dataesr/scientific-paragraphs-categorization huggingface dataset:
Mars Structure Service The Mars Structure Service (MSS) is the operational service for the SEIS instrument responsible for delivering interior seismic structure models. This is one of the primary science products of the InSight mission and the MSS is responsible for producing and updating such models throughout the course of the mission and delivering a final version to the Planetary Data System (PDS) at the end of the mission. A detailed discussion of the range of modeling products planned to be produced by the MSS is described by  Panning et al. (2017). This is anticipated to be a range of models on different scales ranging from the shallow subsurface to global-scale models using many different seismic observations (Fig. 120 ). The general approach to most of the modeling planned relies on Bayesian methods.
The scientific website of the SEIS Portal is dedicated to more professional seismologists and will provide access to different sets of data and specific documentation. Data distribution is described in Sect. 9. 2. The third website encompassed in the SEIS Portal is focused on education and outreach described in Sect. 9. 4. It will give access to diverse education and public outreach initiatives and to multiple sets of educational resources. Two additional areas on the SEIS Portal are dedicated to news and media. With a few exceptions, content is presented both in English and French.
SEIS Data Distribution The SEIS data flow is described in Fig. 121. Raw spacecraft data are downlinked through the Deep Space Network to the Multi-mission Instrument Processing Laboratory (MIPL) at JPL. These are transferred to SISMOC via the File Exchange Tool (FEI). Data are then archived in SEED format (Standard for the Exchange of Earthquake Data). Reader not used to SEED can found additional informations on this data format in the SEED manual  (FDSN 2012)  and in Appendix B. IPGP will provide SISMOC with the static dataless SEED associated with the Instrument Transfer function and other non-flight software (F/SW) tunable parameters. SISMOC will then: -Complete the SEED dataless with dynamic information from F/SW tunable parameters. -Generate raw and calibrated data from the raw spacecraft data and static SEED dataless.
-Generate processed data from the "Black Box. " -Distribute all above data in miniSEED/dataless SEED to MSDS (Mars SEIS Data Service) and MQS (Mars Quake Service). Relevant APSS data (such as Magnetic field, Temperature, Pressure, Wind Direction and speed) will be packaged with the SEIS data. The IPGP SEIS Team (with support from the SP and APSS teams) will certify the integrity of the SEIS/APSS SEED data to MSDS. MSDS will then archive and make the data available through FDSN Web Services (fdsnws-station, fdsnws-dataselect) in FDSN StationXML and miniSEED formats. Meta- SEIS, InSight science, outreach and education teams will be able to access all scientific SEIS and APSS data both through authenticated FDSN Web services and through the SEIS Portal (SEIS Team members Intranet). MSDS is responsible for final delivery of SEIS data in SEED (dataless, miniSEED) format to the IRIS and the PDS archive generation team.
The data formatted according to the SEED format will be distributed under the reserved FDSN Temporary Network Code XB  (period 2016-2022). Two station codes are planned: ELYSE for the scientific data and ELYHK for the housekeeping data. In addition, a Digital Object Identifier (DOI) is planned for these data. IPGP will also deliver the Mars Reference Internal Structure catalogues through the MSS  (Panning et al. 2017 and Sect. 8. 3 ) while ETHZ will be in charge of delivering the Mars Quake catalogue through MQS  (Clinton et al. 2018, this issue, and Sect. 8. 2 ). See Sects. 8. 2 and 8. 3 for more details. The PDS will distribute and maintain all InSight archives for the NASA planetary science community and the general public. SEIS data will be archived with the Geosciences Node. Release of SEIS data in open access and in SEED format at both IPGP and IRIS DMC will be synchronized with the release of these data at PDS.
