dataesr/scientific-paragraphs-categorization huggingface dataset:
BLASTx searches revealed that these contigs potentially encode proteins from four different mastrevirus-like genes (V1, V2, C1 and C2) that were between 86 and 100% identical to those encoded by the genome of a sugarcane-infecting mastrevirus from China that was deposited in GenBank in September 2015 (isolate WZG, GenBank accession number KR150789). It is noteworthy that this virus genome from China was identified by its depositor (Wen and collaborators, Guangxi University, China) as a sugarcane streak virus isolate, despite the fact that at the time it shared less than 64% pairwise genome sequence identity with any other known mastrevirus and should therefore have been identified as a novel species. Only one of the nine plants within which mastrevirus sequences were detected (a S. officinarum from the Miami germplasm collection, NG28-020) displayed any discernable chlorotic streak-or striation-like symptoms such as those caused by many of the known monocotyledonous plant-infecting mastreviruses (Fig.
However, cloning of these amplicons was unsuccessful after several attempts and for unknown reasons. BLASTn comparisons between the 17 genome sequences and those in GenBank indicated that they were all most similar (89 to 97% identity) to the novel sugarcane-infecting mastrevirus genome sequence from China deposited under GenBank accession number KR150789. One representative virus isolate was selected for each of the six host plants that yielded entire genome sequences: FL_434-2 (S. spontaneum Iranspon), FL_579-1 (S. barberi Ketari), FL_362-1 (S. officinarum NG28-020), GP_EK2-1 (S. officinarum EK2), GP_TC3-1 (Saccharum hybrid TC3), and GP_TC9-1 (Saccharum hybrid TC9). These six new sugarcane-infecting mastrevirus sequences were aligned together with the sugarcane mastrevirus sequence from China and a representative set of mastrevirus genome sequences that were available in GenBank in January 2017.
Acknowledgements This research would not have been possible without funding provided by the  Florida Sugar Cane League  (Project #  00107475  and Fund #  F000057 ). The work is supported by the  USDA National Institute of Food and Agriculture  [project  Hatch/Rott FLA-BGL-005404 ], the  Région Réunion  and the  European Union  (FEDER; Action # 1. 03 ). DPM and AV are supported by the  National Research Foundation of South Africa. PR is supported by  EU  grant  FP7-PEOPLE-2013-IOF (N°PIOF-GA-2013-622571 ).
Author JML has received research grants from the  Région Réunion  and the  European Union  (FEDER; Action # 1. 03 ). Authors DPM and AV have received research grants from the  National Research Foundation of South Africa. Author PR has received an  EU  grant  FP7-PEOPLE-2013-IOF (N°PIOF-GA-2013-622 , 571 ). Author PCR has received research grants from the  Florida Sugar Cane League  (Project #  00107475  and Fund #  F000057 ). Authors JML, DPM, AV, PR, and PCR declare that they have no competing interests.
Finally, the coding potential of all transcripts was determined using BLAST  [47]  and phyloCSF  [48]. Thresholds for determining the coding potential of loci were defined through comprehensive analysis of scores associated with known proteincoding and non-coding loci derived from Ensembl  [49]   S1 ). The largest proportion of lncRNAs are classified as intergenic followed closely by those overlapping the 5 0 TSS of known protein-coding genes (Fig  1B ). To investigate the quality of assigned TSSs in our assembly, in the absence of cell type-specific 5 0 Cap analysis gene expression (CAGE) tags, we explored the 5 0 localization of ENCODE  [50]   LncRNA transcripts also associate with Pol II peaks with a weaker signal, perhaps due to the lower level of expression of lncRNAs in general  [7].
To explore the quality of TSSs, we compared 5 0 ends of assembled transcripts to FANTOM5 CAGE peaks (Appendix Fig S3A ) and Ensembl annotation (Appendix Fig  S3B ). In general, TSSs associated with higher read depth more closely resemble those in Ensembl  (Appendix Fig S3B). It is clear from these analyses that although a large number of our assembled TSSs have CAGE evidence, a proportion of the assembly likely represent fragments of transcripts. This is to be expected, as cell-specific matched CAGE samples were not available for filtering (Appendix Supplementary Methods). However, even had these data been available, transcripts with repetitive promoter regions (such as those discussed below) would be discarded at a higher frequency when using approaches reliant on the mapping of tags to TSSs.
