dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses For continuous data, the statistical analyses were performed using SAS software (SAS Institute Inc. , Cary, NC, USA). In experiment 1, the daily melatonin concentrations were examined by ANOVA using a repeated measures model with the GLM procedure  (Little et al. , 1998). Classically for hormonal data and consistent with  Guillaume et al. (2006) , data were previously transformed by a logarithmic transformation because of their limitation by 0, and for their normalization and the equality of variances between groups required by the ANOVA. The analysis tested the following factors: treatment, dark or light periods (in reference to the control group) and rams nested under treatment. The testicular volume variations in the four experiments and testosterone concentrations in experiment 1 were compared between periods and/or between treatments by ANOVA using a repeated measures model GLM procedure  (Little et al.
Finally, in experiment 4, five periods were distinguished relative to the evolution of the testicular volume of the LD-LL 4 group, with a minimum during long days and during the onset of LL treatment (T <18 ), rapid growth (T 18 to 61 ), onset of plateau (T 62 to 90 ), end of plateau (T 91 to 120 ) and decrease (T >120 ). These periods were indicated in the results on different curves. For discrete data, in experiments 2 and 3, the fertility between groups was compared using the number of pregnant ewes with a non-parametric Pearson χ 2 exact test using the StatXact ® a statistical software developed by Cytel ® (Paris, France Business Development Office, Batiment, Antony, France, http://www. cytel. com/software/statxact). All results are expressed as the mean ± SEM.
The thermal profile for SYBR Green qPCR was 95 • C for 8 min, followed by 40 cycles at 95 • C for 15 s, 60 • C for 15 s, and 72 • C for 30 s. To exclude PCR products amplified from genomic DNA, primers were designed, when possible, to span one exon-exon junction. Data were collected and analyzed using the LightCycler R 480 software, release version 1. 5. 0 (Roche). Primers sequences used in this study are available on request. The gene expression stability of Hmbs (Hydroxymethylbilane synthase) was used as the reference transcript.
ACKNOWLEDGMENTS This work was supported by funds from  CNRS ,  INSERM, University Pierre&Marie Curie , " Association Française contre les Myopathies" (AFM)  (contract n •  16605 ) and the  LabEx REVIVE  ( ANR-10-LABX-73 ). RD was supported by a Ph. D. fellowship from  UPMC University. Authors thank  Dr. Solène Emmanuelle Boitard  ( UMR CNRS 8256 ,  Paris-France ) for scientific discussion. We thank the animal facilities of  UPMC.
Application model The finite element model displayed in Fig. 2  and used to create the blisk superelement was built using ANSYS software and Solid226 coupled-field elements. In order to obtain a CPU efficient model, a simplified geometry as well as a coarse mesh were studied. Each blade of this model possesses a boundary node (i. e. retained during reduction)on its tip with three nodal displacements and a nodal temperature. The system is constrained both mechanically and thermally on its internal bore. All other degrees-of-freedom are considered free of any constraint. Thus, external boundaries are perfectly insulated. Such conditions do not represent actual turbomachinery applications. However, test rigs dedicated to the study of bladecasing contact events on actual engine components work under low pressure conditions in order to avoid aerodynamic perturbations  [8].
The highly accurate measurements of the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) are used in numerical weather prediction (NWP), atmospheric chemistry and climate monitoring. As the second generation of the European Polar System (EPS-SG) is being developed, a new generation of IASI instruments has been designed to fly on board the MetOp-SG constellation: IASI New Generation (IASI-NG). In order to prepare the arrival of this new instrument, and to evaluate its impact on NWP and atmospheric chemistry applications, a set of IASI and IASI-NG simulated data was built and made available to the public to set a common framework for future impact studies. This paper describes the information available in this database and the procedure followed to run the IASI and IASI-NG simulations.
