dataesr/scientific-paragraphs-categorization huggingface dataset:
Association analysis The imputed sequences were subjected to single-trait association analysis for milk and semen production traits using mixed linear models with the mlma option of GCTA software  [25]  and the following model: y ¼ 1μ þ xb þ u þ e where y represents pre-adjusted phenotypes of the trait; μ is the overall mean; b is the additive fixed effect of the variant tested; x is the vector of imputed genotypes coded in 0, 1, 2 (copy number of the alternative allele); u is the vector of random additive polygenic effects, u~N(0, Gσ 2 ) with G the genomic relationship matrix; e is the vector of random residual effects normally distributed. The genomic relationship G matrix was calculated on 50 k genotypes using PLINK  [24]. The four traits were subjected to within-breed association analysis (Table  1 ).
Variants with a within-breed MAF lower than 1% were excluded, leaving 11,933,965 and 12,449,740 variants in Alpine and Saanen goats, respectively, when sequences were imputed within-breed, and 14,695,413 and 15,404,361 variants in Alpine and Saanen goats, respectively, when imputation was performed using the French multi-breed panel. A Bonferroni correction was applied to the significance thresholds to account for multiple testing. The average chromosomal significance level was calculated as follows: -log 10 (0. 05/(number of variants/29)). The results of the sequence data association analysis were then compared with 50 k-genotypes results, by performing a GWAS on the 40,491 SNPs found both in the filtered sequence data and the cleaned GoatSNP50 Bead-Chip SNPs. Annotations were extracted from VCF files for variants with a -log 10 (p-value) above the chromosomal threshold.
55 and 27. 68 Mb. When using a French Fig. 2  Principal Component Analysis performed on 1,546,528 filtered variants on chromosome 1 using Plink software. The 'World' group comprises all sequences available except for wild animals. The 'Europe' group comprises all sequences from dairy goats from European countries (therefore excluding Boer, Angora and Creole breeds). The 'France' group comprises all sequences of goats from France (excluding Angora and Creole breeds)   4 ) including 441 on chromosome 19 between 24. 70 and 28. 15 Mb and 7 on chromosome 5 between 44. 80 and 44. 81 Mb (Fig. 5 ).
37 between true and imputed sequences. One reason explaining our low correlations could be a negative effect of the large number of variants with a low MAF in our dataset (Fig. 4 ), for which the correlations decrease rapidly at lower MAF values (Fig. 3 ). Also, a slight drop in R was observed for variants with a MAF of 0. 50 in Saanen goats (Fig. 3 ). This could be linked to the small number of variants with a high MAF (Fig. 4 ), and thus implies that an imputation error would have a major impact on the final correlation. Nonetheless, the correlations that we obtained even for high MAF were low in comparison with other studies in livestock  [3, 29, 30]  or humans  [31]  where the correlation between true and imputed genotypes could reach 0. 8. As our imputation study involved very few sequenced individuals (33 and 40), a single imputation error would drastically reduce this correlation.
65 when an intermediate HD step was introduced. A HD chip is not yet available for caprine species, it would be a very powerful tool that would improve imputation to sequence level by overcoming imputation errors. Our results varied depending on the imputation scenario used to impute the available 50 k genotypes to sequence level. In the Alpine breed, using a multi-breed reference panel resulted in the detection of a new signal for milk yield on chromosome 2 (Fig. 5 ). This signal does not appear when using the 50 k genotypes (Fig. 7 ). Significant variants in this region are annotated for CRYBA2, CDK5R2 and FEV genes which are not explicitly related to the mammary gland or any metabolic path linked to milk production. According to the Rumi-miR database, the region close to the signal is rich in miRNA: 12 miRNAs are located in a range of 1 Mb around the signal in the caprine species.
However, among them, only 2 are expressed in mammary tissue: chr2_2187 (29. 47 Mb) and chr2_2972 (29. 80 Mb) and 4 are expressed in the ovaries: _Novel: bta-miR-153 (28. 57 Mb), _Novel: bta-miR-26a (29. 39 Mb), LO-m0073 (29. 80 Mb) and FO-m0047 (29. 80 Mb). Their exact role and impact on milk yield is still unknown. Further analysis is therefore required to confirm or disprove the signal detected on chromosome 2 for milk yield. In the Saanen breed, the multi-breed reference panel led to the detection of a new signal for milk yield on chromosome 5 while confirming the involvement of a large area on chromosome 19. Significant variants on chromosome 5 are annotated for MDM1 gene, however the link between this gene and milk production is not clear. According to the RumimiR database, there are a few miRNAs in goats that are also located on chromosome 5 near our signal: novel_mir299 (44.
