dataesr/scientific-paragraphs-categorization huggingface dataset:
The ion images were acquired with a pixel size of 40 μm ('medium' focus setting) and the spectrum of each pixel represented ion signals summed from 500 laser shots. The mass spectra were acquired in reflectron mode over a mass range of m/z 120 to 1700. Mass calibration was achieved using calibration standard PepMix 5 (LaserBio Labs, Sophia Antipolis, France) which was deposited onto the matrix coated slide. Data acquisition and processing were performed using flexControl 3. 4 and flexImaging 4. 1 (Bruker Daltonics, Wissembourg, France), respectively. 'Median' method was employed for normalization of all the mass spectra given that it provided slightly higher signal to noise ratio.
Ion images were generated from areas of 500 µm × 500 µm divided by 256 × 256 pixels. The total ion dose applied on each area is ~ 5×10 11 ions/cm 2. Data processing was performed using SurfaceLab 7 software (IONTOF GmbH, Münster, Germany). Mass spectra were internally calibrated using small fragments commonly observed in SIMS spectra such as CH.
Acknowledgments This work was supported by the  French National Research Agency (ANR)  (young investigator grant,  ANR-18-CE34-0008 PLAN-TOX ), the  Chemistry Institute of Lyon  (young investigator inter-laboratory cooperation grant, ICL-2017 DOSAGE), the  ISA  research funds grant, and also the  French GDR "Aquatic Ecotoxicology " framework which aims at fostering stimulating discussions and collaborations for more integrative approaches. MALDI-TOF instrument at CNRS-ICSN was founded by the  Région Ile-de-France (DIM Analytics). We would like to thank  Elodie Chauvet ,  Yves de Puydt from Tescan Analytics (Fuveau, France ) for the access to TOF-SIMS instrument and the help with the experiments. We thank  Jean-Valery Guillaubez  for his technical assistance on the lipid MS analysis of the hepatopancreas sample.
For more details on the monitoring of FTIR solar absorption spectra at these stations, we refer to  Vigouroux et al. (2018)  and references therein, and for the FTIR retrieval principles we refer to e. g. Vigouroux et al. (2009). The same retrieval settings are used at all the stations to avoid introducing possible bias in the HCHO total columns between the stations and inconsistent comparisons with the satellite. Details are given in  Vigouroux et al. (2018). The main settings that might be responsible for internal biases within the network are the spectroscopic database and the fitted spectral windows, the spectroscopic parameters being the main source of the FTIR HCHO systematic uncertainties. The HCHO spectral signatures lie in the 3. 6 µm region and belong to the ν 1 and ν 5 bands (fitted windows are 2763.
42-2764. 17, 2765. 65-2766. 01, 2778. 15-2779. 1, and 2780. 65-2782. 0 cm -1 ). The spectroscopic database used is the atm16 linelist by Geoffrey Toon (JPL), which can be found at http:// mark4sun. jpl. nasa. gov/toon/linelist/linelist. html (last access: 7 July 2020). This linelist is optimized for the main absorbing gases in the fitted windows (HDO, CH 4 , O 3 , N 2 O, CO 2 ) and is based on HITRAN 2012  (Rothman et al. , 2013)  for HCHO, which used the work of  Jacquemart et al. (2010). The retrieval codes used in the FTIR NDACC community are PROFITT9  (Hase et al. , 2006)  and SFIT4. 0. 9. 4 (updated from SFIT2;  Pougatchev et al. , 1995) , which are both based on the optimal estimation method  (Rodgers, 2000). A past comparison exercise has shown a very good agreement between the retrieved products obtained with these two codes  (Hase et al.
5). The presence of TROPOMI outliers is minimized by using the median, but they should ideally be removed by the QA filter. An improvement of the QA value is foreseen in the next product version, which should improve, for example, the filtering at Arctic sites (SZA > 75 • ). In the next section, we also compare the obtained BIAS with the systematic uncertainty in the difference σ syst to evaluate the TROPOMI uncertainty budget: σ 2 syst = (σ S,syst ) 2 + a T S S F,syst a S + a T S (I -A F )S var,syst (I -A F ) T a S , (6) where σ S,syst is the systematic uncertainty of TROPOMI columns, as provided in the public release database (but scaled for altitude; see Sect. 4. 2); a S is the TROPOMI total column averaging kernel; and S F,syst is the FTIR systematic covariance matrix provided in volume mixing ratio units in the standardized GEOMS format, converted to partial column units in Eq.
