dataesr/scientific-paragraphs-categorization huggingface dataset:
MRL acknowledges funding from the  Canadian Institutes of Health Research (CIHR ; grant  MOP 142243 ) and the  March of Dimes , as well as a senior investigator award from the  Michael Smith Foundation for Health Research (MSFHR). EMV is funded by grants from the  European Research Council  ( ERC Starting Grant   260888 ) and  Telethon Foundation Italy  (grant  GGP13146 ). OEB is funded by grants from  Science Foundation Ireland  ( SFI-11-1037 ) and the  European Community 's  Seventh Framework Programme   FP7/2009  ( 241955  suggest an MKS-5 and CEP-290-dependent assembly pathway for building a functional TZ. SYSCILIA). JFR is funded by grants from the  NIH  ( AR054396  and  GM095941 ),  Burroughs Wellcome Fund , the  Packard Foundation , and the  Sandler Family Supporting Foundation. CTR was supported by the  GIS-Institut des Maladies Rares , the  French Fondation for Rare Disease , the  French Ministry of Health  (PHRC national  2010-A01014-35  and  2013 ), and the  Regional Council of Burgundy.
Population Structure Analyses Principal Component Analysis (PCA) We performed principal component analysis (PCA) to assess the population structure of the sampled honey bees. PCA is a classical non-parametric linear reduction technique used to reveal population structure by arranging all principal components (PCs) according to the explained variance without resorting to a model  (Menozzi et al. , 1978; Price et al. , 2006; Gao and Starmer, 2008). Here, we applied PCA on a genetic relationship matrix (n × n) with pairwise identities by state (IBS) between all individuals (N = 151) as provided by PLINK 1. 9  (Chang et al. , 2015). To infer the number of significant PCs and to determine significant differences between subpopulations, we used Horn's parallel analysis (PA;  Horn, 1965; Dinno, 2009)  as implemented in the R package paran  (Dinno, 2012).
Hierarchical Population Structure To determine the uppermost hierarchical population structure, we applied an unsupervised network-based clustering algorithm called super paramagnetic clustering (SPC,  Blatt et al. , 1996; Tetko et al. , 2005). The input to SPC is a dissimilarity matrix D (n × n) with pairwise genetic distances between all individuals calculated using allele sharing distance (ASD; one minus IBS). Given D, each individual gets associated with a Pott spin variable (q). Once q have been assigned, the clustering is performed along a range of temperature ( T) limiting the interactions to a given number of k nearest neighbors (k-NN). We applied the algorithm using the following settings: k-NN = 15, q = 20 and ( T) = 0. 01. For the visualization of hierarchical population structure and cluster solution, we used the R software package phytools  (Revell, 2012).
Admixture To estimate the admixture proportion of each individual, we performed model-based cluster analyses as implemented in the program ADMIXTURE  (Alexander et al. , 2009). The program was run unsupervised with 10,000 iterations and a prespecified number of clusters K (K = 1-6). Convergence between independent runs at the same K was monitored by comparing the resulting log-likelihood scores (LLS) following 10,000 iterations, and was inferred from stabilized LLS with less than 1 LL unit of variation between runs. Cross validation (CV) error estimation for each cluster was performed with ADMIXTURE  (Alexander et al. , 2009)  and used to determine the optimal number of K clusters. To investigate the differences in introgression from introduced honey bees into the native A. m. mellifera gene pool, we compared admixture proportions from individuals originating from different sampling locations.
In order to express the strength of relationship between two individuals, the line width of an edge is proportional to the genetic relatedness between them (IBS). This approach is described in  Neuditschko et al. (2012)  and a recent implementation of this workflow is now available as R package  (Steinig et al. , 2015)  posted at https://github. com/ esteinig/netview. In network theory, so-called communities, which are more densely and strongly connected within a group than outside a group, can be detected. Based on the high-resolution population network at K = 4 (see Result section), we selected a subset (communities) of core bees (gray dashed circles in Figure  4B ) for each subpopulation which were clustering together and showed no or low levels of admixture (<0. 1; except for Buckfast which was a more heterogeneous group).