Conclusions This study provides insights on how to implement a robust quality check and an imputation pipeline based on caprine sequence data that will ensure the quality of subsequent analyses. New signals for milk yield traits were detected in both Alpine and Saanen breeds. Signals for semen and milk production traits were detected in the Saanen breed on chromosome 19. The latter regions however require further investigation and annotation to determine the genes involved and determine more precisely their impact. Imputation using a within-breed scenario appears to be more efficient because it is less time consuming. Signals detected after within-breed imputation show less noise and are more significant. However, due to the small size of our sequenced panel, withinbreed imputation might not be able to detect smaller weaker signals.
Acknowledgements This study would not have been possible without the sequence data provided by the  VarGoats Consortium  (http://www. goatgenome. org/vargoats. html)and previous work by the  International Goat Genome Consortium  (IGGC, http://www. goatgenome. org/)and ADAPTmap Consortium (http://www. goatadaptmap. org/)providing relevant DNA samples, genotyping tools and genotyping data through their collaborative networks. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées  and the  CTIG (Centre de Traitement de l'Information Génétique) of INRAE Jouy-en-Josas  for providing computing resources. Discussions on sequence data quality and imputation methods with  Mekki Boussaha  ( INRAE, UMR GABI ) are gratefully acknowledged. We also would like to thank the CapGenes breeding organization for the data provided.
Availability of data and materials The final sequence dataset will be made publicly available by the VarGoats Consortium. The use of the sequence data is under a data sharing agreement which is available here: http://www. goatgenome. org/vargoats_ agreement. html and states that everyone will contact the VarGoats steering committee to discuss any publication plans that utilize this data to avoid the overlap of any planned analyses. Performance data and 50 k genotypes are not publicly available as they involve private professional partnerships.
INTRODUCTION The Copernicus programme is a European initiative for the implementation of information services dealing with environment and security, mainly based on observation data received from Earth Observation (EO) satellites. In the frame of this programme, ESA launched the Copernicus Sentinel-2A and Sentinel-2B optical imaging satellites, which are fully operating since June 2017. They are equipped with optical imaging sensor MSI (Multi-Spectral Instrument) acquiring optical data products with spatial resolution up to 10m in 13 spectral bands from the VIS to the SWIR spectral domains  1. Sentinel-2 data are applied for a wide field of applications on land surface related to agriculture  2, 3  , forestry  4  and landcover change  5. They are also used to monitor coastal and inland waters  6, 7.
SEN2COR HISTORY Sen2Cor is used by ESA since June 2017 for systematic L2A-processing of Sentinel-2 acquisitions over Europe. Global L2A-processing by Sentinel-2 ground segment (PDGS) started in March 2018. This ESA L2A core production gives the same results as can be obtained by the user if the same software version, auxiliary data and processing configuration are used. The only differences are related to product format, manifest generation, product quality metadata generation and JPEG2000 encoding library used. Users may observe differences due to DEM used. Whereas ESA L2A core production is generated with Planet DEM  18  , the "user" version has access to SRTM-DEM  19  by default. Differences may also be observed because updates of Sen2Cor were not realized at the same time for the ESA L2A core production and for the public version of Sen2Cor (Figure  2 ).
Most remarkable cases had been the use of CCI data package for scene classification and the LUT updates for the S2A spectral response functions version 3. 0  10. The version history shows also that the archive of ESA L2A core products contains no homogenous time series. The only way to get a homogenous time series including data before May 2019 presently is to process the required data with the same Sen2Cor "user" version and configuration. Moreover, "user" processing gives the chance for processing with non-default configuration if necessary whereas ESA L2A core production can only be done with default configuration. Those non-default configurations could be switching on cirrus correction or increasing adjacency range for adjacency correction over dark water surrounded by forest. A reprocessing of the complete L2A archive is being discussed and prepared for collection 1 of ESA L2A core products archive.
