dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Sourcing The reanalysis data is the ERA5-Land reanalysis  [2]. ERA5-Land is the latest climate reanalysis being produced by ECMWF as part of implementing the EU-funded Copernicus Climate Change Service (C3S), providing hourly data on atmospheric, land-surface and sea-state parameters together with estimates of uncertainty from 1979 to present day. ERA5-Land data are available on the C3S Climate Data Store on regular latitude-longitude grids at 0. 1° x 0. 1° resolution. We compute the daily data from the ERA5-Land hourly data for all necessary variables. The climate simulations hail from The Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment  [1]. They support the Fifth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). We use projections from 2 emissions scenarios: SSP1-2.
6 (mitigation policy aligned with a 2° pre Paris agreement target) and SSP5-8. 5 (a no mitigation policy). Daily data of necessary variables are extracted from the Copernicus Climate Change Service that hosts a subset of the CMIP6 archive. The data covers the period from January 1 st 1951 to December 31 st 2100 (except for some models) with the historical period ending in 2015 and the SSP's starting the following year. All models have different spatial resolutions ranging between 0. 9° to 2. 5°. Because the downscaling procedure requires important computational resources, we used simulations from the 5 climate models that were selected by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP,  [5] ): GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. Those five models are considered as a good choice because in terms of climate sensitivity (i.
Remapping Remapping is a preliminary task required by the downscaling methodology. It consists in spatially interpolating the raw simulations (between 0. 93° and 2. 5° resolution) onto the ERA5-Land grid (0. 1° x 0. 1°). We use the Climate Data Operators (CDO, 2016) software from the Max Planck Institute that gathers various algorithms for interpolation used by the scientific community. Daily temperature (mean, minimum, maximum) daily wind speed are interpolated with a bicubic method. Daily precipitation is interpolated sequentially (to 1. 5° then to 0. 75°, and then 0. 1°) with a conservative method.
Vautard personal communication): • Temperature between [-2 ; 2] in K. • Precipitation between [-1 ; 1] in mm. day-1. In this case, values are higher than previously to account for higher discrepancies but small enough to avoid unrealistic changes. If values are outside the range, the quality control raises an error and the simulation is rejected and thus not included in the dataset. CRediT author statement T. N. : methodology, software, data curation; H. L. : supervision, funding acquisition and writing revue and editing; D. D. : writing original draft, visualization, M. V. : methodology, writing revue and editing; G. L. : resources, software.
Acknowledgments To process the data, this study benefited from the IPSL mesocenter ESPRI facility that is supported by  CNRS ,  SU , and  Ecole Polytechnique  partly funded by IS- ENES3  project. We also thank the  Institut Pierre Simon Laplace  for assistance with the Synda software and the  Deutsches Klimarechenzentrum  for the Quality Assurance tool.
Cell number at the end of the experiment was determined by counting upon trypsinization (Supplementary Figure  S1d ) and cell viability evaluated by trypan blue. Procedures for sEV preparation and PKH67 staining were realized as described previously  [25] , according to ISEV guidelines  [30]. All relevant data were submitted to the EV-TRACK knowledgebase (EV-TRACK ID: EV210154) and obtained an EV-METRIC score of 100% for HIPEC and placental explant sEVs [31].
Nanoparticle Tracking Analysis (NTA) sEV preparations were tracked using a NanoSight LM10 (Malvern Panalytical, Malvern, UK) equipped with a 405 nm laser. Videos were recorded three times for each sample at constant temperature (22 • C) during 60 s and analyzed with NTA Software 2. 0 (Malvern instruments Ltd. ). Data were analyzed with Excel and GraphPad Prism (v8) software.
Bioinformatics Data Analysis of Mass Spectrometry Raw Files Raw MS files were processed with the Mascot software for database search and with Proline  [34]  for label-free quantitative analysis. Data were searched against human herpesvirus 5 and human entries of the UniProtKB protein database (Human betaherpesvirus 5 clone VHL-E-BAC19 and release Uniprot Swiss-Prot February 2018). Carbamidomethylation of cysteines was set as a fixed modification, whereas oxidation of methionine was set as variable modification. Specificity of trypsin/P digestion was set for cleavage after K or R, and two missed trypsin cleavage sites were allowed. The mass tolerance was set to 10 ppm for the precursor and to 20 mmu in tandem MS mode. Minimum peptide length was set to 7 amino acids, and identification results were further validated in Proline by the target decoy approach using a reverse database at both a PSM and protein false-discovery rate of 1%.
