dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and methods The guideline was developed according to a well-documented methodology that is universal to ESHRE guidelines  (Vermeulen et al. , 2017). The guideline development group (GDG) was composed of past and present members of the coordination of the Special Interest groups (SIGs) Fertility Preservation and Quality and Safety in ART, with representation of other SIGs (SIG Psychology and counselling, and SIG Ethics and law), and addition of experts in the field, including oncologists, a scientist, and patient representatives. In short, 21 key questions were formulated by the GDG, of which 7 were answered as narrative questions, and 14 as PICO (Patient, Intervention, Comparison, Outcome) questions. For each PICO question, databases (PUBMED/MEDLINE and the Cochrane library) were searched from inception to 1 November 2019, limited to studies written in English.
Strong recommendations should be used as a recommendation to be applied for most patients, while weak recommendations require discussion and shared decision-making. For the narrative questions, a similar literature search was conducted. Collected data were summarized in a narrative summary and conclusions were formulated. The guideline draft and an invitation to participate in the stakeholder review were published on the ESHRE website between 6 May and 17 June 2020. All comments were processed by the GDG, either by adapting the content of the guideline and/or by replying to the reviewer. The review process was summarized in the review report which is published on the ESHRE website (www. eshre. eu/Guidelines). Overall, 71. 5% of the 231 comments resulted in an adaptation or correction in the guideline text.
FP can be considered one option in preventing infertility and an important part of realizing the right to have a family. The importance of FP has also been demonstrated at the start of the COVID-19 pandemic, when ESHRE recommended not starting fertility treatments with the exception of treatments for FP. It has been shown that although most fertility treatments were suspended, FP interventions were continued in most centres throughout Europe  (Vermeulen et al. , 2020). Notwithstanding the importance and relevance of FP, research data on many aspects are scarce. As a basis for the current guideline, a broad and formal literature review was conducted. Few relevant RCTs have been performed, with evidence for most interventions deriving from case series. Research gaps were detected in several areas, and these are documented in a list of recommendations for further research (Supplementary data III).
Three hydrogen bonds are observed with PBP1b, between the side chain of Trp 83 and the main-chain carbonyl of Gln 384 (a11a12 loop, GTase domain), between the main-chain carbonyl of Leu 89 and side-chain hydroxyl of Thr 140 (a1b2 loop, UB2H domain), and between the Glu 90 side chain and the Arg 141 side chain (a1b2 loop, UB2H), as well as the Leu 344 main chain (a9a10 loop, GTase) (Fig. 3, C-F ). In addition, Tyr 85 , Ile 86 , and Leu 89 from E FtsN form a hydrophobic cluster with Leu 224 (a2), Leu 344 (a9a10 loop), Ile 386 , Leu 390 , and Leu 394 (a12) from the GTase domain (Fig. 3, C-F ). Compared with other PBP1b structures available, no major conformation change was observed upon E FtsN binding with a RMSD of 0. 85 Å over 685 common a carbons (PDB code 5HLA) (Fig. 3B ). The differences are however larger in the GTase domain (RMSD 1.
Cross-linking experiments PBP1bg (10 mM) was incubated with a 10-fold excess of FITC-E FtsN peptide and two conditions of the heterobifunctional cross-linker EDC (protein:EDC molar ratio 1:1000 and 1:2000) in buffer containing 50 mM HEPES-NaOH, pH 7. 5, 0. 3 mM NaCl, 0. 7% CHAPS. The mixture was incubated for 2 h at room temperature and analyzed by SDS-PAGE followed by fluorescence imaging using a Typhoon Trio1 imager and Image Quant TL software (GE Healthcare). The PBP1b control (2 mM) was incubated with fluorescent ampicillin 10 mM for 30 min at 37 °C prior to SDS-PAGE.
Crystallization, data collection, and structure determination Crystallization was carried out using the hanging drop vapor diffusion method at 20 °C. The PBP1b (Lys 58 -Ser 804 ) concentration was 20 mg/ml in the buffer solution 20 mM Tris-HCl, pH 8. 0, 0. 3 M NaCl, and 4. 5 mM DM and contained 1:1 molar ratio of moenomycin A and a synthetic peptide corresponding to E FtsN (LPPKPEERWRYIKELESRQ) in a 3:1 molar ratio. The crystals were grown in drops made of 2 ml of protein solution and 2 ml of precipitant solution containing 0. 1 M (NH4) 2 SO 4 , 0. 3 M sodium formate, 0. 1 M Tris-HCl, pH 7. 8, 3% (w/v) lowmolecular-weight poly-g-glutamic acids, and 20% (v/v) PEG 550 monomethyl ether. The cryoprotectant solution was made of 22% (w/v) PEG 6000 and 30% (v/v) PEG 400. The diffraction data were collected on the Proxima 1 beamline of the Soleil Synchrotron (Paris-Saclay).
