dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA Extraction and Sequencing Total genomic DNA was extracted using the Puregene DNA Purification Kit (Gentra Systems). The complete cytb mitochondrial gene was then amplified using primers H15915 and L14123 following procedures detailed in Lecompte et al. ,  [44]. The resulting PCR products were purified and then sequenced in both directions on a MegaBACE 1000 (Amersham Biosciences). Sequences were carefully checked by eye, and aligned using the ClustalW Multiple Alignment option implemented in BioEdit v. 7. 0. 4. 1  [45]. In total, our cytb dataset includes 169 sequences encompassing 146 A. niloticus, two A. cf. niloticus, 11 other Arvicanthis spp. and 10 representatives of other rodent genera (Table  1 ). The complete non translated intron 7 of the Fibrinogen nuclear gene was amplified using primers BFIBR1 and BFIBR2, following Seddon et al.
[46]. These PCR products were purified and then sequenced in both directions by Macrogen (Seoul, Korea). In total, our Fib7 dataset groups 67 sequences that represent 60 A. niloticus, two A. cf. niloticus from Kenya, four representatives of other Arvicanthis species as well as one representative of genus Mastomys, to be used as an outgroup (Table  1 ). As stated previously, all individuals sequenced for Fib7 were sequenced for cytb (except for the A. niloticus specimen labelled 'Mal 299, and the representative of genus Mastomys), thus allowing us to build a combined dataset of 1,871 nucleotides (nt) encompassing 171 individuals. Importantly, all 10 countries that are represented for A. niloticus or A. cf. niloticus individuals in our cytb dataset are also represented in our Fib7 dataset. Similarly, 57 out of the 72 localities sampled in the A.
Phylogenetic and Dating Analyses Bayesian inference (BI) was used to co-estimate phylogenetic relationships and divergence times using the BEAST v1. 7. 5 package  [47]. BEAST uses Bayesian Markov Chain Monte Carlo (MCMC) procedures to approximate phylogenies and simultaneously infer nodes ages. To infer the time-calibrated phylogeny, we used the Bayesian relaxed clock (BRC) approach  [48]  implemented in BEAST. This method accounts for rate variation across lineages and assumes that substitution rates are uncorrelated across the tree (there is thus no a priori correlation between a lineage rate and that of its ancestor). To maximize the amount of available information, analyses were performed on the combined dataset, using specific sets of parameters for each gene  [49]. Bestfit models of evolution for each gene were selected with jModelTest  [50]  using the Bayesian information criterion (BIC).
For the molecular dating analyses, each gene was associated with a specific uncorrelated lognormal relaxed clock (ULRC) model. A coalescent model tree prior with a constant population size was also preferentially used to account for the fact that our trees mostly describe intra-specific relationships  [51]. BEAST. xml files were also modified to implement the path-sampling (PS) procedure  [52] , which allows a better approximation of the marginal likelihood of runs  [53]. In a complementary way, we carried out maximum likelihood (ML) analyses with the software RAxML v. 7. 0. 8  [54]  using default settings and 100 random-addition replicates. For the corresponding partitioned analyses, the same best-fit models of evolution (as in BI) were used. Clade support for ML was assessed with non-parametric bootstrap values (BV; 1,000 replicates were used).
[42]  to set a series of secondary calibrations for the nodes that are shared between our respective datasets. Though the use of secondary calibration is not advocated  [56, 57] , we conducted these analyses for comparison purpose. The fact that our outgroup choice was based on the result of the study of Lecompte et al. [42]  allowed us to identify six shared nodes that were used for secondary calibrations. To do so, we used normal parametric distributions to set minimum and maximum ages for the corresponding nodes. These constraints were enforced as follows: (i) most recent common ancestor (MRCA) of Arvicanthis and Aethomys, normalPrior mean = 6. For each calibration set ('exponential distribution'; 'lognormal distribution'; 'secondary calibrations'), two distinct runs were carried out, each one with 50 million generations, default priors and trees sampled every 5000th generation.
