dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  B. Hajj  for providing Matlab routines for MFM image registration, and  A. Wright  and  S. Jun  for the gift of the hu-mCherry frt-kan-frt strain ( SS6282 ). This research was supported by funding from the  European Research Council  under the  7th Framework Program  ( FP7/2010-2015 ,  ERC  grant agreement  260 , 787 ) and from the  Agence Nationale pour la Recherche  ( ANR-14-CE09-0025-01 ). 3D-SIM experiments.
For each individual, each test was repeated with a 1-week interval between sessions to assess behavioral repeatability (table  S1 ). Exploration, maintenance activity, and aggression tests were recorded with DFK 21AU04 video cameras (The Imaging Source) connected to the Noldus Media Recorder 2 software (version 2. 0. 212; Noldus Information Technology). Exploration. This test closely followed the procedure described by Bergmüller and Taborsky  (45). The novel environment consisted of a 450-liter tank (150 cm wide by 50 cm deep by 60 cm high) divided into two compartments: a small compartment (40 cm wide) separated from the larger compartment by opaque plastic partitions, equipped with a sliding door. The novel environment contained a gravel substrate, two plastic plants (25 cm high), and six artificial nests (8 cm in diameter, typically used in breeding experiments) to provide shelters to explore.
After the fish had spent 5 min to acclimatize in the small compartment, we gently opened the partition between the two parts with a pulley system, and the fish was allowed to explore the large compartment for 15 min. Locomotor activity (swimming distance) was analyzed using automated trajectometry software (EthoVision; Noldus Information Technology). Food neophobia. The test closely followed the procedure described by Schweitzer et al. (43). A novel sort of food was presented to the fish at the usual feeding schedule and within the feeding area to record the latency to consume unfamiliar food. If a fish had not eaten after 15 min, a maximum score of 900 s was recorded. Two different novel foods (Tetra Delica Krill Freeze Dried Shrimp or colored pellets) were used in randomized order to avoid familiarization.
Statistical analysis Repeatability of the behavioral measures was assessed using the intraclass correlation coefficient of Nakagawa and Schielzeth  (47). When the data followed a nonstandard distribution, we relied on the repeatability measurement based on rank, and behavioral consistency over time was assessed by Spearman's rank correlation coefficients. Repeatability was achieved for each behavioral test. To analyze the effects of the pairing context on the similarity index and number of agonistic behaviors, we used a mixed-effects model with pair identity and individual identity as a random variable, respectively. We followed a stepwise selection model procedure to identify the best model. For covariates with a significant effect, we performed post hoc pairwise comparisons. To limit the inflation of type I error, we used permutation tests (n = 10,000 permutations)  (48)  in comparing variables with a small sample size.
Attempts to resolve these questions may be achieved through the development of morphological and genetic datasets of Culicoides spp. from India that are comparable with those being produced elsewhere (for review, see Harrup et al. [28] ). In addition to accurate species identification, a second fundamental requirement for accurate surveillance of Culicoides populations in southern India is the selection of appropriate monitoring tools. Systematic sampling of Culicoides populations using light-suction trapping next to livestock has been used to demarcate geographic and temporal risk of BTV transmission in Europe, Africa and Australia  [29] [30] [31] [32] [33]. In both Europe and Africa, Culicoides spp. surveillance programmes are reliant upon the use of ultraviolet (UV) (~390 nm peak wavelength) light-suction traps, which have previously been shown to be highly effective at collecting C.
PCR purification and COI sequencing Dimer formation from the primers was not observed and purification of the remaining PCR product was performed using the MinElute® PCR purification kit (QIAGEN) following manufacturers recommended guidelines (v. 03/2008). The resulting products were sent for bi-directional sequencing using primers HCO2198 (reverse) and LCO1490 (forward) at a commercial facility (Eurofins, Bangalore, India). The resulting electropherograms were edited and forward and reverse sequences assembled and trimmed to remove primer sequence using CodonCode Aligner v. 5. 1. 5 (CodonCode Aligner, Centerville, MA, USA). Corresponding specimen collection data and DNA sequences including electropherograms have been made publically available via the Barcode of Life Data System (BOLD)  [44]  as dataset DS-CULIN (dx.
