dataesr/scientific-paragraphs-categorization huggingface dataset:
For this first step, one interested, for example, in potential full-length LTR retrotransposons could alternatively use existing tools  [11, 20]  to build the sets of candidates based on their LTR pairs. The output of this first step is a list of potential LTR retrotransposon candidates, which will be searched for conserved protein domains (Step 2) and assigned probabilities of belonging to a particular superfamily (Step 3). Even though this first step generates a lot of false positives, the following steps will correct for these. Step 2: Predicting conserved protein domains. The second step consists in screening every candidate TE, obtained previously, for the presence of conserved protein domains that can be found in LTR retrotransposons. To that aim, we use the RPS-Blast program  [21]. RPS-Blast (Reverse Position-Specific Blast) uses a query sequence to search a database of precalculated position specific score matrices (PSSMs), built from multiple sequence alignments.
In order to accelerate the search process, we constructed a database that only contains the PSSMs related to conserved protein domains found in LTR retrotransposons. More precisely, our database consists of 26 PSSMs, corresponding to the list of conserved subdomains shown in Table  1. The resulting domain hits are used to provide an estimation of the boundaries of the TE candidate. In particular, we considered that each candidate starts at the beginning of the first predicted domain and ends at the final position of the last predicted domain. In case a domain is found in the overlap region between two sequences, we merged these sequences before determining their boundaries. In case a candidate contains predicted domains in both DNA strands (direct and reverse), we split it in two or more new candidates.
RPS-Blast: We constructed the database used by RPS-Blast by taking for each domain of interest the set of sequences from the Conserved Domain Database (CDD) (http://www. ncbi. nlm. nih. gov/Structure/cdd/cdd. shtml)  [28] , used to generate the original multiple sequence alignment, except that we excluded the sequences of the organisms used in our tests (D. melanogaster and A. thaliana, respectively). The reason to exclude the D. melanogaster or A. thaliana sequences is that we want to provide an evaluation as blindly as possible, without using any known information from the target organism. The PSI-Blast (Position-Specific Iterative Blast) program was used to obtain the new PSSMs and Makeprofiledb application for creating the database for RPS-Blast. FORF: The relational trees were built with default parameters, except for the minimum number of examples in a leaf, which was set to 5.
No pruning was used. The forests consist of 100 trees, with a feature sample size at each node equal to the square root of the number of possible features. For the training of FORF we used sequences from Repbase (http://www. girinst. org/server/ RepBase/), volume 17-issue 3, one set for each superfamily of interest here. In order to provide a fair evaluation, as before, we excluded from these sets the sequences of the target organism. Note that each analysis was performed twice: each time leaving out one target organism. The resulting sets were also used as the databases for RepeatMasker and Censor applicationsas described further. We ran the RPS-Blast program, with the PSSM database created in Step 2 of our framework, to search these sequences for regions related to the conserved domains of interest (Table  1 ), retrieving the same types of information obtained from the screening of the candidates (Step 2).
Consequently, dromedary intestinal microbiota can be a source of novel carbohydrate-active enzymes (CAZymes)  [7]  responsible for the degradation, modification or synthesis of glycosidic bonds  [8]. They have been described to offer attractive opportunities to the animal feed industry including the increase of hemicellulose digestibility in feed stuffs, the improvement of feed conversion efficiency and the mitigation of sanitary problems such as sticky droppings  [9, 10]. So far, all the CAZymes used in the feed industry have been discovered by means of classic culture-dependent methods  [11]. However, knowledge of gut ecosystems, where most of the microbiota remains unidentified, is largely incomplete  [12]. Only less than 1% of CAZyme sequences in databases have indeed been identified from uncultured microorganisms  [13] , representing between 60% and 80% of bacteria in digestive ecosystems  [14].
Sequencing and functional annotation Fosmid DNA of selected clones was extracted using the alkaline lysis as previously described  [20]. The nucleotide sequences were determined using the MiSeq technology (Illumina MiSeq (San Diego, CA)) allowing the generation of thousands to millions of short sequencing reads. This raw sequence dataset was assembled using the SPAdes Genome Assembler (http://bioinf. spbau. ru/en/ spades). The fosmid pCC1FOS sequences were identified and discarded. The Rapid Annotation Server (RAST, http://www. nmpdr. org/FIG/wiki/view. cgi/FIG/RapidAnnotationServer) was used to predict open reading frames from the resulting sequences and unveil the repertoire of predicted genes and gene functions. The CAZy family annotation was based on ORFs sequence similarities with proteins of sequenced genomes using BLASTP analysis against the non-redundant NCBI and the SWISSPROT databases.