3). Similarly, the precision of the TROPOMI HCHO products is estimated in Sect. 5. 2 not with the usual standard deviation, which is not robust in the case of outliers, but with the median absolute deviation (MAD; see  Huber, 1981 ) of the differences (DIFF i = TROP i -FTIR i ): MAD = k × med(abs(DIFF i -med(DIFF i ))), (7) where k = 1. 4826 for a correspondence with the 1σ standard deviation for normal distribution without outliers. In Sect. 5. 2, we compare the obtained MAD to the random uncertainty in the differences σ rand , which is calculated following  Rodgers and Connor (2003) : σ 2 rand = (σ S,rand ) 2 + a T S S F,rand a S + a T S (I -A F )S var,rand (I -A F ) T a S , (8) where σ S,rand is the random uncertainty of TROPOMI columns, as provided in the public release database (but scaled for altitude; see Sect.
5 × 10 15 molec. cm -2 ), it is negative and very consistent for the stations with higher HCHO levels ranging from 8. 7 to 28. 6 × 10 15 molec. cm -2 (-29 % to -36 %), with a small error in the bias (2 % to 6 %). Note that the BIAS is also consistent at Paramaribo (-26 %) but with a larger error (14 %) due to a small number of collocations. This dependence of the TROPOMI bias on the HCHO concentration levels is visualized in Fig. 3 , where the BIAS at each station is plotted as a function of the mean FTIR columns. It is therefore not appropriate to use the median bias obtained using the data from all stations together (-10 %) if one wants to correct the TROPOMI HCHO data in model inversion studies. If we calculate the median of the differences for HCHO FTIR columns > 8. 0 × 10 15 molec. cm -2 , we obtain a significant negative bias of -30.
The precision pre-launch requirement is provided at each site, taking into account the mean number of pixels n pix involved in the collocated TROPOMI data (Requ. = 1. 2 × 10 16 molec. cm -2 / √ n pix ). We see that for all the cleanest sites (< 2. 5 × 10 15 molec. cm -2 ), where an additional collocation uncertainty is expected to be small, the MAD is well within the pre-launch requirements. The MAD for these cleanest sites has a median of 1. 3 × 10 15 molec. cm -2 and a minimum of 0. 9 × 10 15 molec. cm -2. This is a good estimate of the precision that TROPOMI can reach in remote conditions. For a single pixel, the best TROPOMI precision under remote conditions is therefore 5-8 × 10 15 molec. cm -2. It must be noted that the pre-launch HCHO precision requirements were chosen based on pre-launch requirements for the instrument signal-to-noise ratio (equivalent to OMI).
Observed TROPOMI monthly variability The Pearson correlation coefficient is very good for the collocated monthly means of TROPOMI and FTIR data (0. 91; see Table  3  and Fig. 4 ) and is usually good for individual sites. However, Pearson correlation is not robust and can give a wrong conclusion when only few data are coincident, especially when outliers are present. We have 17 months of coincident TROPOMI and FTIR measurements in the best cases, while there are only 4 for the newest stations Palau and Porto Velho. We therefore verify that the TROPOMI precision allows the seasonal variability to be well captured even at very clean sites, which can be at the limit of the satellite detection, by plotting the individual monthly mean time series in Fig. 5. The seasonal variability, with a maximum in July-August, is well observed at all the Arctic sites (Eureka, Ny-Ålesund, Thule, Kiruna, and Sodankylä).
org/10. 5194/amt-13-3751-2020 Atmos. Meas. Tech. , 13, 3751-3767, 2020 of coincidences. A similar situation is observed at the other recent oceanic site Palau, where only 4 months of data are available. At the oceanic site Maïdo, we find a good agreement in most of the months but not in October-December, which are the predominant biomass-burning months in the region, so the collocation of the plumes might play a role there. Finally, at Lauder, TROPOMI shows many negative values in the beginning of the period (May-September 2018), which is responsible for a lower correlation (0. 65) and for the negative bias there (although not significant), while other clean sites usually show positive ones (see Table  3 ). The higher-HCHO-level sites show a TROPOMI seasonal variability in very good agreement with FTIR, with correlation larger than 0.
Conclusions We have used a network of 25 FTIR stations, most of them affiliated with the NDACC, to validate the latest TROPOMI ). This network covers a wide range of concentrations, from very clean Arctic, oceanic, and mountain sites with columns that can be lower than 10 14 molec. cm -2 to high-HCHO-level sites such as Mexico City or Porto Velho, near the Amazon rainforest, where columns up to 7 × 10 16 molec. cm -2 have been observed. We found an overestimation (+26 ± 5 %) of TROPOMI OFFL products for very small HCHO columns (< 2. 5 × 10 15 molec. cm -2 ) and an underestimation of TROPOMI of about -30. 8 % (±1. 4 %) for high HCHO columns (> 8. 0 × 10 15 molec. cm -2 ), which can be used, for example, to correct TROPOMI data near emission sources. The results are very similar for NRTI products (+22 ± 7 % and -31.
