dataesr/scientific-paragraphs-categorization huggingface dataset:
For B, we used published datasets for rpa2a (Stroud et al. [22] ) and pold2 (Zhang et al. [16, 17] ). C, D Average methylation rates in CHG context in L5 and pol2a-12 in the absence (0 mm) or presence (2 mM) of hydroxyurea (HU) calculated at all TEs (C) or LTR/Gypsy TEs (D). E CHG methylation rates over non-overlapping 100 kb bins on chromosome 4. F Proportion of γ-H2A. X labeled nuclei in L5 and pol2a-12 plants treated or not with HU. A two-way ANOVA showed the significant effect (P < 2e-16) of HU treatment. Lowercase letters indicate significant differences between groups using Tukey's post hoc tests (P < 0. 05). Error bars represent standard error of the mean across three or four biological replicates, as indicated above bars. G Number of γ-H2A. X foci per nucleus in L5 and pol2a-12 plants treated or not with HU, excluding nuclei without γ-H2A.
Transcript analysis About 30-40 mg of fresh tissues were used for total RNA extraction with TRI Reagent (Sigma), following the manufacturer's instructions. RNA (8 μg) was treated with 12 units of RQ1 DNase (Promega) for 1 h at 37 °C and further purified by phenolchloroform extraction and ethanol precipitation. One-step reverse-transcription quantitative PCR (RT-qPCR) was performed starting from 50 ng of RNA using the Sensi-FAST™ SYBR® No-ROX One-Step kit (Bioline) on an Eco™ Real-Time PCR System (Ilumina), following a program of 10 min at 45 °C, 5 min at 95 °C, 40 cycles of 20 s at 95 °C, and 30 s at 60 °C. Amplification specificity was evaluated by analyzing a melting curve generated at the end of the reaction. Amplification of the ACTIN2 gene transcripts was used as a reference for normalization and data were analyzed according to the 2 -ΔΔCt method.
Nuclei were visualized on a Zeiss Axio Imager Z1 epifluorescence microscope equipped with a PL Apochromat 100X/1. 40 oil objective and images were captured with a Zeiss AxioCam MRm camera using the Zeiss ZEN software. The number of DAPI-stained foci and their area relative to that of the entire nucleus was calculated with the ImageJ software to evaluate the number of chromocenters per nucleus and the relative chromocenter area, respectively. The relative heterochromatin fraction was computed for each nucleus by calculating the ratio of the signal intensity at chromocenters over that of the entire nucleus. Immunocytology and fluorescent in situ hybridization were performed as previously described  [35]. Immunolocalization of γ-H2A. X was performed as described previously  [80]. γ-H2A. X foci were counted by using IMARIS 7.
Bisulfite sequencing (BS-seq) Genomic DNA was extracted from 13-day-old seedlings with the Wizard Genomic DNA Purification Kit (Promega), following the manufacturer's instructions. Sodium bisulfite conversion, library preparation, and sequencing on a Hiseq 2000 or a Hiseq4000 were performed at the Beijing Genomics Institute (Hong Kong) from one microgram of DNA, producing ~27-69 M 101-bp paired-end reads (see Additional file 2: Table  S4 ). Our analysis also included publicly available BS-seq datasets for the following mutants: 1st generation fas2-4 (GSM2800760, GSM2800761  [47] ), pold2 (GSM2090064, GSM2090065  [16, 17] ), atxr5/6 (GSM3038964, GSM3038965, GSM2060541, GSM2060542  [11, 43] ), ddb2-3 (GSM2031992, GSM2031993  [81] ), mail1 (ERR1593765-ERR1593768,  [35] ), and rpa2a and bru1 (GSM981048, GSM980999, GSM980986  [22] ).
