dataesr/scientific-paragraphs-categorization huggingface dataset:
Microarray analysis Differential expression analysis of the Agilent microarray expression data was performed using limma  18  from the Bioconductor project. Raw data were normalized by first performing background correction and then normalizing between arrays for all brain and blood samples also using limma. Probes were annotated using Agilent's array information provided on Array Express (https://www. ebi. ac. uk/arrayexpress/arrays/A-GEOD-9861/?ref=E-GEOD-20043) and gene information provided by Genosplice. The limma package performs differential expression analysis by first fitting the expression data of each gene to a linear model. It then utilizes Empirical Bayes to borrow information across genes, which allows us to perform analysis across a small number of arrays. 19.
External validation We attempted to verify our results using data from a study by Cook et al. 5  This study also examined stroke transcriptomics in gyrencephalic primate, however they used cynomolgus macaques, a close relative of Macaca mulatta. 20 In order to validate the results found in Macaca mulatta, we analyzed placebo and nonischemic transcriptomic data from the Cook et al. study. The placebo ischemic and nonischemic data were accessed from GEO (accession: GSE35589) and analyzed using the methods described above.
The resulting gene sets were sorted by the normalized enrichment score (NES). Empirical P-values obtained based on the 10,000 permutations are provided. The interactions between genes in these gene sets were visualized using the STRING database. 23 Genes were entered into String if they had a fold change of at least 2 in the brain, and 1. 5 in the blood. The thickness of the lines indicates the confidence that a relationship exists and the large nodes indicate proteins for which there is information about the tertiary structure.
Gene expression changes: ischemic versus nonischemic brain tissues In this study, changes in expression between ischemic and nonischemic brain tissues were measured for individual genes, and the effect of these changes was examined at the level of gene sets. A differential expression analysis was performed comparing expression data from ischemic brain tissue to data from the corresponding region of the contralateral hemisphere, which did not undergo ischemia. This analysis revealed that the expression patterns of ischemic and nonischemic brain samples are visibly different (Fig. 3A ). When hierarchical clustering was applied to the top differentially expressed genes and the most variable genes regardless of the experimental condition, the ischemic samples clearly cluster independently from the nonischemic samples.
Internal validation To confirm the robustness of the microarray results, mRNA levels of three ischemic-sensitive genes (HSPA1B, GADD45G, CDKN1A) were quantified by RT-qPCR using samples from the animals. SMC2, which was found to exhibit invariant expression level across all test samples, was used as internal reference (housekeeping). Three reverse transcriptions were performed for each RNA samples followed by three independent qPCR runs, with replicate assay measurements for both target and reference genes. For all genes, the direction and magnitude of the change agreed well with the microarray data (Fig. 4 ).
Interaction of genes products The STRING database  23  was used in order to visualize the interaction of the gene products of genes that are highly differentially expressed in the brain and blood within the top gene sets identified in the brain (fold change ≥ 2 in the brain, fold change ≥ 1. 5 in the blood) (Fig. 5 ). It was noted that IL6 is a highly connected member in the networks representing TNF-a signaling via NF-jB, apoptosis, and hypoxia. The gene ATF3 also appears in all four networks and it interacts with IL6 in the three networks mentioned above. The fifth ranking gene set in the brain (Table  2 ), UV response up, is not shown because none of the top differentially expressed genes in this set have any known interactions.
Acknowledgments The authors thank  Denis Vivien  ( Department of Physiopathology and Imaging of Neurological Disorders, INSERM U1237, University Caen Normandie, GIP Cyceron ) for welcoming Marie-Lise Quill e,  Estelle Rousselet  and  Serge Timsit  in their laboratory to perform animal stroke experiments. This work was supported by grants from  Association Ga€ etan Saleun  and by  ANR (Research French National Agency)   Strokinin  project:  ANR-10-BIOT-005.
