dataesr/scientific-paragraphs-categorization huggingface dataset:
We use the same criteria for deriving O 3 / CO in the LMT from models (Weather Research and Forecasting with Chemistry (WRF-Chem) and CHASER; see Sects. 2. 3 and 4). We also use single-band IASI-only retrievals of ozone  (Eremenko et al. , 2008; LISA product in Dufour et al. , 2012)  in order to analyse the distribution of ozone between 3 and 6 km a. s. l. This single-band IASI product is similar to IASI + GOME2 but only use infrared measurements (both approaches using a Tikhonov-Philips regularisation and the KOPRA radiative transfer code in similar configuration). By comparing IASI-only and IASI + GOME2 ozone retrievals, one may identify ozone plumes located below 3 km a. s. l. and those located between 3 and 6 km a. s. l. (as remarked by  Cuesta et al. , 2013). Indeed, we expect that high ozone concentrations clearly depicted by IASI + GOME2 and not by IASI are located in the LMT below 3 km a.
s. l. Ozone plumes located at 3-6 km a. s. l. are shown by both IASI + GOME2 and IASI. Note that, since the DOF for IASI + GOME2 is lower than 1 in the LMT, multispectral outputs depend as well on ozone concentrations up to 5 or 6 km a. s. l. , and they alone do no reveal whether the ozone plumes are located in the LMT or at 3-6 km a. s. l. Satellite retrievals of NO 2 and CH 2 O from GOME-2 (overpass around 09:30 local time) and OMI (13:30 local time) are used to indicate the availability of ozone precursors. These datasets are provided by TEMIS (Tropospheric Emission Monitoring Internet Service, http://www. temis. nl) and BIRA-IASB (Belgian Institute for Space Aeronomy-Institut royal d'Aéronomie Spatiale de Belgique) for CH 2 O (http: //h2co. aeronomie. be). Retrievals of NO 2 from GOME-2 and OMI are derived respectively by the algorithms TM4NO2A version 2.
g. surface concentrations) and diurnal evolution of tropospheric O 3 and CO over East Asia, in complement to daily satellite observations. The daily evolution of ozone enhancement in the LMT is compared between the models and the satellite data. For this, we estimate background levels of O 3 and CO for each dataset using the same criteria (i. e. daily average minus SD over the domain; see Sect. 2. 2). We derive concentrations of O 3 and CO in the LMT by vertical integration from the surface up to 3 km a. s. l. (without any smoothing by the satellite AVKs). Moreover, we perform sensitivity studies with CHASER (in forecast mode), accounting or not for the stratospheric contribution of ozone in order to identify the tropospheric or stratospheric origin of this pollutant. In the figures of the paper, we show one of the models or both of them according to the following criteria: (i) WRF-Chem describes the structure of plumes of LMT O 3 and CO with finer spatial resolution (Sect.
Murano, personal communication) and the JCAP (Japan Clean Air Program;  Kannari et al. , 2007)  over Japan for automobiles. Surface emissions over China and North and South Korea are taken from REAS (Regional Emission Inventory in Asia;  Ohara et al. , 2007)  version 1. 11, and over Russia from EDGAR (Emission Database for Global Atmospheric Research;  Olivier et al. , 1996)  version 3. 2. Biogenic emis-sions are based on  Guenther et al. (1993). The lateral boundaries of chemical species are taken from the global CHASER model every 3 h. The system is driven by meteorological data from the Mesoscale Model (MSM) of the Japan Meteorological Agency (JMA). CHASER analyses are obtained from an advanced chemical data assimilation system combining satellite observations of several chemical compounds: NO 2 , O 3 , CO and HNO 3 measurements from OMI, TES, MOPITT and the Microwave Limb Sounder (MLS).
