dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors acknowledge all the key contributors to the  PIRATA program  from its inception, that is, the institutions NOAA, IRD,  Météo-France ,  Instituto Nacional de Pesquisas Espaciais , and DHN; all past members of the  PIRATA Resources Board  and of the PIRATA Scientific Steering Group; all engineers and technicians ensuring the yearly cruises, data acquisition, data treatment, and material maintenance (of  NOAA , DHN, and  IRD 's IMAGO service unit); and Research Vessel crews. All PIRATA data used in this paper are made available for free through the PIRATA main website at https://www. pmel. noaa. gov/gtmba/pmel-theme/atlantic-ocean-pirata and related linked websites. We are indebted to  H. Pillar  for the analysis and the figure related to near inertial waves. Part of the work was funded by the  EU   FP7 /2007-2013 under Grant  603521 ,  PREFACE , and by the  EU Horizon 2020  under Grant  2014-633211 , AtlantOS.
Whole-Cell Recordings of Spontaneous Postsynaptic Currents. These electrophysiological recordings use the methods of Fernandez et al. and our description partly reproduces their wording  [49]. Whole-cell recordings of spontaneous glutamatergic and GABAergic currents in CA3 pyramidal neurons at P0 and P15 were performed using an EPC-10 amplifier (HEKA Elektronik Dr. Schulze GmbH, Germany) or a MultiClamp 700B amplifier (Molecular Devices, CA, USA) with custom-made software based on IGOR Pro. Data were acquired at 10 kHz and filtered at 2. 4 kHz. Patch pipette solution contained (in mM) 130 Kgluconate, 10 Na-gluconate, 7 NaCl, 4 Mg-ATP, 10 HEPES, 4 phosphocreatine, and 0. 3 Na-GTP, pH 7. 3 equilibrated with KOH. Spontaneous glutamatergic postsynaptic currents (sEPSCs) were recorded at the reversal potential for GABAergic currents and spontaneous inhibitory postsynaptic currents (sIPSCs) were recorded at the reversal potential for glutamatergic currents.
Reversal potentials were experimentally detected by changing the holding potential step by step with 1 mV increments starting at -75 mV for GABAergic and +10 mV for glutamatergic currents. The holding potential at which spontaneous GABAergic and glutamatergic currents were zero was taken as a holding potential for recordings of sEPSCs and sIPSCs, respectively. sIPSCs were recorded for 15 min at a holding potential of +10 ± 2 mV while sEPSCs were recorded for 15 min at a holding potential of -75 ± 2 mV. Data were filtered post recording and analyzed using Mini Analysis 6. 0 (Synaptosoft Inc. , GA, USA), Clampfit 10. 4 (Molecular Devices, CA, USA), and OriginPro (OriginLab, MA, USA).
Following isoguvacine application, spike frequency was recorded for an extra 15 min (wash-out period). Signals were recorded with a low-noise multichannel DAM-80A amplifier (WPI, UK; low-pass filter 1 Hz; high-pass filter 3 kHz; gain ×1000) and digitized online with a Digidata 1400A digitizer (Molecular Devices, CA, USA). Recordings were analyzed using Clampfit 10. 4 software (Molecular Devices, CA, USA). The spike detection threshold was defined as three times the standard deviation of the noise recorded in the bath solution. Spike frequency was calculated for control, isoguvacine, and wash-out periods. Slices for which wash-out spike frequency did not come back to control levels (±20% of control) were excluded from this study.
Then, an inverted copy of this mask was generated to isolate the background and subtract it from the original image. Finally, background-subtracted images were processed for mean intensity measurement in the defined regions of interest within CA3: stratum oriens, stratum pyramidale, stratum lucidum, and stratum radiatum. Fluorescence intensity was normalized by the mean intensity of the control condition, and the mean ± SEM was calculated for each condition. Data for cell-attached recordings, KCC2 immunohistochemistry, sEPSCs at P0, sIPSCs at P15, mEPSCs amplitude and decay time, and mIPSCs decay time were analyzed with the Mann-Whitney test. sEPSCs at P15, mEPSCs frequency and rise time, and mIPSCs frequency, amplitude, and rise time were analyzed with the two-tailed t-test. Extracellular field recordings were analyzed with the repeated measures Friedman test with Dunn's multiple comparison post hoc test.
