dataesr/scientific-paragraphs-categorization huggingface dataset:
Approximately 1 × 10 6 cells in 1. 2 ml ice-cold PBS were fixed by slowly adding 3 ml cold 100% ethanol while vortexing to obtain a 70% final concentration. The incubation procedure with primary and secondary antibodies was as described for immunofluorescence, with centrifugation at 2,200 rpm for 10 min at each step. Finally, cells were resuspended in 500 µl PI/RNase solution (550825; BD) for 15 min in the dark. Acquisition was performed on a FAC SCalibur (BD) with a minimum of 1 × 10 4 cells analyzed with CellQuestPro software (BD). For analysis of neuronal ploidy, cortices of P0 pups from distinct litters were collected in ice-cold PBS-2% BSA and mechanically dissociated, and cells were sieved through a 40-µm nylon mesh (542-040; Greiner). Cells were washed with ice-cold PBS and fixed as described earlier in 70% ethanol.
Incubation procedures with primary (NeuN) and secondary antibodies were as described earlier. Cells were resuspended in 500 µl PI/RNase (550825; BD) solution for 15 min in the dark. Acquisition was performed on a FAC SCalibur (BD) with 2 × 10 5 cells analyzed with CellQuestPro software (BD). Debris and doublets were excluded using morphometric parameters and pulse area parameters from PI emission, respectively. To set the threshold of NeuN-positive cells, control samples were cells incubated with secondary antibody and PI only. Neuronal DNA content was estimated by analyzing PI emission intensity of NeuN-positive cells only. Five and three embryos were analyzed for controls (WT and Efnb1 lox ; Efnb2 loxlox ) and mutants (Nes-Cre; Efnb1 lox ; Efnb2 loxlox ), respectively.
Synchronization and stimulation were performed as described earlier. Acquisition was performed with the Zen software (ZEI SS) calibrated as follows: seven fields per well, one frame every 10 min for 12 h, 15 z-stacks of 2 µm. Other microscope acquisitions were performed on either an SP5 DM600B confocal microscope (40×/1. 3 APO; 20×/0. 7 APO, PMT; Leica Biosystems) or an Eclipse 80i (20×/0. 5 Nikon; DXM1200C camera; Nikon). Observation was performed using Type-F mineral oil (1153859; Leica Biosystems). Cell counting and pixel quantification were performed with the use of ImageJ software. On fixed cells, the ICB was defined as a thin tubulin-positive structure connecting two cells.
Database search and data analysis Peak lists extraction from Xcalibur raw files were automatically performed using Proteome Discoverer software (version 1. 4. 0. 288; Thermo Fisher Scientific). The following parameters were set for creation of the peak lists: parent ions in the mass range 300-5,000 and no grouping of MS/MS scans. The nonfragment filter was used to simplify ETD spectra with the following settings: the precursor peak was removed within a 4-D window, charged reduced precursors were removed within a 2-D window, and neutral losses from charged reduced precursors were removed within a 2D window (the maximum neutral loss mass was set to 120 D). Peak lists were searched against the SwissProt mouse database implemented with the His-tagged CitK sequence and using Mascot software (version 2.
Statistical analysis For experiments involving a single pair of conditions, statistical significance between the two sets of data were analyzed with a two-tailed, unpaired Student's t test with Prism5 (GraphPad software). For datasets containing more than two samples, one-way analysis of variance with a classical Bonferroni multiple comparison posttest was used to determine adjusted p-values. Sample sizes of sufficient power were chosen on the basis of similar published research and were confirmed statistically by appropriate tests. Each experiment was performed at least three times, and 200-1,000 cells were counted for each condition of each experiment. Statistically significant differences are reported at P < 0. 05, P < 0. 01, P < 0. 001, or exact value mentioned on the graphs.
Online supplemental material Fig. S1  shows validation of siRNAs. Fig. S2  shows still images from Incucyte recordings. Fig. S3  shows immunofluorescence staining for Anillin and MgcRacGAP. Fig. S4  shows the effect of activation of other RTKs on abscission and phosphorylation of CitK. Fig. S5  shows cytometry parameters used for the analysis of neuronal polyploidy. Video 1 shows a stimulated EphB2 + HeLa cell undergoing cell death after entering telophase. Table  S1  shows primer sequences used. Table  S2  shows the mass spectrometry dataset. Online supplemental material is available at http ://www. jcb. org /cgi /content /full /jcb. 201602057 /DC1.
