dataesr/scientific-paragraphs-categorization huggingface dataset:
Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2019-147 Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 26 April 2019 c Author(s) 2019. CC BY 4. 0 License. et al. , 2014; Orphal et al. , 2016). These findings illustrate the current uncertainty associated to the use of different ozone crosssection measurements between platforms and should be considered when examining biases between the different TROPOMI TOC algorithms when validated against the Brewer and Dobson observations. The lists of the stations used in this validation work for each instrument/database category are displayed in Tables S. 1 -S. 5 in the Supplement. In Fig. S. 1 the respective maps show the very good geographical coverage of the Earth by the ground-based measurement sites used herein.
Specifically, in panel (a) the WOUDC Network is shown, in panels (b) and (c) the two Brewer networks, Eubrewnet and Canadian, are shown and in panel (d) the SAOZ stations are displayed. It should be noted that when Brewer ground-based (GB) measurements from WOUDC are used, only the Northern Hemisphere co-locations are considered because of the limited number and poor spatial distribution of stations with Brewer instruments in the Southern Hemisphere (SH) available in the specific database.
European Space Agency (ESA) has established a dedicated S5p validation site, which is maintained by BIRA-IASB, where one can find up-to-date validation reports and comparison results. The link of the site is: https://mpc-vdafserver. tropomi. eu/o3-total-column for financial support. We warmly thank the ESA Ozone Climate Change Initiative project for providing the GODFITv4 datasets, the EUMETSAT ACSAF project for providing the GDP4. 8 datasets, the Copernicus Services Data Hub for providing the TROPOMI/S5p data on a timely manner, the World Ozone and UV Data Centre for providing the Brewer and Dobson spectrophotometer observations, the European Cooperation in Science & Technology Action (COST Action ES1207) for the Eubrewnet measurements and the Environment Climate Change Canada for the Canadian Brewer observations.
Acknowledgments The authors acknowledge the financial support of the  European Space Agency "Preparation and Operations of the Mission Performance Centre (MPC)  for the Copernicus Sentinel-5 Precursor Satellite" Contract No. 4000117151/16/1-LG. The French scientists are grateful to  Centre National d'Etudes Spatiales (CNES)  and  Centre National de la Recherche Scientifique (CNRS) Atmos. Meas. Tech. Discuss. , https://doi. org/10. 5194/amt-2019-147Manuscript under review for journal Atmos. Meas. Tech. Discussion started: 26 April 2019 c Author(s)  2019. CC BY 4. 0 License.
Data availability The Level-2 TROPOMI TOC datasets are available at https://s5phub. copernicus. eu/ and https://s5pexp. copernicus. eu/. The Brewer and Dobson daily datasets used in this work can be downloaded from the WOUDC database (http://www. woudc. org, WMO/GAW Ozone Monitoring Community, 2017), while the individual Brewer measurements can be acquired by the Eubrewnet site (http://rbcce. aemet. es/eubrewnet/; Rimmer et al. , 2018)  and the Canadian Brewer Network site (http://expstudies. tor. ec. gc. ca/). The SAOZ ground based data are available at the NDACC database (www. ndacc. org) and from http://saoz. obs. uvsq. fr/ (Pommereau and Goutail, 1988). Rapid delivery SAOZ data are available from the LATMOS Real Time (RT) facility at http://saoz. obs. uvsq. fr/SAOZ-RT. html (A. Pazmino, private communication).
The European Space Agency (ESA) has established a dedicated S5p validation site, which is maintained by BIRA-IASB, where one can find up-to-date validation reports and comparison results. The link of the site is: https://mpc-vdafserver. tropomi. eu/o3-total-column for financial support. We warmly thank the ESA Ozone Climate Change Initiative project for providing the GODFITv4 datasets, the EUMETSAT ACSAF project for providing the GDP4. 8 datasets, the Copernicus Services Data Hub for providing the TROPOMI/S5p data on a timely manner, the World Ozone and UV Data Centre for providing the Brewer and Dobson spectrophotometer observations, the European Cooperation in Science & Technology Action (COST Action ES1207) for the Eubrewnet measurements and the Environment Climate Change Canada for the Canadian Brewer observations.