After mean of replicate injections, the abundance values were log2 transformed and missing values were replaced by random numbers drawn from a normal distribution with a width of 0. 3 and down shift of 1. 8 using the Perseus toolbox (version 1. 6. 7. 0). For statistical analysis, a Student t-test (two-tailed t-test, equal variances) was then performed on log2 transformed values to analyse differences in protein abundances in all biologic group comparisons. Significance level was set at p = 0. 05, and log2 ratios were considered relevant if higher than 1 or lower than -1. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [35] partner repository with the dataset identifier PXD029146.
Flow Cytometry Analysis After incubation of cells with PKH67-stained sEVs, cells were washed twice with PBS and trypsinized, before proceeding to flow cytometry analysis. PKH67 positive cells were analyzed on a Macsquant VYB Flow Cytometer (Miltenyi Biotec, Paris, France), by using FCS and FITC fluorescence parameters, and by subtracting cell autofluorescence background. Data were analyzed with FlowJo (BD) and GraphPad Prism (v8) software.
DAPI staining (1 µg/mL; Sigma) was performed for 10 min at room temperature. ProLong Gold without DAPI (Thermo Fischer Scientific) was used for coverslip mounting. Widefield acquisitions were realized using Apotome microscope (Zeiss, Iena, Germany) and confocal acquisitions were made on SP8-STED microscope (Leica, Wetzlar, Germany). Image processing was performed using ImageJ. GraphPad Prism (v8) software was used to perform data statistical analysis.
HIPEC Infection by hCMV Leads to an Increase of sEV Secretion without Modifying Their General Features To study the consequences of hCMV infection on placental sEV secretion, composition, and function in early pregnancy, conditioned media of HIPECs, cells described to be fully permissive for hCMV  [38, 39] , were collected between 48 and 72 h post-infection before sEV preparation, times at which 50-80% of cells were infected (Supplementary Figure  S1b ). To exclude any viral contamination of sEVs, an infectivity test was systematically carried out for each preparation. No infection was detected in cells incubated with sEVs alone (Supplementary Figure  S1c ). Moreover, no structure evoking viral particles was observed by transmission electron microscopy (TEM) on sEVs from infected HIPECs (Supplementary Figure  S2 , and data not shown).
sEVs Secreted by Infected HIPECs Harbor a Potential Proviral Protein Cargo To extend the molecular characterization of sEVs secreted by hCMV-infected HIPECs, a comprehensive proteomic approach that allows for a deeper analysis of sEV composition was carried out. The analysis by mass spectrometry-based quantitative proteomics of equivalent amounts of sEVs from NI or hCMV-infected HIPECs led to the identification of 3079 proteins across all samples (3048 human and 31 viral; Supplementary Table  S1 ). Among the 3048 human proteins identified, the gProfiler2 R package was able to interrogate 2936 proteins, for which the term "extracellular exosome" (Gene ontology GO:0070062) appeared as the most significantly enriched (false discovery rate (FDR) = 1. 087859e-259, R package gProfiler2), with 962 of them (32.
8%) associated with the "extracellular exosome" GO term. Conversely, these 962 proteins constituted 44. 1% of the proteins which define the GO term, and the 94 of the top 100 most frequently identified exosomal proteins, as defined by the Exocarta database (http://exocarta. org/exosome_markers_new (accessed on 1 November 2021)), were detected in the sEV preparations. Proteomics data indicated that 31 viral proteins were loaded in sEVs from hCMVinfected HIPECs, as well as 6 human proteins that were significantly over-represented and 15 under-represented (Figure  2a ). Interestingly, the Thy-1 cellular protein, known to play an important role to facilitate hCMV entry into cells via macropinocytosis  [40, 41] , was significantly enriched in sEVs upon infection (Supplementary Figure  S5 ). Moreover, by using the Ingenuity Pathway Analysis (IPA) tool, several biological functions were found to be significantly over-or under-represented in sEVs upon infection (Figure  2b ).
