dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic Analysis The obtained tree (Figure  10 ) is divided into four clades. A first clade including members of the genus Potamothrix and the Tubificinae sp. T1-3 (that probably belong to Potamothrix) branches at the base of the other clades. This is the only clade whose branching is supported (BV of 99 and 100%). Limnodrilus, with Branchiura sowerbyi Beddard, 1892 branching at the base. The lineage of Q. multisetosus was separated from S. ferox, E. velutinus, T. montanus and T. kessleri by more than 20% of genetic variation (in COI). The maximum intra-lineage genetic divergence (in COI) of Q. multisetosus was 1. 2%. The numbers placed at the internal nodes correspond to bootstrap values of ML and FastMe distance analyses; only those higher than 70% are indicated. For each lineage, the name of the taxon is indicated, followed by GenBank accession number and lineage name (of our Swiss database) or by the respective isolate numbers (for Q.
2018)  on protein sequences to mask nonhomologous segments. Protein sequences were aligned with ClustalOmega (Sievers and Higgins 2018); multiple alignments included a total of 305,634 sites. We then used IQTREE  (Nguyen et al. 2015)  (version 2. 0. 5 for Linux 64-bit built May 15, 2020) to calculate a species tree using each protein alignment as a partition ("LG" model with substitution rate heterogeneity among sites following a discrete gamma model with four parameters and invariable sites). Branch support values were calculated with SH-aLRT and ultrafast bootstrap with a value of 5,000. iqtree2.
Paramecium sexaurelia: A Novel mtA-Based Mechanism Not Affecting Transcription The last maternal-inheritance species, P. sexaurelia, does not contain the mtB and mtC genes. We probed the structure of the MAC mtA gene in two different strains, AZ8-4 and 159, which happened to be of opposite mating types in our stocks. Consistent with the transcriptome data indicating that mtA is transcribed in both mating types, its promoter was found to be retained in both strains. We then checked the maternal inheritance of mating types in a cross between these strains. After conjugation, the two F1 exconjugants were checked for heterozygosity by resequencing SNPs, and the maternal (cytoplasmic) origin of each was ascertained by typing a mitochondrial polymorphism. F1s were then tested for mating types; each of them was indeed of the same mating type as its maternal parent.
Acknowledgments Experiments and sequencing were partly performed thanks to the  Core Facility Centers "Cultivation of Microorganisms" and "Molecular and Cell Technologies" of St. Petersburg State University. We thank the  French National Sequencing Center (Genoscope CEA ) and the  France G enomique Paramecium Sequencing Project  coordinated by  S. Duharcourt , as well as  M. Lynch  and collaborators, for permission to use MAC and MIC genomes prior to publication. RNA sequencing benefited from the facilities and expertise of the high-throughput sequencing platform of I2BC. This study was supported by  National Science Centre  (Grant No. 2013/ 09/N/NZ8/03198 ) ( Paramecium tredecaurelia study)  to N. S. -G. and S. T. and fellowship  2013/08/T/NZ1/00306  to N. S. -G. ( Paramecium biaurelia study ), by the  Agence Nationale de la Recherche  (Grant No.
ANR 18-CE12-0005-01 ) to E. M. , and by the  Russian Foundation for Basic Research  (Grant No. 19-04-00710a ) to  I. N. (Paramecium sexaurelia, Paramecium dodecaurelia, and Paramecium tredecaurelia studies). It received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by  ANR  with the references  ANR-10-LABX-54   MEMOLIFE  and  ANR-10-IDEX-0001-02 PSL Research. Mobility to Paris was supported by  St. Petersburg State University  grants  1. 42. 1436. 2015  to I. N. and  COLLAB2020_1 50414369  to A. P.
Data Availability The RNA-seq data underlying this article are available in the European Nucleotide Archive, at http://www. ebi. ac. uk/ena/ data/view/PRJEB38593, and accession numbers for individual sequencing data sets are listed in supplementary table  S2 , Supplementary Material online. The mtA and mtA-like, mtB and mtB-like, and mtC protein sequences used are listed in supplementary files S3-S5, Supplementary Material online, respectively; individual gene sequences determined in this study are shown in supplementary file S6, Supplementary Material online.
