dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, Figure  2A  shows index values derived for Gaussian cluster with 100 members, using a nearest neighbour number of N = 5. On visual inspection the highest values have been assigned to stars with the highest degree of association. Figures  2B  and 2C  show the same cluster, but with an observed angular dispersion and surface density the cluster would have if its distance was a factor of 4 and 16 times larger than A respectively. Applying IN-DICATE under the same conditions as for 2A, the index values for each star remain unchanged in 2B and 2C i. e. for all members ∆ I 5 ≡ 0 despite the surface density increasing by a factor of 256 between 2A and 2C, as the local spatial distribution of members in all three clusters is identical. Hence the index can be used to directly analyse variation in the spatial distribution of points, in any desired parameter space, (a) within a dataset and/or (b) comparatively between two or more datasets.
g. in observational datasets) typically do not significantly affect the index values of true cluster members. Table  1. Statistics of index values derived for clusters' D, E, F (Fig. 3 ). The percentage of members clustered above random (I 5 > I max ) is given for each cluster. Cluster Mo(I 5 ) max(I 5 ) % stars I 5 > I max D 8. 2 10. 6 81. 2 E 1. 6 4. 8 17. 5 F 3. 0 6. 4 34. 1 6. If interloping field stars are distributed in a gradient, the index derived for true cluster members is independent of gradient shape for small nearest neighbour numbers (N = 3). We note that in samples which contain interloping field stars, that the field stars are also assigned index values by INDICATE, so care must be taken when interpreting the values and drawing conclusions on the physical origins of the clustering tendencies of stars in these samples.
Implementation Example In Section 3 we apply our tool on a real stellar catalogue of NGC 3372. Here we demonstrate using synthetic datasets IN-DICATE's ability to quantify the degree of association for each point in a 2D discrete dataset and suitability as a statistical measure for comparative analysis of the spatial distributions of points in multiple datasets. Figure  3  shows three clusters (D, E and F) with different degrees of elongation, angular dispersion, sub-structure, surface density and number of members. We apply INDICATE to each using a N th nearest neighbour number of N = 5 and a standard control distribution (CDA -see Appendix B). Table  1  shows their mode and maximum index values, and the percentage of members with I 5 > I max (Eq. A. 2). Cluster D is the most elongated, and its members have been identified by INDICATE as having the strongest clustering tendencies (highest index values) of the three clusters.
Of course, as the clusters are being analysed in a 2D parameter space it is conceivable that stars in cluster D and E may have very similar spatial distributions but are being viewed from different rotations around the 3D axis. We therefore advise caution when drawing conclusions from comparisons of Cluster F has three concentrations of high index members (marked on Fig. 3  as R 1 , R 2 , R 3 ). Stars that form part of R 1 and R 3 have similar index values, that is they have similar clustering tendencies, but those of R 2 are less spatially clustered. If this were a real dataset, where clustering behaviours are dictated by underlying physics, these index values could be used as a starting point to explore the physical causes of the identified discrepancies between the clustering behaviours of the three concentrations (e.
In addition, Tr14 also contains the most spatially clustered stars in the Carina region, centrally concentrated at its core, with stars here having up to an additional 137 stars in their local neighbourhoods above that expected in a spatially random distribution. By contrast, stars above the threshold within the bounds of Bochum 11 display the lowest degree of clustering behaviour of the five clusters -having a maximum of just 3-4 stars in their local neighbourhoods above random. The high/low proportion of stars within the radial boundary of Tr14/Bochum 11 with an index value above the significance threshold, suggests these clusters are the most and least tightly clustered respectively in the region. In the absence of kinematic data however, we refrain from drawing any conclusions as to the physical origin of this trend.
Clustering Tendencies of the OB Population We create a sub-sample of the OB stars in the catalogue. To identify OB stars a cross-match search of the catalogue with the SIMBAD 3 database was performed, finding 134 stars listed as either O or B spectral type. Thirteen of these have an ambiguous spectral type or are flagged as being higher order systems so are excluded from the sub-sample, leaving a final selection of 121 stars. The term 'mass segregation' is used interchangeably in the literature to describe two quite different realisations. The first definition (hereafter Type 1) refers to a system in which the massive stars are concentrated together at its centre; whereas the second definition (hereafter Type 2) refers to a system in which the massive stars are in stellar concentrated regions, but are not necessarily concentrated together.