These simulated data were evaluated by comparing IASI-NG to IASI observations. The result is also presented here. Additionally, preliminary impact studies of the benefit of IASI-NG compared to IASI on the retrieval of temperature and humidity in a NWP framework are also shown in the present work. With a channel dataset located in the same wave numbers for both instruments, we showed an improvement of the temperature retrievals throughout the atmosphere, with a maximum in the troposphere with IASI-NG and a lower benefit for the tropospheric humidity.
optimally derived channel selections and that it appears to be robust. IASI is the second advanced infrared sounder launched in the last decade  (2006). IASI is an infrared Fourier transform interferometer and is the first instrument of this type to fly as a part of the MetOp operational satellite series. IASI registers the IR spectrum between 3. 6 and 15. 5 µm, providing 8461 channels with a spectral apodised resolution of 0. 5 cm -1 at a spectral sampling of 0. 25 cm -1. As in the case of AIRS, preliminary studies showed that a subset of channels was preferred for an assimilation because of the computing costs and of the existence of correlated information between contiguous channels  (Rabier et al. , 2002). Preliminary studies about the assimilation of IASI data in the ECMWF (European Centre for Medium-range Weather Forecast) model found a mainly positive impact for IASI assimilation and a better quality of the measurements in the 15 µm CO 2 band compared to AIRS  (Collard and McNally, 2009).
, 2010)  or air quality  (Timmermans et al. , 2015). During these experiments, the full observation database is built from a realistic description of the state of the atmosphere, the Nature Run. The content of the database is fed into NWP models or chemistry algorithms, whose results are evaluated against a reference set. OSSEs are very useful to estimate future observing system impact but at a high computation cost because OSSEs typically mimic state-of-the-art data assimilation systems. In this work we built a database of IASI/IASI-NG radiances to evaluate the impact of IASI-NG data with respect to IASI data using two radiative transfer (RT) models: 4A (Automatised Atmospheric Absorption Atlas;  Scott and Chedin, 1981; Tournier et al. , 1995; Chaumat et al. , 2012)  and RT-TOV (Radiative Transfer for TOVS, TIROS (Television Infrared Observational Satellite) Operational Vertical Sounder;  Matricardi et al.
, 2004; Hocking et al. , 2015). Having two sets of simulations enables one to carry out observation impact experiments using a different RT model than the one linked to the simulation algorithm; i. e. if the user employs RTTOV in the retrieval algorithm, the 4A simulation dataset could be used. This use of a different RT for the retrieval adds a more realistic error to the radiance data. To illustrate the potential of this database, it is then employed to evaluate the impact of IASI-NG with respect to IASI on the retrieval of the temperature and humidity profiles. First, we present here a common database of IASI and IASI-NG simulations to assess the expected benefits of the latter instrument. This database will serve as a common base for future impact studies on the impact of IASI-NG. A preliminary impact study in a one-dimensional variational analysis (1D-Var) retrieval context is presented in the later sections of this work.
An exhaustive description of the atmospheric state has been built in the middle of four different dates for each year's season to serve as a basis for future evaluation of IASI-NG impact. This paper is organised as follows: Sect. 2 describes in detail the IASI-NG sounder and the main differences with the IASI sounder. Sections 3 and 4 depict the procedure devised to build the simulated observation database, and the main results of the information are included in the latter. Section 5 deals with the methodology used for the evaluation of using IASI-NG for the retrieval of temperature and water vapour profiles. Finally, the summary and conclusions are provided in Sect. 6. 2 IASI and IASI-NG instruments IASI  (Cayla, 2001; Simeoni et al. , 1997 ) is a space-borne interferometer able to characterise the Earth infrared spectra in the range from 645 to 2760 cm -1 (15.
Future studies using the database presented in this work may help to decide which of the two materials should be used for the final instrument. The IASI-NG noise reduction is achieved by using twice the integration time of IASI, and the increment of the instrument spectral resolution is accomplished thanks to the 8 cm optical path of IASI-NG, twice the 4 cm IASI optical path. IASI-NG will have an additional band compared to IASI passing from three to four bands. IASI band 3 will be divided into two different bands, whereas the limits for the first two bands are quite similar to IASI ones. The limits for the four IASI-NG bands are as follows: B1 from 645 to 1150 cm - 1  , B2 from 1150 to 1950 cm -1 , B3 from 1950 to 2300 cm -1 and B4 from 2300 to 2760 cm -1. Table  1  compares the main features of IASI and IASI-NG instruments.