After applying a conservative burn-in of 12. 5 million generations for each run, convergence of runs was further assessed by examining the effective sample size (ESS) of parameters with Tracer available at www. tree. bio. ed. ac. uk/software/tracer/. The resulting log and tree files were then combined using LogCombiner. We then directly estimated both node support (posterior probabilities: PP) and node age (median age estimates and 95% higher posterior densities: 95% HPD) using TreeAnnotator. Only nodes with posterior probabilities (PP) $0. 95 were considered strongly supported  [58]. Bayes factor (B F ;  [59] ) were then estimated using the log files of the three distinct calibration sets, using scripts detailed in Baele et al. [53]. The resulting time-calibrated trees (one per calibration set) were further modified under Mesquite v2.
75  [60]  by pruning the most basal outgroup taxa. For comparison purpose, additional runs (same settings for the MCMC) were also conducted for the cytb dataset (169 taxa; 1,113 nt) and the Fib7 dataset (67 taxa; 758 nt). Additional phylogenetic-based analyses were also conducted to precise the geographic origin of the various Arvicanthis niloticus populations. To do so, we relied on the tree resulting from the analyses of the combined dataset. This tree was further pruned under Mesquite in order to include one specimen for each of the major lineages of A. niloticus, and one specimen for each other Arvicanthis species. Character optimizations were then performed to infer the ancestral areas of distribution. Three major areas were categorized for this analysis: (i) 'West Africa', which includes Benin, Burkina-Faso, Ghana, Mali, Niger, Senegal, Togo; (ii) 'Central Africa', which includes Cameroon, Central African Republic, Chad; and (iii) 'North-East and East Africa', which here includes Egypt, Ethiopia, Kenya, Somalia, Sudan, Tanzania.
Genetic Differentiation, Genetic Diversity and Network Analyses Analyses at the intraspecific level were performed on datasets that encompass only A. niloticus individuals (146 individuals for the cytb dataset and 60 individuals for the Fib7 one). The genetic differentiation between specimens belonging to the major phylogroups inferred by the phylogenetic analyses of the combined dataset was assessed for each gene using DnaSP v. 5. 1. 0 [61]. However, the fact that DnaSP does not take into account missing data was problematic for the Fib7 dataset since it led to the exclusion of 113 nucleotide positions. Therefore, we analyzed a subset of the original dataset from which two sequences with a lot of missing data were removed  [62]. In order to assess the level of genetic differentiation among the four phylogroups, we used three distinct statistics (F ST , K ST* , and S nn ).
The F ST (fixation index) is a statistic that compares the level of diversity of randomly chosen alleles in a given population with those found in the entire geographical sample; the K ST* is a statistic that takes into account the number of nucleotide differences between different haplotypes but does not give much weighting to large numbers of differences  [63] ; the nearest-neighbour statistic (S nn) measures how often the nearest neighbours within a matrix of sequences originate from the same population  [64]. Because these three indices are known to be more or less sensitive to specific dataset features (such as low level of genetic diversity or low sample size), we used them in combination to ensure a more robust detection of genetic differentiation  [65]. For each statistics, a permutation test of 1,000 replicates was performed to assess the significance of the subdivision parameters.
DnaSP was then used to infer the following parameters of genetic diversity: number of segregating sites (S), number of haplotypes (h), haplotypic (Hd) and nucleotide (p) diversities. We also performed neutrality tests for each gene using Tajima's D [66] and Fu's F statistics  [67]. For the latter, values close to zero are expected for historically stable populations, whereas negative values would be indicative of recent population expansion. Finally, networks analyses were conducted on the reduced cytb and fib7 datasets using the reduced median joining method  [68] , which has the ability to deal with missing data as well as to infer ancestral haplotypes. This method also performs well against, or outperforms other network approaches  [69]. It was implemented using the software Network v. 4.
Phylogenetic Analyses The BIC returned a general time reversible (GTR) model+C as best-fit model for each gene. For all analyses (combined dataset, or Fib7 and cytb gene alone), convergence was reached as indicated by the high ESS values (. 200) recovered for all runs. Analyses of the combined dataset yield a similar topology whatever the method used (BI or ML; Figure  1 ). Overall the corresponding trees are well supported, and most interspecific nodes are supported by PP$0. 95 under BI or bootstrap values $70% under ML (Figure  1 ). Within the genus Arvicanthis, two main plurispecific clades emerge: specimens of A. ansorgei, A. cf. rufinus and ANI-2 cytotype (sensu  [30] ) form a well-supported monophyletic group (PP of 1. 0/BV of 93%) whose sister assemblage contains (PP of 1. 0/BV of 99%) representatives identified as A.
