dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2000)  and cingulate regions  (Pandya et al. , 1981)  to the amygdala, which in turn projects directly to the vagal nuclei in the medulla, the nucleus ambiguus, and dorsal motor nucleus of the vagus nerve  (Hopkins and Holstege, 1978; Schwaber et al. , 1982; Spyer, 1994). Still, the analysis we performed cannot rule out the possibility that the link between firing rate and CCD is mediated by another structure, not investigated in the present dataset, that would influence both firing in entorhinal and cingulate cortices on the one hand, and CCD on the other. In line with previous results from  Frysinger and Harper (1989) , the correlation between firing rate and CCD did not appear to be linked to the respiratory-related changes in heart rate  (Eckberg, 2009). Our results further emphasize the colocalization of neurons that increases or decreases heart rate within the same region.
research and innovation  program  (Grant Agreement No. 670325 , Advanced Grant Bravius) and a senior fellowship from the  Canadian Institute for Advanced Research program in Brain ,  Mind & Consciousness  to C. T. -B. , as well as from  ANR-10-LABX-0087 Institut d'E ´tudes cognitives ,  ANR-10-IDEX-0001-02   PSL University ,  ANR-10-IAIHU-06 , and the  Brain & Spine Institute-L'Institute du Cerveau et de las Moelle epinie  `re. M. B. -R. was supported by Fundaca ˜o para a  Ciencia e Tecnologia Grant   SFRH/BD/85127/2012. The authors thank neurosurgeons Stephane Clemenceau and  Bernard Mathon , the clinical staff of the epileptology unit, and the patients who participated in the study. The authors declare no competing financial interests.
This study aims at validating the cloud mask produced by the Land Surface Reflectance Code (LaSRC) for Landsat 8 data. To detect clouds in optical satellite imagery, LaSRC uses quality assurance (QA) layers, which are produced during the atmospheric correction process. The QA layers include a "Cloud mask", which is based on the estimation of a residual metric showing the quality of aerosol inversion, and "High aerosol", which shows the impact of aerosols on the derived surface reflectance. Validation is performed using the "L8 Biome" cloud validation dataset, which is produced by the US Geological Survey (USGS), and consists of 96 Landsat 8 scenes distributed globally over 12 different biomes. We show that the LaSRC cloud detection algorithm reliably identifies thick clouds with commission and omission errors less than 4%.
I. INTRODUCTION LOUD detection in optical satellite imagery is a prerequisite to producing and delivering high quality higher-order products. Cloud misdetection (cloud omission) may lead to the retrieval of an erroneous signal from a satellite sensor (e. g. surface reflectance or vegetation indices), and these errors can propagate into higher level thematic products used, for example in agricultural, environmental or natural hazards monitoring applications. On the other hand, overdetection of clouds (cloud commission) may reduce the amount of valuable cloud-free data which, in this case, would be incorrectly identified as clouds. With the availability of high temporal resolution images (5-10 days) acquired by moderate spatial resolution (10-30 m) sensors aboard Landsat 8 and Sentinel-2 satellites, it is of great importance to develop and validate cloud detection algorithms, which can be applied globally for multiple conditions within an operational context.
The Fmask algorithm  [5]  builds upon ACCA and other algorithms and utilizes a set of physical based rules to identify cloudy pixels in Landsat images. The potentially cloudy pixels are further refined based on segmentation and geometrical properties of identified objects. The Fmask algorithm produces cloud and shadow layers, as well as a snow layer, which is produced using the Normalized Difference Snow Index (NDSI) and brightness temperature. The Fmask algorithm is constantly improving, and several further modifications have recently been proposed, including to be applied to other satellites such as Sentinel-2  [6] ,  [7] ,  [8]. The C programming language version of the FMask algorithm (called CFmask) is used by the US Geological Survey (USGS) in a production environment to deliver the quality assessment band for Landsat 8 products  [9].