Among them, nine statistically significant and recurrently dysregulated circRNAs (occurrent in both two CRC samples) were further selected as validation candidates (Fig. 1a ). Next, we compared the expression of nine circRNAs in tumor tissues compared to that in matched normal tissues derived from 97 CRC patients. While compared with the microarray data, we found that only four circRNAs showed the uniform tendency of expression change in ≥75% CRC patients (Fig. 1b  and Supplementary Data1). The role of circNSUN2 in CRCs aggressiveness. To investigate the clinical significance among these dysregulated circRNAs in CRC patients, the cohort of 97 CRC patients with survival data was included (Supplementary Data 3). From Kaplan-Meier analyses, we found that high expression of circRNA_103783 (hg 19, chr5: 6623326-6625782), located on the chromosome 5p15.
The expression levels of circRNA were quantified by qRT-PCR with divergent primers (Supplementary Data 2). CircNSUN2 was clearly upregulated in three CRC tumor tissues, five CRC cell lines compared to normal colorectal tissue, particularly in HCT116 cells (Supplementary Fig. 1a ). Additionally, we compared the abundance of circNSUN2 with some other cancer-related circular RNAs, such as circHIPK3  31  and circFOXO3  32, 33. Consistent with the expression levels in Circular Microarray, circNSUN2 was significantly more abundant compared to these cancer-related circular RNAs in HCT116 cell lines (Supplementary Fig. 1b ). We aimed to characterize circNSUN2 by RT-PCR, Sanger sequencing (Fig. 2a ), RNase R treatment (Fig. 2b ), and northern blot (Fig. 2c ) in HCT116 cells according to previously described methodology  18, 33.
The P values were determined by a two-tailed unpaired Student's t test. Source data are provided as a Source Data file and the canonical export receptor NXF1, mediating export and metabolism of m 6 A-modified mRNAs  37. We identified that cir-cNSUN2 is interacted with YTHDC1, SRSF3 and NXF1 from mass spectrometry (Fig. 4a , PeptideAtlas Dataset ID: PASS01424), suggesting that the export of circNSUN2 from the nucleus is through YTHDC1-dependent export. Herein, our results strongly suggest that YTHDC1 exhibits a distinct function compared with YTHDF2 in promoting the cytoplasmic export of m 6 A-modified circNSUN2 and supporting an emerging paradigm of m 6 A as a potential selective signal for the metabolism of mammalian circular RNAs. So far, there are three well-studied mechanisms by which cir-cRNAs exert their biological functions.
Methods Patients and tissue specimen collection. This study has been approved by Institutional Review Board of Sun Yat-Sen University Cancer Center (SYSUCC, Guangzhou, China), and the study was informed in accordance with Declaration of Helsinki. Written informed consent was obtained from the patients before the study began. Human CRC and adjacent normal tissues were collected from 97 patients receiving surgery with informed consent at Sun Yat-Sen University Cancer Center (SYSUCC) from 2005 to 2015. Clinical information of the CRC patients is summarized in Supplementary Data 3. The tumor grade and stage were defined according to the criteria of the World Health Organization (WHO) and the sixth edition of the TNM classification of the International Union Against Cancer (UICC, 2009). All of the patients were followed up on a regular basis, overall survival (OS) time was determined from the date of surgery to the date of death or the date of the last follow-up visit for survivors.