Interestingly, this result was recovered despite the fact that the number of sampled individuals for phylogroup C-1 is lower than those of phylogroups C-3 and C-4 (and even C-2 when considering the Fib7 dataset). The results of the neutrality tests for the two genes showed that A. niloticus populations were stable over time. We only find evidence for expansion of populations' size for the phylogroup C-1 (both Tajima's D and Fu's F statistics) and in the phylogroup C-3 (Fu's F statistics). Network analyses recovered a relatively congruent pattern for both genes (Figure  2  and Figure  3 ), in which the haplotypes from East Africa (phylogroup C-1) are connected with haplotypes from Central Africa (phylogroup C-2), which are further connected with haplotypes from West Africa (phylogroups C-3 and C-4), thus indicating a clear East to West pattern of colonization.
Furthermore, the cytb dataset suggests that the genetic pool that is now present in the eastern part of West Africa (phylogroup C-4) originated from the haplotypic population that currently lies in the western part of West Africa (phylogroup C-3). Time-calibrated tree resulting from the partitioned Bayesian analysis of the combined dataset. Age estimates correspond to the results of the BRC analysis using the fossil constraint with an exponential distribution. Labels on nodes correspond to the nodes listed in Table  2. Posterior probabilities (left) and bootstrap values (right) are also indicated for major nodes (values below,0. 50 are not figured). On the bottom left (lower panel), a map is included to figure the localities of almost all sampled Arvicanthis niloticus specimens (with the exception of the specimen from Ethiopia and the two specimens from Egypt).
niloticus specimens by Ducroz et al. [29] , tends to consolidate the sister status of A. abyssinicus and A. niloticus. The two species are sympatric, but apparently not syntopic, in Ethiopia where the former is considered an endemic of the High Plateaus, whereas the latter would range at lower altitudes, west of the Rift Valley  [24]. The remaining part of this clade corresponds to Arvicanthis niloticus, whose phylogeography is discussed in detail below, thanks to a large and wide-ranging sample. It was previously shown that A. niloticus may display two distinct karyomorphs differing by an inversion: ANI-1a with 2N = 62/ NFa = 62 and ANI-1b with 2N = 62/NFa = 64 on the Eastern and Figure  3. Haplotype network and phylogenetic tree resulting from the analysis of the Fib7 dataset. The haplotype network reconstruction takes into account missing data and gaps, so that the inferred number of haplotypes is higher than the one presented in Table  4.
If true, this would imply that this inversion polymorphism has been segregating in A. niloticus since at least the C-3/C-4 divergence, ,1. 16-1. 43 Ma ago, hence is hemiplasic  [72]. These two explanations (introgression and incomplete lineage sorting) are not mutually exclusive. Moreover, the detection of animals with heterozygous karyotypes (i. e. , ANI-1a6ANI-1b) within the range of both C-3 and C-4 mtDNA clades suggest at least persisting nuclear gene flows between these two lineages. The present work is the first one to include both mitochondrial and nuclear DNA sequences for phylogeographic purposes in African rodents. These two genes led to rather congruent patterns, although the nuclear dataset was much less phylogenetically informative. This is expected since nuclear DNA in coding regions usually evolves under slower mutation rates.
g. , in Central Mali and around Lake Chad) poorly support the local adaptation hypothesis. In the same manner, although putative geographic barriers that currently delimit A. niloticus intraspecific clades (Niger River, Lake Chad basin) were identified, vicariance appears as an inaccurate explanation here since no admixture at all was detected in our dataset. Indeed, this would imply that propagules crossed each of those barriers only 'once' and then remained reproductively isolated for hundreds thousand years, something that sounds highly improbable. On the contrary, the refuge theory  [77]  fits well to the phylogeographic pattern observed in the Nile grass rats: lineages' splits all occurred during the Pleistocene which is characterised by deep climatic variations that induced extensive modifications of the open grasslands habitat range in Sub-Saharan Africa.
