dataesr/scientific-paragraphs-categorization huggingface dataset:
The total electron content (TEC) is one of the most useful parameters to evaluate the behavior of the Martian ionosphere because it contains information on the total amount of free electrons, the main component of the Martian ionospheric plasma. The Mars Express Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) radar is able to derive TEC from both of its operation modes: (1) the active ionospheric sounding (AIS) mode and (2) the subsurface mode. TEC estimates from the subsurface sounding mode can be computed from the same raw data independently using different algorithms, which should yield similar results. Significant differences on the dayside, however, have been found from two of the algorithms. Moreover, both algorithms seem also to disagree with the TEC results from the AIS mode.
This paper gives a critical, quantitative, and independent assessment of these discrepancies and indicates the possible uncertainty of these databases. In addition, a comparison between the results given by the empirical model of the Martian ionosphere developed by  Sánchez-Cano et al. (2013)  and the different data sets has been performed. The main result is that for solar zenith angles higher than 75°, where the maximum plasma frequency is typically small compared with the radar frequencies, the two subsurface algorithms can be confidently used. For solar zenith angles less than 75°, where the maximum plasma frequency is very close to the radar frequencies, both algorithms suffer limitations. Nevertheless, despite the solar zenith angle restrictions, the dayside TEC of one of the two algorithms is consistent with the modeled TEC.
, 2008]. This method estimates H, n o , and N e , taking advantage of the Taylor expansion of equation (  4 ), by doing an optimization of the SNR of the surface echo. Also, this method uses an external data set (surface range from the altimeter Mars Orbiter Laser Altimeter (MOLA) on board Mars Global Surveyor) to constrain the ionospheric correction. At the end of the process, the outputs are not dependent on any model. TEC estimates thus obtained are available at the Planetary Science Archive (PSA) of the European Space Agency (http://www. rssd. esa. int/index. php?project=PSA). On the other hand, the algorithm of Cartacci et al. is based on the so-called contrast method  [Picardi and Sorge, 2000] , which was developed to correct phase distortion, and it is implemented in MARSIS onboard software, as well as in the ground processing software  [Picardi et al.
MARSIS TEC Discrepancy: Description of the Problem The two TEC retrieval methods based on subsurface data should yield similar results, at least within the uncertainties of each method, since they use the same data set. However, significant differences in the results have been observed. Attempts to explain such differences using numerical simulations or TEC estimates based on other measurement techniques, such as radio occultation, have been so far unsuccessful. Additionally, TEC from the numerical integration of an empirical model for the Mars dayside ionosphere has been included  [Sánchez-Cano et al. , 2013]. This model is based on AIS data and radio occultation data from Mars Global Surveyor mission. The model was run for the entire ionosphere (in cyan dots) and for the topside ionosphere only (in green triangles).
Statistical Analysis In order to advance further the understanding of the variability in the MARSIS data sets, a statistical analysis has been carried out. Nineteen Mars Express orbits with MARSIS data acquired in both operational modes were selected, five of them belonging to a special interleaved mode campaign, which the Mars Express project runs specifically for this purpose. The list of orbits is available in Table  1. ). On the dayside, the discrepancy can be up to 1 TECu at solar zenith angle of 60°, which is the same order of magnitude as the TEC value itself. Journal of Geophysical Research: Space Physics 10. 1002/2014JA020630.
Subsurface Mode MARSIS Data Versus AIS Mode MARSIS Data Here we investigate the TEC from the two algorithms using the subsurface mode with TEC estimates from the AIS mode. For a given Mars Express orbit, the heliocentric distance, solar longitude, and solar activity values are the same; the latitude and solar zenith angle are the only parameters that vary. For the 19 selected orbits (Table  1 ), ionospheric and subsurface measurements are consecutive. This means that they were never acquired at the same time. This characteristic, although useful to visually examine the consecutive data, can be a weakness in the statistical comparison. Therefore, to compare data acquired in subsurface and ionospheric modes at the same position, it has been decided to compute the best fit to the subsurface data.