Acknowledgments The EphB2-GFP, EphB2-3YF-GFP, HA-EphB2 WT , and HA-EphB2 K661R constructs were gifts from  Dr. Tony Pawson. The CitK cDNA was provided by  Dr. Ferdinando Di Cunto. We are grateful to  Dr. Elena Pasquale  and  Dr. Andreas Merdes  for sharing plasmids. FACS analyses were performed at the  Centre de Biologie Intégrative platform , and images were acquired in the Toulouse Rio Imaging facility with the help of  Brice Ronsin  and  Stéphanie Bosch  for live imaging. We thank  Maxime Bertrand  for technical help and our laboratory colleagues for critical reading of the manuscript. This work was supported by core funding from the  Centre National de la Recherche Scientifique , a grant from  Fondation ARC pour la Recherche sur le Cancer  to  A. Davy  ( SFI20101201607 ), an  Agence Nationale de la Recherche  grant to  A.
Davy  ( ANR-15-CE13-0010-01 ), and in part by the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( Proteomics French Infrastructure  project,  ANR-10-INBS-08 ). A. Besson  is supported by grants from the  Fondation ARC pour la Recherche sur le Cancer and Ligue Contre le Cancer. The authors declare no competing financial interests.
approaches offer a promising alternative to these methods. They permit sequencing of pre-selected regions of the genome (see the review from ref. 9) that have been determined a priori to be informative for the research question and are generally effective across a range of sample types and qualities. This allows the generation of relatively unbiased, more complete datasets than can be obtained using RAD technologies, and at an affordable cost compared with whole-genome re-sequencing. The population structure of the study organism is another factor that may complicate our ability to make robust demographic inferences from population genomic data. Previous works have shown that ignoring metapopulation structure can sometimes mislead interpretations of the demographic and evolutionary history of a species  [10] [11] [12] [13].
This separation of time scales holds true for several metapopulation models, including range expansions  19. Contrasting the shape of the genealogy at different sampling levels (e. g. "single deme" vs. "scatter", where each lineage comes from a different deme) indirectly provides information on the long term Nm  18. It is therefore possible to make inferences about the evolutionary history of the metapopulation using relatively simple demographic models that investigate changes in effective population size across specific sampling schemes. A few individuals judiciously sampled from the range distribution of a species can be sufficient to uncover its demography, when enough loci are available for statistical inferences. Such an approach is now tenable with the large datasets that are being generated using NGS and has important applications for the study of endangered animals, or of any organism for which specimens are not easily obtained.
Results Genetic diversity and data summary. The demographic history of the metapopulation of C. melanopterus was examined using two datasets: the first based on estimates from a "single deme" (SID) in Northern Australia, the second based on a collection of samples taken from various locations in the Indo-Pacific, which we refer to as the "scatter" sample (SCD) (see Fig. 1 , Table  1  and Supplementary Table  1 ). We sequenced 18 individuals in total with an average coverage of 75×. After applying strict filters and removing duplicate reads (see SI), we obtained sequence data for 995 loci for SCD and 998 loci for SID, spanning 606,647 bp and 632,160 bp respectively (Table  1  and Supplementary Table  S2 ). The average length of the regions is ~600 bp and details of the length distribution for SCD and SID are shown in Supplementary Fig.
1. Overall, 2605 and 1946 high quality single nucleotide polymorphisms (SNPs) were called for the scatter and single deme dataset, respectively. As expected, we detected a higher number of SNPs from SCD (considering samples from different locations) than SID, and SNP density is higher in introns than in exons for both datasets (no significant difference was found between SNP density in intron 5′ and intron 3′ ; Supplementary Table  S2 ). We also performed a Principal Component Analysis (PCA) including all 18 samples to assess the level of population structure within our dataset (Supplementary Fig. S2 ). At least three distinctive clusters are suggested by the first two principal components, explaining ~40% of the variance and separating Australia from the Indo-Pacific and Oman samples. Additional population sub-structure within the Australian and the Indonesian clusters is highlighted by the third principal component (Supplementary Fig.