Data availability. Satellite observations of lowermost tropospheric ozone from the IASI + GOME2 multispectral approach in 2009 and 2010 used here can be provided upon request to Juan Cuesta from the LISA laboratory (cuesta@lisa. u-pec. fr). From 2017, global IASI + GOME2 ozone retrievals are freely available at the AERIS data centre at http://www. aeris-data. fr. Chemistry-transport simulations with the CHASER model globally and with the WRF-Chem model over Asia used here may be requested from the JAM-STEC Institute (Kazuyuki Miyazaki, kmiyazaki@jamstec. go. jp for CHASER; Masayuki Takigawa, takigawa@jamstec. go. jp for WRF-Chem). In situ measurements of surface ozone over Fukue Island may be requested from JAMSTEC Institute (Yugo Kanaya, yugo@jamstec. go. jp). Competing interests. The authors declare that they have no conflict of interest.
Special issue statement. This article is part of the special issue "Quadrennial Ozone Symposium 2016 -Status and trends of atmospheric ozone (ACP/AMT inter-journal SI)". It is a result of the Quadrennial Ozone Symposium 2016, Edinburgh, United Kingdom, 4-9 September 2016. simulations from Prof. K. Yamaji from the University of Kobe, ozonesondes data from WOUDC/SHADOZ/GMD (World Ozone and Ultraviolet Data Centre/Southern Hemisphere Additional Ozonesondes/Global Monitoring Division) networks, surface in situ measurements of ozone from the GAW/EANET (Global Atmosphere Watch/Acid Deposition Monitoring Network in East Asia) networks and meteorological reanalysis (ESPRI ClimServ centre for providing access to data) from ECMWF. IASI is a joint mission of EUMETSAT and CNES. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www.
ready. noaa. gov) used in this publication. We acknowledge the Institut für Meteorologie und Klimaforschung (Germany) and RT Solutions (USA) for licences to use respectively the KOPRA and VLIDORT radiative transfer models. We also thank Z. Cai from the Chinese Academy of Sciences (China) and X. Liu from Harvard-Smithsonian Center for Astrophysics (USA) for their support in producing IASI + GOME2 data and fruitful discussions, and C. Caumont from LISA for contributing to the validation of IASI + GOME2 data. Edited by: Stefan Reis Reviewed by: two anonymous referees.
Acknowledgements. Authors are grateful for the essential support of the  Sakura Hubert Curien Partnership (PHC)  for this French-Japanese cooperative study of ozone pollution over East Asia. This programme is supported by the  Japan Society for the Promotion of Science (JSPS)  and the  Ministère des affaires étrangères et du développement international (MAEDI)  and the  Ministère de l'Éducation Nationale, de l'Enseignement Supérieur et de la Recherche (MENESR) , and the  French Embassy in Japan. We thank the financial support of the  Centre National des Etudes Spatiales (CNES , the  French Space Agency)  via the " SURVEY-OZON " project from  TOSCA (Terre Ocean Surface Continentale Atmosphère ), the  Programme National de Télédétection Spatiale  (PNTS, www. insu. cnrs. fr/pnts,grant  PNTS-2013-05 , project " SYNAEROZON "), the  PolEASIA  project ( ANR-15-CE04-0005 ) from the  Agence Nationale de la Recherche (ANR) , the  Université Paris Est Créteil (UPEC)  and the  Centre National des Recherches Scientifiques-Institut National des Sciences de l'Univers (CNRS-INSU)  for making this research work and its publication possible.
We warmly acknowledge all datasets provided for this study: CO satellite retrievals from IASI from  ULB/LATMOS  ( Université Libre de Bruxelles/Laboratoire Atmosphères, Milieux, Observations Spatiales ) laboratories (special thanks to  C. Clerbaux  and  J. Hadji-Lazaro ), IASI data and support in the production of IASI + GOME2 from the  French atmospheric data centre AERIS  (www. aeris-data. fr;special thanks to  C. Boonne ), tropospheric NO 2 column data and CH 2 O from the GOME-2 and OMI sensors respectively from TEMIS (www. temis. nl)and BIRA-IASB (h2co. aeronomie. be), GOME-2 Level 1 data from EUMETSAT  (provided by the  NOAA CLASS data portal) ,  WRF-CMAQ.