(a). Frequency of mutations in UM and the associated prognostic value: BRCA1 associated protein 1 (BAP1) mutations are mostly associated with chromosome 3 monosomy and an early metastatic risk (~5 years after primary UM diagnosis), splicing factor 3b subunit 1 (SF3B1) and serine/arginine-rich splicing factor 2 (SRSF2) mutations are mainly associated with chromosome 3 disomy and a late-onset metastatic risk (~8 years after primary UM diagnosis), while eukaryotic translation initiation factor 1A X-linked (EIF1AX) mutations are associated with chromosome 3 disomy and a low risk of metastasis. Data is retrieved from The Cancer Genome Atlas (TCGA) UM dataset (cBioportal for Cancer Genomics)  [56, 57]. (b). Main biological processes impacted by the recurrent mutations in UM. Mutations in components of G protein-coupled receptors (GPCRs) lead to the constitutive activation of Gαq signaling and several downstream pathways.
BAP1 loss leads to increased H3K27 that, in turn, raises the expression level of EZH2  [98]. However, the UM cells were reported to resist EZH2 inhibition regardless of their BAP1 status  [99]. Additionally, BAP1 forms a complex with BRCA1 and BARD1, which takes part in double-strand break repair through homologous recombination (HR)  [100]. BAP1 deficiency results in impaired HR, which may suggest an increased dependency on other DNA repair pathways and a consequent sensitivity to PARP inhibition  [100] [101] [102]. A clinical trial of a PARP inhibitor (Niraparib) in BAP1-deficient neoplasms including UM is ongoing (NCT03207347). Overall, targeting BAP1-related processes is a potential therapeutic strategy. Nevertheless, successful approaches to target metastatic malignancies may require combined treatment in order to block all the related processes.
Specific compounds are then needed to restore the normal splicing level, rather than inhibiting the whole process of splicing. Recently, encouraging results were obtained with H3B-8800, a small molecule that is derived from pladienolide that targets SF3B1 complex. Cells harboring SF3B1 mutations presented higher sensitivity to this inhibitor than cells with WT SF3B1, a feature that may overcome the high cytotoxicity of splicing inhibition. The preferential inhibition is associated with an enrichment of alternative 3 ss in SF3B1 mutant cells as compared to WT cells  [137]. Further studies are ongoing to confirm the specificity of H3B-8800 in vivo and in a clinical trial in patients with advanced myeloid malignancies, including MDS, AML, and CMML (NCT02841540). New perspectives also emerged from the studies of neopeptides that were generated by the aberrant transcripts in SF3B1-mutant cells.
Figure1. Genomic and genetic alterations in uveal melanoma (UM) and the affected biological processes. (a). Frequency of mutations in UM and the associated prognostic value: BRCA1 associated protein 1 (BAP1) mutations are mostly associated with chromosome 3 monosomy and an early metastatic risk (~5 years after primary UM diagnosis), splicing factor 3b subunit 1 (SF3B1) and serine/arginine-rich splicing factor 2 (SRSF2) mutations are mainly associated with chromosome 3 disomy and a late-onset metastatic risk (~8 years after primary UM diagnosis), while eukaryotic translation initiation factor 1A X-linked (EIF1AX) mutations are associated with chromosome 3 disomy and a low risk of metastasis. Data is retrieved from The Cancer Genome Atlas (TCGA) UM dataset (cBioportal for Cancer Genomics) [56, 57].
As well as cloud cover, the vegetation can obstruct visible and infrared information about snow, especially where forest canopy protruding above the snowpack reduces the surface albedo  [38]  and partially or completely shades the underlying surface  [39, 40]. Nevertheless, since satellite-based data are indirect measurements of snow-related quantities, they require a quantitative understanding of their accuracy, mainly depending on the uncertainty in retrieval algorithms  [37, 41]. Therefore, the comprehensive validation of satellite snow products is of key importance to properly assess and quantify their reliability, to identify possible errors and to provide input for further improvements. Indeed, the availability of information on the quality control of remotely-sensed data is critically needed by the scientific community, as one of the main key criteria for the selection of the most proper dataset to be effectively used, according to the final purpose.
Numerous studies have addressed the validation of satellite snow products at local and global scale by assessing the accuracy of remotely-sensed observations against ground-based data, which is one of the most widely used validation procedures  [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54]. Lacking any available in-situ reference data, a common approach relies on a cross-sensor comparison among different satellite-derived snow products by assuming one of the analyzed datasets as the reference truth  [55] [56] [57] [58]. This approach is even necessary when assessing the accuracy of satellite-derived products of fractional snow cover (FSC) requiring spatially distributed observations of reference  [33]. Even though currently there is no agreed-upon methodology to perform a cross-sensor comparison, the most commonly used approach assumes the high-resolution satellite imagery as the reference effective dataset to assess.
