dataesr/scientific-paragraphs-categorization huggingface dataset:
Species delimitation analyses: TCS, GMYC and STACEY The statistical parsimony analysis of the COI data set, rendered 28 separate haplotype networks, while TCS analyses of ITS2x and ITS2s resulted in 24 and 23 networks respectively (S20-S22 Appendixes). GMYC analysis of the COI data set rendered 28 putative species, and GMYC of ITS2x and ITS2s resulted both in 24 putative species (S23-S31 Appendixes). In STA-CEY we treated the 28 haplotype networks from the COI data as the species to be tested, and in 98. 8% of the resulting trees, all of these 28 clades were recovered and in 1. 2% of the trees, clades 20 and 28 were lumped together (S32 Appendix) (see  Fig 5). We used the most inclusive data sets for each species delimitation analyses, and in TCS all sequences of COI (n = 462) and ITS2 (n = 402) were included, in GMYC all unique sequences of COI (n = 271) and ITS2 (n = 136) were included, while all terminals with both COI and ITS2-data (n = 351) were included in STACEY.
Clades 4, 26 and 27, all represented by single haplotypes in ITS2, are connected in all four analyses of the ITS2-data. The haplotypes are separated by between three and eight mutations in the minimum-spanning haplotype network in the two TCS-analyses. In summary, we suggest that clades 12 and 13 represent different species even though they are connected in one of the ITS2-analyses. The two clades do not share any ITS2-haplotypes (Fig 6 ), and both lineages are fairly well sampled with 23 (clade 12) and 27 specimens (clade 13). There are also insertion/deletion events in the ITS2-sequence alignments that support the two clades, however, in the analyses presented here, we treated indels as missing data. We further conclude that the separate putative species in clade 8 found in the GMYC-analysis of COIdata could be ignored as intraspecific genetic variation (only seven mutations in the TCS-analysis), and there is neither any differences in the ITS2-data to support such a conclusion.
We do think that there is evidence that clades 20 and 28 should be regarded as the same species even though they have separate haplotype networks in the TCS-analysis on the COI-data, both lineages are under-sampled with only two (clade 20) and five specimens (clade 28), and the difference between the lineages is within the variation that is found in better sampled clades (compare clades 20 and 28 in , and there is a good chance that the haplotypes would be connected given a larger sample size. ITS2-data also support this conclusion as clades 20 and 28 share the same ITS2-haplotype (Fig  6 ). Results from STACEY also give some support to this deduction. In contrast, we believe that it is likely that clade 21 represents a separate species even though it is connected with clade 20/28 in the GMYC of the ITS2s, differences in COI between 20/28 and 21 is substantial (12.
Collecting data for specimens, together with voucher and GenBank accession numbers can be found in S36 Appendix and Table1. Specimens are deposited in one of the following museums: Department of Natural History, University Museum of Bergen (ZMBN 116171-116514, 344 specimens), The Gothenburg Museum of Natural History (GNM 14625-15137, 74 specimens), Norwegian University of Science and Technology, NTNU University Museum, Trondheim (NTNU-VM 59990-72567, 36 specimens), and Senckenberg Museum Frankfurt (SMF 24368-24693, 59 specimens). All specimens are publicly deposited and accessible in a permanent repository.
23), and 16S rDNA (GenBank accession number MG025442), specimen voucher ZMBN 120609), or downloaded from GenBank (Polycirrus: COI = JX423769, 16S rDNA = JX423681, 28S rDNA = JN936481, and Pista cristata: COI = EU239688, 16S rDNA = NC011011, 28S rDNA = DQ790057).
Financial support was provided by the  Norwegian Taxonomy Initiative  [http://www. biodiversity. no/Pages/135523]to AN (Cryptic polychaete species in Norwegian waters, knr  49-13 , pnr 70184228), to EO, TB and JAK (Polychaetes in Skagerrak, knr  53-09 , pnr  70184216 ), to TB, EO and JAK (Polychaetes in the Norwegian Sea, knr  55-12 , pnr 70184227); and by the  Swedish Taxonomy Initiative  [https://www. artdatabanken. se/en/the-swedish-taxonomyinitiative/](Polychaete species complexes in Swedish waters, dnr  140/07 1. 4  and  166/08 1. 4 ), and  Kungliga Fysiografiska  sa ¨llskapet Nilsson-Ehle donationerna [https://www. fysiografen. se/sv/]to AN; and by the  ForBio Research School  funded by the  Research Council of Norway  [https://www. forskningsradet. no/en/Home_page/1177315753906] (project no.