Design and assembly of the Syn-HiC region Designer chromosome Syn-HiC closely resembles the native chromosome with respect to genetic elements (see Materials and Methods for details and Dataset EV1 for the sequence), but was "designed" to yield high resolution and high visibility in 3C experiments by providing nearly equally spaced restriction sites  (Fig EV1). The RSs of four different enzymes were removed with point mutations from the native sequence of the S. cerevisiae SK1 strain background and subsequently reintroduced within the sequence at regularly spaced positions (400, 1,500, 2,000 and 6,000 bp for DpnII, XbaI, HindIII, and NdeI, respectively; Figs  1B  and EV2 ; Table  1 ). As shown on Fig  1C , the DpnII and HindIII RF size distributions in the redesigned Syn-HiC region display sharp contrasts to the native genome-wide distributions that are skewed toward smaller fragments.
Cis-contact pattern of the Syn-HiC region To estimate the quality of Hi-C data in the Syn-HiC region, Hi-C experiments were performed in parallel on G1 synchronized cells of strain carrying the Syn-HiC redesigned chromosome (YRSG181) and the corresponding parental strain (BY4742) using DpnII and HindIII (Materials and Methods). The raw DpnII contact map of chromosome IV exhibits a remarkably "smooth" pattern within the redesigned region compared to the native flanking regions (Fig  1D ). The read coverage over the region also exhibits a dramatic and compelling change, with a more homogeneous and regular distribution in the synthetic regions for both enzymes compared to a heterogeneous distribution in the native sequence, where clusters of restriction sites result in an increased capture frequency of neighboring fragments  (Fig 2A and B).
The standard deviations (r) of those two distributions (Poisson and Gaussian) scale respectively as the square root of the mean (√l) or as the mean (l). We took advantage of the fact that the overall contact number l decreases with increasing genomic distances (s) to check whether these relations between l and r hold for real data  (Fig 3A). We first aggregated the results from 12 Hi-C experiments performed in G1/early S phase and with or without the Syn-HiC region (data from Lazar-Stefanita et al, 2017). In the following, we focused our analysis on the Syn-HiC region. For five different distances  (Fig 3A) , the contact distributions computed from each restriction fragment pair were re-scaled by their mean and superposed on the same plot  (Fig 3A, inset). The collapse of these rescaled distributions clearly indicates a Gaussian behavior (i.
F Illustration of the large structural changes affecting chromosomes during prophase and supported by the ratio maps displayed in (E). For clarity, no chromatin loops are represented and only the purple chromosome is represented with two homologs. The dotted arrows represent the telomere-driven meiotic movements that initiate at zygotene. G Boxplots representing the variation in number of normalized contacts for centromeres (left) and telomeres (right) between 0, 3, and 4 h in SPM. For each dataset the box represents the 25 th and 75 th percentiles. Horizontal red line: median. The whiskers default is to cover 99. 3 percent of the data (x: outliers). Variations between t = 0 h replicates, 3 h and 0 h, and 4 h and 0 h were computed. A relative Wilcoxon test supports a decrease in centromere contacts at t = 3 and 4 h compared to t = 0 h.
5 kb). The fact that a fraction of the population passed anaphase I at 4 h shows that the synchronization is not perfect. Contact maps of pachytene in ndt80D-arrested and wildtype cells at 6 h were also generated (Fig  EV6A ). Individual 2D maps can also be represented as 3D structures to illustrate the transformation of chromosomes into well-individualized entities throughout prophase (Fig 4D;  Lesne et al, 2014). Differences between the Hi-C datasets were assessed by computing the element-wise log-ratio between the normalized contact maps (bin: 25 kb; Figs 4E and EV6B; Materials and Methods). Under this representation, the color scale of the map reflects the local variations in contact frequencies between the two contact maps. Overall, our results illustrate and recapitulate known features of meiotic prophase.
