dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting set of motifs included sites for the various krüppel-like factors as well as the GR (Table  S13 ). The most significantly enriched motif, the Klf14 binding motif RGKGGGCGKGGC, matches the Klf9 consensus motif in the JASPAR database  28, 29  and would be expected to bind Klf9, which is in the same KLF subfamily as Klf14  30, 31. In contrast, HOMER analysis of the 408 genes identified as klf9-dependent in Fig. 4  recovered a somewhat different list of motifs that included binding sites for several immunoregulatory transcription factors (Table  S13 ; note that there are 65 genes in common between the 408 identified in Fig. 4  and the 149 identified in Fig. 6 ), suggesting that the larger set includes more indirect targets of feedforward regulation downstream of Klf9. Consistent with this, the latter set of motifs includes sites for two immunoregulatory genes, irf1 and stat4 that are both consistently upregulated by chronic cortisol treatment (i.
05% phenol red dye, with an injection volume of ~ 20% cell volume. Injected embryos were snap-frozen at 6 h post fertilization for RNA extraction and qRT-PCR. Visual background adaptation (VBA) screen. To identify larvae lacking a functional GR a VBA screen was performed on 4-day old larvae as described  12. Briefly, larvae were incubated for 20 min in a dark incubator then transferred to a white background and immediately examined under a stereomicroscope with brightfield optics. Larvae that failed to mount a VBA response were identified by the failure of melanophores to disperse, remaining clustered in a dark patch on the dorsal surface  12. Larvae were segregated as VBA+ and VBA-cohorts and returned to culture for an additional day before they were collected for RNA-seq. RNA-seq and data analysis.
Fastq formatted read files were preprocessed with Trimmomatic 51 version 0. 38 with default options, and then aligned to the Zebrafish genome version 11 as presented in Ensembl 52 version 93, using the STAR aligner  53  version 2. 6. 1b. The Ensembl transcriptome was preprocessed with a splice junction overhang of 100 nt. Following alignment, the resulting BAM files were processed with RSEM 54 version 1. 3. 0 for isoform and gene-level expression estimates. The resulting gene-level expression values were merged into a single expression matrix with an in-house python script. EDASeq 55 carried out in R version 3. 6. 1 was used to further normalize data for systematic effects, using gene-level length and GC-content as downloaded from Ensembl version 98 using EDAseq's included scripts. "WithinLane" normalization with GC content was judged as superior to that based on length.
In order to derive the temperature and photochemical compound global spatial and vertical distributions, we used dedicated FP3 and FP4 CIRS limb observation sequences, in which an entire hemisphere was scanned while Cassini was approaching or leaving Titan during a given flyby. Such observations had an apodized spectral resolution of 15. 5 cm -1. More details on this observation type, called midinfrared limb maps (MIRLMBMAP), are given in  Nixon et al. (2019). Data used here are extracted from the "global calibration" version  (Jennings et al. 2017 ) archived in the NASA Planetary Data System. We analyzed 11 MIRLMBMAP sequences and combined those acquired within two consecutive Titan flybys, usually within a two-month time period, to derive pole-to-pole spatial distributions of the temperature and photochemical compound VMRs.
For both steps we determined the vertical profiles using an inversion algorithm based on the fit of the observed limb spectra using a line-by-line radiative transfer code. The inversion algorithm and the spectroscopic files used here are described in  Vinatier et al. (2010b Vinatier et al. ( , 2015)). We additionally utilized here the propane pseudo-line list of  Sung et al. (2013)  to reproduce the C 3 H 8 ν 7 , ν 21 , ν 20 , and ν 7 emission bands centered at 869, 922, 1054, and 1158 cm -1 , respectively. We used the spectral dependence of the aerosol extinction coefficient from  Vinatier et al. (2012)  to reproduce the continuum of all selected limb spectra. As in our previous studies  (Vinatier et al. 2007 (Vinatier et al. , 2010a (Vinatier et al. , 2015)) , we had to apply a shift on the CIRS database nominal line-of-sight altitudes of our selected limb spectra to reproduce the P-and Q-branches of the ν 4 -CH 4 band of the deepest limb spectra, which display the highest S/Ns.
