dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. Satellite imagery and derived products are available from the authors upon request. HAIC-HIWC datasets (Strapp, 2016 (Strapp,   , b, 2017;; SAFIRE and RASTA datasets)  are available from the UCAR/NCAR Earth Observing Laboratory at http://data. eol. Atmos. Meas. Tech. , 11, 1615-1637, 2018 www. atmos-meas-tech. net/11/1615/2018/.
We found that, globally, SUS1 deletion increased global mRNA stability (See figure on previous page. ) Fig. 1  Positional organization of Sus1 before and after heat stress. a Heat map showing shifted 5′-end sequencing reads (tags) for Sus1-WT at 25 °C (blue) and after 15 min at 37 °C (red), aligned by the midpoint in between transcription start site (TSS) and transcription end site (TES). Data presented are divided into three subgroups: ribosomal protein genes (RP, n = 137), SAGA-dominated genes (SAGA, n = 471) and TFIID-dominated genes (TFIID, n = 4351) genes and sorted by gene length in each subgroup. The results of two replicates are shown. As a control, the signal of an isogenic strain bearing no-tagged Sus1 was also monitored (No-tag). b Gene-averaged 5′-ends of shifted relative read counts (representing points of cross-linking) of Sus1-WT at 25 °C (blue line) and at 37 °C (red line) around the transcription start site (TSS) in three gene classes: ribosomal protein (RP) genes, SAGA-dominated genes and TFIID-dominated genes, with TSS oriented to the right.
ChIP-exo and data analyses Two biological replicates of each ChIP-exo of Sus1-MYCtagged protein and a no-tag strain were performed. Cells were grown in YPD at 25 °C. The experiments were performed as in  [30]. The heat-shock treatment was performed at 37 °C for 15 min. Prechilled formaldehyde was added to achieve a final concentration of 1%. Reactions were quenched and cells collected by centrifugation. Solubilized sonicated chromatin was prepared from cell extracts to yield a DNA fragment size of < 500 bp. For mapping nucleosomes, data from  [39]  were used. DNA was trimmed in the 5-3 direction with lambda exonuclease, until stopped by formaldehyde cross-link. The resulting DNA was subjected to deep sequencing. Sequencing reads were mapped to yeast genome (sacCer3) using BWA (version 0. 5.
Funding VG-M was supported by the  FPU program  from  MEC  ( AP2009-0917 ). This study was supported by funds from  MINECO  ( BFU2011-23418  and  BFU2014-57636  to SR-N,  BIO2012-40244  to AC and  BFU2016-77728-C3-3-P  to JEP-O) and GV ( PROMETEO/2012/061 ,  PROMETEO/2016/093  and  ACOMP/2014/061  to SR-N, and  PROMETEOII/2015/006 ,  ACOMP/2014/253  to JEP-O). The contributions of PF-T and AC were supported by the  FP7 Project STATegra 306000. BFP was supported by  NIH   GM059055. BFP has a financial interest in Peconic, LLC, which utilizes the ChIP-exo technology implemented in this study and could potentially benefit from the outcomes of this research.
Annotation of S. frugiperda miRNA genes miRDeep2 analysis To detect miRNA genes, raw sequencing data were analyzed with miRDeep2 software. miRDeep2  [17, 18]  maps the sRNA reads to the genome and excises potential miRNA precursors sequences from the genome. The secondary structures of the miRNA precursors are predicted and their stability is estimated by RNAfold. mirDeep2 uses a probabilistic model of miRNA biogenesis by the Dicer protein to score frequency and compatibility of mapping of the small RNA sequence reads (the signature) on the secondary structure of the miRNA genomic precursors (the structure) as compared to a non-miRNA precursor hairpin  [17, 18]. Read stacks correspond to mature miRNA sequences. The score reflects the likelihood of each precursor to be a genuine miRNA. Furthermore, since the algorithm may generate hairpins with read stacks that have no connection with miRNA biology, corresponding to false positives, miRDeep2 estimates the rate of false positive by shuffling the observed combinations of structures and signatures and submit them to the core algorithm.
