dataesr/scientific-paragraphs-categorization huggingface dataset:
This method does not require any modern calibration data (although in reality vegetation models are largely parameterized based on what we know of modern vegetation), and since it is process based, it can provide reconstructions in no-analogue situations such as low CO 2 climates  (Wu et al. , 2007). The importance of pollen-based climate reconstructions are likely to increase in future as more climate models simulations are made in transient mode and at increasing spatial resolutions. At the same time, more fossil and modern calibration pollen data becomes available in public relational databases. Improvements in transfer function performance can also be expected, particularly through the application of Bayesian approaches that include multi-sample and multi-site analysis.
Online data sharing for regional and global environmental data syntheses using plant microfossil data Terrestrial plant microfossil data from a sedimentary archive are slow to generate but rich in information on past local to sub-regional changes in vegetation and fire dynamics, paleoclimates and paleoecology. It is therefore important to continue the training of the next generation of scientists on terrestrial plant microfossils. Data needs to be carefully archived for long-term storage. Data from a specific site or a dataset can be archived easily (for instance, the NOAA, http://www. noaa. gov/ and Pangaea, https://www. pangaea. de/, offer this service to the palaeo scientific community). Over the past 15 years, the increasing amount of plant microfossil data and the establishment of data sharing in the scientific culture (although some barriers still exist, hampering data sharing and good management practice, Neylon, 2017), and the development of large dataset analysis skills has opened the possibility to tackle new questions at regional and global scales.
Databases including plant microfossils are useful tools for comparing different records, for reconstructing past regional dynamics of vegetation, fire and climate, and for examining their feedbacks to climate. Those databases are also key for evaluating modeling results of past climate, vegetation and fire. For example, the analysis of the European Pollen Database allowed re-examination of the mean annual temperature trend over the Holocene  (Marsicek et al. , 2018). Databases will make it possible also to tackle questions arising from global environmental change such as acclimation, adaptation, migration, risk and safe-operating space ecosystems. Some databases can be focused on one proxy, on one continent or on a specific time period of interest. The structure of the existing palaeo databases is generally similar and includes key metadata as geospatial and dating (chronological) information as well as data in the form of pollen and charcoal counts.
The main structure includes sites, samples, pollen or charcoal data, and dating tables. The Global Pollen Database (GPD) has now been replaced by the new Neotoma multi-proxy palaeoecological database. The North American Pollen Database is already included in Neotoma. The other regional constituent databases of the GPD, such as the European Pollen Database, the Latin American Pollen Database and the African Pollen Database, are in the process of being absorbed into the new structure as quickly as resources permit it (largely through voluntary efforts). It is hoped that regional pollen databases presently outside of the public domain such as the Chinese and Indo-Pacific Pollen Databases will ultimately be made available through Neotoma over the next few years. It should be noted that all of these databases are relational databases composed of interlinked tables, together with a harmonized and standardized taxonomy, and including additional information on related aspects such as chronologies.
In this way, they differ markedly from simple data archives such as NOAA palaeoclimate and Pangaea, which only provide file storage. By creating a global interlinked database, Neotoma is providing the scientific community with a powerful new analysis tool, and one that will allow analysis of not just pollen data but all kinds of palaeocological information. The Global Charcoal Database (GCD) holds hundreds of fire history records from six continents mostly based on the analyses of macro and micro-charcoal particles in terrestrial and marine sediments. Successive global syntheses based on increasing number of quantitative data demonstrated a strong relationship between fire and climate over the past 21,000 years  (Daniau et al. , 2012; Marlon et al. , 2016; Power et al. , 2008). As charcoal values vary by orders of magnitude between and within sites the data have to be standardised to facilitate comparisons between sites and through time.
A full description of the procedure and details about the contents of the database can be found in  Marlon et al. (2008)  and  Power et al. (2010b). The current version of the database is now stored under the MySQL environment at gpwg. paleofire. org. The paleofire R package  (Blarquez et al. , 2014)  allows easy access to the GCD data and manipulation of its contents to produce biomass burning reconstructions for subsets of records. The ACER (Abrupt Climate Changes and Environmental Responses) global pollen and charcoal database  (Sánchez Goñi et al. , 2017)  aims to examine changes in vegetation and fire during the last glacial period (73-15 ka), a period of rapid climate changes marked by the socalled Dansgaard-Oeschger (D-O) cycles. The database includes 93 pollen records with a temporal resolution better than 1000 years, 32 of these sites also provide charcoal records.
