dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors acknowledge the strong support of the  European Commission, Airbus , and the airlines (Lufthansa,  Air-France ,  Austrian ,  Air Namibia ,  Cathay Pacific ,  Iberia , and China Airlines so far) who carry the MOZAIC or IAGOS equipment and have performed the maintenance since 1994. In its last 10 years of operation, MOZAIC has been funded by  INSU-CNRS (France) ,  Météo-France ,  Université Paul Sabatier (Toulouse, France) , and  Research Center Jülich (FZJ, Jülich, Germany ). IA-GOS has been additionally funded by the  EU projects IAGOS-DS and IAGOS-ERI. We also wish to acknowledge our colleagues from the  IAGOS team in FZJ, Jülich , for useful discussions. The MOZAIC-IAGOS database is supported by  AERIS  ( CNES  and  INSU-CNRS ). Data are also available via the AERIS website (www.
Data availability. All ozone and carbon monoxide data used in this study were obtained from the existing IAGOS database, freely available on the IAGOS website (http://www. iagos. org) and via the AERIS website (http://www. aeris-data. fr). Atmos. Chem. Phys. , 18, 5415-5453, 2018   www. atmos-chem-phys. net/18/5415/2018/.
Sampling Between January and March 2015, ten populations of R. pseudoacacia were sampled per range, in the Belgian region of Wallonia and in the French region of Aquitaine (fig. 1 , electronic appendix 1). We sampled unplanted populations derived from natural regeneration. Mean, Minimal and Maximal temperatures of May were extracted from WorldClim version 2 (http://worldclim. org/version2) using the 30 seconds resolution raster. WorldClim version 2  (Fick & Hijman 2017)  provides average monthly climate data for minimum, mean, and maximum temperature over the 1970-2000 period. The different populations were spread over geographic areas of the same dimensions (i. e. Gironde and Lot et Garonne, French departments in Aquitaine: 15 000 km² and Belgian Wallonia: 16 000 km²). Overall, French and Belgian populations were distant almost 800 km from one another.
2016). Four multiplexes of 36 SNPs each were further designed with the MassArray assay design 4. 1 software and genotyped with the Sequenom MassARRAY iPLEX platform at the Bordeaux Genome Transcriptome facility (https://pgtb. cgfb. u-bordeaux. fr), using the iPLEX Gold genotyping kit according to the manufacturer's instructions. Products were then analysed using the MassArray mass spectrophotometer (Sequenom) and data were acquired in real time using the MassArray RT software. SNP data were then visualized and validated using ViClust, a R program that we implemented for Galaxy (https://usegalaxy. org/) and that is also available as a standalone R script for Linux or Windows at https://github. com/garniergere/Vi-Clust/. Briefly, ViClust imports the unmodified xml file to export from the Typer-4_0_20 in-house Sequenom software.
STRUCTURE runs were computed on the GenoToul bioinformatics facility (http://bioinfo. genotoul. fr/). The script StrAuto  (Chhatre and Emerson 2017)  was used to produce STRUCTURE mainparams and extraparams and to automatize and parallelise STRUCTURE analysis. STRUCTURE HARVESTER  (Earl & vonHoldt 2012)  was employed to calculate ΔK as described in  Evanno et al. (2005)  in order to determine the most likely K. Finally, CLUMPAK  (Kopelman et al. 2015)  was utilised to synthetize STRUCTURE outputs and to compute graphical STRUCTURE outputs. To evaluate the germination rate of R. pseudoacacia seeds among ranges and temperatures, we constructed a germination curve on the fraction of germinated seeds, G(t) (for more details see  Bouteiller et al. 2017). A Gompertz model was fitted using a Bayesian procedure (equation 1).
2. 2 (R Development Core Team 2015) to compute the Bayesian model. For each life history traits and functional traits, 150,000 iterations were run with a burn-in of 125 000 iterations and a thinning interval of 10, using 4 different chains. Autocorrelation and convergence were assessed using the "autocorr. plot" and "gelman. plot" native JAGS functions and Rhat convergence criterion was inferior to 1. 01 as recommended  (Kruschke 2014). Codes provided by O'Hara &  Merilä (2005)  and Kruschke (2014) were adapted to our design using uninformative inverse gamma conjugate priors for random effects and normal priors for fixed effects. Genetic differentiation among populations can be defined from molecular markers analyses using F ST indices calculated as follows  (Wright 1949) : ( ) FST B W B 2 2 2 v v v = + (4) The F ST value for the whole dataset was estimated using the HICKORY 1.