Among the gene targets predicted by TargetScan, we filtered out those showing variation in their expression in the RT experiments (according to  [7] ). We also checked whether they were predicted as miR targets by at least one of the three other softwares. The list of target genes that are differentially expressed in SfC on different host plants can be found in Additional file 10: Excel file S5. The target genes that are differentially expressed in SfR compared to SfC on corn are listed in Additional file 11: Excel file S6.
Small RNA extraction and sequencing Small RNA was extracted from 12 L4 larvae using the mirVana miRNA Isolation Kit (Ambion) according to the manufacturer's instructions. Extracted small RNA was quantified using a NanoDrop 2000 spectrophotometer (Thermo Scientific) and analyzed by PAGE and silver-staining. Small RNA samples from each replicate of CC, RC, CR and RR larvae were used for cDNA library preparation using the TruSeq ®Small RNA Sample Prep Kit (Illumina) and sequencing on Illumina HiSeq 2500. A size selection targeting snRNAs in the range of 15 to 40 nt was done. Library preparation and sequencing were performed by the platform MGX-Montpellier GenomiX (Montpellier, France). Computational analysis of small RNA sequencing data and miRNA identification. High throughput sequencing generates small RNA reads of 50 nucleotides in length (single reads).
Raw reads were trimmed to remove adapter sequences using cutadapt software (version 1. 4. 1) [53]. For downstream analysis, only reads having length between 15 and 40 bases were considered. miRNA genes annotation miRDeep2 algorithm  [18]  was used to detect miRNA from small RNA deep sequencing data. This algorithm uses a probabilistic model to score the fit of sequenced RNA to the biological model of miRNA biogenesis  [17]. Briefly, reads are aligned to the S. frugiperda genome (Corn variant assembly version 3. 1 or Rice variant assembly version 1. 0), and only reads that do not map more than five times to the genome were used for miRNA detection. Then, using the read mappings as guidelines, potential miRNA precursors are excised from the genome and the miRDeep2 core algorithm scores their likelihood to be a real miRNA precursor.
Analysis of differential miRNA expression miRDeep2 algorithm also provides read counts for the detected miRNA. To assess changes in miRNA expression between S. frugiperda variants and host-plant conditions, the read counts data for known and novel miRNA were used as input for the R package DESeq2  [56]. DESeq2 uses negative binomial generalized linear models to test for differential expression. An adjusted p-value for multiple testing was computed with the Benjamini-Hochberg procedure to control false discovery rate (FDR). Results with a FDR < 0. 05 were considered statistically significant.
2_UTR3 of the C strain available on the webportal  [62]. Predictions obtained by TargetScan were confirmed by at least one of the three following softwares: MiRanda, Rna22 and miRmap. MiRanda (version v3. 3a)  [63]  allows one wobble pairing in the seed region when it is compensated by matches in the 3′ end of the miRNA, it calculates the binding energy of the duplex structure and its position within the 3'UTR, it was used with the same parameters as in  [64]. Rna22 (version v2)  [65]  is a tool based on a search for patterns that are statistically significant miRNA motifs created after a sequence analysis of known mature miRNAs, it was used with default parameters. MiRmap  [66]  is a web-based application  [67]  that combines many thermodynamic, evolutionary, probabilistic and sequence-based features.
Funding This work was supported by a grant from the  French National Research Agency  ( ANR-12-BSV7-0004-01 ; http://www. agence-nationale-recherche. fr/) for E. A. including a post-doctoral fellowship for Y. M. H. P. acknowledges financial support from the  "France Génomique" National infrastructure , funded as part of " Investissement d'Avenir" ( ANR-10-INBS-09 ). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Availability of data and materials The datasets generated during this study are available from NCBI SRA (SRP132165). https://www. ncbi. nlm. nih. gov/Traces/study/?acc=SRP132165 https://www. ncbi. nlm. nih. gov/sra/?term=SRP132165 The Genbank accession numbers of microRNA precursors identified in this study are MH340548 -MH341001.