These observations are crucial to the study of, understanding of and follow-up work on atmospheric compounds to monitor greenhouse gases. Météo-France operational numerical weather prediction (NWP) systems use RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) as a radiative transfer model (RTM) during data assimilation, as in many other NWP centres. In order to assimilate the satellite sounder observations, the actual observations have to be compared to the simulation from the model state with a RTM. Simulating infrared satellite observations from a RTM requires a priori knowledge of the state of the atmosphere and its chemical composition. The RTTOV uses chemical reference profiles that are constant in time and in space. This approximation may lead to possible errors in the simulations.
However, satellite sounding in the atmospheric boundary layer is more difficult, especially for infrared sensors because of cloud and aerosols that interfere with the signal; land surface emissivity and temperature uncertainties also are part of the problem. The data assimilation of satellite observations sensitive to atmospheric composition allows for an accurate chemical description of the atmosphere, especially in the UTLS, to be obtained. Presently, C-IFS assimilates Level 2 products from several satellite instruments. To assess the quality of the CTM forecasts of chemical fields, we need to have accurate measurements of these compounds in the atmospheric column. Despite many chemistry measurements at ground stations, information on chemistry is not widely available at high altitudes. To overcome this lack of data, many projects have been started such as the APOGEE (Atmospheric Profiles of Greenhouse Gases) experiment.
Then in Sect. 3, we will describe the different characteristics of the AMULSE instrument as well as make comparisons with another instrument. Finally, we will use the data from the APOGEE measurement experiment on one study case to evaluate the sensitivity of infrared observation simulations to CO 2 , CH 4 and O 3 information and we will use the in situ profiles to evaluate a priori profiles from the CTMs and retrieval profiles by 1D assimilation experiments. 2 Atmospheric composition measurements 2. 1 Molecules of interest CO 2 and CH 4 are long-lived gases. Atmospheric residence time is a few decades for CH 4 , whose reactivity makes it an important player in atmospheric chemistry  (Voulgarakis et al. , 2013) , and a few hundred to thousands of years for CO 2 which is inert in the atmosphere from a chemical point of view  (Archer and Brovkin, 2008; Eby et al.
Tethered balloon application (2017) Tethered balloons offer great opportunities to characterize the temporal atmospheric evolution of the three measured species up to an 800 m altitude. In such applications we can control the motion speed of the balloon, have a bigger payload (which depends on the type of the balloon used) and we can even acquire data from a fixed stationary point. A tethered balloon is a carrier that allows measurements to be made at heights between fixed-tower and under-aircraft measurements. The costs of a tethered balloon are much lower than the installations mentioned above. An experiment was conducted to monitor the evolution of the atmospheric boundary layer early in the morning at https://doi. org/10. 5194/amt-13-3099-2020 Atmos. Meas. Tech. , 13, 3099-3118, 2020 sunrise.
Objectives One of the goals of AMULSE is to carry out weather balloon measurements in co-location with the IASI satellite in orbit around the Earth, developed jointly by CNES (Centre national d'études spatiales) and EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). The measurements made by the AMULSE instrument will provide comparative data that can be used to validate CNRM-Météo France meteorological models. In addition, the combination of all these measures offers an opportunity to analyse physicochemical processes that have not yet been studied in the stratosphere and the interface between the troposphere and the stratosphere, commonly known as the UTLS (upper troposphere-lower stratosphere). To achieve these objectives, we carried out measurements from the site of our laboratory using meteorological balloons for the measurements of CO 2 , CH 4 , H 2 O, O 3 , P , T and relative humidity in co-location with Metop-A, Metop-B and Suomi NPP.
