dataesr/scientific-paragraphs-categorization huggingface dataset:
7 At first, the vernacular Psalter and liturgical gospels and epistles escaped the prohibition. 8 See Fernández López, Lectura y prohibición, 142-143. 9 It would be of little use to present a detailed history of the scholarship on each one of these codices here. For a relatively recent bibliographical survey, see Gemma Avenoza and Andrés Enrique-Arias, "Bibliografía sobre las biblias romanceadas castellanas medievales," Boletín bibliográfico de la Asociación Hispánica de Literatura Medieval 19 (2005): 411-454, and for more-recent updates see Andrés Enrique-Arias and F. Javier Pueyo Mena, eds. , Biblia Medieval database, http://www. bibliamedieval. es, which includes a searchable corpus of all medieval translations of the Bible into Castilian and detailed information on each of the codices and fragments included therein.
However, these units have not incorporated some sites of immature turtle aggregation, in particular in the North-East Atlantic where immature loggerheads, greens and Kemp's ridleys are frequently observed  (Bellido et al. , 2010; Morinière and Dell'Amico, 2011; Monzón-Argüello et al. , 2012; Nicolau et al. , 2016; Orós et al. , 2016; Botterell et al. , 2020). Satellite tracks and stranding reports collected from European waters therefore provide a unique source of data to update these RMUs and design more appropriate units of protection taking into account juvenile sea turtle habitats. By combining sightings  and tracking data  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020)  collected by the CESTM (Center d'Études et de Soins pour les Tortues Marines) of the Aquarium La Rochelle along the French Atlantic and English Channel coasts, our study aims to assess if the Bay of Biscay could be a favorable habitat or rather an ecological trap for immature sea turtles.
Sightings Sightings of sea turtles are reported throughout the year to the CESTM from the English Channel-North Sea to the Bay of Biscay-Iberian Coast (Figure  1A ). This large dataset covering 33 years of sightings  complements the work of  Witt et al. (2007)  to investigate the temporal variability of the turtle size throughout the year, over a longer period. These sightings gather individuals stranded, bycaught in fishing nets or drifting at sea. Initiated in 1988 and compiling data from the four species found along Western Europe (leatherback, loggerhead, Kemp's ridley and green), this dataset has been partly published  (Witt et al. , 2007; Morinière and Dell'Amico, 2011). To be consistent with the tracking analysis, we restricted this dataset to the three species satellite tracked in our study, i.
Data Prefiltering Location data were obtained from the Argos Data Collection and Location System. The Least Square positioning algorithm was used for tags deployed before 2011 and the improved Kalman filtered positions were obtained for the other tags  (Lopez et al. , 2014). All statistical analyses were performed using R software version 4. 0. 2 (R Core Team, 2021). We restricted our dataset to positions associated with a travel speed lower than 10 km/h  (Fossette et al. , 2010). Locations on land and those associated with a location class Z were also discarded.
Movement Analysis The individual tracks were then predicted daily using the foieGras package with a simple continuous-time random walk algorithm  (Jonsen and Patterson, 2020; Jonsen et al. , 2020). The algorithm accounted for the Argos quality to predict the daily locations using Argos error ellipses for the tags deployed after 2011, while the location classes (0, 1, 2, 3, A or B) were used for the remaining tags. Following  Hays et al. (2021)  and  Hart et al. (2021) , assessment of tag failure was conducted on the data available using the three following metrics: battery voltage, wet/dry switches and the number of Argos transmissions. To identify turtle aggregation and hotspot habitats, we generated hexagonal maps by summing the number of predicted locations (from the random walk) in each grid cell of 0.
Habitat Modeling The geographic space available to each animal was assessed by simulating a series of tracks for each individual using the availability package  (Raymond et al. , 2021) , i. e. , n = 50 simulated tracks per individual as suggested by  Hindell et al. (2020). Using the mgcv package  (Wood, 2017) , the habitat selected by the turtles was then characterized using a Generalized Additive Model to relate the presence of the turtles to a series of environmental factors, i. e. , SST, zooplankton biomass, micronekton biomass, ocean current velocity, zonal and meridional components of ocean currents and sea surface salinity. The turtle's presence was used as a binary response variable (real track vs. simulated tracks). All numeric variables were first checked for collinearity using the VIF function from the usdm package  (Naimi et al.