Metallopeptidases are a family of proteins with domains that remain highly conserved throughout evolution. These hydrolases require divalent metal cation(s) to activate the water molecule in order to carry out their catalytic action on peptide bonds by nucleophilic attack. Metallopeptidases from parasitic protozoa, including Toxoplasma, are investigated because of their crucial role in parasite biology. In the present study, we screened the T. gondii database using PFAM motifs specific for metallopeptidases in association with the MEROPS peptidase Database (release 10. 0). In all, 49 genes encoding proteins with metallopeptidase signatures were identified in the Toxoplasma genome. An Interpro Search enabled us to uncover their domain/motif organization, and orthologs with the highest similarity by BLAST were used for annotation.
These 49 Toxoplasma metallopeptidases clustered into 15 families described in the MEROPS database. Experimental expression analysis of their genes in the tachyzoite stage revealed transcription for all genes studied. Further research on the role of these peptidases should increase our knowledge of basic Toxoplasma biology and provide opportunities to identify novel therapeutic targets. This type of study would also open a path towards the comparative biology of apicomplexans.
As seen with other apicomplexan parasites such as Plasmodium, Eimeria and Cryptosporidium, toxoplasmic proteases could thus be considered potential therapeutic targets in light of this involvement in hostparasites interactions  [16, 37, [45] [46]. Metallopeptidases represent a very diverse catalytic type of peptidase and are classified in the MEROPS database (https://www. ebi. ac. uk/merops/) based on homologous sets of peptidases containing related sequences that are grouped together in families, which are then grouped in clans based on their related primary structures  [56]. All known metallopeptidases have been divided into 16 different clans as described in MEROPS: MA (divided in MA(E) also called gluzincins and MA(M) called metzincins subclans), MC, MD, ME, MF, MG, MH, MJ, MM (with a motif like that of clan MA but bound to plasma membranes), MN, MO, MP, MQ, MS, MT, and Mwhich includes metallopeptidases which are not yet well characterized.
Only a few of these clans are represented in T. gondii. Studies on T. gondii metallopeptidases remain scarce today, whereas complete surveys of protease homologs in Plasmodium falciparum  [16, 66]  and Eimeria tenella predicted proteomes  [31]  have been published. To date, seven metallopeptidases have been experimentally explored in T. gondii  [5, 23, 25, [29] [30] 34, 67, [68] [69]. The ToxoDB database (http://tox odb. org/toxo/, Release 29), that gathers T. gondii genome and post-genome data for numerous strains, provides an invaluable resource to investigate the most complete set of metallopeptidases for this parasite. Using human and protozoan metallopeptidase sequences and peptidase family domains (PFAM motifs  [19] ) defined in the MEROPS database, we identified, in ToxoDB, 49 putative toxoplasmic metallopeptidases clustered into 15 families corresponding to 7 clans.
T. gondii metallopeptidase identification, in silico analysis and classification In this manuscript, we chose to classify metallopeptidases according to their MEROPS classification in families, beyond their amino-, carboxy-or endopeptidase predicted activity. Putative metallopeptidase Toxoplasma genes were identified from the T. gondii ToxoDB database, (http://toxodb. org/toxo/, Release 29) using peptidase family domain (PFAM motifs) recorded in the MEROPS Database (https://www. ebi. ac. uk/merops/) for this family of enzymes. The TGME49 genome was chosen as a reference in our study. The domain/motif organization of predicted proteases was studied using the Interpro Search (http://www. ebi. ac. uk/interpro/). At the end of this search, a total of 49 genes encoding proteins with metallopeptidase signature motifs were identified in the Toxoplasma gondii ME49 genome.
They were subsequently assigned to families and sub-families of metallopeptidase annotations by amino acid sequence comparisons using the BLASTp program in the Washington University (http://www. ebi. ac. uk/Tools/blast) and the BLAST MEROPS server using the MEROPS Database. The deduced amino acid sequences of these putative Toxoplasma metallopeptidases proteins were aligned with sequences from other organisms according to the ClustalW multiple sequence alignment algorithm on the EMBL-EBI website (European Bioinformatics Institute, www. ebi. ac. uk/Tools/clustalw2) using the Blosum62 matrix. The prediction of protein localization sites in parasites was performed by using a computer program Psort II (http://www. psort. org/).