In order to compare patterns of change from different regions, harmonized and consistent chronology based mostly on radiometric dating and few additional tie points based on event stratigraphy below 14 C dating limit or below 14 C levels has been constructed for ninety six of these records. The ACER synthesis emphasized the scarcity of palaeoclimatic records from the tropical regions with enough resolution to investigate millennial-scale climate events  (Harrison and Goñi, 2010; Sánchez Goñi et al. , 2017). The Latin American ACER (LaACER) project was conceived to fill this gap by compiling and synthesizing data from the American tropics and.
Fire The different syntheses of the Global Charcoal Database  (Daniau et al. , 2012; Marlon et al. , 2016; Power et al. , 2008)  showed that biomass burning varies closely with climate changes. For example, biomass burning increased globally from the Last Glacial Maximum to the Holocene (Fig  6d ). Daniau et al. (2012)  demonstrated this global increase in biomass burning is controlled by rising mean annual temperature and moisture, with temperature being the primary control. Few biomass burning records document older glacial/interglacial transition. The synthesis of twenty long records of charcoal, registering not only the Holocene but also the Eemian and part of the last glacial period, suggested that biomass burning is generally high during interglacials and low during glacials  (Daniau et al.
, 2008)  while the decreasing fire activity/frequency recorded since the mid-Holocene is attributed to decreasing seasonality and/or wetter summers  (Rius et al. , 2011; Vannière et al. , 2011). The role of fire, of either anthropogenic or natural origin, in the expansion of some emblematic tree species such as Fagus sylvatica (e. g. Giesecke et al. , 2007; Tinner and Lotter, 2006; Valsecchi et al. , 2008) , on forest composition  (Carrión, 2002; Carrión et al. , 2003; Gil-Romera et al. , 2010) , and on the historical vegetation dynamics of currently protected areas  (Morales-Molino et al. , 2017a; Morales-Molino et al. , 2017b)  remains matter of debate. The palaeofire research has made available a large dataset of individual charcoal and fire frequency records that has enabled a more comprehensive assessment of fire-vegetationclimate-human activities linkages at the continental scale.
North America Due to a long history in palynological research in North America, more than 1 300 pollen sequences are included in the Neotoma paleoecology database  (Williams et al. , 2018)  covering entirely or part of the last 20 kyr, although most of them only contain the Holocene period. Several state of the art papers aim at retracing the North American postglacial vegetation history, such as  Gavin and Hu (2013) ,  Wigand (2013) ,  Whitlock and Brunelle (2007)  and  Whitlock (1992)  for the western part of the sub-continent,  Williams and Shuman (2013) ,  Willard (2013) ,  Grimm and Jacobson Jr (2004) ,  Webb et al. (2003) ,  Davis (2015) ,  Davis (1984) ,  Naughton et al. (2015)  and  Blarquez and Aleman (2016)  for the eastern part and Bigelow (2013) for regions above 60°N. In North America, the deglacial warming (19-11 ka) along with the retreat North American ice-sheets resulted in large range shifts of terrestrial ecosystems.
subtropics. Palaeoclimate records of these regions may help understanding globally-important oceanic and atmospheric systems in the climate variability since these regions are influenced by the Pacific and Atlantic oceans, and by large-scale atmospheric features including the Intertropical convergence Zone (ITCZ), El Niño-Southern Oscillation (ENSO), the North American Monsoon (NAM) and the South American Summer Monsoon (SASM) (Garreaud et al. , 2009). LaACER sits within the Palaeoclimate commission of INQUA, and was cosponsored by PAGES (Urrego et al. , 2014). List of databases including pollen and microcharcoal sedimentary records 5. Glacial-interglacials cycles of the 100-ky world and the climatic shift of the Mid-Bruhnes event during the Middle Pleistocene.
It is important therefore to continue improving the identification and interpretation of these microfossils and to keep training the next generation of scientists. The datasets generated also need to be carefully archived and should benefit from long term storage. Over the past 15 years, the growing wealth of plant microfossil data, the establishment of data sharing protocols in the scientific community and the development of large (continental to global) datasets, and the development of new analytical approaches have provided the opportunity to tackle new scientific questions. Key topics at regional and global scales include the response of vegetation and fire to climate change, as well as feedback mechanisms related to both vegetation, such as the vegetation-(snow)-albedo feedback, and fire, such as fire feedback on the carbon cycle and on albedo.