Phylogenetic analysis Consensus sequences were compared to previously published sequences in the GenBank database using the standard nucleotide BLAST tool  [45] , in addition to comparison to as yet unreleased sequence data in the BOLD database  [44]  using the Barcode Identification Engine in BOLD v3. Sequences from GenBank included in the phylogenetic analysis (n = 196) are listed in Additional file 1: Table  S1. GenBank sequences were included in the analysis to assess if the morphological identifications made within this study were conspecific with those made from other geographical regions and were not confounded with species morphologically similar enough to result in misidentification, e. g. C. brevitarsis and C. bolitinos Meiswinkel, 1989  [26]. All sequences were aligned using MUSCLE  [46]  and quality checked using GUIDANCE  [47]  (100 bootstraps).
Statistical analysis Relationships between female Culicoides abundance and five fixed effects were examined using generalised linear mixed models with a negative binomial error distribution (fitted using the glmmadmb package v 0. 7. 5  [67, 68]  in R  [61] ), three main effects of light type, position and species (including the five most abundant species) and two possible interactions (species × light type, to reflect that light types may be more attractive to some species than others and species × position, to reflect that a trap position may have been closer or further from a larval development site which may vary by Culicoides species). In all models, a random effect of trapping day was used. All possible combinations of the fixed effects were examined (including an intercept only model) and the model with the lowest AIC  [69]  was selected.
Phylogenetic analysis Full length primer truncated DNA barcode sequences of 658 bp were recovered from  71   S2 ), and one currently unidentified species. Amino acid frame shifts and stop codons were not evident among sequence translations, indicating pseudogenes were not likely to be included in the alignments. The COI sequences obtained from the GenBank (Additional file 1: Table  S1 ) overlapped the alignment of the sequences generated in this study by between 434 and 658 bp. Thirteen species from the collections in this study and a further six potentially morphologically confounding species (19 in total) were represented within the phylogenetic study (Figs. 4, 5, 6, 7, 8 and 9). Species clades represented in the Bayesian Inference (BI) phylogeny were concordant with morphological identifications with the exception of one specimen, TPI:ENT:IBVNET-CULI-TN-65, which could not be assigned to a species based on the morphological descriptions currently available in the literature, and is therefore recorded as 'Unknown Species (I)' (Fig.
10 ). The greatest intraspecific sequence differences were reported from within specimens morphologically identified as C. actoni (mean: 5. 8 %; range: 1. 5-8. 1 %), followed by C. brevitarsis (mean: 3. 2 %; range: 0- 5. 6 %), C. huffi (mean: 2. 5 %; range: 0-4. 9 %), C. imicola (mean: 1. 2 %; range: 0-9. 0 %) and C. oxystoma (mean: 2. 9 %; range: 0-5. 8 %) (Additional file 2: Table  S3 ; Fig. 11 ). These levels of sequence variation is more akin to interspecific values (Additional file 2, Table  S3 ), indicating cryptic taxa in the samples sequenced within these taxa, or morphological misidentifications in our dataset. All other pairwise intraspecific sequence differences were less than or equal to 2. 0 %, i. e. within published ranges of intraspecific variation  [74]  (Additional file 2: Table  S3 ; Fig.
11 ). The least mean sequence difference was between C. dubius Arnaud, 1956 and C. peliliouensis   S3 ). All other pairwise interspecific sequence difference were greater than 10. 0 % (Additional file 2: Table  S3 ). No misidentifications occurred between species identified within the study and those considered isomorphic or morphologically similar for which COI DNA barcode sequence data was available. No geographic clustering was observed in the C. imicola haplotypes (Figs. 5  and 7 ). Three specimens collected in previous studies from Spain (GenBank: AF080528, AF080529 and AF080536) were, however, found to have between 2. 7 and 4. 6 %, 3. 5-5. 4 % and 6. 0-9. 0 % sequence difference, respectively, to other specimens identified as C. imicola. The range of intraspecific sequence differences within C.
Trap catches from both trap types were dominated by C. oxystoma and C. peregrinus which together (See figure on previous page. ) Fig. 4  Bayesian Inference phylogenetic tree inferred from COI DNA barcode sequences with species, and subgeneric (thick black line, italic font) or species group (thick black line, Roman font) indicated. Bayesian posterior probability node support values greater than 0. 7 shown. Coloured diamonds indicate specimens from the IBVNet project coloured by collection site (KA1: red; TN01: purple; TN02: green; TN08: blue; TN10: orange; TN11: yellow; TN12: black) with specimen number followed by GenBank accession number in brackets. Culicoides imicola and C. oxystoma clade summarised, see Figs. 5  and 6  for further details represented 65. 6 and 32. 7 % of Culicoides specimens in the UV, and 82.