Design of specific primers for each metallopeptidase sequence Primers were designed based on the selective sequences of the RH T. gondii genomic DNA (gDNA). Positions of introns in putative metallopeptidase genes were obtained by alignment of gDNA with complementary DNA (cDNA). One pair of primers was designed per gene following two conditions if possible: the pair of primers should flank a genomic region spanning an intron and/or amplify the metallopeptidase catalytic domain. All primer pairs were designed using Primer Pro 3. 4 software (www. changbioscience. com/primo/pri mo. html). The primers used to assess metallopeptidase gene expression are listed in Table  1  with their corresponding gene.
Results and discussion The T. gondii reference genome database ToxoDB was screened to identify putative metallopeptidase sequences. In all, 49 metallopeptidases containing PFAM domains that characterize the metallopeptidase enzyme superfamily were identified. The genome of the RH strain (genotype I) shares high similarity to the archived genomes of the T. gondii GT1 (genotype I), ME49 (genotype II) and VEG strains (genotype III)  [35]. We then decided to investigate metalloprotease expression in total RNA from RH tachyzoites by conventional RT-PCR. To do so, genespecific primer pairs flanking a region spanning intron(s) were designed in order to amplify fragments of distinct length from cDNA and gDNA templates. Amplifications with these different primers pairs yielded PCR and RT-PCR products of expected sizes for each gene (Figure  1 ).
These results confirmed their transcription and are in agreement with the currently proposed intron-exons gene model boundaries in ToxoDB. In view of the high structural diversity seen in metallopeptidase families, putative metallopeptidases from the T. gondii genome database were assigned according to the MEROPS classification, as described below. In this study, we used the MEROPS Nomenclature system (release 10. 0) as described in Rawlings et al. (2016)  [56]. In this system, proteases are classified into 8 catalytic superfamilies (aspartic, cysteine, glutamic, metallo-, mixed catalytic type, serine, threonine, and unknown catalytic type peptidases), and metallopeptidases into 16 clans based on their related structures. Metallopeptidases from the T. gondii genome database (ToxoDB) were therefore classified based on their domain organization and sequence similarities to metallopeptidases from other organisms.
We have found 49 putative metallopeptidases as shown in Table  2  (see also  Appendix 1) , which details protease MEROPS clans and families, number of metal ions, T. gondii ME49 gene ID, chromosomal location, protein length (amino acids), ToxoDB product description, protease homolog with highest BLAST score using BLASTp program combined with MEROPS BLAST server, primer name correspondence, alias, related publication and PFAM ID, and signal peptide presence/ prediction. A total of 49 putative metallopeptidases have thus been identified and ascribed to 15 metallopeptidases families described in the MEROPS database: four M1, three M3, one M13, four M14, eleven M16 (the most represented family), one M17, one M18, one M20, eight M24, two M28, three M41, one M48, two M50, six M67 and one M76 peptidase families.
falciparum  [1, 3, 7, [14] [15] 20, 24, 43, 62] , NCLIV_048240 (NcAPN1) and NCLIV_048230 (NcAPN3) from N. caninum  [22] , cgd8_3430 from C. parvum (strain Iowa II)  [49] , and Table  2. Metallopeptidase genes identified and classified in the T. gondii genome database (strain ME-49, genotype II). We used Pfam motifs (http://pfam. xfam. org/) in association with the MEROPS Database to screen the T. gondii database (http://toxodb. org/ toxo/, Release 29). The motif organization of predicted peptidases was studied using the InterProScan Search (http://www. ebi. ac. uk/ interpro/) and family assignment is based on MEROPS À the peptidase Database À classification (https://www. ebi. ac. uk/merops/). ETH_00013105 and ETH_00013105 from E. tenella (strain Houghton) also called EtAPN1  [22]  and EtAPN2  [31].
