dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other side of this demographic trajectory, evolutionary biologists have theorized that growing populations should experience a rise in genetic diversity over time, at a base level of the rate of mutation in closed populations  (Nei et al. , 1975)  and at a higher rate with contributions from migrants in an open population or metapopulation system  (Hansson et al. , 2000; Keller et al. , 2001). Furthermore, geographically expanding populations are predicted to vary spatially, with lower overall genetic diversity at the edge of their expansion wave and differences in the frequency of specific genetic variants as a result of sampling bias or selection for certain traits in successful migrants (reviewed in  Excoffier, Foll, & Petit, 2009). These evolutionary processes that occur during bottleneck, growth, and spatial expansion have been primarily explored through simulated datasets that undergo artificial changes in population size and number.
From such simulations, we have learned how factors such as mating systems  (Armburster & Pfenninger, 2003) , epistasis  (Turelli & Barton, 2006) , baseline allele frequency distributions  (Luikart, Allendorf, Cornuet, & Sherwin, 1998) , intensity and length of bottleneck  (England et al. , 2003) , and timing of recovery  (Hoban, Gaggiotti, & Bertorelle, 2013)  are likely to affect the loss of genetic diversity during bottlenecks. More recent studies that compare the power of traditional and genomic markers to detect bottlenecks also show us that the type and amount of data and the selected model parameters can significantly impact conclusions  (Cabrera & Palsbøll, 2017; Elleouet & Aitken, 2018; Hoban et al. , 2013; Peery et al. , 2012; Shafer, Gattepaille, Stewart, & Wolf, 2015). While advances in computational power have enabled increasingly complex demographic models and the incorporation of Bayesian approaches provide more nuanced ways to draw predictions and interpret uncertainty, simulated datasets inherently lack natural variability that without a doubt influences these processes in the natural environment.
The program then builds a catalog of RAD loci with user-defined parameters through "stacking" sequence reads that putatively originated from the same location in the genome. The selection of parameters can have significant effects on downstream analyses and subsequent conclusions  (Paris, Stevens, & Catchen, 2017; Shafer et al. , 2017). We therefore systematically selected Stacks parameters to optimize the number of polymorphic loci using the r80 optimization approach described in  Paris et al. (2017). Optimization tests were run on a subset of 30 individuals of each species, distributed equally such that the optimization datasets included 10 individuals from each harbor seal cohort and 10 individuals from each gray seal colony (and an equal number from each cohort within the colony, if relevant).
All libraries and sequencing lanes were represented in this optimization dataset, and the samples represented the full range in sequence coverage per individual. We tested values of m (the minimum number of identical raw reads required to create a stack) from 1 to 6, M (the minimum number of mismatches allowed between loci when processing a single individual) from 0 to 6, and r (minimum percentage of individuals per population) of 0. 4, 0. 6, and 0. 8, using default values of m = 3, M = 2, and n = 0. We evaluated the effects of m and M parameter selection on the number of assembled loci, the number of polymorphic loci, and the number of SNPs for each value of r. We also evaluated the effects of m on stack depth, or coverage. An optimal value of m was chosen that produced the highest number of polymorphic loci and SNPs.
As the number of SNPs continued to rise with increasing M in our datasets, we selected an optimal M after which point there was little increase in polymorphic loci, in order to balance trade-offs between increasing detection of true polymorphisms and incorrectly identifying sequencing error in overmerged loci as M increases. Following  Paris et al. (2017) 's recommendation, we set n (the minimum number of mismatches allowed between loci when building the catalogue) equal to M. With the selected optimal parameters, we built two catalogs of RAD loci, one for each species. The harbor seal RAD locus catalog was built using sequence data from all 55 individuals, but for computational efficiency, it was necessary to build the gray seal catalog from a subset of individuals. The gray seal catalog was built using sequence data from 7 individuals from each sample group (cohort/population), distributed evenly across RAD libraries and sequencing runs and selecting for individuals with the greatest coverage, for a total of 56 individuals.
| Historical demography-bottleneck period We estimated historical changes in population size from a subset of the genetic data using ABC analyses implemented in DIYABC v2. 1. 0  (Cornuet et al. , 2014)  with one million simulated datasets representing four different demographic models (Figure  3 ): (1) constant population size over time (null hypothesis), (2) expansion, (3) bottleneck, and (4) expansion followed by bottleneck. We used scaled parameters, to avoid the potential losses in accuracy or increases in error that have been observed in prior simulations comparing scaled and unscaled parameters  (Cabrera & Palsbøll, 2017). This approach, which reveals relative changes in population size at relative times from present, further avoids incorporating uncertainties in generation time and estimates of absolute population size into the models, and allows us to use the same parameters for both species, enabling a direct comparison.