, 2014). To account for the inter-individual variability, turtle ID was added as a random factor. An autocorrelation term was also added to the model to account for the correlation structure in the data. A threefold cross-validation was used by partitioning the dataset into the training (2/3 of the data) and the validation dataset (1/3). Model evaluation was then done on the validation dataset using four performance metrics calculated for each model: the area under the curve (AUC), the sensitivity, the specificity and the true skill statistics (TSS). To test the influence of the spatial pattern on the thermal and biological habitat used by the turtles, a generalized logistic model was performed using the mlogit package  (Croissant, 2020)  using the data collected along the entire route for each individual.
While a suitable habitat should be productive, it also needs to be thermally optimal or at least acceptable for the species to survive. Similarly to other ectotherm species, ambient temperature plays a crucial role in reptile development and survival  (Angilletta et al. , 2002) , driving the at-sea distribution of sea turtles. For this reason, immature loggerhead and green turtles during their pelagic phase generally target the 17-18 • C isotherms  (Polovina et al. , 2000; Mansfield et al. , 2014 Mansfield et al. , , 2021;; Patel et al. , 2021) , and are rarely seen in habitats colder than 14 • C  (Robinson et al. , 2020). Ten percent of our dataset was, however, associated with such cold waters, including 26% of the individuals remaining in these unsuitable areas for weeks or even months (range: 5-128 days, mean: 53 days).
Conservation Implications The identification of a species geographical range is crucial to implement effective management of endangered species, reinforcing the interest of our findings. All sea turtle species inhabiting the North Atlantic are listed on the IUCN Red list, with status vulnerable (loggerhead turtle), endangered (green turtle) and critically endangered (Kemp's ridley turtle,  Wibbels and Bevan, 2019). The French East Atlantic sea turtle network (RTMAE), coordinated by the CESTM of Aquarium La Rochelle, therefore provides an unprecedented dataset to confirm the turtle presence in the BoB. It is worth mentioning that all the satellite tracked turtles were found in distress (stranded, bycaught or drifting at the sea surface), supporting the idea that without the rescue center and rehabilitation, these turtles may have likely died.
Given the number of rescued individuals per year is relatively low, it is hard to estimate the real proportion of immature sea turtles inhabiting the European waters, especially in winter. Future work should therefore be dedicated to genetic analysis and model simulations to assess if the BoB is either an ecological trap or an important habitat used by these immature sea turtles year-round. Such results will undoubtedly support conservation measures, especially the update of the RMUs by potentially including the BoB as an important habitat for loggerhead turtles. As RMUs are population specific, the dataset of the two other species (Kemp's ridley and green turtles) need to be first augmented to draw reliable conclusions regarding their distributions. Alternative approaches such as aerial surveys are available to asses marine megafauna species distribution in the BoB and English Channel  (Darmon et al.
Among other marine megafauna species (e. g. , cetaceans, seabirds), sea turtles are used as bioindicators of the ocean health to reach the good environmental status. The work carried out by Rescue Centers such as the CESTM of Aquarium La Rochelle is therefore critical and should be supported since they offer the unique possibility to collect a wide variety of data from both rescued and dead animals, e. g. , satellite tracking for movement analysis (Descriptor 1), tissue sample for genetics (Descriptor 1), pollutants analyses (Descriptor 8) and interactions with marine litter from necropsies (Descriptor 10).
METHODS The genome-wide analysis was done using the human genome assembly hg19. Only perfect repeats of three or more repeats were searched using an ad hoc pipeline. The precise location of the HR (GGGGCC or GGCCCC) in genomic regions such as promoters, introns and exons was identified using the genome browsers Ensembl and UCSC  (Flicek et al. , 2014; Kent et al. , 2002). We assessed if this HR pattern was statistically overrepresented in the human genome using a Monte Carlo test with 1,000 iterations. The expression and function of genes with HR were analysed using the gene annotation portal bioGPS (bioGPS. org) and Medline database (pubmed. org). Variant Call Format (VCF) files extracted for each gene from 1000 Genome database (http://www. ncbi. nlm. nih. gov/variation/tools/1000genomes/) were used to search polymorphisms describes at repeat positions.