SEIS Services Higher Level Products The key higher-level products that will be delivered from the SEIS experiment will be seismicity catalogues and models of the Martian structure. Seismicity catalogues will be created and curated by the Mars Quake Service team. The methods used to detect and characterize seismicity are described in detail in  Clinton et al. (2018)  and summarized in Sect. 8. 2. The catalogue is expected to include both tectonic and impact events but will not include lander or weather related activity. The format will be quakeML1. 2 (https://quake. ethz. ch/quakeml/), with InSight-specific extensions to reflect the single-station methods, the probabilistic location formulation and the Mars-specific event types (marsquakes, meteor impacts). Typically, events will be identified within hours of data being received on Earth.
g. Woodhouse 1988 ), but modified to be more flexible and include more possible structural parameters to be compatible with the model format used by the AxiSEM numerical wave propagation code  (Nissen-Meyer et al. 2014). 3D crustal models based on gravity and topography (e. g. Neumann et al. 2004 ) constrain only crustal thickness variation while not directly constraining mean crustal thickness. As seismic data constrains the crustal thickness at the landing site, updated 3D crustal models will also be produced, released in both latitude and longitude sampled files as well as spherical harmonic expansions useful for gravity comparisons. These models will be delivered and updated regularly during the course of the mission for use by the MQS probabilistic location algorithm and periodically made available via the InSight data portal.
Resource Distribution The resources will be organized by topics to facilitate the teachers' school activities. Topic 'DATA' will be one of the most highlighted topics. Hundreds of secondary schools and high schools mostly in US, UK, France and Switzerland, will receive SEIS (and weather) data from Mars daily following the public data release. This wide distribution of data is made possible by the worldwide partnership already existing between seismological educational networks (OCA-France, IRIS-US, ETHZ-Swiss, GS-England). See  Courboulex et al. (2012)  and for activities in France  (Berenguer and Virieux 2008). Students will have access to data selected for educational use, then teachers will be able to propose case studies to investigate the planet Mars. Topic 'TELLURIC' concerns a lot of hands-on about planetology, seismology experiments, meteorites impacts and/or physical states of water, pressure, temperature, etc.
Share with the Educational Community All of these resources and data must be provided and shared through the educational community. Data and activities, ranked by topic and by level of school age, will be displayed on the web site for Education. Teachers and the general public will be able to access these web pages through the SEIS portal. The teachers will find data (data selected and case studies), hands-on activities (described step by step), sensors and replica plans (helpful to build models) and dedicated software (to read data, to simulate phenomena) and it will be easy to download all these educational packages. The success of this specific program for schools is dependent on good teacher training. It is necessary not to forget tutorials and documentation so that the teachers can access the resources easily and quickly.
Pressure data will therefore be recorded continuously in order to minimize the pressure related ground deformation noise. Thanks to InSight's robotic arm, SEIS will therefore benefit from possibly the best installation scenario to be made by a static lander and will be able to detect quakes three orders of magnitude smaller than Viking. This results in a predicted detection threshold of moment magnitude M w ∼ 4 for a global detection and M w ∼ 3 for up to 40°epicentral distance. In addition, SEIS measurements will be continuously supported by the APSS suite of pressure, wind and magnetic sensors, which will not only allow systematic noise decorrelation and event validation but will also make joint event monitoring possible, from regional impacts with joint seismic and infrasound signals to local dust devils with seismic, pressure and magnetic signals.
SEIS is in essence a true discovery experiment on a Discovery mission, in the sense that it will possibly perform a sequence of discoveries comparable to those made on Earth 120-100 years ago: first marsquake detection, first solid tide observation on Mars' surface, first seismic impact detection and first constraints on the crust, upper mantle and core size. In addition, SEIS will also explore a planet where micro-seismic noise is likely only generated by the atmosphere, in contrast to the Earth were micro-seismic noise is dominated by oceanic waves and anthropogenic activity. SEIS data will be distributed in SEED format, following the schedule of NASA's Planetary Data System. In addition, these data will be made available at the IRIS DMC and at IPGP Data Center. Data will also be distributed to a wide international network of schools and colleges, through the educational programs in the USA and in several European countries.
C. 1. 5 SEED Network and Station As the landing site is close to the Elysium mount, the InSight station name for the final configuration is Elysium Planitia and its acronym is ELYSE while data produced during cruise post-landing and during deployment will be identified as CRUI(1/2) and ELYS(0/1). The station name for synthetic is SYNT1 (e. g. Ceylan et al. 2017; Clinton et al. 2018). The position of the station is given in Mars geographic coordinates for ELYSE, while the position of the spacecraft with respect to Earth is chosen for CRUI coordinates at the time of the first data on the cruise check. The mission network label is XB and is used for data produced during the test, cruise, commissioning and normal science operation mission phases. Therefore, data may have different network codes such as 7I for the test data produced during the pre and post-flight phases and 7J for the synthetic data.
