dataesr/scientific-paragraphs-categorization huggingface dataset:
69 ± 0. 19. For the proteins combined here for phylogenomic purposes, tunicates (with the exception of O. dioica) displayed on average a twice-higher number of amino acid substitutions than vertebrates. Such substitution rate variation among lineageswithin tunicates, and between tunicates and other deuterostomesneeds to be accounted for in molecular dating analyses by using models of clock relaxation  [52]. The selection of the clock model is often arbitrary and appears mostly dependent on the software choice, with an overwhelming majority of studies relying on the BEAST software  [71]  using an uncorrelated gamma (UGAM, also known as UCLN) model of clock relaxation. However, it has been shown that autocorrelated rate models, such as the autocorrelated LN model, often provide a better fit with phylogenomic data  [54, 72, 73].
Consequently, we compared the fit of both the UGAM and LN models to the fit of a strict CL model for our dataset using cross-validation tests under the CAT-GTR + Γ 4 model. As expected given the large lineage-specific rate variation, both relaxed clock models largely outperformed the strict clock model (UGAM vs. CL: ΔlnL = 4068 ± 125; LN vs. CL: ΔlnL = 4057 ± 118). Among relaxed clock models, UGAM and LN were statistically equivalent in offering a very similar fit to our data (UGAM vs. LN: ΔlnL = 11  ± 38). The use of a relaxed clock model allowed us to perform evolutionary rate comparisons in terms of number of substitutions per site per million years for the 63 terminal taxa considered (Fig. 2 ). The box plots clearly showed that tunicates evolved faster than other groups, especially compared to vertebrates, which were the slowest evolving.
Even though the difference in fit between the two relaxed clock models was not significant for our dataset, Fig. 2  Evolutionary rate variation across sampled species. The bar plots represent average rate estimates (in number of substitutions per site per million years) obtained for the 63 terminal taxa regrouped by taxonomy. The rates were calculated using a rate-autocorrelated log-normal (LN) relaxed molecular clock model under the CAT-GTR + Γ 4 mixture model with a birth-death prior on the diversification process and 13 soft calibration constraints. Data points are plotted as open circles with n = 10, 1, 18, 34 sample points in each taxonomic categories. Centre lines show the medians, crosses represent sample means, and box limits indicate the 25 th and 75 th percentiles with whiskers extending 1.
Second, the ancient age of their major divergence events can heavily complicate orthology assessment among tunicates, as well as between tunicates and vertebrates, thus reducing the quality of genome annotations. Indeed, the fast-paced molecular evolution of tunicates prevents the identification of some genes by simple similarity methods (e. g. BLAST), even when orthologs do exist in databases. For instance, in terms of evolutionary depth, a comparative study of the genus Molgula is roughly equivalent to a comparative study among turtles representing about 180 million years of evolution. In terms of amino acid sequence divergence, the differences are much more pronounced between Molgula occidentalis / M. tectiformis (88. 1% similarity) than between Phrynops/ Chrysemys (98. 0% similarity; see Fig.
Acknowledgements We thank  Nicolas Galtier  for giving early access to the  Clavelina and Cystodytes Illumina data. We are thankful to  Stefano Tiozzo ,  Hector Escriba ,  Sébastien Darras ,  Frédérique Viard , and the diving staffs of the marine stations of Villefranche-sur-Mer, Banyuls, and  Roscoff  for their help in sample collection. We also thank two anonymous referees for their comments. This is contribution ISEM 2018-024 of the  Institut des Sciences de l'Evolution de Montpellier.
Funding This work was supported by the  Centre National de la Recherche Scientifique  and the  Agence Nationale de la Recherche  (Contract  ANR-13-BSV2-0011-01 ) to P. L and E. J. P. D. and by the  Labex TULIP  ( ANR-10-LABX-41 ) to H. P. Computations were made on the  Montpellier Biodiversity Bioinformatics (MBB) platform of the Labex CeMEB , and on the Mp2 and Ms2 supercomputers from the  Université de Sherbrooke , managed by  Calcul Québec and Compute Canada. The operation of this supercomputer is funded by the  Canada Foundation for Innovation (CFI) , the  ministère de l'Économie, de la science et de l'innovation du Québec (MESI) , and the  Fonds de recherche du Québec -Nature et technologies (FRQ-NT).