Conclusions We have developed a powerful novel statistical clustering tool called "INDICATE" (INdex to Define Inherent Clustering And TEndencies) to study the intensity, correlation and spatial distribution of point processes in discrete astronomical datasets. The tool assesses the clustering tendency of each object in a dataset and assigns it an index I j,N (Eq. 5), using a nearest neighbour approach by comparing the spatial distribution of objects in its local neighbourhood with that expected in an evenly spaced uniform (i. e. definitively non-clustered) distribution. INDICATE requires no a priori knowledge of, and makes no assumptions about, the shape of a distribution, presence/number of clusters and/or sub-structure of a dataset. For any application of INDICATE there are three variable parameters: (1) size and (2) number density of the distribution it is being applied to; and (3) the N th nearest neighbour number used by the tool.
5. Uniformly distributed (interloping) field stars in observational datasets typically do not significantly affect the index values of true cluster members. The error on the index value derived for true cluster members is given by Eqs. C. 1, C. 2 and C. 3. 6. If interloping field stars are distributed in a gradient, the index derived for true cluster members is independent of gradient shape for small nearest neighbour numbers (N = 3). However, as field stars are also assigned index values, care must be taken when drawing conclusions on the physical origins of the clustering tendencies of stars in the dataset. One of the primary strengths of our tool is its versatility and flexibility to be applied to a user-defined analysis. In this paper we demonstrated one potential application of the tool -to look for signals of mass segregation and trace variations in degree of stellar association in star forming regions/clusters.
4) The proportion of all points with ∆I j,N > 0 increases with decreasing sample size and increasing nearest neighbour number, reaching ∼ 90% for sample size of S = 50 using N = 9; it is independent of field density. The number of edge points with ∆I j,N > 0 is proportionally lower than non-edge points i. e. expanding the control distribution has less of an effect on edge points than non-edge points. This is because the r j measured for edge points in CDB is (slightly) smaller than in CDA (as it is no longer artificially increased due a lack of control points in given direction), which subsequently causes a small decrease in r (Eq. 3). In both control distributions a radius of r from an edge point can partially encompass an area outside the bounds of the dataset (where there can be no neighbouring points), but for nonedge points a radius of r always encompasses an area within the bounds of the dataset (neighbouring points can be present in any given direction within r).
As we are simulating an observational dataset for which cluster membership is uncertain, N tot = S = 500 + (F × 500). We find the modal difference for all combinations of F and N is Mo[∆I j,N ] = 0, i. e. typically the index values of true cluster members are unaffected by the presence of interloping field stars. The proportion of cluster members with ∆I j,N 0 increases with increasing F and N, reaching a maximum of ∼ 95% for F = 1. 0 and N = 9. In observationally obtained datasets the error on the index value derived for true cluster members is therefore   C. 1). In our second test F = 1. 0 field stars are distributed in three large scale gradient patterns (Figure  C. 1) which are randomly generated in the same parameter space as the Gaussian cluster. For each gradient 100 realisations are made, and for each realisation the difference, ∆I j,N , between the index values derived for cluster members in the dataset that does not contain field stars and the current level of field star contamination is measured for a N th nearest neighbour number of N = 3, 5, 7 and 9.
As we are simulating an observational dataset for which cluster membership is uncertain, N tot = S = 500 + (F × 500) = 1000. We find the modal difference for all gradients with N = 3 is Mo[∆I j,N ] = 0, i. e. for small values of N the index derived for cluster members is independent of gradient shape. This is expected as the index is a local measure, and the value of N essentially defines its resolution (the smaller N, the higher the resolution). Thus index values are more susceptible to the effects of As noted previously, INDICATE is distance independent for a fully resolved dataset. However, in practice, clearly INDICATE cannot detect unresolved binaries and higher order systems in datasets nor a priori know any difference between a member of a grouping and a fore-or background field star.
Even with best efforts, not all field stars will be removed from observationally obtained datasets before analysis, so consideration must be given before drawing conclusions about the clustering tendencies of region stars. In particular, when a pronounced large scale 2D spatial distribution gradient of the field population is present, and cluster membership is uncertain, caution must be taken when drawing conclusions about the physical origins of the clustering tendencies of stars -as field stars within the denser regions of the gradient naturally will have a higher degree of association and thus index. Similar care must be taken when interpreting index values for 2D datasets in which a smaller angular resolution cluster is superimposed onto a larger angular resolution cluster, or that contains two clusters at significantly different distances.