Genes with repeats expressed in the nervous and neuromuscular systems From the list of 74 genes we extracted all genes expressed in the central nervous system (CNS) or in muscles (bioGPS. org), and with known functions (Table  1 )  (Wu et al. , 2009). The 36 selected genes were located on 18 chromosomes, with 5 genes in 19q13. VCF files extracted from the 1000 genomes database and corresponding to these 36 genes showed two genes with a different number of HR compared to the reference genome. RRP36 lost 2 out of 4 repeats in the reference genome in 27% of cases, and ADGRA1 lost 1 out of 4 repeats in the reference genome in 29% of cases. Given this set of 36 genes, a GO analysis found over-representations in four pathways with non-corrected p value between 1. 05e -5 and 4. 31e -5 : regulation of ion transmembrane transport, calcium ion import and membrane depolarization during action potential, and protein linear polyubiquitination (Table  2 ).
Photometric calibration of Y1A1 was performed globally on a CCD-to-CCD basis, and maps of the survey coverage and depth were assembled with the mangle software suite  (Hamilton & Tegmark 2004; Swanson et al. 2008 ). The Y1A1 data set covers ~2000 deg 2 in any single filter with inhomogeneous coverage and depth. In total, ~1800 deg 2 of the Y1A1 footprint has simultaneous coverage in all five DES filters. The desired precision of DES cosmological analyses motivates further refinement of Y1A1. The resulting data set, referred to as Y1A1 GOLD, is accompanied by extensive validation and ancillary data products to facilitate cosmological analyses. The primary components of Y1A1 GOLD are (Figure  1 ) (1) a multiband photometric object catalog subselected from the Y1A1 COADD object catalog; (2) an adjusted photometric calibration to improve uniformity over the survey footprint; (3) shape and photometry information from a simultaneous multiepoch, multiobject fit; (4) a set of ancillary maps quantifying survey characteristics using the HEALPix rasterization scheme  (Górski et al.
Multiepoch, Multiobject Fitting The Y1A1 co-added images provide deeper and more sensitive object detection than individual single-epoch images. However, the co-addition process averages across multiple images, resulting in a discontinuous PSF and correlated noise properties. Precision measurements that rely on an accurate PSF determination, such as galaxy shape measurements for cosmic shear, require a joint fit of pixel-level data from multiple single-epoch images. We used the ngmix 68  code  (Sheldon 2014; Jarvis et al. 2016; Sheldon & Huff 2017)  to reanalyze pixel-level data from multiepoch postage stamps of each object in the Y1A1 GOLD co-add catalog. We used PSFEx (Bertin 2011) to model and interpolate the PSF at the location of each object, and then we generated an image of the PSF using the python package, psfex.
69 We then used the ngmix code to fit this reconstructed PSF image to a set of three free, independent Gaussians. We used ngmix in "multiepoch" mode to simultaneously fit a model to all available epochs and bands. In this mode, a model is convolved by the local PSF in each single-epoch image, and a χ 2 sum is calculated over all pixels in a postage stamp. This is repeated for each epoch and band, and a total χ 2 sum is calculated. We then find the parameters of the model that maximize the likelihood. We took this procedure one step further, performing simultaneous multiepoch, multiband, and multiobject fits, which we call "MOF. " We first identified groups of objects using a friends-of-friends algorithm (e. g. ,  Huchra & Geller 1982; Berlind et al. 2006). We then fit the members of the group using the following procedure: 1.
The DES Y3 processing has largely corrected for variations in quantum efficiency, while  Burke et al. (2017)  show that gradients in the star flats can be successfully removed by the photometric calibration. The code used to perform the nightly fits is called the Photometric Standards Module (PSM). 79 We note that PSM not only fits Equations (6)-(  10 ) but also performs an automated initial culling of nonphotometric data using the outputs of RASICAM  (Lewis et al. 2010; Reil et al. 2014). PSM also culls dome-occulted exposures (identified by a strong gradient in a across the focal plane) and performs iterative sigma-clipping to achieve a good solution for a night. For the Y1 data set, we also culled nights with rms fit residuals >0. 025 mag or an atypical (∼2σ-3σ outlier) fit value for the first-order extinction.