Sentinel-2 Imagery S-2 is a polar-orbiting, multispectral high-resolution imaging mission of the European Space Agency (ESA) for land, ocean and atmospheric monitoring. With the aim of fulfilling revisit and coverage requirements and providing robust datasets, the constellation consists of two identical satellites, Sentinel-2A (S-2A) and Sentinel-2B (S-2B), which were launched on 23 June 2015 (operational in early 2016) and 7 March 2017, respectively. Since the twin satellites are in the same sun-synchronous orbit with a phase delay of 180 • , they guarantee an effective revisit time of 5 days at the equator and 2/3 days over mid-latitudes, with a 290-km swath width. Multi-Spectral Imager (MSI) instruments provide fine spatial resolution optical images (Figure  1 ) having 13 bands spanning from the visible and the near infrared to the shortwave infrared, covering wavelengths from 0.
Indeed, the daily snow cover product is derived for a multi-temporal analysis of SEVIRI 15-min images, that are processed as new data are available to collect the largest possible number of cloud-free pixels. The sampling is performed at 3km intervals, which degrade to ~5 km over Europe. The resulting daily map has a spatial coverage delimited between longitude 25° W-45° E and latitude 25°-75° N  [56, 70]  and it consists of four different classes: snow, cloud, water and bare ground (Figure  2 ). H-SAF H10 (SN-OBS-1) is a daily operational product of snow extent generated from the visible (VIS) and infrared (IR) radiometry of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary Meteosat Second Generation (MSG) satellites. The high temporal resolution and wide aerial coverage of SEVIRI imagery make it highly suitable for snow-cover mapping, since cloud cover is continuously monitored.
Indeed, the daily snow cover product is derived for a multi-temporal analysis of SEVIRI 15-min images, that are processed as new data are available to collect the largest possible number of cloud-free pixels. The sampling is performed at 3-km intervals, which degrade to ~5 km over Europe. The resulting daily map has a spatial coverage delimited between longitude 25 • W-45 • E and latitude 25 • -75 • N  [56, 70]  and it consists of four different classes: snow, cloud, water and bare ground (Figure  2 ).
Test Sites and Data Collection With the aim of properly investigating the reliability of satellite snow products and their consistency under different topographical conditions (i. e. , mountainous and flat areas) and vegetation cover, this study includes three case studies located in Finland, the Italian Alps and Turkey (Figures  4 5 6 ). In each country, eight S-2 tiles of interest have been selected to properly ensure a sizeable sample of satellite images. The selection of S-2 tiles has targeted those providing significant datasets over the analyzed period by minimizing possible overlapping. Furthermore, when selecting S-2 tiles the location of in-situ monitoring instruments has been considered to allow the validation of.
Test Sites and Data Collection With the aim of properly investigating the reliability of satellite snow products and their consistency under different topographical conditions (i. e. , mountainous and flat areas) and vegetation cover, this study includes three case studies located in Finland, the Italian Alps and Turkey (Figures  4 5 6 ). In each country, eight S-2 tiles of interest have been selected to properly ensure a sizeable sample of satellite images. The selection of S-2 tiles has targeted those providing significant datasets over the analyzed period by minimizing possible overlapping. Furthermore, when selecting S-2 tiles the location of in-situ monitoring instruments has been considered to allow the validation of S-2 imagery against ground-based data. It is noteworthy to consider that in this study both snow extent and FSC are referred to the snow cover viewable over the satellite field of view, and not at the ground level.
e. , deciduous forest). The selected tiles are reported in Table  3 , where the percentage values of the main vegetation categories are reported according to the GlobCover 300 m land cover map. The analysis period extends throughout two winter seasons, namely 2016/2017 and 2017/2018. With the aim of properly taking account of the local climatology, the duration of the snow season has been independently set from October to May (eight months) in Finland and over the Italian Alps, and from November to April (six months) in Turkey. Since this study is focused on assessing how satellite products succeed in detecting snow cover, cloud free scenes or scenes with minor cloud cover are primarily selected. Indeed, only S-2 images with cloud cover lower than 20% have been included in the analysis. The resulting datasets of S-2 imagery for the analyzed case studies are reported in Table  4.
