dataesr/scientific-paragraphs-categorization huggingface dataset:
The model is not defined for K 1 = 0 or K 2 = 0. In this scenario, the corresponding value is replaced by 10 -9. The flexit model is included as a function in the HelpersMG R package (version 3. 7 and higher)  (Girondot, 2019b)  and is included in the tsd() function of the embryogrowth R package (version 7. 5 and higher)  (Girondot, 2019a). Overall, only two models are acceptable for our purpose: logistic and flexit. If an asymmetrical model is required, the Hill, A-logistic or Hulin models are not flexible enough when compared to a flexit version.
Here we use sex ratio as being relative male frequency, to be compatible with previous publications  (Girondot, 1999; Godfrey, Delmas & Girondot, 2003; Hulin et al. , 2009). A possible alternative would be to incorporate the data on intersex hatchlings into the model, which tend to be more frequent at intermediate temperatures, and use a multinomial distribution. This possible solution has not yet been developed but could be an interesting avenue to explore in future studies for some species. The likelihood value is often presented as its inverse natural logarithm (-ln L) for practical reasons: likelihoods are generally small numbers, thus -ln L will be positive numbers that are easier to manipulate. The likelihood of a dataset of several incubation temperatures t i with M i and F i within a model is simply the product of the likelihoods for each temperature, L i , or the sum of the -ln L i.
To be able to fit the sex ratio thermal reaction norm using maximum likelihood, at least one incubation temperature producing a mixed sex ratio should be present in the study dataset. A rule of thumb is that fitting a model with p parameters necessitates at least p temperatures with mixed sex ratios. For example, if a logistic model (2 parameters) is fitted for a dataset with zero or only one temperature that produced a mixed-sex ratio, an infinite number of combinations of P and S will share the same likelihood. It is still possible to fit a logistic model to a dataset with only one temperature producing a mixed sex ratio, but standard error of parameters will be generally high. If a dataset has no mixed sex ratio, it is still possible to use a Bayesian model to describe the credibility interval of the parameters and the outputs (see below).
The estimate of parameters for TSD patterns using maximum likelihood serves various purposes: (i) It allows an estimate of the confidence interval of the outputs (see below), (ii) it provides an estimate of the quality of fit (see below), (iii) it facilitates the comparison across datasets because their fitted parameters can be compared even if incubation temperatures were different, (iv) it can be used as a prior for Bayesian analysis (see below), and (v) it can be used as a starting point for iterations using a Metropolis-Hastings algorithm with a Monte-Carlo Markov chain in Bayesian analyses (see below).
Comparison of models: Akaike information criterion and Akaike weight When several models are fitted to the same datasets of observations, the comparison of the performance of the different models can be assayed using AIC (Akaike Information Criterion)  (Akaike, 1974). AIC is a measure of the quality of the fit, while it simultaneously penalizes for the number of parameters in the model. It facilitates the selection from a set of models the best compromise between fit quality and over-parametrization. AIC j = -2lnL j + 2p j with L j being the likelihood of the model and p j the number of parameters of the model j. When a set of k models are tested, the model with lowest AIC is considered to be the best non-overparametrized fit. It is important to note that AIC value itself is not strictly informative in terms of absolute model fit.
A corrected version of AIC for small sample sizes, named AICc, has been proposed when the model is univariate and linear with normal residuals  (Hurvich & Tsai, 1995) : AICc = AIC + 2p(p + 1) n -p -1 The exact formula can be difficult to determine when these conditions are not met, in which case the previous formula could be used  (Burnham & Anderson, 2002). In general, the AICc should always be used instead of AIC  (Burnham & Anderson, 2002) , especially for datasets comprised of small sample sizes, which is typical for sex ratio studies, and particularly when the study targets protected species. When a set of models is compared, it is possible to estimate the relative probability that each model is the best among those tested using the Akaike weight  (Burnham & Anderson, 2002) : Akaike weight j = e 1 2 AICc j -min(AICc) k i=1 e 1 2 AICc i -min(AICc) We use both AICc and Akaike weight to compare the fitted logistic and flexit models for our datasets.