1 software  (Holsinger 1999). F ST was estimated by running the software native's full model (i. e. assuming non-null inbreeding coefficient and significant genetic structure among populations) and compared to the non-inbreeding and no population structure models, as recommended by  Holsinger (1999). The full model outperformed the other models, based on the DIC criterion values (see HICKORY documentation for more details on the models). Pairwise F ST were calculated between all pairs of the 20 populations using Genepop v4. 3. 3  (Raymond & Rousset 1995 , Rousset 2008 ). For each quantitative life history traits and functional traits, the Q ST index of quantitative genetic differentiation among populations was calculated as follows  (Spitze 1993 ): ( ) Q 2 ST B W B 2 2 2 v v v = + (5) where W 2 v is the within population genetic variance and B 2 v is the between population genetic variance estimated using pop 2 v.
U Assuming that individuals obtained from seeds of the same tree were half-sibs, within population variance can be estimated as 4 W t ree 2 2 v v = U U Q ST values were computed per temperature chamber condition using the same model that presented above (equation 1) but without the temperature and temperature x population interaction effects. The code used in this study is provided on the open source GitHub platform (https://github. com/xbouteiller/BayesMix). It was tested by comparison to the same model computed with SAS software using the frequentist PROC MIXED procedure (SAS, version 9. 1, SAS Institute, Cary, NC, USA); Bayesian Q ST estimations (mean + CI) were compared to estimates using first the delta method computed with SAS (O'Hara &  Merilä 2005 , Isik 2009 ) and second using the R package QstFstComp  (Gilbert & Whitlock 2015).
ACKNOWLEDGEMENTS We are very grateful to  Alexandra Quénu ,  Benjamin Dencausse ,  Nastasia Merceron ,  Patrick Reynet  for their help in choosing populations, and to  Adline Delcamp  and  Myriam Paillet  for their help in realizing the lab work. This study has been carried out with financial support from the  French National Research Agency (ANR)  in the frame of the Investments for the future Program, within the Cluster of  Excellence COTE  ( ANR-10-LABX-45 ). This study was funded by the  ANR-10-EQPX-16  Xyloforest. AUTHORS CONTRIBUTIONS  L. Lassois ,  S. Mariette ,  A. Monty ,  A. J. Porté  and  C. Verdu  conceived the experiments; X. P. Bouteiller conducted the experiments with the contribution of  C. Verdu ,  S. Mariette ,  A. J. Porté ,  A. Raimbault  and  R. Segura ;  P. Garnier-Géré ,  N.
The striking similarities between the CMIP5 models and a toy model (Fig. 2 ), on the one hand, and the lack of obvious link between model complexity and process strength (Extended Data Fig. 1 ), on the other hand, all underline that the first-order thermodynamic behaviour of sea ice in GCMs is remarkably consistent and simple at the temporal and spatial scales considered here. In particular, the level of sophistication of a sea-ice model appears relatively unimportant in shaping the sea-ice mean state of that model with regard to other influences. It must be noticed, however, that model diversity is relatively poor in the CMIP5 ensemble: the sea-ice components share very similar dynamic cores, while the main thermodynamic differences stand from the ice thickness distribution scheme. In any case, understanding how atmospheric or oceanic biases affect the sea-ice state as well as a more diligent documentation on tuning methods  7, 20  are likely to give clear insights about the source of spread in current sea-ice projections.
The grey envelopes are the one and two standard deviation confidence intervals from a 1/x fit of the CMIP5 data presented in a. c, same as a but for the open-water-formation efficiency (OWFE). d, same as b but for the OWFE. Figure  4  | Challenges in reducing uncertainties of sea-ice volume projections. Time series of annual mean Arctic sea-ice volume from historical and RCP8. 5 forcings 32 (72 simulations available). Colours are referenced in Extended Table  1. Grey time series correspond to simulations with sea-ice volume north of 80°N deemed incompatible with three observational references (see Methods). The statistics reported at the top of the figure refer to the ensemble mean and standard deviation of annual mean sea-ice volume linear change over 2020-2050 (for the full set of models, "ALL"; and for the subset, "SUB").