It is noteworthy to consider that the effective number of S-2 images in the snow season 2017/2018 is significantly higher than in the previous one (Table  4 ), since S-2B data has become available in March  Since this study is focused on assessing how satellite products succeed in detecting snow cover, cloud free scenes or scenes with minor cloud cover are primarily selected. Indeed, only S-2 images with cloud cover lower than 20% have been included in the analysis. The resulting datasets of S-2 imagery for the analyzed case studies are reported in Table  4. It is noteworthy to consider that the effective number of S-2 images in the snow season 2017/2018 is significantly higher than in the previous one (Table  4 ), since S-2B data has become available in March 2017. Throughout the analyzed period, only one daily H10 image is missing during the first snow season and 7 images are missing in the second one.
In-Situ Webcam Imagery In Finland and in Italy, in-situ webcam imagery has been used to assess the consistency of FSC maps based on S-2 data (S-2-derived FSC), which are derived by counting the number of S-2 snow pixels versus the total number of S-2 pixels over the camera FOV. Webcams have been selected according to two main criteria. The first constraint requires a sufficiently wide webcam field of view (FOV) enabling the comparison with S-2-derived FSC. Secondly, webcams providing a properly representative dataset of observations have been primarily selected. With the aim of complying with these conditions, five webcams have been selected (Table  5 ), only one of which located over Italian Alps, mainly due to the complex topography, which strongly limits the extent of the webcam FOV. The four cameras selected in Northern Finland are part of the camera network deployed in the frame of the MONIMET project  [24].
In-Situ Snow Measurements In Turkey, binary snow maps derived from S-2 imagery have been validated against groundbased measurements for the winter season 2017/18. Snow data from automatic weather stations (i. e. , AWOS: Automated Weather Observing System, and SPA: Snow Pack Analyser) operated by Turkish state meteorological service (TSMS) have been used. Daily snow depth (SD) values have been obtained by processing and filtering the raw data supplied by these stations (e. g. , removal of possible  In Turkey, binary snow maps derived from S-2 imagery have been validated against ground-based measurements for the winter season 2017/18. Snow data from automatic weather stations (i. e. , AWOS: Automated Weather Observing System, and SPA: Snow Pack Analyser) operated by Turkish state meteorological service (TSMS) have been used.
Daily snow depth (SD) values have been obtained by processing and filtering the raw data supplied by these stations (e. g. , removal of possible false snow detection due to grass). This analysis relies on SD measurements provided by 75 ground stations and 205 S-2 images available between November 2017 and April 2018 over Turkey. The validation has been performed over 25 S-2 tiles and 286 in-situ SD observations have been analyzed. Relative positions of S-2 tiles and the ground stations are shown in Figure  8.
3. 1. 1. Sen2Cor Algorithm S-2 L1C data are downloaded from the Copernicus open access hub. The L1C image product consists of a series of 100 × 100 km 2 -tiles, each of which is made of thirteen compressed JPEG-2000 images, one for every single band. The MSI TOA reflectance images are processed through the Sen2Cor version 2. 5. 5, namely the last version of Sentinel-2 L2A prototype processor provided by ESA. Sen2Cor consists of ten main modules and it can perform the tasks of atmospheric, terrain and cirrus correction of L1C input data to generate optimally corrected BOA reflectance images. In this study the L2A_SceneClass (SC) module is used to perform the classification of the input images and to generate Scene Classification (SCL) maps at a spatial resolution of 20 m. The SC algorithm allows the detection of clouds, snow and cloud shadows, and the generation of a classification map consisting of four different classes for clouds (including cirrus), together with six different classifications for shadows, cloud shadows, vegetation, soils/deserts, water and snow (Table  6 ).