The utility of model selection can be further extended to test for potential differences in the results from two or more datasets. In this case, the complete data are split into several subsets with each individual dataset being represented once and only once. The test question is: can the collection of datasets be better modeled with a single set of parameters or should each dataset be modeled with its own set? In this situation, Bayesian Information Criterion (or BIC) should be used instead of AIC or AICc, because the true model is obviously among the tested alternatives: BIC = -2lnL + plnn When the BIC statistic is used, all the priors of the models tested are assumed to be identical. It is also possible to estimate BIC weights by replacing AICc with BIC in the Akaike weight formula. The w-value has been defined as the probability that several datasets can be correctly modeled by grouping instead of independently  (Girondot & Guillon, 2018).
In our example, a model will be fitted first to the combined datasets and BIC (combined) will be estimated with p parameters. Then each dataset will be fitted separately and a set of k BIC (separate) values will be generated, each with p parameters, thus using a total of k. p parameters. This is similar to the case of fitted models with a dataset effect. The global -ln likelihood for the separate fits is simply the sum of the -ln likelihoods. Then the BIC weights will provide a statistically sound method of choosing between the hypotheses that a single model is sufficient for all datasets or that each dataset is best fitted with a different model. We named w-value the BIC weight and we propose this statistic as a replacement for the contentious p-value  (Girondot & Guillon, 2018).
Bayesian approach using Metropolis-Hastings with Monte-Carlo Markov chain The Metropolis-Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution  (Hastings, 1970; Metropolis et al. , 1953). This method is now widely used as it offers a high-performance tool to fit a model. To run a Bayesian analysis with this algorithm, several parameters must be defined for each estimator in the model. The following terms are those used in the function tsd_MHmcmc() of the R package embryogrowth: 1. Density: The R function for density distribution used for the prior. Generally, uniform or Gaussian distribution are used with dunif or dnorm, respectively. 2. Prior1 and Prior2: The parameters describing the prior distribution. For dunif, Prior1 and Prior2 are respectively the minimum and maximum of the distribution, and for dnorm, they are the mean and standard deviation of the distribution; 3.
234, which is the optimal acceptance rate  (Roberts & Rosenthal, 2001). The burn-in value is the number of iterations necessary to stabilize the likelihood. It can be low (around 10) when the starting values are the maximum likelihood estimators. The total number of iterations required is defined after an initial run of 10,000 iterations  (Raftery & Lewis, 1992). The result of the MCMC analysis is a table with one set of values for the estimators at each iteration. The mean and standard deviation summary statistics can be calculated from this table using the coda R package  (Plummer et al. , 2011) , or it is also possible to estimate quantiles. The use of quantiles has the advantage that it does not require any hypothesis on the output distribution; hence an asymmetric distribution is well accommodated.
Maximum likelihood estimates for grouped data Logistic and flexit models were both fitted to the comprehensive dataset: 40 incubations with temperatures from 24 • C to 34 • C and 468 sexed embryos identified as males or females. Mixed-sex ratios were observed in embryos or hatchlings from 8 incubations (Table  1 ). Upon comparing both models (Fig. 2  and Table  2 ), the flexit model was selected based on AICc and Akaike weight as the latter suggested there was a 97% probability that the flexit model was the best. While the logistic model showed the typical symmetric shape, the flexit model showed a strong asymmetrical pattern: transition from all-male condition to the pivotal temperature is smooth, whereas the transition from the pivotal temperature to all-female production was abrupt (Fig.
All these tests gave a probability of more than 0. 05, indicating that the models fit the observations relatively well. From these observations, we recommend against the use of χ 2 approximations to test the degree to which the models fit the data and, instead, use comparisons based on generated null distributions. Based on this methodology, the function tsd() of the phenology R package returns the probability that the observations fit the null model in the object p. Deviance. Null. model (Girondot, 2019a). Finally, because some parameters of the flexit model are nearly impossible to fit with these datasets (coefficient of variation for K 1 = 0. 92, and for K 2 = 12. 43), the flexit model is not applied further in this paper (see Supplemental Information 2 for a Bayesian MCMC with flexit model).