Acknowledgments The authors wish to thank  Maxime Billon-Grand ,  François Michel ,  Alexandre Pons-Bennaceur ,  Amandine Dufour ,  Robin Cloarec ,  Hamed Rabiei , and  Vera Tsintsadze  for their technical assistance and  Nail Burnashev  for criticism. This research was supported by  Neurochlore ;  Fondation Bettencourt Schueller; France's Agence Nationale de la.
DNA methylation has been one of the most studied epigenetic marks since its discovery in 1948  4. Methylation occurs at positions 4 and 5 of the pyrimidine ring of cytosine forming either 4-methyl-cytosine (4mC) or 5-methyl-cytosine (5mC), or at position 6 of the purine ring in 6-methyl-adenine (6mA). 6mA and 4mC were believed to occur only in bacteria but recent advances in sequencing technology made it possible to detect them also in eukaryotic species. A specific database (MethSMRT) was dedicated to these modifications  5  , and the available experimental data were used to train an algorithm to predict the occurrence of 4mC  6  in DNA based on sequence features. We will focus here on 5mC and to facilitate the readability use the term DNA methylation for this purpose. In most eukaryotes, 5mC is overrepresented or even restricted to the dinucleotide CpG context, where 'p' stands for the phosphodiester linkage between the cytosine (C) and the guanine (G).
Essentially, the question is whether or not there is universality in the DNA methylation epigenetic code, conceptually similar to the universality of the genetic code, or if DNA methylation is non-universal and specific to every evolutionary unit. We wished to address this question through the analysis of the evolution of DNA methylation. Here, an obstacle is that a comprehensive analysis of DNA methylation patterns in a wide range of different species is missing. There are currently methods available that allow, in principle, for determining genome-wide DNA methylation patterns ("methylomes") at a single base resolution. Since DNA methylation patterns can be different in different organisms of a species or even tissues of an individual, for a given species several methylomes can exist. A review of the available data in different databases and in the literature showed that there is a strong bias towards model organisms: there are at least 300 methylomes available for human, mouse and the model plant A.
thaliana, but only 63 for a total of 16 other species (http://smithlabresearch. org/software/methbase/ and Céline Cosseau, pers. communication). As a consequence, global conclusions about the function and importance of DNA methylation are actually based on a very limited and biased amount of data. For this reason, it remains challenging to derive the general rules (if any) that govern DNA methylation in the different branches of the "tree of life". A potential solution to the caveat that experimental "wet bench" data is missing is to infer DNA methylation indirectly with computational method  24, 25. The basis for this is that methylated CpG sites mutate relatively frequently compared to the other dinucleotides over evolutionary time  26. If a cytosine is deamined, a deoxy-uracil will form, which is not stable in DNA: it will rapidly be excised by uracil glycosylase and replaced by cytosine.
Here, we have applied this software to predict DNA methylation with CpGo/e ratios in a total of 634 species and to use the results in combination with publicly available experimental data to infer evolution of DNA methylation over the eukaryotic tree of life. We applied Notos on coding sequences coming from three databases (dbEST, CleanEST, and CDS/cDNA). Our results show clearly (i) that DNA methylation prediction by CpGo/e ratio is robust, (ii) that only four types of DNA methylation can be identified in all species despite their wide range of genome sizes, environments, body plans, reproduction types etc. , and (iii) that DNA methylation types does not follow phylogeny but is consistent within clades suggesting evolutionary constraints. Taken together our analysis delivers arguments for the idea of the universality of the role of DNA methylation that is preserved through evolution.
Results CDS and cDNAs are the less biased data and thus the best choice for a pan-species study. We focused in this study on gene body DNA methylation. Annotated genomes are now available for many species, but messenger RNA sequence data is even more abundant and mRNA is representative of gene body DNA sequences. They could therefore be used instead of DNA sequence, but mRNA data is for historical reasons stored in different forms and in different databases. We reasoned that data quality will be critical for providing unbiased estimation of DNA methylation in gene bodies and therefore conducted a comparative pilot study to identify the best possible data source for the subsequent pan-species study. We used coding/transcript sequences from full genome annotations (CDS), dbEST, and cleanEST (details in Supplementary File 2).
