dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, the linear correlation coefficient was r=0. 15 (P=. 07) when the full set of observations was taken into account. However, the number of the connection times were clearly too long (more than 3 hours) or too short (a few minutes) and therefore could not be considered as objective measures of the amount of work. We, therefore, limited our correlation analysis to reasonable connection times of between 30 min and 90 min, but the correlation was again not statistically significant (r=-0. 173; P=. 40). The same result was obtained when considering the in class mark with the whole dataset (r=0. 123; P=. 15) and with reasonable page connection times alone (r=-0. 070; P=. 66).
The Students' Opinion of the Course A total of 4 questions addressed the module's structure (Figure  2 ). Furthermore, 60% of the students considered that they had been well informed about the course's learning objectives and that the proportion of independent learning required had been accurately specified. The students generally considered that the relative proportions of online work and in-class tutorials had been explained and that the evaluation procedures had been well described. A total of 21% of the students stated that they had not consulted any of the learning resources on Moodle, and 26% of the students stated that they had consulted all the resources. In addition to the online teaching, 5 in-class tutorial sessions had been based around simulations of professional situations, so that the students could apply the theoretical knowledge acquired online to practical situations in a context managed by the lecturer.
Using this technique, a database of more than 309 000 temperature profiles has been created from GOMOS measurements. A global climatology was constructed using the new GO-MOS database and is compared to an external model. In the upper stratosphere, the external model is based on the ECMWF reanalysis and the agreement with GOMOS is better than 2 K. In the mesosphere the external model follows the MSIS climatology and 5 to 10 K differences are observed with respect to the GOMOS temperature profiles. Comparisons to night-time collocated Rayleigh lidar profiles above the south of France show some vertical structured temper-ature differences, which may be partially explained by the contributions of the thermal diurnal tide. The equatorial temperature series shows clear examples of mesospheric inversion layers in the temperature profiles.
Limb viewing satellite sounders, such as MLS (Microwave Limb Sounder on the Aura satellite) and SABER (Sounding of the Atmosphere using Broadband Emission Radiometry on the TIMED mission), provide temperature profiles up to the upper mesosphere with good vertical resolution. However these datasets are not assimilated into the NWP models because MLS and SABER are not operational meteorological satellites. The scattering of sunlight (near the UV and visible wavelengths) by the Earth's atmosphere above the top of the stratospheric layer (30-35 km) is solely due to Rayleigh scattering by atmospheric molecules. The elastic scattering intensity is directly proportional to the atmospheric density. It is thus possible to retrieve an absolute temperature profile using the hydrostatic equation and the perfect gas law.
Data processing For this study we used the full level 1 GOMOS database between June 2002 and April 2012, which comprises more than 418 000 bright limb occultations. A screening is conducted to select the measurements used for the generation of the temperature profiles: -Occultations with a solar zenith angle larger than 84 • are eliminated to avoid spectra with too much absorption between the sun and the limb. -Profiles which do not cover the altitude range between 35 and 125 km are not considered. The lower limit is set to retrieve a temperature profile covering the full altitude range 35-85 km and the upper limit is set to have enough data at the top of the profile to correctly estimate the measurement noise. -Occultations with the presence of polar mesospheric clouds (PMCs) are also removed.
Conclusions A database of more than 309 000 temperature profiles from 35 to 85 km, covering the period June 2002 to April 2012, has been created within the framework of the ESA-funded MesosphEO project using the daytime Rayleigh scattering at limb observed by GOMOS. Comparisons of the GOMOS temperature profiles with night-time Rayleigh lidar temperature profiles measured at OHP show some differences, which possess a vertical structure that may be partially explained by the contribution of the thermal diurnal tides. The GOMOS data set was used to build a temperature climatology as a function of latitude and month. Subsequent comparison with the GOMOS external model has yielded an agreement, which is better than 2 K in the upper stratosphere (below 48 km (1 hPa) where the model is driven by ECMWF), and between 5 and 10 K in the mesosphere (from 50 to 80 km where the model follows NRLMSISE-00 climatology).