A total of 18 normal colorectal tissues and 22 colorectal adenomas were collected. In addition, 25 CRC tissues (including 20 pairs with matched LM) were obtained from SYSUCC. Clinical information of the CRC patients is summarized in Supplementary Data 4. Serum samples from 18 healthy individuals, 20 CRC patients without LM and 20 CRC patients with LM were used in this study. Cell cultures. The DLD1 (ATCC CCL-221), HCT116 (ATCC CCL-247), LoVo (ATCC CCL-229), SW620 (ATCC CCL-227), SW1116 (ATCC CCL-233) and 293T (ATCC CRL-11268) cell lines were purchased from American Type Culture Collection (ATCC). The TC71 patient-derived xenograft cell line was obtained from XENTECH (MRF reference: XTM-233_CXT-399/R5700). DLD1, HCT116, LoVo, SW620 and SW1116 were cultured in RPMI-1640 medium (Invitrogen, Carlsbad, USA) with 10% fetal bovine serum (HyClone, USA) as routine.
Subsequently, the labeled circRNAs were hybridized onto the Arraystar Human circRNA Array V2 (8 × 15 K, Arraystar), and then scanned by the Agilent Scanner G2505C (Jamul, CA, USA). Differentially expressed cir-cRNAs demonstrating statistical significance (the |average normalized fold change| ≥ 1. 3) between groups were identified by utilizing fold change cut-off, respectively. RNA interference (RNAi) and transfection. shRNAs for knockdown of cir-cNSUN2 were obtained from GeneCopoeia (MD, USA). siRNAs duplexes were synthesized by GenePharma (Suzhou, China). The target sequences for constructing lentiviral shRNAs and siRNAs are listed in Supplementary Data 2. Plasmid construction. CircNSUN2 overexpression plasmid, circNSUN2-m  6  A mutation plasmid and circNSUN2-HMGA2 binding mutation plasmid were obtained from Furuibio (Guangzhou, China).
Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, USA). Two micrograms of total RNAs was used to synthesize cDNA, a portion of which (1 µl, equal to 0. 2 µg cDNA) were used in a PCR. Real-time polymerase chain reaction (RT-PCR) was carried out using SYBR Green SuperMix (Roche, Basel, Switzerland) and ABI7900HT Fast Real-Time PCR system (Applied Biosystems, CA, USA) using 1 µl cDNA as template  32, 33. Either β-actin or U3 was used as an internal control. The primer sequences are listed in Supplementary Data 2. Western blotting. Equal amounts of protein lysates were resolved by SDS-PAGE gels and then transferred on a PVDF membrane (Millipore, Massachusetts, USA). After incubation with a primary antibody at 4 °C overnight, the membranes were hybridized with a secondary antibody at room temperature for 1 h.
Cytoplasmic and nuclear fractions were isolated as described by the manufacturer, using the reagents supplied in PARIS™ Kit (AM1556, Thermo Fisher Scientific, Waltham, USA). Briefly, HCT116 was lysed in Cell Fraction Buffer on ice for 10 min. After centrifugation at 500 × g for 3 min at 4 °C, the supernatant was collected as cytoplasmic fraction. Followed by washing the pellet with Cell Fraction Buffer, the nuclei were collected. Total RNA extraction and RNA-SEQ. RNAs from control or sh-circNSUN2 HCT116 cells were used to generate mRNA Sequencing libraries using the NEB Next Ultra RNA Library Prep Kit (Illumina). RNA-SEQ libraries were then sequenced on the HiSeq 2000 platform according to the manufacturer's recommendations by CapitalBio Technology (Beijing, China). The raw reads filtered by RNA-BisSeq data analysis method were mapped to the hg19 genome with HISAT2 (Johns Hopkins University, USA)  56.