A total of 127 species are in common between CDS and dbEST, and 92 species were in common between dbEST and cleanEST. Only 29 species were common to all three databases (Supplementary File 3). We produced Notos CpGo/e profiles for all intersecting datasets and proceeded to visual inspection. In 11 out of 29 cases (38%) we identified discrepancies in at least one out of the three profiles and decided to clarify their origins by a detailed analysis of the sequences under the differential peaks. An in-depth analysis revealed that these discrepancies were either due to contaminations during the sequencing process, reflect co-occurrence of other species, or are due to bias in data acquisition. For instance, for Trichoplax adhaerens, Anolis carolinensis (green anole lizard) and Cordyceps militaris one or two additional shoulder peaks in dbEST and CleanEST datasets.
We isolated the 769 sequences under this peak (0. 40-0. 60). The gene ontology showed that all sequences coded for the ribosomal protein SA (RPSA). In human, RPSA genes are indeed highly expressed in the ovary but no data are available for other species. Nevertheless, it seems unlikely that the high abundance of RPSA ESTs reflects an expression bias. We speculate that the research interest of the submitters focused on this particular gene and that therefore many individual EST were submitted (Supplementary Fig. 3 ). Also in the duck (Anas platyrhynchos), we found a shoulder peak at 0. 57 and 0. 59 in data from dbEST and Clean EST. Sequences under this peak corresponded to an EST library exclusively composed of immunoglobulins (146 sequences), reflecting probably a bias introduced by specific research interests (Supplementary Fig.
We therefore hypothesized that RNA abundance induced the bias in EST data. To test this hypothesis, we performed a RNA-seq analysis in these four species. We found that genes under the low CpGo/e peak (presumably hypermethylated) show higher median RNA amounts than genes under the high CpGo/e peak (this presumably hypomethylated). mRNA FPKM medians are 1. 95 to 5. 45 higher in presumably hypermethylated gene bodies (Supplementary Figs  5 6 7 8 ). We conclude that this expression difference is probably the origin of the bias in EST datasets. In summary, dbEST and cleanEST have the advantage of being large repositories with data for many species, but for the purpose of our study we considered them too noisy. A complete list of species for each dataset is in Supplementary File 4. CpGo/e clustering identifies four types of gene body DNA methylation.
86%) are present in this cluster, belonging to various phylogenetic groups (apicomplexa, sponges and nematodes, arthropoda, a large panel of chordata, and embryophytes). Many of them are important model organisms. For 26 (60%) literature data on DNA methylation was found. Gene bodies are methylated. In 19 species DNMTs were analyzed. Twelve species have all three DNMTs. One species (Naegleria gruberi) has DNMT1 and 2, but methylation of DNA was not yet studied. Four species have only DNMT2. Two species has DNMT1 and 3, and one only DNMT1 (Supplementary File 5). We qualify this cluster as with "gene body methylation" (type 3). Finally, cluster 4 contains species that show bimodality or are strongly negatively skewed CpGo/e distributions (mean absolute Q50 skewness = -0. 0424, mean CpGo/e position of mode 1 is 0.
5 for ultra-low gene body methylation. This is due to the fact that our algorithm does not differentiate well between low and ultra-low methylation. If we consider the dataset as a whole, out of the 54 species with known DNA methylation types, 41 were predicted correctly (total PPV = 0. 76). There are some particularly interesting cases of "wrong" prediction. Cryptosporium parvum is a monoxenic unicellular parasite of vertebrates. It belongs to the apicomplexan its exact phylogenetic position is controversial. Exysted oocysts are the only stage that can be used to produce host DNA free genomic DNA preparations. Notos predicts clearly high gene body methylation but LC-ESI-MS did not detect 5mC in exysted oocysts purified from infected cattle  48. Genome analysis of C. hominis to which C. parvum has only 3-5% sequence divergence  49  , showed that the number of genes is reduced (3,952 genes) compared to other apicomplexan, relying heavily on host gene activity.
Methods Origin of sequences, data cleaning and Notos parameters. In this study, coding sequences (CDS) or cDNA sequences of 147 species were downloaded from Ensembl and VEGA databases. Expressed sequence tags (ESTs) were downloaded from two different databases: dbEST 70 (605 species) and CleanEST 71 (110 species) (Supplementary File 3). We used Notos  36  for the calculation and modelling of CpGo/e distribution with the three datasets, with a minimal length L = mi200 bp and formula 1 72 : = × × - CpGo e number of CpG number of C number of G L L / 1 (1) 2 All the values outside the interval, and all the values with a score of 0 were removed. For each species, the number of mode, the position of mode(s), the Q50 skewness coefficient and the standard deviation (SD) were calculated. Blast searches and gene ontology analysis.
