dataesr/scientific-paragraphs-categorization huggingface dataset:
The clear and cloudy statistics are comparable especially in terms of RMSD (smaller during nighttime). The bias is generally negative in the clear sky cases (except in January, night-time case). Moreover, it is larger during daytime for the clear sky cases; and larger in the nighttime for cloudy cases, which could be due to lower reliability of the cloud mask as visible channels are not available. In Figure  11  the MSG/SEVIRI-AMSR-E differences are analyzed by land-cover type, for January and July, separated into day/nighttime and for clear/cloudy conditions. Land-cover types from the IGBP database  [86]  were aggregated into more general classes to simplify the analysis (forest, mixed forest, closed shrub lands, savannas, grass lands, permanent wetlands, croplands and newly vegetated areas, urban, snow and ice, barren, open water and inland water).
This dataset was re-projected to the regular grid used to perform the comparisons and the most frequent land cover class for each grid point is considered. Due to re-gridding and land cover class aggregation, some differences between the land cover used in the processing of each dataset may be present. In Figure  11  the MSG/SEVIRI-AMSR-E differences are analyzed by land-cover type, for January and July, separated into day/nighttime and for clear/cloudy conditions. Land-cover types from the IGBP database  [88]  were aggregated into more general classes to simplify the analysis (forest, mixed forest, closed shrub lands, savannas, grass lands, permanent wetlands, croplands and newly vegetated areas, urban, snow and ice, barren, open water and inland water). This dataset was reprojected to the regular grid used to perform the comparisons and the most frequent land cover class for each grid point is considered.
Due to re-gridding and land cover class aggregation, some differences between the land cover used in the processing of each dataset may be present. For clear-sky pixels, the daytime bias is negative for all land cover classes (in January and July), except forests and wetlands (in July). Classes with significant seasonal variability of vegetation (e. g. grasslands) show larger differences in the statistics between January and July, possibly due to differences in how surface emissivity is taken into account in the retrieval algorithms. However, other factors may play a role, namely a larger spatial heterogeneity often experienced by those land-cover types (e. g. , closed shrubs, grass) during the dry season, which may increase directional effects and lower representativeness. Standard deviations are in general larger for the July case, except for mixed forest.
The comparison of the EB model skin temperature with IR LST for clear sky cases (Figure  4 ) revealed some of the caveats that one might expect from such products. Apart from differences in cloud classification in the two datasets (discussed below), high aerosol loads (which are generally not well-accounted for) may also introduce errors into IR LST, and also into estimates of short-wave (and to a lesser extent long-wave) incoming radiation. It is known that aerosol loads are often underestimated (e. g. ,  [93] ), as is the case over northwest Africa in July (lower left panel in Figure  4 ). This explains the high positive bias at daytime: the underestimation of aerosol loads may actually lead to an underestimation of IR LST (attenuation of IR radiances not properly accounted for) and to an over-estimation of skin temperature (essentially due to overestimating solar radiation).
The validation of the EB skin temperatures estimated under cloudy conditions with in situ observations yielded somewhat better summary statistics than for clear-sky conditions, except for Gobabeb, which has significantly fewer clouds than Kalahari or Evora. Furthermore, when IR-based LST and EB T Sk are blended into an all-weather LST, the RMSDs against in situ data range between 2. 2 and 3. 1 K, which corresponds to an increase of 0. 1 to 0. 8 K compared to the RMSD of the clear sky IR LST. It is worth noting that the accuracy (bias) of the two LST products is considerably better than 2 K for clear skies as well as for cloudy skies. Moreover, the RMSD is only slightly above 2. 5 K, the threshold value of the Climate Monitoring-SAF LST climate data record  [95]. Guillevic et al. [82]  proposed tighter thresholds of 1 K for both precision and uncertainty for an LST product to be useful for climate applications.