, 2017). The solid phase competition ELISA (SPCE) test was used for the detection of antibodies against FMDV serotypes SAT1, SAT2 and SAT3  (Li et al. , 2012). The sensitivity (Se) and specificity (Sp) of these PrioCHECK ® commercial tests were known, based on the validation data established by the FMD reference laboratory: ELISA PrioCHECK ® FMDV NSP (Se: 86%-100% and Sp: 87%-99. 5% data consistent with those reported by  Brocchi et al. , 2006 and Parida et al. , 2007) , ELISA PrioCHECK ® FMDV type O (Se: 85. 7%-98. 8% and Sp: 99%-100%), or given by the supplier for the ELISA PrioCHECK ® FMDV type A (Se: 90% and Sp: 99%). For the ELISA SPCE, the Se is 100% and the Sp is 99. 41%-99. 7% (Li et al. , 2012). Serotyping tests were carried out only on sera from young animals under 1 year of age to determine the different serotypes of FMD virus which circulated in 2016.
| Bioinformatic analysis of nucleotide sequences The sequences obtained were assembled and verified using ContigExpress software (Vector NTI, Invitrogen). A complete sequence of the VP1 coding region was analysed and compared to the homologous genomic regions available in the NCBI GenBank database. Multiple sequence alignment and phylogenetic analyses were conducted using MEGA version 6 software  (Tamura, Stecher, Peterson, Filipski, & Kumar, 2013). FMDV sequences were aligned using the Muscle program with default parameters  (Edgar, 2004). Phylogenetic analyses were conducted by Neighbour-Joining (NJ) method  (Saitou & Nei, 1987)  with the Kimura two-parameter model  (Kimura, 1980). The confidence of the NJ tree was assessed by bootstrap analysis with 1,000 replicates.
| Statistical analyses Binomial regression was conducted using the package R-INLA  (Rue, Martino, & Chopin, 2009)  of the R software  (R Core Team, 2018)  to estimate overall disease seroprevalence by species, age category and region. Since infection patterns are very different between species, species-specific seroprevalence was estimated using separate models. However, one estimate was presented for small ruminants that is sheep and goats. The epidemiological unit (EU) of the analysis was the group of animals of the same species and age category within a herd (thus, within a region as well). The EU was considered infected whenever at least one of the animals in the EU tested positive. The number of positive EUy ij in region i and age category j for a given species has been modelled with a binomial likelihood, where n ij is the number of sampled EU in region i and age group j.
Spectra were acquired with an UltrafleXtreme MALDI-TOF instrument (Bruker Daltonics, Bremen, Germany) equipped with a Smartbeam laser at 2 kHz laser repetition controlled by flexControl 3. 4 software (Bruker Daltonics, Bremen, Germany). A total of 10 spectra per sample (2 spectra per technical replicate, 5000 shots per spectra) were acquired in the positive reflectron ion mode in the mass/charge (m/z) range of 100-1200. External calibration was performed using a mixture of caffein, MRFA peptide, leu-enkephalin, bradykinine 2-9 and glu1-fibrinopeptide at 1 mM each, 1. 64 mM of reserpine, 942 µM of bradykinine and 771 µM of angiotensine in 1 µL of α-cyano-4-hydroxycinnamic acid (CHCA, 20 mg/mL) solubilized in 50% acetonitrile and 0. 1% (w/v) TFA. To increase the mass accuracy (mass error <0.
05%), an internal calibration using a lock mass at m/z 604. 3375 corresponding to PC 20:0 was subsequently applied to all spectra by flexAnalysis 3. 4 software (Bruker Daltonics), as previously described  [25]. The data were integrated in the R software (version 3. 5. 0) and treated for baseline subtraction using the Statistics-sensitive Non-linear Iterative Peak-clipping algorithm (SNIP, minimum 15 iterations), smoothing (Savitzky-Golay algorithm, m/z range = 0. 1), spectra alignment using prominent peaks (difference between spectra < 0. 002%) and normalization on intensity using total ion count (TIC). Peaks were detected using a total average spectrum with a signal/background noise > 2 in the mass range of 400-1000 m/z. The different isotopes of a given mass were not considered for analysis.
