dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1999; Naeimi et al. , 2009). ASCAT is an active C-band radar with a spatial resolution of 25 km. Soil moisture is retrieved as the degree of saturation and sampled onto a 12. 5 km discrete global grid. Data can be obtained upon registration from http://hsaf. meteoam. it/soil-moisture. php (last access: 1 July 2019). SMOS data are the reprocessed L2 soil moisture retrievals version V650, which can be obtained upon registration from https://smos-diss. eo. esa. int/ (last access: 1 July 2019;  Kerr et al. , 2012). SMOS is a passive L-band interferometric radiometer with an average spatial resolution of 43 km. Soil moisture is retrieved in volumetric units and sampled on a 15 km discrete global grid. SMAP data used are the 36 km L2 radiometer-only soil moisture retrievals (SPL2SMP), algorithm version 5 (R16010; DOI: https://doi.
org/10. 5067/SODMLCE6LGLL; O'  Neill et al. , 2018). The passive SMAP radiometer operates at L-band at a spatial resolution of 40 km. Soil moisture is retrieved in volumetric units and sampled on the 36 km EASE grid version 2  (Brodzik et al. , 2012). MERRA-2  (Gelaro et al. , 2017)  is the latest atmospheric reanalysis produced by NASA's Global Modelling and Assimilation Office. Soil moisture is estimated on a 0. 5 ∘ × 0. 625 ∘ grid in volumetric units as internal state variable of its land surface component, the Catchment Land Surface Model  (Koster et al. , 2000). Here we use soil moisture estimates of the surface layer, which refers to the top 5 cm of the soil  (GMAO, 2015). MERRA-2 data can be downloaded from https://gmao. gsfc. nasa. gov/reanalysis/MERRA-2/data_access/ (last access: 1 July 2019).
A. 2. Pre-processing Unreliable soil moisture retrievals of the individual satellite products are masked out following the recommendations of the data providers. ASCAT soil moisture retrievals are masked out if the correction flag has a value other than 0 or 4, if the confidence flag and the processing flag have values other than 0, or if the surface state flag  (Naeimi et al. , 2012)  has a value other than 1. SMOS retrievals are masked out if the RFI probability exceeds 0. 1 or if the Chi-2 probability drops below 0. 05. SMAP data are masked out if the retrieval quality flag has a value other than 0 or 8. In addition, soil moisture retrievals of all satellite products are masked out at time steps where MERRA-2 estimates a soil temperature below 4 ∘ C or nonzero snow mass. ASCAT, SMOS and MERRA-2 are resampled to the 36 km EASE v2 grid that is used for SMAP retrievals using a nearest-neighbor approach.
A. 3. 1. Sample size All metrics are calculated from the same collocated data points, i. e. days where all four data sets provide valid soil moisture estimates. The number of temporal matches at each grid cell within our study domain is shown in Fig. A. 1. As discussed in Section 3, sample size directly translates into statistical power, i. e. reliability (in terms of confidence intervals) of the calculated skill metrics. Sample sizes obtained here, which range from 150 in the more mountainous areas to up to about 300-500 in the rest of the CONUS, are typically considered high and associated with reasonably low confidence intervals for validation purposes. However, as discussed in Section 3. 6, confidence intervals are affected by temporal auto-correlation. "Effective" sample sizes, corrected for autocorrelation using Eq.
A. 3. 2. Relative metrics Figs. A. 4, A. 5 and A. 6  show spatial plots of relative (mean) bias, ubRMSD and R 2 (coefficient of determination or squared Pearson correlation) estimates for raw soil moisture values, respectively, and Figs. A. 7 and A. 8 show ubRMSD and R 2 estimates for soil moisture anomalies, respectively. Biases are only calculated for raw soil moisture time series and between soil moisture estimates that are expressed in the same unit, i. e. for SMOS, SMAP, and MERRA-2 which provide estimates of volumetric soil moisture. ASCAT estimates of the degree of saturation could be converted into volumetric units using porosity information, but since the quality of soil texture maps on these scales is questionable, this is not recommended for bias estimation purposes. Note also, that the biases between the remaining three data sets also include collocation and (vertical and horizontal) scale mismatches and should therefore be interpreted with care.
