dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, the Wollongong station, which is situated near the east coast of Australia, is a dark surface (with nearinfrared albedos over land of 0. 3) and lies between the Tasman Sea to the east and the Illawarra escarpment to the west (Fig. 8 ). The OCO-2 retrievals of X CO 2 in target mode are systematically higher than those from the TCCON, and are particularly high (up to 5 ppm higher than TCCON) in July and August (Fig. 9 ), due to the problem discussed below in Sect. 4. OCO-2 data over Białystok, located in a dark, forested region, also has a persistent high bias (on the order of 1 ppm) compared with TCCON. Even for sites at which OCO-2 X CO 2 does not appear to have a significant bias with respect to TCCON, the retrievals can show spurious spatially correlated errors. The Lauder TCCON station is situated in a valley between rolling hills (Fig.
Nadir and glint-mode comparisons to TCCON In this section, we evaluate the bias-corrected OCO-2 glint and nadir modes against ground-based TCCON data to reveal other biases that were not eliminated using the standard version 7 bias correction. We use the version B7 retrospective OCO-2 "lite" files here, which have had the footprint-dependent, parameter-dependent, and scaling biases (described in Sect. 3. 2) removed. The residual footprint correction was applied manually to the data. The "lite" files are available from the CO 2 Virtual Science Data Environment (JPL-Caltech, 2016, http://co2. jpl. nasa. gov) and from GES-DISC (2016). We limit ourselves to data for which the warn level is less than or equal to 11, as recommended by  Mandrake et al. (2015) , and for which the "xco2_quality_flag" is zero.
(2011b)  and  Nguyen et al. (2014)  are not required for OCO-2, due to OCO-2's much higher data density. Figure  11  shows the differences between coincident OCO-2 X CO 2 and that from TCCON, separated by viewing mode and season. The bottom panel collects the viewing modes together, still separating by season. The OCO-2 X CO 2 appears to have a bias with respect to TCCON that increases Figure  12. Land glint OCO-2 one-to-one plot against TCCON. The slope of the relationship is represented by "m" in the figure, and the coefficient of determination is represented by "R 2 ". The number of points on the graph is indicated by "N" and the root-mean-square value (RMS) of the differences between OCO-2 and TCCON X CO 2 is also shown. Each point represents a daily median of coincident OCO-2 and TCCON measurements.
Many points are overlaid in this graph, obscuring the density of points along the best fit line. with increasing latitude in the land glint and nadir data north of 45 • N (Park Falls). This latitude-dependent bias is consistent with the target-mode results (Fig. 6 ). A seasonal bias is not apparent at latitudes for which all four seasons have sufficient coincident measurements (Lamont, Edwards, Ascension, Réunion, Wollongong), indicating that the latitudinal bias is not likely caused by an airmass-dependent bias (in either OCO-2 or TCCON). In general, however, the number of coincident measurements is low (Table  3 ), especially in the Northern Hemisphere north of 45 • N. In the Southern Hemisphere winter, there is a significant high bias in the retrieved X CO 2 from the OCO-2 ocean glint data.
The "Total" row is calculated by considering all the coincidences in the table independently. (Fig. 5 ). Appendix Fig. A1r  and s also show this problem as a function of time. The bias is also seen in preliminary comparisons to models (not shown), which also indicate a low bias of OCO-2 ocean glint X CO 2 in the tropical oceans. However, this latter bias has not been clearly detected in compar-isons with TCCON data (e. g. , Fig. A1p  and r ). The Southern Hemisphere ocean glint bias does not impact the overall scaling bias between OCO-2 and TCCON X CO 2 within the uncertainty but does impact the latitudinal gradients (and hence fluxes) inferred by the OCO-2 data. While the cause of the bias in the southern winter is currently unclear, there is a promising hypothesis related to the OCO-2 B7r algorithm's misrepresentation of stratospheric aerosols, exacerbated by the eruption of Mount Calbuco in Chile on 22 April 2015  (Romero et al.
, 2016). The overall comparisons between the OCO-2 data and TCCON data are reported in Tables  3  and 5  and shown in Figs. 12-14 for data from land glint mode, ocean glint mode, and nadir mode. The differences between aggregated, bias-corrected OCO-2 X CO 2 data coincident with all available TCCON daily median measurements are -0. 3, 0. 2, and 0. 2 ppm for land glint, ocean glint, and nadir, respectively. The RMS values of these differences are 1. 3, 1. 4, and 1. 3 ppm, respectively. The differences between the biascorrected OCO-2 values and the TCCON medians differ from site to site; sites with more than 10 coincident measurements have differences in land glint mode ranging from -0. 7 ppm (Wollongong) to 0. 9 ppm (Karlsruhe), in ocean glint mode ranging from -1. 1 ppm (Saga) to 0. 4 ppm (Park D.
