dataesr/scientific-paragraphs-categorization huggingface dataset:
61 genes are reported in the core data set only. (b) Of 31 confirmed genes Rep 31 , 2 showed concordant upregulation, 4 downregulation. In erestingly, 10 genes are concordantly regulated in cell culture models with discordant regulation in animal/human models. (c) Complementing published data sets, CD 178 combined regulation by Pkd1 -/-and Pkd2 -/-. A total of 61 genes appeared in CD 178 only (New 61 ). (d) Combining the cross-species meta-analysis with function-based mapping and gene enrichment analysis of ABC 25 highlighted 8 yet unreported and 6 confirmed genes. Both, confirmed and new genes were represented in clusters A 10 and B 8. Interestingly, none of the Rep 31 but 3 of New 61 genes were represented in functional cluster C 7. Thus, combining Pkd1 -/-and Pkd2 -/-identified new targets for in-depth analysis of ADPKD pathogenesis.
transient polycystin-dependent ion flux into stable cellular differentiation and renal tubular structure (Figs. 4e, 6d ). Derived from the largest volume of ADPKD mRNA expression data available, the PKD1/2 > TF 7 > ABC 25 signaling cascade-and in particular the 6 replicated and 8 newly identified genes-generated novel, testable hypotheses that may broaden our understanding of PKD-dependent tissue homeostasis in health and disease. For example, sex has been described as significant determinant of ADPKD disease severity  61. Men with ADPKD often have worse renal cystic disease but less severe liver cystic disease  62. Supported by the male origin of mIMCD3 cells, PKD1/2 > AR > CDH2 signaling in cluster A 10 may contribute to this phenotype (Figs. 4e, 6d  and Table  S9 ). The AR gene in TF 7 encodes the steroid-hormone activated androgen receptor, which regulates eukaryotic gene expression and affects cellular proliferation and differentiation in many target tissues  63.
PIK3R1 encodes phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), the major suppressive regulatory subunit of phosphoinositide 3-kinase (PI3K). PI3K participates in a broad range of regulatory processes, including cell growth, proliferation, metabolism and secretion  82. In cancer, loss of PIK3R1 has been shown to promote EMT and cellular proliferation by over-activation of downstream AKT signaling  83. In ADPKD, increased PI3K/AKT signaling has been described as disease modifying, but both, loss and gain of PKD1 have been linked to mis-regulated pathway activity  [84] [85] [86] [87] [88]. Increased PIK3R1 protein expression has been observed in conditionally immortalized human tubular epithelial cells from ADPKD patients  89. Disease status, species differences or the observed Pkd-dependent Pik3r1 exon usage may reconcile these divergent findings, but additional experimental data are needed to clarify the molecular function of PIK3R1 in ADPKD.
Similar network effects cause well-studied genes to continuously accrue data, while the non-randomness of missing annotations is more likely to disadvantage primate specific genes or new targets without a wealth of literature  94, 95. With more data becoming publicly available, future in silico re-analyses of CD 178 have therefore the intriguing potential to further clarify core functional connections currently still hidden in the ADPKD transcriptomic profile. Taken together, our data indicate the robust power of integrative transcriptomics to select targets for subsequent functional studies in ADPKD, including the 6 replicated (C3, FSTL1, PCOLCE, PCSK9, SPP1 and ZFP37) and 8 newly identified (CD34, CDH2, CSF2RA, DLX5, HOXC9, PIK3R1, PLCB1 and TLR6) genes (Fig. 6d ). were isolated from healthy mice expressing the early region of simian virus SV40 (Tg(SV40E)Bri/7) to sustain continuous proliferation in vitro  22.
RNA sequencing (RNA-seq) analysis. RNA-seq was performed in samples from wild-type as well as from one Pkd1 -/-and one Pkd2 -/-mIMCD3 clone. Despite multiple sampling, untargeted analysis of such clonal cell lines may be confounded by undetected, random genome editing-induced off-target effects or clonal artifacts. To minimize false discovery, mIMCD3-based transcriptomic profiling of ADPKD was hence restricted to consistent changes across the two independent Pkd genotypes. Data was analyzed with Galaxy Europe 103 (https:// usega laxy. eu/). Reads were first trimmed and quality checked with Trim Galore (Galaxy Tool Version 0. 4. 3. 1) and FastQC (Galaxy Tool Version 0. 72), respectively. Reads were aligned to the mouse genome version mm10 (GRCm38) applying HISAT2 (Galaxy Tool Version 2. 1. 0 + galaxy3)  104.
