model's prognostic SST and surface currents, and the bulk formulae described in  Large and Yeager (2004, 2009). There is no restoring term applied to SST. SSS restoring is used to prevent unbounded salinity trends in all the model simulations used for this study. The NEMO-based models convert SSS restoring to a freshwater flux.
Observations and reanalysis data used for the model evaluation We make use of the following observational and reanalysis data to evaluate the CORE-II simulations. • Monthly one degree gridded optimum interpolation (OI) SST product  (Reynolds et al. , 2002)  for 1982-2007 is taken from the National Oceanic and Atmospheric Administration (NOAA). • The subsurface temperature and salinity are taken from the World Ocean Atlas (WOA09) climatology  (Locarnini et al. , 2010; Antonov et al. , 2010; Boyer et al. , 2009). We also use WOA09   Bonjean and Lagerloef, 2002). The OSCAR product is available at 0. 33 • spatial resolution and 5 day averaged. For this study, we computed monthly climatologies from the 5day averaged data for the period 1993-2007. We also use ship drift climatology from  Cutler and Swallow (1984)  as well as the near-surface current (0-15 m) climatology (version 2.
Datasets used for the wind speed evaluation We used buoy observed wind speed data at 3 m height provided by National Institute of Ocean Technology (NIOT) under National Data Buoy Programme (NDBP) of the Ministry of Earth Sciences, Government of India  (Premkumar et al. , 2000) , to compare the most widely used forcing fields from CORE-II, JRA55-do  (Tsujino et al. , 2018)  Table  1  List of models and their configurations used for the CORE-II inter-annual simulations following  Danabasoglu et al. (2014). Note that z* represents the stretched geopotential vertical coordinate that absorbs motion of the free surface (see  Adcroft and Campin, 2004 and Griffies (2009) GFDL-CM3 2 • × 2. 5 • L48 MOM4p1 Geophysical Fluid Dynamics Laboratory, USA nominal 1 • GFDL-ESM2M 2 • × 2. 5 • L24 nominal 1 • GFDL-ESM2G 2 • × 2.
5 • L24 GOLD nominal 1 • MRI-CGCM3 T159L48 MRI. COM3 Meteorological Research Institute, Japan nominal 1 • NorESM1-M 1. 875 • × 2. 5 • L26 NorESM-Ocean Norwegian Climate Center, Norway nominal 1 • and DRAKKAR  (Brodeau et al. , 2010). The buoy location is at 94 • E, 10. 5 • N in the BoB and shown in Fig. 1a  as red dot. Daily wind speed from buoy is used for the comparison. The 3-hourly 55-km horizontal resolution JRA55-do, and the 6-hourly 75-km resolution DRAKKAR wind speed were used for the comparison. However, daily averages were computed from these 3-hourly and 6-hourly data to compare with daily buoy wind speed. These datasets have been corrected relative to reanalysis product ERA-Interim  (Dee et al. , 2011)  for DRAKKAR and Japanese 55-year Reanalysis  (Kobayashi et al. , 2015)  for JRA55-do products, analogous to how CORE-II has been corrected.
We also used Cross-Calibrated Multi-Platform (CCMP; available at http://rda. ucar. edu/datasets/ds745. 1/#!access) Surface Wind Vector Analyses  (Atlas et al. , 2009)  for the spatial wind speed comparison. Here we provide a comparison of CORE-II with DRAKKAR and JRA55-do to understand the reliability of these products for the Indian Ocean simulation. There are notable differences in spatial resolution (CORE-II: 2 • and JRA55do: 0. 5 • ) as well as differences in temporal resolutions (CORE-II and DRAKKAR : 6-hourly and JRA55-do: 3-hourly). Yet for the comparison we only considered daily averaged datasets from all products. The corrected CORE-II, JRA55-do and DRAKKAR data are available at 10 m height. For comparison, we interpolate the CORE-II, JRA55-do and DRAKKAR 10 m winds to 3 m using the logarithmic scale given by Stull (2011).