In order to cross-compare operationally ready cloud detection algorithms to be used for Landsat quality assurance (QA) data products,  Foga et al. (2017)   [9]  performed a largescale analysis using USGS cloud reference datasets. These validation datasets included "L7 Irish" (for Landsat 7)  [4] ,  [1] , "L8 SPARCS"  [16]  and "L8 Biome"  [9]  for Landsat 8. Among the algorithms tested were various versions of Fmask, ACCA, See5, and the Land Surface Reflectance Code (LaSRC) cloud mask. The LaSRC cloud mask is one of the products generated within the atmospheric correction process  [19]. Foga et al. (2017)   [9]  reported a 73. 07% overall accuracy (OA) for the LaSRC cloud mask with omission and commission errors 4. 70% and 23. 90%, respectively. However, that study did not provide algorithm description, did not describe criteria used, did not investigate the source of discrepancy between the LaSRC produced cloud masks and reference cloud masks, and results were not reported for the different types of clouds (thin and thick) available in the reference datasets and different types of biomes.
The present study aims to fill this gap and provide a more comprehensive description and analysis of the LaSRC cloud detection algorithm for the "L8 Biome" validation data. In particular, we show that there are two major factors leading to relatively large LaSRC's commission errors reported in  [9] : (1) snow cover, for which LaSRC should not be directly applied; (2) thin (cirrus) clouds, which have a high degree of subjectivity in the reference dataset. We also show some inconsistencies in the "L8 Biome" reference cloud mask, which can be attributed to the subjectivity of manual cloud classification.
Residual = * + , - This residual metric (Eq. 1) is the main criterion for detecting thick clouds, since the latter will either prevent the AOT inversion process from convergence, or will drive the residual metric to high values. It should be noted that, unlike most cloud detection algorithms which use TOA reflectance values, the LaSRC cloud detection algorithm uses surface reflectance residual values and incorporates data on atmospheric conditions. These differentiate the proposed approach from the previous ones. A specific threshold should be selected to determine cloudy and clear pixels in Landsat 8 imagery. This threshold was set to 0. 05. Pixels with residual values exceeding this threshold are marked as cloudy ones. Pixels adjacent to clouds within 5 pixels (150 m) are separately masked as being 'adjacent to clouds'.
B. Data description The "L8 Biome" cloud validation dataset  [27]  consists of 96 Landsat 8 scenes, which were selected using a semi-random sampling by biomes  [9]. These biomes included barren, forest, grass/crops, shrubland, snow/ice, urban, water and wetlands. For each biome, 12 Landsat 8 scenes were selected, and each scene was manually classified into the following classes: clear, thin cloud, cloud, and cloud shadow. It should be noted that no specific threshold was used to detect thin clouds, which were primarily determined by the analyst. Also, the cloud shadow class in the validation dataset was not provided for all the Landsat 8 scenes. Out of 96 reference Landsat 8 scenes in the "L8 Biome" validation dataset, only 79 scenes were included for evaluating the LaSRC cloud detection algorithm.
Among the 17 scenes not considered, 16 scenes were fully covered with snow/ice (12 from the snow/ice biome and 4 from other biomes), and 1 scene had inconsistencies in depicting thin clouds (Fig. 2 ). Fig. 2  shows a subset of the Landsat 8 scene (LC80350192014190LGN00) with inconsistent detection of thin clouds. While the reference image shows the existence of the boundary between thin clouds and clear pixels, no clear boundary exists in the satellite imagery, including when using true/false color combinations or the cirrus band. As per our analysis, this was the only scene with such major inconsistencies in the validation dataset. The reason for removing snow/ice scenes is that snow/ice pixels are detected as cloudy or high aerosol by the LaSRC algorithm, are considered low quality pixels and should not be used for further processing.
C. Performance metrics We used a set of performance metrics, similar to those used in  [9] , to validate the LaSRC cloud detection algorithm against the "L8 Biome" dataset. These metrics included overall 𝑂𝐸 = 100 × K total_cloud RK thin_as_cloud RK thick_as_cloud K total_cloud , (3) 𝐶𝐸 = 100 × T K clear_as_cloud MK shadow_as_cloud K thin_as_cloud MK thick_as_cloud MK clear_as_cloud MK shadow_as_cloud V, (4) where  total is the total number of valid pixels in the scene;  clear_as_clear ,  thin_as_cloud ,  thick_as_cloud are the numbers of correctly classified pixels by the LaSRC algorithm;  total_cloud is the total number of cloud pixels (both thin and thick) in the "L8 Biome" dataset;  clear_as_cloud and  shadow_as_cloud are the numbers of pixels incorrectly classified as cloud by the LaSRC algorithm.