Taxonomic assignment of the metagenomic sequences Two methods were used to perform taxonomic assignment of protein-coding genes. The first was based on ORFs sequence similarities with sequenced genomes, using BLASTX analysis against the non-redundant NCBI database (E-value 10 -8 , identity ! 90%, and query length coverage ! 50%). A class, genus or species was assigned to a contig when protein-coding genes of this contig were derived from the same organism. The most probable common ancestor from which the non-assignable contig has evolved was retrieved using MEGAN v. 5. 10. 6  [21]  based on BLASTX analysis against non-redundant NCBI.
Sequencing and annotation Sequencing of fosmid DNA from the selected clones, 26H3 and 36A23, was performed with the MiSeq (Illumina MiSeq (San Diego, CA)). Read assembly resulted in one large contig of 24131 bp for 26H3 (Accession number: LT796703) and 31146 bp for 36A23 (Accession number: LT796702). Gene prediction of resulting contigs showed that the metagenomic inserts contained 20 ORFs and 29 ORFs respectively for 26H3 and 36A23. The taxonomic assignment of the predicted genes based on sequence similarities with the non-redundant (NR) protein sequences database of the NCBI showed that sequences of the two contigs present low similarity with proteins of sequenced genomes, showing that they derived from microorganisms whose genome sequences were not available. MEGAN analysis revealed that 16 and 17 ORFs respectively belonging to the clones 26H3 and 36A23 were assigned to the Bacteroidetes/Chlorobi group.
Ghanem and coworkers  [30]  revealed that the main residues in crude extract mucilage of halophytes were rhamnose, galactose, and glucose. In addition, the main monosaccharide detected in seeds of Alhagi species (camel thorn) is glucose, while mannose is dominated the epigeal part  [31]. This would explain the high hit rate obtained on galactomannan and β-glucans, while no hit was obtained on xylan. Functional annotation of 26H3 contig revealed predicted genes coding for proteins being involved in plant cell wall hydrolysis. We predict that 6 ORFs are included in a polysaccharide utilization locus to target acetylated mannans. Nevertheless, since ORF5 product is predicted to be a carbohydrate transporter from the MFS family, and thus does not present any significant similarity with SusC carbohydrate transporters that are the 'labels' of the bacteroidete Polysaccharide Utilization Loci (PULs) described in the PUL database (http://www.
Interestingly, the comparison of the 36A23CAZy gene cluster with Polysaccharide Utilization Loci (PULs) described in the PUL database revealed that it has no similarity with archived PULs. In order to assess the originality of these metagenomics CAZymes, their sequences were compared to the amino acid sequences of characterized CAZymes derived from the CAZy database (Tables  6  and 7 ). Genes assigned to the GH5, GH16 and GH26 families share less than 55% identity with their nearest characterized neighbors. Those assigned to the GH3, GH130 and GH94 families showed less than 75% identity with their nearest characterized exo-acting homologs. Finally, the CAZymes assigned to the CE7 and CE6 families displayed only 36% and 29% identity with characterized esterases. The most original putative CAZyme is that encoded by ORF3 of the clone 36A23, function.
1 [34] CAJ19136. 1 4 conserved hypothetical protein 99 82 0. 0 β-1,4-mannosylglucose phosphorylase (MGP;BF0772) 99 73 0. 0 [unidentified microorganism] Bacteroides fragilis NCTC 9343 GenBank: CAH06518. 1 GenBank: CAJ19137. 1 (GH130) [35] 5 cation transporter [Bacteroides 99 68 0. 0 hypothetical protein [uncultured bacterium contig00010 99 56 3e-173 intestinalis] GenBank: (2014)] GenBank: AIA99573. 1 [36] WP_044533049 8 beta-glucosidase [uncultured 99 69 0. 0 β-glucosidase (BglF3;Fjoh_0775) [Flavobacterium 96 43 0. 0 microorganism] GenBank: johnsoniae UW101] GenBank: ABQ03809. 1 (GH3) [37] ADB80109. 1 9 Hypothetical protein [Bacteroides 100 83 0. 0 Cellobionicacid phosphorylase 94B (Cep94B;CBAP; 97 70 0. 0 uniformis] GenBank: SdCBAP;Sde_0906) (Cep94B) (GH94) [Saccharophagus WP_016273975. 1 degradans 2-40] GenBank: ABD80580.
