dataesr/scientific-paragraphs-categorization huggingface dataset:
For example,  Parker et al. (2016)  presented hypothetical surface compositions for Sedna out to 5 microns, so far indistinguishable in the current dataset, which is limited mostly to < 2. 5 microns. Our greatest laboratories for studying irradiation chemistry may be Makemake, Quaoar and 2007 OR10. The surface of Makemake is dominated by methane. However, some deviations from the CH 4 spectrum have been identified as due to the presence of C 2 H 6 , then C 2 H 2 , C 2 H 4 and propane (C 3 H 8 )  (Brown et al. , 2015; Lorenzi et al. , 2015; Perna et al. , 2017). Quaoar has a surface dominated by the presence of water ice, with significantly less coverage in methane than Makemake  (Jewitt and Luu, 2004; Schaller and Brown, 2007b). However, its red color could be interpreted as due to the presence of irradiated hydrocarbons.
6-5. 3 µm spectral range at once. The possible observing modes for TNO observations are the fixed slits and the integral field unit (IFU;  Closs et al. , 2008) ; all filter/grating combinations are available for use with these modes. In reality, due to uncertainties in the apparent positions of many TNOs (though most large-and mid-size TNOs have 1-year uncertainties >1 arcsec, nearly half of the TNOs in the MPC database have orbital uncertainties larger than 1 arcsec), blind pointing with the IFU will be the only option for spectral observations of the majority of them unless additional, more accurate astrometry using ground-based facilities becomes available in the time leading up to JWST science operations. The wide aperture target acquisition (WATA) available for accurate positioning of a target in the fixed slits or the IFU required to know the position of the target to better than 1" to be able to blindly position it in the 1.
6" × 1. 6" WATA aperture. Unfortunately, the standard target acquisition with micro-shutter arrays (MSA) that would have allowed to perform target acquisition using reference targets over a much larger field of view is not possible for moving targets. While NIRSpec IFU observations are better-suited for TNOs with poorly-constrained orbits, the signal-to-noise ratio (SNR) of the spectra extracted from the IFU data cube will be lower than would be obtained for the same filter/grating strategy and total photon collection time with the fixed slits. After being processed through the pipeline the IFU data are in the form of a data cube: two spatial dimensions and one spectral dimension. This means there is an image for each wavelength "slice" rather than a single column in a fixed slit spectrum.
We use the water-ice optical constants from  Clark et al. (2012) , and optical constants from the GhoSST database (now part of the SSHADE database;  Schmitt et al. , 2018)  for the other species (https:// ghosst. osug. fr, https://www. sshade. eu). At this stage, we do not include any dust or reddening component. These would not change the position of the absorption features, only their depths due to the relative amount of each compound in the mixture.
Figures  2, 3  show examples of simulated spectra corresponding to mixtures of water and methanol ice (9:1 and 7:3) and water and methane ice (9:1, 7:3, and 5:5), using from top to bottom the high-spectral resolution, mediumspectral resolution (exposure times equal to those used for the high-spectral resolution, see Table  2  for Orcus), and low-spectral resolution options. It is obvious in these examples that the lower the spectral resolution, the higher the SNR. As for data obtained from the ground, higher SNRs can be achieved with the high-spectral resolution by binning the data and thus artificially lowering the effective spectral resolution. However, most of the time, NIRSpec observations of faint targets are detector-noise dominated so the SNR after binning will be lower than one that would be obtained for direct observations at lower spectral resolution.
3 +4. 9 -2. 9 Salacia 4. 1 Actaea 1. 29 +0. 29 -0. 23 829 ± 30 4. 4 ± 0. 4 Class BB, BR 1. 26 ± 0. 16 866 ± 37 4. 2 ± 0. 4 Inconclusive 1. 16 +0. 59 -0. 36 H values are from the JPL Solar System Database at http://ssd. jpl. nasa. gov. Diameters and albedos from the "TNOs are cool" key program on Herschel are given on a gray background for the primary in case of a multiple system (available at http://public-tnosarecool. lesia. obspm. fr/Published-results. html). Other measurements are also provided for comparison: values from stellar occultations are given in bold. * : Varuna and 2003 AZ 84 may have a non-spherical shape, Varuna in particular has shown variable albedo, size and surface composition since it was first observed. Stellar occultations by 2003 AZ 84 provide the following size: (470 ± 20)×(383 ± 10) × (245 ± 8) km (Dias.