A. 3. 3. Triple collocation metrics As discussed in Section 3, TCA requires three data sets with independent random errors. Since errors of SMAP and SMOS are expected to be correlated (see Section 4. 3), two independent data set triplets can be formed, i. e. ASCAT -SMOS -MERRA-2 and ASCAT -SMAP -MERRA-2. This results in unambiguous skill estimates for SMAP and SMOS, and in two skill estimates for ASCAT, which are averaged for increased precision. Figs. A. 10 and A. 11  show spatial plots of TCA-based ubRMSE and R 2 (coefficient of determination w. r. t. the unknown truth) estimates, respectively, and Figs. A. 12 and A. 13 show ubRMSE and R 2 estimates for short-term soil moisture anomalies, respectively. The skill estimates represent the median of the bootstrapped sampling distribution, which are more robust than the direct estimates, and 80% confidence intervals (i.
A. 4. Final remarks In this appendix, we provide an illustrative validation example that follows the good practice guidelines presented in this paper. For brevity, we omit the presentation of ground data comparisons, which can be calculated and presented in the exact same way as the area-wide coarse-scale comparisons shown above. For simplicity, results are presented in spatial maps and boxplots that cover all of CONUS without further stratification. For summary information or if metrics are only computed at a few locations using ground reference data, results could be further presented in tabular format. Some examples of comprehensive ground reference data comparison including both sparse networks and core validation sites can be found in                 -3 ] and R 2 estimates  [-]  for raw soil moisture estimates (top) and soil moisture anomaly estimates (bottom) of ASCAT when using SMOS as third data set minus when using SMAP as third data set in the triplet.
Acknowledgements We acknowledge the support from the  International Space Science Institute  (ISSI; http://www. issibern. ch/;last access: 1 July 2019). This publication is an outcome of the  ISSI's Team on  " Adding value to soil moisture information for climate studies " and has received funding from the  eartH2Observe project (European Union 's  Seventh Framework Programme , Grant Agreement No. 603608 ), from the  KU Leuven   C1  internal fund  C14/16/045 , from the  Research Foundation Flanders  ( FWO-1224320N  and  FWO-1530019N ), and from the  European Space Agency's Climate Change Initiative for Soil Moisture  (Grant Agreement No. 4000126684/19/I-NB ).
CVS) data that have been specifically processed for SMAP validation. https://nsidc. org/data/nsidc-0712 GLDAS NASA's global modelling and data assimilation system https://ldas. gsfc. nasa. gov/gldas/ MERRA NASA's global reanalysis data sets https://gmao. gsfc. nasa. gov/reanalysis/MERRA-2/ ERA ECMWF's global reanalysis data sets https://www. ecmwf. int/en/forecasts/datasets/browse-reanalysisdatasets/.
The RNAsequencing data are accessible through Gene Expression Omnibus (GEO) series accession number GSE154905. Single gene expression via RT-qPCR (real-time quantitative PCR) was also performed to further validate results from the RNA-sequencing (RNA-seq) data. Prior to reverse transcription, DNase treatment was performed on 300 ng RNA using RNAse-free DNAse I (04716728001, Roche, Basel, Switzerland) at 37 °C for 10 min. Reaction was then stopped by increasing the temperature to 70 °C for 10 min. Complementary DNA (cDNA) was synthesized from DNase I-treated total RNA (300 ng) using M-MLV reverse transcriptase (M1705, Promega, Southampton, UK) as per supplier's protocol (1 h at 37 °C followed by 10 min at 70 °C). Real-time quantitative PCR (qPCR) was performed in a total volume of 15 μL using 2× LightCycler ® 480 SYBR Green I Master (04707516001, Roche, Basel, Switzerland), 1.