III. RESULTS Fig. 3  shows examples of LaSRC cloud detection performance in some of the Landsat 8 images from the reference dataset. Fig. 4  shows a histogram of the residual metric values (Eq. 1) for clear and cloudy pixels from all scenes of the reference datasets. We selected a rather conservative threshold value of 0. 05 (Table  I ) and Fig. 4  validates the selection of this threshold, so clear and cloudy pixels can be discriminated. Table  II  shows overall performance of the LaSRC cloud detection algorithm for Landsat 8 images in terms of OA, cloud OE and CE, depending on the QA layers applied. Thick clouds were reliably detected by the LaSRC algorithm with OE=3. 86% and CE=3. 16%. Adding the adjacent cloud layer to the cloud mask decreased OE from 3. 86% to 2. 19%, while increased CE from 3.
16% to 13. 02%. This finding relates to the ambiguity of cloud boundary detection both in the validation dataset and LaSRC derived cloud mask. Adding the high aerosol layer to the cloud mask decreased OE from 3. 86% to 0. 63%, while substantially increased CE from 3. 16% to 27. 58%. This shows that the LaSRC cloud and high aerosol masks basically captured all the thick cloud pixels in the validation dataset. As expected, thin clouds from the validation dataset were in disagreement with the LaSRC cloud layer only yielding OE=42. 86% and CE=14. 91%. Adding the high aerosol layer substantially improved detection of thin clouds with OE improving from 42. 86% to 4. 94%, however at the expense of overdetection, i. e. with CE increasing from 14. 91% to 55. 86%. Adding the adjacent cloud layer also improved detection of thin clouds: CE decreased from 42.
86% to 23. 13%. This means that a portion of thin clouds in the validation dataset were detected as the boundary of thick clouds. Further visual inspection confirms this explanation. Overall, performance of the LaSRC algorithm for thin clouds suggests that the LaSRC high aerosol layer can be used to detect thin clouds; however, large discrepancies with the "L8 Biome" validation dataset exist in terms of overdetection, which corresponds to the statement of subjectivity of detecting thin clouds in the reference dataset  [9]. Moreover, when analyzing the distribution of the Landsat 8 cirrus band TOA reflectance values for thin clouds from the "L8 Biome" dataset, approximately 47% pixels identified as thin cloud are uniformly distributed in the range 0. 0005 to 0. 01 (reflectance units) (Fig. 5 ).
This means that to detect such thin clouds, a low threshold should be selected for the cirrus band. These thin clouds will be misidentified by most existing algorithms, which use the following thresholds (reflectance units): 0. 0113  [10] , 0. 02 and 0. 04  [5] ,  [6]. The other 53% of pixels identified as thin cloud in the "L8 Biome" dataset have reflectance values in the cirrus band of more than 0. 01. When thin and thick cloud masks from the validation dataset are combined together, the LaSRC cloud detection algorithm yielded the following performance (Table  II ): OE=13. 17% and CE=2. 68% for the cloud layer only, and OE=0. 62% and CE=24. 62% for the combined cloud, adjacent cloud and high aerosol layers. Again, these numbers are dependent on the subjective criteria used to define the cloud boundary and thin cloud.
Users, working on snow/ice applications and requiring discrimination between clouds and snow/ice, are recommended to use other algorithms, for example Fmask  [6] , as LaSRC will mask out both snow/ice and clouds as unreliable retrievals. Further research should be performed to provide a more objective validation of the LaSRC cloud mask, especially for thin clouds. A more formal and quantitative definition of thin cloud should be adopted, which will reduce subjectivity in producing 'ground truth' datasets for cloud validation. This, in our opinion, should combine, in addition to photointerpretation, ground based photos of the sky  [28] , e. g. from the Atmospheric Radiation Measurement (ARM) Climate Research Facility  [29] , and data on the atmospheric properties measured by NASA's Aerosol Robotic Network (AERONET)  [30]  of sites, which are distributed globally.
Although ground based photos provide a limited field of view, which makes it difficult to cover the full satellite scene swath (e. g. from Landsat 8), they can be used as a 'seed point' to direct an analyst to manually produce a cloud mask or as training data to classify clouds for the full scene using machine learning approaches. Also, ground based photos together with AOT retrievals from the Aeronet stations can provide a cloud opacity classification  [28]  and, thus, can provide a quantitative approach for thin clouds definition. An example of a geo-referenced ground photo of clouds made synchronously with Landsat 8 acquisitions over NASA's Goddard Space Flight Center (GSFC) is shown in Fig. 6.