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/v14092030/s1, Figure  S1   S1 : The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD029146; Table  S2 : Bibliography references for establishment of Figure  2d.
Acknowledgments: The authors warmly thank  C. Sinzger  for VHL/E strain of hCMV. The authors greatly thank  C. Chouquet ,  B. Rauwel ,  M. Mouysset ,  L. Battut ,  C. Mengelle ,  S. Lhomme ,  F. Chauvrier ,  P. Verdy , and  N. Kopf , as well as the whole ViNeDys team, for their technical assistance and their numerous advice and discussions which allowed the progress of this work. The authors also thank  AL. Iscache ,  V. Duplan-Eche , and  F. L'Faqihi-Olive , from the cytometry facility of Infinity, as well as  S. Allart  and  D. Daviaud , from the imaging facility of Infinity, and finally, the  GeT technical service of Infinity. The authors thank the medical and paramedical staff of the gynecology unit at  Paule de Viguier Hospital , the patients who agreed to participate in the study, as well as  L.
Data collection and collation. In this study, we used a part of the dataset assembled in Virgili et al. ( 31 ; Fig. 4 ), we only considered beaked and sperm whale sightings and effort data recorded in the Bay of Biscay, eastern North Atlantic. Visual shipboard and aerial surveys performed by seven independent organisations between 1998 and 2016 were assembled (details of the surveys are listed in Appendix A). A single common dataset was created, aggregating all survey datasets standardised for units and formats. Effort data were linearized and divided into 5 km segments using ArcGIS 10. 3  57  and the Marine Geospatial Ecology Tools software  58. Cetacean sightings were recorded following line-transect methodologies that allow Effective Strip Width (ESW) to be estimated from the measurement of the perpendicular distances to the sightings  59  , except for the JNCC-ESAS surveys that used a 300-m strip-transect methodology.
A total of 113 sightings representing 236 individuals of beaked whales and 52 sightings totalling 106 individuals of sperm whales were assembled for the present study (Fig. 4 ). Aggregated data represented about 150,400 km of on-effort transects (i. e. following a transect at specified speed and altitude or height above sea level, with a specified level of visual effort) of which 53% was carried out by boat and the rest by plane (Fig. 4 , Table  2 ). Moran's and Geary's indexes were calculated to ensure there was no spatial autocorrelation in the data using the 'spdep' R-package  60.
Data processing. Delimitation of depth classes. To determine whether the presence of deep-divers would be related to surface oceanographic processes or to processes taking place in the water column, four depth classes were delimited according to the water masses reported in the Bay of Biscay (see 4. 1) in association with the dive profiles of deep-divers (Fig. 5 ). Environmental variables were extracted for each of the selected depth classes. The first class was the "surface zone"; the deep-diver distribution may be related to mechanisms at the surface or these mechanisms may influence processes at depth. The second class was the epipelagic zone (named "0-200 m"), extending vertically from 0 to 200 m. We delineated this class between 0 and 200 m because most of beaked whale recovery dives occur between 0 and 200 m  61   Static and oceanographic variables.
Habitat-based density modelling. To model the distribution of beaked whales and sperm whales, we fitted GAMs  30  with a Tweedie distribution to account for over-dispersion in the cetacean count data  65  with the 'mgcv' R-package  66. GAMs extend generalised linear models to allow for smooth, nonlinear functions of predictor variables to be determined by observed data rather than by strict parametric relationships  30. The mean number of individuals per segment was linked to the additive predictors with a log-function with four degrees of freedom. An offset equal to segment length multiplied by twice the ESW, or twice the 300 m-strip for JNCC-ESAS surveys, was included ( 67 ; refer to Virgili et al. 31  for the ESW estimation); ESWs were calculated for each combination of platform, class of Beaufort sea-state and class of observation height.