Only one SNP per RAD locus was included in this analysis, to reduce biases previously observed in simulations with four segregating SNPs per locus as compared to a single SNP  (Shafer et al. , 2015). Models were tested both with and without a minimum minor allele frequency (MAF) filter of 0. 05; rare minor alleles are often removed from downstream RAD analyses as a conservative measure to remove sequencing errors, but rare alleles can also be particularly informative for studies of demography  (Keinan & Clark, 2012; Maruyama & Fuerst, 1985a,b). We assessed the likelihood of each model using four summary statistics: proportion of zero values, mean and variance of nonzero values, and mean of complete distribution. The posterior probability of each demographic model was estimated using logistic regression on the top 1% of the simulated datasets based on similarity of summary statistics to the observed values.
The error associated with model selection was calculated as the probability of selecting a given model when it is not true, that is, its incorrect selection among 1,000 datasets simulated for each of the alternate models  (Cornuet, Ravigné, & Estoup, 2010). Finally, we estimated the posterior probabilities of the model parameters (effective population sizes and times of population size change) using a logit-transformation and the top 1% closest simulated datasets.
Spatial and temporal analyses were then merged in a hierarchical analysis of molecular variance (AMOVA) implemented in Arlequin v3. 5  (Excoffier & Lischer, 2010). We further compared observed rates of change in allele frequency to those predicted under neutral expectations of genetic drift in a single population experiencing logistic growth. Neutral expectations were generated using a forward simulator built for high-throughput sequencing genomic datasets, popRangeSim  (McManus, 2015)  in R. We made minor modifications to the source code to model logistic growth as N t+1 = N t + rN t (1-N t /K), where N is the population size at present (t) and following one generation of growth (t+1), r is the population growth rate, and K is the carrying capacity. popRangeSim implements a Wright-Fisher approach using the binomial distribution of probabilities to model stochastic genetic drift each generation prior to population growth.
We did not incorporate mutation, given its likely minimal impact over the time period assessed here. For Sable Island gray seals, for which we have the longest dataset of population surveys and genetic samples, the model was F I G U R E 3 Demographic scenarios, with priors, tested with approximate Bayesian computation using DIYABC. Scenarios are defined by relative changes in effective population size (N) at given times (t) since present Scenario 1 Constant N 0 Scenario 4 Expansion & Bottleneck N pb N pe N b t e t b t r N 0 Scenario 3 Bottleneck N b N pb t r t b N 0 Scenario 2 Expansion t e N pe N 0 present Priors N pe = 1 x 10 3 -1 x 10 4 N pb = 2 x 10 4 -1 x 10 5 N b = 1 x 10 2 -1 x 10 4 N 0 = 1 x 10 5 t e = 1 x 10 4 t r = 1 -1 x 10 2 t b = 1 x 10 2 -5 x 10 3 parameterized using the following values informed by the literature to represent the time period 1970 to 2015: starting population size = 5,000  (Bowen, McMillan, & Mohn, 2003) ; r =.
| RE SULTS RAD sequencing resulted in a total of 628. 4 million gray seal reads and 111. 5 million harbor seal reads, with an average of 2. 4 (±1. 4 SD) million reads per individual, following the implementation of sequence quality filters. The systematic evaluation of Stacks parameters, following  Paris et al. (2017) , identified similar optimal parameters for both species. As expected, and previously demonstrated in  Paris et al. (2017) , the mean coverage per locus increased and the number of assembled loci per individual decreased as m increased for both species (Figure  4 ). The number of polymorphic loci and SNPs for both gray and harbor seals was greatest at m = 2, which was therefore selected as the optimal value for both datasets. Changing the M parameter had little impact on the number of assembled loci, although we observed a slight drop after M = 1 (Figure  4 ).