Comparison of the maximum likelihood estimates for RMU data The characteristics of the fitted logistic model for the 3 RMUs and Mexico and Costa Rica within the East Pacific RMU are shown in Table  3. The fitted S values for East Pacific (Mexico) and Northeast India observations have substantial standard errors and confidence intervals. These two datasets have only one mixed-sex ratio, and the maximum likelihood approximation failed to provide a correct estimate of the S parameter. The analysis shows that, within the East Pacific RMU, a single TSD pattern is sufficient to model the combined Mexico and Costa Rica data (w-value = 0. 87), whereas there is a 0. 97 probability (w-value) that at a worldwide scale there are differences in TSD parameters for the different olive ridley populations (Table  4 ).
Bayesian estimates Priors for the Bayesian process were chosen with a Gaussian distribution, with an average set as the fitted parameters, which were estimated from the global maximum likelihood analysis (Fig. 2 , Table  2 ), and a standard deviation large enough to avoid imposing too high a constraint on the output. Minimum and maximum values for P were 25 and 35 • C, respectively, with S spanning from -2 to 2 and K x from -500 to +500. The standard deviation for the new proposal was initially chosen to be 2 for P and 0. 5 for S, K 1 , and K 2. They were adjusted during the iterations to have acceptance rates closer to 0. 234. A total of 100,000 iterations were performed. Though both the logistic and flexit models were fitted using Bayesian MCMC, only the results for the logistic will be discussed here because some parameters of the flexit model are nearly impossible to fit with these datasets (coefficient of variation for K 2 = 12.
Starting in 1999, new statistical methods were developed  (Girondot, 1999; Godfrey, Delmas & Girondot, 2003; Hulin et al. , 2009)  and the R package embryogrowth  (Girondot, 2019a)  has included the tsd() function since version 2. 0. 0 in 2013. More recently, attempts to correlate TSD parameters with life-history parameters were successful  (Carter et al. , 2019; Hulin et al. , 2009)  but sometimes produced unexpected results. For example, when these were compared between populations in Chrysemys picta, P could not be explained by geography or local thermal conditions, but the TRT was wider at lower latitudes  (Carter et al. , 2019 ). An explanation for these difficulties could be that the models for the TSD pattern used did not correctly reflect the true TSD pattern. The TSD pattern has also been used to estimate sex ratios from natural nests using the average nest temperature for the total incubation period, which can result in biased data because temperature determines sex only during the thermosensitive part of development.
e. until 23 m s -1 ), WASP has been fitted to datasets used to build the COARE 3. 5 parameterization  (Edson et al. , 2013). The temperature and humidity roughness lengths z 0T and z 0q (see Eq. 6), which define the corresponding neutral transfer coefficients, have been adjusted for the resulting sensible and latent heat fluxes to match the COARE 3. 0  (Fairall et al. , 2003)  parametrization for surface wind speeds up to 45 m s -1. The stability functions ψ x (Eq. 5) are a blend of the Kansas-type functions  (Businger et al. , 1971 ) and a profile matching the asymptotic convective limit  (Fairall et al. , 1996). Thus, WASP enables us to represent the Charnock parameter's behaviour and its dependency on waves in a more physical way. Moreover, it reproduces the observed decrease in the drag coefficient due to very strong wind, which would not be possible using a wave-age-only Charnock parameter as in  Drennan et al.
Available observations In order to validate the simulations, we collected several observations of the surface and near-surface in the northwestern Mediterranean area (see Fig. 1b , which displays the observations over the WW3 domain). Data from 14 moored buoys (listed in Table  1  and plotted in Fig. 1b ), available either from the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine. copernicus. eu/, last access: 3 Feruary 2020) database or from the HyMeX programme database (http://mistrals. sedoo. fr/ HyMeX/, last access: 3 Feruary 2020), were first used for validation. These platforms measure a wide variety of nearsurface variables that may include sea temperature, salinity and wave parameters, such as the significant wave height (H s ) and peak period (T p ), as well as some atmospheric parameters, such as the 2 m air temperature, relative humidity, the 10 m wind speed, wind direction and gusts.
In addition to these buoys, six coastal surface weather stations from the Météo-France network were used (mainly around the Gulf of Lion and in Corsica) in order to complete the coverage over the area of interest with respect to atmospheric in situ data. Altimetric data from two satellites crossing the area during the event were used for the significant wave height validation. The first satellite was Jason-2 (OSTM/Jason-2 Products Handbook, 2008) from the joint CNES/NASA oceanography mission Jason. The altimetric measurements used were To validate the rainfall accumulation, the ANTILOPE product from Météo-France was used  (Laurantin, 2008). This product merges rain gauges and radar data. This analysis was complemented by the use of the Météo-France radar composite images over western Europe.
