dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by the  INRA Animal Genetics Department , www. ga. inra. fr(grant to AD),  ApisGene  (grant to DB) www. apis-gene. com, the  French National Research Agency  ( ANR-14_CE 19-0011  to DB;  ANR-13-ISV1-00002  to adult Charolais cattle. Finally, this model provides useful information for SPG58/SPAX2 disease and other demyelinating lesions. received funding from the  Verum Foundation  (to AB and GS) www. verum-foundation. com, the  Fondation GIS-Maladies Rares  (to GS) www. fondation-maladiesrares. org, the  Fondation Roger de Spoelberch  (to AB)www. fondation-roger-despoelberch. ch, the  Seventh Framework Programme  - FP7  (Omics call to GS) https://ec. europa. eu/research/fp7/index_en. cfm and the  E-Rare  programme, www. erare. eu(Neurolipid call to GS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
| Step 2. Compilation of preliminary lists of potential IAS Each thematic group was asked to assemble preliminary lists of potential IAS that they considered to constitute the highest risk with respect to the likelihood of arrival, establishment, spread and the magnitude of their potential negative impact on biodiversity and ecosystem services, within the EU region over the next 10 years. It was expected that each thematic group would derive these lists from a combination of systematic literature searches (including academic journals, risk assessments, reports, authoritative websites and other "grey" literature), querying of IAS databases (Supporting information S3) and their own expert knowledge. As expected, the approaches adopted by each thematic group differed slightly with respect to methods followed to derive the preliminary lists because of the diverse nature of the taxonomic groups and variation in the sources of information available (details given in Supporting information S4).
ACKNOWLEDGEMENTS The authors are grateful to the  European Commission  for funding the study (Invasive alien speciesframework for the identification of invasive alien species of  EU  concern  ENV. B. 2/ETU/2013/0026 ) on which this publication is based on. The study was also supported by project  COST TD1209 (Alien Challenge ). JP was partly funded by long-term research development project  RVO 67985939  ( Academy of Sciences of the Czech Republic ). MV acknowledges the  Ministerio de Economia y Competitividad projects IMPLANTIN  ( CGL2015-65346R ) and the  Severo Ochoa Program for Centres of Excellence  ( SEV-2012-0262 ). GHC acknowledges the  UK Department of Environment, Food & Rural Affairs  for its complementary support. We thank the editor and anonymous reviewers for their insightful comments.
, San Diego, CA). This association study was based on both linkage analyses and linkage disequilibrium using QTLmap software (http:// dga7. jouy. inra. fr/ qtlmap/ ) interval mapping was performed with the likelihood ratio test using linear regressions. Breeds were analyzed together and separately. The study highlighted 37 chromosome-wide significant quantitative trait loci (QTL) with linkage analyses and 222 genome-wide significant QTL for linkage disequilibrium, for type and SCC traits in dairy goats. Genomic control of those traits was mostly polygenic and breed-specific, suggesting that within-breed selection would be favored for those traits. Of note, Capra hircus autosome (CHI) 19 appeared to be highly enriched in single nucleotide polymorphisms associated with type and SCC, with 2 highly significant regions in the Saanen breed.
, 2016a) , and milk production  (Martin et al. , 2017). However, very little is known about the loci controlling type traits and lactation SCS (LSCS) in this species. To our knowledge, only one study on QTL detection of caprine body conformation traits was published  (Marrube et al. , 2007) , based on a low-density panel of microsatellites in Angora goats. In this paper, we report a genome scan for 12 type and health traits, including udder type traits, SCC, and stature in Saanen and Alpine dairy breeds. We performed linkage analyses (LA) and linkage disequilibrium (LD) analyses in a large family design of 2,209 goats, based on Illumina GoatSNP50 BeadChip (Illumina Inc. , San Diego, CA) data to identify genomic regions responsible for type and health traits and to nominate potential candidate genes.