Discussion This study presents the first detailed multi-site genetic analysis of Culicoides in southern India with new COI DNA barcode sequence data presented for 12 previously recognised species, and potentially up to four cryptic or unidentified taxa. To the best of our knowledge, Culicoides mesghalii and C. kepongensis are recorded for the first time in India and C. peliliouensis and C. similis are recorded for the first time in southern India. The study orange; TN11: yellow; TN12: black) with specimen number followed by GenBank accession number in brackets. See Fig. 4  for the relative placement of the C. imicola clade with respect to other specimens analysed within this study also reveals the potential for deep sequences differences symptomatic of cryptic species diversity within C. actoni, C.
An east-west split in haplotypes of C. imicola specimens from the Mediterranean basin was previously identified using COI sequencing by Calvo et al. [77] , Dallas et al. [78]  and Nolan et al. [79]. The present study has shown that four C. imicola COI haplotypes are present in southern India; two are unique to India, one is identical to specimens collected in Israel and South Africa and the other is identical to specimens collected in China, Israel, South Africa and Portugal. The status of the latter sample from Portugal (GenBank: AF079975) requires reevaluation as this sample does not fit with previously proposed eastern-western haplotype demarcation in the Mediterranean basin. In addition, three publically available sequences (GenBank: AF080528, AF080529 and AF080536) labelled as C. imicola but with sequences differences of 2.
brevitarsis was suggested by analysis of the COI region, but this was not supported by analysis of the fused carbamoyl phosphate synthetase, aspartate transcarbamylase and dihydroorotase (CAD) nuclear genes  [26]. Similar investigations are required to explore the potential cryptic diversity identified between the Indian and Australian specimens of C. brevitarsis. There is no evidence that the morphologically indistinguishable C. bolitinos  [26, 90]  is present in southern India as this species is genetically distinct from the Indian C. brevitarsis (mean sequence difference compared to South African C. bolitinos: 12. 2 %; range: 11. 6-12. 6 %). The Indian C. brevitarsis is also genetically distinct (mean sequence difference: 11. 1 %; range: 10. 6-11. 5 %) from C. bolitinos specimens recently collected in Reunion Island (GenBank: KF186129 and KF186130)  [91] , which have been provisionally identified as C.
The sequence difference between C. peregrinus specimens from China, Japan, India and Thailand was 1. 2-1. 7 %. However, Australian specimens have been the basis of C. peregrinus BTV vector incrimination studies  [93]  and the south Indian specimens of C. peregrinus assessed within this study show only limited sequence divergence (mean sequence difference: 2. 0 % (1. 8-2. 3 %) from specimens recently collected in Australia (GenBank: KR075719-KR075721)  [94] , indicating these associations are intraspecific and are still valid. Culicoides anophelis has previously been reported from southern India  [11, 12, 95]  and is widespread throughout southern Asia  [18]  possibly ranging as far east as New Guinea  [96]. The sequence identity of C. anophelis specimens collected in this study were concordant with the only available COI sequence for this species, also from southern India, and further study is required to investigate the relationship of populations of this species across its extensive range.
g. C. actoni  [92]. This utility is, however, proportional to the number of species represented within the reference dataset and a strong integration with morphological taxonomy  [28]. Further DNA barcode data is also required across the full range of the Culicoides subgenera in order to test the 'barcode gap' hypothesis related to the definition of species boundaries with regard to intra-and inter-specific variation  [72]. The current subgeneric classification of the Culicoides  [103] , however, remains largely unvalidated (for review see Harrup et al. [28] ) and there is mounting evidence to suggest that at least some of the current subgenera are polyphyletic, i. e. derived from more than one common ancestor  [104] [105] [106] [107]. The additional Avaritia subgenus species sequence data presented within this study, however, supports the monophyly of this subgenus within the context of the species included in the phylogenetic analysis with the Avaritia subgenus supported by BI as a monophyletic clade (100 % posterior probability).
If this were to occur during surveillance activities, the reduced sensitivity of the green LED-based light-suction traps would result in pseudo-absences within the Culicoides abundance dataset potentially resulting in epidemiologically significant errors in models of Culicoides seasonality and/or distribution. Ultraviolet LED-based light-suction traps, however, did collect a greater by-catch of other insects in comparison to green LED-based traps and hence incurred a greater handling time for each sample collected. While taking into account the limited spatial-scale and temporal duration of this comparison of green and UV wavelength-based light-suction traps the increased sensitivity of detection of the UV LED-based trap makes it the preferred choice for on-going surveillance efforts in India compared to a green LED-based light-suction trap.