Analysis of MALDI-MS data Statistical analyses were performed using the MALDIquant and MALDIquantForeign packages (v 1. 17 & v0. 11. 1) of the R software (version 3. 5. 0) [26]. MS data did not pass the Kolmogorov-Smirnov's test of normality. All lipidomic data without preselection were submitted to the non-parametric Wilcoxon test to identify changes between sides of ovulation and to the Kruskall-Wallis test followed by Tukey's post-tests to identify changes between stages of the estrous cycle. Masses were considered differentially abundant between groups with a P-value < 0. 05. Hierarchical clustering of differential masses was performed using the gplot (v 3. 0. 1) and RcolorBrewer (v 1. 1-2) packages of the R software. Principal Component Analysis (PCA) of differential masses was performed using the FactoMineR package (v1.
Extracts were resolubilized in 10 µL of methanol for the positive mode and in 10 µL of ammonium acetate at 20 mM in methanol for the negative mode, then analyzed on a dual linear ion trap Fourier Transform Mass Spectrometer (FT-MS) LTQ Orbitrap Velos (Thermo Fisher Bremen, Germany) using the Xcalibur software (version 2. 1, Thermo Fisher Scientific, Bremen, Germany), as previously described  [25]. Briefly, FT-MS spectra were acquired using the profile mode in the 400-2000 m/z mass range, and the FT-MS/MS spectrum using highenergy collisional dissociation (HCD) fragmentation ion mode. Target resolution was set at 100,000 for FT-MS and FT-MS/MS analysis. The selected precursor width for fragmentation was 1. 5 m/z. FT-MS and FT-MS/MS spectra were acquired, over 10 min, in triplicate, using different collision energy levels (20 to 60 keV with a step of 5 keV for the positive mode, 30 and 40 keV for the negative mode).
Validation and analysis of HR-MS/MS data FT-MS and FT-MS/MS data were converted to Mascot generic format (MGF) using the Proteome Discoverer (version 1. 4, ThermoFisher Scientific, San Jose, USA) and the LipidMatch softwares  [27]. Each automatic lipid identification performed by LipidMatch (mass accuracy of 0. 05 m/z for precursor and 50 ppm for fragment ions, score = level 1) was validated using LipidBlast 2. 0 software  [28]. For phospholipids and sphingolipids, all identifications with a Rev-Dot score >800 were accepted considering the presence of specific fragments ions error mass accuracy of 0. 8 Da. For each identified lipid, theoretical mass (error mass accuracy < 0. 05 Da) and formula were obtained through interrogation of the LIPID MAPS database (http://www. lipidmaps. org). Masses highlighted by the hierarchical clustering were annotated with the LIPID MAPS database using the same criteria.
Randomly picked recombinant colonies were PCR amplified with Rsa21 primer. PCR products were run on a 1. 2% agarose gel and transferred onto a Hybond N + membrane (Amersham) which were hybridized with [γ 32 P]dATP end-labelled (GA) 15 and (GT) 15 probes to improve fragment selection. Among these clones 55% gave a satisfactory positive signal. A total of 192 positive clones were sent for sequencing (Genome Express). Sequences were analyzed and primers were designed using the PRIMER 3 program  (Rozen and Skaletsky, 2000).
Genotypic equilibrium among pairs of loci was tested over all samples with the randomization log-likelihood G-based test that is implemented in FSTAT software, and by performing 10 000 within samples permutations of bi-loci genotypes. This generates a distribution in P-values that was compared to an exact binomial test undertaken with R 2. 15. 2 (R Development Core  Team, 2018)  in order to detect cases where the multiplicity of tests resulted in a false rejection of the null hypothesis tested  (de Meeûs et al. , 2009). We tested whether the genotypic composition of tick samples clustered accordingly to the sampling geography and/or sampling dates using the principal component analyses adapted to genotypic data that is implemented in PCA-GEN v. 1. 2 (http://www2. unil. ch/popgen/ softwares/pcagen.
htm). Permutation tests included in the program enabled testing the significance of individual axes inertia (10 000 genotype randomizations). Estimation of the effective population sizes (N e ) was performed using the two methods implemented in NEESTIMATOR v1. 3 software  (Peel et al. , 2004) , both insensitive to heterozygosity. The first is a temporal method based on sequential sampling of different populations  (Waples, 1989)  while the second is based on linkage disequilibrium  (Bartley et al. , 1992). The temporal method was run considering the tick samples collected in November 2007 and their parental generation sampled either in May or July. Complementarily, we also applied Balloux (  2004 )'s method, a method based on heterozygote excess; it is worthy to note that this last estimate Ne is sensitive to deviations from HWE since it is directly driven from the  Weir and Cockerham (1984) 's F IS estimator so that Ne = 1/(-2F IS ) + 1/2(1-F IS ).