Although being first and foremost a product derived from model outputs, this reanalysis shows reasonable agreement with independent data  22. assuming that it is uniformly distributed between 0 m and twice the mean thickness 37. If the net energy balance at the sea-ice top surface is positive, sea-ice thickness is reduced uniformly assuming again that it is uniformly distributed between 0 and twice the mean value; this results in a decrease in sea-ice concentration. An energy budget is finally computed at the base of the ice. Here, a parameterised ocean-ice turbulent heat flux 37 is used assuming constant sea-ice velocity (1 cm/s), seawater density (1024. 458 kg/m 3 ) and drag coefficient (0. 005). The energy imbalance is used to grow or melt ice at the base of the existing ice floe. Reference experiment.
Ice-formation efficiency (IFE). The evaluation of the IFE is graphically illustrated in Extended Data Fig. 7 (a, b ). First, the time series of the Arctic sea-ice volume north of 80°N (see Domain for investigation of sea-ice thermodynamics above) is computed. Then, for each calendar year of the time series but the last one, (1) the annual minimum sea-ice volume is recorded for that year (V min ) and (2) the annual maximum of the next year is recorded (V max ). The ice volume created ( V=V max V min ) is then computed. Finally, a linear regression is conducted between V min (x, predictor) and V (y, predictand) over all years. The IFE is defined as the slope of the regression line between V and V min. By default, both V and V min are linearly detrended prior to the regression in order to avoid spurious relationships between those variables due to possible secular trends.
This detrending does not affect the conclusions of the manuscript (Extended Data Fig. 8a ). The IFE is a dimensionless number and can be interpreted as the efficiency of a model to recover a summer anomaly of sea-ice volume either completely (IFE = 1. 0) or not at all (IFE = 0. 0). Extended Data Fig. 9  illustrates the methodology for all 44 CMIP5 models. Open-water-formation Efficiency (OWFE). The diagnostic derives from from Holland et al. , 2006  8. The evaluation of the OWFE is graphically illustrated in Extended Data Fig. 7 (c, d ). First, the time series of the Arctic sea-ice volume and area north of 80°N (see 'Domain for investigation of sea-ice thermodynamics' above) are computed. Then, for each calendar year of the time series, the months of annual maximum and minimum sea-ice volumes are recorded (t min and t max , respectively).
No sensitivity to reference period. The analyses with CMIP5 models are conducted over the reference period 1955-2004, which corresponds to the last 50 years of the historical period defined by the CMIP5 protocol 34. The robustness of the findings was tested using different periods. Results were found to be insensitive to this choice (Extended Data Fig. 11 ). Results were also found to be robust with respect to the separation in time: computation of OWFE and IFE on an earlier period than the Arctic sea-ice variability indices yields similar results (Extended Data Fig. 12 ).
We discard simulations that have a monthly mean sea-ice volume north of 80°N systematically higher or lower than three standard observational references: IceSat, CryoSat2 and the ITRP datasets  10, 41, 42  over the period of observational data availability (2000-2017, Extended Data Fig. 14 ). In other words, we disregard simulations for which the sea-ice volume north of 80°N for each month of each year is always outside the observational range. Applying this constraint on the CMIP5 ensemble (RCP8. 5, 2005-2100), we discard 14 simulations out of 72 available. The ensemble mean of 2020-2050 projected ice-volume loss hardly changes after the application of this constraint (from 6. 85 to 6. 80 10 3 km 3 ) and the spread around these estimates is only reduced by about 17% (from 3. 08 to 2. 56 10 3 km 3 ) (Fig.
Acknowledgments The research leading to these results has received funding from the  Belgian Fonds National de la Recherche Scientifique (F. R. S. -FNRS) , the  European Commission 's  Horizon 2020 projects APPLICATE  ( GA 727862 ) and  PRIMAVERA  ( GA 641727 ). We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in the supplement) for producing and making available their model output. We acknowledge the  CESM Large Ensemble Community Project  and supercomputing resources provided by  NSF/CISL/Yellowstone  for access to the CESM-LE data. The authors thank  C. M. Bitz  and  D. Notz  for useful discussions, and  F. Kauker  for providing the ITRP data. The authors thank  M.