1. 0 + galaxy3)  104. De novo transcript reconstruction was conducted with StringTie (Galaxy Tool Version 1. 3. 6)  108. The transcriptome was assembled using StringTie-merge (Galaxy Tool Version 1. 3. 6). Assembled transcripts were compared to the reference annotation (Mus musculus, mm9) with GFFCompare (Galaxy Tool Version 0. 11. 2)  109. Exon abundancies were then estimated using DEXSeq-Count (Galaxy Tool Version 1. 28. 1. 0)  99, 100. Differential exon usage (DEU) was tested with DEXSeq (Galaxy Tool Version 1. 28. 1 + galaxy1)  99, 100. False discovery rate of DEU was controlled with a p-value < 0. 05 adjusted for multiple testing (Benjamini-Hochberg procedure). CellNet analysis. CellNet Analysis of RNA-seq data was performed as described previously  27. Data was uploaded to an Amazon web services (AWS) instance.
No alterations to CellNet standard settings were performed. For the original code to work with the chosen AWS platform, all AWS-related URLs (https:// s3. amazo naws. com/) were changed to https:// s3. conso le. aws. amazon. com/ s3/, e. g. in the "cn_s3_fetchFastq" function. Kidney cell explorer analysis. Kidney Cell Explorer Analysis was performed using 47 https:// cello. shiny apps. io/ kidne ycell explo rer/. "Adult mouse kidney" was selected as reference data set. CD 178 were used as input by pasting the respective gene IDs in the search field. Expression was displayed in "Average expression (rescaled)" mode and "RdYIBu" Palette. Genes were counted as "expressed" if rescaled average expression was > 0, e. g. if color was lighter than dark blue (HEX code #4575b4ff. ). Nephro cell analysis.
Nephro cell analysis was performed using http:// nephr ocell. miktmc. org/. "Adult normal kidney" was selected as reference data set. Rep 31 and New 61 were used as input; one gene at a time. Violin plots displayed were analyzed and divided into 5 categories: 0 = not measured in the nephrocell data set; 1 = no expression; 2 = low expression, no violin plots displayed; 3 = high expression, violin plots displayed; 4 = very high expression, striking violin plots displayed. A heatmap was generated to visualize these values. Transcription factor enrichment analysis. Testing for transcription factors regulating the core data set genes was performed with ShinyGO (Tool Version 0. 51) using the function TF. Target. TFacts  41, 42. False discovery rate was set to < 0. 05. Pathway analysis. Functional protein network and transcriptional factor network were determined using the STRING (Search tool for the retrieval of interacting genes/proteins) database version 11.
0 37 (https:// versi on-11-0. string-db. org/). Functional enrichment analysis was performed against the Molecular Signature Database (MSigDB) v7. 1 using standard hypergeometric distribution with correction for multiple hypotheses testing using FDR < 0. 05 according to Benjamini and Hochberg  38, 42. Pathway databases used: Hallmark Gene Sets, Positional Gene Sets, GO Gene Sets, KEGG, All Transcription Factor Targets. Cross-species meta-analysis. Available PKD1-responsive transcriptional data was retrieved from a literature research covering publications between 2008 and 2018  [12] [13] [14] [15] [16] [17]. Transgenic models were not considered for comparison. Studies were included if they provided lists of differentially expressed genes and raw data had been deposited for public access. No PKD2 data matching above criteria were found.
Ref. 18  was included for PKD2responsive expression. Lists of differentially expressed genes were obtained from the primary or supplementary data of the respective publications. Analysis of  13, 16  was based on the previously described PKD1 Signature by  15. Criteria for differential gene expression were adopted from the respective studies. RNA-seq and profiling of Pkd1 tm2Ggg /Pax8 rtTA mice. Pkd1 tm2Ggg /Pax8 rtTA (C57BL/6 genetic background) mice have been described previously  110. All animal experiments were conducted according to the guidelines of the National Institutes of Health Guide for the Care and Use of Laboratory Animals, as well as the German law for the welfare of animals, and were approved by regional authorities (Regierungspräsidium Freiburg G-13/18, G-15/58, and G-16/28).
RNAseq was performed by GATC Biotech AG (Konstanz, D) on Illumina HiSeq Genome Sequencer. Data analysis was performed using the same work-flow as for CD 178 (see "RNA Sequencing analysis"). Differentially expressed genes (FDR < 0. 05) were overlapped to top 20% genes contributing to principal component 2 in the principal component analysis. Overlapping genes were summarized in the Pkd1 -/-mouse data set. Quantification and statistical analysis. Statistical analyses of RNA-seq data were carried out in Galaxy and R using DESeq2  103, 106. False discovery rate of DGE was set to < 0. 05 (Benjamini-Hochberg procedure). The number of biological replicates (n), statistical test performed and resulting significance for each experiment is.