Sea surface temperature In this section, we offer a particularly extensive analysis of the SST given its importance for Indian Ocean climate variability and due to its relatively precise observational measures. For the analysis purpose, we regrided all models and observed SST and NHF dataset uniformly on a 1 • x 1 • grid. data is based on advance very high resolution radiometer (AVHRR) satellite data  (Reynolds et al. , 2002)  which is available from April 1981, hence we used 1982-2007 to compute monthly climatology. Observations (Fig. 5a ) show SST cooler in the west and warmer in the east  (Murtugudde and Busalacchi, 1999; Schott et al. , 2009)  and a tongue of relatively warm SST (>29 • C) in the equatorial Indian Ocean, forming the Indian Ocean warm pool  (Fasullo and Webster, 1999; Rao et al.
Acknowledgments The encouragement and facilities provided by  Dr. S S C Shenoi , Director,  Indian National Centre for Ocean Information Services (IN-COIS) , are gratefully acknowledged. Graphics were generated using the NOAA product Ferret. We thank  Prof. Raghu Murthugude  for useful suggestions that greatly improved the manuscript. We thank  N Kiran Kumar  for his help to generate the schematic diagram in Fig. 1. The fourth author (J. V. D) acknowledges funding from the  Helmholtz-Gemeinschaft  and the  GEOMAR Helmholtz Centre for Ocean Research Kiel  (grant  IV014/GH018 ). NCAR is a major facility sponsored by the  U. S. National Science Foundation  under Cooperative Agreement 1852977. Q. Wang  and  D. Sidorenko  are supported by the  German Helmholtz Climate Initiative REKLIM (Regional Climate Change ).
We thank  Matthew Harrison  and  P S Swathi  for their comments and discussions that have helped this paper. This is INCOIS contribution no. 358  and NCPOR contribution no. J-38/2019-20. We thank all five anonymous reviewers for their constructive comments by which we have improved the manuscript immensely. Fig. 32b. Transport across the equator from MOM, MOM025, KIEL,  KIEL025 , all model ensemble and ORAS4 in July. Units are in m 2 /s. GOLD: Generalized Ocean Layer Dynamics GOOS: Global Ocean Observing System HYCOM: Hybrid Coordinate Ocean Model ICTP: International Centre for Theoretical Physics IOC:Intergovernmental Oceanographic Commission IOD: Indian Ocean Dipole IODZM: Indian Ocean Dipole/Zonal mode IOMOC: Indian Ocean Meridional overturning circulation ISMR: Indian Summer Monsoon Rainfall ITF: Indonesian through flow ITCZ: Inter Tropical Convergence Zone   JRA55 -do: Japanese 55-year atmospheric reanalysis ( JRA-55 ) based surface dataset for driving ocean-sea-ice models ( JRA55-do ) (Tsujino et al.
EDM2-deficient Arabidopsis lines exhibit reduced RPP4-mediated immunity Our RNA-seq data suggested the impact of EDM2 on RPP4 expression to be complex. Besides suppressing proximal polyadenylation in the 3'UTR, EDM2 seems to have additional effects on RPP4 transcripts. Our RNA-seq data revealed a significant reduction of RPP4 exon 1 transcript levels in edm2-2 compared to WT (Figs 2B, 5A and 5B). No significant transcript level difference was observed for exons 2-6 (Fig  5B ). This observation is consistent with the annotation of an alternatively initiated RPP4 transcript (TAIR10, Araport 11) bearing its transcription start site (TSS) in intron 1 slightly upstream from exon 2 (Figs  2B  and 5A ). Although we did not confirm this TSS, the existence of alternative TSSs directing expression of transcripts starting with exon 2 sequences is further supported by TSS-sequencing data from Kindgren et al.