The mid-and low-latitude stratopause was located at the same pressure in 2012  (Vinatier et al. 2015)  and 2015, but the 10-20 • N stratopause was 2K colder in 2015 than in 2012 at 0. 2-0. 3 mbar, which may result from Saturn's orbit eccentricity  (Bézard et al. 2018). The temperature profiles we derived can be compared with those of  Mathé et al. (2020)  inferred from CIRS limb spectra acquired at 0. 5-cm -1 spectral resolution at comparable latitudes and time. Their dataset probed the same pressure region as ours. The agreement with their mid-northern and highnorthern latitude profiles in 2015 and 2016 is very good. Our derived temperatures are also consistent below the mesosphere with their equatorial and mid-southern latitude profiles. However, we derived mesospheric temperatures that were higher by ∼5-10 K than their 2017 equatorial values and their 2016-2017 mid-southern values.
22 × 10 -3 derived from the far-IR to UV spectral dependence of the aerosol cross sections of  Vinatier et al. (2012, see Fig. 2 therein). A comparison of our haze extinction coefficient maps with those derived from ISS observations acquired at similar dates is displayed in Appendix D. In the overlapping altitude range, agreement is overall within a factor of 2, which is typically the uncertainty on the haze extinction coefficient derived from CIRS limb spectra above 350 km. The largest difference is inferred in September 2017, which corresponds to an ISS observation with a limited latitudinal coverage for which Titan's center adjustment, and therefore the altitude grid extraction, is not as well constrained as for other ISS datasets (see  Seignovert et al. 2020).
(2011)  from winter to northern spring, and extended by  Achterberg et al. (2018)  over early northern summer but deeper than 0. 05 mbar as they used CIRS nadir datasets. Our zonal wind integration differs from these previous studies as we did not integrate the thermal wind equation over cylinders concentric around Titan's rotation axis  (Flasar et al. 2005) , but instead performed integration in spherical coordinates (see Appendix E). We set the zonal wind at 5 mbar to 120 m s -1 , which is compatible with in situ measurements acquired by the Doppler Wind Experiment aboard Huygens during the probe descent  (Bird et al. 2005; Folkner et al. 2006). Figure  5  shows the meridional cross sections of zonal wind velocity (eastward) derived from the thermal fields displayed in Fig. 2. During the studied period all the derived meridional cross sections of zonal wind show a clear asymmetry between the northern and southern hemispheres with zonal winds much faster in the southern hemisphere than in the northern one.
Acknowledgements. This work was funded by the  French Centre National d'Etudes Spatiales (CNES)  and the  Programme National de Planétologie (INSU). M. S. and N. A. T. acknowledge support from the  UK Science and Technology Facilities Council  (grant number  ST/MOO7715/1 ). A116, page 25 of  33 A&A 641 ,  A116  ( 2020 ).
D'alessandro et al. (2019)  also retrieved molecular information from their adriatic specimens but the publication is missing sequence information (e. g. Genbank accession numbers). More importantly, the presented phylogenetic analysis of Spiophanes species undertaken by  D'alessandro et al. (2019)  includes only species distantly related to S. adriaticus/S. afer (based on our judgement of their morphology), and hence neither supports nor rejects the hypothesis of S. adriaticus being a formerly unknown Spiophanes species. thus, the separation of S. afer and S. adriaticus is not well resolved yet, and we reject a formal acceptance of this species and urge the authors to provide registration numbers for type material. all our specimens are in good agreement with the original description of S.
viriosus (obIS, 2019;; rESoMar, 2019)  and has not been recorded since then. In 2012, S. afer was reported between Italy and Spain, including Corsica (obIS 2019), suggesting that the previous reports of Spiophanes in the Mediterranean Sea may also refer to this species. therefore, despite the fact that we are reporting S. afer as present on the French Mediterranean coast at least since 2010, the literature and databases strongly suggest that it has been present in the area for a longer period of time. In summary, the present state of knowledge on the spatio-temporal distribution of the species of Spiophanes, and particularly S. afer, does not allow an assessment of its real distribution in Europe. Further investigations will require that museum and private collections be checked, and new morphological and molecular analyses on fresh material (or preserved in a compatible way for DNa extraction) be carried out.