SNP Thinning and Selection of Informative SNP Panels The systemic homozygosity in haploid sequence data results in strong linkage disequilibrium (LD) between SNPs (see Supplementary Figure  1 ), and consequently a large number of uninformative or "redundant" SNPs  (Weale et al. , 2003; Nicolas et al. , 2006; Paschou et al. , 2008). To reduce SNP density and redundancy without adversely affecting LD-associated finestructure, the dataset was randomly down-sampled to 10,000 SNPs using PLINK 1. 9  (Chang et al. , 2015). To ensure that there was no loss of information after the stringent thinning step, we computed and compared ASD-based distance matrices and PCA results between the entire (3. 375 M SNPs) and thinned (10 K SNPs) datasets. First, to verify whether the genetic relationships between individuals are reflected in the thinned dataset, we tested the concordance between the ASD distance matrix with all bees (N = 151) inferred from the whole-genome (3.
375 M SNPs) and the pairwise distances inferred from the thinned (10 K SNPs) dataset using Mantel R  (Mantel, 1967). Mantel R is a permutation test to estimate the correlation between two matrices and was calculated using the package vegan in R  (Oksanen et al. , 2016). Second, to visualize whether the thinned dataset is able to capture the population structure revealed by all SNPs, we computed PCA for each dataset and plotted the core bees (N = 95) on the first and second PCs. To distinguish introduced from native honey bees for conservation or breeding purposes, ancestry informative SNPs for subspecies discrimination were identified. Out of the thinned dataset, we generated panels with different number of SNPs (1000, 500, 100, and 50 SNPs) employing three different selection methods: (1) F ST  (Weir and Cockerham, 1984)  between native and introduced honey bees.
F ST per site was calculated between core bees of A. m. mellifera from Switzerland and France (N = 56) and core bees of A. m. carnica and Buckfast bees (N = 39) using VCFtools  (Danecek et al. , 2011). Four SNP panels with the highest F ST per SNP were defined as: F ST -1000, F ST -500, F ST -100 and F ST -50. In addition, we selected a fifth panel correcting for any remaining redundancy in the SNP informativeness by computing a sorted QR-decomposition to effectively remove correlated markers  (Paschou et al. , 2008). The input for QR-decomposition is a genotype matrix with dimensions n × m (samples × SNPs), where each SNP is encoded with either -1 for homozygous reference or 1 for homozygous alternate allele. Applying this approach with a custom script in GNU octave  (Eaton et al. , 2014) , we extracted the most uncorrelated SNPs out of the F ST -100 panel resulting in the fifth SNP panel: UncorrF ST -50.
(2) PCA-derived, whereby a subset PCA-correlated SNPs were selected to reproduce the structure found by PCA without use of prior ancestry information  (Paschou et al. , 2007). This approach measures the correlation between a SNP and the significant principal components giving each SNP a PCA-score  (Paschou et al. , 2007). The input is a genotype matrix (samples × SNPs) encoded as above (1,-1) and PCAscores were calculated using a custom script in R. Finally, we defined four informative SNP panels with the highest PCA-scores: PCA-1000, PCA-500, PCA-100, and PCA-50. Additionally, we generated a fifth panel applying the same approach as for the UncorrF ST -50 (see above). We thus selected the 50 most uncorrelated out of the PCA-100 SNPs resulting in the panel: UncorrPCA-50. (3) Randomly chosen SNPs, selected using the sample-function in R to generate panels with 1000, 500, 100, and 50 SNPs, respectively.
Since the variation when using randomly selected SNPs is expected to be high, we run the samplefunction five times each to produce a total of 20 SNP panels: 5x Random-1000, 5x Random-500, 5x Random-100, and 5x Random-50. After generating the SNP panels, we tested their accuracy in estimating admixture proportions and genetic relationships between individuals by reference the earlier results from the complete SNP dataset. The random panel replicates were tested independently and the averaged statistic and variation are reported.
Mapping and Variant Calling On average 19,549,894 paired-end reads were generated per bee of which 96. 3% mapped in pairs to the reference genome Amel4. 5  (Elsik et al. , 2014). Approximately 2. 2% of the mapped reads were marked as duplicates that aligned with the identical start and end positions onto the reference genome. Sequencing depth per sample ranged from 3. 3 to 20. 7X with mean 9. 9X resulting in 97. 2% of the genome being covered on average. Individual mapping and alignment statistics are presented in Supplementary Table  2. Individual variant calling statistics for each drone genome in the Swiss dataset, comparing the three different applied variant calling tools, can be found in Supplementary Table  3. Once filtered on depth of coverage, the master variant file identified by combining single sample VCF files resulted in 4.