Reads were filtered to remove PCR duplicates, using a custom program that considered a read pair duplicated if both reads from a pair were identical to both reads of another read pair. Libraries were mapped to TAIR10 with BS-Seeker2 v2. 1. 5 [82]  using Bowtie2 with 4% mismatches and methylation values were called from uniquely mapped reads. Only cytosines with a minimum coverage of 6 reads were retained. We used a previously described method to detect DMPs and DMRs  [35] , with minimum methylation differences after smoothing of 0. 4, 0. 2, and 0. 1 respectively for the CG, CHG, and CHH contexts. To calculate average methylation levels at specific regions, we first determined the methylation rate of individual cytosines and extracted the average methylation rate of all cytosines in the region.
Statistical analysis Means and standard errors of the mean were calculated from independent biological samples. All analyses were conducted with R version 3. 6. 1  [83]. All boxplots had whiskers extend to the furthest data point that is less than 1. 5-fold interquartile range from the box (Tukey's definition). Differences in mean for RT-qPCR data were tested using a two-sided unpaired Student's t test with Welch's correction with the t. test function. Kruskal-Wallis rank-sum test was performed with the native kruskal. test R function, and Dwass-Steel-Critchlow-Fligner post hoc tests were made using the pSDCFlig function with the asymptotic method from the NSM3 package  [84].
a b s t r a c t In this article, we present the datasets which are used to estimate the turbulent kinetic energy dissipation rates and vertical diffusivity in the Indonesian seas. An archived CTD (conductivity, temperature, depth) datasets collected between 1990 and 2016 with 1 m vertical resolution is presented and analyzed using an improved Thorpe method. The direct estimates dataset of the dissipation rate from two research expeditions, i. e. , INDOMIX Program in 2010 and TOMTOM Program in 2015 were also presented, available to be compared with the indirect estimates from CTD profiles. We also present the dissipation rate output of three recent regional internal tide models in the Indonesian seas for comparison with microstructure measurements and improved Thorpe estimates. The datasets refer to "Spatial structure of turbulent mixing inferred from historical CTD datasets in the Indonesian seas"  [1].
© 2020 Published by Elsevier Inc. The model output of the dissipation rates presented here are the latest high-resolution regional model including tidal forcing in the Indonesian seas. • The datasets on turbulent mixing in the Indonesian seas presented here are useful for oceanographers who want to analyze the seawater and mixing properties in the Indonesian seas as well as a proper input for any oceanography modeling related to oceanic mixing. • The observed and modeled dissipation rates dataset presented here provide a baseline datasets to validate the representation of mixing in circulation models.
Data (1. 1) Historical CTD datasets Seawater properties were collected using standardized instruments, CTD (conductivity, temperature, depth) probe. We consider only the CTD cast with vertical resolution higher than or equal to 1 m. The dataset consists of 822 geographically distributed single casts CTD profiles and hourly CTD cast in some sub-regions in the Indonesian seas (Sulawesi Sea/SLA, Makassar Strait/MAK, Flores Sea/FLO, Banda Sea/BAN, Maluku Sea/MAL, Lesser Sunda/LES). Please see Fig. 2  in  [1]  for the region map. (1. 2) Selected World Ocean Dataset (WOD) The dataset consists of 1405 CTD casts sampled with CTD probe that can be downloaded from World Ocean Database (WOD) ( https://www. nodc. noaa. gov/OC5/WOD/pr _ wod. html ). We only consider the CTD casts with vertical resolutions greater or equal to 1 m.
(1. 4) Microstructure INDOMIX 2010 There are five stations of dissipation rate inferred from microstructure measurements sampled in the Halmahera Sea, Banda Sea and Ombai Strait (see Fig. 4  in  [1]  ). (1. 5) Microstructure TOMTOM 2015 The dissipation rate profiles from three stations in the Timor Passage obtained using microstructure measurements are available for comparison (see Fig. 4  in  [1]  ). (1. 6) A half degree grid of Thorpe estimates The datasets contain a half-degree grids of dissipation rates and vertical diffusivities at several depth ranges: 50-30 0 m, 30 0-50 0 m, 50 0-80 0 m, 80 0-20 0 0 m ( Figs. 1  and 2  ). (  1.