Model Comparison A possible explanation for the discrepancy between the two subsurface data sets is the presence of secondary peaks below the main peaks. Since there are no coordinated observations that allow the estimation of these peaks, the use of a model is necessary. The subsurface data sets are compared with the NeMars empirical model  [Sánchez-Cano et al. , 2013]. NeMars models the dayside Martian ionosphere assuming that it is made of two layers. The upper layer is modeled based on the data from the Active Ionospheric Sounding mode of MARSIS instrument (and therefore is in excellent agreement with the AIS data by design) and the secondary one based on radio science data from the Mars Global Surveyor mission. In Figure  7 , three examples of NeMars electron density profiles have been plotted with their corresponding MARSIS AIS electron density profile.
The final output is the electron density profile, which can be easily numerically integrated to obtain TEC. Since the main layer model is based on AIS data, the model was not compared with this data set because by design, a good agreement is expected. The model has been only compared with  Mouginot et al. and Cartacci et al. TEC  estimates to analyze if the contribution of the bottomside is the reason for the main difference between both subsurface algorithms. TEC has been calculated with the NeMars model using the same values for solar zenith angle, solar activity, and heliocentric distance (inputs of the model) for the selected orbits. An example is shown in Figure  8 , where the TEC values are displayed together with the modeled TEC. The discrepancies have been quantified with another statistical study of the differences between both modeled TEC data and subsurface data (Figure  9 ).
EChO performance requirements EChO's top-level requirement is that the photometric stability over the frequency band of interest shall not add significantly to the photometric noise from the astrophysical scene (star, planet and zodiacal light). The frequency band over which the requirement applies is between 2. 8×10 -5 Hz and 3. 7 mHz, or ~5 minutes to 10 hours  (Puig et al. , 2014; Eccleston et al. , 2104; Pascale et al. , 2014; Waldmann and Pascale, 2014). This implies having the capability to remove any residual systematics and to co-add the elementary observations from many repeat visits to a given target. The photometric stability budget is described by  Puig et al. (2014) ,  Pascale et al. (2014) , and  Waldmann and Pascale (2014). To achieve the required performance, particular attention is required to: • the design of the instrument • the calibration strategy to characterise all possible systematic variations in performance • the data processing pipeline(s).
The data processing It is crucial to correct the raw observed signal time series to account for variations in the signal which are not directly linked to the planetary transit or occultation. The methods for doing this will be encapsulated in the data processing algorithms to be employed in the data pipeline; the final data quality and performance of EChO are highly dependent on the performance of these algorithms. There may be many systematic variations to account for, most of which will be negligible, but we highlight two areas requiring particular attention: • The astrophysical scene contributions: the stellar variability, the local zodiacal cloud contribution, the exozodiacal cloud contribution and any contaminating stars. These are independent of the instrument performance but may add systematic signals resembling the transiting planet.
1996; Jenkins et al. 2010) , the instrument response functions are well characterised in advance and conceived to reach the required 10 -4 to10 -5 photometric precision. EChO aims at reaching the same level of photometric precision. For general purpose instruments, not calibrated to reach this required precision, poorly sampled OSVs or a missing parameterisation of the instrument often become critical issues. Even if the parameterisation is sufficient, it is often difficult to determine which combination of these OSVs may best capture the systematic effects of the instrument. This approach has caused some debates for current instruments regarding the use of different parametric choices for the removal of systematic errors. (Grillmair et al. , 2008). Right: NIR and MIR observations compared to synthetic spectra for three models that illustrate the range of temperature/composition possibilities consistent with the data  (Swain et al.