Interestingly, when we compute similar data for coding transcripts (Appendix Fig S7 ), we also observe an effect, again only for overlapping ERV elements. However, here the effect appears to be predominantly antisense for many subfamilies of ERV. Hence, using FANTOM5 CAGE data, we confirm that ERV elements are a source of genomic transcripts in the testes. As a complementary approach, we reanalysed our RNA-Seq data using a method to allow directional TSSs to be easily recognized and to demonstrate the potential for transcriptional activity within repeat subsets. We divide promoter ERV elements into two sets according to whether they directly overlap the TSS of an assembled transcript regardless of coding potential. For each of these two sets, we compute the coverage of uniquely mapped reads across individual elements and then calculate the mean coverage across all aligned subfamily members.
EMBO reports r t E n d + 2 K b -2 K b S ta r t E n d + 2 K b Antisense Sense -2 K b S ta r t E n d + 2 K b -2 K b S ta ª Having observed ERV families driving expression of lncRNAs in spermatocytes and spermatids, we sought to determine whether this is a conserved feature of vertebrate spermatogenesis. We obtained and analysed data from rat (R. norvegicus) and zebrafish (D. rerio). Rat diverged from mouse approximately 30 Mya  [58] , while zebrafish diverged from mammals over 400 Mya. We performed strandspecific RNA-Seq from ribosomal-depleted RNA isolated from rat spermatocytes and zebrafish whole testis. We tested these samples to determine whether a broad panel of promoter-associated repeat elements are more likely to be associated with lncRNAs or coding genes. Again, we observe ERV elements as the most highly enriched repeats in lncRNA promoters in rat  (Fig 4A and Appendix Fig S8C).
We therefore investigated the expression of individual repeats for three members of the ORR1 group of elements (Fig  6B ). Remarkably, individual repeats within the same subfamily have very distinct expression profiles. This is perhaps most striking for ORR1E with many elements expressed almost exclusively in the male germline, while others are either expressed in many tissues or upregulated in the accessory axillary lymph node, spleen, intestinal mucosa and uterus. Having established that ERV elements are expressed across many tissues, we wished to expand this analysis further to explore whether ERV-regulated lncRNA expression is phenomenonrestricted to the germline. Using Ensembl annotation and mouse ENCODE RNA-Seq data, we identified tissue-specific lncRNAs in a panel of 13 tissues. For these genes, we assessed the absolute number and fraction of transcripts with a promoter or TSS-associated ERV element  (Appendix Fig S9).
However, in these situations, independent and matched end evidence is difficult to ascertain and in many cases challenging to map to a single specific loci. We believe that our data set can also provide a backbone for further improvements and observations. Importantly with respect to this work, we confirmed many of our observations using alternative approaches, incorporating data sets from Ensembl, ENCODE and FANTOM  [49, 50, 57]. As previously described we note an increase in promiscuous transcription in spermatocytes and round spermatids  [9]. However, uniquely, we observe MaLR, ERVK and ERVL ERVs driving significant expression of lncRNAs in mouse post-mitotic spermatocyte and round spermatid populations, with the ORR1 family the most striking example. lncRNA expression is characterized by dramatic increases in the number of loci expressed and their overall degree of expression as spermatogenesis progresses, peaking at the round spermatid stage.
Intriguingly, our analysis of FANTOM5 CAGE data demonstrated that LTR-driven transcription can be far more widespread than we were expecting. This is in agreement with the work of Faulkner et al  [31]  where the authors went further to demonstrate, as an example, the tissue specificity of individual members of the VL30 subfamily of LTRs using an earlier iteration of the FANTOM data. Subsequent to this earlier analysis, we were able to investigate the expression of repetitive elements to a relatively high resolution using the latest CAGE data. In doing so, it appears that individual LTRs of subfamilies selected as drivers of lncRNA expression in gametogenesis themselves have intricate, divergent and tissue-specific expression profiles (Fig  6 ). Similarly intricate cell-specific expression patterns have been noted in the early embryo  [34].