In 2015, Zheng et al. demonstrated that a T. gondii leucine aminopeptidase gene knockout influenced the growth of T. gondii without completely blocking parasite development, virulence or enzymatic activity  [68]. We have found in the ToxoDB database that this LAP has an ortholog (NCLIV_042660) in the N. caninum genome, an expected situation considering the phylogenetic proximity of the two species. Interestingly, we can also note that one M17 was identified in the P. falciparum genome as PF3D7_1446200, and this has been studied extensively  [42, 44, 57, 59 ]. This protease is expressed in all intra-erythrocytic stages and particularly at the trophozoite stage where protein synthesis increases  [8, 38]. It appears involved in the regulation of free amino acid spool  [59]. Bestatin, a broad spectrum aminopeptidase inhibitor, prevents the growth of P.
M24 Peptidase family (Methionyl aminopeptidase 1 and aminopeptidase P families) Clan MG contains exopeptidases that required two cocatalytic ions of cobalt and/or manganese, and contains only family M24. Peptidases belonging to this M24 peptidase family are also called methionine aminotransferase and cleave methionine residues at the N-terminal level. The M24 family has been divided into two subfamilies: M24A (methionyl aminopeptidase) and M24B (X-pro aminopeptidase and X-pro dipeptidase). Most members of family M24 are cytosolic, and do not require proteolytic activation. As of today, eight T. gondii methionine aminopeptidases have been found in ToxoDB with signature PFAM PF00557. Only one publication describes a Toxoplasma M24 peptidase (ToxoDB accession number: TGME49_261600) also called TgAPP (aminopeptidase P) and a recombinant form of this TgAPP has been expressed to evaluate its enzymatic parameters  [67].
gondii apicoplast. From pulse-chase assays, the authors showed that two cleavages occurred within this protein sequence: a first one in the N-terminal part and a second one in the C-terminal part, allowing specific apicoplast targeting of this FtsH1  [29] [30]. The authors suggested that the roles of FtsH1 in T. gondii could include protein Table  3. Comparative study of the metallopeptidase repertoires for T. gondii (Tg) strain ME49, N. caninum (Nc) strain Liverpool, H. hammondii strain HH34, E. tenella (Et) strain Houghton, P. falciparum (Pf) strain 3D7, and C. parvum (Cp) strain Iowa. Metallopeptidases are indicated by their EupathDB accession numbers and are classified into MEROPS families using PFAM domains and Blast similarity searches. surveillance, chaperone activity, and import  [29].
The enigma of M22 Peptidase family During this study, we identified proteins ascribed to the "M22 peptidase family" in the Eupath database, including two members in the T. gondii genome, TGME49_274110 and TGME49_202310. While studying them, we however discovered that this family has been retracted from the MEROPS database, because there is a lack of experimental evidence to support peptidase activity as a general property of this family. The only evidence for any proteolytic activity in M22 was attributed to the O-sialoglycopeptidase from Pasteurella haemolytica. Homologs are almost universally distributed, but peptidase activity for members of this family has never been found. Structural studies have shown that members of "M22" have a very different fold to any known metallopeptidase (Rawlings, personal communication), and therefore they have been retracted from the MEROPS Database.
de pendent metallopeptidase HflB subfamily protein 100%/ 3,10e-113 (M41) M41B - - Ye s TGME49_259260 VIIb 1250 membrane protein FtsH1 100%/ 6,50e-120 (M41) M41C FtsH1 Karnataki A. 2007 No MA(E) 1 Zn 2+ M48 TGME49_221170 II 432 CAAX metallo endopeptidase 94,51%/ 1,20e-141 (M48A) M48 - - PF01435 PF16491 No TGME49_266140 IX 378 peptidase, M50 family protein 34,27%/ 1,40e-23 (M50B) M50 Clan Numbe r of metal ion MEROPS familie s Gene ID 49 strain ME- Chro. Prote in Le ngth (aa) ToxoDB Product Description (ME-49) Blast MEROPS % identities/ e-score (Familie s) Prime r Name Alias Publication PFAM SignalP peptid TGME49_224350 X 1419 aminopeptidase N, putative 92,18%/ 3,40e-213 (M1) M1 - - No TGME49_221310 II 1069 aminopeptidase N protein 57,30% / 3 , 6 e - 1 2 3 ( 1 M ) 1 M A o N TGME49_224460 X 970 aminopeptidase N, putative 100%/ 2,6e-238 (M1) M1B Tg110 Berthonneau J.