Correcting for stellar activity The impact of stellar activity on the EChO data has been carefully evaluated by many teams working on EChO. Results from the Kepler mission  (Basri et al. 2013)  indicate that most G dwarfs have photometric dispersions less than 50 ppm over a period of 6 hours, while most late-K and M dwarfs vary at a level of some 500 ppm. Note that Kepler operates in the visible where stellar photometric variability is over a factor of 2 higher than in the "sweet spot" of EChO -the NIR and MIR -because of the contrast between spots and the stellar photosphere. The effects of stellar activity on EChO's observations will vary for transit and eclipse observations. Alterations in the spot distribution across the stellar surface can modify the transit depth (because of the changing ratio of photosphere and spotted areas on the face of the star) when multiple transit observations are combined, potentially giving rise to spurious planetary radius variations.
The situation is simpler for occultations, where the planetary emission follows directly from the depth measurement. In this case, only activity-induced variations on the timescale of the duration of the occultation need to be corrected for to ensure that the proper stellar flux baseline is used. The EChO mission has been designed to be selfsufficient in its ability to correct for the effects of stellar activity. This is possible thanks to the instantaneous, broad-wavelength coverage and the strong chromatic dependence of light modulations caused by stellar photospheric inhomogeneities (star-spots and faculae). We have explored several possible approaches to evaluate the effect of stellar activity and developed methodologies to prove the performance of EChO data in reaching the required precision  (Herrero et al.
, 2014; Micela, 2014; Danielski et al. , 2015; Scandariato et al. , 2014). Method 1 -We have investigated a direct method of correlating activity-induced variations in the visible with those in the IR. The underlying hypothesis is that variations of the transit depth in the visible are solely caused by stellar activity effects and not influenced by the atmosphere of the transiting planet. To test this approach, a realistic stellar simulator has been developed that produces time series data with the same properties as the measurements from EChO. The simulator considers surface inhomogeneities in the form of (dark) starspots and (bright) faculae, takes into account limb darkening (or brightening in the case of faculae), and includes time-variable effects such as differential rotation and active region evolution.
We have generated series of transits at wavelengths 0. 8, 2. 5, and 5. 0 µm. Then, we have measured the transit depths and calculated the variations of those depths with time. We have found that there is a well-defined correlation between activity-induced transit depth variations in the visible (0. 8 µm) and the IR (2. 5 and 5. 0 µm). An illustration of the transit light curves generated by the simulator and the correlation between visible and IR transit depth variations (TDV) can be seen in Figure  21  (left & middle). In practice, the correction of EChO data for stellar activity using, for example, a series of measurements in the visible and an IR band can be done using the following expression:  !" !"## =  !" +  ! +  ! •  !"# - !"# , where  stands for the transit depth, and  ! and  ! are the coefficients of a linear fit that can be determined from simulations.
, 2015). Figure  22 : Left: Kepler time series of an active M0 star (blue dots). Using Independent Component Analysis, the periodic pulsation filter at t=202, 218 and 235 was filtered from other correlated noise in the time series. The filtered signal is shown in red  (Waldmann, 2012). Centre and Right: Kepler time series of another active K4 star. Using a Gaussian Process based method the stellar activity was successfully filtered out, with residuals as small as 10 -5 when considering daily observations of 10 hours (Centre) and 10 -4 when data are acquired for 10 hours every 5 days (Right)  (Danielski et al. , 2015).
Overall noise allocation Using the EChOSim tool, we can evaluate the performance by calculating the overall noise allocation and comparing this to the scientific requirements. The procedure is extensively described in  (Pascale et al. , 2014 ) and here we only summarise the main results. • Noise associated to the astrophysical scene: The number of detected photons from the planet and star, N 0 , and the zodiacal background photons in a sampling interval Δt, Zodi, are used to estimate the level of photon noise from the astrophysical scene: (1) It is convenient to refer the noise in one sampling interval to the noise per unit time: (2) • Noise associated with the instrument: All sources of instrumental noise contribute to the total system noise level, σ SN. The system noise level is then given by the sum in quadrature of all individual noise components: (3) σ RO is the detector readout noise, σ DC is the dark current noise, σ Tel is the combined photon noise associated to the thermal emission of all optical surfaces in the line of sight, σ Opt is the photon noise associated to the thermal emission of the module enclosure, and σ RPE+PDE expresses the photometric noise associated to the pointing jitter.