Genome-Wide SNP Genotyping and Quality Control All 2,209 animals were genotyped using the Illumina GoatSNP50 BeadChip (53,347 SNP); DNA was extracted from blood samples and genotyping was performed at LABOGENA (www. labogena. fr). Data were cleaned using an in-house pipeline as described by  Martin et al. (2017). In brief, any individual with a call rate below 95% (n = 16) or showing pedigree inconsistency (n = 228; i. e. , 10%) was discarded. Quality control of SNP included the following inclusion criteria: call rate >99%, minor allele frequency >1%, and Hardy-Weinberg P-value >10 -6. After edits, a total of 47,174 out of 53,347 synthesized SNP remained, and a total of 44,612 SNP distributed on goat (Capra hircus, CHI) autosomes CHI 1 to CHI 29 were included for further analyses. The marker order and positions were based on the caprine Assembly CHIR_1.
For LA, the empirical 5 and 1% chromosome-wide significance levels of the test statistics were estimated from 1,000 within-family permutations  (Churchill and Doerge, 1994)  for each chromosome. For LD, the empirical  chromosome-wide significance level of the test statistics was estimated from 1,000 simulations for each chromosome, assuming a heritability of 0. 35 for the trait. The 5% genome-wide thresholds were obtained by applying the Bonferroni correction P genome-wise = 1 -(1 -P chromosome-wise ) n , where n was the number of autosomes analyzed (i. e. , 29). The 95% CI of the QTL locations were estimated with the logarithm of odds drop-off  (Lander and Botstein, 1989)  implemented in QTLMap software. In practice, the bounds of the interval were the 2 locations where the likelihood was equal to the maximum likelihood minus 3.
202); K α varied from 1. 13 to 1. 17. The CLIP test was performed in the Saanen breed for a 241-SNP region located between 20 and 32 Mb on CHI 19. Quantitative trait loci for milk production traits had previously been detected in the same genomic region of CHI 19 using the same data set  (Martin et al. , 2017). Indeed, QTL for milk yield (MY), protein yield (PY), and fat yield (FY) were mapped on CHI 19 between 22 and 29 Mb. Accordingly, the CLIP test was carried out for UFP, RUA, MY, PY, and FY. In addition, TF-for which there was no QTL in the region-was tested as a negative control. Multi-Trait Association Mapping. The multitrait QTL on CHI 19 was further investigated using 2 approaches. First, a PCA was implemented on the 5 traits MY, FY PY, RUA, and UFP, using the SAS PRINCOMP procedure  (SAS Institute Inc.
, 2008). The number of principal components to be retained was based on the proportion of variability explained and on their relationships with the original variables. Principal component (PC) scores were then calculated and treated as a new phenotype for performing LD QTL mapping as described above. A linear model was then fitted to the SNP that was close to the most significant position, to quantify its effect on the different traits (MIXED procedure; SAS Institute Inc. , 2008). The model included the fixed effects of sire and SNP. Second, a multivariate QTL detection was implemented using LA implemented in QTLMap. In this approach developed by  Gilbert and Le Roy (2003) , the joint influence on the traits is assumed to follow a multinormal distribution. Significance levels were estimated by Monte Carlo simulations, with trait values being simulated with multinormal distributions  (Gilbert and Le Roy, 2003).
ncbi. nlm. nih. gov/ genome ?term = capra %20hircus). Further fine mapping and functional analyses are, therefore, needed to identify the causative gene(s) and variant(s). Indeed, to our knowledge, this is the only way to determine whether one or several variants of the regions are responsible for the phenotypes we observe. As stated previously, genotypes on new animals for SNP in this region will be available in the near future thanks to a custom-built chip. Also, whole-genome sequence data for a few dozen Saanen animals will be available in the Vargoats project (http:// www. goatgenome. org/ vargoats. html). This data might help in fine mapping the QTL, possibly using imputation from 50K to sequence. The present results may explain some part of the adverse genetic correlation between udder type and milk production that exists in our goat populations.