Figure1. Time-calibrated tree resulting from the partitioned Bayesian analysis of the combined dataset. Age estimates correspond to the results of the BRC analysis using the fossil constraint with an exponential distribution. Labels on nodes correspond to the nodes listed in Table2. Posterior probabilities (left) and bootstrap values (right) are also indicated for major nodes (values below,0. 50 are not figured). On the bottom left (lower panel), a map is included to figure the localities of almost all sampled Arvicanthis niloticus specimens (with the exception of the specimen from Ethiopia and the two specimens from Egypt). Additional information on mountain and hydrogeographic formations of interest is also provided. A red line is also used to figure the distribution limits of the two distinct A.
Figure2. Haplotype network and phylogenetic tree resulting from the analysis of the cytb dataset. The haplotype network reconstruction takes into account missing data and gap so the inferred number of haplotype I higher than the one presented in Table4. Red values on nodes indicate the inferred number of mutation steps between haplotypes or ancestral haplotypes (symbolized by a red node). The absence of value means that the number of steps is inferior to 10. The phylogenetic tree corresponds to the results of a Bayesian inference analysis (see text for details); posterior probabilities (PP) are indicated for major nodes (values below,0. 50 are not figured). On the right side, the general origin of Arvicanthis niloticus specimens is figured using circles filled with different colours that directly refer to the map presented in Figure1.
Acknowledgments We would like to thank the associate editor  Dr. Roscoe Stanyon  and four anonymous reviewers for their constructive comments on the present manuscript. We are grateful to  Drs C. Brouat  and  F. L. Condamine  for useful advices on a preliminary version of the manuscript. We also thank the numerous collectors and providers of samples that helped us with the sampling. Some of the molecular data used in this work were produced through technical facilities of the  ''Laboratoire d'Excellence -Centre Mediterraneen de l'Environnement et de la Biodiversite'' (LabEx -CeMEB ), Montpellier, France.
(ANI-2) GenBank: HM635839 Cameroon, Gamnaga 10u57N 14u03E x x ANI-2 1 GenBank: AF004584 CAR, Koumbala 09u14N 20u42E x ANI-2 2 Arvicanthis cf. rufinus GenBank: AF004582 Benin, Lokossa 06u38N 01u43E x 1 GenBank: AF004583 Benin, Tanougou 10u49N 01u26E x 2 non-Arvicanthis species Aethomys chrysophilus (GenBank: AJ604526. 1) Tanzania x Desmomys harringtoni (GenBank: AF141206) Ethiopia x Golunda ellioti (GenBank: AM408338. 1) India x Lemniscomys striatus (MNHN: ZM-2008-020) Benin x Mastomys erythroleucus (CBGP: M4136) Chad x Mylomys dybowskii (GenBank: AF141212. 1) Ivory Coast x Pellomys fallax (GenBank: DQ022382. 1) Tanzania x Otomys irroratus (GenBank: EU874434) South Africa x Otomys sungae (GenBank: JF795993) ? x Otomys tropicalis (GenBank: JF795995) ? x Rhabdomys pumilio (GenBank: AF533116) South Africa x [29]GP'', ''DM'' and ''MNHN'' stand for Centre de Biologie pour la Gestion des Populations (Montpellier, France), Durban Natural Science Museum (Durban, South Africa) and Museum National d'Histoire Naturelle (Paris, France), respectively.
Line source formulation The Gaussian formulation of the concentration field for a pollutant emitted from a line source is the result of the integration of the point source solution over the line source (reflection terms are taken into account in the models, but neglected here for simplicity): C(x, y, z) = y 2 y 1 Q 2πuσ y (s)σ z (s) exp -z 2 2σ 2 z (s) - (y -s) 2 2σ 2 y (s) ds (1) where C is the pollutant concentration in g m -3 at location (x, y, z), x is the distance from the source along the wind direction in m, y and z are the horizontal and vertical crosswind distances, respectively, from the plume centerline in m, u is the wind velocity in m s -1 , Q is the emission rate in g s -1 , y 1 and y 2 the ordinates of the source extremities, and σ y and σ z are the standard deviations representing pollutant dispersion in the cross-wind directions in m, which are derived from experimental datasets.