We removed combinations of variables with Pearson correlation coefficients higher than |0. 5| and tested all models with combinations of one to four variables to avoid excessive complexity  68. To assess the correlation between the variables we created a correlation matrix using the R package 'corrplot' ( 69 ; Fig. 6 ). Many variables with depths greater than 200 m were correlated with each other but also with surface variables (depth < 200 m) and with static variables such as slope, roughness and surface of canyons; showing the importance of considering correlations in the model selection. The Akaike information criterion (AIC, the lower the better  32  ) and Akaike weight (w; 'akaike. weights' function from 'qpcR' R package 70 ;) were used for model selection. The Akaike weight of a model is the probability that this model is the best among all tested models  32, 71.
The four variables could therefore be used to obtain functional relationships and to predict the relative densities of beaked whales and sperm whales in the Bay of Biscay. If not, all models had to be considered to predict the species densities. There were not enough data to fit a model by month or by season (the number of individuals in winter was too low) so we fitted models to all data of beaked whales and sperm whales and obtained climatological predictions maps for all seasons combined in the 1998-2016 period, although most of sighting data were collected in summer and the prediction maps most likely reflected the summer species distribution. The uncertainties associated with the predictions were also estimated as the standard deviation associated with the predicted relative densities; high values indicated high errors associated with density estimates.
However, it should be noted that the uncertainty associated with the model prediction with the four most important variables was certainly lower than the uncertainty associated with the mean prediction of all models and it was therefore underestimated. Finally, the model fit of each model was assessed thanks to the percentage of explained deviance  30, 72  , the Root Mean Squared Error (RMSE) which measured the prediction errors and the model accuracy (the lower, the better  73  ; 'qpcR' R-package  70  ) and a visual inspection of predicted and observed distributions  74. Table  3. Candidate environmental predictors used for the habitat-based density modelling. A: https:// www. gebco. net/; 15 arc-second is approximately equal to 0. 004°. B: Harris et al. 62. C: Iberian Biscay Irish-Ocean Physics Reanalysis model from Copernicus (https:// doi.
Acknowledgements We are grateful to the many observers who participated in the surveys and collected all the data but also the ships' captains, crews and pilots. We thank  Phil Hammond  and his team for providing SCANS and CODA survey data. THUNNUS survey was carried out thanks to the collaboration of the  General Directorate of Fisheries  and  Maritime Affairs ,  Government of Galicia. We thank the  Direction Générale de l' Armement (DGA) , including  Odile Gérard , for funding AV's research grant. ML was funded by a  Ramón y Cajal  ( RYC-2012-09897 ) postdoctoral contract of the  Spanish Ministry of Economy, Industry and Competitiveness. This study is a contribution to the  CHALLENGES  ( CTM2013-47032-R ) project of the  Spanish Ministry of Economy, Industry and Competitiveness. We would particularly like to thank  Matthieu Authier  for his advice in carrying out the analyses.
In particular, the unprecedented high volume of highquality images produced by TLI systems has already been leveraged using modern Artificial Intelligence (AI) methods, like deep learning (DL). An important limitation to the development of AI-based solutions for IVF is the absence of a public reference dataset to train and evaluate deep learning (DL) models. In this work, we describe a fully annotated dataset of 704 TLI videos of developing embryos with all 7 focal planes available, for a total of 2,4M images. Of note, we propose highly detailed annotations with 16 different development phases, including early cell division phases, but also late cell divisions, phases after morulation, and very early phases, which have never been used before. This is the first public dataset that will allow the community to evaluate morphokinetic models and the first step towards deep learning-powered IVF.
We postulate that this dataset will help improve the overall performance of DL approaches on time-lapse videos of embryo development, ultimately benefiting infertile patients with improved clinical success rates. © The data comes from infertile couples that underwent Intracytoplasmic Sperm Injection (ICSI) cycles. Patient treatment and embryo culture protocol were described in a previous study  [1]. Videos corresponding to embryos with less than 6 annotated phases were rejected to keep only videos with detailed annotations. Images were extracted using the manufacturer's (Vitrolife©) API.