ACKNOWLEDGMENTS The authors thank  Pierre Martin  and the  Capgenes  ( Mignaloux Beauvoir , France) breeding organizations for the data provided and their helpful contributions. This work was supported by grants from  French  organizations through " Genomcap" [a research program  including  INRA ,  APIS-GENE, Union Nationale des Cooperatives d'Elevage et d'Insemination Animale (UNCEIA) ,  Capgenes , and  France Conseil Elevage (FCEL)]  and  EC  ( FP7/2007-2013 ), grant no. 245140 , " 3SR ,"  Sustainable Solutions  for Small Ruminants (http:// www. 3srbreeding. eu/). The authors thank the involved partners. Cyrielle Maroteau  received the support of the  Union Nationale des Cooperatives d'Elevage et d'Insémination Animale (UNCEIA) , Apis-gene, and Capgenes for her CIFRE scholarship. Pauline Martin  acknowledges the support of  Région Midi-Pyrénées ,  Animal Genetics Department  from  INRA  and  GE-NOMCAP research program  including  INRA, APIS-GENE ,  ALLICE (formerly UNCEIA ), and FCEL (all located in Paris, France) and  CAPGENE (Mignaloux-Beauvoir, France).
Their plastic hatching delay showed several characteristics of diapause III which is constitutive in annuals. Since then,  Furness, Reznick, Tatarenkov, and Avise (2018)  showed that developmental properties presumed to be limited to annuals do occur in some sister taxa. For annual killifish, there are no data allowing a detailed analysis of rates of development and survival during and after a desiccation period. We have no embryonal life history that we can use to assess fitness effects and optimal strategies, and we are therefore not approaching the use of modeling tools advocated by  Childs et al. (2010). Here, we collect demographic data and model structured embryonal life histories, while we impose desiccation instead of cues such as temperature which are associated with seasonal change but also with short-term (even diurnal) fluctuations.
| Housing and data collection Temperature differences between eggs and with adults were unplanned and due to within-and between room variability. Next to the control treatment, which consisted of eggs continually incubated in water, we exposed the eggs to different "desiccation" treatments in the range where expected survival of most stages would allow developmental responses to be observed  (Podrabsky, Carpenter, & Hand, 2001). We set up desiccators with demineralized water and saturated salt solutions of KNO 3 , NH 4 H 2 PO 4 and KCl that would generate air humidities from 100% relative humidity (Water), 94%-93%, 93% and 86%, respectively  (O'Brien, 1948). The humidity levels were verified with dataloggers (Gemini Tinytag PlusII, https://www. geminidataloggers. com) which could only confirm that the relative humidity was below 100% for KCl (relative humidity 85%).
| Sensitivities of fitness components to rates In any developmental stage, development and death are risks that are competing. Inspecting risks of a separate process is insufficient to estimate distributions of individuals over states at different points in time. We used multistate estimation of fractions per stage and made plots of these fractions as a function of age using the mstate library for R (de Wreede, Fiocco, & Putter, 2011). These fractions were used to assess the importance of each separate survival rate and rate of development on two fitness components: (a) total number of surviving embryos at the end of the observation period and (b) the fraction in diapause I. The data for the control group were bootstrapped (100 resamples) and multistate estimation was carried out for each pseudo-dataset.
In stage 1, most deaths occur in the first month. After that, remaining in this stage does not impose an immediate disadvantage in terms of further mortality relative to the other stages. The death rate decreases with age to become zero in most stages and treatment groups. By the age where all surviving embryos are in the rewetting phase, there are hardly any new deaths occurring. Table  2  summarizes which variables were retained in the models for survival per stage. Table  S1  does that for a dataset restricted to the observations made before the start of the long interval (days 96-133, Figure  2 ). There are significant fixed effects for stages two and five, and other effects suggested by Figure  3  were not significant. Embryos that are desiccated in stage 2 have an increased death rate (confidence interval [0.
We also see a similar amount of individuals that do not make a developmental transition from stage 4 into 5, where no diapause is expected. What we clearly observe is that the curves for development in stage 2, three, and four, all show a period of about twenty days between days 20 and 50 with fewer developmental transitions. When data are censored before the single long time interval between observations, there is no such period. We conclude that there are no slow/fast alternative developmental trajectories in these stages. For the shortened dataset and relative to the control, embryos in saturated air develop significantly more slowly (-0. 37 (0. 17)) from stage 1 into 2. However, the effect is not clearly visible in graphs, there are no significant effects for the other desiccation levels and the effect is not detected in the complete data or when the dry period is separately analyzed.