Figure3. Complementary mathematical analysis of Pkd1-and Pkd2-responsive transcripts identified a core ADPKD data set of 178 consistently regulated genes (CD 178 ). To identify genes involved in ADPKD pathogenesis, two independent mathematical approaches were used: (1) statistical analysis of differential gene expression (DGE); and (2) unbiased data simplification by principal component analysis (PCA). (a) and (b) Applying RNA-seq on Pkd1-and Pkd2-deficient mIMCD3 cells revealed a total of 791 differentially expressed genes in Pkd1 -/-and 1250 in Pkd2 -/-cells (large, dark-yellow dots: FDR < 0. 05, log 2 fold change ≥|1|). In Pkd1 -/-cells, > 54% (429 genes) and in Pkd2 -/-cells, > 63% (790 genes) were downregulated. (c) Filtering DGE of Pkd1 -/-and Pkd2 -/-for concordant changes provided 254 genes (DGE 254 ), of which 38 were up-and 216 downregulated genes (orange dots).
(d) To elucidate CD 178 transcriptional regulation, ShinyGO and GeneSetDB meta-databases were queried (FDR < 0. 05) 41, 42. This revealed a total of 17 CD 178 genes regulated by 7 trancriptional factors (TF 7 ). (e) 6 of 7 TF 7 were shown to form a k-means based cluster. Also, ABC 25 identified several regulatory cascades: 5 ABC 25 genes (Cd34, Spp1, Cdh2, C3 and Mmp2) were linked to 6 of the TF 7 (Ar, Esr1, Ets, Gli1, Rbpj, Tp53). Spp1 and Cdh2 were regulated by more than one TF 7 , whereas Ets2, Rbpj and Tp53 regulated more than one ABC 25 gene.
Acknowledgements We would like to thank  Simone Diederichsen  and  Lukas Peintner  for technical assistance and  Anna Reznichenko  for her input on the manuscript. The following illustrations for figure design were obtained from the  DBCLS Togo TV  picture gallery: mouse (Figures 1,6,  S5 ,  S6 ); human (Figure 1, 6,  S6 ); and cell culture (Figure 1, 6,  S6 ).
Data collection The global SSCC and SST datasets used in the present study were obtained from the daily Level-3 products of MODIS-Aqua with a spatial resolution of ~ 4 km and a temporal resolution of 1 day during the period July 1st, 2002 to February 28th, 2019. The daily SSCC and SST data can be downloaded from the ocean color website (http://oceanc olor. gsfc. nasa. gov/) of the National Aeronautics and Space Administration (NASA).
Data pre-processing Given that the spatiotemporal coverage of daily remote sensing data is not sufficient due to the noise produced by cloud and sun glint, the daily SSCC and SST datasets were pre-processed by an upscaling technique, as follows: (a) grid points with the spatial resolution 1 • ×1 • were set up across the world oceans; (b) a spatial moving window centered at each grid's centroid with a searching radius of ± 0. 5 • was employed to search for neighboring daily SSCC and SST data; and (c) the monthly mean SSCC and SST data were computed at each grid point.
Spatiotemporal Bayesian maximum entropy (BME) interpolation In order to obtain more complete time series of monthly SSCC and SST data at each grid point, BME was used for SSCC and SST interpolation purposes. BME was used to map the space-time distribution of the SSCC (or SST) data characterized by the space-time vector p = (s, t), where s = (s 1 , s 2 ) denotes location coordinates and t is the time instant. It sets up a S/TRF model, X(p), to represent the spatiotemporal variability and in-situ uncertainty of SSCC (or SST), i. e. , X(p) ≡ { SSCC(p) or SST(p) (1) so that X(p)～f X (p, χ), (2) 1 The term "space-time" describes a domain described by two or three spatial coordinate axes and a single time axis. All physical variations occur within this space-time domain. where the symbol ~ denotes that the random field is mathematically described by a probability density function (PDF) f X , and χ is a possible SSCC (or SST) field realization at the space-time point p.