Problems in the representation of surface parameters in numerical models have already been acknowledged in the literature  [9, 10]. Downward short-and long-wave radiative fluxes may be estimated from satellite observations, mainly through their cloud signatures. Within the LSA-SAF, these are estimated every 30-minutes from MSG/SEVIRI measurements on a pixel-by-pixel basis. The respective algorithms described in Geiger et al. [53]  and in  Trigo et al. [50] make use of bulk parameterization schemes combined with information on cloud cover and type. When validated against 6 in situ stations spread across the MSG disk, the biases of LSA-SAF Downwelling Surface Shortwave and Longwave Flux products mostly within the target accuracy of 10%  [50, 53, 77]. Furthermore, the two fluxes form a consistent dataset derived from the same source data, where errors in cloud amount and type tend to partially compensate each other  [50, 77].
provides time-series of the various LST datasets for Evora (in situ with and without directional correction, IR split-window LST, all-weather EB model  and AMSR-E LST), for a spring (23 March-7 April 2010) and summer(7-    22  August 2010) period. The area surrounding Evora station experiences strong seasonal (and interannual) vegetation variability as the understory grows and desiccates. This introduces some emissivity variability as well as significant temperature contrasts between surface elements, particularly during summer. A correction that accounts for directional effects was introduced in order to improve comparisons with MSG/SEVIRI level 2 clear-sky LST estimates [18, 92]. The adjusted time series (shown in green) show differences to the original in situ estimate up to 2 K, with lower LST values around the daily maximum for the periods shown.
Acknowledgments: The  MSG/SEVIRI IR  and EB model LST data were generated by the  LSA SAF. The work at RMI has been funded by  EUMETSAT  and the  European Space Agency  through the  PRODEX programme of the Belgian Science Policy. The AMSR-E/Aqua LST data was obtained through the  GlobTemperature portal  (http://data. globtemperature. info/). CJ acknowledges support from the  European Space Agency (ESA)   Data User Element  ( DUE) GlobTemperature  project.
Acknowledgements The field work was conducted under an agreement for paleontological researches signed between  Lisea and Cosea (VINCI group ) and the  PALAIOS society. Financial support was provided in part by the  Lisea foundation , the  Vienne department , the municipality communities of the Pays Chauvinois and the Valdivienne municipality. We are grateful to  J. A. Doyle  and the second anonymous referee for their thorough reviews, and helpful comments on the manuscript.
Modulation of Fungal Transcriptomes in Response to Water Stress In order to identify potential effectors of the response of A. brassicicola to dehydration stress, we first focused on the analysis of the transcriptional response in germinating conidia exposed to previously defined water stress (i. e. , exposure to sorbitol 1. 2 M for 0. 5 and 2 h and exposure to silica gel beads for 1 h and 4 h). Each treated sample was compared to control sample and, thus, we produced four transcriptome data sets (each set was obtained from three biological replicates). We used A. brassicicola Nimblegen microarrays bearing one probe for each of the 10 633 ORFs predicted in the A. brassicicola automatically annotated genome database (JGI Genome Portal 1 ). Only probes with a P-values ≤0. 05 and a log2 ratio ≥0.
Expression of Hydrophilins During Osmotic and Hydric Stress Then, we explored the expression pattern of genes encoding putative hydrophilins from transcriptome data sets generated after exposing germinating conidia of A. brassicicola to 1. 2 M sorbitol and to low relative humidity (silica gel beads). These data sets were obtained using microarrays designed based on the genome of the A. brassicicola ATCC 96836 strain available on the JGI Genome Portal (see footnote 1). This genomic sequence contained 10,514 predicted genes and the proportion of gaps between scaffolding boards in this sequence was relatively high (estimated to 5. 2% by  Dang et al. , 2015). As described above, the in silico analysis was performed from the recently published genome of A. brassicicola strain Abra43 (Belmas et al.
Two mutant strains ( abnik1 and abhog1) were described in previous studies  (Dongo et al. , 2009; Joubert et al. , 2011)  and were deficient for genes encoding key regulators of the HOG pathway, the HK AbNik1 and the MAP kinase AbHog1, respectively. The third mutant was obtained in this study and was deficient for the kinase AbSch9, which is also known to be involved in the fungal response to osmotic constraints  (Pascual-Ahuir and Proft, 2007). Under non-stress conditions, no significant differences in mycelium growth were detected in any of the tested mutants as compared to the wild-type (data not shown). To assess the impact of low RH exposure on A. brassicicola, viability tests were conducted on young hyphae of wild-type and mutant strains. About 200 conidia were first incubated for 24 h on cellophane disks overlaid on solid PDA medium.