7) Nagai 2017 Model The dissipation datasets from the 3D non-hydrostatic MITGCM with 1/600 °horizontal resolution. Since the datasets only cover the Halmahera Sea region, there are only three vertical profiles corresponding to a spring-neap average in this region, which correspond to the microstructure measurements of the INDOMIX 2010 stations (see Fig. 4  in  [1]  ). (1. 8) Nugroho 2018 model The datasets contain dissipation rates inferred from 3D hydrostatic model NEMO 2. 3 for the Indonesian seas region (INDESO Project) with 1/12 °horizontal resolution. There are five vertical profiles of the dissipation rates presented in this dataset, which are located at the same location as the microstructure measurements of the INDOMIX 2010 (see Fig. 4  in  [1]  ). (1. 9) Nagai 2015 model The dissipation rate datasets were the output of 3D hydrostatic MITGCM with 1/100 °that has been re-gridded at 0.
This is an open access article under the CC BY-NC-ND license. ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ) The historical CTD datasets presented here were collected between 1990 and 2016, and most of them have never been published on the World Ocean Datasets (WOD) platform. The Thorpe estimates are presented based on a recent improved method. The turbulent kinetic energy dissipation rate from vertical microstructure profiler (VMP) observations presented here are the latest in situ observation on mixing estimates in the Indonesian seas (INDOMIX Program in 2010 and TOMTOM expedition in 2015).
Acknowledgments We are grateful to  Research Center for Oceanography-Indonesian Institute of Sciences (RCO-LIPI)  for the data sets of the  RV Baruna Jaya I, VII and VIII, Balai Riset Perikanan Laut (BRPL) -Indonesian Ministry of Marine Affairs  for allowing us to analyze some of their historical CTD datasets. We thank  Muhadjirin ,  Praditya Avianto ,  Priyadi Dwi Santoso  and  Dewi Surinati  from.
Data accessibility Repository name: Mendeley Data Data identification number: 01 Direct URL to data: (1) Historical CTD Dataset https://data. mendeley. com/datasets/4ysksfvtyw/draft?a= 1c781591-21cb-4e1d-a3df-2dcef6b3c79d (2) Selected World Ocean Dataset (WOD) CTD profiles https://data. mendeley. com/datasets/tgfsb766vr/draft?a= 542e9320-a3a8-48de-9aba-625b62f427e9 (3) Thorpe Scale Output https://data. mendeley. com/datasets/nndbfdc96j/draft?a= 173c6a14-2321-4adb-bc16-5abece4726cb (4) Microstructure INDOMIX 2010 https://data. mendeley. com/datasets/pnpty39v4t/draft?a= 79c878c1-8a46-475f-8cb4-aa82d3872976 (5) Microstructure TOMTOM 2015 https://data. mendeley. com/datasets/ybch479j8h/draft?a= 488d639c-1860-4227-917d-eabf4ac576b1 (6) Nagai 2015 Model https://data. mendeley. com/datasets/b2djdt3bmy/draft?a= a90e0ec3-e3f2-43db-a459-d0746ffa39a2 (7) Nagai 2017 Model https://data.
mendeley. com/datasets/9f9krnjxrr/draft?a= d7d19916-9c7f-4f7a-8c79-eff346813841 (8) Nugroho 2018 Model https://data. mendeley. com/datasets/kcm8bdjpgy/draft?a= c8b1b296-c9ca-40f4-b98b-7ed176e455ca (9) Gridded 0. 5 °dissipation rate and diffusivity https://data. mendeley. com/ datasets/s5xd2hry83/draft?a=5d5f18cb-b7d5-4e64-8956-14773e8c209d (10) Thorpe Analysis scripts https://data. mendeley. com/datasets/b64669fjg7/draft?a= 1bf72908-4ccd-4bd1-bd64-8f7a710f96e5.