The transit spectrum exhibits various spectral features associated not only with water vapour but numerous other molecules. Similar simulations have been done for the eclipse spectra, which provide the planet to star contrast as a function of wavelength (Figure  23 ) Figure  23 : Left: Transit depth as a function of wavelength for GJ3470b as observed with EChO. We assumed the co-addition of 21 transits and the current design of the instrument and data processing pipeline. Right: Planet to star contrast as a function of wavelength assuming the co-addition of 170 eclipses. Using both transit and eclipse spectra together, one can determine the mean molecular weight of the atmosphere, the atmospheric components, the temperature as a function of pressure, the presence and type of clouds. These calculations have been repeated for all the targets observable by EChO (see next section).
The optimization of the observation program The ability to fulfil the scientific program strongly depends on the optimization of the observation program. Because the planetary transits and occultations happen at specific epochs (given by ephemerides), the observation program, the data transfer sequences and the on-board calibration phases have to be well-defined and are time critical. The final performance evaluation of EChO also needs to take into account the way the observation and calibration/data transfer phases are optimized. We have simulated an observing programme with an assumed target reference sample using scheduling simulation tools  (Garcia-Piquer et al. , 2014; Morales et al. , 2014). These tools aim to check the feasibility and efficiency of the observation program. They include optimisation routines that allow the scheduling assuming knowledge of the visibility of the objects, the transit/occultation ephemerides, the expected spacecraft performance and some assumed calibration and data transfer phases.
The leitmotif of home movies is thus defined by realworld (and not reconstructed) scenarios in which people are captured in different settings: the houses in which they live, the roads on which they drive, the public places they visit. Is this amateur heritage available? Amateur film has been inaccessible as long as it remained confined to the spaces of its social use: family, private clubs, associations, etc. Today this immense patrimony could become a significant archival source, but a proper cataloguing of it is needed to supply historians and filmmakers with the necessary metadata for analysis and use. Fortunately, in the past twenty years there has been increasing awareness of this heritage, which has been raised by practices of collecting, preserving and re-using as well as by scholars' interest in it.
And, as an applied concept, it may have far-reaching implications for planning and urban regeneration purposes as well as for heritage and conservation. 27  conclude, the digital environment that enables the creation of online databases can increase the accessibility of amateur film heritage. The historical, cultural and social implications of this online working environment are yet to be fully understood; however, this allows exploring the potential of amateur footage for discovering and narrating the history in a myriad of ways, including by recreating the eyewitness experience on the past.
Immunohistochemistry and scoring TMAs were cut at 4 μm thickness onto Superfrost + glass slides and were processed using the Ventana Discovery XT, and the Ventana Benchmark XT and Benchmark Ultra automated systems (Ventana Medical Systems, Tucson, AZ, USA). Immunohistochemical staining was performed with antibodies to SMARCA4 (1:25, clone EPNCIR111A, ab110641; Abcam, Toronto, Ontario, Canada), SMARCA2 (1:50, clone HPA029981; Sigma, St Louis, MO, USA), and SMARCB1/BAF47/ INI1 (1:50, 25/BAF47, 612110; BD Biosciences, Mississauga, Ontario, Canada). All TMAs were scored twice by a pathologist (ANK). For SMARCA4, tumours were scored as positive if any tumour cell nuclei showed staining; tumours scored as positive usually showed diffuse, moderate to strong staining. Tumours were scored as negative if tumour cell nuclei showed no staining only if adequate staining was detected in internal positive control cells (endothelium, lymphocytes or fibroblasts); absence of staining has previously been shown to correlate with the presence of inactivating mutations in SMARCA4  [2] [3] [4] [5] 15].