METHODS Data Availability. All the results produced in this manuscript can be reproduced bit wise. The scripts used for creating figures and statistics are available through the following public Github repository: https://github. com/fmassonn/paper-arctic-processes Specifically, the two functions evaluating the IFE and OWFE will be incorporated in the Earth System Evaluation tool (ESMValTool, http://esmvaltool. org) for wider use by the climate community. The data used in the scripts above can be retrieved from the following archive: https://doi. pangaea. de/10. 1594/PANGAEA. 889757 Instructions on how to use this data and how to run the scripts can be found in the README. md file coming with the Github project above.
ABR measurements Click-evoked ABRs and the BIC were measured using a setup replicating that described in more details in previous publications  (Beutelmann et al. , 2015; Ferber et al. , 2016) , and only briefly outlined here. Ethics statement. All experimental procedures complied with guidelines set forth by the National Institutes of Health and were approved under a protocol submitted to the University of Colorado Health Sciences Center Animal Care and Use Committee. Animal preparation. Data were acquired for adult animals of either sex and included nine Mongolian gerbils (Meriones unguiculatus; four female), five Sprague Dawley rats (Rattus norvegicus; three female), four long-tailed chinchillas (Chinchilla lanigera; four female), five guinea pigs (Cavia porcellus; two female), and nine C57BL/6N mice (Mus musculus; three female).
An automated artifact rejection threshold was set before each recording session and was typically ϳ10 V. Analysis. Recorded signals were averaged by condition and processed through a digital third-order Butterworth filter (cutoffs at 100 Hz and 1 kHz). The BIC waveform was then calculated for each ITD. Each monaural trace is shifted by ITD/2 in either direction (Fig. 1G , top) to obtain the "summed response," termed S (Fig. 1E ), so that the delay between monaural traces is equal to the ITD. Note that our convention may be different from that of other studies  (Ungan et al. , 1997; Riedel and Kollmeier, 2006). The BIC is then computed by subtracting the summed response from the binaurally evoked ABR: BIC ϭ B Ϫ S (Fig. 1 F, G ). Gaussian fits. A bell-shaped function was fitted to the peak amplitudes versus ITD data obtained in the recordings.
The fitting procedure was an implementation of the Broyden-Fletcher-Goldfarb-Shanno algorithm (a modified quasi-Newton approach;  Nocedal and Wright, 2006)  as provided by the SciPy library. The Jacobian matrix of SS needed by the optimization routine was evaluated in part by hand (for the amplitudes), and in part (the latencies) using the SymPy symbolic computation package  (Meurer et al. , 2017). Python code for the fitting is available on-line (https://github. com/ victorbenichoux/bic_ei_model. git). The amplitudes fitted here were the cross-correlation gains (therefore equal to 1 for ITD ϭ 0). The latencies were the cross-correlation latencies. Measurements from all within Ϯ750 s were fitted, because this is the range over which the ITD axis was sampled uniformly in all species tested.
4A ). For all species considered, the magnitude of the BIC remains high even for ITDs well beyond the physiological range of ITDs of that species (Fig. 4A ). In addition, it is striking that the width of the curves in Figure  4A  is not related to the head size (horizontal bars). A very similar result holds with the analysis of individually labeled DP1 (Fig. 4D ) and DN1 peaks (Fig. 4E ). In addition, we report the amplitudes of DN1 peaks as measured in previous studies for two additional species: cats (Fig. 4E , dark gray;  Ungan et al. , 1997) , whose head size is twice that of guinea pigs; and humans (Fig. 4E , black;  Riedel and Kollmeier, 2006) , whose head size is twice that of cats (Fig. 4E ). As a result, our dataset now includes animals with an eightfold difference in head sizes from mice to humans, and thus an eightfold difference in the ecological range of ITDs.
Dot plots are widely used to quickly compare sequence sets. They provide a synthetic similarity overview, highlighting repetitions, breaks and inversions. Different tools have been developed to easily generated genomic alignment dot plots, but they are often limited in the input sequence size. D-GENIES is a standalone and web application performing large genome alignments using minimap2 software package and generating interactive dot plots. It enables users to sort query sequences along the reference, zoom in the plot and download several image, alignment or sequence files. D-GENIES is an easy-to-install, open-source software package.