Acknowledgements We thank  PAGES  and  INQUA  for supporting the  Global Paleofire Working Group (GPWG) , the projects on " Abrupt Climate Changes and Environmental Responses" (International Focus Group ACER ) and " Latin American Abrupt Climate Changes and Environmental Responses " (LaACER). Financial support was provided by the  INSU-IMAGO projects WarmClim, PuLSE,MIcMAC  and  CAMPFIRE. Basil Davis  was supported by " SNF HORNET  Project Grant No. 200021_169598 " and  Filipa Naughton  by  ClimHol  ( PTDC/AAC-CLI/100157/2008 ) and  Ultimatum  ( IF/01489/2015 ) research grants.
Using the Cap Analysis of Gene Expression (CAGE) technology  [20, 21] , the FANTOM5 consortium provided one of the most comprehensive maps of TSSs in several species  [2]. Integrating multiple collections of transcript models with FANTOM CAGE datasets, Hon et al. built an atlas of 27,919 human lncRNAs, among them 19,175 potentially functional RNAs, and provided a new annotation of the human genome (FANTOM5 CAGE Associated Transcriptome, FANTOM CAT)  [4]. Despite this annotation, many CAGE peaks remain unassigned to a specific gene and/or initiate at unconventional regions, outside promoters or enhancers, providing an unprecedented mean to further characterize non-coding transcription within the genome 'dark matter'  [13]  and to decode part of the transcriptional noise. Here, we probed CAGE data collected by the FANTOM5 consortium  [2]  and specifically looked for repeating DNA motifs around CAGE peak summits.
The motif enrichment is therefore more indicative of transcription initiation at T STRs than of the existence of a true motif located precisely 2bp after T STRs. In line with this, ENCODE CAGE data, which were generated using Illumina technology, confirmed several FANTOM CAGE peaks (generated by HeliscopeCAGE) but were not exactly aligned on that of FANTOM (Supplementary Figure  S2B ). Note that the FANTOM CAT annotation was actually shown to be more accurate in 5' end transcript definitions compared to other catalog  [4]. Moreover, a small fraction of Start-seq  [27]  and DECAP-seq  [28]  TSSs can also initiate in sequences with a preference for T and this clearly does not represent a genuine motif (Supplementary Figure  S3A  and B ). A similar observation was made with TSS-seq data collected in Arabidopsis thaliana  [29]  (Supplementary Figure  S4 , motif #2).
This artificially introduces mismatched Gs at Illumina tag 5' end, which is not detected with Heliscope  [31]. Although such G bias is not clearly observed when considering the whole CAGE peak, it is readily detectable when considering the 3' end of T STRs (position -2 from FANTOM CAGE summit) using Illumina EN-CODE CAGE data produced in Hela-S3 nuclei (Figure  2A ). This bias is observed in other cell types and is comparable to that observed with CAGE tags assigned to gene TSSs (Figure  2B  and Supplementary Figure  S11 ). The G bias is even more pronounced in intergenic than intragenic T STR CAGEs, suggesting that reads corresponding to host gene mRNA can mask transcription at STRs (Figure  2B  and Supplementary Figure  S11 ). Conversely, most CAGE tag 5' ends perfectly match the sequences of pre-miRNA 3'end, as previously reported  [31] , or that of 61,907 randomly chosen genomic positions (Figure  2B  and Supplementary Figure  S11 ).
We concluded that, overall, detection of CAGEs at T STRs is associated with transcription-related chromatin marks. Several T STR CAGEs correspond to annotated transcript and enhancer TSSs. We found that 11,180 T STRs (end+2bp) were associated with a 'robust' CAGE peak i. e. peaks confirmed by external data of ESTs and associated with H3K4me3 marks and DNase hypersensitivity sites (DHSs)  [2] , representing > 17% of all T STR CAGEs , and thereby confirming the results shown in Figure  3. We then assessed the presence of T STR transcription among annotated TSSs. Motif enrichment around FANTOM CAT TSSs  [4]  shows that ∼ 1. 5% of them initiate downstream T STRs (Figure  4A ). Looking directly at TSS coordinates of FANTOM CAT robust transcripts  [4] , we noticed that 6,734 TSSs, corresponding to 10,606 robust transcripts (out of 525,237, ∼ 2%), initiate 2bp after a T STR, with a clear enrichment in lncRNA intergenic, lncRNA sense intronic or sense overlap RNA (hypergeometric test p-values < 2.