Diurnal activity has been reported in C. oxystoma  [111]  and C. actoni  [18]  indicating that any trap utilising light as an attractant has the potential to underestimate the abundance and distribution of these species in comparison to species with a principally crepuscular or nocturnal activity pattern. In addition, the preferential attraction to different light wavelength of the highly competent BTV vector C. fulvus, which is known from northern  [1, 5, 6] , but not southern India, remains unknown as it was not detected during this study. Further studies across different environments in this region would be useful in elucidating the distribution of this and other putative BTV vectors in this region. In addition, the continued expansion of the depth of the reference dataset of matched morphological and genetic data for Culicoides specimens from India and the surrounding regions is essential to enable accurate assessments of species abundance and diversity in relation to BTV epidemiology to be made in the future and will enable the Indian Culicoides fauna to be placed in a global context.
Acknowledgements This study was funded by the  Biotechnology and Biological Sciences Research Council (BBSRC) ,  Department for International Development (DFID)  and  Scottish Government  grant  BB/H009205/1 ,  BB/H009167/1  and  BB/ H009493/1  as part of the  Combating Infectious Diseases of Livestock for International Development initiative. With additional support provided by  BBSRC  grant  BB/K021214/1 ; AB was also supported with funding from the  London School of Hygiene and Tropical Medicine. The Indian Council of Agricultural Research  supported this study through its Hisar,  Chennai, Bengaluru and Hyderabad centres  under the  All Indian Network Programme on Bluetongue.
Since then, several new bacteria, including new genera and species, have been reported in the human gut  [5] [6] [7] [8]. To describe these bacteria, 16S ribosomal RNA sequencing coupled with a taxonogenomic strategy was recently developed. This strategy is based on a systematic comparison of genomic and phenotypic characteristics, especially the matrixassisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) spectrum, with the phylogenetically closest species existing in databases  [5] [6] [7] [8]. Using the culturomics method, a new bacterium was isolated from a healthy Gabonese male teenager. The strain exhibited 97. 9% of similarity with the 16S nucleotide sequence of Kallipyga massiliensis (JN837487). However, this value was lower than the 98. 7% 16S rRNA gene sequence threshold recommended by Stackebrandt and Ebers  [9]  to delineate a new species without carrying out DNA-DNA hybridization.
MALDI-TOF and 16S rRNA identification In order to identify bacteria, MALDI-TOF was used following the same protocol as previously described  [5] [6] [7] [8]. Identification of bacteria continued with a 16S rRNA standard PCR coupled with sequencing. The apparatuses used were GeneAmp PCR System 2720 thermal cyclers (Applied Biosystems, Foster City, CA, USA) and the ABI Prism 3130xl Genetic Analyzer capillary sequencer (Applied Biosystems), respectively  [10]. The 16S rRNA nucleotide sequence obtained after amplification and sequencing was corrected using Chromas Pro 1. 34 software (Technelysium, Tewantin, Australia). Then a BLASTN was systematically performed in the online PubMed National Center for Biotechnology Information (NCBI) database (http://blast. ncbi. nlm. nih. gov. gate1. inist. fr/Blast.
NMNI of Orthologous Groups (COGs) databases using BLASTP on the NCBI website (http://blast. ncbi. nlm. nih. gov/Blast. cgi). The tRNAScan-SE and RNAmmer tools allowed us to predict respectively the tRNAs and rRNAs  [12, 13]. SignalP software and the TMHMM program helped to predict, respectively, signal peptides and numbers of transmembrane helices  [14, 15]. Mobile genetic elements were predicted using PHAST and RAST tools  [16, 17]. ORFans were identified if the BLASTP E value was lower than 1e-03 for alignment lengths greater than 80 amino acids. An E value of 1e-05 was used if alignment lengths were smaller than 80 amino acids. These threshold parameters were already used in previous works to define ORFans  [5] [6] [7] [8]. For the data management and genomic features visualization, Artemis and DNAPlotter were respectively used  [18, 19].