They are often limited to single sequence FASTA files and do not enable any interaction with the produced graphic. Both mentioned tools are only running on Unix computers. The software packages of the second generation have been developed in java in order to be platform independent and user friendlier. They include tools such as JDotter  (Brodie, Roper & Upton, 2004) , Gepard  (Krumsiek, Arnold & Rattei, 2007 ) and r2cat  (Husemann & Stoye, 2009). User interaction permitted to add new dynamic features such as sequence orientation and ordering to maximize the diagonal alignment matches in order to ease visual comparison. These tools have also input sequence size limitations. For example, Gepard takes over an hour to align human chromosomes 1 vs. itself and plot the result. A new generation of JavaScript based dot plot visualization tools emerges.
One of its early members is https://dnanexus. github. io/dot/. Users have first to produce coordinates and index files before uploading them in order to render the dot plot. They can add annotations which will be displayed in the graph margins. We present hereafter D-GENIES, an interactive, rapid and easy-to-use standalone and web application permitting to produce a complete human vs chimpanzee genome dot plot in one hour and 10 min.
IMPLEMENTATION Fast dot plot computation D-GENIES takes advantage of minimap2  (Li, 2017) , one of the latest nucleic sequence alignment program which is able to map very large lowly similar multi-FASTA files. D-GENIES can only produce dot plots for nucleic sequences. In order to limit memory consumption and lower processing time, the program splits large sequence queries, such as chromosomes, in ten mega-base chunks. Processing time and memory consumption are presented in the results and discussion section hereafter.
Simple interactive user friendly interface The Pairwise mApping Format (PAF) file is rendered in the dot plot by a JavaScript client developed with d3. js  (Bostock, Ogievetsky & Heer, 2011 ; https://d3js. org/). To limit drawing time, only the hundred thousand largest alignments are plotted. Both the standalone and web application are accessed through a web browser. The page top menu (Fig. 1 ) permits to launch a new alignment, visualize plots, browse the example gallery or documentation and contact support. To produce a dot plot, a user clicks on the Run menu item and fills three or four input boxes. A modifiable job name is automatically attributed to the dot plot. The user email address is mandatory. The application will send a message once the dot plot is rendered. The application has two modes called New alignment and Plot alignment.
Easy standalone or web server installation D-GENIES can be installed and run as a standalone application on Unix or MS-Windows or as a web server on Unix only. It uses the Flask framework http://flask. pocoo. org/ back-end to serve web pages and submit jobs. In standalone mode only one process should be run in a given instance. In web server mode, several processes will be run simultaneously. Three steps are time and disk space consuming when working with large genomes: file upload, data preparation and alignment. D-GENIES uses three mechanisms to ensure robustness. When installed as a web server, it can use a computer cluster to run memory and disk intensive processes through the DRMAA layer. It also uses a local scheduler storing jobs in a MySQL database which defines process order and manages concurrency on the available cores.
Because files can be large and may saturate the server, their size is tested before upload. DRMAA, MySQL parameters and maximum file size are set in the configuration file. The file folder storing the input and output files can be cleaned using the delete job button in standalone or web server mode. A cron job deleting files having more than a given number of days can also be launched periodically in web server instances. D-GENIES can be installed using the-pip install dgenies-command and and run with the-dgenies run-command. By default, on Unix, all data is stored in the user. dgenies folder. This can be modified in the application. properties configuration file located in the /etc/dgenies folder. The source code can be downloaded from https://github. com/genotoul-bioinfo/dgenies.
DISCUSSION The comparison with Gepard and r2cat shows that D-GENIES processing times are respectively from eight to 83 and 3 to 55 times shorter. More often than not the difference between D-GENIES and the two other software packages increases with genome size. Using minimap2 enables to very quickly compare large sets of lowly similar sequences. The compared sequence sets correspond to full size large genomes which can not be processed by second generation dot plot software packages. D-GENIES uses the default minimap2 parameters. To limit minimap2 time and memory consumption, D-GENIES implements a chunking strategy. Large sequences are split in ten mega-base chunks which are aligned individually. When chunk alignments are contiguous they are visually joined in the graphical panel. With chunking, alignment duration dropped from 9 h and 45 min to 1 h and 7 min and memory consumption from 81 to 36 Gb for human vs chimpanzee comparison.