2005) , starting with a relatively fine grid, nside = 512 (~0. 01 deg 2 ). If there were fewer than 200 stars in a pixel, then we appended neighboring pixels using the get_all_neighbors function from healpy, enlarging the pixel chunks until they contained at least 200 stars. To reduce computation time in high-density regions near the LMC, when there were more than 2000 stars per pixel we randomly down-sampled. Approximately 97% of the wide-area survey footprint was fit in chunks of 9 pixels containing a median of ∼400 stars and yielding an effective resolution of ∼0. 1 deg 2. We applied a modified version of the BigMACS SLR code  (Kelly et al. 2014)   81  to calibrate each star from the reference exposure with respect to the empirical stellar locus. The absolute calibration was set against the i-band   Glazebrook et al.
2006). We selected stellar objects from the Y1A1 GOLD catalog using MODEST_CLASS (Section 8. 1) and perform a 2″ match to the APASS and 2MASS catalogs. We then fit a set of transformation equations to map from g apass , r apass , and J 2mass to a predicted magnitude in each of the DES filters: Equations (  21 )-(  23 ) are derived from a global fit of the Y1A1 GOLD data set and are valid for stars for which -< r J 1. 81 apass 2mass = - ´- - ( ) (. We find a cleaner and tighter relation using the hybrid APASS/2MASS -( ) r J apass 2mass color rather than a purely APASS -( ) r i apass apass color for these transformation equations. These transformation equations explicitly remove any absolute calibration offset between the two data sets and can be used to test for spatial nonuniformity between the GCM calibration and these external catalogs (Figure  23 ).
We note that the residual structure seen in the S82 region of Figure  23  does not appear in comparisons with SDSS DR10 or DES Y3, suggesting that this structure is a feature introduced by APASS. To validate the completeness and contamination of the Y1A1 GOLD catalog, we perform a comparison with the CFHTLenS data in the W4 field. In this case, we are interested in the transformed magnitude of all objects, so we perform no stellar selection. We use matched objects to derive a set of transformation equations from the CFHTLenS g′, r′, i′, z′ filters to the DES g, r, i, z system: We find that these equations should be valid for objects with g-r<1. 2 and i-z<1. 0. = + - + ( ) (.
The DES data management system is supported by the  National Science Foundation  under grant no. AST-1138766. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-88861 ,  FPA2015-68048 ,  SEV-2012-0234 ,  SEV-2012-0249 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. This manuscript has been authored by  Fermi Research Alliance, LLC  under contract no. DE-AC02-07CH11359  with the  U. S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.
Although it does not provide the exact mortality associated with the pandemic or epidemic, it is considered to provide an objective indicator  ( Beaney et al. , 2020 ). The excess mortality resulting from the COVID-19 pandemic is being tracked by several groups. Data from the World Mortality Dataset, which tracks mortality in 103 countries, found the highest excess mortality per 10 0,0 0 0 population in Latin American and Eastern European countries: Peru (590), Bulgaria (460), North Macedonia (420), Serbia (400), Mexico (360), Ecuador (350), Lithuania (350) and Russia (340)  ( Karlinsky & Kobak 2021 ). Some countries, including Uruguay, Australia and New Zealand, had negative excess mortality i. e. fewer deaths than expected. Similar results were obtained from other groups ( The Economist, 2021 ,  Sanmarchi et al, 2021 ).
The Universal Influenza Vaccine Technology Landscape is a publicly available database of universal influenza vaccine technologies that have reached clinical or late pre-clinical development  ( CIDRAP, 2020 , Ostrowsky et al. , 2020 ). It includes 22 vaccine candidates in clinical development and 74 in late pre-clinical development, utilising a variety of vaccine platforms  ( CIDRAP, 2020 , Ostrowsky et al. , 2020 ). Target antigens are aimed at inducing neutralising antibody responses, cross-reactive T-cell responses or a combination of both. Targets for neutralising antibodies include the stalk domain of the HA glycoprotein, the NA glycoprotein and the matrix 2 membrane protein (M2). T-cell targets are mainly focused on internal proteins such as nucleoprotein, matrix protein (M1) and nonstructural proteins  ( CIDRAP, 2020 , Ostrowsky et al.