3b ). A meteorological in situ sounding with a GPS probe fixed on the instrumentation and connected to a ground station allows the determination of the landing site which helps recovery of the instrumentation. Flight simulation software is used to estimate the trajectories and the landing point of the probe. This estimation is based on the wind forecasts from both Météo-France operational numerical weather prediction models: the mesoscale model AROME (Applications de la Recherche à l'Opérationnel à Méso-Echelle;  Seity et al. , 2011; Brousseau et al. , 2016)  and the global model ARPEGE (Action de Recherche Petite Echelle Grande Echelle;  Courtier and Geleyn, 1988; Déqué et al. , 1994). Hypotheses are made about both the ascending and the descending speed of the system and the release of the carrier balloon in order to run a first simulation.
Use of data from the APOGEE measurement experiment The data produced during the APOGEE measurement experiment have different applications. Initially, they were used as satellite validation data for the IASI and CrIS infrared sounders. They were then used to evaluate the quality of vertical ozone and methane profiles extracted from the MOCAGE and CAMS chemistry transport models. Finally, they were used as verification data to compare the ozone profiles returned in one-dimensional variational assimilation experiments (1D-Var) with the a priori ozone profiles from MOCAGE and CAMS. , with a spectral sampling of 0. 25 cm -1 and spectral resolution of 0. 5 cm -1 after apodization. CrIS is part of the payload of the US polarorbiting satellites Suomi NPP and NOAA-20 launched in 2011 and 2017, respectively.
Only the so-called CrIS normal spectral resolution data are used in this study, leaving the CrIS spectrum divided into three parts: between 650 and 1095 cm -1 with a spectral resolution of 0. 625 cm -1 , between 1210 and 1750 -1 (1. 25 cm -1 spectral resolution), and between 2155 and 2550 cm -1 (2. 5 cm -1 spectral resolution).
Thus a polynomial function was used to link the in situ profiles to the reference profiles from the levels where the data are missing. Then, modelled atmospheric profiles of temperature, humidity, surface temperature, surface humidity, surface pressure, and zonal and meridian wind come from the global model ARPEGE forecasts (every 3 h), which have been extracted for the same period and location as for the in situ sounding. The coefficient file used during the simulation process in RTTOV being on 101 fixed-pressure levels, the thermodynamic profiles of ARPEGE were interpolated on these same levels. In this study we simulated satellite observations from two types of infrared instruments: IASI and CrIS. In order to evaluate the quality of our simulations, we sought to spatially and temporally co-locate the IASI (Metop-B) and CrIS (Suomi NPP) pixels as close as possible to the in situ soundings.
10a ) and CrIS-monitored (Fig. 10b ) channels for in situ sounding from 17 April 2018 at 10:00 UTC. IASI and CrIS have 314 and 330 monitored channels, respectively. For this case study the differences (O-B) using reference chemical (REF) profiles and simulations using in situ chemical (SONDE) profiles are small over all IASI (a) and CrIS (b) spectra. However, there are significant differences for ozonesensitive channels with O-B values closer to zero when using the reference ozone. This could be explained by a smaller accuracy of ozone measurement with ozonesondes than with the AMULSE instrument. Another possibility is the existence of other biases (e. g. depending on the scan position) which are compensated for by bias in the opposite sign in the case of REF profiles. The same experiments were performed on other in situ sounding data with generally the same results (not shown here).
Forecasts of atmospheric composition One possible use of these in situ profiles of CO 2 , CH 4 and O 3 is as comparison data to the a priori chemical profiles from chemistry transport models (CTMs). As part of our study, we compared our measured data against the profiles extracted from the CTMs MOCAGE and C-IFS. These two models differ in that the global version of the MOCAGE model does not assimilate any observation in operation, while C-IFS assimilates Level 2 products from several instruments such as MLS, OMI, SBUV/2, GOME-2A and GOME-2B, IASI, MOPITT, OMPS, and PMAp  (Basart et al. , 2018). C-IFS provides analyses and 5 d forecasts of atmospheric composition at regional and global scales in near real time. MOCAGE is an off-line global three-dimensional chemistry transport model  (Guth et al.
Acknowledgements. This research based on data from the APOGEE measurement experiment has been conducted within the framework of  Olivier Coopmann 's PhD thesis, which funded by  CNES  ( Centre national d'études spatiales ) and the  Région Occitanie. We thank  Michel Ramonet  (from  LSCE ) and his team for allowing us to perform the measurements on their ICOS site. Review statement. This paper was edited by  Troy Thornberry  and reviewed by two anonymous referees.