We made a first-pass annotation with the MAKER pipeline using evidence drawn from the Swiss-Prot database, the hidden Markov model (HMM) profile computed from GeneMark  (Lukashin and Borodovsky, 1998) , and the Augustus  (Stanke and Waack, 2003)  profile generated with BUSCO  (Waterhouse et al. , 2018). We then used the predicted genes from the MAKER output to train and run Augustus to produce the final annotation. Our assembly of Halicephalobus sp. NKZ332 spans 47 Mb in 5,085 contigs with an N50 of 60 kb. This assembly size is smaller than the genomes of most sequenced nematodes  (International Helminth Genomes Consortium, 2018). Likewise, the assembly is much smaller than those of other Panagrolaimus species, whose genomes are ~140 to ~180 Mb in gonochoristic species and even larger (~230-270 Mb) in parthenogenic strains, which likely originated from an allopolyploidy event  (Schiffer et al.
under review). In our annotation, we predicted a total of 11,023 genes with a mean of 1,511 bp in length. We assessed the completeness of the genome assembly using  BUSCO (Simão et al. 2015) , with which our annotation shared 83. 2% (79. 8% complete genes) of the nematode ortholog set. Given such representation of conserved orthologs in the annotation, the genome of Halicephalobus sp. NKZ332 is strikingly minimal. Finally, using the MITOS Web Server  (Bernt et al. 2013) , we assembled the mitochondrial genome sequence, which has a length of 13,886 bp and GC content of 20%. In summary, we present a compact nematode genome representing a genus of small, parthenogenic, and ecologically opportunistic nematodes. The nuclear and mitochondrial genome sequences have been deposited in NCBI GenBank under accession numbers VOSG00000000 and MN207311, respectively.
ACKNOWLEDGMENT We thank  Prof. Isabelle Chataigner ,  Dr. Michael De Paolis  ( Univ. Rouen ) and  Dr. Xavier Moreau  ( Univ. Versailles ) for helpful discussions. DR thanks the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI)  for a PhD fellowship. We also gratefully acknowledge the  ANR  ( ANR-17-CE07-0050 ; " ArDCo "),  Université Paris Sud  and the  CNRS  for financial support.
, 2015; Huang et al. , 2015)  and surface  (Baars et al. , 2012)  platforms does not provide the compre-hensive dataset best suited to examining the drivers of multipollutant vertical distribution. When simulating the pollutant vertical distribution over TSA there is a large diversity between AeroCom global models  (Koffi et al. , 2012)  and between a subset of regional models  (Andreae et al. , 2012). In particular, open questions remain regarding treatment of convective mixing  (Archer-Nicholls et al. , 2015)  and the injection of buoyant plumes into the vertical column  (Paugam et al. , 2016). Furthermore, to ensure consistency with remotely sensed AOD measurements, aerosol emissions are typically up-scaled by a factor of 2 to 5  (Johnson et al. , 2016; Reddington et al. , 2016). This represents a key uncertainty in model treatment of biomass burning aerosol, with deficiencies in both emissions inventories and model process posited.
Plumes identified on take-off and approach to an airport were removed from further analyses as they were likely contaminated from urban emissions. Satellite remote sensing products were obtained for the SAMBBA period and September 2008-2017. The MODIS (Moderate Resolution Imaging Spectroradiometer) instrument provided retrievals of aerosol optical depth and active fire data. Precipitation rates were acquired from instruments on board TRMM  (Tropical Rainfall Measuring Missions). Land cover data were acquired from the ESA CCI Land Cover project and used the United Nations Land Cover Classification System. Specific details on the acquisition and processing of satellite data are provided in the Supplement.
Together, this structure can explain the lidar-derived extinction coefficient distribution (first published by  Marenco et al. , 2016). Aerosol is capped below the first wind speed jet, is well mixed within the mixed layer and features a maximum at ∼ 1. 5-2 km. Visible plumes at ∼ 59 and ∼ 52. 5 • W lie at injection heights typical of those observed in the in situ profile data. The similarity of the in situ σ sp_amb and lidar extinction coefficient profile shapes at the regional (Fig. 11 ) and local (Fig. S7 ) scales engenders confidence in the representativity of both datasets. Disparity in the absolute magnitudes is primarily explainable by differences in the sampling coverage (Fig. S8 ). Consistent values of the scattering Ångström exponent at 700/550 nm within the boundary layer (Fig. S5 ) indicate a similar aerosol type throughout.