2000 Yes TGME49_262575 VIIb 290 hypothetical protein 39,33%/ 4,7e-10 (M1) M1C - - No TGME49_226420 X 667 peptidase family M3 protein 99,29%/ 9,70e-152 (M3B) M3 - - Ye s TGME49_272670 VIII 1094 peptidase family M3 protein 81,91%/ 2,60e-270 (M3A) M3A - - Ye s TGME49_216150 XI 506 peptidase family M3 protein 95,47%/ 2,50e-144 (M3B) M3B - - No MA(E ) 1 Zn 2+ M13 TGME49_295640 Ia 1038 peptidase family M13 protein 98,44%/ 0 (M13) M13 - - PF01431 Ye s TGME49_265780 IX 2803 flagellar/ basal body protein 100%/ 7,50e-55 (M14D) M14 - - Ye s TGME49_271870 VIII 1321 zinc carboxypeptidase superfamily protein 44,44%/ 2,30e-51 (M14A) M14A - - Ye s TGME49_253170 III 2204 zinc carboxypeptidase, putative 47,71%/ 5,20e-55 (M14B) M14B - - No TGME49_202910 VIIa 318 zinc carboxypeptidase superfamily protein 49,62%/ 1,30e-67 (M14B) M14C - - Ye s TGME49_202680 VIIa 563 peptidase M16 alpha subunit, putative 100%/ 3,10e-107 (M16B) M16 MPPA - Ye s TGME49_253890 III 1604 peptidase M16 inactive domain-containing protein 100%/ 3,8e-105 (M16B) M16A - - Ye s TGME49_235680 X 1692 peptidase M16 inactive domain-containing protein 76,28%/ 2,3e-95 (M16B) M16B - - No TGME49_244480 VI 357 peptidase M16 inactive domain-containing protein 75,00%/ 5,00e-80 (M16A) M16C - - No 4 E M G T 9 _ 2 0 6 5 1 0 a I I V 2 3 4 0 o T o x y l s e n i 4 - 8 1 , 5 0 % / 2,70e-81 (M16A) M16D TLN4 Laliberté J.
2011 No TGME49_257010 VIIb 1023 sporozoite developmental protein 100%/ 1,10e-109 (M16A) M16E - - Ye s TGME49_269885 VIII 1645 rhoptry metalloprotease toxolysin TLN1 (TLN1) 93,50%/ 3,50e-99 (M16A) M16F TLN1 Hajagos BE. 2012 Yes TGME49_227948 X 1306 peptidase M16 inactive domain-containing protein 90,95%/ 7,50e -212 (M16C) M16G - - Ye s TGME49_236210 X 509 peptidase M16 family potein, putative 66,01%/ 7,00e-145 (M16B) M16H - - Ye s TGME49_314850 XI 2174 hypothtical protein 45,59%/ 2,70e-06 (M16A) M16I - - No TGME49_214490 X 1353 peptidase M16 inactive domain-containing protein 83,77%/ 7,50e-235 (M16C) M16J - - No MF 2 Zn 2+ /Mn 2+ M17 TGME49_290670 IX 781 leucyl aminopeptidase LAP (LAP) 94,13%/ 7,00e-184 (M17) M17 TgLAP Jia H. 2010 PF00883 PF02789 yes MH 2 Zn 2+ M18 TGME49_297970 II 508 aspartyl aminopeptidase 97,86%/ 1,70e-246 (M18) M18 TgAAP Zheng J.