Naturally, various mathematical forms are possible, depending on the available G-ad S-KB. More detailed information can be found in the relevant literature and references therein (e. g. ,  He and Kolovos, 2018). It is noteworthy that in the present work only the time series with the number of data larger than three quarters of series length (i. e. , 150 months) was interpolated by BME approach and only the time series with full length of data was selected for further wavelet coherency analysis. The BME method was implemented using the SEKS-GUI software equipped on the MATLAB platform  (Yu et al. , 2007) , and it can be downloaded from http://seksg ui. org/SEKSHome/SEKSHome. html.
Space-time BME mapping of coherency values and hotspot analysis At each grid point where complete SSCC and SST time series were available, the wavelet coherency values were extracted at the characteristic period, i. e. the period characterized by high coherency values. Then, a number of coherency time series were gathered together for constructing a coherency space-time dataset. Again, the BME approach was employed to map the spatiotemporal distribution of SSCC-SST association strength with a 0. 5 • ×0. 5 • spatial resolution. Given that the SSCC-SST association displays local cluster characteristics, hotspot analysis was considered appropriate for detecting high-value clusters and low-value clusters, respectively, representing strong and weak associations at various regions of the global ocean.
Specifically, a hot (or cold) spot is a location where two conditions apply: (a) a high (or low) value occurs at this location and (b) high (or low) values are also found at its surrounding locations. The hotspot analysis was performed with the help of the ArcGIS 10. 2 software and the corresponding Python codes (https://desktop. arcgis. com/en/arcmap/10. 3/tools/spatial-statis tics-toolbox/hot-spot-analysis. htm). The hot (or cold) spots were determined and mapped at the 0, 90%, 95% and 99% significance level.
Spatiotemporal BME interpolation of SSCC and SST After excluding the land grid points, 43,337 grid points with a spatial resolution 1 • ×1 • were finally set up across the global ocean. Figs. S2a-2b and S3a-3b represent the number of original MODIS remote sensing data at each grid point, including daily SSCC and SST data and monthly mean data. The descriptive statistics is summarized as follows: (a) among the 43,337 grid points, there are 15,227 points and 11,659 points with a number of monthly mean SSCC and SST data smaller than 150; (b) 7809 and 3330 grid points have a number of monthly mean SSCC and SST data larger than 150 but smaller than 200, respectively; (c) 20,301 and 28,348 grid points have complete times series of SSCC and SST (i. e. , the number of data in the time series equals to 200), respectively.
e. , association strength varies a lot at various spatial grid points in 1-year period); and (c) some local strong SSCC-SST associations exist in other time--frequency domains. Specifically, the coherency values during the 5-year period were larger than that during the 1-year period, indicating that the SSCC and SST have a stronger association and a closer relationship than that during the 5-year period. However, there exist distinct edge effects of wavelet coherency analysis with nonnegligible uncertainty, almost accounting for half of the entire 200 months time series (Fig. 1 ). Therefore, the 1-year period was selected as the main period for describing the covariation characteristics between SSCC and SST. Further, the coherency values at various time instants and spatial grid points were extracted for constructing space-time coherency datasets (totally 5.
Data quality and validation Data quality is of an important component of remote sensing techniques, especially due to the long-distance optical transmission from the sea surface to the satellite causing non-neglected uncertainty. In order to validate the accuracy of MODIS-retrieved sea surface chlorophyll concentration (SSCC) and sea surface temperature (SST) products, in-situ SSCC and SST measurements were collected from the seaBASS website (https://seabass. gsfc. nasa. gov/) and subsequently matched with the MODIS products, for comparison purposes. The scatter plots of the accuracy of the MODIS SSCC and SST products are shown in Fig. S10. The mean absolute error (MAE) and the root mean squared error (RMSE) of the MODIS SSCC products are 2. 0956 and 4. 3279 mg/m 3 , respectively; the corresponding two indicators of the MODIS SST products are 0.
, 2016). Regarding the MODIS SST data, Minnett (2010) also reported that the error between insitu SST and MODIS SST is rather low, i. e. , -0. 15 K. In addition, before implementing the BME method for spatiotemporal SSCC and SST interpolation, leave-one-out cross validation for both SSCC and SST were conducted. The scatter plots of the validation results are shown in Fig. S12. Regarding the cross validation of SSCC, the R 2 , MAE and RMSE are 0. 6033, 0. 0610 mg/m 3 and 0. 4296 mg/m 3 , respectively. Most of the SSCC data were located in open seas, and the SSCC values were rather small, leading to low MAE; on the other hand, the number of high SSCC was small, located in the coastal regions, and they may not be well validated, leading to the separated distribution of data points in Fig. S12a  and a not very high R 2.