The number of polymorphic loci and SNPs jumped from M = 1 to 2 and then continued to rise for greater values of M. After the transition from M = 1 to 2, the number of polymorphic loci rose by fewer than 150 loci with each incremental increase of M, and we therefore selected M = 2 and n = M = 2 as the optimal value for both datasets. Interestingly, it was clearly apparent in our evaluation of Stacks parameters that the gray seal data set was significantly more polymorphic than the harbor seal dataset. Gray seals had more than twice the number of polymorphic loci and SNPs than harbor seals at the selected m = 2 for any tested value of r, and this trend remained across all tested values of m and r (Figure  4 ). Similarly, across all tested values of M and r, gray seals had twice, or nearly twice, the number of polymorphic loci and a greater number of SNPs.
HARBOUR SEAL GREY SEAL Using the selected, optimal parameters and following the removal of highly repetitive stacks, gray and harbor seal RAD catalogs contained 244,822 and 195,906 de novo loci, respectively. RAD loci of the two species were compared through alignment to the reference Weddell seal genome. For gray seals and harbor seals, respectively, 187,696 (76. 67%) and 145,591 (74. 32%) loci aligned to the Weddell seal genome, and 100,251 loci were sequenced in both study species. We identified, genotyped, and further analyzed 8,716 and 3,761 polymorphic r80 loci containing 10,097 and 4,312 SNPs that were sequenced in at least 80% of the individuals in at least one sample group in gray and harbor seals, respectively. The polymorphisms were largely species-specific. Although 9,211 of these loci were present in the sequence data of both species (loci determined to be equivalent in the two species if aligned to the same position in the Weddell seal genome), only 560 of the loci were polymorphic in both species.
| Historical demography-bottleneck period With no MAF filter, simulations of past changes in gray and harbor seal population size implemented in DIYABC identified the historical expansion with recent bottleneck scenario (4) as the most likely model in all cases, with a relatively low error rate around 5% (Table  2 ). In all cases but gray seals at Muskeget Island, the second most likely model was the bottleneck scenario without historical expansion (3), and there was very little to no support found for either of the other scenarios. All summary statistics for the observed datasets fall within the range of the simulated datasets using the defined scenarios and priors, confirming that the selected models do fit the observed data well. When a minimum MAF filter of 0. 05 was implemented, the bottleneck scenarios remained most likely only for harbor seals from the Northeast United States (Table  2 ).
Here, we highlight the value evaluating such analytical approaches with empirical data as well, using cases of well-described demographic history that accurately represent the variation inherent in data from samples of natural populations. Our study contributes to a small, but rapidly growing number of RAD studies that compare data across, rather than or in addition to within, species and genera. These studies, using both simulated and empirical data, have quantified the number of retained homologous TA B L E 2 Likelihood (± 95% confidence interval) for each of four historical demographic scenarios, as depicted in Figure  3 , for three gray seal breeding colonies and harbor seals by-caught in the Northeast United States. For each sample set, demographic models were tested using datasets with and without a minor allele frequency (MAF) filter of 0.
05. Gray lines at the bottom of each plot represent the prior. Scenarios and parameters as depicted in Figure  3. 2015 ) reports little change in the number of homologous loci across multiple pipelines), but more likely reflects true differences in rates of molecular evolution across lineages. Our pinniped study  and Viricel et al. 's (2014)  cetacean study are more promising in the applicability of RADseq to studies of divergent species, while  Pante et al. (2015)  findings of lower than 30% shared loci in octocoral species that diverged only 1-2 mya offer a cautionary note. Ultimately, pilot testing of RADseq protocols across multiple study species is necessary to evaluate their potential for generation of large-scale SNP datasets for interspecific analyses. Among the relatively large number of shared homologous loci between gray and harbor seals, we found clear differences in the rates of polymorphism.
While optimizing the parameters for ddRAD sequence analysis with a representative subset of individuals of equal size from both gray and harbor seals, it became evident that the contemporary genomes of gray seals were generally more diverse than those of harbor seals in the Northwest Atlantic. While both datasets included similar numbers of loci and sequences per individual, many more polymorphic loci and SNPs were identified among gray seal loci than harbor seal loci (Figure  4 ). We acknowledge that there exist several sources of bias in estimates of genetic diversity from RAD sequencing data  (Arnold, Corbett-Detig, Hartl, & Bomblies, 2013; Cariou, Duret, & Charlat, 2016a,b; DaCosta & Sorenson, 2014; Davey et al. , 2013; Gautier et al. , 2013) , but contend that relative comparisons of diversity between species for which data have been collected and processed in a similar manner should be robust  (Lozier, 2014).