2e-16 for the 3 RNA classes). A list of these TSSs is provided in Supplementary Table  S3A. Similar results were obtained for 5,889 stringent transcripts (total n = 402,813)  [4]. Importantly, transcript models in FANTOM CAT combine various independent sources (GENCODE release 19, Human BodyMap 2. 0, miTranscriptome, ENCODE and an RNA-seq assembly from 70 FANTOM5 samples) and FANTOM CAT TSSs were validated with Roadmap Epigenome DHS and RAMPAGE data sets  [4]. As expected, these TSSs only moderately contribute to gene expression (Supplementary Figure  S16 ). No specific Open Reading Frame could be detected 2kb downstream T STR CAGEs (Supplementary Figure  S17 ). The T homopolymer is also observed for ∼ 4% of FANTOM enhancer TSSs [3] (Figure  4B ), though no enrichment in enhancer epigenetic marks are predicted by chromHMM/Segway (Figure  3A ).
Clinically relevant variants are predicted to impact transcription at T STRs. Previous studies showed that genomic variations at T STRs can impact gene expression. For instance, the variant rs10524523, located in TOMM40 intron 6, which corresponds to a T STR associated with a CAGE signal in several FANTOM libraries, is linked to the age of onset of cognitive decline, Alzheimer's disease and sporadic inclusion body myositis  [43] [44] [45] [46] [47] [48]. The TOMM40 mRNA brain expression appears to be linked to the length of the T STR, with the longer the variant the higher the expression  [43, 44]. Conversely, the KIAA0800/ENSG00000145041 promoter is repressed by an upstream element involving a T STR and two Alu repeats  [49]. In that case, the CAGE signal detected at this particular T STR has been annotated as one TSS of KIAA0800/ENSG00000145041 in FANTOM CAT (namely ENCT00000303320.
1, FTMT20900010597. 1, MICT00000243080. 1) [4]. More widely, the length of T STRs have been reported to impact gene expression (called expression STR or eSTR) in a directional and strand-specific manner  [50]. We further used our model to predict the effects of genomic variations located within sequences surrounding T STR (end of T STR ± 50bp). First, we observed, in the case of TOMM40 intron 6 variant, that predicted transcription is positively correlated to the length of the T STR (Figure  7A ). Second, we compared the effect of variants listed in dbSNP, the world's largest database for nucleotide variations  [51] , and variants listed in ClinVar, which pinpoints medically important variants  [52]. Almost all T STRs harbor variations listed in dbSNP (962,241 out of 1,169,145), while 2,577 T STRs are associated with variations listed in ClinVar.
Discussion We looked at recurring DNA motifs associated with unassigned FANTOM CAGE peaks  [2]  and showed that a significant portion of them initiate at T STRs in several species. We do not exclude that some CAGE peaks initiating at T STRs are generated by internal priming during Heliscope sequencing. Yet we provide evidence that a fraction represents genuine TSSs as (i) T STR CAGEs are truly capped and associated with typical epigenetic marks, (ii) several of them correspond to annotated long non-coding transcripts or enhancer RNAs and are confirmed by various technologies, not requiring oligodT priming, and (iii) their expression can be predicted by a deep learning model, which uses features located downstream T repeats, unlikely involved in internal priming. We show that GRO-seq coincides with CAGE at T STRs (Figure  3B  and Supplementary Figures  S11  and S12 ).
Implicitly, the human transcriptome should contain a large portion of RNAs derived from repetitive elements  [57]. We provide here an example of such RNAs, which may represent the counterparts of satellite non-coding RNAs  [58]. Homopolymers of > 10 Ts appear at frequencies well above chance in the non-coding regions of several organisms, suggesting selective advantages that lead to their overrepresentation  [26]. Additionally, using FANTOM and GENCODE genome annotations, we noticed that T STRs are enriched in introns of coding, as opposed to non-coding, genes (Supplementary Figures  S1  and S2 ). In human and mouse genomes, T STRs can correspond to the polyA tails of short interspersed nuclear elements (SINEs)  [59]. According to RepeatMasker, only a fraction of human T STRs associated with CAGE peaks correspond to SINEs (∼ 37% vs.