Acknowledgements. This work is a contribution to the  HyMeX programme (Hydrological cycle  in the Mediterranean EXperimenthttp://www. hymex. org,last access: 3  February 2020 ) and is supported by  INSU-MISTRALS. The authors acknowledge the  Occitanie region, France , for its contribution to  César Sauvage 's PhD at CNRM. The authors acknowledge the MISTRALS/HyMeX database teams (ESPRI/IPSL and  SEDOO/OMP)  for their help with accessing the surface weather station data. The authors gratefully acknowledge  Mickaël Accensi  and  Fabrice Ardhuin (LOPS)  for their invaluable help and advice concerning the development and application of the north-western Mediterranean configuration of the WaveWatch III model. Finally, the authors wish to thank  Olivier Nuissier  ( CNRM ) for discussions regarding the characteristics of the Mediterranean heavy precipitation event that was studied.
82 • N CMEMS 4-Martigues 5. 05 • E 43. 33 • N Météo-France 9-Banyuls-sur-mer 3. 17 • E 42. 49 • N CMEMS 5-Bonifacio 9. 18 • W 41. 37 • N Météo-France 10-Sète 3. 78 • E 43. 37 • N CMEMS 6-Ersa 9. 36 • W 43 • N Météo-France the Geophysical Data Record (GDR) from the MLE4 (maxi- mum likelihood estimator) altimeters' retracking algorithm that were corrected following a buoy comparison method: H s _cor = 1. 0149 × H s + 0. 0277. The second dataset was obtained from GDR data from the SARAL/AltiKa satellite (SARAL/AltiKa Products handbook, 2013) that also uses the MLE4 altimeters' retracking algorithm but simply removes erroneous H s using a threshold relationship. Both satellites combined gathered 292 measures of H s during the period between 12 and 14 October 2016.
, 2015). Their boiling points range from 188 • C to 290 • C under atmospheric pressure. The characterization of the solutes by 13 C NMR spectroscopy can be carried out on extracts or on fractions obtained by chromatographic methods. The fractions of interest may also contain an important amount of these high boiling point solvents. NMR data acquisition of series of samples is often carried out in automation mode with standard acquisition parameters. An accurate calibration of pulses on the 1 H RF channel is necessary to record 13 C{ 1 H} spectra in proper decoupling conditions. The optimal power of the decoupling pulses depend on probe tuning quality and on the nature the analytes. Decoupler power miscalibration may cause decoupling artifacts around the intense solvent signals, at a point their intensity is comparable to the one of the signals of interest  (Blechta and Schraml, 2015).
7 Hz to 5. 9 Hz. The intensity of the two residual signals were similar: signal attenuation was always at least 95 %. Based on this result, an intensity of 11. 7 Hz was retained for presaturation pulses in all subsequent spectra recordings. Multiple site presaturation was extended to other heavy solvents used as natural product extractants: 1,2-propanediol, 1,3propanediol, 1,2-butanediol and 1,3-butanediol. For all but 1,2-propanediol, presaturation reduced solvent signal intensity by at least 94 %. Presaturation was also carried out on samples containing sucrose and each of the heavy solvents mentioned here above. The spectra recorded with and without presaturation as well as the corresponding raw NMR data are available for download. As expected, presaturation has resulted in a strong decrease of targeted signals and the removal of decoupling artifacts.
13 C NMR spectra acquisition time. The pulse length for excitation was 13. 7 µs and the relaxation delay was 3 s. Spectra were referenced for a central signal of DMSO-d 6 at δ 39. 52. The computer source code used in the present study was written in C language; it relied on the libxml2 library for the reading of the input data file (this may be an overkill for such a task, admittedly) and on the libsimu1 library for the calculation of rotation matrices by means of Eqn. (4), as programmed for the design of SLURP pulses. The libsimu1 archive file also contains a proof of Eqn. (4). The computer code for saturation simulations is available from GitHub; its installation was tested with Cygwin in Windows 10 but should be easily carried out on any other platform that provides a C language compiler and UNIX-like tools.