The Photoperiod Sensitivity index (PS) was calculated according to the formula: [(heading date under LD -heading date under SD) / heading date under LD]. For the 247 varieties listed in Supplementary Table  S1 , the phenotypic data of the CT95-37 RESGEN project 'Constitution, description and dynamic management of rice genetic resources of European vocation' supported by the European Community, DG VI, were used  (Feyt et al. , 2001). The data were collected from experiments conducted between 1996 and 1998 in Arles (France), Alcala del Rio, in Andalusia (Spain), Thessaloniki (Greece) and Salvaterra de Magos in Estremadura (Portugal). Plants were grown under similar conditions in irrigated fields. The tested cultivars were arbitrarily divided into three groups in order to spread the work load over three years as described in  Feyt et al.
(2001). The experimental design was an augmented design with a common set of seven control varieties present all years in all trials. The control varieties (Ariete, Baldo, Cigalon, Loto, Koral, Senia and Thaibonnet) represented a broad range of flowering date variation from early to late. Dates of 50% flowering were recorded for all accessions and time to 50% flowering computed based on the trial sowing dates. Data were adjusted for the year effect. The Italy_2012 and Italy_2013 datasets were collected at the northernmost European location (Vercelli, Italy) in 2012 and 2013, respectively. The final data used in association mapping analyses are presented in Supplementary Table  S1.
Candidate gene genotyping Seedlings were grown in a greenhouse for two weeks. The aerial part was collected and grinded using steel balls in 2 ml tubes after freezing with liquid nitrogen. Genomic DNA from all varieties was extracted using the Edwards method  (Edwards et al. , 1991). A total of 17 different non-functional alleles in five different genes were genotyped using markers listed in Supplementary Table  S2 : six for hd1, three for prr37, three for ghd7, three for ghd8 and two for el1 (Supplementary Table  S1 ). Primers for dCAPS design were obtained using dCAPS Finder 2. 0 (http://helix. wustl. edu/dcaps/dcaps. html) allowing up to two mismatches. PCRs were conducted as described before, and digestions with restriction enzymes were carried out at 37°C overnight. The PCR products were separated by agarose gel electrophoresis, and visualized using a Fluorchem imaging system (Alpha Innotech) with Alpha Ease FC software.
Quantification of gene expression and correlation between gene expression and heading dates To quantify gene expression from developmental time courses, leaves were sampled at the indicated day, two hours after lights-on (ZT2). For diurnal time courses, leaves were sampled every three hours from three-week-old plants grown under under SD and six-week-old plants grown under LD. Leaf samples from plants grown under field conditions were collected during floral induction (from the middle of May until the end of July) every two weeks, two hours after dawn. Gene expression data from field-grown plants were collected in 2012 and 2013 with similar results. Data from 2012 are presented. Leaves were collected from at least three independent plants for each RNA preparation. Total RNA was isolated using either TRI reagent (Sigma-Aldrich) or the Spectrum Plant Total RNA Kit (Sigma-Aldrich) following manufacturer's instructions.
Association mapping All accessions had been previously genotyped with SNPs spaced of at least 0. 4 Mb in the genome  (Courtois et al. , 2012). A set of 85 SNPs polymorphic in the studied accession set, hereafter called 'neutral SNP set', was retained for further analysis. All analyses were conducted using Tassel v. 4. 0  (Bradbury et al. , 2007). A Principal Component Analysis was conducted on the neutral SNP set using a correlation matrix. Three axes together explaining 40% of the variation were retained. The matrix constituted of the loadings of the accessions on these three axes was used to control population structure. A kinship matrix was computed using the same dataset to capture more complex familial relationships. Several models were compared to test the significance of each SNP × trait association: a simple linear model without any control of population structure (GLM0), a linear model with control of structure (GLM1), a mixed model with control of kinship (MLM1) and a mixed model involving structure and kinship (MLM2).