The NDACC FTIR network, which covers a large number of atmospheric species at wide ranges of concentrations, is a powerful source of reference data to detect such nadir satellites' biases. Although significant, the observed overestimation and underestimation of TROPOMI are within the lower limits of the pre-launch requirements (±40 %), as are the biases at individual sites for 20 of the 25 stations. The TROPOMI systematic uncertainty budget is in very good agreement with the observed bias, larger uncertainties being reported at stations where the bias exceeds the 40 % requirements. Possible improvements in the TROPOMI biases could be achieved by taking into account aerosol effects over polluted sites, improving the QA values at high SZAs, and using an albedo climatology and a priori HCHO profiles at the TROPOMI spatial resolution.
3 between MAD and the random uncertainty in the comparisons (dominated by TROPOMI random uncertainty) at the high-level sites, where an additional effect of collocation might play a role as well. The underestimation of the TROPOMI random uncertainty could be due to a random effect of the uncertainty associated with the air mass factor calculation that is not currently included in the budget. This would also explain a larger underestimation of random error at high-level sites (factor of 2. 3 vs 1. 6 at clean sites). Furthermore, a systematic uncertainty component over a short term (so not included in the TROPOMI random uncertainty) can have a random effect on our longer-term comparisons. We have shown that the TROPOMI data capture the HCHO seasonal variability very well, even at very clean sites.
The Pearson correlation coefficient for monthly mean coincident data is 0. 91. Although we have found room for a refinement of the TROPOMI random uncertainty estimation and for an improvement of the QA values for a better filtering of the remaining few outliers and negative columns (exceeding the expected statistical distribution), this validation work has demonstrated the very good quality of the TROPOMI HCHO product, which is well within the pre-launch requirements for both accuracy and precision. This work has also shown the high value of the FTIR HCHO network, providing harmonized and well-characterized data covering a wide range of HCHO columns. These ground-based FTIR data are continuously extended by new measurements and will be used in the coming years for the routine S5P validation within the ESA-dedicated validation server (https: //mpc-vdaf-server.
tropomi. eu/, last access: 7 July 2020). The FTIR network will also be used in the near future for the validation of previous satellite missions such as OMI or GOME-2. New FTIR measurements are continuously performed and can be used in the coming years for the validation of new satellite generations, such as TEMPO, GEMS, Sentinel 5P, or Sentinel 4. An extension of this TROPOMI HCHO validation with ground-based MAX-DOAS and Pandora instruments, especially at sites where both FTIR and UV-visible techniques are available (e. g. Xianghe, Maïdo, and Lauder) or in uncovered regions (Africa), would bring additional knowledge. However, there is first a need for data product harmonization within the MAX-DOAS network as was done with the FTIR network used here. This work is ongoing as part of the ESA FRM4DOAS and Pandonia projects.
Data availability. The TROPOMI HCHO data are publicly available at the Copernicus Open Access Hub (https://scihub. copernicus. eu, last access: 7 July 2020). The access and use of any Copernicus Sentinel data available through the Copernicus Open Access Hub are governed by the legal notice on the use of Copernicus Sentinel Data and Service Information, which is given here: https://sentinels. copernicus. eu/documents/247904/ 690755/Sentinel_Data_Legal_Notice (last access: 7 July 2020). The FTIR data sets can be provided in the public NDACC repository (ftp://ftp. cpc. ncep. noaa. gov/ndacc/station/, last access: 7 July 2020) depending on each PI decision. Please pay attention to the NDACC data policy. The whole data set used in this publication can be provided upon request by Corinne Vigouroux (corinne.
Financial support. This study has been supported by the ESA PRODEX projects TROVA and TROVA-E2, funded by the Belgian Science Policy Office (Belspo). The measurements at Reunion Island have also been supported by the Université de La Réunion and CNRS (LACy-UMR8105 and UMS3365) as well as at Porto Velho by the BRAIN-be pioneer project IKARE, funded by Belspo, with the collaboration of the Instituto Federal de Educaçao, Ciência e Tecnologia de Rondônia (IFRO). The measurements at Reunion Island have been supported by BIRA-IASB and the Université de La Réunion and CNRS (LACy-UMR8105 and UMS3365). The measurements at Porto Velho were funded by the BRAINbe pioneer project IKARE (Belspo), with the collaboration of the Instituto Federal de Educaçao, Ciência e Tecnologia de Rondônia (IFRO). The SPbU FTIR team has been supported by the Russian Foundation for Basic Research project no.