Klimova et al. (2014)  analyzed 350 bp of the mitochondrial hypervariable region and nine microsatellites in gray seals from Sable Island. Prior evaluations of DIYABC with simulated datasets have suggested that SNP data have greater power to recover correct scenarios than mitochondrial or microsatellite data, alone or combined  (Cabrera & Palsbøll, 2017 ). Yet, few empirical studies have used genotyping-by-sequencing data (e. g. , RADseq, GBS) to evaluate signatures of bottlenecks, and fewer yet have compared the effectiveness of bottleneck detection using RAD sequencing and other markers. Those that do also report variable conclusions across marker types. In declining and stable populations of bumble bees (Bombus impatiens and B. pensylvanicus), Lozier and colleagues detected population bottlenecks using both microsatellites  (Lozier, Strange, Stewart, & Cameron, 2011)  and RADseq  (Lozier, 2014) , but report a difference in relative microsatellite and RADseq diversity between species.
Without an annotated reference genome for either study species, with limited knowledge of linkage disequilibrium, and with measured variation in primarily noncoding regions, our ability to further evaluate these potential signatures of selection is limited in this study system, but the markers represent interesting targets for future study. As gray seal populations have recovered in the Northwest Atlantic, several new sites have been recolonized and their geographic distribution has expanded. With samples from Canada to Massachusetts, our dataset spans this range expansion. In contrast to theoretical expectations of a linear decline in diversity along the expansion axis due to founder effects  (Austerlitz, Jung-Muller, Godelle, & Gouyon, 1997; Nei et al. , 1975) , we did not observe any significant difference in diversity between samples collected from Canadian source populations and those from Muskeget Island at the edge of the range expansion.
ACK N OWLED G M ENTS We thank the following individuals who helped with access to sample archives and ongoing collections:  Christine Bubac ,  Johanne Guerin ,  Matthew Harnden ,  Nell den Heyer ,  Elizabeth Josephson , and  Gordon Waring. Timothy Frasier  provided valuable input on project design and analyses. Two anonymous reviewers provided helpful feedback on a prior version of this manuscript. Funding for initial sample collection was provided by the  Department of Fisheries and Oceans Canada, Natural Sciences and Engineering Research Council of Canada ,  NOAA Northeast Fisheries Science Center, Northeast Fisheries Observer Program , and the  NIH/NIAID Centers of Excellence for Influenza Research and Surveillance  ( HHSN272201400008C ). K. M. C. was supported by a  National Science Foundation Postdoctoral Research Fellowship in Biology  under Grant No.
DATA ACCE SS I B I LIT Y RAD loci sequences are available in the NCBI Short Read Archive (Accession no. SRP140440). Results of the Stacks analysis are archived at Dryad https://doi. org/10. 5061/dryad. fg52075. O RCI D Kristina M. Cammen http://orcid. org/0000-0002-3923-6438 R E FE R E N C E S.
8,9-Dimethoxynaphtho Yields of products 3, 4 (see Scheme 1) are resonable enough; moreover, their formation occurs from readily available styryl derivatives upon mercury lamp irradiation at room temperature for 30 min. Alternative synthetic pathways to similar fused compounds are laborious and multi-stage. Of particular interest is the fact that sterically hindered isomers 3a and 4a are formed in a higher yield. To rationalize such a preference, ground state energies of different conformers of the E-1a-d were calculated by MOPAC2016 program with PM7 Hamiltonian as well as their equilibrium amounts were calculated using the Boltzmann distribution model (Table  1 ). Isomerization of E-isomers leads to the corresponding Z-isomers a-d (see Scheme 1, Table  1 ). Due to low reactivity of C-atom in the 4-position of the thiophene residue, the participation of Z-isomers b and d in the cyclization reaction can be disregarded.