Loadings reduce to near baseline values above ∼ 4 km in all. Only in  Andreae et al. (2012)  have similar regionally averaged profiles of CO been presented. These exhibit a similar profile shape as here -greatest near the surface and reducing thereafter to a minima at ∼ 4 km. Andreae et al. (2012)  therefore also demonstrated the same relationship of aerosol : CO with altitude as observed here. Whilst studies displaying only a few profiles (e. g. Andreae et al. , 1988 Andreae et al. , , 2004;; Chand et al. , 2006; Pereira et al. , 1996; Yokelson et al. , 2007)  may not mirror these average distributions, comparable profiles are present in our library of individual profiles (Sect. S5), indicating that our more extensive dataset is consistent with previous reports and captures variability between different studies.
Acknowledgements). Processed individual and averaged vertical profiles, data masks, plume composition, model output and satellite fields are currently available on request from Eoghan Darbyshire. Lidar data are available on request from  Franco Marenco  (franco. marenco@metoffice. gov. uk). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-5771-2019-supplement. Acknowledgements. We would like to thank those involved in the  SAMBBA  project. This includes the  Facility for Airborne Atmospheric Measurement (FAAM)  and  DirectFlight Ltd. who manage and operate the BAe-146-301  Atmospheric Research Aircraft (ARA) , which is jointly funded by the  Natural Environment Research Council (NERC)  and the  Met Office. A number of institutions were involved in logistics, planning and support for the SAMBBA campaign: the  Met Office, INPE, University of Sao Paulo  and the  Brazilian Ministry of Science and Technology.
Data availability. All raw time series data used to derive the vertical profiles from the FAAM research aircraft are publicly available from the Centre for Environmental Data Analysis website (http://www. ceda. ac. uk/, last access: 31 August 2018). Direct links to the flight data records are given in the reference list (Facility for Airborne Atmospheric Measurements et al. , 2014a, b, c, d, e, f, g -https://doi. org/10. 5285/6034214ae46c48a7835608866a823f56, h, i, j, k, l -https://doi. org/10. 5285/7e7783fcd44e4a3890f3bd67e89e585e, m, n, o, p, q). Raw active fire and land use data used in the paper are available publicly from NASA and ESA respectively (see.
2013; Kim et al. 2016). The WRKY TFs family has been widely documented and possesses an associated Arabidopsis database  (Eulgem et al. 2000). For example, AtWRKY57 is known to repress leaf senescence by interacting with the jasmonic acid (JA) pathway. AtWRKY54 and 70 have cooperative functions in negative regulation of senescence  (Hu et al. 2012) , whereas AtWRKY53, an early factor of drought response  (Sun and Yu 2015) , regulates senescence positively  (Zentgraf et al. 2010 ). These three WRKYs then interact independently with AtWRKY30  (Besseau et al. 2012). AtWKRY22, 29, 33 and 53  are also implicated in the oxidative-stress response and regulate oxidation during senescence  (Zentgraf 2007). Finally, some small RNAs have also been described as senescence-inducible and to set posttranscriptional controls during senescence  (Thatcher et al.
4-RNA-seq data alignment and quantification The RNA-seq library read qualities were assessed using FastQC (Babraham Institute, Cambridge, UK)]. The remaining sequencing adapters were removed using trim_galore (Babraham Institute, Cambridge, UK). The reads were splice-aligned on the genome using STAR  (Dobin et al. 2013 ) (version 2. 4. 0i using standard parameters) on the lettuce genome version 5 (Reyes-Chin-Wo et al. 2017). The expression was quantified using featureCounts  (Liao et al. 2014) (version V1. 4. 5-P1 using standard parameters).
5-Statistical analysis One sample (packaged lettuce) consisted of 4 leaves collected from 4 lettuces. The whole experiment was repeated 3 times (Data reflect 3 biological replicates per time point (day 0, day 2 and day 7). Differential gene expression analyses were performed in R 3. 3. 2 (R core Team 2016). The normalization between each pair of samples  (Robinson and Oshlack 2010; Robinson et al. 2010; McCarthy et al. 2012). Gene enrichment analysis was performed with the Gene Ontology package topGO (Alexa and Rahnenfuhrer 2016) and pathway related analyses were performed with the Kyoto Encyclopdia of Genes and Genomes (KEGG) pathway (Kanehisa Laboratories, Japan). DE gene annotations were verified with SwissProt, GO enrichment, and the KEGG pathways according to conserved domains in the sequence.
