dataesr/scientific-paragraphs-categorization huggingface dataset:
This dynamism has short-term effects on flooding and sediment dispersal patterns, as well as longer term effects on the fluvial landscape and sedimentary successions  [14] [15] [16]. In remote playas, rare flood events may be beneficial for ecohydrology, but in drylands more generally as well as in other hydroclimatic settings, floods also can be one of the most significant and widespread hazards  [17]. In particular, large or extreme floods disrupt riparian ecosystems and present great threats to human life, properties, and other infrastructure, and hydrological and hydraulic models are needed to predict their impacts. Modeling of flood propagation and extent, both in natural and anthropogenically-induced events such as dam-break/dam-breach floods, requires accurate calibration and validation datasets  [18] [19] [20] [21].
In addition, Sentinel-2 data are used alongside very high resolution WorldView satellite imagery to investigate the geomorphological changes resulting from the flood. The combination of MNDWI and ICA methods on Sentinel-2 MSI imagery, and the complementary use of very high resolution WorldView satellite imagery, provides fundamental information on flood characteristics and impacts, and the approach potentially could be applied in this and other settings to derive data to help calibrate and validate hydrological and hydraulic models.
Materials Daily precipitation data in the Uyuni area were collected for the period 1976 through 2017 from the Bolivian Servicio Nacional de Meteorología e Hidrología. Although we have precipitation data from only one gauge, the major moisture supply by strong low-level northwesterly winds along the eastern flank of the central Andes makes the precipitation pattern consistent in the drainage catchment of the study area  [40]. Launched in June 2015, Sentinel-2A Level-1C (L1C) Multispectral Instrument (MSI) data are publically available for free download from Sentinels Scientific Data Hub (https://scihub. copernicus. eu/). MSI data are characterized by a span of 13 spectral bands with a spatial resolution on the ground ranging from 10 m to 60 m (Table  1 ). These spectral and spatial resolutions, along with the availability of images free of charge, make Sentinel-2 very appealing for water mapping and flood monitoring as well as for other applications such as crop monitoring in agriculture (e.
g. ,  [28, 42, 43] ). The Sentinel-2 L1C datasets are the standard product of Top of Atmosphere (TOA) reflectance and were pre-processed to generate and format bottom of atmosphere (BOA) Level-2A products using the Sen2Cor processor (version 2. 3) under Anaconda Python platform  [44]. Sen2Cor enables Sentinel-2 L1C products to be processed for physical atmospheric, terrain and cirrus correction and creates BOA reflectance corrected bands  [43]. The output product format is a collection of JPEG-2000 images with bands reproduced at three different resolutions (10, 20 and 60 m). Sen2Cor also produces a resampled version of the higher resolution bands (10 and 20 m) to 20 and 60 m, respectively. In this study, we used bands with resolution of 20 m to extract flood extent and undertake classifications.
Methodology Daily precipitation data can be used for maximum daily precipitation frequency analysis and to pinpoint the peak flood events. Pre-and post-flood imagery can then be selected for extracting flood extents and patterns and analyzing the associated geomorphological changes (Figure  2 ). In this study, the daily precipitation data were processed to extract maximum daily precipitation and return periods. The maximum daily precipitation frequency analysis showed that the peak rainfall Remote Sens. 2018, 10, 725 6 of 19 (40. 7 mm) on 6 January 2017 is an approximately 40-year event (Figure  3 ), and this corresponded with an extreme flood. This flood event was within the observation periods of Sentinel-2. The Sentinel-2 image available from immediately after this event is from 9 January 2017 with cloud cover of up to 50% but the image on 19 January 2017 is less cloudy, particularly in the medial and distal regions of the lower Río Colorado, which is the main region of interest for investigating geomorphological changes.
Therefore, we selected the Sentinel-2 data acquired on 19 January 2017 for the post-flood image. For the pre-flood image, the data acquired on 20 December 2016 were selected, due to high cloud cover on the image of 30 December 2016 (Table  2 ). This is justified by the limited rainfall between 20 December 2016 and 6 January 2017. The selected pre-flood and post-flood Sentinel-2 data were then processed for flood extent extraction and assessments (Figure  2 ). Before processing, using visual inspection, clouds and cloud shadows were detected and removed. The lower Río Colorado is characterized by low gradients (~0. 000575 m/m declining to ~0. 000148 m/m) and a surrounding, largely homogeneous, red-brown oxidized, fine-grained surface with no vegetation, although white, salt-rich surfaces also occur locally  [40, 41, 45].