The cells were washed with 200 ml and resuspended in 500 ml of prewarmed sporulation media (SPM; 1% potassium acetate, 0. 2× of uracil, arginine, and leucine, two drops of anti-foaming agent, and 7. 5 ll 50% PEG350) and put back with robust agitation at 30°C. Samples were collected for Hi-C or Southern blot analysis (below) at 0, 3, and 4 h for the wild-type strain (YRSG190), and at 6 h for the wild-type (YRSG190) and ndt80D-arrested cells (strain YRSG154). Note that t = 6 h on the one hand and t = 0, 3, and 4 h on the other are from two different meiotic time courses. Since t = 6 h and pachytene-arrested cells gave very similar Hi-C patterns, we moved forward to compare these datasets with the ones obtained from the 0-, 3-, and 4-h kinetics. RNA isolation from yeast for RNA sequencing Three independent RNA-seq libraries were generated for BY4742, SK1, and Syn-HiC strains.
Processing of the reads and contact map generations The raw data from each 3C experiment were processed as follows: First, PCR duplicates were collapsed using the six Ns present on each of the custom-made adapter and trimmed. Reads were aligned independently using Bowtie2 in its most sensitive mode against the S. cerevisiae reference genome (native genome) or against the S. cerevisiae reference adapted for the Syn-HiC region on chromosome IV (Syn-HiC genome). For SK1 strains, the new reference genome was recovered from  Yue et al (2017). An iterative alignment procedure was used: For each read, the length of the sequence being aligned was gradually increased from 20 bp until the mapping became unambiguous (mapping quality > 30). Paired reads were aligned independently, and each mapped read was assigned to a restriction fragment.
Statistical analysis The CV is defined as the ratio between the standard deviation and the mean of the contact histograms at fixed distance s; to take into account the finite-size effect, we discarded bins at the edge of the contact matrix in order to keep the statistics (number of bins) for different values of s constant, up to s < 15,000 bp in DpnII and s < 70,000 bp in HindIII datasets. To show that the improvement is specific to the new restriction pattern and is unlikely to be found spontaneously within the genome, we compared the Syn-HiC results with seven regions of similar size along chromosome IV  (460,856-590,306 bp; 590,306-719,756 bp; 849,206-978,656 bp; 978,656-1,108,106 bp; 1,108,106-1,237,556 bp; 1,2375,56-1,367,006 bp; 1,367,006-1,496,456 bp). The quality improvement was assessed by computing the logarithm of the ratio of the CVs of the Syn-HiC and native regions  (Fig EV5).
Acknowledgements We thank  Jef Boeke ,  Nancy Kleckner , and  Gianni Liti  for fruitful discussions and comments on the manuscript. We are also grateful to  Jef Boeke  for providing us with the Sc2. 0 PCRTags sequences for chromosome 4. We thank  Elodie Pirayre  and  Ivan Moszer  for contributing to the initial steps of the design of the algorithm. We also thank  Martial Marbouty ,  Axel Cournac , and  Rémi Montagne  for advices during the course of this work. Vittore Scolari  and  Héloïse Muller  were recipients of  Pasteur Roux Cantarini  fellowships, and  Aurèle Piazza  was supported by the  Framework  Project 7 of the  European Union  ( Marie Curie International Outgoing Fellowship   628355 ). This research was supported by funding to R. K. from the  European Research Council  under the  7th Framework Program  ( H2020 ERC grant  agreement  DLV-771813 ) and from  ERASynBio and Agence Nationale pour la Recherche  ( IESY ANR-14-SYNB-0001-03 ), and to R.