2007 ). However, these data have been supplemented progressively and now raise more than 3,500 SAGs through the leaf senescence database  (Li et al. 2017 ). 4-Chloroplast and primary metabolism related genes Among the cellular compartments, the chloroplast is the most affected by abiotic stress and senescence (Pintó-Marijuan and Munné-Bosch 2014). Free Chl breakdown products, especially pheophorbide a, are strong light-absorbing molecules that can produce reactive oxygen species that in turn induce oxidative damage and accelerate senescence  (Pružinská et al. 2005 ). In addition, hydroxylations of chlorophyllide a in b were hypothesized to be catalyzed by cytochrome P450-type enzymes, also known to take a role in detoxification processes  (Hörtensteiner and Lee 2007). During the storage of lettuce leaves, the RNA-seq data highlighted the modulation of many Chl hemostasis-related genes: a chlorophyllide b reductase, a protochlorophyllide reductase and two chlorophyllases (Table  2 ).
Finally, cytokinins-related markers did not display such an asymmetrical pattern between early-and late-responsive genes. Eleven genes associated with the NAC TF family, known to play a role in the hormonal response to abiotic stress and senescence  (Nakashima et al. 2012; Takasaki et al. 2015) , were upregulated during postharvest storage (supplementary data 3). The auxin signaling pathway involves a large group of responsive genes such as auxin-responsive and induced proteins, auxin response factors (ARF), small auxin upregulated RNA (SAUR), auxin-responsive Gretchen Hagen3 (GH3) and YUCCA genes (Mueller-Roeber and Balazadeh 2014). In dark-induced and developmental senescence, IAA levels were found to decrease while RNAs of the GH3 genes family were increased; GH3 genes aim to conjugate IAA into amino acids, contributing to auxin homeostasis (Mueller-Roeber and Balazadeh 2014).
d) Evaluating effectiveness of OoC compared to current in vivo profiles, through the analysis of the corresponding data derived from conventional drug development models, and correlation with clinical data or manifestations. e) Performing intra-and inter-laboratory assays (ring trials) to assess the reproducibility and accuracy of OoC devices as well as monitoring technological performances (stability and robustness). These last aspects should be considered as iterative approaches, supported by ongoing pharmaceutical projects to bring added value, aiming at investigating the correlation between OoCs and in vivo data, if relevant, and to establish the critical link to clinical expectations. Ideally, all qualification studies should be performed by a third party, as proposed by the testing center initiatives in the US funded by NCATS, to ensure an independent analytical characterization (Fig.
Towards a centralized database information sharing and promoting OoC development To achieve optimal results from the qualification studies, a key challenge is to establish an evolutive database, clustering all available data on a reference set of the most appropriate com-is beneficial for the commercialization of OoC devices, this acceptance should not hinder the development process. Indeed, regulatory agencies should be considered key players, involved in the early stages of OoC development along with end users, in order to better understand the potential of the technology and its applications. Industry needs confidence in the robustness of the data retrieved through the devices, whereas regulators typically require a case-by-case analysis. Therefore, while considered necessary, the qualification of a device does not necessarily prelude to its regulatory acceptance nor to user adoption.
Towards standardization: open technology platforms for OoC One approach that experts recommended to encourage the OoC community to converge towards standardization was the realization of open technology platforms. These can be seen as shared technology platforms created to gather knowledge and expertise into a centralized database, in which potential users could contribute by developing and sharing building blocks of modular sys-pounds and biomarkers, together with the results on the performance and accuracy of the specific OoC systems under test and the context of use for the target tissue(s). The aim of this centralized and publicly accessible database would be to provide the scientific community with in-depth information (including raw data and negative results) on well-characterized pharmacological and toxic compounds to demonstrate in vivo-like responses in OoC devices and to go beyond a simple and linear annotation of the compounds' effects.