Value of the Data • This dataset is composed of 704 TLI videos of developing embryos with all 7 focal planes available, for a total of 2,4M images. • We propose highly detailed annotations with 16 different development phases, including early cell division phases, but also late cell divisions, phases after morulation, and very early phases, which have never been used before. • This dataset can be used to train machine learning models to identify the various phases of embryo development from polar body appearance to blastocyst hatching. • Researchers and developers of time-lapse automated analysis software can use this dataset to evaluate and compare new models against previously proposed models. • To the best of our knowledge, this is the first embryo time-lapse dataset publicly available.
Data Description This dataset is composed of 704 videos, each recorded at 7 focal planes, accompanied by the annotations of 16 cellular events. First, we describe how the events were annotated, then how each frame was assigned a label, followed by the file descriptions and some dataset statistics and samples. Dataset annotation. Each video was annotated by a qualified and experienced embryologist undergoing regular internal quality control. For each video, the annotation consists of the timing of 16 cellular events noted tPB2, tPNa, tPNf, t2, t3, t4, t5, t6, t7, t8, t9 + , tM, tSB, tB, tEB, and finally tHB. We use the definition of the events proposed by Ciray et al. [3]  : polar body appearance (tPB2), pronuclei appearance and disappearance (tPNa and tPNf), blastomere division from 2-cell stage to 9 (and more) cells-stage (t2,t3,t4,t5,t6,t8 and t9 + ), compaction (tM), blastocyst formation (tSB, tB), expansion and hatching (tEB and tHB).
We chose to use more events than previous work  [4] [5] [6] [7]  to develop models that can more precisely describe embryo development in a controlled environment. We started prospective annotation of the database according to this reference work in 2014, while annotations made before 2014 were retrospectively checked. From event timing to frame labels. We formulate the task as an image classification problem. This means that we need to assign a label to each frame that the model will be trained to predict. However, the annotations given by the biologists are timings in hours post-fertilization that indicates the temporal position of events in the video. Knowing the timing at which each frame was taken, we identify the frames corresponding to each event and assigns them a label corresponding to the event they show (noted pPB2, pPNa, pPNf, p2, p3, p4, p5, p6, p7, p8, p9 + , pM, pSB, pB, pEB or pHB), as illustrated in Fig.
1. The other frames are assigned the label corresponding to the most recent event that has occurred in the previous frames. This labeling constructs the succession of embryo development phases, delimited by the cellular events. Dataset files. The dataset is composed of several compressed folders: -embryo_dataset. tar. gz contains 704 folders, each containing all the frames of one video. An embryo is a 3D object and the focal plane of the microscope can be changed to better visualize the embryo. This compressed folder contains images recorded at the central focal plane called F0. The images are JPEG files in shades of gray with a 500 × 500 resolution. -embryo_dataset_annotations. tar. gz contains the 704 CSV annotations files. Each CSV is the annotation of one video and has three columns: phase, start frame index and end frame index that indicates the frame interval of each phase.
2 (a) ). Most videos have at least 8 annotated phases and approximately 360 videos have more than 13 phases annotated, illustrating the richness of annotation of our dataset ( Fig. 2 (b) ). Dataset samples. Sample images allow one to have a clear view of the content of the dataset and the annotations associated with the images ( Fig. 3  ). Note that, depending on their position Fig. 1. The method used to assign a label to every frame of the video. First, we identify at which frame each event occurs and assign to these frames a label corresponding to the event they show. The other frames are assigned the label corresponding to the most recent event that has occurred in the previous frames. Note that all frames are labeled except the frames before tPB2 as they precede all the events. The video used as an example here is AG274-2.
To select the videos, we first excluded videos with strictly less than 6 phases annotated to keep only videos with highly detailed annotations and then randomly selected 10% of the remaining videos, which constitutes a dataset of 704 videos. Among these videos, 499 correspond to embryos considered to be morphologically viable and subsequently chosen for transfer, while the other videos correspond to discarded embryos because of poor development. These discarded embryos allowed us to study a variety of abnormal embryonic features (abnormal morphology, abnormal fertilization/number of pro-nuclei, necrosis, fragmentation, developmental delay, etc. ) or problems during image acquisition (sharpness, change of focus, brightness, etc. ). We subsequently extracted all images using the Application Programming Interface (API) provided by the TLI manufacturer (Vitrolife©).