The images were acquired using a fluorescence microscopy (OLYMPUS FV1000 confocal microscopy, Japan). Fluorescence intensity was analyzed by ImageJ. Pearson's correlation coefficient was analyzed by OLYMPUS FV1000 software. The probe sequences were shown as below: circNSUN2: 5′-biotin labeling-AAGUUCUUCAGGAUACCUUAUGAUGA GGCCGCACGUUGAGGA-3′ HMGA2: 5′-Cy3-GGCAGACUCUUGUGAGGAUGUCUCUUCAGUUUC CUCCUGAGCAGGCUUC-3′ Animal models. All mouse procedures were approved by the Institutional Animal Care and Use of SYSUCC. All of the BALB/c nude mice in each experimental group were obtained from Vital River Laboratory (Beijing, China). For the LM model, 2 × 10 6 cells suspended in 40 μl PBS were injected into the inferior hemispleen into each 6-week-old BALB/c nude mouse. Eight weeks after cell injection, mice were euthanatized.
For the lung metastasis model, 5 × 10 5 cells suspended in 100 μl PBS were injected intravenously in the tail vein into each 4-week-old BALB/c nude mouse. Six weeks after cell injection, the mice were sacrificed. The lungs and livers were removed and fixed with phosphate-buffered formalin. Subsequently, the numbers of metastatic nodules in the lungs and livers were carefully examined. For imaging tumors in live animals, VivoGloluciferin (Promega, Madison, WI) was dissolved in sterilized PBS (final concentration: 15 mg/ml). Mice were anaesthetized with isoflurane and injected intraperitoneally with 100 µl of the luciferin solution. After 15 min, images were acquired with the Xenogen IVIS Lumina series II for 5 min and analyzed using the LivingImage 2. 11 software package (Xenogen Corp. ). Transwell assays.
The medium was exchanged every second days for 1 week. Bright-field images were acquired and the quantification of the invading area was analyzed using Olympus cellSens Standard 1. 9. Immunofluorescence. CRC cells grown on the confocal dish (Corning, United States) were fixed with 4% paraformaldehyde in PBS for 20 min on ice and then permeabilized with 0. 1% TritonX-100 in PBS for 10 min. After washing twice with PBS, cells were blocked with 5% BSA for 30 min at 37 °C and incubated with primary antibody overnight at 4 °C. The next day, cells were washed with PBS and then incubated with corresponding secondary antibody for 30 min at 37 °C, followed by staining with DAPI for nucleus staining. Fluorescent images were acquired using OLYMPUS FV1000 confocal microscopy. The relative fluorescence densities were analyzed by ImageJ, and plotted using GraphPad Prism 6 software.
Afterwards, protein identification and quantification were accomplished by Proteome Discoverer software (version 1. 4; Thermo Fisher Scientific, USA). A protein with area ratio (circNSUN2/CTRL) > 2, unique peptides > 2 was considered significant. RNA-binding protein immunoprecipitation (RIP). RIP assay was performed by using a Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore), according to the manufacturer's instructions. Briefly, HCT116 cells were harvested after 48 h post transfection and lysed in RIP lysis buffer on ice for 30 min. After centrifugation, the supernatant was incubated with 30 μl of Protein-A/G agarose beads (Roche, USA) and antibodies. After overnight the immune complexes were centrifuged then washed six times with washing buffer. The beadsbound proteins were further analyzed using western blotting.
Statistical analysis. All experiments were carried out at least three times and data from one representative experiment are shown. Data are presented as mean ± standard deviation (S. D. ). The statistical significance of differences was evaluated by two-tailed Student's t test or two-way ANOVA. OS were assessed with the Kaplan-Meier method and compared by the Log-rank test. The correlations among circNSUN2, HMGA2 and CXCR4 expression in CRC patients were calculated by Pearson correlation analysis and P value computed using the R language function. P values < 0. 05 were considered statistically significant. All statistical analyses were carried out using R or GraphPad Prism (version 6. 0). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by grants from the  National Key R&D Program of China  (No. 2017YFC1309001  and No. 2016YFC1302305 ), the  National Natural Science Foundation of China  (grant nos. 81572359 ,  81730072 ,  81772595 ), the  Natural Science Foundation of Guangdong  (No. S2014030001589 ), and the  Guangzhou Science and Technology Plan Projects (Health Medical Collaborative Innovation Program of Guangzhou)  (No. 201803040019 ). The authors thank  Dr. Jean-Gabrie Judde  and  Dr. Olivier Deas  ( Xen-Tech SAS, Evry, France ) for providing colorectal carcinoma PDX cells. The authors thank  Dr. Jian-Hua Yang  ( Sun Yat-Sen University, Guangzhou, China ) for providing circScan 34 bioinformatic analysis. The authors thank  Dr. Yun-Gui Yang  ( Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China ) for providing SFB-YTHDC1-WT, SFB-YTHDC1-DM,  Myc-YTHDC1-WT Ins, Myc-YTHDC1-DM Ins, Myc-METTL3-WT Ins, Myc-METTL3-BM Ins , and Myc-METTL3-CM Ins constructs.