Here, we applied a stage-structured matrix population model to a longitudinal dataset of 625 individually known female spotted hyenas Crocuta crocuta (hereafter hyenas) in the Serengeti National Park, Tanzania, spanning two decades. We investigated the population consequences of a canine distemper virus (CDV) epidemic  10, 18  , caused by a novel genotype better adapted to non-canids such as hyenas and lions Panthera leo than to canids  19. By quantifying and conducting a sensitivity analysis for R 0 of this CDV strain for the first time in hyenas, we formally demonstrate that it was highly contagious among hyenas during the epidemic and that the most influential parameter to decrease R 0 was the probability of becoming a socially high-ranking breeder. CDV infection during the epidemic decreased the survival of cubs and subadults, particularly low-ranking ones.
We thus ended up with a 22 × 22 meta-matrix M (Eq. (  1 )). Asymptotic analyses of the matrix population model. We used R v. 3. 5. 0. (R Core Team 2017)  41. We used the function 'is. matrix_irreducible' in R's package popdemo v 1. 3-0  42  to verify that the meta-matrix was irreducible. Matrix models are termed irreducible when their associated life cycles contain the transition rates to facilitate pathways from all states to all other states. Irreducible matrices are ergodic: the stable asymptotic growth rate is independent from the initial stage structure in the population projection. Both conditions should ideally be met for further analyses  43. When M(t) = M is considered constant (i. e. , no time dependence, no stochasticity), the behaviour of such a deterministic model is well-known  22.
The modelled population is then characterised by an asymptotic growth rate and a stable asymptotic distribution over the 22 states, which are the dominant eigenvalue and the corresponding right eigenvector, respectively  22. We used the functions 'stable. state' and 'reproductive. value' in R's package popbio 2. 4. 4  44  to determine the stable stage distribution and the reproductive values, based on the definitions provided by  22.
Acknowledgements We are grateful to the  Commission for Science and Technology of Tanzania (COST-ECH) , the  Tanzania Wildlife Research Institute (TAWIRI)  and  Tanzania National Parks (TANAPA)  for their support of our research. We thank the  Deutsche Forschungsgemeinschaft DFG  (grants  EA 5/3-1 ,  KR 4266/2-1 ,  DFG-Grako 1121 ), the  Leibniz-Institute for Zoo and Wildlife Research , the  Fritz-Thyssen-Stiftung , the  Stifterverband der deutschen Wissenschaft  and the  Max-Planck-Gesellschaft  for financial assistance. We thank  Annie Francis ,  Thomas Shabani ,  Malvina Andris ,  Nelly Boyer ,  Robert Fyumagwa ,  Traudi Golla ,  Katja Goller ,  Nicole Gusset-Burgener ,  Richard Hoare ,  Karin Hönig ,  Mark Jago ,  Stephan Karl ,  Berit Kostka ,  Michelle Lindson ,  Sonja Metzger ,  Melody Roelke-Parker ,  Dagmar Thierer ,  Agnes Türk ,  Harald Wiik  and  Kerstin Wilhelm  for assistance.
Code availability. The R programming code to replicate all our analyses and figures is available from Github: https://github. com/LucileMarescot/SIR-Matrixmodel. It is divided in three main sections: (1) code 1_Model construction loads the input values for the three epidemic periods and builds the matrix model, (2) code 2_Model analysis_asymptotic presents the asymptotic analysis of the matrix model and (3) code 3_Model analysis_stochastic presents the stochastic analysis of the matrix model. Each section is fully annotated so that each step can be replicated.
EEG recordings and analysis Electroencephalograms (EEGs) were recorded from 64 Ag/Acl BioSemi active electrodes mounted in an elastic cap. Electrodes were placed according to the standard 10-20 system designed to cover a large median area of the scalp. An online band-pass filter was applied (0,01 -100 Hz). Data were sampled at a rate of 512 Hz. For EEG analysis, the task was divided into six periods (p1/p2/p3/p4/p5/p6), with each period corresponding, in terms of duration, to 5 mins.
Phase-amplitude coupling (PAC): To evaluate the modulation of task-relevant regions we used the PAC method. In the PAC (or nested oscillations) the low-frequency (nesting) phase will drive the power of a coupled higher frequency (nested). Therefore, we analyze the synchronization of amplitude envelope of gamma frequency with the phase of theta band. For the PAC analysis, region of interest was defined thanks to the granger causality analysis, only occipital regions modulated in the theta band were considered. PAC analysis was also performed with Brainstorm software  (Tadel et al. 2011).