OGT9-6a is an offspring of yCNOD226-1a crossed with BY4741. OGT8-11a is an offspring of BY4742 crossed with Y1196. Strains OGT9-6a and OGT8-11a were plated on rich media and UV irradiated (5W/m 2 during 5 second), resulting in 50% survival. LH514D and LH11D are suppressor clones of the growth defect selected from UV-irradiated OGT9-6a grown at 25˚C. AH29R is a suppressor clone of the growth defect selected from UV-irradiated OGT8-11a grown at 25˚C. Genetic interaction mapping (GIM) analysis of the RPA49 deletion mutant was performed as described previously  [33]. Microarray data were normalized using MATLAB (MathWorks, Inc. , Natick, MA) as previously described  [29]. OGT15-7b is an offspring of LH514D with BY4741, followed by homologous recombination using PCR-amplified fragments generated with oligos 1716 and 1717 and pCR4-HIS3 as template.
Mapping extragenic suppressors allele by genetic linkage Extragenic suppressors allele SGR were mapped using GIM methods, in which a strain is crossed with the entire pool of haploid deletion strains  [57]. Here, deletions are used to evaluate linkage to SGR locus. Due to the genetic suppression, Individual deletions genetically linked to SGR are counter-selected in rpa49Δ background. Genetic mapping of SGR locus is based on strong genetic linkage of counter-selected deletion alleles (see S1 Fig), evaluated using micro-array of deletion bar-code (ArrayExpress accession E-MTAB-7831)  [58].
Miller spreads experiments and analysis Chromatin spreading was mainly performed as described previously with minor modifications  [61]. Carbon-coated grids were rendered hydrophilic by glow discharge instead of ethanol treatment. Images were obtained using a JEOL JEM-1400 HC electron microscope (40 to 120 kV) with an Orius camera (11Mpixels). The position of the RNA polymerase I molecules and the rDNA fiber were determined by visual inspection of micrographs using Image J (http:// rsb. info. nih. gov/ij/). Digital images were processed by software programs Image J and Adobe Photoshop (v. CS6).
Acknowledgments We would like to thank the imaging platform of Toulouse TRI for their assistance. We thank  Margaux Cescato ,  Lilian Guillot  and  Jorge Perez-Fernandez  for strains and plasmid constructions. YCp50-26 carrying RPA49 was kindly provided by  P. Thuriaux. We also thank  Melanie Panarotto  for the Miller spreads analysis. This work benefited from exchanges with all unit members, including  Anthony Henras ,  Frederic Beckoue  ¨t and Lise Dauban. We thank  Stephanie Balor  and  Vanessa Soldan  from the  METI  platform for the TEM acquisitions.
A bivariate fit of the data yields the relation [XO2]ECHAMP = 0. 88 ([HO2]+[RO2])IU_cal + 6. 6 ppt, with an R 2 of 0. 99. This level of agreement is within the combined analytical uncertainties for the two instruments' calibration methods. A linear fit of all the 30-minute averaged [XO2] ambient data with the 1-minute averaged [HO2*] data (one point per 30 minutes) yields the relation [XO2] = 0. 85 [HO2*] + 3. 3 ppt, with an R 2 of 0. 67. Day to day variability in the [XO2]/[HO2*] ratio was observed. The lowest [XO2]/[HO2*] ratios between 13:00 and 16:00 were 0. 8 on 13 and 18 July, whereas the highest ratios of 1. 1 to 1. 3 were observed on 24 and 25 Julythe same two days on which the highest concentrations of isoprene and ozone were observed. Although the exact composition of the peroxy radicals during IRRONIC is not known, 0-dimensional photochemical modeling of the IRRONIC dataset using the RACM2, RACM2-LIM1, MCM 3.