Film thickness and film morphology was obtained using atomic force microscopy (AFM). AFM images were obtained in tapping mode using a Veeco Dimension 3100. The morphological images and thickness measurements were analyzed using the WSxM 4. 0 software. 40 FTIR measurements were performed using an Equinox 55 spectrometer (Bruker). FTIR spectra were acquired in transmission mode using a resolution of 4 cm À1 and 64 scans. Spectra were baseline corrected. Undoped SiO 2 wafers were employed as the substrate for measurements. A Nikon SMZ1500 microscope with a DS-Fi1 CCD sensor was used to acquire the magnified images. Electrochemical measurements were performed using a three electrode setup on a AUTOLAB potentiostat (Metrohm) in 1 M aqueous KCl solution. Samples of PEDOT and PPy with different exposure times (0 s, 150 s, 300 s, 600 s and 900 s) were prepared on gold-coated glass substrates.
Acknowledgements The authors acknowledge by the  Knut and Alice Wallenberg foundation KAW (Tail of the Sun ,  KAW 2014. 0041  and  Power Papers ,  KAW 2011. 0050 ) and the  Swedish Foundation for Strategic Research  ( Synergy  project), XC is financed by the  European Research Council  ( ERC-starting-grant   307596 ), and the  Advanced Functional Materials Center at Linko ¨ping University. The authors would like to thank  Dr Eliot Gomez  and  Dr Daniel Tordera  for the photography and videography of the electrochromic devices.
Hornet nests location Technicians from the city hall of Nantes and from Bionéo (sarl Prophy Végétal) located Vespa velutina nests in and around the botanical garden of Nantes. These data were useful to determinate if Sarracenia plants we collected could be present in the foraging area of hornet colonies. According to their flight capacities, workers can forage in an area of 2. 000 m around their nest  (Sauvard et al. 2018). We performed heatmaps with the software QGIS 2. 18. 20. We used EPSG: 2154, RGF93/Lambert 93 as reference coordinate system.
data analysis We performed data analyses with the software  Rstudio (1. 0. 143 version -2009 -2016 RStudio, Inc. ). Analyses were used to compare the relative abundances of the insect orders found in the urns (Diptera, Hymenoptera, Lepidoptera, Coleoptera and Heteroptera), and in Hymenoptera (Vespa velutina, V. crabro, Vespula sp. , Apis sp. and Bombus sp. ). To compare the abundances of the insect within each sampling year, we used a chi-squared test and a Wilcoxon signed-rank test for paired samples. We used a Bonferroni correction to reduce type I errors per year (corrected α = (0. 05/10) = 0. 005).
Acknowledgments We gratefully acknowledge  J. Pearce  for her English editing services,  P. Gacel  from  Bioneo  and  R. Salvador  from  Nantes city hall  for the V. velutina nests location data in Nantes. This study was supported by funding from the  Centre-Val de Loire region  (a  French regional grant ) for our FRELON 2 project. M-A. Wycke received support from Vetopharma.
It is important to consider that the ROS level reported is quantified by the probe DCFDA and since the nature of the ROS detected with this probe is not fully clear we cannot exclude that using other ROS-reporting probes would directly correlate with Redox sensitivity. We have tried mitoSOX for mitochondrial superoxides detection but did not detect differences between control and patient cell lines (data not shown). Nevertheless, KH176(m) was found to be an effective antioxidant capable of scavenging ROS of different nature (H 2 O 2 , superoxide) and at different cellular locations (cytosol, membrane, mitochondrion). Based on the current understanding of the effects at the cellular level of OXPHOS system defects, various new small molecules able to counteract such consequences are in preclinical development and some have reached the clinical development phase  12, 35, 36.
For instance, Prdx2, upon H 2 O 2 sensing, can pass reducing equivalents to STAT3, a transcription factor involved in cell growth and apoptosis, attenuating thereby its transcriptional activity  48. We envision that KH176m might in fact alter the transmission of such signal when interacting with the peroxiredoxins and therefore inhibiting the cell death signalling triggered by the BSO treatment. Further work is required to decipher the exact effect KH176m has on the peroxiredoxins, acting as a cofactor to increase its peroxidase activity or altering the peroxiredoxin-dependent redox signalling to other proteins. Based on the detailed characterization of KH176, its mode of action, pharmacokinetics and toxicokinetics properties, a Phase 1 clinical trial in healthy adult male volunteers was performed (ClinicalTrials.