Data availability The datasets and computer code produced in this study are available in the following databases: Hi-C and RNA-seq data: Bioproject PRJNA464299 (https://www. ncbi. nlm. nih. gov/bioproject/?term= PRJNA464299). The fasta sequences of the Syn-HiC region and its native counterpart are available as a supplementary file (Dataset EV1). Previously published datasets used in this work are described in Table  3. Expanded View for this article is available online.
RNA Isolation, Library Preparation, and Sequencing Total RNA was isolated from whole root, nodule primordia, and nodule tissue samples using a NucleoSpin R RNA Plant kit (Macherey-Nagel) according to the manufacturer's instructions. mRNA was directly isolated from root hair samples using a Dynabeads mRNA DIRECT kit (Thermo Fisher Scientific) according to the manufacturer's instructions. RNA quality was assessed on an Agilent 2100 Bioanalyser and samples were sent to GATC-Biotech 1  for library preparation and sequencing. Sequencing data is deposited at the Short Read Archive with the BioProject ID SRP127678. Sequencing data for roots and shoots 3 dpi with R7A was obtained previously  (Munch et al. , 2018)  and is deposited at the Short Read Archive with the BioProject ID PRJNA384655.
Bioinformatics and Statistical Analysis Reads were mapped to the L. japonicus v. 3. 0 genome and differential gene expression was analyzed using CLC Genomics Workbench 9. 5. 3 (Qiagen). For each sample a minimum of 30 million reads were obtained with >90% of reads mapped to the reference genome. A criterion of fourfold change with a FDR p-value ≤ 0. 05 was applied to determine differential gene expression. Results of the analysis are available on Lotus Base 2   (Mun et al. , 2016). Multidimensional scaling (MDS) analysis was performed in R (R Core Team, 2017) from total read count data using the script outlined in Supplementary Data 1. Heatmaps were generated in R using Heatmap and Heatmap3 functions  (Zhao et al. , 2014) , an example script is provided in Supplementary Data 1. Venn diagrams were constructed using BioVenn  (Hulsen et al.
, 2003; Maekawa-Yoshikawa and Murooka, 2009). Transcriptome responses during nodule colonization were examined through the harvesting of 7 dpi nodule primordia that are in the early stages of colonization by R7A and mature nitrogen-fixing nodules that are fully colonized by R7A. RNAseq of whole roots (minus root tips) and shoots collected at 3 dpi to provide a basis for tissue-specific responses was previously performed  (Munch et al. , 2018). For each condition examined, biological triplicate samples were harvested and total RNA was isolated. The reads were mapped to the L. japonicus v3. 0 genome and all data is available from the Lotus Base resource website  (Mun et al. , 2016)  2. Multidimensional scaling analysis clearly separated the samples based on their tissue origin (Figure  1A ).
japonicus, as previously described  (Nagata et al. , 2008). Whole roots (minus root tips) treated with the spectrum of diverse bacterial strains or water were harvested at 1 and 3 dpi in biological duplicates. RNA isolation, Illumina sequencing and read mapping were performed as described above for the tissue samples. All data are available from the Lotus Base resource website  (Mun et al. , 2016)  2. We detected outlier replicates for each inoculum and these were removed before proceeding with downstream analysis. The remaining three datasets, consisting of two 3 dpi and one 1 dpi samples, were combined to allow robust detection of differentially expressed genes. The effects of combining replicates from two 3 dpi and one 1 dpi samples were investigated using the R package DEGseq  (Wang et al.
Homologs of some of these genes were also reported as differentially expressed in response to R. solanacearum in M. truncatula according to proteomic and transcriptomic analyses  (Yamchi et al. , 2018). Interestingly, a gene annotated as a WAT1 (Walls Are Thin1)-related gene (Lj1g3v0913340) was downregulated in response to Rs JS763. In A. thaliana, inactivation of WAT1 gene, which is required for secondary cell-wall deposition, conferred broad-spectrum resistance to vascular pathogens, including R. solanacearum  (Denance et al. , 2013). A transient defense response in L. japonicus roots during the early stages of symbiosis with compatible rhizobia had previously been reported  (Kouchi et al. , 2004). We investigated expression of the defense related genes identified by  Kouchi et al. (2004)  but did not observe comparable expression in response to symbiotic or pathogenic strains in our dataset (Figure  5A  and Supplementary Data 4).