S2, panel b  and d ). Overall, this confirms that the signal in our dataset is incompatible with panmixia and thus a simple demographic model of a single isolated population is not appropriate to fully describe the demographic history of these samples. Demographic inferences: indirect approach. First, simple population genetics models were applied to indirectly estimate the demographic parameters of the metapopulation by contrasting the characteristics of the genealogies under the two sampling schemes, SID and SCD. A constant-size model (model COS) and a demographic-change model (model CHG1) were initially compared (Fig. 2 , panel a and b). Both models were found to have equal posterior probability (0. 5) in SID, suggesting a pattern compatible with either a weak signal of expansion (N mod was slightly higher than N anc , Supplementary Table  S3 ) or a constant population size.
Therefore, a model with two demographic changes (model CHG2; Fig. 2, panel c ) was also tested in order to account for possible more recent demographic events (e. g. post-glacial expansion), as described for other marine species  30, 31. Posterior probabilities of model CHG1 and model CHG2 are similar for both datasets (Supplementary Table  S4 ). When the effective population size was estimated at different time points in the past, CHG1 and CHG2 showed the same pattern (Fig. 3  and Supplementary Fig. S3 ). The estimation of T c1 in CHG2 for both datasets reflects the estimate of T c in CHG1 suggesting that CHG2 identifies only the change in effective population size corresponding to the demographic expansion that was also identified by CHG1. Demographic inferences: direct approach. Considering the results from the indirect approach, we therefore explored the demographic signals in the data by fitting a non-equilibrium finite island model  20  (model FIM; Fig.
2, panel d ) with 100 demes to both datasets. The choice of an island model is justified by the absence of panmixia in the PCA and by the near-uniform Gst matrix (Supplementary Table  S5 ), suggesting similar genetic distances across all samples except Oman. Model FIM is defined by three parameters, namely Nm, the time of the onset of the island T i and the effective size of the ancestral deme, N anc. Demographic parameters of the metapopulation were estimated directly for the two different sampling schemes, SCD and SID. The FIM model showed the highest posterior probability in the model choice for both SID and SCD (Table  2 ). Cross-validation Table  1. Summary of the "scatter" (SCD) and "single deme" (SID) dataset. of the model choice confirmed that FIM is distinguished from COS and CHG1 with high probability for both datasets (see SI and Supplementary Table  S6 ).
Importantly, the two datasets produced similar estimates of both Nm and T i (Table  3 ). We further confirmed this result by applying FIM to the complete dataset of 18 samples (SCD + SID without shared samples) (Table  3 ). With this value of Nm ≈ 40 in the metapopulation, the signature of the ancestral expansion at T i is lost in SID but not in SCD, consistent with expectation based on results in simulation studies of range expansion (see ref. 19,32,33). Indeed, for similar Nm values the gene genealogy of lineages sampled from a single deme is a mixture of recent coalescent events occurring during the scattering phase and more ancient coalescent events occurring during the collecting phase. This generates a gene genealogy similar to that typical of a constant size population. Conversely, a scatter sample will show a signature of expansion as most of the coalescent events will occur at the time of the foundation of the metapopulation.
Detection of a recent bottleneck. We used ABC to test whether the absence of a bottleneck in our dataset reflects the real demographic history of C. melanopterus, or a lack of power to detect it. To this end, we simulated pseudo observed data sets (pods) under a modified FIM model (FIM-BOTT) characterised by two additional parameters (Supplementary Fig. S4 , panel b): T bott (the time when an instantaneous decrease in Nm occurred) and I bott (the ratio of the ancestral Nm to the new one). When I bott = 1 FIM-BOTT reduces to FIM (Fig. 2 panel d ). FIM-BOTT is defined by two major events: the onset of the island at T i and the reduction in connectivity (i. e. , a reduction in the effective population size of a deme, a reduction in the migration rate, or both) at T bott. We simulated 1,000 pods under model FIM-BOTT for each parameter combination and we reanalysed them using the CHG2 model.