SNP Thinning The pairwise genetic distance matrices calculated using the total (3. 375 M SNPs) and the thinned (10 K SNPs) datasets are highly concordant [Mantel R = 0. 999, 95% CI (0. 998, 0. 999), p < 0. 001]. Moreover, when visualized the PCA scatter plot generated with the thinned dataset (10 K SNPs) reflects the same pattern found using all SNPs, yet with a less clear distinction between Swiss and French A. m. mellifera (Figures  5A, B ). Hence, the population structure and genetic relationships observed using all SNPs is conserved well with the 10 K SNPs dataset, with only minimal loss of information despite the stringent thinning step applied and could thus be used to select ancestry informative SNPs.
In conclusion, with only 50 SNPs the UncorrF ST -50 panel clearly outperformed the other selection methods (SNP positions in Supplementary Table  4 ). Admixture proportions computed with the UncorrF ST -50 panel were very highly correlated with admixture proportions using the whole-genome (3. 375 M SNPs) dataset [Pearson's r = 0. 975, 95% CI (0. 958, 0. 985), p < 0. 001] while accounting for the lowest error [SE = 0. 043, 95% CI (0. 039, 0. 046)]. Additionally, the genetic relationships inferred from the UncorrF ST -50 panel were also highly correlated with all 3. 375 M SNPs [Mantel R = 0. 977, 95% CI (0. 976, 0. 980), p < 0. 001]. The PCA scatter plot (Figure  5C ) visually clearly highlights the ability of the UncorrF ST -50 SNP panel to reveal the significant difference and to separate introduced from native honey bees, as shown by the first PC explaining 99.
DISCUSSION Network-based clustering was used for the first time in honey bees enabling effective identification of fine-scale population Admixture estimates of all panels are highly correlated (>0. 9) with admixture proportions calculated with the entire dataset (3. 375 M SNPs) and there is no difference between the selection methods F ST , PCA and Random. Yet, with only 50 SNPs the UncorrF ST -50 panel significantly better predicts admixture than the other 50-SNP panels. (B) Concordance between pairwise genetic distance matrices computed using all SNPs (3. 375 M) and each of the SNP panels (1000, 500, 100, and 50 SNPs) with the different selection methods (F ST , PCA, Random, UncorrF ST , and UncorrPCA). Mantel R with 95% CI was estimated on the distance matrices using all individuals (N = 151) with 999 permutations.
, 2012; Greenbaum et al. , 2016). This is the advantage of our combined network visualization; it illustrates both the genetic relationships between individuals and admixture levels, which is crucial for the interpretation of the observed population structures  (Neuditschko et al. , 2012). Therefore, the network clustering is highly suitable to identify community structures corresponding to the four subpopulations in this dataset (A. m. carnica, Buckfast, A. m. mellifera from Switzerland and France). Furthermore, admixed individuals can be easily spotted. The substructure between Swiss and French A. m. mellifera is surprising, given the large mating distances of honey bee drones from their colony of origin  (Neumann et al. , 1999c). Here, we found already some population differentiation on a short distance of ∼40 km between the conservatory in the French Alps and Swiss A.
m. carnica-beekeeper, who recently moved to the area. Thus, our data demonstrates that the genetic consequences are immediately measurable. Moreover, there is a need for an increased legal protection of conservation areas, as evidenced by the drastic effects of this single migratory beekeeper on conservation efforts. The French conservatory in Savoie (CSA) also displays admixed individuals. In this breeding program identification has been mainly based on morphometrics, which can have less resolution than genetic markers  (Francis et al. , 2014). In addition, France has a long history of foreign queen importation with many large-scale commercial apiarists keeping C-lineage bees  (Franck et al. , 1998; Garnery et al. , 1998; Wragg et al. , 2016). This increases the chance of introgression in the native gene pool.
Such low density SNP panels have been developed successfully for breeding and breed assignment for a number of different livestock species  (Boichard et al. , 2012; Wellmann et al. , 2013; Henshall et al. , 2014). For honey bees,  Muñoz et al. (2015)  have previously generated sets of ancestry informative markers (AIMs) based on different selection methods to infer admixture between A. m. mellifera and C-lineage (A. m. carnica and A. m. ligustica). However, due to the hybrid origin of Buckfast bees, it might pose more difficulties to detect admixture with Buckfast than with pure C-lineage bees. Moreover, the full dataset of  Muñoz et al. (2015)  consisted of 1183 preselected SNPs from a previous study, while here we selected the most informative SNPs from whole-genome sequence information.