V. CONCLUSION In this study, we validated the LaSRC derived cloud mask for snow/ice-free Landsat 8 images using the "L8 Biome" cloud validation dataset, generated by USGS. Within the atmospheric correction process, the LaSRC algorithm generates several QA layers related to clouds, which, unlike many other cloud detection algorithms, are based on land surface reflectance and incorporate information on atmospheric properties. The analysis was performed using 79 Landsat 8 scenes, which are distributed globally over multiple biomes. The LaSRC cloud detection algorithm was reliable in identifying thick clouds from the reference data with commission and omission errors less than 4%. Larger discrepancies were observed for thin clouds, which can be related to the subjectivity in identifying thin cloud in the reference data.
VLT/MUSE observations All five CARS targets were observed with MUSE at the Very Large Telescope (VLT) under ESO programs 094. B-0345(A) and 095. B-0015(A). The MUSE data cover 1 × 1 FoV at a 0. 2 spatial sampling and a wavelength coverage of 4650-9300 Å with R ∼ 2500. Integration times range from 400 s-900 s split up into two or three exposures, which are rotated by 90 • against each other for cosmic-ray rejection and better image cosmetics. The data were reduced with the standard MUSE pipeline (version 1. 6. 0, Weilbacher et al. 2012 Weilbacher et al. , 2014)). As a first step in the post-processing, we subtracted the bright point-like AGN emission from the reconstructed datacube using QDeblend 3D , as described in  Husemann et al. (2013 Husemann et al. ( , 2014 Husemann et al. ( , 2019)).
SED fitting and FIR luminosities We constructed spectral energy distributions (SEDs) for all targets using publicly available broad-band photometry from the following catalogs: the Herschel/SPIRE Point Source Catalogue 1. 17 ± 0. 41 1. 94 ± 0. 25 (*) Integrated over 42. 5-122. 5 µm  (Schulz et al. 2017 ), the AKARI/FIS Bright Source Catalogue  (Yamamura et al. 2009 ), the 2MASS Extended Source Catalog  (Jarrett et al. 2000) , and the GALEX Source Catalog  (Bianchi et al. 2017). For the following datasets we applied aperture measurements on the survey images: Herschel/PACS images from the Herschel Science Archive 2  , WISE Image Atlas  (Cutri et al. 2011) , optical grizy photometric images 3  from the Pan-STARRS DR1  (Chambers et al. 2016) , and Swift/UVOT images from the Barbara A. Mikulski Archive for Space Telescopes 4.
Acknowledgements. We thank the anonymous referee for helpful comments that improved the quality of the manuscript. Based on observations made with the  NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA is jointly operated by the  Universities Space Research Association, Inc. (USRA) , under  NASA  contract  NNA17BF53C , and the  Deutsches SOFIA Institut (DSI)  under  DLR  contract  50 OK 0901  to the  University of Stuttgart. Based on observations collected at the  European Organization for Astronomical Research in the Southern Hemisphere  under  ESO  programme  094. B-0345 (A) and  095. B-0015 (A). M. G. is supported by the  Lyman Spitzer Jr. Fellowship (Princeton University ) and by  NASA  Chandra  GO7-18121X  and  GO8-19104X. MPT acknowledges support from the  Spanish MINECO  through grant  AYA2015-63939-C2-1-P.
Western Blot Protein extracts from CFBE41o-cell lines expressing CFTR were separated on 7% (w/v) polyacrylamide gels, transferred to PVDF membranes, and analyzed by Western blot (WB). Membranes were blocked and probed with 1:5000 mouse anti-CFTR 596 (CFF, Cat No. A4) or with 1:3000 mouse anti-calnexin (BD Transduction Laboratories™, Cat No. 610523, San Jose, CA, USA), which were used as the loading control. Membranes were washed and incubated for 1 h with anti-mouse secondary antibody. Chemiluminescent detection was performed using the Clarity™ Western ECL substrate (BioRad, #1705061, Hercules, CA, USA) and the Chemidoc™ XRS system (BioRad). The quantification of band intensity was performed using the Image Lab software (BioRad) and normalized to the loading control as appropriate.