Acknowledgments the authors would like to thank those involved in sampling campaigns on board the rv " Côtes de la Manche " (JErICo-Next-h2020 and voG CNrS lEFE-EC2Co programs, bruno Deflandre, SGC CDM Fred (2016) JErICobENt, https://doi. org/10. 18142/284 ),rv  "l'Estran" (la rochelle université ) and " JIF Surveyor" (Jifmar offshore Services , bayonne), rv "tethys II" (rEDIt 2010, http://dx. doi. org/10. 17600/10450130 )funded by  "agence de l'Eau Corse-Méditerranée " (convention n°2010  0871 ) and the  "agence des aires Marines Protégées " (Marché N° 2009-aaMP-16; lot N°9; aSCoNIt/GIS Posidonie), and rv " Nereis II " (rNMCb funded by  Conseil Départemental des Pyrénées orientales and IbIS). We thank tarik  Meziane (MNhN Paris)  for providing accession numbers in the difficult context of Covid-19 confinement.
& bachelet, G. (2019) Molecular analysis and new records of the invasive polychaete Boccardia proboscidea (annelida: Spionidae). Mediterranean Marine Science, 20 (2),  393-408. https://doi. org/10. 12681/mms. 20363ravara, a. & Moreira M. h. (2013) Polychaeta (annelida) from the continental shelf off aveiro (NW Portugal): Species composition and community structure. Check list, 9 (3),  533-539. https://doi. org/10. 15560/9. 3. 533read,  G. & Fauchald , K. (Ed. ) (2019) World Polychaeta database. Spionidae Grube, 1850. available from: http://marinespecies. org/aphia. php?p=taxdetails&id=913 (accessed 2 September 2019) rESoMar (2019) French marine stations and observatories network Database. available from: http://resomar. cnrs. fr/bases/index. php(accessed 18 September 2019) Söderström, a. (1920)  Studien über die Polychätenfamilie Spionidae.
The NUCAPS CO, CH 4 , and CO 2 profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH 4 and CO 2 product accuracies are both found to be within ±1%, with precisions of ≈1. 5% and 0. 5%, respectively, throughout the tropospheric column.
The validation of the NUCAPS T, q and O 3 profile EDRs with respect to high-quality reference datasets has been previously reported in Nalli et al. [21, 22] , where it was demonstrated that the SNPP EDRs meet JPSS Level 1 requirements; additional independent assessments of the SNPP T and q EDRs versus other reference datasets have been reported elsewhere  [23, 24]. Similar performances have been established for the EDR products from the NOAA-20 satellite, launched since the original SNPP validation effort. Since that time, the NUCAPS algorithm development team has focused on improvements to the operational carbon trace gas EDR products mentioned above. The improvements include updated a priori profiles (based on current zonal climatologies) and RTA tuning (empirically removing residual biases between the model and observations), along with optimized quality assurance (QA) flags (based upon the algorithm chi-square, χ 2 , degrees-of-freedom, and other quality measures).
Methodology Carbon trace gas EDR validation was a new requirement within the JPSS calibration/validation (cal/val) program  [25]  beginning with the transition to the full spectral-resolution (FSR) CrIS NUCAPS. The JPSS Level 1 requirements for carbon trace gas profile EDRs are given in Table  1 , which are defined for the global ensemble of total column, cloud-cleared cases. These requirements serve as the program metrics by which the system is considered to have reached Validated Maturity and meets mission requirements. Satellite sounder validation methodology has been well-established for T, q and O 3 profile EDRs within previous validation work, with the various coarse-layer statistical uncertainty characterizations conducted relative to baseline reference datasets (i. e. , "truth") roughly classified within a "hierarchy"  [26].
Profile statistics for layer gas concentrations are defined in terms of fractional errors, including systemic (i. e. , bias or "accuracy"), random (i. e. , 1σ variability or "precision"), and total combined error (i. e. , root mean square error, RMSE). For carbon trace gases we have adopted a similar hierarchical approach based upon available reference datasets, consisting of (1) numerical model global comparisons, (2) satellite EDR intercomparisons, (3) surface-based observing network assessments, and (4) intensive field campaign in situ data assessments. Those at the base of the hierarchy may be readily employed during the early cal/val stages (or anytime thereafter) of a satellite's lifetime, whereas those near the top are employed during later stages. These are briefly overviewed below.