The Abra_v1. 0 chip was in situ synthesized by Nimblegen (Madison, WI) and contained 10,633 60-mer oligoprobes that were designed from the A. brassicicola automatically annotated genome database 4 , containing one probe per sequence. Slides were washed, dried, and scanned at 2 µm resolution and high sensitivity with a Roche-NimbleGen MS200. DEVA Software version 1. 2. 1 was used to extract pair data files from the scanned images. Three biological replicates were analyzed per comparison using the dye-switch method. Statistical analyses of the gene expression data were performed using the R language, version 2. 14, and the Linear Models for Microarray Analysis package  (Smyth et al. , 2005)  from the Bioconductor project. For the preprocessing step, data were normalized by the LOWESS method.
There are multiples lines of evidence  [18, 19]  showing that solar variability has been a key forcing in the history of the Earth's climate. Correlations between solar proxies and atmospheric/climate indicators have been established in present day datasets and in sedimentary and ice core archives. However, most of the apparent correlations and associated solar signals tend to be very variable and intermittent. Some are also very difficult to reproduce in climate models. Establishing a quantitative forcing-response relationship for the Sun-Earth link is problematic without a clear understanding of the key mechanisms engaged in the action of solar variability on the atmosphere and climate, notably at regional scales. There is no general consensus on those mechanisms. The overall response of the atmosphere and surface climate to solar variability involves a wide range of coupled chemical, dynamical, and radiative processes and the interactions between different atmospheric layers and between the atmosphere and the ocean.
Materials and Methods UVSQ-SAT is a LATMOS mission based on the deployment of one CubeSat and with the goal to monitor essential climate variables. The UVSQ-SAT mission contains a space segment and a ground segment that uses at least one UHF/VHF antenna located at Observatoire de Versailles Saint-Quentin-en-Yvelines (France). The ground segment includes all the activities from CubeSat monitoring/control to data product generation and distribution. LATMOS is a prime contractor of the 1U CubeSat with the support of a manufacturer (Innovative Solutions In Space (ISIS)) to build a dedicated satellite platform. Although the CubeSat is small, it contains all the critical subsystems and functions present in larger satellites. A configuration of the UVSQ-SAT nano-satellite is shown in Figure  2 , and an overview of the UVSQ-SAT CubeSat properties is given in Table  2.
6704 ×10 -8 Wm -2 K -4 ), and Qr i is the absorbed residual power (Moon, planets, etc. ). This yields solving a set of N differential non-linear equations to obtain OLR λ EI(λ)dλ and OSR λ a SI(λ)dλ data without having knowledge of the satellite's attitude. Then, for ERS data processing, the first step will consist of developing deep learning methods to estimate the attitude of the UVSQ-SAT CubeSat using all housekeeping data (platform and payload inertial measurement units, three axis payload compass, coarse platform photodiodes for solar orientation, power on each solar panel, etc. ). Indeed, UVSQ-SAT does not have an active Attitude Determination and Control System (ADCS), which allows precise pointing of the CubeSat. This is one of the reasons why the UVSQ-SAT CubeSat is equipped with broadband ERS sensors (with a large field of view) on each side.
This aspect should prevent them from degradation due to contamination  [33]. They are designed to measure the UV solar spectral irradiance from a 180 • field of view angle with an active area of 1 mm ×1 mm and a responsivity of ∼ 4 A/W. Figure  4  shows the DEVINS photodiodes' manufacturing process. The DEVINS technology will be validated in the framework of the UVSQ-SAT mission (check in orbit that the detector degradation is weak despite the exposure to radiation (both ionization and displacement-damage effects)). UVSQ-SAT will use a total of four DEVINS photodiodes. For DEVINS data processing, we will use the six coarse platform photodiodes located on each solar panel for determining the Sun's direction. From the knowledge of the Sun's direction (θ), we will determine the DEVINS solar spectral irradiance (SSI(λ)) from the following instrumental equations based on our experience and history in solar instrumentation developments and observations  [42] [43] [44].