Availability of data and materials All data generated or analysed during this study are included in this published article and its additional files. Raw sequencing reads have been deposited under NCBI Bioproject PRJNA414754. Transcriptome assemblies, alignments, and trees are included in Additional file 4 and are also available from a GitHub repository (https://github. com/psimion/SuppData_Delsuc_ BMCBiol_2018_Dating_Tunicata).
OBSERVATIONS AND DATA PROCESSING PSR J1713+0747 was observed using the Lovell Telescope (LT) at the Jodrell Bank Observatory between 2011 April and 2015 July with an average cadence of about 4. 5 days. Between 2013 February and 2014 April, the pulsar was observed more frequently with an average cadence of approximately every two days. All the LT observations were carried out at L-band (with a centre frequency of 1532 MHz) and the data were recorded using the 'ROACH' pulsar backend (see  Bassa et al. 2016). In addition to these highcadence LT observations, we included the observations of the pulsar obtained from three other telescopes -Effelsberg Telescope (EFF) in Germany, Nançay Radio Telescope (NRT) in France, and Westerbork Synthesis Radio Telescope (WSRT) in the Netherlands -in the analysis to further improve the cadence.
2016). To be consistent with all observations, we selected the beginning and the end time of each data set to be equal to that of the LT observations, but we note that the NRT observations started four months after the beginning of the LT observations. The details of the bandwidths, data span, and the number of observations in each data set is given in Table  1. Combining the data from all four telescopes, 952 observation epochs were included in our analysis with a resulting average cadence of about 1. 6 days. We also note that the combination of data from other telescopes improved the average cadence of the pulsar to approximately daily within the period between 2013 February and 2014 April, including 420 TOAs in total, in which it was monitored more frequently using the LT. We processed the data using the pulsar analysis software package PSRCHIVE 1   (Hotan et al.
TIMING THE PULSAR We fit a previously published timing model  (Desvignes et al. 2016)  to our observed TOAs and update the solution by Table  1. Details of observations used in the analysis. Two 1. 347 GHz Effelsberg data sets have been included in the analysis; the data obtained using the multi-beam receiver (denoted as ' m ') and the single-beam receiver (denoted as ' s '). The observation length varies and typical values for data sets are given. In total, 952 observation epochs were included in the analysis, resulting in an average cadence of about 1. 6 days. The last column shows EFAC and log 10 [EQUAD(s)] determined for each data set in the timing model given in  Table 2  a Note that there were only 512 pulse phase bins in the early LT observations (i. e. before 2011 July -MJD 55754).
Therefore, we fit for an additional 'JUMP' in the timing analysis to correct for the time delay of these TOAs. minimising the chi-square of timing residuals using the pulsar timing software package TEMPO2  (Edwards et al. 2006; Hobbs et al. 2006). Since the pulsar is in a binary orbit, the timing model includes Keplerian and Post-Keplerian (PK) parameters (see  Lorimer & Kramer 2005). We combine TOAs from different telescopes/backends by fitting for time offsets or 'JUMPs' in the timing model to account for any systemic delays between the data sets (e. g. Verbiest et al. 2016). Note that some of our observations were obtained simultaneously using all/several telescopes as a part of LEAP observations  (Bassa et al. 2016 ). Therefore, these simultaneous observations further help in constraining the 'JUMPs' between the relevant data sets in the timing analysis.
g. 4. 3 yrs of data in our work compared to 17. 7 yrs of data in Desvignes et al. (  2016 )), or some covariance between parameters. Therefore, we use their parallax of 0. 90(3) and keep it fixed. σ = E 2 q + E 2 F σ 2 t (see The observing frequencies of our data sets span ∼350 -2627 MHz (see Table  1 ). This broad frequency range al-3 https://github. com/LindleyLentati/TempoNest lows us to fit for the DM and its first two time derivatives, Ḋ M(≡ dDM/dt) and DM(≡ d 2 DM/dt 2 ), in the timing model, and we measure a DM with a 3. 5σ significance. The pulsar showed a significant DM event towards the end of 2008 (see  Desvignes et al. 2016; Arzoumanian et al. 2015; Zhu et al. 2015). However, the TOAs in our data sets begin in 2011 and therefore, no effect from this event is seen in the analysis and we see no evidence for any subsequent events.