Discussion Despite a relative abundance of microsatellite markers in A. variegatum, the main difficulty encountered was to identify loci that could be amplified specifically (i. e. without any suspicion of locus duplication) and unambiguously genotyped. Eight polymorphic microsatellite markers were characterized in the tropical bont tick A. variegatum. Half of them displayed, within cohorts and villages, polymorphism patterns Table  1  Characterization of eight microsatellite markers in Amblyomma variegatum: description of marker (name, GenBank access to reference clone, primers' sequence, structure of repeated array), thermocycling conditions (annealing temperature, Ta; MgCl 2 concentration) and genetic variation (alleles number, Na; allele size range; allelic richness, A R ). A R is based on the minimum sample size of 12 diploid individuals.
Acknowledgments This work was supported by the transversal project  AAP INRA-CIRAD « Dynamic  and genetic diversity of tick populations and transmitted pathogens », the " Chercheur d'Avenir 2011 " call from the  Languedoc-Roussillon region, France , the  Centre de Coopération Internationale en Recherche Agronomiquepour le Développement (CIRAD) , and the  Ministère de l'Agriculture et de l'Alimentation. We are indebted to  Sébastien Zoungrana ,  Maurice Konkobo  and  Laurent Kaboré  for A. variegatum sampling, and to  E. Renier  and  R. Aprelon  for technical help. We also wish to thank the members of the group 'Ticks and tick-borne diseases' of the " Réseau Ecologie des Interactions Durables" (REID)  for useful discussions.
Experimental Design and Data Collection All the trials were laid out in the same Randomized Complete Blocks design with four replicates (randomization was different from one trial to another), each containing the ten genotypes. This gave a total of 40 plots per trial. Each plot consisted of 7 rows of 40 hills, occupying an area of 44. 8 m 2 (5. 6 m × 8 m). At physiological maturity, plants were sampled from a well delimited 3. 36 m 2 (3 rows × 7 hills) sub-plot to assess biomass (leaves and stems) and grain yields. Biomass dry weight was determined after air-drying in a greenhouse, followed by 48 h in an oven at 65 • C, and grain dry weight after panicle threshing. Grain and dry biomass yields were calculated in kg ha -1.
Acknowledgments: The authors thank  Mbaye Sarr Diop ,  Mamadou Sonko ,  Abdou Faye ,  Ibrahima Ndong ,  Massiré Badji ,  Bounama Mbengue ,  Yagouba Diao ,  Jean Gabriel Diatta  and  Thierno Ndiaye  for their invaluable help and cooperation in the laboratory and field work.
Epidemiological data During winter 2016/2017, a total of 105 records about dead or diseased birds essentially C. chloris and C. carduelis were collected in the first quarter of 2017. In all, 97 reports originated from bird ringers working with the Association Cap-Ornis Baguage, Steenwerck, Pas-de-Calais  (62) , France during their winter monitoring through the SPOL-Mangeoire protocol (a standardised protocol to study wintering strategies of the common passerines in France at provisioned feeding stations)  [36] ; one originated from the Regional Centre for Wildlife Healthcare, Moncavrel, France; and seven from individuals who reported to the Research Centre on the Biology of Bird Populations (CRBPO), National Museum of Natural History (MNHN), Paris, France. Among these data, the ring returns accounted for 14 records that provided information about survival of the infected birds as well as further illustrated their dispersion movements for seven of them.
Phylogenetic analysis For the ITS region, multiple alignment of the sequences obtained for T. gallinae and 53 sequences of Trichomonas retrieved from GenBank was generated using the Clustal W algorithm  [43]. Molecular phylogeny was inferred by the maximum likelihood (ML) method based on the Tamura 3-parameter + C + I model of evolution  [80]  with the most appropriate model of nucleotide substitution for ML analysis chosen using the Bayesian Information Criterion score  [73]. Nodal robustness and reliability of inferred trees topologies were assessed by non-parametric bootstrap analysis using 1000 replicates. Initial trees for the heuristic search were obtained automatically by applying NJ and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood approach, and then selecting the topology with superior log likelihood value.