Briefly, lysates were mixed with 70% ethanol and loaded onto a silica-membrane column. Columns were washed and total RNA was eluted in nuclease-free water and stored at -80 • C until use. The quality of all samples was assessed with a TapeStation 4200 (Agilent Technologies, Santa Clara, CA, USA) to verify RNA integrity. All samples had an RNA integrity number > 8. Sequencing libraries were prepared using a TruSeq stranded mRNA sample preparation kit (Illumina, San Diego, CA, USA) following manufacturer's instructions. Pooled libraries were sequenced at >15 million (75 bp) single reads per sample using a NextSeq 500 and high output kit (75 cycles) (Illumina). The RNA-sequencing data are accessible through Gene Expression Omnibus (GEO) series accession number GSE154905. Single gene expression via RT-qPCR (real-time quantitative PCR) was also performed to further validate results from the RNA-sequencing (RNA-seq) data.
Alcian blue-periodic acid-Schiff (AB-PAS) staining was then performed by incubating the slides for 5 min in Alcian blue (B8438, Sigma-Aldrich), periodic acid solution (Reagent 1 of the PAS staining kit; 1016460001, Sigma-Aldrich) for 10 min and Schiff's reagent (Reagent 2 of the PAS staining kit) for 15 min (slides were rinsed in running water and then dH 2 0 between each incubation). Following this, slides were dehydrated by sequential incubation in solutions containing increasing ethanol concentrations and cleared in xylene for 5 min before being mounted using DPX mountant (06522, Sigma-Aldrich) and a 22-mm square coverslip. Slides were left to dry overnight before imaging using a Zeiss Axio Imager 2 (Cambridge, UK). Epithelial thickness was later measured using Fiji imaging software  [50].
The transepithelial short-circuit current (Isc) and the transepithelial electrical resistance (TEER) were recorded using Ag-AgCl electrodes in 3 M KCl agar bridges and the Acquire & Analyze software (Physiologic Instruments, San Diego, CA, USA), as previously described  [52] , and results normalized to an area of 1 cm 2 and expressed as Isc (µAmp. cm -  ).
The data analysis was performed as previously described  [53] : after subtracting background values from pHrodo and Alexa Fluor ® 488, ratios were generated for each time point and pH was calculated from a standard curve where pH was clamped using highly buffered solutions between 5. 5 and 8. To reduce inter-experiment variability, the standard curve calibration was performed at the end of each independent experiment. 2. 9. Data Analysis 2. 9. 1. RNA-Sequencing Analysis Quality of FASTQ files was assessed with FastQC, Version 11. 7. No read trimming was necessary, and all samples were retained for further analysis. Reads were quantified against transcripts using Salmon, a program for quantifying expression of transcripts from RNA-Seq data. Salmon quantifies reads against transcripts. To obtain gene-level counts, the R package 'tximport' was used.
Gene annotation was obtained from Ensembl gene ids using the R package 'biomaRt'. Differential expression analysis was carried out using the R package DESeq2  [54]. DESeq2 estimates fold change between experimental conditions using a Negative Binomial GLM with a logarithmic link function. The resulting estimates of logarithmic fold change (Log 2 FC) were tested for significance using a Wald test, in which the estimate of Log 2 FC was divided by its standard error to obtain a test statistic, which was then compared to a standard normal distribution.
Airway Epithelial Cell Subtype Identification The 50 most expressed markers for each airway cell subtype were extracted from the "Lung Atlas: Epithelial Cells" dataset from the "Teichmann Lung and Asthma Atlases-available from UCSC Cell Browser (https://cells. ucsc. edu) [2]. The Log 2 FC values obtained from our RNA-seq dataset were then matched to these genes when present.
Patterns of Gene Expression and Signaling Pathways Using NetworkAnalyst NetworkAnalyst (https://www. networkanalyst. ca, [55] ) was used to identify specific patterns of gene expression and signaling pathways (KEGG-Kyoto Encyclopedia of Genes and Genomes-and Reactome analysis), biological processes, molecular functions. and cellular compartments (PANTHER analysis) that were significantly regulated by the differentiation media. Organism (H. sapiens) and ID type (ensemble gene ID) were specified and lists of upregulated and downregulated genes were uploaded along with the corresponding Log 2 FC. The list enrichment network of the assorted visual analytics was then selected and enrichment analysis performed using the KEGG, Reactome, PANTHER BP (Biological Process), MF (Molecular Function), and CC (Cellular Component) databases.