Bioinformatics tools. Intersections between genomic coordinates were computed with BEDTools  [67]. Signal were extracted from bigwig files using bwtool (agg and extract subcommands)  [68]  and deepTools  [69]. Statistical tests were done using R  [70]  (fisher. test, wilcox. test, cor ) or Python (fisher exact, mannwhitneyu, spearmanr from scipy. stats (http://www. scipy. org/)). Graphs were made using ggplot2 R package  [71]  or Python matpotlib library (https://matplotlib. org/api/pyplot api. html). All scripts are available upon request. Evaluating mismatched G bias at Illumina 5'end CAGE reads. Comparison between Heliscope vs. Illumina CAGE sequencing was performed as in de Rie et al. [31]. Briefly, ENCODE CAGE data were downloaded as bam file (Supplementary Table  S7 ) and converted into bed file using samtools view  [72]  and unix awk as follow: samtools view file.
Epigenetic marks at T repeats. Average chromatin CAGE signal around T STR CAGEs (Figure  S9 ) were computed using the agg subcommand of the bwtool  [68]. For RNAP-II ChIA-PET, interacting regions within the same chromosome were extracted using the column name $4 of the bed file. Roadmap and GENCODE ChIP-seq as well as GROseq data were extracted from URLs indicated in Supplementary Table  S7. To look at epigenetic marks around T repeats into H1-hESC cell line, the coordinates of 'expressed' and 'non-expressed' T repeats ends+2 bp (see results for details) were intersected with Roadmap bedfiles using bedtools intesect  [67]. The fractions of intersecting T repeats within each set were compared for each available epigenetic mark and each of the 3 H1-hESC samples using Fisher's exact test. The same procedure was used in other cell lines with ENCODE broadpeaks data for the H3K4me1, H3K4me2, H3K4me3 and H3K36me3.
Convolutional Neural Network. A Zenbu  [73]  xml custom script was used to calculate the mean raw tag count (Q3 filter, no normalization) of each base genome-wide in 988 FANTOM libraries. We summed these means along each T STR + 5bp. This values were used as predicted variables in a CNN model built on sequences centered around each T repeat end (n = 1,169,236). The same procedure was repeated with mouse (397 libraries) and chicken data (58 libraries) using the same architecture. The coordinates of the T repeats with the mean tag count values (score column) used to train our models are provided as bed files https://gite. lirmm. fr/ibc/t-repeats. The fasta file corresponding to T repeats sequences with their tag count was parsed using Biopython  [74]  library. To build the convolutional neural network, we used Keras  [75]  library in Python with Tensorflow  [76]  backend.
The complete code can be found at https://gite. lirmm. fr/ ibc/t-repeats. Comparison with other models. To compare our deep learning regression model with other parametric and non parametric approaches, we used linear regression with l1-norm penalty via LASSO  [41]  and Random Forest  [42]  to predict the mean tag count signal. For both approaches, the predictive variables are the nucleotide, dinucleotide and trinucleotide content computed into the 50 bp downstream and 50 bp upstream the T homopolymer end, excluding the T homopolymers. We also integrated the length of the T repeat and the nature of the nucleotide following the T repeat end (A, C or G at position -1 referring to the CAGE summit at 0). LASSO inference was performed using the function cv. glmnet from the R package glmnet and Random Forest was performed using the randomForest R package.
To compute the distribution of the minimal depth we used the min depth distribution function from the R package randomForestExplainer. [2]. C. Among 158,966 mouse CAGEs, 8,274 are associated with T homopolymers. Note that another repeat of 2-5 Gs is also identified in FANTOM CAGE data with strong similarities in both human and mouse (A and C). However, this repeat is less strict and is not conserved in dog, chicken, macaque and rat (Supplementary Figure  S1 ) and is absent in Start-seq and DECAP-seq data (Supplementary Figure  S3 ). Though this motif may represent a biologically relevant signal in human and mouse, these features make it less relevant for our study. Figure  6  Probing sequence-level instructions for transcription at T repeats A. Length of T STRs associated (blue) or not (red) with CAGE peaks.