Limitations and future work Certain limitations of the current work should be mentioned: (1) as noted in subsection 4. 3, due to the turbidity in the coastal regions, the quality of the SSCC and SST products was low, in which case the association analysis results may be questionable. Therefore, future work should focus on developing and/or applying local algorithms  (Hu et al. , 2000; Ruddick et al. , 2000; Lavender et al. , 2005; Schroeder et al. , 2007)  to establish a precise atmospheric correction process and high quality SSCC and SST product retrieval; then the WCA-BME methodology can be further employed to define the SSCC-SST association in specific coastal regions. (2) Some other SSCC or SST interpolation techniques have been also developed or compared. For example,  Kusuma et al. , (2018)  compared the performance of inverse distance weighted (IDW), spatial kriging, natural neighbor interpolation and data spline in spatial SST interpolation, and they found that IDW and kriging performed better than the other two methods; and it suggested that the geostatistical methods might show a stronger ability in SST interpolation than the other two methods.
Both a streams provide ZTD estimates with a sampling of 5 minutes. Apart from the usage of different clock and orbit products, the processing streams run by both analysis centres also differ by their software packages and related processing options as summarised in Table  1. Most importantly, IGN uses the Bernese GNSS version 5. 2 software in double-difference processing mode  (Dach et al. , 2015)  while ENSTA_B/IPGP use the GIPSY OASIS II version 6. 2 software in precise point positioning (PPP) mode  (Zumberge et al. , 1997). Various studies have compared the results and discussed the benefits and drawbacks of both approaches. When consistent clock and orbit products are used, there is generally a good level of agreement (i. e. a few mm RMS differences in ZTD estimates). Finally, the regional network including 49 GNSS stations was reprocessed by ENSTA_B/IPGP using again GIPSY OASIS II version 6.
RGP NRT and daily results This dataset is provided only for the BCON and BCOS stations which benefit from colocated surface air pressure and temperature measurements. The measurements come from Vaisala PTU200 Sensors connected directly to the GNSS receivers and are included in the raw GNSS data files. The ZHD estimates are therefore computed using Eq. 2 and the T m values are computed using the widely used  Bevis et al. (1992)  formula: T m = 70. 2 + 0. 72T s (6) where T s is the surface pressure. This formula was derived by  Bevis et al. (1992)  from a radiosonde data set over the USA with a RMS error of 4. 7K. The refractivity coefficients used for the computation of ZHD and κ are from  Thayer (1974) , the thermodynamics constants are from  ICAO (1993) , and the g m formula is from  Saastamoinen (1972).
GIPSY rapid and final results This dataset is also provided only for the BCON and BCOS stations but it used the a priori values for ZHD available during the data processing. The values were obtained from the Technical University of Vienna (TUV) as part of the VMF1 tropospheric parameters computed from the ECMWF operational analysis  (Boehm et al. , 2006). The TUV also computes T m values using Eq. 5 from the same analysis. Both products are made available on a global grid with 2°latitude and 2. 5°longitude resolution every 6 hours. The refractivity coefficients and g m are the same as for the RGP data set.
-In the RGP daily and GIPSY repro1 comparison, the small bias in RGP daily is a result of almost exact cancelling of a ZTD bias and a K bias in the RGP data. The random errors in the RGP data are again dominated by ZTD errors. -In the GIPSY final and repro1 comparison, the bias in the GIPSY final is almost only due to a bias in ZHD, while the random errors are due to ZTD and ZHD differences of the same magnitude. In conclusion, compared to the GIPSY repro1 dataset which uses the most accurate data processing and conversion parameters, both operational streams show small systematic and random errors on the level of ± 0. 5 kg m -2 and ± 1. 0 kg m -2 , respectively.
Conclusions This paper describes the data processing streams and discussed the quality of GNSS ZTD and IWV retrievals from four operational streams run by IGN and ENSTA-B/IPGP and one research stream (GIPSY repro1), see Table  1. The two operational streams run by IGN (RGP NRT and RGP daily) provide data for stations BCON and BCOS, the two GNSS stations installed at the BCO for EUREC 4 A in October 2019, as a well as a few other sites in the French overseas territories of the Antilles. The RGP NRT stream provides ZTD estimate within 45 min after the end of measurements which are generally available for assimilation into NWP models. We showed that this stream had some instabilities, especially in most recent 1-hour time slot, which are corrected in the t-2h time slot. This instability was found to be related with the processing scheme and was corrected since then by IGN.