comparison to nonannual rivulids where initially over 10% of individuals changed coiling per day (Varela-Lasheras & Van Dooren, 2014), the activity of prehatching annual fish embryos seems overall lower from the start. Overall, the effects of desiccation are limited and different from the pattern observed among nonannual rivulids. The desiccation regimes in this study are much prolonged in comparison to Varela-Lasheras and Van Dooren (2014), where the median duration was eight days, with visible effects on the state of the eggs after one hour. There, delays in response to brief desiccation were early in development and desiccation provoked an increased incidence of delayed hatching (similar or equal to diapause III) in stage 5 in some species. Regarding early development in the first stage of the Argentinean pearlfish, we only found a significant desiccation effect in a reduced dataset and for the weakest desiccation level, casting doubt on its existence.
, 2016a). Moreover, in fall, the concentrations of Hg 0 peaked during the night. In winter (May-August), as expected, the diurnal cycle of Hg 0 disappeared, and a gradual decline of Hg 0 was seen in nearsurface air. Overall, these observed seasonal and diurnal features of atmospheric mercury on the plateau are not well understood and not reproduced by global chemical transport models, likely due to their imperfect representations of boundary layer dynamics and chemical reaction pathways  (Angot et al. , 2016a)  and to the singularity of their longitude-latitude grid at the poles. Here, we present detailed box model calculations to interpret observational data collected at Dome C in 2013, and to explore important chemical and physical processes controlling diurnal and seasonal variations of atmospheric mercury.
Meteorology A surface-based temperature inversion layer exists at Dome C for most of the year, mainly due to radiation imbalance, while a convective mixed layer up to several hundred meters in depth develops during the daytime in summer in response to surface heating (see the Supplement, Sect. S1)  (Pietroni et al. , 2014). Here, the depth of the inversion/mixed layers is specified as ∼ 500 m in our model, and the air above is regarded as the free troposphere. The vertical atmospheric transport is represented with turbulent diffusion coefficients (K z ) from the polar-oriented regional climate model MAR (Modèle Atmosphérique Régional) (Sect. S1). The MAR data have been used to simulate several other atmospheric species (e. g. , NO x and HONO) in the 2011-2012 summer Oxidant Production in Antarctic Lands and Export (OPALE) campaign at Dome C  (Legrand et al.
(5) The chemical mechanisms and reaction rates for snow mercury processes, including photoreduction and dark reduction, should be further investigated. Future modeling work should also improve the representation of those processes (e. g. , diamond dust) shown in Table  2. Given the rapid exchange of mercury between the surface snowpack and the above atmosphere (especially during summer), regional modeling studies should be conducted in the future in order to understand the total and speciated mercury budgets over the entire Antarctic Plateau and the influence of the plateau on the coastal environments. Data availability. The mercury box model is available at http:// github. com/shaojiesong/Hg_DomeConcordia (last access: 30 October 2018). The mercury measurement data at Dome C are available upon request at http://sdi.
iia. cnr. it/geoint/publicpage/GMOS/gmos_ historical. zul (GMOS, 2018). The ozone and NO x measurement data at Dome C are available upon request to the authors. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-15825-2018-supplement. Author contributions. SS, NES, and AD initialized and designed the study. SS, HA, and NES developed the model. HG provided MAR data. HA, FS, NP, OM, and AD conducted Hg observations, and DH and JS conducted NO x observations used in the model. SS wrote the paper with input from all the authors. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. The authors acknowledge the  MISTI Global Seed Funds (MIT-France). The model work at  MIT  is supported by the  US NSF   Atmospheric Chemistry Program  no. 1053648. Mercury measurements at  Concordia Station were  initiated with the  FP7   GMOS  project and are supported by  IPEV   GMOstral Program 1028. Detlev Helmig  acknowledges logistical and financial support by the  French Polar Institute  (Program 1011, SUNITEDC) and support from the  US NSF Office of Polar Programs  through grant no. 1142145. Aurélien Dommergue  and  Joël Savarino  thank  LEFE/INSU  for their financial support. Shaojie Song  thanks the  MIT Henry Houghton Fund , and  Hélène Angot  the  Univ. Grenoble Alpes Doctoral School for Earth, Planetary and Environmental Sciences  for travel support. We thank  Tomás Sherwen  for providing GEOS-Chem bromine model results,  Xin Yang  for p-TOMCAT bromine model results,  Oleg Travnikov  for GLEMOS mercury model results,  Michel Legrand  for ozone observational data,  Christophe Genthon  for the meteorological observational data, and  Jack Dibb  for access to the measurement data from Summit Station.