In this context ground based transit photometry surveys like SuperWASP  (Pollacco et al. 2006) , targeting bright stars, are essential contributors. In this paper, we present the WASP and SOPHIE discovery of two hot Saturns and one warm super Neptune, with mass and radius measured with a precision better than 15 %, and discuss their impact on the formation and evolution theories of ice and gas giants. In Section 2, we describe the photometric and radial velocity observations acquired on the three systems. In Section 3, we present our analysis of the data with the resulting stellar and planetary parameters. Finally in Section 4, we discuss the nature and composition of these planets and their impact on planet formation and evolution theory with a focus on the migration of the hot giant planet population and the upper boundary of the Neptunian desert.
g. UCAC4,  Zacharias et al. 2013; 2MASS, Skrutskie et al. 2006). These additional analyses are essential for rejection of false positives, and for identification of the best candidates. This process allowed to detect three transit planets that we will now introduce. 1SWASPJ231615. 22+001824. 5 (2MASS23161522+0018242), hereafter WASP-151, lies very close to the celestial equator and is thus visible to both WASP arrays. A total of 45, 945 data were obtained between  2008-06-12 and 2012-11-28, 16, 375  from SuperWASP and 29, 570 by WASP-South. A search for periodic modulation in the WASP light curves, such as might be caused by stellar activity or rotation, was carried out using the method of  Maxted et al. (2011). No significant periodicity was identified, and we place an upper limit of 2 mmag on the amplitude of any modulation.
During these observations a total of 195 transits were covered of which 27 were full or quasi-full events. The WASP data show a periodic reduction in stellar brightness of approximately 0. 01 mag, with a period of roughly 4. 5 days, a duration of approximately 3. 7 hours, and a shape indicative of a planetary transit. The WASP thumbnails of WASP-151 show some contamination from a background galaxy about 20" from the target and thus within our first aperture. The galaxy is about 3 magnitude fainter in V than our target. We calculated a dilution factor for WASP-151 of about 1% and thus negligible when considering WASP data. We found no significant periodic modulation, and we place an upper limit of 1. 5 mmag on the amplitude of any such light curve variation. There are a total of 688 transits observed of which 54 are good events 2.
K2 observations of WASP-151 In addition to the ground-based photometric observations described in the previous sections, WASP-151 (alias EPIC 246441449) was observed by NASA's Kepler Space Telescope in its two-reaction wheel mission K2  (Howell et al. 2014)  during Campaign 12. The observations span over ∼ 79 days (from 15th December 2016 to 4th March 2017) apart from 5 days (from 1st February 2017 to 6th February 2017) when the spacecraft was in safe mode. Since Campaign 9, the K2 consortium releases the raw cadence data shortly after downlink from the Kepler satellite. These data are raw, as opposed to the science cadence data like the target pixel files (tpf), for two main reasons 5. First their format, the raw cadence data are provided as one file per cadence delivering the pixel counts for the whole focal plane as a table.
In order to construct the image time series of a target, we need the pixel mapping reference file which specifies the (column, row) CCD coordinates for each value in the raw cadence data tables. Second, the raw cadence data are not calibrated. It means that they are not reduced with the Kepler pipeline  (Quintana et al. 2010)  and thus not corrected for background, dark, smearing trails, undershoot or non-linearity of the pixels response. The formatting and calibration of the raw cadence data for all the targets of a K2 campaign is a very lengthy procedure and even if the raw cadence data for Campaign 12 have been released several months ago, the calibrated tpf are, at this moment, still unavailable. Therefore, to be able to benefit from the high quality light-curves of the WASP-151 system provided by the K2 mission, we decided to format and reduce ourselves the raw cadence data.
The light-curve clearly display transit features at the ephemeris inferred from the WASP data with no sign of outof-transit variations. A search for periodic modulation caused by stellar activity showed a tentative detection with an amplitude of 1 ppt (∼ 1 mmag) at a period of 35 days. We then searched the light-curve for additional transit features (apart from WASP-151b's transit). We investigated a tentative mono transit-shaped feature which proved to be an artifact due to the position-flux decorrelation technique used by Polar. For this decorrelation, we cut the K2 image time-series in several parts where the behavior of the pointing jitter of the Kepler satellite can be safely assumed to be 1 dimensional (for more details see  Barros et al. 2016). The mono transit-shaped feature was appearing precisely at the junction of two of those parts.