FUNDING This work was funded by the  Swiss Federal Office for Agriculture FOAG  and the  Fondation Sur-la-Croix ,  Basel. Data  for the Savoie samples was provided by the SeqApiPop programme, funded by the  FranceAgriMer  grant  14-21-AT. The GeT platform, Toulouse (France), is a partner of the National Infrastructure France Génomique, thanks to support by the  Commissariat aux Grands Invetissements  ( ANR-10-INBS-0009 ).
DATA ACCESS Swiss bee sequence data is deposited at the European Nucleotide Archive (ENA: http://www. ebi. ac. uk/ena) under study accession number: PRJEB16533. The sequence data of the bees from Savoie are available through the SeqApiPop programme on the Sequence Read Archive (SRA: https://www. ncbi. nlm. nih. gov/sra) with accession: SRP069814.
The structured coalescent is often used to estimate the effective size of populations in a context similar to that of our simulations: each deme corresponds to a sampling region as opposed to a genuine element of a partitioned population. We used MultiTypeTree  [46]  from the BEAST2 package  [10]  to estimate the parameters of the structured coalescent model applied to our simulated data (see  SI 17). Posterior medians and 95% credibility intervals for the population densities estimated with the structured coalescent and the ΛV models are presented in Figure  1. The structured coalescent generally overestimates population densities with the strongest biases observed with two demes and more accurate estimates obtained with ten. Estimates obtained under the ΛV model are better overall, both in terms of accuracy and, to a lesser extent, precision, with very little difference between two and ten demes.
6-d Influenza seasons in the USA Homologous nucleotide sequences from the NA segment of the Influenza A virus (H1N1 sub-type) were retrieved from the Influenza Research Database  [45]. Five flu seasons (2009-2010 to 2013-2014) in the USA were considered in five separate analyses. Hawaii and Alaska were excluded from the analysis in order to approximate the shape of the habitat with a rectangle. Multiple sequences are generally available for each state and season. Two distinct sets of sequences (with a single sequence per state for each set) were analyzed for each season, thereby providing two independent biological replicates for each of the five flu seasons. Each sequence alignment was analyzed using the HKY  [19]  model of nucleotide substitution with the FreeRate model of rate variation across sites  [44]  and a covarion-like model of site-specific rate variation across lineages  [18].
15 Bayesian estimates of neighborhood sizes Figure  9 : Cumulative incidence rate of individuals (per 100,000 population in the USA) that underwent laboratory-confirmed influenza related hospitalization. Data from http://gis. cdc. gov/GRASP/Fluview/ FluHospRates. html. TypeTree The MultiTypeTree software from the BEAST2 software package  [46, 11]  was used to estimate the rate at which pairs of lineages coalesce under the structured coalescent model in our simulations. The number of demes was set to two or ten depending on the simulation setting. Relative rates of migration between demes were estimated from the data. Log-normal prior distributions with default hyper-parameter values were used here. The rate of coalescence was estimated in each deme and a log-normal distribution with default hyper-parameter values was also used to model the prior distribution of the inverse of each of these rates.
This leads to a rapidly growing population of ancestors, whose structure we wish to understand. This is primarily investigated using stochastic simulations of the ancestral processes just defined. These simulations use the discsim Python module, which is freely available at https://pypi. python. org/pypi/discsim. The large effective densities and numbers of loci used in this article would not be possible without recent advances in simulating these ancestral processes  (Kelleher et al. , 2014). The code used to run the simulations and generate the plots here is freely available under the terms of the GNU General Public License at https://github. com/ jeromekelleher/ancestral-waves. In the model, time proceeds by incrementing a global clock by an exponentially distributed value with parameter λL d each time an event occurs in T d L.
Acknowledgments We thank the two anonymous referees for their very careful reading and their comments, which led to many improvements in the presentation of the manuscript. The first author's work supported by  EPSRC  grant  EP/I013091/1  and  Wellcome Trust  grant  100956/Z/13/Z. The second author's work supported in part by  EPSRC  grants  EP/I01361X/1  and  EP/K034316/1. The third author's work supported by the chaire Modélisation Mathématique et  Biodiversité of Veolia Environment-École Polytechnique-Museum National d'Histoire Naturelle-Fondation X. The fourth author's work supported by  ERC  Advanced Grant 'Selection and information' 250152.