Land glint Ocean glint Nadir Bias RMS R 2 N Bias RMS R 2 N Bias RMS R 2 N Eureka 0. The nadir mode data show the best correlation of the three science modes (R 2 = 0. 81), followed closely by land glint (R 2 = 0. 79) and finally ocean glint (R 2 = 0. 63). The low correlation coefficient in the ocean glint data is partially driven by the high anomalies in the Southern Hemisphere winter, most obviously in the data over Wollongong (Fig. 11 ). If the Southern Hemisphere winter data (June-September) are excluded from the ocean glint correlations, the R 2 improves to 0. 75. The slopes of all three regressions are within uncertainty of 1. 0. The agreement between the science-mode OCO-2 data and TCCON is poorer than that for the targetmode measurements. Halving the spatial coincidence criteria over each site does not significantly improve the correlation coefficients.
Conclusions Aggregated OCO-2 X CO 2 estimates filtered with warn level ≤ 11 and xco2_quality_flag = 0 generally compare well with coincident TCCON data at global scales, with absolute median biases less than 0. 4 ppm and RMS differences less than 1. 5 ppm. While the bias correction clearly improves the relationship between TCCON and OCO-2 globally, some biases remain. Spurious local X CO 2 variability that is correlated with topography and surface brightness is apparent in the target-mode measurements, particularly over Edwards, Wollongong, and Lauder. Ocean glint measurements from OCO-2 at southern high latitudes during the Southern Hemisphere winter are biased high, possibly due to stratospheric aerosol interference. In all observation modes, there is an apparent latitude-dependent bias, with the largest north of 45 • N.
Acknowledgements. Part of this work was performed at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with  NASA. The operation of the Ascension Island site was funded by the  Max Planck Society. The  Bremen, Białystok , and Orléans TCCON sites are funded by the  EU projects InGOS  and  ICOS-INWIRE  as well as by the  Senate of Bremen. The Darwin and Wollongong TCCON sites are funded by  NASA  grants  NAG512247  and  NNG05GD07G  and by  Australian Research Council  grants  DP140101552 ,  DP110103118 ,  DP0879468 ,  LE0668470 , and  LP0562346. We are grateful to the  DOE ARM program  for technical support at the Darwin TCCON site. Nicholas M. Deutscher  was supported by an  Australian Research Council  fellowship,  DE140100178. The TCCON site at Réunion Island is operated by the  Royal Belgian Institute for Space Aeronomy  with financial support in 2014, 2015, and  2016  under the  EU  project  ICOS-Inwire and the ministerial decree for ICOS  ( FR/35/IC2 ) and local activities supported by  LACy/UMR8105 -Université de La Réunion.
TCCON is funded by  NASA  grants  NNX14AI60G ,  NNX11AG01G ,  NAG5-12247 ,  NNG05-GD07G , and  NASA 's  Orbiting Carbon Observatory Program. We are grateful to the  DOE ARM program  for technical support in Lamont and  Jeff Ayers  for technical support in Park Falls. From 2004 to 2011 the Lauder TCCON program was funded by the  New Zealand Foundation of Research Science and Technology  contracts  CO1X0204 ,  CO1X0703 , and  CO1X0406. Since 2011 the program has been funded by  NIWA 's  Atmosphere Research Programme 3 (2011/13 Statement of Corporate Intent ). The TCCON measurements at Eureka are made by the  Canadian Network for the Detection of Atmospheric Change (CANDAC) , led by  James R. Drummond , and in part by the  Canadian Arctic ACE Validation Campaigns , led by  Kaley A. Walker. They are supported by the  Atlantic Innovation Fund/Nova Scotia Research Innovation Trust ,  Canada Foundation for Innovation ,  Canadian Foundation for Climate and Atmospheric Sciences ,  Canadian Space Agency, Environment Canada, Government of Canada International Polar Year funding, Natural Sciences and Engineering Research Council ,  Northern Scientific Training Program ,  Ontario Innovation Trust , and  Ontario Research Fund and Polar Continental Shelf Program.