The data were indexed, integrated, and scaled using XDS and reached a 2. 4 Å resolution  (60). Because of the anisotropy of the data, additional processing was carried out with Staraniso  (32). The three axes of the ellipsoid used to cutoff the resolution were 2. 3, 4. 3, and 2. 3 Å. The crystal used belongs to the orthorhombic P2 1 2 1 2 space group. The structure was solved by molecular replacement with Phaser (61) using the PBP1b structure of PBD code 3VMA as search model  (30). The refinement and model building cycles were respectively performed with buster (BUSTER version 2. 10. 2, Global Phasing Ltd. , Cambridge, UK) and Coot  (62). A summary of the relevant statistics of the data collection and refinement is given in Table  1. The figures were prepared using PyMOL (PyMOL molecular graphics system, version 1.
Microscopy and image analysis Samples were taken from the complementation cultures (42 °C) at different time (1-h intervals), and the cells were fixed as described  (63). Photographs were taken with a cooled AxioCam MRm (Zeiss) mounted on a Zeiss Axio Imager. Z1 microscope, and the images were acquired in phase-contrast mode using the AxioVision Rel. 4. 5 (Zeiss) software. The cell morphologies analysis were determined using ImageJ software (RRID:SCR_003070) running under plugin ObjectJ (RRID:SCR_019190).
RNA-seq assay Total RNAs were extracted from 180 mg of the aerial part of seedlings with the ZR Plant RNA MiniPrep kit (Zymo Research), according to the manufacturer's instructions. HiSeq 50 bp singleton reads from RNA-Seq were first adaptor trimmed and then analyzed using the TopHat and Cufflinks software. TopHat (v2. 0. 9) was used for alignment of short reads to the Arabidopsis thaliana genome TAIR10, Cufflinks (v2. 2. 0) for transcript assembly and differential expression, and commeRbund (v2. 0. 0) for visualization of differential analysis. Default parameters were used. A total of three biological replicates were generated for the WT and gcn5 RNA-seq data.
We combined these ChIP-seq results with transcriptomic data of WT plants and found that both GCN5 binding and H3K14ac levels positively correlate with gene expression (Figure  1B -C and Supplemental Figure  S2 ). Using additional ChIP-seq experiments, we also compared the distribution of GCN5 with H3K9ac and RNAPII binding (the latter dataset previously published  (48) ), as well as with the repressive mark H3K27me3. As expected from its known role as a positive regulator of gene expression, we observed that GCN5 binding correlates with H3K9ac and RNAPII binding, while it largely anticorrelates with H3K27me3 in WT seedlings (Figure  1A ). We next refined our analysis of the GCN5 mode of action by precisely mapping its binding to different types of genomic regions. After peak annotation, we compared GCN5 binding to the genomic regions of the entire Arabidopsis genome and observed an enrichment of GCN5 binding sites on regions upstream of transcription start sites (TSSs), usually corresponding to promoter regions and to 5 untranslated regions (UTRs) (Figure  1D ).
The comparison of the GCN5 binding sites with nucleosome distribution and chromatin accessibility, analyzed through MNase-seq and ATAC-seq respectively (datasets that have been previously published (49)), revealed that GCN5 binding correlates partially with nucleosome occupancy. GCN5 binding displays two peaks, one before and one after the TSS (Figure  1B ). The peak immediately downstream the TSS matched perfectly histone distribution and H3K14ac enrichment (Figure  1B, E-F ), suggesting that this protein is recruited on chromatin through its bromodomain, which recognizes acetylated histones (Figure  1E -F, Supplemental Figures  S3  and S4 ). By contrast, the upstream peakwhich does not coincide with H3K14ac enrichment -corresponds to the nucleosome-free region (Figure  1C ), suggesting that GCN5 can also be recruited on chromatin in- dependently of acetylated histone recognition.