Conclusions The combination of MNDWI and ICA methods on Sentinel-2 MSI imagery has led to advances in flood mapping along the non-vegetated reaches of the lower Río Colorado approaching its terminus on the margins of Salar de Uyuni, Bolivia. Maximum daily precipitation frequency analysis based on a 42-year record of daily precipitation data (1976 through 2017) indicated that an approximately 40-year event (40. 7 mm) happened on 6 January 2017 in the study area, and this was associated with an extreme flood. Sentinel-2 data acquired after this extreme flood were used to separate water bodies and land using modified normalized difference water index (MNDWI), and then independent component analysis (ICA) was applied to the land section of the combined pre-and post-flood images to extract flooding areas.
Small-scale waves were observed along the boundary between Jupiter's North Equatorial Belt and North Tropical Zone, ~16. 5° N planetographic latitude in Hubble Space Telescope data in 2012 and throughout 2015 to 2018, observable at all wavelengths from the UV to the near IR. At peak visibility, the waves have sufficient contrast (~10%) to be observed from ground-based telescopes. They have a typical wavelength of about 1. 2° (1400 km), variable-length wave trains, and westward phase speeds of a few m/s or less. New analysis of Voyager 2 data shows similar wave trains over at least 300 hours. Some waves appear curved when over cyclones and anticyclones, but most are straight, but tilted, shifting in latitude as they pass vortices. Based on their wavelengths, phase speeds, and faint appearance at high-altitude sensitive passbands, the observed NEB waves are consistent with inertia-gravity waves at the 500-mbar pressure level, though formation altitude is not well constrained.
Here we re-examine HST multi-color imaging data sets from 2012 to 2018, as well as the full Voyager 2 approach data. In addition, we report on NEB wave appearances in multiple visible and infrared ground-based data sets. In the past few years, the NEB waves have been observed sporadically, with life times of at least tens of hours, and are sometimes prominent enough to be seen in images acquired with ground-based telescopes. Section 2 summarizes these observational data. Section 3 describes the measurable aspects of the NEB waves. Preliminary General Circulation Modeling (GCM) is discussed in Section 4, with discussion of wave types and Earth analogies in Section 5. Smith et al. (1979)  showed a faint wave in Voyager 2 Imaging Subsystem Narrow-Angle Camera (NAC) data, reported in a single violet image on 3 July 1979 (see their Figure  3 ) in the NEB.
However, the longitudinal wavelength has been nearly constant from the Voyager era to HST (Table  3 ) and in good agreement with groundbased data (Table  2 ). In the Voyager 2 data, a NEB wave is seen over at least 12 days, while the HST data sets are limited to approximately 20 hours of data, so a complete wave life cycle cannot be determined. Sometimes the NEB wave remains prominent over the full HST 20-hr observations, but on other dates it begins to fade over that time period. The amateur data sets provide evidence that some wave trains may last more than 30 days, though again, temporal sampling is incomplete. Despite the many observations and multiple wave crests seen on all these dates, velocities are difficult to determine. In particular, it is impossible to conclusively identify the same wave crest from frame to frame with time separations of 10 hours, as aliasing may occur.
Models show that cloud opacity maps can differ significantly from potential vorticity maps  (Marcus 2004 , Palotai et al. 2014 ). Although our simulations imply that the waves may be commonly produced by vortex interactions, details of cloud microphysics may play a role in determining whether the waves are visible in imaging data. ³12 20 8 20 Notes: 1. The length of individual wave trains is difficult to distinguish because of the background cloud features. 2. The lifetime observed in amateur data may exceed 30 days. 3. In the simulations the wavelength changes as a function of time as seen in Fig. 7  Figure  6. Potential vorticity maps for cases #1 (top), #2 (middle) and #3 (bottom) at days 17, 39, and 23 simulated days respectively. Green indicates cyclonic vorticity, while red is anticyclonic.