CONCLUSIONS AND OUTLOOK Reported investigations confirm good performance of Sen2Cor for all of scene classification, WV retrieval and SR estimation. Several updates of the processor are on the way to improve some data quality weaknesses found. It was shown, that ESA L2A core product gives equivalent results to 'user' products obtained with the public available Sen2Cor. ESA L2A core products are the easiest way to get atmospheric corrected SR products. However, they are based on a default configuration. 'User' product gives the opportunity to process with non-default configuration, which may have advantages and give better results in some situations than relying on default configuration. ESA archive of Sentinel-2 L2A-products has still no 'collection' providing a (long) homogenous time series until the archive is reprocessed.
'User' processing gives the opportunity to generate a homogenous time series for an area of interest. Main updates resp. product evolutions foreseen for Sen2Cor are summarized in Figure  5. Sen2Cor 2. 9 will provide some processing fixes. Sen2Cor 2. 10 will contain several scene classification improvements, it will support the usage of CAMS data for AOT fallback and Copernicus DEM. It will support changes of the product structure corresponding to a new Product Specification Document (PSD) like output of band 1 in the 20m spatial resolution product and extending the product with quality indicators with regard to L2A processing. Sen2Cor 3. 0 will be migrated to Python 3. 7 and will support processing of Landsat-8 data.
Introduction In light of the recent publication of  Greaves et al. (2020a)  reporting on the detection of phosphine above the Venusian clouds, we decided to explore the Solar Occultation at Infrared (SOIR) database in search of a spectral signature of PH 3 in the infrared (IR) range. Greaves et al. (2020a)  identified a phosphine line in spectra taken by two different instruments, James Clerk Maxwell Telescope (JCMT) and Atacama Large Millimetre/submillimetre Array (ALMA) in June 2017 and March 2019, respectively. They claim that it corresponds to 20 ppb of phosphine at an altitude of 53 to 61 km. In addition to the difficulties in understanding how phosphine can be present in Venus' atmosphere, this possible detection still needs confirmation and validation from other spectral lines of phosphine.
The SOIR instrument has already proven to be very sensitive to the detection and quantification of trace gases  (Wilquet et al. 2012; Vandaele et al. 2015; Mahieux et al. 2015a,b)  thanks to its solar occultation measurements delivering spectra with a high signal-to-noise ratio (S/N). Providing that the species have a spectral signature in the SOIR instrument's spectral range, the SOIR dataset can corroborate the possible presence of trace gases in the atmosphere of Venus or, otherwise, help to constrain their upper limits.
SOIR delivered height sets of spectra per solar occultation, each of them is referred to as "dataset" hereafter (see Appendix B for more details). We selected SOIR orders 105 to 110 (see Table  1  for the spectral range of each order) by considering the spectral signature of PH 3 in the SOIR spectral range. In this region, CO 2 and SO 2 also have a spectral signature and Fig. 1  shows the intensities of their theoretical lines.
An alternative attempt to detect PH 3 was carried out with a machine learning tool that had already been used to detect the CO 2 quadrupole and to infer the absence of methane in NOMAD-SO spectra  (Schmidt et al. 2020). The method aims to summarize the dataset with statistical endmembers, hereafter "sources". First, data are pretreated to remove the baseline and converted into absorbance. The method consists of a linear blind source separation under positivity constraints and uses the probabilistic sparse Non-negative Matrix Factorization (psNMF) described in  Hinrich & Mørup (2018). Appendix E provides more information on this analysis. In addition to these detection methods, we determined the detection limits (DLs) of PH 3 in SOIR spectra in the volume mixing ratio (VMR). For each SOIR spectrum, we compared the measurement noise to a synthetic spectrum of PH 3 simulated by considering the SOIR instrumental function.
We expect some strong variations in the DLs with respect to the diffraction orders. The altitude of interest is closer to 60 km and thus a few kilometers below the typical ALDL. For order 108, the lowest DL is 3 ppb at 67 km and 4 ppb at 61 km (the lowest tangent altitude probed). For order 107, the lowest DL is 0. 7 ppb at 67 km and 2 ppb at 61 km. For order 106, the lowest DL is 0. 3 ppb at 69 km and 1 ppb at 65 km. For order 105, the lowest DL is 0. 2 ppb at 69 km and 0. 4 ppb at 61 km. The DLs lower than 20 ppb and below 62 km corresponds to ten different datasets of SOIR and five different occultations. The DLs below 20 ppb and below 61 km correspond to six datasets from four different occultations. The SOIR datasets presented here mostly cover latitudes above 60 • north. Still eleven datasets cover the latitudes below 60 • , corresponding to three different occultations.