, 2011; Willers et al. , 2013). Once inhaled, PM notably has the capacity to generate reactive oxygen species (ROS), which leads to pro-inflammatory responses that can ultimately result in apoptosis  (Ayres et al. , 2008; Jin et al. , 2018; Nel, 2005; Piao et al. , 2018; Yang et al. , 2018). Investigating the PM oxidative potential (OP) in light of its major emission sources in various urban environments can then provide valuable information to instigate air pollution abatement policies limiting health outcomes. However, spatially resolved PM source apportionment at a city scale remains a challenging task  (Dai et al. , 2020a, b; Pandolfi et al. , 2020). Receptor models demonstrated their ability to extract information by variable reduction techniques, especially in large datasets, in different branches of scientific research.
In this paper, we present results of a study conducted over 1 year at three sites within 15 km of each other in the Grenoble metropolitan area within the framework of the Mobil'Air project (available in https://mobilair. univ-grenoble-alpes. fr/, last access: 2 November 2020). The sources of PM 10 were apportioned considering major chemical components contributing to the PM mass, including organic and elemental carbon, ions, a condensed set of commonly used organic markers (anhydride monosaccharides, polyols, MSA), and metals. Additional fit-for-purpose tracers, including free cellulose and several organic acids, were also added in the PMF input datasets to tackle specific sources that are difficult to discriminate using a traditional PMF dataset only. Results obtained from this improved PMF analysis were then used to investigate the spatial and seasonal variabilities in the source contributions for different urban typologies inside a metropolitan area.
PMF input dataset Source apportionment of PM 10 was conducted using the United States Environmental Protection Agency (US-EPA) software PMF 5. 0  (Norris et al. , 2014) , aiming at the identification and quantification of the major sources of PM 10 for the three urban sites in the Grenoble basin. Briefly, PMF is based on the factor analysis technique  (Paatero and Tapper, 1994)  applying a weighted least-squares fit algorithm allowing the resolution of Eq. (S1) (in the Supplement). In our study, 35 chemical species were used as input variables, namely OC * , EC, ions (Na + , K + , NH + 4 , Mg 2+ , Ca 2+ , NO - 3 , SO 2- 4 and Cl -), trace metals (Al, As, Cd, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Ti, V and Zn) and organic tracers (MSA, levoglucosan, mannosan, polyols (sum of arabitol and mannitol), pinic acid, 3-MBTCA, phthalic acid, and cellulose), as summarized in Table  S1  in the Supplement.
Comparison with a "classic" PMF solution In order to quantify the added value and the changes brought in by the additional tracers, a reference PMF using a chemical dataset (not including cellulose, pinic acid, phthalic acid, and 3-MBTCA) and parameters similar to that in the SOURCES project  (Weber et al. , 2019)  was performed, hereafter called "classic", and the results were compared with those from the present study (called "orga"). the comparison of the yearly average mass contribution of the different factors for these two approaches. A detailed comparison of chemical profiles between the "classic" and "orga" PMF runs in each site is summarized in Sect. S3. One can see that most observations below are consistent in all three sites. Some factors remain unaffected or only marginally modified: it is the case for the biomass burning source with a percentage increase in contribution, only ranging from 1 % to 14 %, in the "orga" compared to the "classic" PMF run across all sites.
, , 2003;; Usher et al. , 2002). In the SPECIEUROPE database, several studies have reported sulfate-rich sources influenced by a variety of different fuel combustion sources  (Bove et al. , 2014; Pernigotti et al. , 2016; Pey et al. , 2013). It is, therefore, not surprising that part of the matter in the sulfate-rich source was re-assigned to different other sources upon addition of the organic tracers in the "orga" PMF run. A comparable study in Metz (France) also used another organic tracer (oxalate) to apportion a secondary organic aerosol (SOA) source from PM, ascribing it possibly to both biogenic and anthropogenic emissions  (Petit et al. , 2019). We also observed an increase in the contributions of the MSA-rich factor at the three sites, with an increase in contributions from specific inorganic species, such as SO 2- 4 and NH + 4 (see Fig.