Growth Conditions Dictate Airway Epithelial Cell Identity The RNA-seq dataset was next used to investigate the impact of the two media on epithelial cell subtype markers of CF cultures. The 50 most expressed markers for each airway cell subtype were extracted from the "Lung Atlas: Epithelial Cells" dataset from the "Teichmann Lung and Asthma Atlases-available from UCSC Cell Browser (https://cells. ucsc. edu) to which the Log 2 FC values obtained from our RNA-Seq dataset were matched. The 15 most expressed markers for each cell subtype are presented in Figure  4. Cells 2020, 9, 2137 11 of 26 extracted from the "Lung Atlas: Epithelial Cells" dataset from the "Teichmann Lung and Asthma Atlases-available from UCSC Cell Browser (https://cells. ucsc. edu) to which the Log2FC values obtained from our RNA-Seq dataset were matched.
Most expressed genes per airway epithelial cell type were extracted from the dataset "Lung Atlas: Epithelial Cells" from the "Teichmann Lung and Asthma Atlases-available from UCSC Cell Browser (https://cells. ucsc. edu). The Log 2 FC, for the 15 most expressed genes for each cell type, were then extracted from our transcriptomic study and plotted on the x-axis. On average, for each set of 50 genes, the results show that basal cell markers (Log 2 FC Basal 1 = -0. 1015 ± 0. 1434; Basal 2, -0. 04046 ± 0. 1190) were not affected by differentiation media but that genes associated with club, goblet 1, and goblet 2 subtypes were significantly increased in UNC-grown epithelial cells compared to SC by 1. 6 fold (Log 2 FC = 0. 6596 ± 0. 2134), 2. 1 fold (Log 2 FC = 1. 060 ± 0. 1698), and 1. 9 fold (Log 2 FC = 0.
Indeed, using the KEGG and Reactome databases, we found that 'ABC transporters', 'salivary secretion', 'transmembrane transport of small molecules', 'passive transport by aquaporins', 'ion channel transport', 'multifunctional anion exchangers', and 'stimuli-sensing channels', respectively, were all downregulated in UNC medium compared to SC medium (Figure  5A, C ). KEGG and Reactome pathways upregulated in UNC medium were those related to 'infection' (KEGG) and 'protease activity' (Reactome) (Figure  5B, D ). Cells 2020, 9, 2137 12 of 26 15 most expressed genes for each cell type, were then extracted from our transcriptomic study and plotted on the x-axis.
Indeed, using the KEGG and Reactome databases, we found that 'ABC transporters', 'salivary secretion', 'transmembrane transport of small molecules', 'passive transport by aquaporins', 'ion channel transport', 'multifunctional anion exchangers', and 'stimuli-sensing channels', respectively, were all downregulated in UNC medium compared to SC medium (Figure  5A, C ). KEGG and Reactome pathways upregulated in UNC medium were those related to 'infection' (KEGG) and 'protease activity' (Reactome) (Figure  5B, D ). The use of PANTHER databases showed an upregulation in 'ion transport', 'ATPase activity coupled to transmembrane movement of substances', 'ion channel activity', 'voltage-gated sodium channel activity', 'chloride channel regulator activity', and 'voltage-gated ion channel activity' when CF cells were grown in SC medium compared to UNC (Figure  6A, C ).
Interestingly, this analysis also  The use of PANTHER databases showed an upregulation in 'ion transport', 'ATPase activity coupled to transmembrane movement of substances', 'ion channel activity', 'voltage-gated sodium channel activity', 'chloride channel regulator activity', and 'voltage-gated ion channel activity' when CF cells were grown in SC medium compared to UNC (Figure  6A, C ). Interestingly, this analysis also showed a decrease in 'microtubule motor activity' in the UNC condition, suggesting that SC medium could upregulate cilia function (Figure  6C ). PANTHER analysis also reported an increase in 'proteolysis', 'immune response', and various enzymes activities (peptidases, hydrolase, oxidoreductase) in cells grown in UNC medium compared to SC (Figure  6B, D ). Performing the analysis using the cellular component PANTHER database did not reveal any major differences between CF cells grown in UNC and SC media (Figure  6E, F ).