As seen in Fig. 5 , they correspond to latitudes 6. 67 • (orbit 108. 1, orders 105 to 108), -30. 96 • (orbit 446. 1, order 108), and -9. 11 • (orbit 591. 1, order 105). For the dataset corresponding to orbit 446. 1 order 108 bin 2, the ALDL is at 62 km with a detection limit of 8 ppb and at 61 km, and the DL is still 9 ppb. For orbit 108. 1 order 107 bin 2, the DL at 62 km is 7 ppb. ASIMAT also derives DLs and the results are the same as the ones shown in this section. The machine learning algorithm DL is estimated from those derived in  Schmidt et al. (2020)  for CH 4 lines at 3067. 3 cm -1 , which are lost in dominant H 2 O lines in a realistic synthetic dataset, including nonlinear radiative transfer and an instrumental effect. In this case, the detection limit was with a noise variance of 0.
SOIR detection limits below 20 ppb for a constant VMR of phosphine below 180 km (DL1) and 68 km (DL2) and no phosphine above the respective altitude. and60•,the lowest DL is 2 ppb at 65 km. There is a difference in the time of measurements as SOIR datasets with DLs lower than 20 ppb extend from August 2006 until January 2010 and the spectra used byGreaves et al. (2020a)were recorded in June 2017 and March 2019. Another difference is that SOIR scanned a localized region of the Venus terminator, while the spectra fromGreaves et al. (2020a)cover more than 15 • of latitude in the Venus disk, as seen from the Earth. These differences might be important if phosphine is really present in Venus' atmosphere and if the process producing phosphine is localized and varies in time.
Acknowledgements. Venus Express is a planetary mission from the  European Space Agency (ESA). We wish to thank all  ESA  members who participated in the mission, in particular,  H. Svedhem  and  D. Titov. The research program was supported by the  Belgian Federal Science Policy Office  and the  European Space Agency  ( ESA, PRODEX program , contracts C 90268, 90113, 17645, 90323, and  4000107727 ). A. Mahieux was supported by the  Marie Sklodowska-Curie Action  from the  European Commission  under grant number  838587. This work was supported by the  Belgian Fonds de la Recherche Scientifique -FNRS  under grant number  30442502  ( ET-HOME ). SR thanks  BELSPO  for the FED-tWIN funding (Prf-2019-077 -RT-MOLEXO). F. Schmidt acknowledges support from the  "Institut National des Sciences de l'Univers" (INSU) , the  "Centre National de la Recherche Scientifique" (CNRS)  and " Centre National d'Etudes Spatiales" (CNES)  through the " Programme National de Planétologie ".
It targets the inactive conformation of ABL1 kinase and is used against chronic myelogenous leukemia (CML)  [11]. Since then, at least one new PKI reaches the market every year, with a significant acceleration since  2011. 2002, 2008, 2010 and 2016  are exceptions to this rule with no compound approved these years. In addition to approved PKIs, many novel compounds are currently being evaluated in clinical trials throughout the pharmaceutical industry. Taken collectively, these compounds show new trends in terms of structures, physicochemical properties and biological activities that foreshadows changes in the PKI landscape. To collect and organize this data as well as keep up-to-date with their evolution, we developed PKIDB  [16] , a curated, annotated and updated database of PKIs in clinical trials.
In order to enter PKIDB, compounds should be currently in one development phase (from Phase 0 to Phase 4), have a disclosed chemical structure as well as an International Nonproprietary Name (INN)  [12]. Each compound is provided with comprehensive descriptive data as well as with links to external databases such as ChEMBL  [13] , PDB  [14] , PubChem  [15]  and others. The type of binding mode specified in PKIDB has been manually entered and comply with o ko k classification  [16]. The database is freely accessible on a dedicated website (http://www. icoa. fr/pkidb). As of 11 th of December 2019, it contains 218 inhibitors, 60 approved and 158 in various stages of clinical trials (from Phase 0 to Phase 3). In this study, we compared PKIDB to a large dataset of 76,504 PKIs retrieved from ChEMBL (referred herein as "PKI_ChEMBL", see Material and Method section).