In background (Ox) mode, the N2 and C2H6 flows were switched: sampled air was mixed with NO and N2 upstream Oxygen) were 21. 1 ppm and 30%, respectively, leading to concentrations in the reaction chamber of 0. 9 ppm and 1. 4%, respectively. Both upstream and downstream injections were delivered with PFA tubing (0. 16 cm i. d. × 6 m). Each reaction chamber alternated between ROx and Ox mode every 45 seconds on an anti-synchronized schedule using four solenoid valves controlled by Labview software (National Instruments). After the downstream reagent addition, the air from each reaction chamber flowed through 1 m of 0. 32 cm ID FEP tubing, a particulate matter filter (United Filtration Systems, Inc. , DIF BN60), and another 6 m of tubing before entering identical CAPS monitors located inside the laboratory.
This amounted to a correction of approximately 20% at a water mixing ratio of 1%. The limit of detection of HO2 * was 0. 8 ppt (30 s average, signal-to-noise ratio of two). The overall accuracy of the HO2* measurements was ±36% (2σ). On all days except 22 July, HO2 * data were collected for 1 minute every 30 minutes and OH was measured during the rest of the 30 minute cycle. On 22 July, OH was not measured and instead the FAGE instrument measured HO2 * continuously.
Supporting Measurements Ambient NO2 was measured using a separate CAPS monitor (Aerodyne Research)  (Kebabian et al. , 2007; Kebabian et al. , 2008). The standard 450 nm bandpass filter used by the CAPS monitor was replaced with a 470 nm bandpass filter to eliminate any interference by glyoxal and methyl glyoxal  (Kebabian et al. , 2008). This reduced the sensitivity by approximately a factor of three but still provided high signal-to-noise ratios (>100) for the ambient measurements. O3 was measured with a UV absorbance monitor (2B Technologies model 202). NO was measured using a Thermo Fisher chemiluminescence sensor (Model 42i Trace Level). NO, NO2, and O3 data were averaged to 1 minute. Additional details regarding the calibrations and baseline measurements for the NO, NO2, and O3 measurements can be found in the SI.
Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-1359 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 14 January 2019 c Author(s) 2019. CC BY 4. 0 License. [XO2]/[HO2*] value, since ECHAMP is sensitive to those peroxy radicals (α>0. 9) whereas the LIF-FAGE HO2* measurement is not (α<0. 1). In contrast, air with a relatively high fraction of alkene-derived RO2 (e. g. , isoprene peroxy radicals), for which both ECHAMP and LIF-FAGE HO2* α values are near one, would be expected to lead to lower [XO2]/[HO2*] values (i. e. , closer to unity). Because the composition of the peroxy radicals during IRRONIC is not exactly known, we examine the predicted speciation generated by zero-dimensional photochemical modeling of the IRRONIC dataset using two versions of the Regional Atmospheric Chemistry Mechanism (RACM2 and RACM2-LIM1) and the Master Chemical Mechanism (MCM 3.
Study population Study participants included in this analysis are a subset of the participants enrolled in the EDEN mother-child cohort between 2003 and 2006  (34). EDEN is a two-center study that has included 2002 pregnant women, mainly Caucasian, before 24 weeks of gestation in the university hospitals of Nancy and Poitiers, France. Exclusion criteria were multiple pregnancies, prepregnancy diabetes, French illiteracy and plans to move outside the region within the following 3 years. Lifestyle, demographic and medical data were collected by questionnaires and interviews during pregnancy and after delivery. DNA methylation (DNAm) was measured in placenta samples from 668 women  (35). Placenta samples were collected at delivery by the midwife or the technician of the study using a standardized procedure.
Raw signals of 450K BeadChips were extracted using the GenomeStudio ® software (v2011. 1. Illumina). The DNA methylation level of each CpG was calculated as the ratio of the intensity of fluorescent signals of the methylated alleles over the sum of methylated and unmethylated alleles (β value). All samples passed initial quality control and had on average more than 98 % of valid data points (detection p-value <0. 01). A refined version of the Subset Quantile Normalization (SQN) pipeline  (36) including a revised annotation file  (37)  was used for data processing, correction and normalization. Data processing and normalization did not change the density distribution of the DNA methylation levels (results not shown). Intensity values were corrected for potential biases in fluorescent dye intensity and background corrected using the lumi R package  (38)  as implemented in the SQN pipeline.