This result is consistent with our previous study showing that GCN5 binding to chromatin can occur in the absence of its bromodomain  (36). We therefore performed de novo motif discovery on the 5 UTR and on promoter regions of GCN5 targets using the HOMER software, identifying highly significant motifs on both. Among them, the G-Box consensus sequence (CACGTG) is one of the most represented cis elements within the GCN5 peaks (Supplemental Figure  S5 ), consistent with the central role of GCN5 in light responses  (23). These data suggest that GCN5 can be recruited to chromatin both through its interaction with specific transcription factors and via the direct interaction of its bromodomain with acetylated histones. To further confirm the role of GCN5 as an activator of gene expression, we integrated our ChIP-seq data of GCN5 binding sites with RNA-seq data from the gcn5 mutant (Supplemental Table  S1 ).
GCN5 can either activate or repress gene expression by controlling H3K14ac distribution on its target genes To further elucidate whether GCN5 acts both as an activator and repressor of gene expression on different loci, we investigated how it influences genome-wide H3K14ac levels, since it specifically catalyzes the deposition of this mark  (24). We analyzed the H3K14ac landscape in the WT and in gcn5-1 and gcn5-2 mutants (Figure  2C ), identifying 31,651 peaks in the WT and 26 627 peaks in the mutants. We then compared the three ChIP-seq datasets using seqMINER and diffReps, which allow qualitative and quantitative comparisons between a reference set of genomic positions and multiple ChIP-seq datasets. Through this approach, we observed a strong reduction of H3K14ac levels at the 5 end of genes in the gcn5-1 and gcn5-2 mutants compared to the WT (Figure  2D ).
05). (E). Susceptibility of NahG, NahG gcn5-2, sid2 and sid2 gcn5-2 mutants to Pseudomonas syringae pv. tomato DC3000 was compared to WT Col-0. Bacteria were quantified 2 hrs (day 0) and three days (day 3) after spraying. NahG gcn5-2 and sid2 gcn5-2 mutant plants showed enhanced susceptibility compared to Col-0. Average values and standard deviations were calculated from two independent experiments. Asterisks represent statistical significance compared to the WT (t-test, P < 0. 05). (F) Relative expression of SA-related genes in 14-day-old seedlings, WT and gcn5 mutants. WRKY33, DND2 and MYC2 expression was extracted from the RNA-seq data. Values are average from three independent replicates. Asterisks represent significant difference with WT (q < 0. 05). (G) (top) Quantification data of the H3K14ac immunoprecipitation results.
Chromatin-protein complexes from WT and gcn5 mutants were immunoprecipitated with H3K14ac specific antibodies. H3K14ac enrichment on the tested regions (WRKY33, DND2 and MYC2) was monitored by qPCR. Asterisks indicate significantly different values with respect to the WT (Student's t-test, P < 0. 05). (bottom) Visualization of ChIP-seq results showing GCN5 binding and H3K14ac levels of a few selected genes, MYC2, WRKY33, DND2, that are GCN5 targets and down-regulated in both gcn5 mutants. The regions highlighted in red above the gene structures were amplified in the ChIP-qPCRs. (H). Quantification data of GCN5 immunoprecipitation results. Chromatin-protein complexes from gcn5-1 35S::GCN5-GFP mutant were immunoprecipitated (Ip) with antibodies specific for GFP or IgG. GCN5 enrichment on the tested regions (WRKY33, DND2 and MYC2) was monitored by qPCR.
FUNDING Agence Nationale de la Recherche (ANR)  [ EPICLISPE ANR-18-CE20-0015 ];  Institut Universitaire de France (IUF) ;  Biotechnology and Biological Science Research Council  [ BB/L019345/1 ,  BB/M017982/1  to V. N. , in part]; V. N. is also supported by the  Royal Society. Funding for open access charge:  ANR  research grant.
The construction of terraces is a key soil conservation practice on agricultural land in China, providing multiple valuable ecosystem services. Accurate spatial information on terraces is needed for both management and research. In this study, the first 30 m resolution terracing map of the entire territory of China is produced by a supervised pixel-based classification using multi-source and multi-temporal data based on the Google Earth Engine (GEE) platform. We extracted time-series spectral features and topographic features from Landsat 8 images and the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data, classifying cropland area (cultivated land of Globeland30) into terraced and nonterraced type through a random forest classifier. The overall accuracy and kappa coefficient were evaluated by 10875 test samples and achieved values of 94 % and 0.