Nonetheless, terrestrial tropical cyclones show a potential wave formation mechanism and if conditions allow (such as wave trapping), the wave train can grow. For Jupiter, the smallest-scale spiral and baroclinic GWs would be below the ~175-km spatial resolution limit of HST. Very fine scale waves are observed in the NEB (and other regions) in recent JunoCam data with wavelengths of 55 to 174 km (e. g. , Sanchez-Lavega et al. submitted). The highest spatial resolution Voyager 2 data did not include any images of the cyclones near the NEB waves. However, Voyager 1 did observe a large well-formed cyclone at a resolution of ~14 km/pixel (Figure  9 ) with a fine-scale structure suggestive of a spiral wave or GW, as well as a few near-horizontal dark streaks with spacings of 250 to 425 km and lengths of 500 to 750 km; similar streaks are seen on many Voyager vortices.
Wave Temporal Appearance Any waves that do form may be damped over time or break in the presence of vertical wind shear and critical layer absorption, depositing their energy and disappearing  (Lane 2015). If conditions are no longer conducive, no new waves appear. The temporal variability of the NEB waves implies either the static stability is temporally variable, or else ever-present waves are simply not visible due to cloud/haze changes. While we do not know the exact time between the end of the Voyager 1 data and when the 1400-km NEB waves first appear, their presence in the earlier Voyager 2 data indicates that onset of wave formation is < 100 days. It is of note that the 2015 NEB waves appeared in the same seasonal cycle as those seen during Voyager (northern autumnal equinox) and that identical waves have not been observed at similar southern latitudes.
Acknowledgements: This work used data from the  NASA/ESA HST Space Telescope , and A. A. S, M. H. W, G. S. O were supported by grants from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. These observations are associated with programs  GO13067 ,  GO13937/14334/14756/15262 ,  GO14661 , and  GO14839. Jupiter maps are available at https://archive. stsci. edu/prepds/opal/and https://archive. stsci. edu/prepds/wfcj/. R. G. C's research was supported by an appointment to the  NASA   Postdoctoral Program  at the  NASA Goddard Space Flight Center , administered by  Universities Space Research Association  under contract with  NASA. R. H. , P. I. and A. S. -L. were supported by the  Spanish MINECO  project  AYA2015-65041-P  with  FEDER , UE support and  Grupos Gobierno Vasco   IT-765-13.
Method This research was approved by the ethics committee of Aix-Marseille University. Participants. Twenty-four students from Aix-Marseille University participated in this study. All participants had normal or corrected-to-normal vision and were native French speakers. Materials. Target items were common nouns, selected from the LEXIQUE database  [22]. Fifty-two plural-dominant and 52 singular-dominant words were selected. Each set consisted of 26 singular and 26 plural forms (see S1 Appendix). An argument raised by one of our reviewers is that optimal power would require a larger number of 1600 observations per cell  [23]  where we had only 624 observations per cell. The plural forms were composed of a stem (e. g. , canard [Engl. duck]) and the regular plural-suffix-s (e. g. , canards [Engl.
Data analyses Accuracy and reaction times (RTs) of vocal responses were corrected using CheckVocal  [25]. Due to technical difficulties, the voice recordings from five participants were non-interpretable and therefore excluded. Incorrect responses (8. 4%) were removed from reaction time analyses. Inverse RTs (1/RT) were calculated for each participant to correct for RT distribution skews and used throughout the analyses. Reaction times (RTs) and error rates (ER%) were analysed for each participant (Table  2 ). Linear mixed-effect modelling was employed to perform the main analyses  [26, 27]. Fixed effects, random effects, and random slopes were only included if they significantly improved the model's fit in a stepwise model selection procedure. Models were selected using chisquared log-likelihood ratio tests with maximum likelihood parameter estimation.
Following Barr et al. [28] , we included the maximal random effect structure justified by the design. The model was refitted after excluding data-points whose standardised residuals were larger than 2. 5 in absolute value [see 26], which led to the removal of 0. 9% of the data. Linear mixed-effects models as implemented in the lme4 package  [29]  in the statistical software R (Version 3. 0. 3; [30] ) were fitted using the above described selection procedure. Two fixed effect factors were examined: factor number (singular, plural) and factor dominance (singular-dominant, pluraldominant). The lmer default coding for treatment contrasts was used (i. e. reference 'plural' for factor number; reference 'plural-dominant' for factor item type). P-values were determined using the package lmerTest  [31].