1 [Bacteroides fragilis NCTC 9343] GenBank: CAH06517. 1(GH26) [35] 14 hypothetical protein [Labilibacter aurantiacus] 98 53 3e-127 Fisuc_2933 / FSU_0196 [Fibrobacter 91 38 8e-61 GenBank: WP_068471806. 1 succinogenessub sp. succinogenes S85] GenBank: ACX76513. 1 (GH5-CBM4) 16 glycoside hydrolase [marine bacterium AO1-C] 83 53 4e-93 laminarinase (LamR) [Rhodothermus 77 49 2e-82 GenBank: OJJ21706. 1 marinus] GenBank: AAC69707. 1(GH16) [40] 17 9-O-acetylesterase [Parabacteroides] GenBank: 99 48 3e-148 Acetylxylan esterase (R. 17;R. 17-1) 37 29 7e-08 WP_005865579. 1 [unidentified microorganism] GenBank: CAJ19109. 1 (CE1-CE6) [41] 26 putative multiple sugar transport system 99 66 1e-166 - - - - substrate-binding protein [Prevotella ceaebacterium HUN156] GenBank:SFW18185. 1 https://doi. org/10. 1371/journal.
Data Analysis The raw FastQ files are deposited at the Short Read Archive (https://www. ncbi. nlm. nih. gov/sra) under accession number SRP136075. Raw numbers of reads prior to subsampling are provided in Supplementary Table  1. The FASTQ files produced on the MiSeq were demultiplexed and UMI-corrected using an analysis pipeline that incorporates Decombinator  (19, (23) (24) (25). All software is freely available at https://github. com/innate2adaptive/ Decombinator. Typically, when analyzing TCR data, the total number of TCRs in a sample are rarefied to either the minimum number of TCRs from all samples (or lower), or an arbitrarily selected percentage of the maximum read depth (usually 10%) of the sample containing the largest number of TCRs. Motivated by concerns over sample representation following rarefication, we developed an algorithm to compute the minimum representative subsampling depth for each sample (Supplementary Figure  1 ).
It can be used to gauge the inequality of the frequency distribution between different clonotypes in each sample, and give a summary of the clonotype abundance distribution. Shannon entropy, a separate measure, quantifies diversity through the incorporation of both evenness and richness, with higher values representing more even populations of TCR rich samples. Shannon entropy, H(X), where X is the CDR3 data from a single sample, is defined as H (X) = - n i=1 p (x i ) log * b p (x i ) (1) where p(x i ) is the proportional abundance of clonotype i and b is the logarithm base of 2. For analyses, the ineq package (version 0. 2. 13) was used in the R environment (version 3. 3. 2) for computing the Gini coefficient along with the vegan package (version 2. 4. 2) for computing Shannon entropy. The ggplot2 package (version 2.
Verification of the Antigen Specificity VDJdb (  26 ) is an open-access TCR database that contains CDR3s annotated for antigen specificity. We investigated whether antigen specificity could be defined for the unique clonotypes in each sample, using VDJdb. We found several examples of TCR sequences from the database in the repertoires of the patients in this study (Figure  4 ). These shared sequences are likely to represent "public" TCRs which may have higher probabilities of being generated owing to the non-uniform nature of the recombination process  (27). Unexpectedly, clonally expanded HIV-1 "specific" sequences were found in each reconstituted repertoire (ranging between 0. 04-0. 45% across all 16 CBT patients), despite all patients being confirmed to be HIV negative prior to transplantation.
AUTHOR CONTRIBUTIONS SA, ASG, BM, and RC conceived the study. SA, ASG, BM, JH, and TO designed the experiments. ASG, LM, and SA performed experiments and collected experimental data. MP, RC, and PV provided sample collection and collected clinical data. SA, ASG, BM, TA, JS, JB, NK, BC, and RC interpreted the data and wrote the manuscript. All authors approved the final version.
ACKNOWLEDGMENTS This work was funded by the  Olivia Hodson Cancer Fund ,  Action Medical Research for Children , the  Henry Smith Charity  and the  Reuben Foundation. All research at Great Ormond Street Hospital NHS Foundation Trust  and  UCL Great Ormond Street Institute of Child Health  is made possible by the  NIHR Great Ormond Street Hospital Biomedical Research Centre. The views expressed of those of the authors are not necessarily those of the  NHS , the  NIHR , or the  Department of Health. We thank the  GOSH Bone Marrow Transplantation team  for their participation in this study and the  GOSH SIHMDS-Haematology Department in the Camelia Botnar laboratories  for generating the clinical data. We would also like to thank the patients and their families for their participation in this study.