(3D) virtual paleohistology  (Sanchez et al. , 2012). The specimens were imaged at the beamlines ID19 and BM05 of the European Synchrotron Radiation Facility (ESRF, France). A multiscale approach, consisting in a series of increasingly higher resolution scans, was used to study the samples of S. sanjuanensis  (Tafforeau et al. , 2007; Sanchez et al. , 2012)  from medium voxel sizes (14. 96 and 6. 96 µm respectively) to a submicron voxel size (0. 75 µm) in regions of interest. The medium-resolution datasets reflect the entire bones imaged either in connection to the rest of the skeleton (CM 28597) or in isolated conditions (MNG 7747). The high-resolution scans were made in selected regions of the midshaft to infer aspects of the evolution and development of S. sanjuanensis' limbs down to the microscopic level.
Method -Image Reconstruction and Three-Dimensional Segmentation Tomographic reconstruction was performed using the filtered back-projection algorithm implemented in the PyHST2 software  (Mirone et al. , 2014) , integrating a single distance phase retrieval modified from  Paganin et al. (2002)  to integrate an unsharp mask of the retrieved phase maps before the tomographic reconstruction to compensate blurring effects  (Sanchez et al. , 2012). The reconstructed volumes were later filtered with a 3D unsharp mask to highlight more specifically the trabecular mesh.
ACKNOWLEDGMENTS We thank  T. Hübner  for access to the collections housed in the  Museum der Natur of Gotha (Germany) ;  D. Germain  and  A. de Ricqlès  for access to the research collections of thin sections housed in the  Muséum national d'Histoire naturelle of Paris (France) ;  A. Durišová  for access to the collections housed in the  Slovak National Museum (Bratislava, Slovakia)  and  D. S. Berman  and  Amy C. Henrici  for access to the collections of the  Carnegie Museum of Natural History, Pittsburgh (United States ). We want to thank the editorial board of Frontiers in Earth Science and more specifically  M. Laurin  who helped editing this article. Previous versions of this article were greatly improved thanks to the three reviewers and  P. Ahlberg  ( Uppsala University ).
The available databases are often assembled for diverse objectives, and often re-processed using statistical methods. Spatialization and crossing of these data pose several methodological di culties and can introduce uncertainties in the cartography process carried out. For this reason, different methods and techniques are employed to speci cally treat environmental databases in order to take bene t from all available information and reduce the uncertainties (see Section 3. 2). This paper presents the methodological framework developed by INERIS to identify a common framework for conceptualizing and operationalizing environmental exposures as an important step towards articulating a science of EHI. In order to build a calculation infrastructure able to characterize the eco-exposome at the territorial level, we had to solve several methodological issues: (1) de ne an integrated exposure assessment framework that rst requires different scienti c limitations to be overcome, such as the linkage of the global sourceeffect chain, (2) provide statistical methods and numerical tools that would allow spatial and temporal data processing from existing environmental and populational databases, (3) link, adapt or develop transport and transfer models.
The coupling of numerical and statistical models has established a scienti c and technical basis for the integration, data processing and assessment of the transfer of contaminants from the environment to the populations. In that way, it is possible to integrate all available data, despite their heterogeneity in a common spatial support. The referent grid selected, allows the re ection of local variations, and the integration of environmental monitoring databases in France. The GIS modeling platform enables the coupling and interoperability of all spatial data via the reference grid on which the input data and the variables of interest are discretized after processing.
Databases in health and environment have been developed for several years. They evolve and are in full expansion. Actions to identify and monitor the quality of the environment for soils, water and air are conducted by different agencies, institutes or observatories. The production of this type of data and advances in computer technology allow their reuse in conceptual frameworks and with objectives different from those that prevailed in their implementation. The emergence of quality data and their integration into GIS make it possible to conduct territorial analysis work. These environmental data re ect the actual contamination of the environment and therefore of the global exposure of the populations. The indicators based on these data allow to characterize the population's exposure and its evolution regarding the implementation of public prevention policies.