Database construction To build the database consisted, first of all, in getting the most accurate description possible of the state of the atmosphere, so that data could be used for the simulations of both IASI and IASI-NG measurements. Four different dates were chosen for the year 2013. To cover the maximum of possible meteorological variability, each date falls in the middle of each season: 4 February (North Hemisphere winter), 6 May (NH spring), 6 August (NH summer) and 4 November (NH autumn). These four periods of 24 h begin at 21:00 UTC on the previous day. Even though the dataset represents more than 5 million IASI observation points (5 242 448) over the globe, it is likely that these four days do not cover all the possible meteorological situations. However it offers the possibility to have different atmospheric profiles covering the whole Earth, with daytime/night-time and sea/land conditions.
Configuration of the simulations The information contained in the IASI observation database has been used to prepare the most realistic possible atmospheric state according to the input of each RT model. Regarding the atmospheric chemistry, a total of 16 vertical atmospheric constituent profiles were ingested by 4A for the simulations: H 2 O; CO 2 ; O 3 ; N 2 O; CO; CH 4 ; SO 2 ; HNO 3 ; OCS; CH 3 D; N 2 ; and the CFCs 11, 12 and 14. For H 2 O, CO 2 , O 3 , CO, CH 4 , and SO 2 species, the vertical profiles were taken from the above database. For the other species, a standard profile provided by 4A was used. All the profiles were linearly interpolated onto the 43 4A levels (43L). The reduction in the number of levels was carried out as no significant gain in accuracy was observed when using 60 levels,   Sherlock and Saunders, 1999)  for sea surface emissivities.
Land surface emissivities were taken for 4A from the University of Wisconsin (UW) IR atlas of emissivities  (Seemann et al. , 2008)  for the year 2013. RTTOV simulations also used the UW atlas of emissivities but for the year 2007. Since RTTOV uses precomputed atlas values to speed up the calculations, it was not possible to use the 2013 atlases. As negligible differences are expected in the UW atlas emissivity values between the years 2007 and 2013, the RTTOV precalculated emissivity files have been used. Table  2  summarises the main differences between both RT runs. The IASI viewing geometry was used for the simulations of both instruments despite the different scanning geometry of IASI-NG because scan geometry was not yet clearly defined at the time the dataset was built. Although the AVHRR cloud cover values corresponding to each IASI pixel and cloud vertical profiles have been included in the database, we have chosen not to consider cloudy conditions because of computational cost of these kinds of simulations and also due to the large uncertainties in cloud radiative properties modelling.
Information contained in the database A total of 5 242 047 IASI observations were compiled for the four selected dates. From this total, 3 463 432 observations correspond to sea pixels, 185 916 were from coastal regions and 1 591 529 were from over land. An overview of the distribution of the observation according to different latitude bands and AVHRR cloud mask values is given in Table 3. Five latitude belts have been considered: the North Pole, the mid-latitudes of the Northern Hemisphere, the tropics, the mid-latitudes of the Southern Hemisphere and the South Pole. For the AVHRR cloud mask, there is no value for   3a  compares the results from full IASI (black) and IASI-NG (grey) spectrum simulations. IASI-NG presents a higher variability of the spectrum than IASI because of its higher spectral resolution.
5 % approximately, making this RTTOV profile training set possibly obsolete. 4A computes optical depths directly using the input vertical profiles, allowing for more accurate values for them than using a set of predefined lookup tables. The fact that RTTOV presents a lower bias than 4A in the rest of the band arises from not having considered the same sea surface model. As mentioned before, 4A uses constant emissivity values from  Snyder et al. (1998) , while in RTTOV the sea surface emissivity is computed by the ISEM model, which takes into account the skin temperature and the instrument viewing angle  (Hong et al. , 2010). Whatever the spectral bands, differences could be due to the spectroscopic parameters used. The spectroscopy used by 4A came from the 2009 version of the GEISA database, and the spectroscopy used by RTTOV came from HITRAN 2012.
In order to evaluate both model simulations over a longer period (the four selected days in 2013), simulations were compared against a subset of 314 channels of IASI observations stored in the Météo-France archive. Only 789 573 clearsky observations over both land and sea from the 5 242 047 total observations, i. e. around 15 % of the recovered IASI observations, were cloud-free observations according to the AVHRR cloud cover. The average and SD of these differences for both 4A and RTTOV simulated datasets are shown in Fig. 5. The SDs of 4A and RTTOV simulations against observation differences are similar all over the measured spectrum. There are only three slight differences: for surface channels in the range between 750 and 950 cm -1 , RTTOV presents a slightly lower values compared to 4A: around 0.