Taken together, the pathway analysis of the transcriptomic data suggests that differentiation media specifically targeted genes involved in ion and fluid transport. As this is of specific interest and very relevant to CF research, the rest of the paper focuses on this aspect. Cells 2020, 9, 2137 13 of 26 showed a decrease in 'microtubule motor activity' in the UNC condition, suggesting that SC medium could upregulate cilia function (Figure  6C ). PANTHER analysis also reported an increase in 'proteolysis', 'immune response', and various enzymes activities (peptidases, hydrolase, oxidoreductase) in cells grown in UNC medium compared to SC (Figure  6B, D ). Performing the analysis using the cellular component PANTHER database did not reveal any major differences between CF cells grown in UNC and SC media (Figure  6E, F ).
Our results show that epithelial architecture as well as cell density were strongly impacted by the choice of medium. In addition, RNA-seq revealed a profound shift in airway cell lineage, with the UNC medium promoting differentiation into Club and Goblet cells whilst the SC medium stimulated a very marked increase in multiciliated cells, as well as ionocytes. Of note, the different databases, used for the pathway analysis of the RNA-seq datasets, identified different regulated pathways. It is known that there exists some heterogeneity between databases  [64]. However, the results returned from these analyses were globally consistent with a regulation of fluid and ion transport systems (Figures  5  and 6 ). Although a reduced TEER was observed in SC-compared to UNC-grown epithelia, this was not because the cells formed a leakier epithelium containing fewer tight junctions, as SC cultures demonstrated robust tight junctional protein expression with an increased cell density.
Acknowledgments: The authors gratefully acknowledge the  Newcastle University BioImaging Unit as well as the Genomics Core Facility  for their support and assistance in this work. The authors thank  Robert Bridges ,  Rosalind Franklin University of Medicine and Science , and  Cystic Fibrosis Foundation Therapeutics  for providing the compound  P5  ( dF508act-02 ) and  John Riordan ,  University of North Carolina-Chapel Hill , and  Cystic Fibrosis Foundation Therapeutics  for providing the CFTR Antibody (596) used in this study. We would like to acknowledge  Scott H Randell  and  Leslie Fulcher  ( Marsico Lung Institute, The University of North Carolina at Chapel Hill, USA ) for providing CF primary airway epithelial cells from the CFFT collection and CF and non-CF primary airway epithelial cells from the  UNC CF Center Tissue Procurement and Cell Culture Core.
Target Manufacturer (Reference) Species Raised in Dilution/Concentration (WB/IF) Primary Antibodies β-actin Sigma-Aldrich (A5441) Mouse 1:5000 (WB) CFTR (Cystic Fibrosis transmembrane conductance regulator CFF Therapeutics (596) Mouse 1:3000 (WB) 1:250 (IF) ATP12A Sigma-Aldrich (HPA039526) Rabbit 1:1000 (WB) 1:400 (IF) MUC5AC Abcam (ab3649, Cambridge, UK) Mouse 1:67 (IF) Acetyl-α-Tubulin (Lys40) (D20G3) Cell Signaling Technology (5335, London, UK) Rabbit 1:800 (IF) Phalloidin (Alexa Fluor 647) Thermo Fisher Scientific (A30107) NA 1. 25 µg/mL DAPI (4 ,6-diamidino-2-phenylindole) Sigma-Aldrich (D9542) NA 1:1000 Secondary Antibodies Anti-mouse Immunoglobulin (Ig)G, HorseRadish Peroxidase (HRP)-linked Cell Signaling Technology (7076) Horse 1:5000 (WB) Anti-rabbit IgG, HRP-linked Cell Signaling Technology (7074) Goat 1:5000 (WB) Precision Protein™ StrepTactin-HRP Conjugate Bio-Rad (161038) NA 1:5000 (WB) Anti-Rabbit IgG (Alexa Fluor 488) Thermo Fisher Scientific (A11034) Goat 5 µg/mL (IF) Anti-Mouse IgG (Alexa Fluor 594) Thermo Fisher Scientific (A11032) Goat 2 µg/mL (IF).