The smaller domain has a resolution of 3 km. The WRF options selected for these simulations are given by  Kim (2011). Since Gaussian models use a single set of meteorological inputs for a given hour, domain-wide average values, over the smallest domain, of the meteorological variables were used. In the initial simulation, the stability classes were defined according to wind speed and cloudiness. In a subsequent simulation, atmospheric stability was defined according to the Monin-Obukhov length. We used the same dataset for the HV and Polyphemus model simulations. Background concentrations and emission rates were computed hourly for the year 2005 instead of 2007 and 2008 because of a lack of year-specific traffic modelling data for the roads studied. Also, available emission rates were daily averaged values, which means that variation in traffic (congestion during rush hours for instance) is not taken into account.
Therefore, performance indicators for those three cases are shown in Supplementary Material only. EMIS temporal profile tends to decrease model performance slightly. Nevertheless, the use of a temporal profile for emissions was considered to be relevant despite the decrease in performance, because our purpose was to decrease the overall input data uncertainty rather than 10 to evaluate the effect of individual changes. Therefore, performance indicators for those three cases are shown in Supplementary Material only. Figure  6  shows satisfactory results for the summer campaign whereas for the winter campaign a significant model underestimation is visible. As discussed above, the uncertainty in measurements is important (on average +17 % in summer and +12 % in winter). Therefore, the fact that passive diffusion tube measurements tend to overestimate NO 2 concentrations could explain why Polyphemus and the HV models underestimate those measurements.
Conclusions The Polyphemus line source model has been presented and evaluated with a case study characteristic of a large roadway system. Uncertainties in input data (emissions, background concentrations, meteorological parameters) and in passive diffusion tube measurements have been discussed. The base simulations reflected operational input datasets and, as such, differed in their levels of detail. As a result, we focused on the uncertainty in traffic emissions and meteorology. According to previous studies, Polyphemus fulfills the criteria to be considered as a "good" model despite the fact that emissions rates were annual averages. Polyphemus and the HV model, give similar results for the one-month average concentrations; ADMS-Urban tends to lead to lower concentrations. Although no major improvement of Polyphemus with respect to the HV model appears in the one-month averaged results, some major differences can be seen in specific situations when the wind is nearly parallel to the road.
Acknowledgements. We acknowledge the  GENEMIS  project, which provides the emission temporal profile. We also acknowledge the  National Centres for Environmental Prediction (NCEP)  for providing initial and boundary conditions that were used for the WRF model simulations. Finally, we acknowledge the  Department of Transportation for the Île-de-France region (DRE IF)  for providing the traffic modelling results. Edited by:  P. Jöckel.
3 Case study 3. 1 Simulation set-up This case study pertains to a very large road network in the Paris region, France. It includes concentration measurements made during winter 2007 and summer 2008. The dataset used for the simulations contains the following: -The coordinates of 1371 road sections divided into 5425 smaller, but straight, sections representing a total of 831 km of linear road length. -The NO x emission rates associated to each road section computed with the CopCETE emission model, of the scientific and technical network (RST) of the French Environment Ministry, from traffic modelling results developed by the Department of Transportation for the Île-de-France region (DRE IF). CopCETE uses the Eu- ropean emission methodology COPERT 3: http://www. emisia. com/copert/Copert3. html (COPERT 4 was not yet available when the original study was conducted).
For comparison, transcriptional responses to auxin were also assayed in intact (undigested) roots treated for 3 h. To establish that the tissue-specific expression profiles gathered here were consistent with the previously published root expression data  (Birnbaum et al, 2003; Nawy et al, 2005; Lee et al, 2006; Levesque et al, 2006; Brady et al, 2007) , we generated a list of cell type-specifically enriched (CTSE) genes using the public data and visualized their expression in our data set. This CTSE list was based on the expression profile template matching in a select data set of 13 non-overlapping, cell type-specific expression profiles of sorted GFP-marker lines (Supplementary Figure  S2B ; Supplementary Table  S2 ). This procedure yielded a total of 3416 genes whose expression is enriched in one specific cell type (maturing xylem, developing xylem, xp pericycle, phloem-pole pericycle, phloem, phloem companion cell, quiescent center, endodermis, cortex, trichoblast, atrichoblast, lateral root cap or columella) or whose expression was enriched in two related cell types (xylem (developing and maturing xylem), pericycle (xp and phloem-pole pericycle), phloem (phloem and phloem companion cell), ground tissue (endodermis and cortex), epidermis (trichoblast and atrichoblast) or root cap (lateral root cap and columella)).