The monsoon rainy season extends to the end of September before reverting back to winter monsoon conditions by November  (Chang et al. , 2006). Due to the importance of ocean-atmosphere interactions in the evolution of the SAM and because we are primarily interested in evaluating model configurations that can be used for making future projections, here we analyze the ability of the coupled EMBRACE models to represent the main features of the summer SAM. Figure  3  shows seasonal mean (June to September, JJAS) precipitation from the coupled models and the differences relative to the satellite product TRMM 3BV43  (Huffman et al. , 2007). Figure  4  and Figure  5  show near-surface temperature and 850hPa zonal wind speed compared to data from the ERA-Interim reanalysis. Also shown are the alternative observation-based datasets GPCP-SG (precipitation) and the NCEP reanalysis  (Kanamitsu et al.
Findings from this analysis may offer pointers to an optimal resolution for other models to aim at with respect to simulating sub-seasonal precipitation variability as well as seasonal mean rainfall. Figure  12  shows 3-10 day band-pass filtered precipitation variance for JJAS over Africa from two observational data sets (TRMM 3B42 and GPCP-1DD) and the six EC-Earth resolutions. The two observations differ markedly with respect to the absolute magnitude of variance on these time scales. This is partly expected as the observational datasets feature a rather different horizontal resolution (0. 25° vs. 1°). They do, however, exhibit some agreement in the spatial distribution of maxima and minima in precipitation variability, with a broad region of high variability stretching from Sudan west across to the Atlantic coast.
Analysis is only performed for the AMIP simulations as the main findings also apply to the coupled models. Figure  16  shows cross-sections from 65° S to 30° S of simulated zonal mean DJF total cloud cover, LWP and IWP, TOA outgoing (SWUP) and surface downwelling (SWD) shortwave radiation compared to satellite observations. For LWP and IWP, ERA-Interim reanalysis data are also included as a second observationally based estimate  (Dee et al. , 2011). Observed cloud cover increases from ~60% at 30° S to more than 90% around 60° S. Most models capture this poleward increase, although all, except HadGEM3-A, exhibit a systematic negative bias (of 5-15%) across the band ~45° S to 65° S. HadGEM3-A has the most accurate cloud cover and is a clear improvement over HadGEM2-A. CNRM-AM-PRE6 also shows improvement compared against its CMIP5 version.
EC-Earth3 shows a small improvement, while the MPI-ESM model shows little change. The impact of clouds on solar radiation can be summarized by cloud optical depth, which is a function of the cloud water content and the effective radius of cloud liquid droplets/ice crystals, integrated over cloud depth  (Slingo, 1989)  microwave data set, IWP observations are MODIS collection 6 data  (Platnick et al. , 2003). Similar to  Jiang et al. (2012) , the MODIS IWP data representing in-cloud values have been multiplied with the observed ice cloud fraction for comparison with the grid-box averages provided by the models. Due to the large differences across remotely sensed LWP/IWP data sets, values from UWisc and MODIS should be viewed as indicative at best. We include LWP/IWP estimates from ERA-Interim as a second constraint to provide some measure of this uncertainty.
Code and data availability This analysis has been done with the ESMValTool, which is released under the Apache License, VERSION 2. 0. The newly added ESMValTool namelist 'namelist_lauer17james. xml' includes the diagnostics that can be used to reproduce the figures of this paper. This version will be available from the ESMValTool webpage at http://www. esmvaltool. org/ and from github (https://github. com/ESMValTool-Core/ESMValTool). Users who apply the software resulting in presentations or papers are kindly asked to cite the ESMValTool documentation paper  (Eyring et al. , 2016b)  alongside with the software doi (doi: 10. 17874/ac8548f0315) and version number. The climate community is encouraged to contribute to this effort and to join the ESMValTool development team for contribution of additional diagnostics for ESM evaluation.