Data availability A reporting summary for this Article is available as a Supplementary Information file. Microarray data are accessible at the Gene Expression Omnibus (GEO) under accession number: GSE121895. RNA-SEQ data have been deposited in the Sequence Read Archive (SRA) database under project PRJNA549980, ID: SRP202052. The mass spectrometry data are accessible using PeptideAtlas Dataset ID: PASS01424. The source data underlying Figs. 2b, 4b, c , f, g, 5b, d, 6a-c, e, f, and 7a-e and Supplementary Figs. 1a-d, 2a-h, 3a-f, 4a-h, 5b-f, 6a-c  and 7a-d  are provided as a Source Data file. Unprocessed gel images for Figs. 2c, 4a, b, d, 5a, b, d, e , Supplementary Figs. 4a, b, 5b, c, f  have been provided in Supplementary Fig. 9. Other data that support the findings of this study are available from the corresponding authors on reasonable request.
Introduction Fire is a major perturbation of the Earth system, which impacts the plant biomass distribution and vegetation structure, the carbon cycle, global atmospheric chemistry, air quality and climate  (Bowman and Balch, 2009). Fire is therefore recognised as an essential climatic variable  (GCOS, 2011) , and the potential impact of global warming on drought severity and fire season length is a key scientific question  (Flannigan et al. , 2009; Krawchuk et al. , 2009; Aragão et al. , 2018)  to understand its role within the Earth system. Most dynamic global vegetation models (DGVMs) have included fire modules (see  Hantson et al. , 2016; Rabin et al. , 2017, for a review)  to improve the prediction of the impact of fire on vegetation dynamics and the carbon cycle. Substantial efforts have been devoted in the past decades to create reliable global burned area (BA), active fire and fire radiative power (FRP) datasets which allow the quantification of the fire perturbation since the beginning of the 2000s  (Mouillot et al.
Here we document and investigate the relationship between fire patch size derived from BA data and FRP at a global scale based on remote-sensing information. FRP measures the energy emitted through radiative processes released during the combustion and can be associated with fire intensity all throughout the fire burning process  (Wooster et al. , 2005 (Wooster et al. , , 2013;; Ichoku et al. , 2008; Barrett and Kasischke, 2013). A positive relationship between fire patch size and the reaction intensity of the fire front is expected at least for small fire size, whose propagation rate has been benchmarked using laboratory experiments. But we do not know if this holds up at global and regional scales for bigger fires, usually reaching longer temporal scales with varying wind directions and atmospheric circulation and larger spa-tial extent.
Fire patch size may not continue to increase with fire intensity above a certain size due to landscape fragmentation acting as a natural barrier against fire propagation. To uncover the fire size-intensity relationships, we assembled the information on fire patch size recovered from the FRY global database  (Laurent et al. , 2018)  based on the MODIS MCD64A1 and the MERIS FireCCI41 BA products, with FRP using active fire pixel data from the MCD14ML dataset.