One of the most common engineering measures applied in comprehensive soil conservation and improved farming projects is to transform slopes into terraces. However, the terrace number and area distribution in China are poorly documented. Current terrace statistics are incomplete or outdated and no national map exists. Accurate and detailed spatial distribution information of terraces is thus lacking. Therefore, it is essential to generate national-scale data to provide information for policy-makers to make decisions and take actions in land planning and agricultural management, and for researchers to evaluate the value and impact of terraces on ecosystem. Remote sensing is often used to rapidly obtain detailed land cover and land use information  (Gong et al. , 2013; Yu et al. , 2013b) , agricultural monitoring being one critical application.
, 2020; Teluguntla et al. , 2018). Thus, it has potential in largescale terrace/non-terrace classification. However, few studies have explored its application in the field. Here, we adapted and extended this approach to systematic agricultural terrace mapping. As for the selected data for classification of terrace/non-terrace, usually, past studies relied on images from high-resolution satellites such as WorldView  (Luo et al. , 2020; Zhao et al. , 2017) , SPOT-5  (Li et al. , 2013) , Gaofen-1  (Zhang et al. , 2017)  and unmanned aerial vehicles  (Diaz-Varela et al. , 2014). Such data are expensive and are not universally accessible, making them unsuitable for large-scale applications. Medium resolution, freely available data, such as Landsat 8 imagery, provide a more practical option but satisfactory accuracy must be demonstrated for the research problem of terrace identification.
Note that medium-resolution images have been used in many previous large-scale agricultural land cover classification studies  (Massey et al. , 2018; Phalke and Özdoğan, 2018). Although the amount of data decreases when conducting classification at medium resolution compared with high resolution images, remote sensing-based land cover mapping at the scale of a large country like China still requires considerable data storage and processing capabilities, which is a major challenge for software packages. The Google Earth Engine (GEE) platform provides a multi-petabyte analysis-ready data catalog and high-performance, intrinsically parallel computation service  (Gorelick et al. , 2017). Thus, the handling of large geospatial datasets and access to substantial computational resources can be easily met on GEE, improving the efficiency of geospatial analysis.
Shuttle Radar Topography Mission digital elevation model (SRTM DEM) data Topographic features being the most distinctive features of terraces, it is crucial to consider them for terrace identification. We selected the 30 m SRTM DEM data for characterizing topographic features. SRTM is an international research effort that obtained digital elevation models on a near-global scale  (Farr et al. , 2007). The product at 1 arc-second (30 m) resolution is available on GEE, which has undergone a void-filling process using open-source data (ASTER GDEM2, GMTED2010, and NED). Note that the acquisition time of SRTM DEM data is 2000, and we suppose the terrain changes little in decades.
GlobeLand30 To improve terrace mapping efficiency, we limited the classification to cropland area of GlobeLand30, a well-established and widely used source of land cover information, generated by integration of pixel-based and objected-based methods with knowledge (POK) using multi-source data. Its accuracy for cultivated land type is above 80%  (Chen et al. , 2017b). The latest version of this land cover dataset is for 2010, which does not correspond to our classification year (2018). However, according to national survey statistics, the cropland area in China changed little in recent years. We thus accepted the use of 2010 data as a mask for our 2018 classification. of dimensionality", accuracy decreases as dimensions increase)  (Hughes, 1968). To reduce the dimension of temporalspectral features, we extracted the percentiles of six optical bands (Blue, Green, Red, NIR, SWIR1, and SWIR2) and one thermal infrared band (TIRS2) from the Landsat 8 time-series images during the whole year.
All 4790 training samples (2151 terrace samples and 2639 non-terrace samples) were collected by visual interpretation of Landsat images, SRTM DEM data, cropland extent data extracted from GlobeLand30, and Google Earth images. All test samples were randomly generated in a hexagonal grid (Icosahedral Snyder Equal Area Discrete Global Grid (ISEA DGG)) created using the DGGRID software  (Sahr, 2019). Based on this strategy, the study area was split into 1460 equalarea regions (Fig. 2 ). Accurate precision evaluation requires sufficient samples for both terrace and non-terrace; however, the randomly generated terrace samples were insufficient due to their small percentage. To increase the number of terrace samples, we took the following strategy. Four samples were first generated randomly in each hexagon of China.