Differences between IASI and IASI-NG simulated spectra A comparison between IASI and IASI-NG radiance simulations is shown in Fig. 6 , from 730 to 740 cm -1 , belonging to the 15 µm CO 2 absorption band. A single IASI observation from 6 August 2013 and acquired at 20:38:00 has been used in order to calculate the Earth IR spectrum with 4A at a resolution of 0. 001 cm -1. The result is shown Fig. 6b. Figure  6a  presents the CO 2 absorption lines as extracted from the GEISA database  (Jacquinet-Husson et al. , 2011). The position of these absorption lines corresponds with the sharp peaks observed in the 4A spectrum calculation of Fig. 6b. The simulation of the IASI and IASI-NG spectrum by RT-TOV and 4A models is shown in Fig. 6c  and d  respectively. The values of the corresponding IASI measurements are also drawn, with dots, in Fig.
Q channels 14 2701 , 2910 , 2951 , 2958 , 2991 , 2993, 3002, 3008, 3014, 3027, 3049, 3058, 3105, 3577 Low-trop. Q channels 7 5368, 5383, 5397, 5401, 5403, 5405, 5483  5 Retrieval of temperature and humidity vertical profiles using the simulated observation database To illustrate the potential gain brought by IASI-NG, a short study using 1D-Var retrievals and a small subset of clearsky observations over sea is proposed in this section. Indeed these conditions represent an easier way to deal with infrared observations even if in the future the IASI-NG data will be used with different assumptions (e. g. assimilation over land and/or for cloudy sky). In this section, retrieval experiments are performed using the 1D-Var code (version 1. 0) provided by the EUMETSAT NWP Satellite Application Facility (Weston, 2014).
This 1D-Var system was interfaced with the RT-TOV model version 11. A subset of 1681 4A simulations was extracted from the global database presented in previous sections. This subset retained only one simulation over sea under clear-sky observations according to the AVHRR cloud mask flag for each date evenly distributed over latitudes and longitudes. Observations above sea ice surface, mainly at the South Pole, were excluded from the dataset. These 1681 simulations were classified by latitude bands (Fig. 7 ), resulting in 522 observations for tropical latitudes (20 • S to 20 • N), 1068 for mid-latitudes (66. 5 • S to -20 • S and 20 • N to 66. 5 • N) and 91 for polar latitudes (90 • S to 66. 5 • S and 66. 5 • N to 90 • N). These 4A simulations will be considered as the truth in this retrieval study.
Conclusions A database of IASI and IASI-NG simulations has been devised. The two sets of simulations for IASI-NG correspond to the two materials under consideration for the IASI-NG prism. The database is presented in two versions according to the radiative transfer model that has been used. The information available in the database has the following structure: coordinates: longitude, latitude, terrain elevation, date and time; observation parameters: surface pressure and temperature, IASI zenith and azimuth angles, solar zenith and azimuth angles, land-sea mask value, cloud cover value from AVHRR, 4A emissivity index, surface emissivity from UW atlas and RTTOV surface type; vertical profiles: temperature, humidity, carbon dioxide, ozone, carbon monoxide, methane and sulfur dioxide; cloud information: vertical profiles of cloud cover, ice water content, liquid water content, rain water content and snow water content; simulations: radiances of IASI (in 8461 channels), 16921 channels for IASI-NG A (KBr) and IASI-NG B (ZnSe); real observations: 314 IASI brightness temperatures.
With a small subset of atmospheric profiles from the database and an 1D-Var framework, the impact of the current IASI channel selection has been evaluated for IASI and IASI-NG configurations over sea and for clear-sky conditions. With channels located in the same wave numbers, retrieval experiments showed an improvement of the temperature retrievals throughout the atmosphere with a maximum in the troposphere. The improvement is lower for the humidity in the troposphere. This improvement may reach 10 % in the tropics at 400 hPa and is about 5 % at mid-latitudes and in polar regions. The improvement obtained for tropospheric humidity is of the same order (5 %). A reduced sensitivity in the low troposphere is confirmed for IASI. This result agrees with the study by  Sellitto et al. (2013) , who produced tropospheric ozone pseudo-observations based on these noise configurations.