RNA seq analysis. RNAseq datasets for Nematostella vectensis, Nasonia vitripennis, Crassostrea gigas and Oryza sativa japonica were downloaded as fastq files from the European Nucleotide Archive and NCBI (details in Supplementary File 3). For each dataset, the reads were filtered with a Fred quality score ≥26. Filtered reads were mapped on their reference genomes (downloaded from Ensembl, details in Supplementary File 3) with RNA STAR  74  on a local Galaxy instance (v2. 4. 0d-2). Resulting BAM files and the gff files (downloaded from Ensembl, details in Supplementary File 3) with the coding sequences were used for FPKM estimations with Cufflinks  75. Annotation gff-files were used to extract CDS in fasta format from their genomes and we calculated the CpGo/e ratios with Notos 36 and detected modes (peaks).
To compare FPKM for the genes under the peaks, a bandwidth of 0. 2 (±0. 1 around mode maximum) was arbitrarily chosen for the CpG o/e ratio. FPKMs were extracted and used for statistical analysis of expression level in gene bodies with low and high predicted methylation. Mood's median test was used with R  76. Meta-analysis of DNA methylation using literature data. For each species for which data was available in the above-mentioned databases, we searched the literature on Google scholar (as of April-June 2016) with the following keywords: DNA methylation, 5-methyl-cytosine, gene body, mosaic methylation, global methylation, DNA methylation pattern. Articles were obtained from Bib CNRS (https://bib. cnrs. fr/) and manually curated to obtain gene body methylation, and presence of DNMTs. Clustering.
The remaining four wrong predictions are actually peculiar cases that were discussed above. The clustering was implemented using R version 3. 4. 0 (Supplementary Files 6 and 7). For visualizing the clustering, the R package dendextend has been used. Parameters were Rscript cluster. r -0. 04 0. 69 0. 12 input_ file_notos input_file_notos_bootstrap with first parameter [skewness], second [Mo], and third [SD], and Notos outputfiles as input. Further details on our method can be found in  25.
Tree of life. We recovered the taxonomic IDs of all investigated species from the NCBI taxonomy database (https://www. ncbi. nlm. nih. gov/taxonomy) and created a tabular file (. txt). We used this file to generate a Phylip tree file based on the classification in the NCBI taxonomy database with the NCBI common taxonomy tree online tool (https://www. ncbi. nlm. nih. gov/Taxonomy/CommonTree/wwwcmt. cgi) and designed a tree with the interactive Tree of life (version 4. 0. 2) (https://itol. embl. de)  76.
Acknowledgements The work on this tool was initiated during a meeting that had received funding of the  French-Norwegian travel program  AURORA. This work has been supported by  Campus France  and the  Norges  forskningsrad (program AURORA, nr. 34040YK ) to  C. Grunau  and  J. Bulla , the grant Felleslegat til fordel for biologisk forskning ved  Universitetet i Bergen  to  J. Bulla , the  ANR  grants  ANR-10-BLAN-1720  ( EpiGEvol ) and  ANR-17-CE12-0005-01  (CHRONOGET), a PhD grant for disabled students by the  French Ministry of Education and Research  to  B. Aliaga , and a  DFG return grant  to  I. Bulla  ( BU 2685/4-1 ). The authors are grateful to  Céline Cosseau  and  Cristian Chaparro  for helpful discussion.
Context. With the discovery over the last two decades of a large diversity of exoplanetary systems, it is now of prime importance to characterize star-planet interactions and how such systems evolve. Aims. We address this question by studying systems formed by a solar-like star and a close-in planet. We focus on the stellar wind spinning down the star along its main sequence phase and tidal interaction causing orbital evolution of the systems. Despite recent significant advances in these fields, all current models use parametric descriptions to study at least one of these effects. Our objective is to introduce simultaneously ab-initio prescriptions of the tidal and braking torques, so as to improve our understanding of the underlying physics. Methods. We develop a 1D numerical model of coplanar circular star-planet systems taking into account stellar structural changes, wind braking and tidal interaction and implement it in a code called ESPEM.