The SC module consists of the cloud/snow algorithm, the cirrus detection algorithm, and the cloud showdown detection algorithm to generate the classification map  [76]. Each algorithm processes the TOA reflectance input data through a sequence of thresholding filters, which are applied to S-2 spectral bands, band ratios, and indexes. In the cloud/snow detection algorithm, each test provides a cloud probability, which is recursively updated at each step. After thresholding the brightness in the red region of the solar spectrum (band 4), all potentially cloudy pixels are filtered by thresholding the NDSI  [72] , which is evaluated from spectral bands 3 and 11. Snow confidence map is generated by detecting snow pixels, according to four successive filters using spectral bands 2, 3, 8, 11. Ancillary information on yearly snow climatology is used to define the monthly snow probability of each pixel and to discard possible false snow detections.
However, a priori information on forests is not needed, because the effective average forest transmissivity is estimated from Earth observation reflectance data. Conversely, the retrieval algorithm for mountainous areas involves the thresholding of NDSI at 0. 4, since it allows the derivation of the resulting average fraction of snow-covered area by retrieving snow and snow-free ground from satellite data according to the reflectance values  [55]. Because in mountainous regions the sun zenith and azimuth angles, as well as direction of observation relative to these are the most limiting factors  [89] , possible terrain effects are properly removed from the measured radiance through a statistical-empirical correction method  [90]. Once the visible channel is corrected due to topographic effect, the average reflectance values are determined from pixels of pure snow-covered area and pure bare ground.
Indeed, especially in dense forests, the lack of intercepted snow can lead to a misleading S-2 classification as snow-free surface despite the presence of snow cover under canopy. Therefore, with the aim of addressing this critical issue, a further analysis has been performed according to the information on different vegetation types supplied by ESA GlobCover 2009 land cover map. The impact of the vegetation cover has been investigated throughout the whole analysis period by considering a sample of one tile in each country. The comparison between S-2-derived FSC and H12 product has been performed after preliminarily filtering out of the vegetated pixels in S-2 data by using the information supplied by GlobCover data for flat areas and without filtering in mountainous regions. For the filtering, S-2 derived FSC maps have been collocated with the GlobCover map.
No Snow c d From these classification results, different scores for dichotomous statistics are evaluated: • Probability of detection: POD = a/(a + c) (2) • False alarm ratio: FAR = b/(a + b) (3) • Probability of false detection: POFD = b/(b + d) (4) • Accuracy: ACC = (a + d)/(a + b + c + d) (5) • Critical success index: CSI = a/(a + b + c) (6) • Heidke skill score: HSS = 2(ad -bc)/[(a + c)(c + d) + (a + b)(b + d)] (7) FSC is assessed through the evaluation of Root Mean Square Error (RMSE) with respect to the reference dataset FSC re f. RMSE = ∑ n i=1 FSC re f ,i -FSC i 2 n (8).
In-Situ Digital Imagery The comparison relies on a total of 50 pairs of FSC values, resulting from the analysis of matching webcam and S-2 images, both in Finland and in Italy. The evaluation has revealed a total RMSE value of 12. 22%. In overall, S-2 snow mapping reveals a general FSC overestimation. When neglecting full-snow and bare-soil classification (i. e. , FSC equal to 0% and 100%), the total RMSE value increases up to 19. 82% for 19 value pairs. It is noteworthy that no outlier affects the distribution and the analyzed data boast a high correlation. Figure  12  shows the data scatterplot resulting from the comparison. FSC is assessed through the evaluation of Root Mean Square Error (RMSE) with respect to the reference dataset. RMSE = ∑ 𝐹𝑆𝐶 , -𝐹𝑆𝐶 𝑛 (8).
The agreement between the mapping of the FSC derived from S-2 imagery and H12 product has been assessed according to the same pixel-to-pixel approach. Like in the assessment of H10 data, the complex topography in mountainous areas affects the consistency between the two analyzed datasets, especially over the Italian Alps, where RMSE values are higher than the other case studies (Figure  17 ). However, RMSE scores are generally lower than 0. 4, except for the same tiles in Italy, in compliance with the product requirements  [105]. The higher consistency between H10 product and S-2 snow masks over flat areas is confirmed when assessing the evaluation metrics throughout the whole analysis period (Table  11 ). While H10 product reveals satisfying evaluation metrics of the same ranges in Finland and in Turkey, the Alpine complex topography strongly affects the snow mapping over this region, as proved by the poorer scores.