In order to examine the involvement of IBM1 in H3K9me2 regulation at loci targeted by EDM2, we performed H3K9me2 ChIP-seq with the ibm1-4 mutant (SALK_035608C). We identified 6,082 genes and 2,706 TEs with significant changes in H3K9me2 levels in ibm1-4 compared to WT (S7 and S8 Tables). While effects of IBM1 on H3K9me2 at TEs have not been reported before, its effects on this silencing mark in genes are consistent with previously published ChIP-chip data  [44]. In our and previous studies  [44]  the majority of significant changes in ibm1-4 are H3K9 hyper-dimethylation in genic regions. As anticipated, we observed a large overlap between the ibm1-4 and edm2-2 specific H3K9me2 profiles. This set of overlapping genes includes 11 NLRs (S4A and S4E Table ). Unexpectedly, however, we observed an effect of IBM1 on H3K9me2 levels in multiple genes and TEs, which are directly targeted by EDM2, including 18 out of 46 HA-EDM2-associated TEs and genes such as RPP7, and EDM2 itself  (Fig 2A and S7 Fig).
Discussion EDM2 was previously shown to be a chromatin-associated protein controlling expression of the RPP7 immune receptor and the histone H3K9 demethylase IBM1 by promoting the synthesis of full-length transcripts encoding these proteins. EDM2 has also been reported to control silencing states of some TEs, and its physical association with heterochromatic TE/repeat sequences was found to be critical for its function in RPP7/IBM1 regulation. Through regulation of IBM1, EDM2 has also been implicated in global suppression of CHG methylation in genic regions associated with heterochromatin. In this study, we expanded on previous findings by genome-wide H3K9me2 ChIP-seq, RNA-seq and HA-EDM2 ChIP-seq analysis. Our new data show that EDM2 controls genome-wide expression of NLR genes and TEs, as we observed in edm2-2 plants effects on H3K9me2 and/or transcript levels at 59 NLR genes and over 2,000 TEs.
H3K9me2 ChIP-seq and data analysis ChIP-seq libraries were prepared using NEBNext Ultra TM II DNA library prep kit for Illumina (E7645, New England Biolabs) according to the manufacturer's instruction. Libraries were sequenced on the Illumina NextSeq500 generating 75 bp -single-end sequence reads. For each Chip-seq library, raw reads quality was first analyzed using FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/), and any base with a quality score below 25 or N was trimmed using Sickle (https://github. com/najoshi/sickle). Trimmed reads were then mapped to the A. thaliana genome (TAIR 10) using BWA 0. 7. 15-r1140 with mem option default parameters  [70]. See S11 Table for read statistics. Uniquely mapped reads were further filtered for calculating H3K9me2 coverage in transcripts, while unfiltered reads were used for calculating H3K9me2 coverage in transposable elements.
The number of reads mapped to each transcript was determined using BEDTools v2. 25. 0  [71] , and Spearman correlation coefficients were calculated between biological replicates. To compare H3K9me2 level between WT and edm2-2 samples, non-expressed transcripts with coverage value below 1 in all libraries were removed. Transcripts representing differentially methylated regions were determined using DEseq2 in R  [72]  with a P-adjusted value of 0. 05 and a 1. 2 fold change.
bioinformatics. babraham. ac. uk/projects/fastqc/). The first 10 bases and the last base were trimmed. Contaminating adaptor reads, reads that were unpaired, bases below 25 and Ns, and reads shorter than 18 bases are also filtered using Sickle (https://github. com/najoshi/sickle). All trimmed reads were mapped to A. thaliana genome (TAIR 10) using HISAT2 v2. 1. 0 [73]  with a known splice site file, built from Araport annotation file v11, and strand information parameter,-rna-strandness RF. See S11 Table for mapping statistics. Uniquely mapped reads were further filtered for calculating coverage in transcripts, while unfiltered reads were used for calculating coverage in transposable elements. Coverage values for each transcript were calculated using BEDTools with-split, -D, and -S parameters  [71].