This is an open access article under the CC BY-NC-ND license. ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ) The historical CTD datasets presented here were collected between 1990 and 2016, and most of them have never been published on the World Ocean Datasets (WOD) platform. The Thorpe estimates are presented based on a recent improved method. The turbulent kinetic energy dissipation rate from vertical microstructure profiler (VMP) observations presented here are the latest in situ observation on mixing estimates in the Indonesian seas (INDOMIX Program in 2010 and TOMTOM expedition in 2015) Specifications table Subject Earth and Planetary Sciences: Oceanography Specific subject area Physical Oceanography Type of data Table How data were acquired (1) CTD (conductivity, temperature depth) dataset: Seawater properties (temperature, depth, salinity) were measured using Seabird Electronics instruments.
, Cuypers, Y. , Bouruet-Aubertot, P. , Nagai, T. , Hibiya, T. , region (INDESO Project) with 1/12 °horizontal resolution. Atmadipoera, A. Spatial structure of turbulent mixing inferred from historical CTD datasets Data format Raw in the Indonesian seas Analyzed Progress in Oceanography Parameters for data collection The CTD datasets contains information of station positions (longitude, DOI: https://doi. org/10. 1016/j. pocean. 2020. 102312 latitude), temperature and salinity at specified depths. The dissipation rates dataset from both microstructure measurements and model contains information of station positions and dissipation rates (m 2 s -3 ) values at certain depths. The Thorpe scale analysis output contains Value of the data various information related to mixing properties (Thorpe vertical displacements, overturn selection criterion, buoyancy frequency, dissipation rate and vertical diffusivity).
Description of data collection • The CTD datasets were acquired from various research expeditions directed by Research Center for Oceanography-Indonesian Institute of Sciences (RCO-LIPI), Indonesian Ministry of Marine Affairs and Agency for the Assessment and Application of Technology (BPPT) under the supervision of Indonesian Minister of Research and Technology. Some of the CTD dataset outside Indonesian territorial waters are available and are freely downloadable from the World Ocean Database (WOD). The microstructure measurements were acquired during INDOMIX 2010 expedition a joint Research between Indonesia and French; and during TOMTOM 2015 expedition, a joint research between French and Australia. Data source location Institution: Research Center for Oceanography-Indonesian Institute of Sciences (RCO-LIPI) and LOCEAN-Sorbonne University City/Town/Region: Jakarta and Paris Country: Indonesia and France Latitude and longitude for collected data: [100 °-138 °E, 13 °N-13 °S] ( continued on next page ).
For the design of HIV-1-specific probes, full-length HIV-1 genomes or HIV-1 DNA fragments were fenestrated using a Perl script into 120 nucleotide-long fragments with a sliding window of 60 nucleotides. The targeted HIV-1 sequences were genomes from the set of HIV-1 reference genomes of the Los Alamos National Laboratory database, National Institute of Allergy and Infectious Diseases, National Institutes of Health (https://www. hiv. lanl. gov/content/sequence/NEWALIGN/align. html#comp), HIV genomes obtained from two patients whose cases were previously described  15  , and HIV-1 DNA that had been recovered from the woman and her transmitting partner. The set of 20,000 probes was synthesized by Arbor Biosciences (Arbor Biosciences, Ann Arbor, MI, USA). Library preparation for the next-generation sequencing.
Reads were filtered based on their quality, generated paired reads were imported into the CLC software (https://www. qiagenbioinformatics. com/products/clc-genomics-workbench/) and then assembled by mapping to the HIV genome GenBank accession no. K03455. 1 (HIV-1 strain HXB2) and to sequences obtained from the woman and transmitting partner PBMCs. Reads identified as corresponding to HIV sequences were exported as fasta and SAM files.