The objective is to be able to better select PKIs from public databases based on structural and physicochemical property information of PKIs already in clinical trials. Firstly, we performed a Principal Component Analysis (PCA) and compared the projection of both datasets in a common factorial space. We also assessed the structural shape diversity of PKIs using a Principal Moments of Inertia (PMI) analysis. Secondly, in addition to comparisons at the global molecular structure level, we performed a substructure analysis based on PKI scaffolds. In medicinal chemistry, scaffolds are mostly used to represent core structures of bioactive compounds. They are relevant for the medicinal and/or computational chemists and have proved to be useful in f c o of "p b c "  [17]  in " c ffo opp "  [18]  or in Structure-Activity Relationships (SAR) analyses  [19].
The concept of scaffold was first defined by Bemis and Murcko as frameworks consisting in rings and linkers from which substituents are removed  [20]. From these scaffolds, two levels of abstraction were derived: the heteroatom framework and the graph representation. The heteroatom framework only takes into account the atom type without considering bond types or aromaticity, whereas the graph representation (also known as cyclic skeleton) turns every atom type to carbon and every bond type to single bond, reducing the initial molecule to a simple graph  [21]. Finally, the rings are obtained by removing bonds between rings. The balance between the molecular diversity of scaffolds and their frequency is an important parameter in a chemical database. A high frequency associated to a small number of scaffolds corresponds to a focused library composed of structurally-similar molecules bearing varying substituents.
On the opposite, a low frequency associated to a large number of scaffolds reflects a high molecular diversity  [16]. Thus, this criterion needs to be addressed when designing or selecting a chemical library depending on its forecasted usage. We assessed scaffold diversity for the PKIDB and PKI_ChEMBL datasets using the molecular Bemis and Murcko scaffolds and cyclic skeleton. The most represented scaffolds (frequency) and the distribution difference (distribution de quoi, à lire plus loin) between the two studied datasets are presented. Finally, an analysis of the rings of all molecules was performed. We first considered all the rings devoid of substituent (first attached atoms were replaced by hydrogen atoms). Then, we encoded the rings while retaining the position and atom type of their original substituents.
Update on PKIDB The description and analysis of PKIDB have been reported in a previous study by Carles et al. [ This brings to 60 the total number of approved drugs on the market referenced in our database. As described in PKIDB, most of the PKIs are targeting more than one protein kinases and since the first version of PKIDB, new targets emerged such as the Wee1-like protein kinase inhibited by adavosertib. Table  1. PKIs approved in 2018 and 2019 with their respective targets extracted from DrugBank (Uniprot ID extracted from https://www. uniprot. org/. ) PKI Unitprot ID Gene name Alpelisib Binimetinib P42336 Q02750 PI3KCA MAP2K1 Dacomitinib P00533 EGFR Duvelisib O00329 P48736 PI3KCD PI3KCG Encorafenib P15056 BRAF Entrectinib P04629 Q16620 Q16288 P08922 Q9UM73 NTRK1 NTRK2 NTRK3 ROS1 ALK Erdafitinib P11362 FGFR1 Fedratinib O60674 P36888 O60885 JAK2 FLT3 BRD4 Fostamatinib P43405 SYK Gilteritinib P36888 P30530 Q9UM73 FLT3 AXL ALK Larotractinib P04629 Q16620 Q16288 NTRK1 NTRK2 NTRK3 Lorlatinib Q9UM73 P08922 ALK ROS1 Pexidartinib P36888 P10721 P07333 FLT3 KIT CSF1R Upadacitinib P23458 JAK1 Zanubrutini b Q06187 BTK.
Here, we updated the statistical analysis with new PKIs included in PKIDB and we compared them to the ChEMBL dataset (Table  2 ). 3. Here again, we found that there is no significant difference between the two kinase subsets over all the descriptors. Molecular weight (MW) and CLogP are the most discriminant descriptors. (2. 7%) 1 RDKit was used to calculate all descriptors including ClogP. % of K c p o o of V b boundaries. From these calculations, we propose a range of descriptors to guide the design of kinase inhibitors. The proposed ranges do not consider the property values beyond two standard deviations from the mean (95. 4% confidence interval). Thus, the upper and lower limits of molecular descriptors better represent the current chemical space of kinase inhibitors, either approved or in clinical trials.