Cellular composition of placenta samples Cellular composition of biological samples is a potential confounder in epigenetic epidemiological studies. In the absence of reference methylomes for placental tissue, we used a reference-free method, the RefFreeEWAS package available in R  (41) , to deconvolute cell-type proportions from DNA methylation array data. The method relies on the identification of latent variables as surrogates for cell-type mixture. From the 10,000 most variable CpGs, we identified the optimal number of cell-types to be 6. We then used the 425,878 CpGs to estimate the proportion of each cell-type per sample.
Characterization of epigenetic context of smoking-associated DMRs We investigated whether the smoking associated DMRs could be associated with specific features in their global epigenomic landscape. Indeed, the identification of a specific epigenome environment could not only explain the particular sensitivity of these regions but also help us anticipating the biological consequences of methylation alterations on the epigenomic control of placenta development. To address this question, we first identified a set of random regions of similar sizes to compare to our smoking associated DMRs. To identify "random DMRs", smoking status was randomly assigned to each participant of our study in an iterative process repeated 122 times. The EWAS and DMRs analyses were performed for each dataset provided by each of the 122 iterations.
geneimprint. com and igc. otago. ac. nz databases, with 102 genes common to the two databases. In order to assess the potential impact of methylation alterations in our smoking associated DMRs on the regulated expression of these genes, we looked for proximity and/or overlaps between our smoking associated DMRs and the known ICRs and promoter regions associated with these genes. 46 )) and igc. otago (http://igc. otago. ac. nz,  (47) ).
Such case could lead to a misclassification of some genomic regions altered by cigarette exposure that could be categorized in the "epigenetic memory" group and rather belongs to the "reversible" group. Finally, the former smokers group likely includes women who stopped smoking in anticipation of a pregnancy (i. e. before the conception) and women who stopped smoking in early pregnancy once they knew they were pregnant (i. e. a few days / weeks after conception). Time since quitting smoking was not available in our data. However, this might influence the methylation levels and would be an interesting piece of information to add to future research to provide insights into the existing risk faced by former smokers even months or years after cessation. The present study includes the largest sample size of placentas investigated to date, which were collected in the context of a longitudinal cohort with repeated data on maternal smoking before and during pregnancy.
Patient and Public Involvement Patients or the public were not involved in the design, or conduct, or reporting, or dissemination of our research. The results are disseminated to study participants through newsletters and the study website (http://eden. vjf. inserm. fr). The left, center and right panels are respectively centered on the DMRs associated with the NNAT locus +/-2kb, the SGCE/PEG10 locus +/-2kb and the H19/MIR675 locus +/-2kb. The top panels show heatmaps respectively corresponding to H3K4me3, H3K4me1 and H3k27ac enrichments in placenta around the center of the region of interest +/-2 kilobases. For each mark we downloaded triplicates from ENCODE data (see methods). We computed the enrichement matrices from bigWig files using the deepTools software and displayed it using a custom R script.
Acknowledgments: Most of the computations presented in this paper were performed using the CIMENT/GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr). We thank the participants of the EDEN cohort. We are grateful to the midwife research assistants ( L. Douhaud. S. Bedel. B. Lortholary. S. Gabriel. M. Rogeon. and  M. Malinbaum ) for data collection and  P. Lavoine  for checking, coding, and entering data. The EDEN mother-child cohort study group includes:  I Annesi-Maesano ,  JY Bernard ,  J Botton ,  M-A Charles ,  P Dargent-Molina ,  B de Lauzon-Guillain ,  P Ducimetière ,  M de Agostini ,  B Foliguet ,  A Forhan ,  X Fritel ,  A Germa ,  V Goua ,  R Hankard ,  B Heude ,  M Kaminski ,  B Larroque ,  N Lelong ,  J Lepeule ,  G Magnin ,  L Marchand ,  C Nabet ,  F Pierre ,  R Slama ,  MJ Saurel-Cubizolles ,  M Schweitzer ,  O Thiebaugeorges.