Code and data availability. The PresatSimul source code is available from https://github. com/nuzillard/PresatSimul. The libsimu1 source code is available from https://github. com/nuzillard/Libsimu1. The data files, pulse sequence and script from which Figs. 3  and 5  were obtained and a supplementary Figure and caption can be downloaded from https://www. zenodo. org/record/3635970 and temporarily from https://mycore. core-cloud. net/index. php/s/kQHZs7GaUxaZmmr.
SARS-CoV-2 phylogenetic data SARS-CoV-2 genome sequences have been continuously submitted to the Global Initiative on Sharing All Influenza Data (GISAID) by laboratories worldwide  21. To circumvent the computational limits of phylogeny reconstruction and time calibration techniques, the sequences of the GISAID database are subsampled before analysis by the Nextstrain initiative, using a balanced subsampling scheme through time and space  20, 43. Phylogenetic reconstruction uses maximum-likelihood phylogenetic inference based on IQ-TREE  44  and time-calibration uses TreeTime  45. See  46  for further details on the Nextstrain bioinformatics pipeline. A dated phylogeny of 5,211 SARS-CoV-2 genomes, along with sampling dates and locations, was retrieved from nextstrain. org/ncov on May 12, 2020.
Data and material availability: Data and analysis code are available online at https://github. com/rasigadelab/covid-npi. Extended Data Figure 2. Length distribution in phylogenetic branches with uncertain country assignation. Shown are box-and-whisker plots of the lengths of internal and terminal branches, depending on branch exclusion due to uncertain (<95% confidence) country assignation. Boxes denote interquartile range (IQR) and median, whiskers extend to lengths at most 1. 5x the IQR away from the median length, and circle marks denote lengths farther than 1. 5 IQR from the median length. Extended Data Figure 3. Correlation of reported and estimated epidemic onset dates. Dates of first estimated autochtonous SARS-CoV-2 transmission per country relative to the dates of the 10 th reported case (left panel) and the 10 th reported death (right panel) in countries with at least 15 assigned internal branches.
Highly variable heterozygosity and accession-specific allele proportions among accessions RNA-seq data were obtained from a set of 14 wild and nine diploid cultivated banana accessions (Table  1 ). The wild accessions included representatives of M. acuminata subspecies reported to be involved in the origin of hybrid 'AAcv' cultivars. The diploid cultivars represented different geographical origins and were selected among the 'AAcv' diversity described by  Perrier et al. (2011). Genomic data from the triploid 'Grande Naine' accession representing the Cavendish cultivars were also used. M. balbisiana was used as an outgroup with the draft genome data  (Davey et al. , 2013)  from the 'Pisang Klutuk Wulung' (hereafter 'PKW') accession. Depending on the accessions, 13. 9-94. 4 mapped million reads were obtained for polymorphism detection, representing 16.
Global assessment of ancestral contributions to banana accessions A global genetic structure analysis of the 24 diploid accessions using the ADMIXTURE program  (Alexander et al. , 2009)  suggested that K = 6 was the best number of ancestries fitting our dataset (Table  S3 ). At K = 6, 14 accessions were homogeneous for one of the six ancestries (Figure  1c ). They corresponded to 13 of the 15 wild accessions and to cultivar 'Pisang Madu'. Unexpectedly, this cultivar was found to be homogenous for one ancestry that was detected only in cultivated accessions. The remaining ancestries generally corresponded to current knowledge on the banana genetic diversity represented here. M. balbisiana, M. acuminata ssp. zebrina and ssp. malaccensis each represented one ancestry, whereas ssp. burmannica, ssp.
(b) Proportion of specific alleles in the studied accessions. This proportion was calculated as the percentage of polymorphic sites in which at least one allele was not found in other accessions of the dataset. (c) Global genetic structure of the dataset obtained via ADMIXTURE analysis with six ancestral populations. To select representative alleles for the ancestral groups discriminated by the COA analysis, alleles were clustered on the first five axes based on their coordinates. Eight clustered allele groups were identified (Figure  3a  and Table  S5 ). Group 1 (53 287 alleles) corresponded to M. balbisiana 'PKW' alleles. Alleles of group 2 were mainly derived from ssp. burmannica/burmannicoides/siamea accessions ('burmannica/siamea' group; 33 464 alleles). Group 3 (20 067 alleles) corresponded to ssp.