Data availability. Unfiltered, uncorrected OCO-2 data are available from the Goddard Data Center (GES-DISC, 2016). The filtered and bias-corrected data are contained in "lite" files, which are available both from JPL's CO 2 portal (https://co2. jpl. nasa. gov/, JPL-Caltech, 2016) and the Goddard Data Center. TCCON data are available from the TCCON data archive, hosted by CDIAC: http://tccon. ornl. gov. Each TCCON dataset used in this paper is cited independently in Table  1  or in the captions of Fig. A1.
The RTM database (http://rtm-onf. ifn. fr/), which is available for all French Alpine massifs, has recorded nearly 30,000 events (including all types of events) over the past few centuries in the Southern Alps and is regularly updated by the RTM services. It lists the impacts of torrential activity without systematically distinguishing the nature of the processes involved (flood or debris flow). The identification of processes and the prediction of their intensity at a regional scale are therefore not conceivable solely on the basis of this partial knowledge of the hazard. The production of datasets at a regional scale and the development of methodologies to produce them are therefore still important research issues. In this context, the aim was to develop a methodology to report debris-flow susceptibility spatially at a regional scale (i.
But the development of a method to map erosion areas from such data sources remains a methodological challenge at this scale. We randomly selected 30 tiles of infrared orthophotos to constitute a training dataset of the erosion classification model. These tiles were automatically segmented into objects of homogeneous texture and classified manually into two categories (erosion/ no erosion). The approach is based on a fusion (weighted sum) of several supervised classifications of infrared orthophotos and Landsat 7 ETM+. Thirty other tiles were randomly selected to create a validation dataset. They were segmented and classified manually by means of photo interpretation and then compared with the different classification methods and the final model in terms of sensitivity and specificity. Details of this analysis are available in  Bertrand (2014).
These images cover a very high temporal resolution, as the coverage is updated daily, and would allow characterising the sediment recharge after high intensity events. The regional assessment of debris-flow susceptibility should also be improved by integrating other feedbacks on the physical reality of torrential phenomena. The validation dataset should be extended with other observation sites, as its size is relatively reduced (56 sites) compared with the length of the hydrographic network we characterised. A systematic analysis of the torrential events that occur after convective rainfalls of high intensity would improve the assessment of the effects of conditions of sediment recharge into headwater catchments. The spatial distribution of these rainfall events can now be reconstructed from a retro-analysis of the radar data from the RhyTMME project and compared with a field analysis of the recent torrential responses.
This kind of study is now feasible and promising not only for the determination of sediment recharge proxies but also for the determination of critical rainfalls for the triggering of phenomena  (Marra et al. , 2014). 35 From a static map of debris-flow susceptibility to a hazard map, integration of meteorological forcing is needed. Critical rainfall conditions allowing the triggering of debris flow have been identified thanks to an intensity duration threshold based on data obtained from a very active torrent in the Alpes-Maritimes department (the Réal torrent)  (Bel et al. , 2016). Implementation of this threshold on real-time data (from meteorological radar of the RhyTMME project) and its integration with the static susceptibility map would permit making predictions of torrential hazards more dynamic.
e. , CatSper proteins)  (Singh and Rajender, 2015)  were strongly upregulated in spermatids (Figure  S1A ). By contrast, expression of  the Sycp2 and Sycp3 genes, encoding synaptonemal proteins  (Wang et al. , 2005) , and of Mlh3 and Spo11, encoding homologous recombination proteins  (Romanienko and Camerini-Otero, 2000; Santucci-Darmanin et al. , 2002) , were upregulated in spermatocytes (Figure  S1A ). These observations confirmed the purity of the germ cell populations and the reliability of the bioinformatics analysis. By using the FAST-DB splicing patterns annotation tool, we also identified 4,090 AS events in 1,753 genes that are modulated across meiosis in male germ cells, with the majority (65. 1%) being upregulated in spermatocytes (Figure  1C  and Table  S1 ). Classification of the events corresponding to annotated splice variants in the FAST-DB reference database highlighted selection of alternative first and last exons and IR as the most regulated splicing modalities (Figure  1D  and Table  S2 ).
RT-PCR analysis of randomly selected events yielded a high validation rate in all splicing patterns (80%; 12/15 events tested) (Figures  1E  and S1B ). Moreover, comparison of our dataset with a previous RNA-seq analysis of spermatocyte and spermatid transcriptomes (GSE43717;  Soumillon et al. , 2013)  revealed a highly significant overlap (p = 0; modified Fisher's test) in splicing-regulated genes (Figure  S1C ) and a very similar distribution of splicing patterns (Figure  S1D ). Although many splicing-regulated genes (44. 8%) were also regulated at the expression level (Figure  S1E ), with almost equal frequency in all splicing patterns (Figure  S1F ), they were underrepresented when compared with the total of genes differentially expressed in the two cell types (60% of expressed genes).