5 , Supplementary Fig. S7 ). SCD does not show any significant reduction in Nm est until 500 generations ago, while SID starts showing a decrease almost immediately, after just 10 generations. The decline of Nm est in SID is faster when I bott = 1,000 than for I bott = 100. In both cases, the distributions of Nm est for SID and SCD up to 500 generations ago show a significant difference. At 1000 generations, both datasets show the same pattern with severe decreases in Nm est (Fig. 5 , Supplementary Fig. S7 ).
When we fit the FIM model independently to both SCD and SID, we found a metapopulation characterised by a value of Nm ~40 (95% CI: 20-110), with an onset of formation around ~59,000 (95% CI: 6,000-165,000) generations ago. This level of connectivity is consistent with philopatry in this species, as previously described in Moorea and Tetiaroa (French Polynesia)  35. Unfortunately our sampling scheme does not allow us to test males and females separately, but it would be interesting to investigate possible differences in the level of connectivity related to sex-specific migration. We further apply the same FIM model to the whole dataset and we obtained similar values of both Nm and T i (Table  3 ). In summary, the indirect approach of comparing gene genealogies under different sampling schemes and directly applying the FIM model (applied to three largely independent datasets) provided consistent results, suggesting that this approach is effective for reconstructing the demography of this species.
Indeed, we simulated from the posterior distribution of the FIM the expected Fst between two demes at 14 independent microsatellites loci with the same mutation rate as in Vignaud et al. 29. We found the Fst distribution to be compatible with the value they found between East and West Australia, which are more distant than the samples from our study area. This further suggests that our model is appropriate for our specific dataset, but we are aware that it might not provide an accurate description of the C. melanopterus demography over its whole range. We made some simplifying assumptions when analysing the C. melanopterus data. We used a single step change of effective population size in our indirect approach and a constant Nm when fitting the FIM model. Obviously, the real evolutionary histories of species are likely to be more complex and it may be challenging to distinguish between local events (such as a bottleneck or expansion in a deme) and changes in connectivity/number of demes in metapopulations  16, 41.
5  and Supplementary Fig. S7 ), suggesting that the comparison of the two datasets may help detecting recent changes in effective population size. These results (both the ABC-skyline models and the Nm estimates) can be interpreted from a coalescent point of view: a recent bottleneck will produce a burst of coalescent events only at the deme level (i. e. , during the scattering phase), having no impact at all on the collecting phase even for high intensity of reduction. A scattered sample, which does not have the scattering phase, will not be affected by recent bottleneck events while a local single deme sample will. These findings suggest that metapopulations are more resilient than unstructured populations to recent changes in effective size (or connectivity), implying that taking population structure into account is crucial when inferring the demography of a species.
Materials and Methods Sampling, library preparation and sequencing. A total of 18 samples of C. melanopterus were included from 8 different locations (Fig. 1 ): Northern Territory and Queensland, Australia (N = 11), Oman (N = 1), Philippines (N = 1), Thailand (N = 1), Malaysia (N = 1), South Kalimantan, Indonesia (N = 1), West Kalimantan, Indonesian Borneo (N = 1) and East Kalimantan, Indonesia (N = 1). Samples were divided in two datasets called "scatter" (SCD) and "single deme" (SID) with 9 and 11 samples respectively (Table  1 , Supplementary Table  S1 ). The SCD dataset aims to mimic a "scatter sample", sensu Wakeley, where all samples come from independent geographical locations. We also included one sample from Oman in order to infer the demographic history of the metapopulation at a larger scale and to cover a more extensive area of the Indian Ocean.
Read mapping, variant calling and filtering. A Burrows-Wheeler Aligner (BWA  50  ) was used to map reads to the reference sequence generated by de novo assembly  51. Duplicate read marking was performed with Picard v. 1. 129 (http://broadinstitute. github. io/picard/), local realignment with the Genome Analysis ToolKit (GATK) v3. 3-0  52  and genotype calling with samtools v1. 1  53. Raw variants were filtered using custom Perl (v5. 18. 2) and R (3. 0. 2) scripts (Supplementary Table  S8 ). Filtering was based on strand bias, minimum depth 6× , removal of triallelic sites, removal of sites with heterozygous calls in more than 80% of samples and removal of any missing calls in order to have a dataset without any missing genotypes, reducing background noise and uncertainty (see SI for details).