Comparisons of HIV-1 sequences obtained from the woman PBMCs and her transmitting partner PBMCs, and analysis of G-to-A mutations and of substitution of tryptophan codons by stop codons. A custom script written in Python language was used to analyze the SAM file generated from the mapping of reads obtained by next-generation sequencing, and to count differences in amino acids between HIV sequences recovered from the PBMCs of the woman and her transmitting partner. In addition, alignments of nucleotide sequences obtained by Sanger and next-generation sequencing were performed using the MUSCLE program  19. The phylogenetic analysis was performed using the MEGA6 software (www. megasoftware. net) with the neighbor-joining method. Moreover, nucleotide sequences were incorporated into a Microsoft Excel file (one nucleotide per cell) for their analysis using sequences from the HIV-1 K03455.
1 genome (HXB2 strain) or those obtained from the transmitting partner PBMCs as references. An alignment was also performed using the MUSCLE program  19  for amino acid sequences obtained by translation of the nucleotide sequences from the three open reading frames using the Transeq online tool (https://www. ebi. ac. uk/Tools/st/emboss_transeq/) and amino acid sequences from the proteins of the HXB2 strain as references. These sequences were added into the Microsoft Excel file (one amino acid per cell) alongside the corresponding nucleotide sequences. G-to-A mutations including those associated with substitution of tryptophan codons (TGG) by stop codons (TGA, TAG, or TAA) and generated by APOBEC3 enzymes  10  were searched using the Microsoft Excel software. In addition, we used the Hypermut tool (www.
Acknowledgements We thank  Annick Abeille ,  Emilie Doudon ,  Emeline Baptiste ,  Nathalie Duclos  and  Gwilherm Penant  for their technical assistance. This work was supported by the  French Government  under the  "Investments for the Future" program  managed by the  National Agency for Research (ANR) ,  Méditerranée-Infection 10-IAHU-03  and was also supported by  Région Provence Alpes Côte d' Azur and European funding FEDER PRIMMI (Fonds Européen de Développement Régional -Plateformes de Recherche et d'Innovation Mutualisées Méditerranée Infection). The funders of the study had no role in study design, data collection, data analysis, data interpretation, and writing of the article. All authors had full access to the data in the study and the corresponding author had final responsibility for the decision to submit for publication.
to December 2015 after a 1-year spin-up run using the prescribed sea surface temperature data  (Ishii et al. , 2005). In the model simulation, the horizontal wind fields in the AGCM were nudged towards the 6hourly Japanese 55-year Reanalysis data  (Kobayashi et al. 2015). We used the monthly anthropogenic 150 emissions dataset by  Lamarque et al. (2010)  and the daily biomass-burning emissions from the global fire assimilation system dataset by  Kaiser et al. (2010). Mineral (dust) and sea-salt emissions were calculated depending on the meteorological conditions in the model  (Tanaka and Chiba, 2005; Yumimoto et al. , 2017).
Data and code availability The data sets used in this publication are available at the ARM Climate Research Facility database for the GoAmazon2014/5 experiment (https://www. arm. gov/research/campaigns/amf2014goamazon). The TEM data and the simulation data used in this publication are available upon request (adachik@mrijma. go. jp). Access to the MRI-ESM2 code is available under a collaboration framework with MRI.
South American (SA) societies are highly vulnerable to droughts and pluvials, but lack of long-term climate observations severely limits our understanding of the global processes driving climatic variability in the region. The number and quality of SA climatesensitive tree ring chronologies have significantly increased in recent decades, now providing a robust network of 286 records for characterizing hydroclimate variability since 1400 CE. We combine this network with a self-calibrated Palmer Drought Severity Index (scPDSI) dataset to derive the South American Drought Atlas (SADA) over the continent south of 12°S. The gridded annual reconstruction of austral summer scPDSI is the most spatially complete estimate of SA hydroclimate to date, and well matches past historical dry/wet events. Relating the SADA to the Australia-New Zealand Drought Atlas, sea surface temperatures and atmospheric pressure fields, we determine that the El Niño-Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) are strongly associated with spatially extended droughts and pluvials over the SADA domain during the past several centuries.