Providing relative data from reference compounds/biomarkers and allowing the community (including stakeholders and regulators) to use it wisely, may help both developers and end users to challenge OoC systems and may influence the decision-making process. However, the quest for qualification of OoC devices entails abundant human data sets being available as well as multi-parameter readouts without bias (achievable via proper automated statistical data analysis). In addition, a relevant list of test compounds/biomarkers is not readily available but may be in the making thanks to several independent initiatives (e. g. , the NIH in the US and the Crack it! Program in the UK). Beyond having a list of compounds for which the outcome and mechanisms of action are agreed, the compounds may not necessarily be available to academic research groups since they may be proprietary or may no longer be synthesized.
Device qualification, per se not corresponding to device adoption or regulation, should pass through independent testing centers, preferably established in Europe following the US lead. A single, worldwide database, eventually emerging from the harmonization and interconnection of local databases, would be ideal to collect and share all related data, following the example of shared compound the data obtained with OoCs included in a regulatory approval and perceived by regulatory bodies as complementary data to the standard benchmark. The applicability of these guidelines to rapidly developing technologies like OoCs remains a key challenge. A convincing way to circumvent these issues would be to encourage multi-partner task forces to conduct showcases capable of fostering end user adoption.
Recommendations 1. Focus on selected pathophysiological areas in the context of OoC models for disease mechanisms, drug efficacy and toxicity, and personalized medicine: cancer, neurodegenerative diseases, cardio-metabolic disorders, autoimmune diseases, fibrosis, and rare diseases. 2. Provide end users with customizable platforms for fitfor-purpose OoC models. 3. Explore a modular approach for the OoC models, with technical and biological modules that can be assembled within standard open technology platforms. 4. Converge towards standardization of components, methods, and data via collective dialogue among experts, facilitated by EUROoCS, and starting very early in the development.
Acknowledgements The authors would like to thank all experts who took part in the ORCHID Strategy workshop (see Appendix B 2 ) for their valuable contributions. This work was funded by the  European Union 's  Horizon 2020 Research and Innovation program  under grant agreement No. 766884 , and the  Netherlands Organ-on-Chip Initiative (NOCI) Gravitation  grant of the  Netherlands Organization for Scientific Research (NWO).
However, since many studies focus on Northern Eurasian October snow cover as the predictor for winter climate, we will include it nonetheless. MERRA2 and 20CRv2c offer snow cover as well as snow depth as a post-process output, however ERA-20C only offers snow depth. We refrain from converting it to snow cover ourselves, but found the index based on snow depth to be extremely similar (also see Supplementary Figure  1 ) to the same index using snow cover. All snow indices are normalized and linearly detrended with respect to their overall time period. Generally, we found the long term reanalyses to be of comparable quality of MERRA2 during the overlapping periods. The ERA20C 10 hPa November-December mean GPH shows remarkable interannual agreement with state-of-the-art reanalyses that assimilate upper air data for the period 1958-2010 (see Supplementary Figure  2 ).
This can hint towards decadal variations in the strength of the regression, but could also be due to biases in the reanalyses. To check for such biases we compared all reanalyses with the SLP reconstruction dataset HadSLP2r (Allen and Ansell 2006), and found that for the regression analysis using the time period 1901-2010, 20CRv2c overestimates the polar sea level pressure response, whereas ERA20C is much closer to HadSLP2r (See Supplement Figure  4 ). This would indeed support the notion of decadal variations in the strength of the relationship between predictor and predictand. However, it is worth highlighting that this overestimation for 20CRv2c is not visible for the 1851-2010 period, where the regression anomalies resemble HadSLP2r much closer. As can be seen from Figure  3 , the 35-year-long analysis in MERRA2 shows November sea ice concentration and early winter stratospheric heights to regress a similar SLP pattern than the November snow index.
What exactly causes the snow variability to drop is difficult to assess, but preliminary results support the notion of low variability in AMO and sea ice concentration to have an influence, which supports the above outlined mechanisms (not shown). Another source of uncertainty is the disagreement between ERA20C and 20CRv2c when it comes to the stationarity of the relationship. 20CRv2c shows negative correlation throughout the whole 20 th century, whereas ERA20C flips the sign of the correlation in the late 1930s and late 1970s. The same relationship but using October snow shows high agreement between the two datasets, which is the same case for the correlations between snow and stratospheric GPH. We therefore conclude, that the information stored in the November snow cover in 20CRv2c is slightly different to the information stored in the ERA20C snow depth.