18-05-00011. St. Petersburg FTIR measurements were carried out by the instrumentation of the GEOMODEL resource centre of SPbU. The NDACC stations Bremen, Izaña, Ny-Ålesund, and Paramaribo have been supported by the German Bundesministerium für Wirtschaft und Energie (BMWi) via DLR under grants 50EE1711A, B, and D. We thank the EU project STRATOCLIM for financial support to U. of Bremen. Measurements made at Lauder by NIWA are funded by New Zealand's Ministry of Business, Innovation and Employment through the Strategic Science Investment Fund. We thank the AWI Bremerhaven for logistical support and the station personnel in Ny-Ålesund. On-site support in Paramaribo, Suriname, was provided by the Meterologische Dienst van Suriname. Logistical and on-site support in Koror, Palau, was provided by the AWI Potsdam and the Coral Reef Foundation.
1 ), rather than a discrete choice. Note that the arbitrator combines the action values, not the actions. Thus, the agent will not end up with a midway action when the two systems encode different preferences. Lesions or partial inactivations of either the DLS or the HPC were achieved by setting limits on P SR (see SI Appendix for more details). Code Availability. The results were generated by using code written in Python. Code is available on ModelDB (accession no. 266836)  (103).
ACKNOWLEDGMENTS. We thank  Dan Bush ,  Will de Cothi ,  Changmin Yu , and  Kevin Miller  for useful comments on the manuscript;  Oliver Vikbladh  and  Mat é Lengyel  for discussions; and our anonymous reviewers for insightful suggestions. This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement 785907  Human Brain  Project  SGA2 ;  European Research Council  Advanced Grant NEUROMEM; the  Wellcome Trust ; and the  Gatsby Charitable Foundation.
Computer Simulation Methods All programs coding the algorithms for experience-dependent synaptic rewiring of geniculo-cortical afferent inputs and the analysis of response properties of individual neurons and orientation representation in the model visual cortex are available at https://github. com/slforg/synaptic_rewiring_ programs. As shown in Figure  4 , our model visual cortex consists of neurons in 48 × 48 square arrangements, each of which has a disk-shaped dendritic field of 254 synaptic sites. The model LGN is composed of four layers of 24 × 24 square arrangements of neurons, where the layers represent ONcenter non-lagged, OFF-center non-lagged, ON-center lagged, and OFF-center lagged types  (Saul and Humphrey, 1990, 1992). The four types of neuron in the model LGN send axons to the model visual cortex.
Accepted Article This article is protected by copyright. All rights reserved database  (35,386 entries). Details on the parameters used are given in Supplementary Methods. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Vizcaíno et al. , 2013; Deutsch et al. , 2017)  partner repository with the dataset identifiers PXD012214 and PXD012215 (DRM/DSM distribution patterns) and PXD012248 (phosphoproteomics). NO 3 -influx studies -Root NO 3 -influx was assayed as described  (Laugier et al. , 2012). Details are given in Supplementary Methods. Statistical analyses and data visualization -Functional classification of proteins was done based on Mapman  (Thimm et al. , 2004)  and Classification SuperViewer Tool  (Provart & Zhu, 2003). Information about subcellular location was derived from SUBA3  (Tanz et al.
, 2013). Detailed protein function was manually updated with the support of TAIR  (Huala et al. , 2001). Other statistical analyses were carried out with Sigma Plot (version 11. 0) and Excel  (Microsoft, 2013). Heat maps were generated with R package Superheat. Over-representation analysis was done on PANTHER website via Fisher's exact test, p-values were adjusted using Bonferroni correction.
Acknowledgments We thank  Adrian Hills  ( Laboratory of Plant Physiology and Biophysics, University of Glasgow ) for us detailed user guides for EZ-R HIZO software. We thank  Anette Mähs  for analyzing CBL transcript levels and  Katrin Held  and  Stefanie Schültke  for help in generating genetic material used in this study. The work was supported by an international grant from the  ANR in France  ( SIPHON ANR-13-ISV6-0002-01 ) and  Deutsche Forschungsgemeinschaft  ( SCHU1533/8-1 ) in Germany.