This is complemented by our findings when using Ensembl annotation to define promoter regions and ENCODE sequence data (Appendix Fig  S10 ). Although the extent to which LTRs influence expression in testes appears to be relatively unparalleled (Appendix Fig S9 ), the phenomenon itself is ubiquitous with the potential for much broader impact, extending beyond the germline adding to the expanding literature describing similar phenomena. Finally, we can show that retrotransposon-driven expression is a conserved feature of vertebrate spermatogenesis and plays a particularly significant role in driving and regulating lncRNA expression in rodents. Understanding the extent to which ERVs rewire transcriptional networks will be an important future direction, but this work helps to transfer ERVs from their traditional status as parasitic and opportunistic DNA elements to promoter elements with a major influence on the regulation, diversification and evolution of vertebrate transcriptomes.
Transcriptome assembly Two biological replicates from erythroblasts, spermatogonial stem cells, spermatocytes and round spermatids were processed. Adapter contamination was removed, replicates merged and sequences deduplicated such that each sequence is unique per sample using Kraken  [71]. Transcriptomes were assembled using two approaches, Cufflinks and Trinity  [72, 73]. For Cufflinks, reads were mapped using TopHat2  [73]. Trinity transcripts were remapped to the genome using gmap  [74]. Cuffmerge merged these into a unified assembly. Transcripts were discarded if they matched any of these.
Transcriptome classification and analysis Transcripts were assigned as non-coding if they had a PhyloCSF  [48]  score < 50 (based on 29 species UCSC alignments  [75] ) and no BLASTx match (E > 1 × 10 À10 ) versus Ensembl  [49]  peptides and PfamA/B  [76]. Transcripts were assigned as "intermediate" if they matched only one of those criteria. In general, expression was obtained using FPKM values from RSEM  [77]  and the replicate mean was used. For heatmap expression visualization, global comparisons of major repeat classes and the three-species analysis, DESeq2  [78]  was used to normalize and transform raw counts generated by HTSeq  [79]. Transcript genomic classification was determined with respect to Ensembl annotations including external enhancer data. An overlap of 1nt is counted as a genomic feature match.
Matches are assigned by strand or as "both" where an overlap is not stranded. Pseudogene annotation was also derived from Ensembl. For RNA Pol II analysis, ChIP-Seq data from ENCODE  [50]  were used. Reads were merged between replicates and remapped to the genome (mm10, Bowtie2). Coverage was computed and visualized using Bedtools2 and deepTools  [80, 81]. When multiple isoforms have TSS sites within 50 bp, only the longest isoform is retained. For exon conservation analysis, exons of the longest isoform of each transcript were matched to PhyloP  [82]  scores for the mouse genome (UCSC). To assess the quality of TSS annotation, the assembly was compared to FANTOM5 CAGE peaks  [57]. Bedtools was used to identify the closest peak to the TSS of transcripts from coding and non-coding clusters.
Repeat and ERV analysis Repeat annotation was obtained using RepeatMasker, NCBI/ RMBLAST and the RepeatMasker database of elements (v20130422) for mouse, rat and zebrafish. When non-overlapping repeat analysis is performed, the lowest scoring repeat match at overlapping sites is trimmed until no overlaps remain. Promoter analysis was performed by searching for overlaps between repeats and sites 1 kb upstream of defined TSSs. Unless otherwise stated (see Appendix Supplementary Methods), when non-redundant promoter analysis is performed a random transcript from each transcript cluster is selected, the promoter is defined and all overlapping promoters are excluded. For ERV expression gradient analysis, aligned reads (Tophat2) were filtered to remove multimappers and retain only the first of each pair.
Evolutionary analysis Detection of conserved lncRNAs was performed via mapping the longest isoform from each lncRNA cluster against 15 genome sequences obtained from Ensembl using WU-BLAST  [83]  seed matches followed by realignment as previously published  [84]. For longest ORF analyses, the longest complete ORF was selected for each transcript cluster. Conservation per nucleotide was calculated using PhyloP (mm10 genome, 60-way vertebrate alignment, UCSC). For each transcript, the fraction of nucleotides with positive or negative PhyloP scores was computed separately (cut-off = 1. 301; P ≤ 0. 05) for both the ORF and paired 3 0 UTR.