Based on the Two Line Elements (TLE), the SatPC32 program allows automatic tracking of the satellite and compensation of the Doppler shift in the radio signal. On the RF transmit line, modulation and AX. 25 encapsulation were performed by hardware (i. e. , TS2000), whereas de-modulation and AX. 25 de-encapsulation were done by software on the RF receiver line (i. e. , RTL-SDR). A custom application was employed to write and read the Consultative Committee for Space Data Systems packet utilization standard (CCSDS PUS) protocol, as well as provide a Graphical User Interface (GUI) to the ground station operator. Since the UVSQ-SAT CubeSat will use satellite amateur radio frequency bands (i. e. , VHF 145. 830 MHz and UHF 437. 020 MHz), an international frequency coordination is under preparation for being submitted to the International Amateur Radio Union.
It is important for continuous monitoring of the ERB (as for CERES, EarthCARE, Megha-Tropiques, and Scanner for Radiation Budget (ScaRaB)) and temporal overlap to realize inter-calibrations. It lacks also a high frequency and sampling in measurements and consequently a more accurate determination of the Earth's global energy imbalance along with the diurnal and multi-directional sampling needed to capture spatiotemporal scales. A constellation consisting of small CubeSat's could provide temporal and spatial coverage that are lacking currently to provide accurate EEI measurements and represent important developments that are needed. The UVSQ-SAT mission will contribute to these future developments both in terms of technology advancement (miniaturized sensors used for high scientific relevance) and innovative data processing (reconstruction of a global map of OLR and OSR using multiple sensors' data).
Then, we obtained Earth's net radiation associated with the sensors' observations. Finally, we performed an interpolation (Delaunay triangulation) on the scattered dataset that resided in 2D space to obtain Earth's net radiation based on UVSQ-SAT observations for a given time period (Figure  6 , left middle and bottom panels). 4. We plotted the differences (Figure  6 , right middle and bottom panels) between the "real" map (Figure  6 , left upper panel) and the map obtained with the satellite observations for a given period (Figure  6 , left middle and bottom panels). For a one-day observation period, the UVSQ-SAT expected performances were degraded (Figure  6 , right middle panel). Earth's net radiation errors with only data processing could be greater than ±30 Wm -2 over large geographical areas.
Indeed, with an LEO orbit (600 km, 98 • ), the satellite made just over 14 orbits in a day, and every point on the Earth was covered at least twice. It was clear that it was insufficient with only one satellite in orbit to cover the entire surface of the Earth (spatial coverage). It took at least 15 days to have a good spatial coverage and to offer a satisfactory Earth net radiation map (Earth's net radiation errors could be greater than ±10 Wm -2 over very small geographical areas), as shown in Figure  6  (right bottom panel). latitude-longitude geographic grid that will be obtained with UVSQ-SAT. The source of data for the left upper panel was from CERES observations. In this case, the result was an average of 15 days. Consequently, we lost the information related to short temporal variations (albedo, clouds, etc.
Conclusions UVSQ-SAT is a scientific and technology demonstration satellite in the domain of Earth observation and solar physics, scheduled to be launched in 2020/2021. The main scientific goals are to measure the top of atmosphere outgoing longwave radiation and shortwave radiation and the solar spectral irradiance in the Herzberg continuum (200-242 nm) with good accuracy. UVSQ-SAT uses disruptive technologies for remote sensing and will improve the miniaturization and compactness of small sensors onboard small satellites. The UVSQ-SAT tools are used in our education program to train students in space technology, Earth observations, and astronomy and astrophysics. UVSQ-SAT represents a CubeSat of the INSPIRE series of satellite missions. The INSPIRE program is a multinational consortium of universities collaborating to develop a constellation of small satellites for cutting edge space and Earth science research, a supporting global ground station network, as well as research and educational programs covering spacecraft design, space systems engineering, operations, and data analysis.
Currently, the UVSQ-SAT mission is not intended to provide a continuity of the essential climate variables' data records. However, the use of small compact space qualified sensors like those used in UVSQ-SAT can facilitate future innovative space programs onboard small satellites and the implementation of a future small satellite constellation dedicated to the measurements of the essential climate variables with full data traceability. The advantages of such a future constellation have been described in this manuscript and allow considering exceptional revisit time and spatial resolution, in particular for the Earth's energy imbalance monitoring. Indeed, a constellation of 50 satellites is needed to estimate global daily mean top of atmosphere outgoing longwave radiation and shortwave radiation.