313, p =. 024). The distribution of visual complexity for singular and plural items is illustrated in Fig 2. The covariate analyses indeed revealed that the effect of visual complexity was significant (t = 5. 32, p <. 001). Importantly, the inclusion of factor visual complexity showed that the effect of number was no longer significant (t = 0. 18, p =. 859). Error rates. The error analyses were performed based on the same principles as the RT analyses. We applied a binomial variance assumption to the trial-level binary data using the function glmer as part of the R-package lme4. First, we analysed the overall error rates in each condition. As in the reaction time analyses, the inclusion of dominance, as well as the interaction between number and dominance did not significantly improve the model's fit and these factors were excluded from the model.
Participants were instructed to respond as quickly and accurately as possible. Responses were recorded with a head-worn microphone. The amplifier was configured individually for each participant. To avoid that participants would be exposed to the same stem twice, we created two counterbalanced lists. Data analyses. The analyses of Experiment 2 were conducted following the same procedures as Experiment 1. Accuracy and response times of vocal responses were corrected using CheckVocal  [25]. Irregular fillers and incorrect responses (2. 4%) were removed from reaction time analyses. Inverse RTs (1/RT) were calculated for each participant to correct for RT distribution skews and used throughout the analyses. Data points whose standardised residuals were larger than 2. 5 in absolute value were excluded which led to the removal of 2.
Acknowledgments Elisabeth Beyersmann was supported by a  Macquarie University   Research Fellowship. Britta Biedermann  was funded by an  Australian Research Council (ARC) ,  Australian Postdoctoral Fellowship  (APD,  DP110100799 ). Antje Lorenz  was funded by the  German Research Council  ( DFG LO 2182/1-1  and  1-2 ). F. -Xavier Alario was supported by grants  ANR-16-CONV-0002  ( ILCB ),  ANR-11-LABX-0036  ( BLRI ) and the  Excellence Initiative of Aix-Marseille University  (A Ã MIDEX).
Elisabeth Beyersmann was supported by a  Macquarie University   Research Fellowship. Britta Biedermann  was funded by an  Australian Research Council (ARC) ,  Australian Postdoctoral Fellowship  (APD,  DP110100799 ). Antje Lorenz  was funded by the  German Research Council  ( DFG LO 2182/1-1 ). F. -Xavier Alario was supported by grants  ANR-16-CONV-0002  ( ILCB ),  ANR-11-LABX-0036  ( BLRI ) and the  Excellence Initiative of Aix-Marseille University  (A Ã MIDEX).
Statistical testing The data from the most affected eye of each patient underwent statistical analysis. For significance testing, the probability threshold was set at 5% for two-sided tests and two-sided CIs. Statistical analyses were carried out with SAS version 9. 4. The primary analysis used the per protocol population. This included all patients from the ITT population that had no major protocol violation. Generally, analyses were performed by treatment group and overall. For adverse events (AEs), only distribution by treatment group is presented. The primary efficacy outcome was change in OSS after 35 treatment days (D0-D35). A difference on the overall OSS of ,2 points led to a conclusion of noninferiority of 0. 1% HA versus 0. 5% CMC. Secondary efficacy outcomes were OSS on D84, TBUT, OCI index with six items, each in frequency and intensity, and evaluation of treatment medications by patients and investigators.
At D0, patients in the 0. 1% HA group had a mean TBUT of 6. 8±2. 4 seconds, while in the 0. 5% CMC group mean TBUT was 7. 2±2. 4 seconds (not a significant difference). Both treatments slightly improved TBUT values. At D84, the increase in TBUT was 1. 5±1. 9 seconds in the 0. 1% HA group and 1. 4±2. 1 seconds in the 0. 5% CMC group compared to D0. A secondary analysis was carried out using OCI primary end-point data of the ITT population at D35, and confirmed the noninferiority of 0. 1% HA vs 0. 5% CMC. No other secondary end points showed any statistically significant differences between the two groups. aes A total of seven AEs were reported in six patients during the study: four AEs in four of 41 patients (9. 7%) in the 0. 1% HA group (chronic arthrosis, episodic crackling feeling under the lid, episodic chalazion, chronic eye stinging) and three AEs in two of 39 patients (5.