Lacto-N-triose II (LNT2, β-GlcNAc-1,3-β-Gal-1,4-Glc) is an HMO core structure in which N-acetylglucosamine is β-1,3-linked to lactose  [6, 16, 17]. A few β-N-acetylhexosaminidases (β-NAHAs; EC 3. 2. 1. 52) of the glycoside hydrolase family 20 (GH20) from bacteria, fungi and plants are reported to produce HMO-type GlcNAc-containing oligosaccharides with 1,3 linkages  [15, 18, 19] , as well as chitooligosaccharides and their analogs in transglycosylation reactions with the formation of 1,6 rather than 1,4 linkages  [18, [20] [21] [22]. In Nature, β-NAHAs from GH3, 20, 84, 109 and 116  [5, 23, 24]  categorized in the CAZy database (www. cazy. org) [4]  degrade N-acetylhexosamine-containing compounds by releasing GlcNAc and GalNAc from the non-reducing ends of N-acetylglucosides, N-acetylgalactosides, glycosphingolipids and glycoproteins  [5, [25] [26] [27].
Interestingly, these families display a variety of mechanisms, either retaining via a substrate-assisted mechanism (GH20 and GH84)  [28, 29]  or a glycosyl-enzyme intermediate (GH3 and GH116)  [30] , or inverting via an oxidized form of nicotinamide adenine dinucleotide (NAD + )-depending mechanism (GH109)  [31]. While being represented in five distinct GH families, the large majority of β-NAHAs belong to GH20. N-acetylated oligo-and polysaccharides, e. g. , chitooligosaccharides and chitin, are prevalent in marine organisms, thus crustaceans represent an abundant source of GlcNAc in marine environments. The National Center for Biotechnology Information (NCBI) database (https://www. ncbi. nlm. nih. gov/) currently has more than 112,000 predicted β-NAHAs, but out of the more than 200 characterized EC 3.
None of these proteins, encoded by genes from Paraglaciecola or related bacteria (Table  S1 ), had been recombinantly produced or characterized. The closest relatives of PhNah20A are GH20 β-NAHAs from Bowmanella denitrificans and Lacimicrobium alkaliphilum with 53%-54% sequence identity (Table  S1 ). PhNah20A contains two domains, the GH20 catalytic (β/α) 8 -barrel domain (Pfam: PF00728) and the N-terminal GH20b domain (also referred to as GH20 domain 2; Pfam: PF02838) of a predicted zincin-like fold similar to zinc-dependent metalloproteases  [49]  consisting of four antiparallel β-strands and an α-helix  [27, 50]. These two domains are typical for GH20 enzymes  [50] , and importantly they constitute an active and stable minimum functional unit of GH20 enzymes, thus requiring both a catalytic GH20 and a GH20b domain  [50].
PhNah20A has no predicted signal peptide sequence and most probably is not secreted, whereas a 28 residues N-terminal signal peptide was predicted for the hypothetical PhNah20B (Figure  1A ). Therefore, during the growth of P. hydrolytica on chitooligosaccharides, PhNah20B probably performs the initial degradation of these substrates. PhNah20B, in addition to the GH20b and GH20 domains, contains a putative carbohydrate binding domain of the CHB_HEX superfamily (Pfam: PF03173) having a predicted β-sandwich structure similar to cellulose binding domains in cellulases  [51] , and a C-terminal CHB_HEX_C domain (Pfam: PF03174) of unknown function resembling an immunoglobulin-like fold  [50, 51]. A similar four-domain architecture was seen in the crystal structure of a chitobiase from S. marcescens  [51] , and has only been reported for bacterial GH20 enzymes  [50, 51].