CAGE analysis of repeat expression Cap analysis gene expression tags were retrieved from FANTOM5  [57]. rRNA matches were filtered using swan (Kraken), and reads that map to a repeat masked genome were discarded (Bowtie, up to 3 mismatches). Remaining CAGE tags were mapped to a database of sequences for individual, non-redundant ERV elements (Bowtie, 2 Both uniquely mapped and multimapped counts were used.
Mass spectrometry A database for LC-MS/MS search was created from a set of ranked ORFs selected based on a series of criteria (see Appendix Supplementary Methods). Spermatocyte and round spermatid cell extract preparation and digestion were performed  [85]. Peptides (200 ng) were injected on EasySpray 50 cm column (Thermo) connected to an Orbitrap Fusion Lumos (Thermo). Mascot (Matrix Biosciences) was used to search for matches with the following settings: MS1 tolerance: 5 ppm, MS2: 0. 1Da, max missed cleavages: 2. Samples were re-analysed with targeted MS, when only ERV-derived peptides selected from previous experiment were measured. Targeted data extraction (MS1 filtering and PRM) was performed using Skyline 3. 5  [86]  with tolerances of dotp > 0. 75, idotp > 0. 8.
Acknowledgements We thank  V. Benes , the staff of the  EMBL Genome Core facility , for highthroughput sequencing support and  EMBL FACS facility  for cell isolation. This research was primarily supported by grants from the  European Research Council  ( FP7/2007-2013/ERC no. GA 310206 ), the  BBSRC  ( BB/J01589X/1 ) and EMBL Core funding. M. P. D. was partly funded by an  MRC   Methodology Research Fellowship  ( MR/L012367/1 ). G. B. was supported by an  EMBL   Interdisciplinary Postdoc (EIPOD) fellowship under Marie Curie Actions Cofund  (no. 291772 ). J. R. was supported by the  Wellcome Trust  (nos. 103139 ,  092076 ,  108504 ). R. S. The  Wellcome Trust Centre for Cell Biology  is supported by core funding from the  Wellcome Trust  ( 092076/Z/10/Z ). R. C. A. is a  Wellcome Trust Principal Research Fellow  ( 095021/Z/10/Z ).
Building use and human behaviour in France The enhancement of TEB described in Sect. 2 allows for the variety of building use and human behaviour at grid point scale to be represented. Detailed information on the urban area of investigation is required to make use of these enhancements. Human behaviours related to building energy consumption depend on the socio-economical characteristics of the population and cultural practices. For example, air conditioning is very common in large parts of the USA  (Sailor and Pavlova, 2003) , whereas it is rare in western Europe. Such information is crucial for a precise simulation of building energy consumption. The determination of all relevant information on building use and behaviour for the entire world is beyond the scope of this study. In this section, we derive these data for France and describe how we take them into account in the enhanced version of TEB.
Our general approach is to assign the behaviourrelated parameters of TEB as a function of building use. In Sect. 3. 1, we describe the datasets we exploit, in Sect. 3. 2 how we determine building use. In Sect. 3. 3 and 3. 4 we describe how we derive the TEB input parameters related to human behaviour. Finally, in Sect. 3. 5 we provide the model configuration for simulation of urban climate and building energy consumption in France.
Datasets In France, administrative datasets that provide centralised information on the morphology and use of buildings and the socio-economical status of the population are readily available. The following administrative datasets are used here: -The French Geographical Institute (IGN) compiles a digital cadastre (the IGN-BDTOPO) providing information on building morphology and use for every building in France (http://professionnels. ign. fr/bdtopo). This dataset is regularly updated; we use the version from 2014. - The data on building morphology included in the IGN-BDTOPO have been used by  Amossé (2015)  to determine the "urban typology" via statistical classification. It describes the building type and alignment (e. g. "detached low rise", "continuous rows of mid-rise", "extended low rise").
The urban typology is comparable to a French version of the built-up classes included in the local climate zone classification by  Stewart and Oke (2012). It can provide valuable information on building use. For example, "detached low rise" corresponds mainly to individual housing, while "extended low rise" is typical for commercial or industrial buildings. The administrative datasets cover all of France, but they do not include details on energy-related human behaviour. More detailed surveys on these aspects are thus required. The following are used in this study: -The ENERGIHAB survey  (Lévy and Roudil, 2012)  provides information on energy-related behaviours for 1950 households in the Paris region (Ile de France). Data are available on the design temperature for heating, the electrical appliances installed and the frequency of their use, and practices concerning ventilation and shading.