Acknowledgments: The  UVSQ-SAT  team acknowledges support from the  Université de Versailles Saint-Quentin-en-Yvelines (UVSQ, France) , the  Sorbonne Université (SU, France) , the  Université Paris-Saclay (France) , the  Centre National de la Recherche Scientifique (CNRS, France) , the  Centre National d'Études Spatiales (CNES, France) , the  Laboratory for Atmospheric and Space Physics (LASP, USA) , the  National Central University (NCU, Taiwan) , the  Nanyang Technological University  ( NTU, Singapore ), and  French Small and Middle Enterprises  ( Carta-Rouxel, Nanovation, ACRI-ST ). The  UVSQ-SAT team gratefully acknowledges David Orlikowski (Director of the Centre d'investigation clinique et d'innovation technologique  ( CIC 1429 , France)) and  Frederic Barbot  (Coordinator of the CIC 1429) for their support in the development of the TW sensor.
Growth Inhibition Zone Experiments For growth inhibition zone determination, an overnight culture of bacteria is diluted in PBS at a concentration of 3 × 10 7 CFUs mL -1 and 10 µL of the diluted bacterial suspension is then spread evenly on the surface of 7 mm MHA (Mueller Hinton Agar) plate. The plates are exposed (in duplicate) to the plasma for 30, 60, 120 and 240 s at distances of 10 and 15 mm from the reactor orifice. After exposure to the plasma, the plates are incubated at 37 • C for 24 h in a static incubator and the diameter of the bacterial growth inhibition zone on the agar surface is determined  [75]. Since the inhibition zone circumference is irregular, the inhibition zone diameter is approximated by applying simple image processing techniques, i. e. , zone edge detection with a Sobel-Feldman operator and optimum circular fit, using Matlab TM.
Patients This study is an ancillary study of the randomised controlled Rotation or Change (ROC) trial. 28 The ROC trial was a 52-week, multicentre, open, parallel-group trial that was conducted from December 2009 to August 2012. The trial included patients with RA fulfilling the 1987 American College of Rheumatology criteria who were at least 18 years old and had an inadequate response to a first-line TNFi therapy. The trial participants were randomly assigned in a 1:1 ratio to receive either a second-line TNFi (etanercept, adalimumab, certolizumab or infliximab) or a non-TNF-targeted biological agent (RTX, TCZ or ABA); these biologics were administered at their licensed dosages ( ClinicalTrials. gov Identifier: NCT01000441). Patients had to present erosions or a Disease Activity Score in 28 joints using an erythrocyte sedimentation rate ≥3.
Statistical analyses All statistical analyses involved the use of the SAS release 9. 4 (SAS Institute, Cary, North Carolina, USA) statistical software package. Type I error was set at α=0. 05. Continuous data are shown as the median and IQR. The comparison of population characteristics between the second-line TNFi and the other biological groups involved the χ 2 test, Student's t-test or Wilcoxon test. We compared the change of each marker between the different treatment groups using an analysis of covariance (ANCOVA) model adjusted for baseline-investigated laboratory parameters and the treatment group. The comparisons of the changes in each marker between inclusion and week 24 were analysed with the non-parametric Wilcoxon rank-sum test or Student's t-test, and the comparisons of changes in each marker according to the EULAR response were analysed with an ANCOVA model.
Patient characteristics Of the 300 patients included in the ROC trial at baseline, 203 patients were investigated in this ancillary study. Of these patients, the 96 patients (47. 3%) in the second-line TNFi group included 41 receiving etanercept (42. 7%), 40 receiving adalimumab (41. 7%), 12 receiving certolizumab (12. 5%) and 3 receiving infliximab (3. 1%). Additionally, 107 (52. 7%) patients received other biological non-TNF-targeted agents, including 47 receiving TCZ (43. 9%), 34 receiving RTX (31. 8%) and 26 receiving ABA (24. 3%). The baseline characteristics were similar between the two treatment groups (table  1 ) and between the patients participating and not participating in our study (data not shown). In patients receiving corticosteroid treatment, the daily dose at baseline was similar between second TNFi and other biologic (median dose in milligram (IQR): 6 (5-8) vs 6.