They showed a clear improvement of low-tropospheric ozone pseudo-observations compared to the IASI ones and the potential to separate lowerfrom upper-tropospheric ozone information. Additional work is thus required to check if IASI-NG will be able to better probe the atmosphere at these levels. For this purpose, a new channel selection needs to be defined, which will be undertaken in a following study. These encouraging results of the IASI-NG impact provided by this study should be confirmed with a more comprehensive atmospheric dataset and a closer context of NWP operations. IASI-NG is dedicated to multiple applications such as NWP, atmospheric chemistry and air quality. The potential of multi-spectral synergy between instruments from the second-generation European Polar System (EPS-SG) for improved sensitivity to ozone in the lowest part of the troposphere should be studied further as proposed by  Costantino et al.
Acknowledgements. The authors would like to acknowledge the  CNES  for funding this work in the frame of the  IASI-NG programme. Jean Maziejewski  and  Jean-Francois Mahfouf  are warmly thanked for their careful review of a previous version of the paper. Two anonymous reviewers and  Pasquale Sellitto  are also acknowledged for their fruitful comments on the manuscript. Edited by:  Helen Worden Reviewed  by: two anonymous referees.
Code availability. A high-level Fortran 90 library has been developed to access the information contained in the simulation dataset files. The library and its documentation can be downloaded from the web page of the EUMETSAT/CNES IASI Sounding Science Working Group at http://iasi. cnes. fr/en/IASI/isswg. htm. Data availability. A total of 96 hourly files for the two simulation dataset are available at the web page of the EUMETSAT/CNES IASI Sounding Science Working Group at http://iasi. cnes. fr/en/ IASI/isswg. htm.
6, 39. 0 and 44. 4 % of the 5 242 047 total simulations. Figure 3 presents the information available in the database associated with an IASI observation taken out in the South Pacific (179. 0 • W, 24. 55 • S) on 3 February 2013 at 21:13:46 UT. The simulated brightness temperatures pre- sented in this figure come from the 4A RT model dataset. Fig- ure.
DNA extraction and genome sequencing. 13 Thermosipho strains were cultured in a modified Ravot medium as previously described in  (Lossouarn et al. 2015)  and used for total DNA extraction as described in  (Charbonnier et al. 1992; Geslin et al. 2003). All strains, except T. africanus Ob7 were submitted for genome sequencing at the Norwegian Sequencing Centre (NSC, Oslo, Norway). A 300 bp insert paired end library was prepared using the genomic DNA sample prep kit (Illumina, San Diego, CA, USA) for each DNA sample. The Library was spiked with phiX DNA. Subsequently, all strains except Ob7 were sequenced on an Illumina MiSeq generating a dataset of 250 bp paired end reads. Strain Ob7 was sequenced at University of Alberta, Canada, using an Iontorrent (Thermo Fisher Scientific, Waltham, MA, USA) approach.
16S rRNA Phylogeny of Thermosipho species Full-length 16S rRNA sequences were manually extracted from the annotated Thermosipho genome assemblies generated in this study as well as publically available genomes. Next we searched the SILVA database version SSU r122 for Thermosipho spp. 16S rRNA sequences with the following setting: sequence length > 1300 bp, alignment quality > 80, pintail quality > 80  (Quast et al. 2013). Matching sequences were downloaded and added to extracted 16S rRNA sequences from the de-novo sequenced genomes. Multiple identical sequences from the same strain with different accession numbers (e. g. the four 16S rRNA sequences of T. melanesiensis BI429, five 16S rRNA sequences T. africanus TCF52B) are represented by a single sequence in the dataset. The NCBI non-redundant database was then queried using BLASTn (blast+ v 2.
2. 26  (Camacho et al. 2009 )) with the 16S rRNA gene dataset described above, to identify Thermosipho sp. like 16S rRNA sequences missed with the above methods. Additional fulllength sequences were downloaded from Genbank and combined with the de-novo / SILVA 16S rRNA sequences. The final 16S rRNA gene alignment consisted of Thermosipho sequences combined with outgroup sequences from other Thermotogales species. The resulting alignment was used to build phylogenies using Maximum Likelihood as implemented in MEGA 6. 0  (Tamura et al. 2013). and the General Time Reversible (GTR) model (G+I, 4 categories ) as suggested by JModelTest v. 2. 1. 7 (Darriba et al. 2012).