Acknowledgements This work has been performed and funded within the  European Commission 's  7th Framework Programme , under Grant Agreement number  282672 , the " Earth system Model Bias Reduction and assessing Abrupt Climate change (EMBRACE) " project, and continued within the European Union's  Horizon 2020 research and innovation programme  under grant agreement No. 641816  (CRESCENDO), and the  DLR  project " Klimarelevanz von atmosphärischen Spurengasen, Aerosolen und Wolken: Auf dem Weg zu EarthCARE und MERLIN (KliSAW) ". We acknowledge the  World Climate Research Program 's (WCRP's) Working Group on Coupled Modelling (WGCM), which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP5 the  US Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals.
Results Landscape of canine lncRNA transcription. We produced 16 RNA-seq in a previous study 9 consolidated by 10 RNA-seq published data  8  , corresponding to a total of 26 stranded RNA-seq data to represent a panel of diverse canine anatomical systems (Supplementary Table  S1 ). We applied a state-of-the-art RNA-seq bioinformatics pipeline  24  to assess gene expression levels for both canine lncRNAs and mRNAs (see the Methods section). For lncRNAs, we used the FEELnc-based classification  9  based on the international gold-standard lncRNA classification given by the GENCODE consortium  25  to differentiate long intergenic non-coding RNAs (lincR-NAs, n = 5,651) from antisense lncRNAs that have overlapping protein-coding genes in antisense (n = 4,793). We determined that a total of 7,763 (74.
Tissue-specific expression of lncRNAs correlates with their transposable element content. TEs are thought to constitute part of the active lncRNA regions  13  and may drive the specific expression patterns observed for lncRNAs  16. Interestingly, in dogs, a canine-specific SINE repeat family (SINEC_Cf) has been shown to provide many polymorphic site insertions  17  and is also associated with multiple disease phenotypes  16, 18. To gain insights into the relationships between canine lncRNAs and transposable elements, we analysed the TE content of the 10,444 lncRNAs in the 4 major TE classes (DNA transposons, LTRs, LINEs and SINEs) as annotated by the RepeatMasker database  36. We first showed that almost 70% of canine lncRNA transcripts contain at least one TE overlapping an lncRNA exon, a result in between human (83%) and mouse (66%) lncRNAs  15.
We then determined that ~20% of the cumulative exon sequences of lncRNAs are composed exclusively of TEs (Fig. 2a ). This proportion of lncRNA exonic TEs is less than that observed in the entire dog genome (37. 7%) but is 2. 5 times higher than for mRNAs exonic sequences (7. 9%), highlighting the high prevalence of TEs in dog lncRNA exons as also observed in other species  15. Compared to the genome-wide distribution of TEs, most TE families are under-represented in lncRNAs with the exception of LTR-ERVL retroviruses (Long Terminal Repeat-Endogenous RetroVirus-Like elements) (Supplementary Fig. S3 ), which are also significantly enriched in human lncRNAs  15. In addition, we computed canonical correlations between TE content in both lncRNAs exons and promoters and lncRNA tissue expressions using the mixOmics program  37  (see Methods).
Identification of dog-human conserved lncRNAs by comparative genomics. DNA conservation among divergent species is a widely used indicator to infer functionality and to suggest conserved functions. Identifying orthologous lncRNA relationships is challenging since lncRNAs are more likely to be gained or lost during evolution  11  than constrained sequences as protein-coding genes. Therefore, we used a positional comparative genomics approach, parameterised at the synteny level, to predict putative lncRNA orthologues between dog and human using the EnsEMBL Compara database  39  (see Methods). Using this strategy, we mapped the positions of canine lncRNAs onto the human genome and identified 939 lncRNAs (9. 0%) with 1:1 human non-coding orthologues from the GENCODE database  25  which can be separated into 560 lincRNAs (9.
9% of the total number of lincRNAs in dogs) and 379 antisense (7. 9% of the total number of antisense in dogs) (Figs  3  and S5 ). We found that lncRNA orthologues that were part of a synteny block shared similar gene structures, with a mean number of exons per gene of 3. 6 and 3. 1 for humans and dogs, respectively (Supplementary Table  S3 ). Based on the GENCODE annotation of the conserved dog-human lncRNAs, we were able to confirm the annotation of the well-studied HOTAIRM  40  , MALAT  41  , NEAT_1  42  and PCA3  43  in dogs and to infer novel canine orthologous relationships such as the CASC family of lncRNAs (CASC15, CASC17, CASC18 and CASC20) and INHBA-AS1  44  or MEG9  45. Complementary to this synteny-based strategy, we evaluated the selective constraints acting on canine lncRNAs and mRNAs by using the Genomic Evolutionary Rate Profiling (GERP) scores  46  (see Methods).