No noticeable red noise is seen within this short, but high-cadence, time span. The significant power drop at the yearly period (i. e. a frequency of ∼ 3 × 10 -8 Hz) in Figure  2  is common for all three cases and it is due to fitting for the pulsar position in the timing model. Similarly, the power absorption at ∼ 1. 7 × 10 -7 Hz is due to fitting for the orbital period, P b = 67. 8 d. We then use the same method outlined in previous studies (see  Yardley et al. 2010; Yi et al. 2014 ) to build the detection threshold using the power spectrum of timing residuals. To remove any spurious effects from any apparent Figure  2. The power spectrum of the timing residuals of the pulsar given in Figure  1. The top panel shows the spectrum generated using the entire data set across the full data span, including (solid curve) and excluding (dotted curve) the low frequency WSRT 350 MHz data set.
For the clarity of the plot, we shifted the dotted curve down by a factor of 0. 1. We note that both curves are very similar, indicating that the GLSP method accounts for the less weighted WSRT 350 MHz data correctly in the periodogram. The middle panel represents the power spectrum generated using the timing residuals after subtracting the wave forms of the red and stochastic DM power-law noise terms as described in Section 3. The bottom panel shows the spectrum for the veryhigh-cadence period of the observations after accounting for all noise parameters in the timing model. Note that the upper bound of the frequency range is extended beyond 3. 5 µs with this veryhigh-cadence observations. The y-axis represents the normalised power (see  Zechmeister & Kürster 2009, for details). The vertical dashed and dashed-dotted lines represent the frequencies of 1 yr -1 and the orbital period of 67.
ACKNOWLEDGMENTS The European Pulsar Timing Array (EPTA: www. epta. eu. org) is a collaboration of European institutes to work towards the direct detection of low-frequency gravitational waves and the running of the Large European Array for Pulsars (LEAP). Pulsar research at the  Jodrell Bank Centre for Astrophysics  and the observations using the Lovell Telescope are supported by a consolidated grant from the  STFC  in the UK. The Nançay radio observatory is operated by the  Paris Observatory , associated with the  French Centre National de la Recherche Scientifique (CNRS). We acknowledge financial support from ' Programme National de Cosmologie et Galaxies' (PNCG)  of  CNRS/INSU, France. The Westerbork Synthesis Radio Telescope is operated by the  Netherlands Institute for Radio Astronomy (ASTRON)  with support from The  Netherlands Foundation for Scientific Research NWO.
Part of this work is based on observations with the 100-m telescope of the  Max-Planck-Institut für Radioastronomie (MPIfR) at Effelsberg. The LEAP is supported by the  ERC   Advanced Grant 'LEAP' , Grant Agreement Number  227947  ( PI M. Kramer ). KL acknowledge the financial support by the  European Research Council  for the  ERC Synergy Grant Black-HoleCam  under contract no. 610058. SO acknowledges support from the  Alexander von Humboldt Foundation and Australian Research Council   grant Laureate Fellowship   FL150100148. AS is supported by the  Royal Society. GJ and GS acknowledge support from the  Netherlands Organisation for Scientific Research NWO  ( TOP2. 614. 001. 602 ). SRT is supported by the  NANOGrav  project, which receives support from  National Science Foundation Physics Frontier Center  award number  1430284.
5 ps, which enables multiple rounds of excitation during the picosecond pulse. Kinetic parameters were estimated through global analysis using the Glotaran software package and are reported as EADS assuming a sequential reaction scheme with increasing lifetimes as with the UV-vis data above. 31, 32 ndividual traces at notable frequencies along with the result of the global fit are provided in Figure  5B. The full set of kinetic traces is given in Figure  S5  (SI), and the transient concentration of the various states is given in Figure  S6 (SI). The first component, with a 3 ps lifetime (Figure  5A , black), has a lifetime very similar to that of the ECN S 1 /ICT state shown above. It exhibits a strong broad negative signal at 1685 cm -1 that is assigned to the bleach of the ECN β1-ring CO stretch mode upon population of the optically forbidden S 1 / ICT state of ECN, which immediately follows excitation of the allowed S 2 state in 100 fs.