They are more accurate and agree with the measurements secured from the cross-correlation function following  Boisse et al. (2010). A value for microturbulence (ξ t ) was determined from Fe lines by requiring that there is no slope between the abundance and the equivalent width. The error estimates for ξ t include the uncertainties in T eff and log g, as well as the scatter due to the measurement and the atomic data uncertainties. Values for macroturbulence (v mac ) were determined from the calibration of  Doyle et al. (2014) , however the value for WASP-156 is extrapolated from the calibration as this star is not within the correct temperature range. With the v mac fixed to the calibration value, the projected stellar rotation velocity (v sin i * ) was determined by fitting the profiles of several unblended lines.
Transit and RV analysis We followed the same method to perform the parameters' inference for the three systems. We analysed jointly all the radial velocity and photometric datasets available for a given system. To model the radial velocity and photometric data, we used the Python packages ajplanet 11   (Espinoza et al. 2016 ) and batman 11  (Kreidberg 2015)  respectively. In order to decrease the correlation between the parameters of our model and ease the fit, we adopted the parametrisation suggested by  Eastman et al. (2013)  with R p /R * the ratio of the planet's radius to that of the star, P the orbital period, t c the planet's time of inferior conjunction, √ e cos ω * and √ e sin ω * where e is the orbital eccentricity and ω * is the stellar orbital argument of periastron, K the radial velocity semi-amplitude, i the orbital inclination, a/R * the ratio of the planet's orbital semi-major axis over the stellar radius, v0 the systemic radial velocity, u and v the two coefficients of the limb-darkening quadratic law.
2 g/mol, ρ * the stellar mean density and log g the stellar log gravity. Both ρ * and log g are, in this case inferred from the transit profile 12. These estimates are marked with (tr. ) in Table  3. After the full mcmc analysis, we computed the value of all these secondary parameters from the main parameters values and at each step of each walker of the second emcee exploration. Then we estimated their map and 68 % confidence level interval with the same method than the main parameters. The specificities for the analysis of each system were: WASP-151: The exposure times of the WASP, IAC80, Euler-Cam and TRAPPIST data are all below 90 s which is negligible compared to the time scale of the transit variations (typically 30 min for the transit ingress and egress). However the exposure time of the K2 light-curve is 29.
When doing so, we used the time of the first sample (t min ) as the origin for the linear function: ∆F oot + (tt min ) ∆F ′ oot ). t min is equal to 2 457 187. 753440000124, 2 457 269. 443920060061, 2 457 328. 353823559824, 2 457 328. 502696809825 HJD for the TRAPPIST, the September IAC80, the first part and the second part of the November IAC80 light-curves respectively. A&A proofs: manuscript no. Demangeon_2017_3WASP We re-analyzed jointly all the datasets with these 8 additional free parameters in our model. The inferred parameter values and error bars are reported in Table  3. Figure  2  and 3  show the photometric and radial velocity data phase folded at the bestfit ephemeris (see Table  3 ) with the best-fit model and residuals. The error bars displayed take into account the best-fit jitter values obtained by the Bayesian inference (see Table  3 ).
WASP-156: The exposure times of the WASP and NITES data being both below 40 s, no supersampling has been applied for those two datasets. The exposure time of the EulerCam data being around 80 s and the ingress and egress for this system being relatively short (∼ 10 min), we decided to supersample the model by a factor 4. A first analysis of this system showed that the 2 datasets collected with EulerCam were not compatible. The 2014 Euler-Cam dataset displayed a very pronounced V-shape that was not supported by the other datasets. As described in Section 2. 2. 1, this dataset was identified earlier as affected by large PSF variations, and stellar counts in the non-linear regime of the EulerCam CCD. So we decided to discard it from the final analysis. We also noticed that the residuals of the 2016 EulerCam light-curve seemed to exhibit a quadratic trend and introduced 3 additional parameters to our model to account for a possible quadratic variation of the out-of-transit level.
When doing so, we used the time of the first sample (t min = 2 457 700. 517166 HJD) as the origin for the quadratic function: ∆F oot + (tt min ) ∆F ′ oot + (tt min ) 2 ∆F ′′′ oot ). We re-analyzed jointly all the datasets with these 3 additional free parameters in our model. The inferred parameter values and error bars are reported in Table  3. Figure  6  and 7  show the photometric and radial velocity data phase folded at the bestfit ephemeris (see Table  3 ) with the best-fit model and residuals. The error bars displayed take into account the best-fit jitter values obtained by the Bayesian inference (see Table  3 ).