Numerical model output (analysis and/or forecast interpolated to NUCAPS footprints) enables the rapid comparison with large, global datasets obtained during "Focus Days" (i. e. , days selected for the acquisition of global SDRs that are used for retrieving EDRs using the latest versions of offline code) and as such are extremely useful for early evaluation of the algorithm and identifying gross problem areas  [27]. Numerical models used for such comparisons include the European Center for Medium-Range Weather Forecast (ECMWF), the NOAA CarbonTracker  [28] , and the Copernicus Atmosphere Monitoring Service (CAMS)  [29]. Such analyses are useful in identifying regional or spectral biases. However, dynamical models (e. g. , ECMWF) do not constitute independent correlative data given that they assimilate radiances, and generally do not model chemistry and/or surface fluxes.
Trace gas EDRs obtained from other satellite sensors or algorithms provide quasi-independent observations for global intercomparisons. Like numerical model comparisons, this approach also allows for the acquisition of large, global data samples that can facilitate early consistency checks. In addition, such data (depending on the sensor/algorithm) may be more reliable than model analyses, especially in the case of previously validated EDR products, thus providing additional global confidence. AIRS is extremely useful for this purpose given it is a mature, high-resolution IR sounder that runs an end-to-end algorithm similar to NUCAPS, with the Aqua satellite flying in the same 01:30, 13:30 local equator crossing time orbit. Other satellite sounder EDR datasets include Tropospheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5, the NASA Orbiting Carbon Observatory (OCO-2), Greenhouse gases Observing Satellite (GOSAT), and the Aura Microwave Limb Sounder (MLS).
However, a limitation of these data for validation is that they may possess similar retrieval error characteristics (in the case of AIRS  [27] ) or different vertical sensitivity, and thus ultimately would require proper treatment of each sensor's averaging kernels  [30]  (cf. Appendix A). Ground-based, remotely sensed observations obtained periodically from surface-based observing networks provide independent truth datasets with a global distribution reasonably representing global latitude zones roughly analogous to radiosonde observations (RAOB) for temperature and moisture. The most notable example of such a dataset is the Total Carbon Column Observing Network (TCCON) [31], a ground-based network of uplooking solar-spectrum FTS instruments that obtain total column measurements of traces gases (discussed more in Section 3.
1). A newer source of in situ data are vertical profiles obtained from the balloon-borne AirCore sampling system  [32, 33]. NUCAPS EDR collocations with these ground-based networks thus provide independent datasets for statistical assessments  [26]. However, limitations in these datasets include the time latencies needed for acquiring reasonable collocation sample sizes, uncertainties in unit conversions, and different sensitivities to atmospheric layers. At the top of the validation data hierarchy are intensive aircraft campaigns that provide episodic, but generally comprehensive sets of in situ and remotely sensed vertical profile data from multiple ascents and descents of dedicated aircraft flying over a specified region. Aircraft campaigns thus allow for detailed performance specification over regions of interest.
Data Following the hierarchical approach described in Section 2, multiple complementary correlative truth datasets are relied upon to provide independent measurements for validation. We have leveraged three datasets for this purpose, namely uplooking spectrometer total-column data from TCCON, balloon-borne profiles from AirCore, and finally aircraft in situ vertical profile data from ATom. Existing satellite EDR datasets from other platforms (viz. , Aqua AIRS and TROPOMI) have also been utilized for global intercomparisons to demonstrate the NUCAPS products look qualitatively reasonable and geographically consistent (Warner et al. , manuscript in prep for Atmos. Chem. Phys. ). Specifics of the layer and unit conversions required for conducting quantitative statistical assessments of NUCAPS (Section 4) are explicitly described for completeness and reproducibility.
TCCON The Total Carbon Column Observing Network (TCCON) [31] is a ground-based network of Bruker 125HR uplooking solar-spectrum FTS that obtain spectral measurements in the near-IR region that encompasses the CO, CH 4 , CO 2 , N 2 O, and O 2 absorption bands  [31] , thus comprising an independent data source for validating NUCAPS. The interferograms are collected with a 45 cm optical path difference (45 cm was chosen deliberately to optimize retrievals of CO 2 in the 6000 cm -1 band) yielding a spectral resolution of ≈0. 02 cm -1. The total column retrievals of these trace gases is achieved via a retrieval algorithm (called GFIT) that includes both the forward and inverse model calculations. The inverse algorithm employs least-squares fitting by scaling an a priori  [31]. The a priori profiles used in the GFIT system, x 0 , were obtained from the NOAA National Centers for Environmental Prediction, National Center for Atmospheric Research (NCEP/NCAR) analysis.