These results uncover an extended transcriptome reprogramming across meiosis and highlight a specific splicing program activated during this crucial phase of spermatogenesis. Intron Retention Is a Specific Feature of the Meiotic Transcriptome An unexpected finding of our analysis was the large extent of IR detected in male germ cells (Figure  1D ). Indeed, unlike other splicing patterns, IR events were strikingly over-represented with respect to their expected frequency in the FAST-DB reference database (Figure  2A ). To test whether this enrichment was due to underestimation of IR events in the FAST-DB, we analyzed RNA-seq data for other developing/differentiation systems using the same approach. Notably, in the comparison between the transcriptome of round spermatids and somatic Sertoli cells of the testis (GSE43717;  Soumillon et al.
, 2013) , IR accounted for 10% of the regulated splicing events, whereas this percentage was even lower (6%) when we compared newborn with adult cardiomyocytes (GSE49906;  Giudice et al. , 2014) (Figures S2A and S2B). By contrast, a similar percentage of IR events (22% compared with 29% in our condition) was regulated between spermatocyte and spermatid transcriptomes from an independent source  (Soumillon et al. , 2013)   (Figures S1C and S2B). Thus, IR regulation is a specific feature of the transmeiotic differentiation of male germ cells. Further classification of all splicing events differentially regulated between spermatocytes and spermatids (thus including novel splice variants besides the annotated ones of Figure  1C ) showed that IR accounts for 40. 9% of the regulated splicing events (Figure  S2C ).
cytes) and spermatids (sp. tids) (black columns) within each AS pattern. p Values above the graph indicate a significant enrichment for regulated events within specific AS pattern in our dataset compared with the reference database (modified Fisher's test). Red box highlights the IR pattern. (B) Bar graph representing percentages of up-and downregulated events among exonic and intronic splicing events. Enrichment in upregulated events in IR with respect to all other AS events was statistically significant (p = 4. 9 3 10 À61 ; c 2 test). (C) (Top) Visualization of the RNA-seq reads profile of the intron-retaining genes Adam3, Adam18, and Stag3 in sp. cytes (upper graph) and sp. tids (lower graph). Sequence reads (vertical gray lines), exons (blue boxes), and introns (horizontal lines) are shown.
These results suggest that meiotically retained introns represent a distinct class characterized by specific features, which might contribute to the mechanism(s) underlying their retention. Intriguingly, by evaluating the mean FPKM (fragments per kilobase of transcript per million mapped reads) value as estimate of the gene expression levels  (Trapnell et al. , 2010) , we observed significantly higher expression in the group of IR genes compared with all the other transcribed genes (Figure  4E ). Moreover, comparison of our dataset with chromatin immunoprecipitation sequencing (ChIP-seq) analyses of histone marks in spermatocytes available from the GEO database (GSE49624;  Hammoud et al. , 2014)  highlighted a significant enrichment of epigenetic marks of active transcription (i. e. , acetylated H3K27, trimethylated H3K4) in the 5 0 region of IR genes with respect to all other genes, whereas repressive marks (i.
METHOD DETAILS RNA Extraction, Library Preparation and RNA-Seq Data Analysis For RNA-seq analysis, purified populations of meiotic spermatocytes (n=2) and post-meiotic round spermatids (n=3) were fractionated by Sta-put and total RNA was extracted and DNAse treated using the miRNEasy extraction kit (Qiagen) according to manufacturer's instruction. PolyA plus RNA-seq libraries were constructed according to Illumina's protocol and sequenced using a 100 bp single-end format on an Illumina HiSeq 2000. RNA-Seq data analysis was performed by GenoSplice technology (www. genopslice. com). Sequencing, data quality, reads repartition (e. g. , for potential ribosomal contamination), and insert size estimation were performed using FastQC, Picard-Tools, Samtools and rseqc. Reads were mapped using STARv2.
e. , for each gene, all possible splicing patterns were defined by comparing exon content of transcripts). All types of alternative events can be analyzed: Alternative first exons, alternative terminal exons, cassette exon, mutually exclusive exons, alternative 5' donor splice site, alternative 3' acceptor splice sites, intron retention, internal exon deletion and complex events corresponding to mix of several alternative event categories). ''exon'' and ''pattern'' analyses were based on the splicing-index calculation as previously described  (Gandoura et al. , 2013; Wang et al. , 2012). Results were considered statistically significant for p-values % 0. 05 and fold-changes R1. 5 for ''PATTERN'' and p-values % 0. 05 and fold-changes R 2. 0 for ''EXON''. Same procedures were carried out for analysis of downloaded published datasets (GSE43717,  Soumillon et al.