Data and methodology We used the FRY database containing the list of fire patches characterised by their morphological traits, including fire patch size, at a global scale  (Laurent et al. , 2018). Fire patches were derived from the MERIS fire_cci v4. 1 (later called FireCCI41;  Chuvieco et al. , 2016)  and the MCD64A1 Collection 6  (Giglio et al. , 2016 ) BA pixel products. The FireCCI41 product provides the pixel burn dates for the period 2005-2011 and is derived from the Envisat MERIS sensor, with a spatial resolution of ∼ 300 m ×300 m and a 3-day revisit frequency at the Equator. The MCD64A1 product, derived from the MODIS sensors, provides pixel burn dates at a global scale over the period 2000-2017 with a coarser resolution (∼ 500 ×500 m) but a more frequent revisit time (1 day at the Equator).
The pixel burned dates are combined using a flood-fill algorithm  (Archibald and Roy, 2009) , which is parametrised by a cut-off value. This cut-off value corresponds to the maximum time difference between the burn date of neighbouring pixels belonging to the same fire patch. These global datasets have been thoroughly compared by the authors of the FRY database, locally compared using the US and Canadian forest service fire patch database  (Chuvieco et al. , 2016)  and validated against Landsat fire polygons in the Brazilian Cerrado  (Nogueira et al. , 2017). The FRY database is organised in eight datasets (two survey times and four cutoff values) and provides for each individual fire patch a set of variables, called fire patch functional traits, including the geolocation of the patch centre, the fire patch size (later called FS, in hectares) and different indices on fire patch morphology.
Standard deviation ellipses (SDEs) are fitted by  Laurent et al. (2018) , over each fire patch larger than 5 pixels (using the aspace R package), and the geolocation of their centres, half-axes and orientation in a longitudinal-latitudinal coordinate system, as well as the values of the minimum, mean and maximum pixel burn dates, are also provided for each fire patch. Active fire pixel data from the MCD14ML dataset  (Giglio et al. , 2006)  consist in a list of geographic coordinates of individual active fire pixels detected by the Terra and Aqua sensors on board the MODIS satellite for the period 2000-2017 with a resolution of ∼ 1 km ×1 km. For each pixel, the dataset provides the date and hour of burn of the active fire pixel, along with its FRP (in megawatts). FRP represents the energy emitted by fire through radiative processes (i.
e. the total fire intensity minus the energy dissipated through convection and conduction) over its total area. It is widely used as a proxy for fire impact assessment  (Barrett and Kasischke, 2013; Sparks et al. , 2018) , biomass combustion rates  (Roberts et al. , 2005)  or fire event  (Hernandez et al. , 2015)  and fire spread  (Johnson et al. , 2017)  modelling. We performed a spatio-temporal matching between active fire pixel data and all the fire patches from the FRY database in order to recover the average FRP for each fire patch. To do so, we consider that an active fire pixel belongs to a fire patch if it fulfils the two following conditions. The centre of the active fire pixel must be located within the SDE of the fire patch. Since the side of an active fire pixel is 1 km, we also consider that an active fire pixel located at a distance of 1 km or less from the area covered by the SDE belongs to the fire patch.
Using a shorter value for the temporal buffer (10 days) slightly increases the failure rate of the matching but had no significant impact on the results presented in this analysis. In the following, we studied the relationship between FRP and FS in each region defined by the Global Fire Emission Database (GFED;  Giglio et al. , 2013, Fig. S1  in the Supplement). Since different vegetation types can occur within a GFED region (and consequently different amount of biomass or drought severity), we split all of them into three vegetation types using the GLCF MODIS Land Cover data  (Channan et al. , 2014)  and explore the relationship between FRP and fire size for each vegetation type in each GFED region. The vegetation types are defined by grouping together MODIS Land Cover categories: "forests" stands for all the forested land cover types (evergreen or deciduous needleleaf or broadleaf forests and mixed forests), "savannas" for savannas with woody savannas, and "grasslands/shrublands" for grasslands with open and closed shrublands.