Genome browser tracks For genome browser tracks, read coverage per nucleotide is calculated using BEDTools. Coverage values were then normalized per million mapped reads. Genome tracks were displayed in IGV (Integrative Genome Viewer)  [74]. For HA-tagged EDM2 samples, signals from biological replicates were first combined, and background signal measured by input sample analysis was subtracted from edm2-2 and WT samples. To visualize HA-EDM2 associated areas, signals from WT were further subtracted from edm2-2 signals. Genome browser tracks for suvh456 RNA-seq data were obtained from NCBI (GEO: GSE111609)  [75].
Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) inoculation Bacterial growth and inoculation were performed as previously described  [77]. Two-week-old plants were sprayed with Pst DC3000 suspension containing 2 × 10 8 cfu/mL in 10 mM MgCl 2 with 0. 04% silwet L-77. Leaves were harvested at 3 hrs, 2 dpi and 3 dpi. Each leaves sample was weighed, surface sterilized in 70% ethanol for 30 s and washed in sterile distilled water for 30 s before ground in 10 mM MgCl 2. Serial dilutions were plated on King's B plates with appropriate antibiotics. Each data point represents the average of three replicates. Each replicate contains the bacterial titer of average of three individual pots with a density of 20 seedlings.
Acknowledgments We thank  Dr. Jeffery L. Dangl  ( University of North Carolina, USA ) for critical reading of the manuscript and helpful discussions. We also thank  Dr. Judith Bender  ( Brown University, Providence, RI ) for providing the Col-0 suvh456 mutant. We thank the  NSF- supported  SIG-NAL T-DNA Express and NSF-supported Arabidopsis Biological Resource Center  for providing seeds.
The hemispheric shocks are scaled to represent deviations from the world production trend, so that their sum is identical to the yield shock variable ( ) used in Roberts and Schlenker. For example, ℎ = -0. 02 indicates a -2% shock to world production related to the deviation of the average yield in hemisphere ℎ from its trend. Similarly, is the region log yield deviation from trend, calculated using jackknifed residuals. FAOSTAT data are available from 1961 to 2014 but our lagged yield shocks mean that our sample starts in 1962. Estimation results are presented in table A1 and include 2SLS and OLS estimates with different specifications of the time trend. The F-statistics indicates that the first stage is strong. The overidentification test does not reject the validity of the instruments. The first-stage coefficients follow the intuitions from theory.
A range of in situ, satellite and reanalysis products on a common daily 1°×1°latitude/longitude grid were extracted from the Frequent Rainfall Observations on Grids database to help facilitate intercomparison and analysis of precipitation extremes on a global scale. 22 products met the criteria for this analysis, namely that daily data were available over global land areas from 50°S to 50°N since at least 2001. From these daily gridded data, 10 annual indices that represent aspects of extreme precipitation frequency, duration and intensity were calculated. Results were analysed for individual products and also for four cluster types: (i) in situ, (ii) corrected satellite, (iii) uncorrected satellite and (iv) reanalyses. Climatologies based on a common 13-year period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) showed substantial differences between some products.
Therefore, it seems necessary to evaluate each product with respect to the other and to make recommendations for the assessment of global precipitation extremes. However, this type of intercomparison has not previously been easy because of the lack of standardisation across products (e. g. resolution, formats etc) and in how precipitation extremes are defined and analysed by different research groups. Some groups have focussed on a small subset of available products (globally e. g. (Roca et al 2019). This database named Frequent Rainfall Observations on GridS (FROGS) is freely available from ftp://ftp. climserv. ipsl. polytechnique. fr/FROGs/ and contains data from in situ, satellite, blended and reanalysis datasets. Here we assess how these products compare using a standard suite of precipitation indices in order to determine whether these products can be used for longterm assessment of precipitation extremes (over land).