Small ncRNA libraries and tRFs analysis Deep sequencing libraries (GSM1919557, GSM1919558, GSM1919559, GSM1919560, GSM1919561, GSM1919562) from RNA of three-week-old Arabidopsis seedlings were retrieved from the GEO database (http://www. ncbi. nlm. nih. gov/geo/) and libraries (SRX1070790, SRX1070791, SRX1070795, SRX1070796) from RNA of leaves of two-week-old plants were retrieved from SRA (https://www. ncbi. nlm. nih. gov/sra/). Bioinformatics analysis of tRFs was performed as described in  (9).
ACKNOWLEDGEMENTS We wish to thank  Marc Bergdol  ( IBMP-CNRS ) for his technical assistance in the structural analysis. Prof. Robert Steinfeld  ( University of Goettingen ) is warmly acknowledged for providing us with purified recombinant human RNase  T2. We are grateful to  Todd Blevins  for providing us with A. thaliana dcl1234 mutant line. We thank  Jessica Warren  for English improvement.
FUNDING Centre National de la Recherche Scientifique (CNRS)  in association with the  University of Strasbourg ;  French National Research Agency  as part of the  Investments for the future program ;  LabEx consortium MitoCross  [ ANR-11-LABX-0057  MITOCROSS to C. M. ). The open access publication charge for this paper has been waived by  Oxford University Press -NAR Editorial Board  members are entitled to one free paper per year in recognition of their work on behalf of the journal. Conflict of interest statement. None declared.
In 2000, Beck et al. underlined that the methodologies only became robust in the past 15 years with the development of many sensors (in terms of spatial and time resolution) and the growing use of field work associated with remotely sensed data. However, based on 438 research papers published in Landscape Ecology between 2004 and 2008, Newton et al. [31]  emphasize that only 36% of the studies explicitly mention remote-sensing, only 5% are carried out at various scales, 3% use remote-sensing to develop new approaches of landscape characterization, 2% exploit multi-source data, and only 0. 5% use very high resolution and/or SAR (Synthetic Aperture RADAR) data. Together with technical considerations, the extreme cost of acquiring such datasets at that time is another explanation for the underuse of SAR.
Study Area: The Amazon Region This study's area of interest is the Amazon region. This region is the largest tropical biome in the world (6. 5 million km 2 ), extending over nine countries (Bolivia, Brazil, Colombia, Ecuador, France/French Guiana, Guyana, Peru, Suriname, and Venezuela). The Amazon region today is inhabited by more than 30 million people, with a strongly heterogeneous population distribution throughout the territory  [37]. It is the area most affected by Anopheles-borne diseases (Figure  1 ) in the American continent. Geographical context of the study area in the Amazon region (the background is made with Natural Earth. Free vector and raster map data @ naturalearthdata. com, and the limits of the Amazon biome were obtained from the World Terrestrial Ecoregions map of the Nature Conservancy  [38] ).
This diversity in sensors offers a wide range of possibilities in terms of spatial resolution, wavelengths, revisit periods and polarizations. In the past, accessing up-to-date SAR imagery could be costly, but it is now possible for all users to access Sentinel 1A and 1B SAR data for free. Much archived data (ERS 1 and 2, ALOS-1, etc. ) are also available for free. This policy strongly encourages the use of SAR products for various applications, including public health. Optical remote-sensing presents various limitations when focusing on wetlands  [40, [43] [44] [45]  such as: (i) the fact that most wetlands are located in tropical or sub-tropical areas where cloud cover is frequent; (ii) possible confusion with other types of land cover; (iii) the strong difficulty in discriminating the various types of wetlands (e.