ENERGIHAB contains only very little information on air conditioning, since it is not common in the Paris region. -The INSEE conducts regular surveys on practices related to building energy consumption, the "Enquête Nationale sur le Logement" (ENL). Here we use the surveys from 2006 and 2013. These comprise a sample of ∼ 36 000 households (∼ 27 000 households) for 2006 (  2013 ) distributed over all of France. The ENL survey contains information on electricity consumption and the presence of air conditioning. However, there is no information on how air conditioning is used. The exploration of the administrative datasets, surveys and our a priori knowledge of the climatic and cultural situation of France allows us to determine basic information about energy-related human behaviour. As nearly all homes are equipped with heating systems, heating during the winter contributes considerably to the building energy consumption and thus needs to be taken into account.
Building use Our general approach is to assign the behaviour-related parameters of TEB as a function of building use. Information on building use is available in the IGN-BDTOPO. Four main categories of building use are defined for each building: industry, commerce, agriculture and "undefined". This basic information allows us to identify industrial and agricultural buildings as well as large commercial buildings. In addition, all buildings of public interest (e. g. schools) are indicated in the IGN-BDTOPO. By combining this information, we can distinguish between residential (individual and collective), office, commerce, education, public health, industry, agriculture, religious and sport use, as well as identify castles and non-heated buildings. However, one important aspect of French cities is not described in the administrative datasets.
4. The dominant building use (Fig. 3a ) is mainly collective housing close to the city centre and individual housing in suburban areas. However, considerable parts of the domain are covered by university campuses, large commercial buildings, and industrial zones. The information included in the IGN-BDTOPO allows us to identify these building uses. The fraction of commercial use is mainly between 0. 1 and 0. 2 close to the centre of the city, which represents the presence of shops on the ground floor of the buildings, which is common in this area. It must be kept in mind that the approach to determine building use described in this section is tailored to the structure of French cities and the availability of administrative datasets. Different approaches are needed for other regions of the world.
Kunze and Hecht (2015)  have shown that information on (fractional) building use might be derived from collaborative geographical datasets like OpenStreetMap. Such approaches are promising, since in many regions no detailed administrative datasets exist. -The efficiency tendency (ET) (ECR in  Bourgeois et al. , 2017)  indicates to which degree inhabitants tend to regulate equipments related with building energy consumption. For implementation in TEB we only consider the relationship between ET and the use of the heating system. In households with high (low) ET, the design temperature for heating is on average lower (higher) and more likely to be reduced when the building is vacant or during the night. Furthermore, for high (low) ET a larger (smaller) part of the building is not heated. The ET is lower for collective than for individual heating systems.
Bourgeois et al. (2017)  calculate the average electricity consumption available from the ENL dataset for the 3 classes of the EIU indicator. These values are used to initialise the parameters related to the internal heat release in TEB (Table  A4 ). The electricity consumption due to domestic warm water has been excluded from the ENL dataset. This is justified for urban climate modelling, since a large part of the energy exits the building through pipes and into the sewage system  (Heiple and Sailor, 2008). The internal heat release due to metabolism is also not included, since in industrial countries it is often negligible compared to the other components of the anthropogenic heat flux  (Grimmond, 1992). Metabolic heat may have to be included for cities in developing countries. -No robust statistical relationships between the behaviours related to ventilation and shading and the INSEE-Census data have been found by  Bourgeois et al.
Urban morphology and building architecture Data related to urban morphology and building architecture is taken from the MApUCE database, which provides information on urban morphology, architecture and behaviours for 80 French cities. The TEB input parameters related to urban morphology (e. g. building density, building height) have been calculated based on the information on building geometry included in the IGN-BDTOPO. The parameters related to building architecture (e. g. roof albedo) are initialised via the architectural database compiled by  Tornay et al. (2017). The urban vegetation has been retrieved via satellite images  (Crombette et al. , 2014). In Fig. 5 , we display the most relevant elements of the urban morphology for the domain of investigation. The building use is displayed in Fig.