, 2013; GSE49906, Giudice et al. , 2014). Analysis for enriched GO functional clusters among IR and other AS regulated genes was performed using DAVID Functional annotation Tool (v6. 8), using the expressed genes as background  (Huang da et al. , 2009). Ontologies were considered as enriched if fold enrichment R 2. 0 and p-value % 0. 05. Extraction of RNA, RT-PCR and Real-Time PCR Analysis Total RNA was extracted using Trizol reagent (Invitrogen) according to the manufacturer's instructions. Isolation of nuclear and cytoplasmic RNA was performed as described  (Rio et al. , 2010). Briefly, isolated germ cells were washed three times with ice-cold PBS by centrifuging at 1000 g for 5 min and then lysed with three volumes of cold buffer A (10 mM KCl; 1,5 mM MgCl 2 ; 20 mM TrisHCl; 1 mM DTT). Cellular lysates were incubated for 10 min on ice and then homogenized in a Dounce homogenizer by applying 15 strokes with a type B pestle.
PTC and NMD Prediction The representative RefSeq transcript was selected for each of the 768 genes with intron retention event. In case no RefSeq transcript existed, the GenBank transcript with a higher number of exons was selected. Sequence of retained intron was then included in transcript sequence. Open Reading Frame (ORF) predictions were performed using ''find_orfs_with_trans'' from Python module Bio. SeqIO (Python V2. 7. 6) on these new transcript sequences. Only ORFs from frame +1, +2 and +3 and with defined stop were selected. If the coding sequence (CDS) start site was already known in the original transcript sequence, ORF with the same start was selected. If no CDS was already known, the longest ORF was selected. If distance between the predicted CDS stop and the last exon-exon junction was greater than 50 nucleotides, the corresponding transcript was predicted to be targeted by NMD.
Comparison with ChIP-Seq Data For comparison with ChIP-seq dataset for histone modifications, bedgraph files from GSE49624  (Hammoud et al. , 2014)  were annotated after changing the mm9 coordinates into the corresponding mm10 coordinates. The proportion of genes from ''IR'' and ''Others'' groups that were present in the GSE49624 data and had histone marks (score>=10) were plotted using R. These proportions were compared using Chi2 test, or Fisher's test when Chi2 conditions were not met (i. e. when expected values of the number of sample observations were < 5).
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analyses for differential gene expression, splicing changes, comparison of different datasets, analysis of cis-acting sequence features of introns, were performed in R according the statistical tests described in the figure legends. Statistical analyses for qPCR analysis were performed in GraphPad Prism according to the statistical tests described in the figure legends. Number of different cellular preparations or of animals independently analyzed is indicated by the ''n'' in each figure legend. In all figures, if p-value is not stated: *, p-value % 0. 05; **, p-value % 0. 01; ***, p-value % 0. 001.
DATA AND SOFTWARE AVAILABILITY The RNA-seq data have been deposited in the GEO database under ID GEO: GSE95138. splicing events between sp. tids and somatic Sertoli cells (left chart) and between newborn and adult cardiomyocytes (right chart) revealed by analysis of previously published RNA-seq datasets (GSE43717,  Soumillon et al. , 2013; GSE49906, Giudice et al. , 2014). B) Analysis of RNA-seq data from both our and previously published studies revealed a significant enrichment of IR events (blue bars) in the comparison between sp. cytes and sp. tids germ cells respect to the other indicated developing/differentiation systems (p-values of Chi2 test are shown). C) Pie chart depicting proportions of exonic and intronic events among the regulated splicing events in sp. cytes compared to sp.
tids. D, E) Venn diagrams showing overlap between IR genes (D) and regulated IR events (E) in sp. cytes compared to sp. tids identified in this study (green circle) and by analysis of a previous dataset  (Soumillon et al. , 2013). P-value = 6. 97E-164 for overlap in D, p-value = 0 for overlap in E according to modified Fisher's test. F) Venn diagram showing overlap in the direction of the regulation of the IR events regulated between sp. cytes and sp. tids in this study and in analysis of  Soumillon et al. , 2013  dataset. P-value = 0 according to modified Fisher's test.