Terrace/non-terrace classification We mapped the terraces over entire China using the GEE platform. Random forest was chosen as the classifier for its high precision, efficiency, and stability  (Breiman, 2001; Gislason et al. , 2006). The two critical parameters of the random forest classifier, the number of decision trees and the number of variables per split, were set to 200 and the square root of the feature number, respectively. The classification process comprised several steps. First, the cropland extent data was used to mask non-cropland area which occupied 80% of the whole study area. As a result, the classification efficiency was improved by at least 80% through masking of non-cropland areas. Second, national, regional, and local training samples were merged to train the classifier of each province and each random forest model was applied to classify the corresponding province.
Second, the irregular shape and small patch size of terraces make the classification more difficult. Mixed pixels are common in medium-resolution images because of the spatial resolution limitations and spectral heterogeneity  (Fisher, 1997). This phenomenon is especially severe in terrace/non-terrace classification. The use of 30 m Landsat data in this research ensures that terraces with large areas can be detected. However, with data of this resolution it is not possible to correctly classify fragmented terrace patches (Fig. 11f ). Moreover, the proportion of terraces within a pixel can cause overestimation or underestimation. Higher resolution images can generate a more accurate classification. To better capture these small and irregular terraces, satellite images with a finer resolution are necessary.
The omitted cropland led to some omission errors in terrace mapping. Besides, the commission errors in the cropland extent data and the non-correspondence of cropland extent data year and terrace/non-terrace classification year led to some uncertainties. Some abandoned terraces, terraces used for reforestation or afforestation in the cropland extent data were classified into terraces. And even some non-cropland vegetation not on the terraces and a few other objects near or on the terraces caused part commission errors. These uncertainties can be avoided by using accurate cropland extent data. However, we had tried several other best cropland extent data at present to limit the classification area, such as cropland of National Land Cover Database for China (NLCD-C)  (Zhang et al. , 2014)  and Global Food Security-support Analysis Data at 30 m (GFSAD30)  (Teluguntla et al.
Data availablity The China terrace proportion map at 1 km resolution (calculated from 30 m resolution map) is available to the public at https://doi. org/10. 5281/zenodo. 3895585 (Cao et al. , 2020). The map values indicate the proportion of terrace within 1×1 km grid cell. The China terrace map at 30 m resolution will be available after publication. The Landsat 8 SR imagery and SRTM DEM data used in this study were available at the GEE platform. The GlobeLand30 data can be downloaded from www. globeland30. org. The detailed information of these data is shown in Table  S2.
Biallelic loci must be characterized by a metagenomic coverage between a limit of median ± 2σ, with a minimum and maximum of 5× and 150× coverage in each sample to avoid low covered and multicopy genomic regions. To keep out rare alleles and potential calling errors, only variants characterized by a BAF comprised between 0. 9 and 0. 1, and a BARE between 0. 95 and 0. 05 in at least one population were chosen for the final dataset resulting in 25,768 biallelic loci (Table  S5 ).
| Population-scale ADE detection using metagenomic and metatranscriptomic data In each population, we first selected heterozygous loci variants (BAF ≠ {0,1}). We tested the correlation between BAF and BARE and modeled their relationship by a linear regression. Then, we computed D = BAF-BARE and estimated the distribution parameters µ and σ 2 by fitting a normal distribution via fitdist function from fitdistrplus R package  (Delignette-Muller & Dutang, 2015). We then tested the psADE of each variant using a two-sided Fisher's exact test on a 2 × 2 table containing the read counts G A , G B , T A , and T B. Given the large number of tests, we applied the Benjamini and Hochberg p-value correction  (Benjamini & Hochberg, 1995)  to control the False Discovery Rate (FDR). This generated seven sets of candidate loci under psADE, one set for each population.
| Estimation of genomic differentiation and detection of variants under selection Pairwise-F ST was computed as follows, F ST = 2 i p i (1-pi) , for each locus between each pair of populations i and the median pairwise-F ST was retained to measure the genomic differentiation between each population. A Mantel test was performed to test for isolation-by-distance between median pairwise-F ST and geographic Euclidean distances using vegan v2. 5-2  (Oksanen et al. 2018 ) and geosphere v1. 5-7 (Hijmans, 2017) R packages. The pcadapt R package v4. 0. 2  (Luu, Bazin, & Blum, 2017)  was used to detect selection among populations from the B-allele frequency matrix. The computation was run on "Pool-seq" mode, with a minimum allele frequency of 0. 05 across the populations, and variants with a corrected Benjamini and Hochberg p-value <.