Data and methods We extract data products from FROGS  (Roca et al 2019)  that have global land coverage (or at least cover a minimum land area from 50°S to 50°N). Table  1  shows the 22 products that meet these criteria, with 13 years being the shortest time period covered (GSMAP-gauges-RNLv6. 0) and 67 years being the longest time period covered (REGEN_ALL_v1. 1/ REGEN_LONG_v1. 1). All products share a common overlapping period of 2001-2013. For each product a suite of 10 'ETCCDI' precipitation indices  (Zhang et al 2011)  are calculated (table 2) resulting in one value per year and representing annual measures of frequency, duration and intensity (see also figure 1 of Alexander et al 2019 in this SI is available online at stacks. iop. org/ ERL/15/055002/mmedia). Our aim is to intercompare datasets using some basic statistics to determine if   (Roca et al 2019)  and analysed in this study.
Dataset name Years available References In situ-based (5) conclusions with respect to extreme precipitation climatologies and trends are robust and product independent. If so, this could inform e. g. IPCC as to the reliability of studies using products other than those that are in situ-based for long-term climate assessment. To achieve our goal we first calculate climatologies for each product over the common 13year period (2001-2013) when all products have data (figure  1 ). Then timeseries are intercompared over their full period of record. For this analysis we also group the datasets by product type: (i) in situ data, (ii) satellite products that have been corrected using in situ data, (iii) uncorrected satellite products and (iv) reanalyses (figure  2 ). A coefficient of variation (cov) is calculated for each cluster (that is, the interproduct spread in %) with covs shown for a selection of indices that cover precipitation intensity (SDII), frequency (R10mm) and duration (CDD) (figure  3 ).
Results Climatology Figures 1 and S1-S9 show a 13-year climatology for each of the indices from table 2 for all 22 precipitation products considered. Globally spatial patterns are similar across climatic zones over the 2001-2013 period. However, there are many important caveats. For annual total wet-day precipitation (PRCPTOT), for example, the 'wettest' product is >50% wetter than the 'driest' product (1256. 5 mm (MERRA2) versus 821. 1 mm (CPC_v1. 0) globally averaged over the 13-year period). Overall extreme precipitation indices in reanalyses tend to be less spatially consistent than the in situ and satellite datasets with, for example, tropical Africa being very wet in some reanalyses and much drier in others (figures 1, S1-S9). This masks the fact, however, that reanalysis products often fall into two groups ('wet' and 'dry').
This can be seen more clearly in table 3 where all the datasets are ranked for each index from driest to wettest on average over the 2001-2013 period based on the global (50°S-50°N) land average. Interestingly MERRA2 mostly falls into the wet group and MERRA1 mostly falls into the dry group (see also figure  2 ). This means that the spread of actual values across reanalyses is larger on the whole than any of the other product clusters. MERRA2 and CFSR are the wettest products overall, ranking highest and second highest in 6 out of the 10 precipitation indices and all but one of the precipitation intensity metrics respectively. The uncorrected satellite products tend to fall in the dry end of the scale except for 3B42_IR_v7. 0 which is much wetter. CHIRP_v2 has some of the driest precipitation intensity, frequency and duration measures.
Figures  S10, S11  show the same information for the remaining precipitation indices studied. As we move through the product clusters from in situ to corrected satellite to uncorrected satellite to reanalyses the cov increases, particularly in the tropics and high-altitude regions. In addition, it is clear that some indices agree better than others, that is, they are less sensitive to the choice of dataset. For example, SDII is not as sensitive as CDD and similarly CDD is not as sensitive as R10mm to dataset choice. This agrees well with the results of  Herold et al (2017)  who used a smaller satellite-only subset of products. The cov should be read as the interproduct spread in the sense that just because a cov is small that does not mean that those products are closer to the 'truth'. Simply they tend to agree better because they may rely on similar source data (in the case of the in situ-based products and satellite-based datasets).
The coherence in trend signatures tends to break down for CDD and CWD with mixed sign and significance of trends across products. This is even the case across the in situ-based products with, for example CDD, the two REGEN datasets and GPCC_FDD_v2018 having non-significant decreasing trends (-0. 41 days/decade, -0. 41 days/decade, -0. 65 days/decade) while GPCC_FDD_v1. 0 has a significant increasing trend (1. 68 days/decade).