MCA identifies a coupled pattern explaining 48% of the total cross-covariance between scPDSI and austral summer (DJF) sea surface temperatures (HadISST dataset; SI Appendix, section 10, and Fig. 4A ) over the period 1901 to 2000. The spatial loadings show an ENSO-like spatial pattern, where positive (negative) DJF SSTs in the tropical Pacific are coupled with wet (dry) conditions in southeastern SA (SESA), central Chile, and New Zealand. Dry (wet) conditions were also indicated in the Altiplano and central-east Australia during positive (negative) DJF SSTs. The correlation coefficients between the DJF SSTs for the Niño 3. 4 sector (HadISST_N3. 4; SI Appendix, section 10) and the scPDSI leading mode and SST leading modes are 0. 63 (Fig. 4C ) and 0. 94, respectively. A similar spatial pattern emerges by applying MCA to instrumental SSTs and scPDSI datasets (SI Appendix, Fig.
Materials and Methods Observational Climate and Tree Ring Data. The observational data used to compute instrumental scPDSI was an ensemble of the interpolated fields of monthly climate observations from three datasets: 1) precipitation, temperature, and potential evapotranspiration data from the Climatic Research Unit Time Series (CRU) TS 4. 01  (42) , enhanced by the incorporation of precipitation and temperature records from the Argentinean Institute of Snow, Ice and Environmental Research (IANIGLA-CONICET) database (SI Appendix, section 1 and Fig. S1 ); 2) precipitation and air temperature dataset from the University of Delaware  (43) ; and 3) the precipitation dataset from Global Precipitation Climatology Centre  (44). The computed monthly scPDSI data were seasonalized to develop average data for the austral summer season (DJF).
The historical sources are precipitation records from Potosí, Bolivia (Altiplano; ref. 21) , the snow and drought records from the Andes region of central Chile (ref. 46  and sources references therein), and the flood records in the city of Santa Fe (mid-Paraná river, La Plata basin; ref. 29). In this analysis, the regional scPDSI records were used as the background time series and the dates of dry/wet years in the historical series as event years (Dataset S1). For each event, a 9-y lag window was used with the event year as the central value plus 4 y before and after the event. The 9-y scPDSI data were averaged for each event to produce a mean pattern related to the historical event. The mean scPDSI pattern for the selected years was statistically evaluated for significance (95% confidence interval) by performing 1,000 Monte Carlo simulations (47) using random years from the scPDSI record.
This technique allows the detection of nonmonotonic trends without imposing parametric restrictions. For this purpose, a Gaussian kernel function was applied in order to estimate the probability of occurrence of one specific extreme event using a 60-y bandwidth. To better interpret these estimates, confidence bands at the 95% level were obtained using 1,000 bootstrap simulations  (48, 49). MCA for Reconstructed scPDSI and Climate Modes. To describe how SST and geopotential height (500 mb) (SI Appendix, section 10, for dataset reference) covary with the summer scPDSI from the SADA and the ANZDA, we used MCA. This method is widely used in climate research and identifies coupled patterns in two data fields that share the maximum amount of covariance  (50). This statistical tool identifies the common signal while separating stochastic noise from other factors.
The leading modes obtained by MCA were used as estimators of ENSO (ENSO-e) and SAM (SAM-e) variability for the past 500 y (Dataset S2). The resulting time series of the difference between both climate index estimators was used to determine the anomalous negative/positive values by the 5th and 95th percentiles, respectively (Dataset S3). The 25 (  26 ) negative (positive) values were associated with coupled anomalous negative (positive) ENSO-e and positive (negative) SAM-e events. Data Deposition. Tree ring chronologies, instrumental and reconstructed scPDSI (SADA) are available at the Center for Climate and Resilience Research (CR) 2 , http://www. cr2. cl/datos-dendro-sada/ (51). Historical hydroclimate reconstructions together with regional scPDSI used to validate each proxy are presented in Dataset S1.