The probe laser was a 1 kHz Ti:sapphire amplifier (Spitfire Ace, Spectra Physics) that pumped a separate optical parametric amplifier (Topas, Light Conversion), producing signal and idler beams that underwent subsequent difference frequency generation in AgGaS 2 , yielding femtosecond mid-IR pulses tunable from 1 to 10 μm center wavelength with ∼100 fs pulse duration. The pump and probe beams were spatially and temporally overlapped on the sample, and data were collected from 0 to 120 μs or to 585 μs distributed logarithmically with background spectra measured at negative time delay. The probe light was spectrally resolved and detected with a liquid-N 2 -cooled 32-pixel MCT array (InfraRed Associates). Difference absorption data were calculated as the difference between probe spectrum with and without exposure to pump light which was modulated in situ by an optical forming the bottlenecks for carotenoid translocation.
■ AUTHOR INFORMATION Corresponding Author *j. t. m. kennis@vu. nlORCID  John T. M. Kennis :  0000-0002-3563-2353  Notes The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS This work was supported by the  Chemical Sciences Council of The Netherlands Organization for Scientific Research (NWO-CW)  through a  VICI grant , a  Middelgroot investment grant  to J. T. M. K, and by the  Life Sciences Council of The Netherlands Organization for Scientific Research (NWO-ALW)  through a  Middelgroot investment grant  to M. L. G. The work of the D. K. group was supported by grants from the  Agence Nationale de la Recherche  ( ANR  projects  RECYFUEL  ( ANR-16-CE05-0026 ), by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 675006  ( SE2B ).
Comparable Corpora Table  1  indicates the size of the source corpora (number of documents, number of words in specialized and simplified versions). The three corpora are built with French data. The Drug corpus contains drug information such as provided to health professionals and patients. Indeed, two distinct sets of documents exist, each of which contains common and specific information. This corpus is built from the public drug database 1  of the French Health ministry. These data have been downloaded in June 2017. We can see that the specialized versions of documents provide more word occurrences. The Scientific corpus contains summaries of meta-reviews of high evidence health-related articles, such as proposed by the Cochrane collaboration  (Sackett et al. , 1996). These reviews have been first intended for health professionals but recently the collaborators started to create simplified versions of the reviews (Plain language summary) so that they can be read and understood by the whole population.
This corpus has been built from the online library of the Cochrane collaboration 2. The data have been downloaded in November 2017. We can see that specialized version of summaries is also larger than the simplified version, although the difference is not very important. The Encyclopedia corpus contains encyclopedia articles from Wikipedia 3  and Vikidia 4. Wikipedia articles are considered as technical texts while Vikidia articles are considered as their simplified versions (they are created for children 8 to 13 year old). Similarly to the works done in English, we associate Vikidia with Simple Wikipedia 5. Only articles related to the medical portal are exploited in this work. These encyclopedia articles have been downloaded in August and September 2017. From Table  1 , we can see that specialized versions (from Wikipedia) are also longer than simplified versions.
Reference Data In this section we describe the data that are used for training and evaluation of the automatic sentence alignments. The reference data are created manually. We have randomly selected 2*14 encyclopedia articles, 2*12 drug documents, and 2*13 scientific summaries. The sentence alignment is done by two annotators following these guidelines: The judgement on semantic closeness may vary according to the annotators. For this reason, the alignments provided by each annotator undergo consensus discussions. This alignment process provides a set of 663 aligned sentence pairs. The inter-annotator agreement is 0. 76  (Cohen, 1960). It is computed within the two sets of sentences proposed for alignment by the two annotators. Table  2  indicates the size of the reference data before (source columns) and after (aligned columns) the alignment.