The multiple genomic sequence alignment was performed with the Mauve alignment tool (version 2. 3. 1) [20]. Homemade average genomic identity of orthologous gene sequences (AGIOS) software was used to estimate the mean level of nucleotide sequence similarity between K. gabonensis and other Clostridiales  [21]. By combining AGIOS and Proteinortho software  [22] , we detected orthologous proteins between genomes compared two by two. The corresponding genes were then retrieved, and the mean percentage of nucleotide sequence identity among orthologous ORFs was determined using the Needleman-Wunsch global alignment algorithm. To make a genome comparison, K. gabonensis strain GM4 was compared with Desulfitobacterium hafniense strain DP7, Peptostreptococcaceae bacterium strain VA2, Fusobacterium nucleatum strain 36A2, Kallipyga massiliensis strain ph2 and Clostridiales bacterium strain 9403326.
MALDI-TOF and phylogenic analysis The spectrum generated from clean Kallipyga gabonensis strain GM4 spots was unable to match those of the Bruker database (Fig. 1 ). Using 16S rRNA phylogeny analysis, we demonstrated that K. gabonensis strain GM4 exhibited 97. 9% similarity with the 16S nucleotide sequence of K. massiliensis (JN837487) (Fig. 2 ). This value of similarity remains lower than the 98. 7% 16S rRNA gene sequence threshold recommended by Stackebrandt and Ebers  [9]  to delineate a new species without  carrying out DNA-DNA hybridization. Thus, this bacterium was considered to be a new species called K. gabonensis sp. nov. strain GM4 belonging the Clostridiales Incertae Sedis XI family created in 2009  [25]  (Table  1 ). 16S rRNA sequence of Kallipyga gabonensis was 1,486 bp long (LN 849790).
massiliensis) (Fig. 3 ). The 16S rRNA sequence of K. gabonensis was deposited in GenBank under accession number LN849790. Phenotypic and biochemical characterization K. gabonensis sp. nov. strain GM4 is Gram positive, comprises non motile cocci, and is non-spore forming (Fig. 4 ). This bacterium is catalase positive and oxidase negative. It is able to grow under temperatures ranging from 28 to 45°C, with an optimum growth at 37°C, after 48 hours of culture. pH test results showed that K. gabonensis can survive under pH conditions ranging between 5 and 8. 5, but has optimal growth at pH 5. It grows better with salinity concentrations of around 0. Individual colonies of K. gabonensis are whitish and exhibit a diameter of 2 mm on 5% sheep's blood-enriched Colombia agar. Its individual cell exhibits a diameter of 0.
Genome properties The genome is 1,621,211 bp long with 50. 01% G+C content (Table  3 ). It contains two scaffolds (composed of two contigs) (Fig. 6 ). Of the 1,536 predicted genes, 1,475 were proteincoding genes and 61 were RNA genes (four are 5S rRNA, four are 16S rRNA, four are 23S rRNA, 49 are tRNA genes). A total of 931 genes (63. 12%) were assigned a putative function (by COGs or by nrBLAST), and 79 genes were identified as ORFans (5. 36%). The remaining genes were annotated as encoding hypothetical proteins (394 genes = 26. 71%). Table  4  summarizes the distribution of genes into COGs functional categories of the K. gabonensis genome. The genome sequence has been deposited in GenBank under accession number CXYV00000000.
Using API gallery systems, K. gabonensis strain GM4 induced positive reactions with D-glucose, esculin ferric citrate, D-melibiose, sucrose, inulin and D-raffinose. It also possesses esterase (C4), leucine arylamidase, naphthol-AS-BI-phosphohydrolase, slightly esterase lipase (C8), acid phosphatase and β-glucosidase activities. The potential pathogenicity of the type of strain GM4 (= CSUR P1915 = DSM 100575) is unknown. This strain exhibited a G+C content of 50. 01% and a genome length of 1. 6 Mb. The 16S rRNA sequence of K. gabonensis was deposited in GenBank under accession number LN849790. The whole genome shotgun sequence of K. gabonensis has been deposited in Gen-Bank under accession number CXYV00000000.
0 Nucleotide transport and metabolism H 31 2. 1 Coenzyme transport and metabolism I 31 2. 1 Lipid transport and metabolism P 42 2. 8 Inorganic ion transport and metabolism Q 7 0. 5 Secondary metabolites biosynthesis, transport and catabolism R 106 7. 2 General function prediction only S 71 4. 8 Function unknown - 562 38. 1 Not in COGs COGs, Clusters of Orthologous Groups database.