In the context of reuse of this type of data for the purpose of expology, a database must be set up in which the variables are associated with the modes of exposure (concentrations in the environmental and exposure media are present, eating behavior, spacetime budget,. ). These variables must know several stages of process to allow the construction of indicators: -the identi cation of data sources allowing the construction of the different variables, -the acquisition of these data in view of the access modalities, the nancial, legal or human aspects, -the analysis of the quality and representativeness of the databases regarding the objective of the study (choice of a database, validity and representativeness of the data) sometimes involving the approximation or the application of simplifying assumptions, -the preprocessing of databases: cleaning the databases, rebuilding missing data, -the construction of ad-hoc data where the appropriate data sources are not available or exhaustive in relation to the objectives of the study, -data transformation (homogenization, aggregation or disaggregation of data).
The estimation of exposure requires knowledge of the concentrations of environmental compartments to which an individual or a population is exposed. These concentrations can be measured or modeled. A wide range of data might potentially be mobilized for integrated assessment. The database selection or study design de nition should be guided to reach the best compromise between data representativeness and method robustness, consistent with the objectives of the study. Characteristics of air pollution (e. g. , chemical components, particle properties) vary spatially  [13]  and may differ between areas near and far from monitors  [14]. Automated monitoring networks operate in Europe providing detailed air quality information on a regular basis. The soil routes of exposure to humans are inhalation of dust and vapor coming from soil contaminants, ingestion of contaminated soil particles (mainly for children) or contaminated food, and dermal absorption through the skin.
Once a site is considered as contaminated, it is necessary to provide enough accurate data to minimize lack of statistical representativeness and increase the spatial quanti cation. The time spent for evaluating the presence and extent of contamination can be reduced by an adequate sampling plan  [15]  which can at the same time, reduce the project costs  [16]. A soil monitoring system could be the source of the comparable and objective data on the current state and evolution of soils. The database of the soil monitoring system allows the creation and maintenance of data for each of the monitoring sites of agricultural land as well as the preparation of data for further processing through specialized programs  [17]. Position information provides a link to the GIS, and thus opens the possibilities for further spatial analysis, the identi cation of risk areas and their assessment.
In France, 300,000 samples are tested each year. Indeed, tap water is one of the most strictly controlled foodstuffs. Each year, the health agencies carry out close on 12. 3 million tests covering all of the country's public water and wastewater services (both publicly and privately managed). In 2013, more than 8. 1 million tests were carried out on services managed by private water company. Work has been carried out by INERIS to identify environmental and spatialized databases for the purpose of characterizing exposures by associating the main producers and data managers identi ed  [19, 20]. It allows to propose elements for the speci cation of environmental health platforms and to improve the integration of data in the framework of building an environmental health tracking information system.
Statistic approaches and optimize data representativeness The data available in a region of interest characterize levels of contamination at very speci c locations, over a given spatial support (i. e. the support on which the data is measured such as point, surface or volume), and for very speci c time frames. In order to construct the exposure maps from spatialized databases in the context of evaluating environmental inequalities, the development of methods is required to process and harmonize the available data, with respect to their speci cities (missing values, limited number of observations, etc. ) in the same resolution and support. In the mathematical eld of numerical analysis, interpolation is a method of constructing new data points within the range of a discrete set of known data points.
Using data from existing monitoring networks remains popular, due to cost considerations, data availability, and population coverage. Such statistical methods are aimed at using multiple types of information to inform exposure estimates and allow to estimate exposure in areas far from monitors. In addition to fused data, several other approaches have been developed to estimate individual-and population-level exposures, including various interpolation methods, land use regression (LUR) models, aerosol measurements obtained from satellites, and sourceand tra c-proximity analysis  [26]. Stochastic methods such as kriging are preferred  [27]. An issue commonly reported is the availability of data. Some databases include some limitation (as a limited number of observations by example) and therefore it is not possible to assess the population's exposure adequately.