Accepted Article This article is protected by copyright. All rights reserved  Tang R-J, Liu H, Yang L, Gao X-S, Garcia VJ ,  Luan S , Zhang H-X. 2012. Tonoplast calcium sensors CBL2 and CBL3 control plant growth and ion homeostasis through regulating V-ATPase activity in Arabidopsis. Cell Research 22:  1650-1665. Tanz SK ,  Castleden I, Hooper CM ,  Vacher M , Small I, Millar HA. 2013. SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis. Nucleic Acids Research 41:  D1185-1191. Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Kruger P ,  Selbig J, Muller LA ,  Rhee SY , Stitt M. 2004. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. The Plant Journal 37(6):  914-939.
Vidal EA,  Alvarez JM ,  Araus V, Riveras E, Brooks M, Krouk G, Ruffel S, Lejay L, Crawford N, Coruzzi GM , et al. 2020. Nitrate 2020: Thirty years from transport to signaling networks. Plant Cell. 32(7):  2094-2119 Vizcaíno JA ,  Côté RG ,  Csordas A, Dianes JA, Fabregat A, Foster JM, Griss J, Alpi E, Birim M, Contell  J, et al. 2013. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Research 41(1):  D1063-D1069. Wang YY ,  Hsu PK , Tsay YF. 2012. Uptake, allocation and signaling of nitrate. Trends in Plant Science 17(8):  458-467. Weinl S, Kudla J. 2009. The CBL-CIPK Ca 2+ -decoding signaling network: function and perspectives. New Phytologist 2009(184): 3. Wisniewski JR, Zougman A, Nagaraj N, Mann M. 2009. Universal sample preparation method for proteome analysis.
In the past decades, the concept of Icacinaceae has been refined greatly, as morphological and molecular data have led to a narrower circumscription of a monophyletic Icacinaceae family with only 23 genera (vs 58 sensu  Sleumer, 1942). This family possesses an extensive fossil record, important to the biogeographic history of the Northern Hemisphere, but the reported fossils need to be carefully evaluated in the current phylogenetic framework. We evaluated 183 fossil reports of Icacinaceae from the literature but considered only 92 as reliably belonging to this family. Most of the accepted records are from endocarp remains. With this sampling, we show an increase of the species richness during the Paleocene. A great increase of diversity in terms of genera, species, and morphological range is shown through the Paleocene-Eocene interval and during the Early Eocene (Ypresian).
Introduction In the past decades, the concept of Icacinaceae has been revised considerably with the results of new molecular and morphological data that show that the former Icacinaceae family concept with about 400 species and 58 genera (from  Sleumer, 1942)  was polyphyletic  (Kårehed, 2001). That phylogenetic study defined the Icacinaceae s. s. as belonging to the Garryales order and placed some genera in the Aquifoliales and Apiales orders. In particular, Kårehed described a new family, Stemonuraceae, and added some species to the Cardiopteridaceae family. The resulting circumscription of Icacinaceae included only 36 genera divided in four informal groups: Icacina, Cassinopsis, Emmotum, and Apodytes. Later, another study showed that this new circumscription of Icacinaceae corresponds to a basal lamiid group  (Lens et al.
Finally, we recorded 38 occurrences (14 species) of fossil wood records, with two fossil genera, Icacinoxylon and Apodytoxylon. The dominance in the fossil record of climbing genera, well represented close to rivers, could be a taphonomic bias  (Burnham, 1989; Burnham et al. , 1992). However, because the climbing genera are well distributed in the family, we consider our sample as representative of the entire family. We decided to accept or reject occurrences based on the initial description and the associated figures in the literature. We checked all occurrences in accordance with new taxonomic and phylogenetic data  (Stull et al. 2015 ). The Icacinaceae s. l. now belongs to four order in Asterids (Fig. 1 ), and thus some Icacinaceae characters used as criteria of fossil recognition (e. g.
It is critical to document these dynamics, gathering time-series data throughout the seasonal growth period, because any shifts in primary production can affect higher trophic level organisms in the nearshore food web. The Antarctic tourism industry maintains a fleet of vessels that visit the peninsula's nearshore waters throughout the austral summer, November to March. We developed a citizen science (CS) program-FjordPhyto-to leverage these vessels as platforms for gathering data about the region. Trained staff and travelers collect environmental data and biological samples that are sent to researchers. Preliminary results include phytoplankton species identification, cell abundance determination, carbon biomass estimates, and euphotic depth measurement at multiple sites. We show that CS is a valid tool that can enhance research in Antarctica, while also providing an enriching experience to travelers interested in learning more about science in polar environments.