TCCON station data can facilitate intercomparisons (acting as a "transfer-standard") between retrievals from multiple satellites. The total column trace gases retrieved by TCCON are in dry mole fractions (DMF), whereas the NUCAPS algorithm retrieves trace gas layer abundances (in molecules/cm 2 ) on the 100 RTA model layers. Thus, a conversion scheme must be implemented. Furthermore, because TCCON and NUCAPS have fundamental differences in vertical sensitivity, it is desirable that the TCCON column averaging kernels (AKs) be utilized in the integration of the NUCAPS observation. For explicitness, the conversion scheme and application of TCCON column AKs in the integration of NUCAPS retrieved trace gas profile EDRs are detailed in Appendix A. In practice the column AKs are dependent only on the solar zenith angle, θ , of the measurements  [31].
Thus a single set of column AKs from the Lamont Site, a(θ ), are provided for gridded values of θ = 10 • , 15 • ,. , 85 • (shown in Figure  1 ), which can then be interpolated to the solar zenith angle of the measurement. From Figure  1  a fundamental limitation in the utility of TCCON data for IR sounder (e. g. , NUCAPS) validation becomes evident, namely the TCCON tendency for higher sensitivity in the upper layers of the atmosphere, except at larger θ for CH 4 and CO 2. The TCCON vertical sensitivity must be taken into account when comparing against the sounder retrieved EDRs, which typically have peak sensitivity in the mid-troposphere (discussed more in Section 4).
g. , Picarro, Inc. ). AirCore thus allows in situ measurement of mole fraction samples for various trace gases (e. g. , CO, CH 4 and CO 2 ) without requiring an aircraft or onboard data transmission system (e. g. , as with an ozonesonde). A distinct advantages of AirCore is the capability for multiple deployments with a distributed geographic coverage over land. In this capacity AirCore has promise to be a surface-based network like TCCON, albeit with calibrated, high-resolution profiles measured in samples that survey 98% column, somewhat analogous to an ozonesonde network. Because the length scale of diffusion is <0. 5 m over the time it takes to analyze an AirCore sample (≈4 h), >100 discrete samples can be measured in a 100 m AirCore tube. The resultant profile resolution surpasses the 100-layer forward model grid employed in the retrieval.
ATom The Atmospheric Tomography (ATom) mission  [34]  deployed an extensive gas and aerosol measurement payload on the NASA DC-8 aircraft for global-scale sampling of the atmosphere, profiling continuously from 0. 2-12 km altitude. Flights occurred during all four seasons, originating from the Armstrong Flight Research Center in Palmdale, California, USA, flying north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, before returning to California across central North America or the North American Arctic. Figure  2  shows the flight paths for the 2016-2018 sampling periods (ATom-1, -2, and -4). ATom-1 and -2 data were first used for SNPP NUCAPS development and validation prior to our J-1 validation effort; we subsequently obtained ATom-4 data for NOAA-20 validation (note that ATom-3 was still pre NOAA-20), and simply combined it with our existing ATom-1 and -2 collocation data for SNPP going forward.
During ATom flights, the aircraft repeatedly ascended to 10-12 km, leveled-off, then descended to different heights at different rates. The raw aircraft data are recorded as a function of time, with reported altitudes featuring small-to-medium scale fluctuations throughout any given flight. Correlative truth profiles must thus be extracted only from smooth, continuous ascent/descents, disregarding small-scale altitude fluctuations and periods when the aircraft leveled off. Through trial and error, we devised an approach for extracting these profiles from the flight data based upon three criteria, namely the ascent/descent rate, ∆z/∆t (to find actual ascents/descents), the time difference between successive ascending/descending profiles (given that level-off periods separate the ascents and descents), and the thickness of the interquartile range covered by the profile (to ensure reasonably complete tropospheric profiles).