Acknowledgements. O. D. S. D acknowledges the support from  Fundação para a Ciência e a Tecnologia (FCT)  through national funds and by  FEDER  through  COMPETE2020  by grants  UID/FIS/04434/2013&POCI-01-0145-FEDER-007672  and  PTDC/FIS-AST/1526/2014&POCI-01-0145-FEDER-016886. SCCB also acknowledges support from  FCT through Investigador FCT  contract  IF/01312/2014/CP1215/CT0004. CAH was supported by  STFC  under grant  ST/P000584/1. A. S. B. acknowledges funding from the  European Union Seventh Framework programme  ( FP7/2007-2013)  under grant agreement No. 313014  (ETAEARTH). D. J. A. B acknowledges funding from the  UKSA  and the  University of Warwick. The  SuperWASP Consortium  consists of astronomers primarily from  University of Warwick, Queens University Belfast, St Andrews, Keele, Leicester, The Open University, Isaac Newton Group La Palma  and  Instituto de Astrofísica de Canarias.
The SuperWASP-N camera is hosted by the  Issac Newton Group on La Palma  and WASPSouth is hosted by SAAO. We are grateful for their support and assistance. Funding for  WASP comes from consortium universities  and from the  UK's Science and Technology Facilities Council. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programmes  ( FP7/2007-2013  and  FP7/2013-2016 ) under grant agreement number  RG226604  and  312430  (OPTICON), respectively. This work is based on radial velocity observations made at  Observatoire de Haute Provence (CNRS ), France and at  ESO La Silla in (Chile)  with the CORALIE Echelle spectrograph mounted on the Swiss telescope. We thank the staff at  Haute-Provence Observatory. TRAPPIST is funded by the  Belgian Fund for Scientific Research (Fond National de la Recherche Scientifique, FNRS)  under the grant  FRFC 2.
annulata and T. parva. The immunity can be adoptively transferred by CD8+, but not CD4+ T cells from vaccinated animals. This strongly suggests that cytotoxic T lymphocytes (CTL) play an important role in developing immunity against Theileria parasites  (18, 19). Further, major histocompatibility complex (MHC) class Irestricted CD8+ cells, the cytotoxic lymphocytes (CTLs), that target schizont-infected lymphocytes also play a primary role in mediating immunity against Theileria in cattle  (20, 21). The availability of comprehensive genomic, transcriptomic and proteomic datasets of Theileria parasite have provided opportunities for in silico mining of novel candidates for vaccine design  (22) (23) (24). Immuno-informatics, which integrates the transcriptomics and proteomics through advances in computational and molecular immunological tools, has emerged as a new tool for identification of the target antigens for vaccine development  (25) (26) (27).
Sequence Retrieval and Sub-cellular Localization All protein sequences of T. annulata were retrieved from the National Centre for Biotechnology Information (NCBI) database. The CD-HIT server was used to remove proteins having 90% or more similarity. All the parameters recommended by the server were set to default  (33). The CELLO v. 2. 5 server was FIGURE 1 | (A) Systematic workflow for identification of potential vaccine candidates and construction of multi-epitope subunit vaccine against Theileria parasites from the available proteomic data. n represents total number of proteins used for analysis. 1 https://www. ncbi. nlm. nih. gov/; 2 http://weizhong-lab. ucsd. edu/cdhitweb-server/cgi-bin/index. cgi?cmd=cd-hit; 3 http://cello. life. nctu. edu. tw/; 4 http://gpcr. biocomp. unibo. it/predgpi/pred.
B-cell Epitopes Prediction The prediction of B-cell epitopes for all the selected antigenic proteins from the previous step was performed using BCPREDS server with 90% specificity  (39). BCPREDS server (B-cell epitope prediction) is based on Support Vector Machine (SVM) classifiers and the training dataset containing 701 linear B-cell epitopes from Bcipep database and 701 random non-B-cell epitopes from SwissProt sequence database. Further, the proteins were analyzed for the presence of putative transmembrane domains, exposed and intracellular region(s) using TMHMM server (http://www. cbs. dtu. dk/ services/TMHMM/)  (40). Finally, those epitopes which were exposed to the extracellular surface of the cells were selected for further analysis.