We classified these families into three groups, based on the homology (or lack thereof ) of composite eukaryotic sequences with prokaryotic sequences from a reference dataset of 2704 complete prokaryotic genomes (2540 from Bacteria and 164 from Archaea, totaling 8,422,211 proteins) (Additional file 1: Figure  S1 ). Initially, we found that 633 gene families comprised composite eukaryotic genes with a prokaryotic origin, i. e. , both the composite eukaryotic genes and at least one prokaryotic gene could be aligned over their full lengths. The origin of these composite genes likely predated LECA. We also found that composite eukaryotic genes in 383 gene families did not share detectable local similarity with prokaryotic sequences, and were thus likely to be eukaryotic innovations. Finally, 605 gene families corresponded to Sgenes, because only partial sequence similarity was detected between composite eukaryotic and prokaryotic sequences.
Thus, even though S-genes only represent about 19% of the yeast informational genes, their evolution contrasts with that of informational genes in general, and is strongly impacted by genes of bacterial origin. Thus, there is a large hidden bacterial contribution to the evolution of eukaryotes, beyond operational functions, consistent with the work of Cotton and McInerney  [21]. Identifying a dominant bacterial signal in eukaryotic informational genes may be explained by the inability to identify bona fide archaeal homologues due to a much smaller database of available genomes from these prokaryotes. Regardless, we feel that explaining this finding is not trivial, and we can only speculate. Because the host was likely an archaeum, replacing genes that encode a significant fraction of the informational machinery of archaeal origin with bacterial genes might have been counter-selected in chimeric eukaryotic lineages.
Dataset construction A protein sequence database was assembled by downloading every archaeal, viral, and plasmid genome that was annotated as 'complete' according to the NCBI Genome database on November 2013 (152, 3769, and 4294 genomes, respectively). Regarding Bacteria, one representative genome was chosen randomly per eubacterial family (230 genomes). Finally, 38 unicellular eukaryotic genomes and their organelle genomes were sampled across the eukaryotic tree of life -19 for photosynthetic organisms and 19 that are non-photosynthetic, with a comparable total gene number and phylogenetic diversity in their ribosomal proteins. The resulting 2,192,940 protein sequences were used to perform an all-versus-all sequence comparison using BLASTP  [30]  (version 2. 2. 26) (30% protein identities cut-off in agreement with  [73] , E-value cutoff 1 × 10 -5 and using the soft-masking parameter for low complexity regions) (see Additional file 20 for the list of genomes used).
Domain and functional annotations Domains were predicted using the Conserved Domain Database  [31]  (CDD) (version 3. 13) (default parameters) and Pfam  [32]  (version 29. 0) (default parameters). Sequences were functionally annotated by the category of their best HmmScan  [74]  match (version 3. 1) (E-value cutoff 1 × 10 -5 ) against eukaryotic EggNog database [75] (version 4. 5). S. cerevisiae genes were annotated with the DEG database  [76]  (version 13. 3) and protein-protein interactions with the BioGRID database  [77]  (version 3. 4. 136).
Detection of S-gene families Composite proteins were detected using FusedTriplets  [29]  (E-value < 1 × 10 -5 ) by scanning the BLASTP output. All sequences were also independently clustered into protein families according to published methods  [1]. Briefly, an undirected graph was constructed in which each node corresponds to a sequence and two nodes are linked if the corresponding sequences show a BLAST hit with an E-value < 1 × 10 -5 , ≤ 30% sequence identity, and a mutual sequence overlap of ≥ 80%. Connected components in this graph were considered protein families. Families with only eukaryotic sequences, at least three different eukaryote species, more than 50% of genes detected as composite by FusedTriplets, and with at least two domains, were kept for further analysis. In order to verify that no proteins from families have full-length homologs with prokaryotic sequences, each protein was blasted against an extensive prokaryotic dataset including Asgard genomes (2540 from Bacteria and 164 from Archaea, 8,422,211 proteins).