Evaluation The training of the system is performed on two thirds of the sentence pairs, and the test is performed on the remaining third. Several classifiers and several combinations of features are tested. Classical evaluation measures are computed: Precision, Recall, F-measure, Mean Square Errors, and True Positives. Our baseline is the combination of length measures with the common words (features 1, 2, 4 and 5). These features are indeed traditionnally exploited in the existing work. We also evaluate the system on data in English that were released for STS competitions 6  : we use 750 sentence pairs from SemEval 2012, 1,500 sentence pairs from SemEval 2013, 3,750 sentence pairs from SemEval 2014. Each pair of sentences is associated with the similarity score [0;5]. We apply our system to these data in two ways: (1) the system is trained and tested on the STS dataset, and (2) the system is trained on our dataset in French and tested on the STS dataset in English.
We assume indeed that the features used and even the models generated can be transposed to data in other languages. For the experiments with the English data, we use the same evaluation measures (Precision, Recall, F-measure, Mean Square Errors, and True Positives). The set of stopwords in English contains 150 entities. In Table  3 , we present the results obtained on French data using the whole set of features (but without the tf*idf similarity scores) on test set, and non-lemmatized texts. The results are indicated in terms of Recall R, Precision P , F-measure F , Mean Square Errors M SE and True positives T P (out of the 221 positive sentence pairs in the test set). We can see that all the classifiers are competitive with F-measure above 0. 80. Overall, several classifiers (LDA, QDA, LogReg, LinSVM) provide stable results, for which we indicate the evaluation scores obtained in one iteration.
93 Fmeasure. We use the whole set of features for the experiments with the STS dataset. In this set of experiments, the classification model is trained and tested on the STS reference data in English. Our assumption is that the features exploited are transferable from one language to another. The reference data and categories in English and in French differ. One difference is that the STS pairs of sentences are scored from 0 to 5 according to their similarity, while in French we do binary classification (a given pair of sentences should be aligned or not). To make the two datasets comparable, we propose to transform the STS scoring in binary categories. We test similarity thresholds within the interval [2. 5;4. 5] by step of 0. 5, which permits not to consider identical sentences (scores close to 5) and very distant sentences (scores lower than 2.
2013; Taylor et al. 2016 ). While we examine methods to minimise these errors and mitigate their effects when searching for GWs in the PTA data, one can also use the data to extract scientific information on topics other than GWs. In particular, PTA data have been employed to develop an independent pulsar-based time standard  (Hobbs et al. 2012)  and to constrain the masses of the solar-system planetary systems (SSPS)  (Champion et al. 2010, henceforth CHM10). The SSEs that we use for pulsar timing are constructed via numerical integrations of the equations of motion for the known solar-system bodies. These integrations are subject to a wealth of observational data from telescopes, radio and laser ranging and spacecrafts orbiting the planets and their moons, when available. Such input data also include estimates of the masses of the planets and other important solarsystem bodies.
Observationally, it is not the mass, M, but the gravitational parameter of the bodies that is determined, i. e. GM where G is the universal constant of gravitation. This parameter can be determined with much higher precision than the gravitational constant (see e. g. Petit & Luzum 2010) , a fact that limits the precision of measurements of M in SI units. For this reason, the masses solar-system bodies such as the planets are expressed as the ratios of their gravitational parameters to the solar gravitational parameter (heliocentric gravitational constant), GM ⊙. New data are added over time, so that newer SSEs are subject to data of better accuracy and observational sampling. Many of the involved parameters are fitted and adjusted while creating the final SSE. As noted in CHM10, while this process gives accurate predictions for the positions of the planetary-systems with respect to the Earth-Moon system, they do not manage to constrain the masses much better than the measurements used as initial values.
This is reflected by the fact that typically the ratios of the gravitational parameters of the planetary systems with respect to the solar parameter are held fixed during numerical integrations. What changes between SSE versions with respect to the reference planetary masses is either the initial mass values of the planetary systems, for example after new mass estimates by spacecraft fly-bys, and/or the estimate of the solar gravitational parameter, which can be a fitted parameter in the SSE. Therefore, the input planetary masses in principle differ between the various SSE versions. With this in mind, CHM10 search for errors in the masses of the planetary systems, as the most possible errors that pulsartiming data could identify. In this paper we focus on extending the work of CHM10 using the first IPTA data release (IPTA DR 1;  Verbiest et al.