AEs were judged to be related to the underlying DED. The proportion of AEs, including treatment discontinuation, associated with AEs was 9. 8% for 0. 1% HA and 5. 1% for 0. 5% CMC. There were no reports on serious AEs throughout the study. We found that the two tested eyedrop types led to clinically and statistically relevant improvements in SSs and OCI values. For several parameters, there was a constantly favorable trend for 0. 1% HA in comparison to 0. 5% CMC. Our data suggest that 0. 1% HA and 0. 5% CMC are efficacious in the treatment of patients presenting moderate DE syndrome with keratitis or keratoconjunctivitis. Two of six OCI items (stinging and itching) showed significantly more favorable results for 0. 1% HA on D84.
(c) Metabolome profiling Metabolome profiling on cell media and HLCs was undertaken using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) using a Thermo Scientific Ultimate 3000 UPLC system coupled to an electrospray Q Exactive Focus mass spectrometer. A hydrophilic interaction liquid chromatography (HILIC) assay to investigate water-soluble metabolites and a C 18 reversed phase method to investigate lipid metabolites were applied. Univariate and multivariate data analyses were performed in METABOANALYST 3. 0  [26] , including principal components analysis (PCA), Mann Whitney U-tests or Kruskal-Wallis tests to identify metabolites demonstrating a statistically significant change in relative concentrations between two or three biological classes. Fold changes were calculated by division of the mean peak response for one biological class by the mean peak response of the second biological class.
Raw sequencing data were quality controlled, filtered and aligned using Ion Torrent suite software (Life Technologies, Paisley, UK) and then normalized to total reads in R using bespoke scripts. Relative 5hmC levels per 150 bp window were determined using the 'sliding windows' function on the Galaxy server. Genomic annotation data for human (hg19) analyses were downloaded from the University of California Santa Cruz Genome Bioinformatics Resource. Further details on hmeDIP bioinformatic processing can be found in Thomson et al. [27]. Raw and processed data files are available for download from the GEO repository accession number GSE109139.
(e) Statistics Prism GraphPad software (GraphPad Software Inc. ) was used for analysis of qPCR and cell culture variables. Data were routinely analysed for outliers and normality of distribution. Non-parametric data or data with significant outliers were log 10 transformed. If data remained non-parametric, a non-parametric test was used as indicated. Data point and bar graphs are shown as mean + s. e. m.
We examined changes in transcription following 48 h LPO exposure using the Illumina HT-12 beadchip microarray (Illumina, San Diego, USA). PCA demonstrated clear separation between treated and control populations (figure  2a ). 48 h LPO treatment resulted in the dysregulation of 2618 transcripts (adjusted p-value , 0. 05, Benjamini Hochberg false discovery rate (FDR)), with upregulation of 1309 transcripts corresponding to 1083 genes and downregulation of 1309 transcripts mapping to 980 genes (greater than 10% transcriptional change, Benjamini-Hochberg adjusted p-value 0. 05; figure  2b  and electronic supplementary material, table  S2 ). The magnitude of change is consistent with a number of human datasets  [20, 30]. GO terms with the greatest enrichment in upregulated transcripts included 'GO:0044255' 'cellular lipid metabolic process', 'GO:0090208' 'positive regulation of triglyceride metabolic process' and 'GO:1901617' 'organic hydroxyl compound biosynthetic process' (electronic supplementary material, figure  S2a ).
TET2 is the dominant TET isoform expressed in liver and expression was appropriately increased during differentiation  [35]. In order to validate that HLCs have a liver-like hydroxymethylome and have transitioned from an ESC state, we performed hmeDIPsequencing (hmeDIP-seq) on HLCs and compared profiles with published datasets for 5hmC in human liver and human ESCs  [36, 37]. We additionally performed hmeDIPseq on human kidney to assess tissue-specificity. Accordingly, we observed high levels of 5hmC across key liver genes, e. g. albumin (Alb) exclusively in HLCs and human liver datasets, while low levels of 5hmC were observed over ESC and kidney 5hmC-enriched loci, e. g. the HoxA cluster (electronic supplementary material, figure  S5a, b ). In both human ESCs and liver, high levels of 5hmC are present at transcription start site (TSS), distal promoter and proximal gene body regions  [38, 39]  and bioinformatic analysis of 5hmC datasets in HLCs showed a similar 5hmC profile (figure  4c ).