Barker & Ogilvie (2009)  investigated the influence of these effects on spin alignment. Ferraz-Mello et al. (2015)  used the creep tide theory from Ferraz-Mello (2013) and a semi-analytical wind model (see  Bouvier 2013, and references therein)  to compute the past evolution of observed star-planet systems. Damiani & Lanza (2015)  assumed a constant tidal efficiency and a Skumanichtype wind braking law, in which the torque is proportional to the stellar rotation cubed, and demonstrated that a pseudo-stable equilibrium state can exist for star-planet systems, in which corotation is not achieved and the ratio of the orbital mean motion divided by the stellar rotation rate is determined by the angular momentum loss rate due to magnetic braking. Zhang & Penev (2014)  implemented a star-planet system secular evolution code based on the two-layer rotational model of  MacGregor & Brenner (1991)  and the constant quality factor framework from  Goldreich (1963)  and performed a statistical analysis on their numerical simulations to constrain the tidal theory.
( 23 ) In this formula, α = R r /R * and β = M r /M * are the mass and aspect ratios of the core, respectively, γ = α 3 (1 -β)/(β(1 -α 3 )) is the ratio of the envelope's density over that of the core and = Ω/ GM /R 3. We computed these ratios from the stellar evolution tracks of the STAREVOL code  (Amard et al. 2016) , which is well adapted to our bi-layer stellar rotation model. Mathis (2015)  showed that, for a given rotation rate, the dissipation could vary over several orders of magnitude with aspect and mass ratios. Moreover, a solar-like star rotation rate may vary during its life on the MS in the range [Ω , 100 Ω ]  (Gallet & Bouvier 2015). Since the dissipation is proportional to the rotation rate squared, a given star may dissipate significantly more at its arrival on the MS than at solar age, as discussed by  Gallet et al.
11  start their orbit below the co-rotation radius of their host star. On the contrary, the initial co-rotation radius of the fast rotator is about 0. 025 AU. As shown in the right panel of Fig. 11 , all planets that start orbiting the fast rotator below this limit are rapidly destroyed, leading to a disentangling of the population in two groups: planets which formed above the co-rotation radius, rapidly pushed away from their stars, which allowed them to eventually survive, and those which formed below and were disrupted within the first million years. This implies a depopulation of the region close to fast rotating stars, as observed by  McQuillan et al. (2013b). Teitler & Königl (2014) , who used a secular evolution code to explain this dearth, concluded that it was due to tidal engulfment.
Conclusion We presented ESPEM, a code implementing a model of secular evolution of star-planet systems under magnetic braking and tidal interaction. Our wind model based on the work of  Réville et al. (2015a)  allows a fine analysis of magnetic phenomena occurring in the stellar corona. Its results are in agreement with recent observations  (Barnes 2010; Gallet & Bouvier 2015)  and has the advantage of explaining the mass dependence of the braking torque (see  Matt et al. 2015). We emphasized the difference between the ab-initio tidal prescription of  Mathis (2015)  and the constant quality factor model used by  Zhang & Penev (2014). The former may predict that a planet will survive whereas the latter predicts its decay. Thus, understanding the physical processes Coordinates of the points of the border between the survival and demise regions (respectively the blue and orange regions of Fig.
The question of the Rossby number was also extensively studied by  Brun et al. (2017). In particular, they highlighted that the fluid Rossby number R of , defined as the ratio of the advection term divided by the Coriolis force, was a good indicator to assess the large-scale rotational behavior of solar-like stars. Based on numerical simulations performed with the ASH code, they gave an expression of this number as a function of stellar mass and surface rotation rate. In a future work, we will take into account the studies mentioned above to improve this part of our model. A better understanding of the magnetic and coronal properties of solar-like stars will also be useful in order to implement star-planet magnetic interactions in the code  (Strugarek et al. 2014 (Strugarek et al. , 2015)).
Acknowledgements. The authors acknowledge  Florian Gallet ,  Emeline Bolmont ,  Antoine Strugarek , and  Rafael A. García  for helpful discussion during the project. We also thank  Jérémy Ahuir  for providing Figs. C. 3 and C. 4. S. Mathis acknowledges funding by the  European Research Council  through  ERC  grant  SPIRE 647383. We also thank the  ERC   STARS2 207430 ,  FP7   SPACEINN , the  ANR  Toupies  SIMI5-6 020 01 , the  INSU/PNST  and CNES support via our PLATO and Solar Orbiter fundings. We thank the referee and editor for useful comments that improved the content of the paper.