Primer design A region of the polymerase (L) gene (nucleotide positions 7170 to 7419 according to the Pasteur virus genome; GenBank accession number M13215) conserved among lyssavirus genomes was selected as the target of the combo RT-qPCR technique, based on the results of previous studies  [15, 21]. This target sequence was amplified from a selected panel of lyssavirus isolates by a previously described RT-hnPCR method  [15] , and the PCR products were Sanger sequenced and analyzed with Sequencher 5. 0 (Gene Codes Corporation) software. The probes and primers of the combo RT-qPCR assay were designed on the basis of a ClustalX 2. 1 multiple nucleotide alignment  [22]. All primers were designed with OligoAnalyzer 3. 1 software, available from http://eu. idtdna. com/calc/analyzer, and checked in silico for the formation of hairpins, self-dimers and hetero-dimers and sequency analogy, by blast analysis (BlastN on the NCBI database).
Preparation of cloned-target plasmids Viral RNA was extracted from selected RABV isolates (n = 9) and from one or two representative LBV, MOKV, DUVV, EBLV-1, EBLV-2 and ABLV lyssaviruses, reverse-transcribed and amplified with the PVO5m/PVO9 primers, as previously described  [15]. The PCR products were then inserted into the Topo TA vector according to the manufacturer's recommendations (Life Technologies, Saint Aubin, France). For the other species and isolates of lyssavirus (WCBV, ARAV, KHUV, IRKV, SHIBV, IKOV, Ozernoe and BBLV), which were not available at the time of this study, the nucleotide region corresponding to the PVO5m/PVO9 fragment was synthesized (Eurofins Genomics, Ebersberg, Germany) based on the reference sequences in GenBank. This region was then inserted into the Topo TA vector, as described above.
Probe and primer design Due to the limited number of nucleotide sequences corresponding to the targeted region of the polymerase gene of lyssavirus available from public databases at the time of this study, a large panel of RABV isolates was selected and sequenced, so as to obtain at least one prototype sequence for each of the principal phyloclades previously defined (S1 Table)  [26] [27] [28]. We also selected and sequenced a panel of isolates from other lyssavirus species. The probes and primers for the combo RT-qPCR assay were designed from two different sequence datasets. The first dataset contained 102 RABV partial polymerase gene sequences, including 93 newly acquired sequences, and was used to design the pan-RABV RT-qPCR assay, which was based on the Taq3long and Taq17revlong primers and the TaqMan probes RABV4 and RABV5 (Fig  1 ) (Table  1 ) (S1 Table ).
These primers amplified a 143-nucleotide sequence. To complete the evaluation of the spectrum of detection of these primers and probes of the pan-RABV RT-qPCR assay, we analyzed in silico another dataset of 91 partial polymerase gene sequences of RABV isolates originated from the New World region (the Americas) and which have been recently published and available in GenBank (S1 Fig)  (S2 Table). The genetic diversity observed in this dataset was high, leading to the presence of cumulative mistaches for some isolates. For the pan-lyssa RT-qPCR assay, we used another dataset contained 45 sequences representing all the other lyssavirus species and including 22 new sequences (S1 Table ). On the basis of multiple nucleotide alignment data, we defined a single set of primers, Taq5long and Taq16revlong, amplifying a 119-nucleotide region (Fig  2 ) (Table  1 ).
All the assays gave negative results for of the negative control materials, indicating a high specificity. To complete the evaluation of the performance of our pan-RABV RT-qPCR assay, we analyzed in silico the targeted nucleotide region of a dataset of RABV isolates originated from New World area, circulating mainly in bats, skunks or raccoons, and which were recently available in GenBank. Indeed, the primers and probes were initially designed based on a limited number of such isolates. A high genetic diversity was found in isolates originated from the Americas, as observed in silico with the presence of cumulative mistmaches in the primers and/or probes hybridization regions, which could interfere with their detection (S1 Fig). Although we demonstrated in our study that the pan-RABV RT-qPCR assay allowed the positive detection of different prototype isolates from this region (such as 9704ARG and 9105USA), further evaluation of this assay has to be performed on a larger and more representative dataset of isolates from New World area.