The ten most intense ions were fragmented by higher energy C-trap dissociation with normalized collision energy of 28, and MS/MS spectra were recorded with a resolution of 17,500 at m/z 200. The maximum ion injection time was 120 ms for both survey and MS/MS scans, whereas AGC target values of 3 × 10 6 and 5 × 10 5 were used for survey and MS/MS scans, respectively. In order to avoid repeat sequencing of peptides, dynamic exclusion was applied for 30 s. Singly-charged ions or ions with an unassigned charge state were also excluded from MS/MS. Data were acquired using Xcalibur software (version 4. 0, Thermo). The intensity-based absolute quantification (iBAQ)  [21] , a label-free proteome quantification method, was used to determine the stoichiometries of the identified proteins. This quantification was based on peak intensity in survey scans, corresponding to the sum of all the peptides intensities divided by the number of observable peptides of a protein and was integrated into the quantitative proteome software package MaxQuant  [22, 23].
All data were searched with Virtual Expert Mass Spectrometrist (VEMS)  [24]  and MaxQuant  [25]. Mass accuracy was set to 5 ppm for peptides and 10 mDa for peptide fragments. Gaussian weight for fragment ions was set to 5, and the six most intense fragment ions per 100 Da were used for scoring fragment ions. Two missed cleavages were specified, and the Human Database from UniProtKB (Release 2015_02) was used, including permutated protein sequences, leaving Lys and Arg in place, together with common contaminants such as human keratins, bovine serum proteins, and proteases. The total number of protein entries searched was 136,314. Fixed modification of carbamidomethyl cysteine was included in the search parameters. A list of 8 variable modifications was considered for all data against the full protein database.
Protein N-terminal Met-loss was not specified for VEMS searches since VEMS by default checks N-terminal Met-loss. The false discovery rate (FDR) for protein identification was set to 1% for peptide and protein identifications unless otherwise specified. No restriction was applied for minimal peptide length. Identified proteins were divided into evidence groups as defined previously  [26]. Statistical analysis and data filtering were performed using the R statistical programming language (https://www. r-project. org/). After identification and quantification of the mass spectrometry results, each protein was normalized to the total amount of protein in the respective sample (iBAQ quantitation). To filter for unspecific interactions, a combination test was performed between each sample and the background (pull-down from parental CFBE41o-cells) using an R script.
Networks and Gene Ontology The Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david. ncifcrf. gov/) [27, 28]  was used to analyze the obtained dataset and identify the Gene Ontology (GO) terms that were enriched in our data. The GO terms considered were the biologic process (BP) and cellular component (CC) using Version 6. 8. Networks were generated using the Agile Protein Interactome DataServed (APID) (http://cicblade. dep. usal. es:8080/APID/init. action) [29] , accessed and visualized/analyzed using Cytoscape (http://cytoscape. org) [30]. Networks were presented with only the proteins detected in the interactomes. The interacting proteins were plotted according to the yFiles Organic Layout algorithm.
Principal Component Analysis Principal component analysis was performed using the package "pca3d" (https://cran. r-project. org/web/packages/pca3d/index. html) within R  [31]. The median of the processed immunoprecipitation values for each CFTR variant was computed for each protein; thus, a data frame with 5 columns and as any rows as the proteins measured was created. The principal components for this dataset were computed, and a 3D scatter plot of the 3 main components was used for visual inspection and clustering analysis. Due to the nature of the data provenance being the same, it was not scaled, nor centered for PCA analysis. Clustering was performed using hierarchical agglomerative clustering through the standard R function "hclust" and using average linkage as a clustering method. The resulting clusters were visually assessed using the 3D plots for different partition thresholds that produced different clustering arrangements, ranging from 3-12.
We then used this cellular system in parallel with wt-CFTR CFBE-expressing cells  [32]  to obtain pulled-down proteins that interact with either wt-or DD/AA-CFTR following a general strategy consisting of: (i) cell lysis and immunoprecipitation (IP) of CFTR and interacting proteins; (ii) identification of proteins by LC-MS/MS; and (iii) analysis of the CFTR interactome (database search and statistical filtering). Parental CFBE cells were used as the control (background sample). By this analysis, we identified a total of 1150 different proteins: 979 proteins for DD/AA-CFTR IP and 945 proteins for wt-CFTR IP (Datasets S1A,B, respectively), 45% being common to both sets. The analysis was performed in triplicate; similarity analysis among replicates showed an overlap of 77% in DD/AA-CFTR and 62% in wt-CFTR, confirming the reproducibility of the approach.