Demographic Inference. We developed an ABC  24  framework to estimate parameters and compare demographic models. The folded site frequency spectrum (SFS) and total number of SNPs were used as summary statistics, to avoid phasing issues. Mutation and recombination rates were allowed to vary across loci using hyperprior distributions (see SI for details). Generation time was set to seven years  36. Four demographic models were tested for both datasets (Fig. 2 ). Model COS represents a constant-size population model described only by the effective population size (N) (Fig. 2 , panel a). Model CHG1 represents a single instantaneous demographic-change model occurring at the time T c (Fig. 2 , panel b). N anc and N mod are the ancestral and modern effective population sizes respectively. Values of the ancestral versus modern effective population size ratio (resize, N anc /N mod ) greater than 1 are compatible with a reduction in effective population size while values lower than 1 suggest a population expansion.
We generated 100,000 simulations for each demographic model using fastsimcoal2 v2. 5. 1  57. Each simulation includes 995 and 998 gene genealogies for SCD and SID respectively, to be consistent with our real data. Prior distributions are displayed in Table  3 , Supplementary Tables  S3  and S9. The posterior probabilities of each model were calculated by a weighted multinomial logistic regression  58  for which we retained the best 25,000 simulations. The parameters of the best-fitting model were estimated from the 5,000 simulations closest to the observed dataset using a local linear regression according to ref. 24. Posterior distributions for model CHG1 and FIM are shown in Supplementary Figs  8  and 9. Both CHG1 and CHG2 models were used to graphically reconstruct the variation of effective population size through time.
Analyses were performed in the R environment  54  with the library abc  59. We performed cross-validation for both model selection and parameter estimation by randomly generating pods from the prior distributions under each model. For each cross-validation experiment we generated 1000 pods and we applied the same inferential procedure as for the observed data (see SI, Supplementary Figs  S10  and S11 ). In addition, a posterior predictive test  60  was carried out to test whether the data can be reproduced under a specific demographic model. Bayesian p-values computed from the posterior distribution of the number of polymorphic sites showed that none of the four models in both datasets could be rejected (SI, Supplementary Fig. S12 ). Recent Bottleneck. We tested for signatures of a recent population bottleneck in the metapopulation data set for both sampling schemes.
S4 , panel a). In this case, pods were simulated with demographic parameters estimated in our real SCD data under model CHG1, to which we added a recent bottleneck characterised by I bott and T bott. Note that here I bott represents the ratio between the ancestral effective population size Ne and the new one (while in FIM-BOTT it represents the ratio of the ancestral Nm to the new one). We tested the same twelve parameters combinations of I bott and T bott that we previously used when simulating FIM-BOTT. CHG1-BOTT reduces to CHG1 when I bott = 1. All pods simulated under both FIM-BOTT (for both sampling schemes) and CHG1-BOTT were analysed using the ABC-skyline produced by the CHG2 model (Fig. 2, panel c ). We applied the same settings used for the real data and for each combination of parameters we plotted the average of the Ne through time across the 1000 pods (see SI).
). N anc : effective population size of the ancestral deme; T i : time of the onset of the island (in generations); Nm: product of the effective population size N and the migration rate m for each deme; SCD: scatter dataset (9 individuals); SID: single deme dataset (11 individuals); pooled dataset: SCD + SID without shared samples (18 individuals). a Upper and lower limits of the 95% credible interval about the estimated mode. b U, uniform probability, in the range of the two values.
Acknowledgements The authors thank  Giorgio Bertorelle  and  Oscar Lao  for helpful comments and suggestions and  Laurent Excoffier  for sharing fastsimcoal v2. 5. 2 before publication. Data analysis was performed on the computer cluster Genotoul bioinformatics platform (www. bioinfo. genotoul. fr). This work was supported by the  Agence Nationale de la Recherche  ( DEMOCHIPS ANR-12-BSV7-0012 ) and by the  US National Science Foundation  ( DEB-1132229 ).
04 38. 61 28. 51 117. 80 U: 0. 05-250 Pooled dataset N anc 52,541 58,148 8176 84,492 U: 100-100,000 T i 65,004 18,329 4255 176,692 U: 100-200,000 Nm 58. 7 51. 4 38. 3 150. 9 U: 0. 05-250.