Data analysis The sequencing data were first analyzed with the FastQC tool to determine the quality of the sequences  [12]. Then, to identify specific HPV genotype sequences present in each cervical sample, the Roche GS Reference Mapper v3. 0 software was used, taking as references all the L1 sequences reported in the Papillomavirus Episteme (PaVE) database  [13]. The parameters considered for mapping were the following: trimming of adapters and primers, 90% of minimum overlap identity, Phred score ≥ 20, minimum read length of 100 bp and exclusion of all the repeated reads. All HPV raw sequence reads were uploaded in the NCBI Sequence Read Archive (accession number SRP130362). Among the nucleotide changes observed in the L1 gene from HPVs 83 (AF151983), 84 (AF293960), 86 (AF349909), 87 (AJ400628), 102 (DQ080083) and 114 (GQ244463), we described those that were present in all the reads from a specific HPV with a minimum depth of 20 reads or those found in two or more distinct samples (independently of the reads number).
Finally, a phylogenetic tree was constructed with L1 consensus sequences from HPV83, 84, 86, 87, 102 and 114 from each sample, including the L1 reference sequences from all alpha3-papillomaviruses. Evolutionary analyses were conducted in MEGA 7 software by using the muscle algorithm for the alignment of sequences and the evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura-Nei model  [14, 15]. All positions with less than 95% site coverage were eliminated and the tree with the highest log likelihood was chosen.
1%), 5 were HPV102+ (35. 7%) and 1 was positive to both genotypes (Table  2 ). L1 gene mutations from HPV83, 84, 87, 102 and 114 After comparing the sequences obtained by NGS with the L1 reference genes (downloaded from PaVE database), nucleotide variations in L1 fragment from HPV genotypes 83, 84, 87, 102 and 114 were determined. Concerning HPV83-L1, only sample S27 contained an L1 gene fragment identical to the reference, and rest of the samples showed diverse synonymous mutations. The most common nucleotide changes were c. 1200A>G (in 6 samples), c. 1191A>G (in 5 samples), c. 1227G>C (in 5 samples), and c. 1008A>G (in 4 samples) (Table  3 ). Regarding HPV84-L1, seventeen out of eighteen samples showed one or more nucleotide changes, being the non-synonymous mutation c. 1323C>A (p. D441E) the most prevalent one (in 16 samples).
Terai and R. D. Burk (accession number AF293960) from a cervicovaginal sample obtained from a 21-year-old Caucasian female with a normal Pap smear  [21]. It is considered as a LR-HPV and is among the most prevalent LR-HPVs found worldwide in cervical samples  [22] [23] [24] [25] [26] [27] [28] [29] [30]. Both HPV83 and HPV84 belong to the Alphapapillomavirus genus, species 3 group (alpha-3) together with HPV 61, 62, 72, 81, 86, 87, 89, 102 and 114. From these genotypes, only 102 and 114 were characterized after LA test was launched  [31, 32] , and are obviously not included in the test, neither do HPV86 and HPV87. In the present study, non-specific hybridization of four alpha-3 HPVs in the LA test, the golden test used to detect HPVs in a patient's sample, is being reported. Indeed, HPV102-L1 cross-reacts with the probe that Table  3  Nucleotide changes identified in HPV83-L1 when compared to the reference sequence (AF151983) in nine HPV83+ samples Sample number c.
Acknowledgements We thank  R. H. Friend  for making this work possible and  A. Rao  for useful discussions. F. A. Y. N. S. , D. H. P. T. , N. D. M. H. and A. W. C. gratefully acknowledge the support of the  Winton Programme for the Physics of Sustainability  and the  Engineering and Physical Sciences Research Council (EPSRC). A. J. M. acknowledges support from  EPSRC  grant  EP/ M025330/1.
, 2018) , the French National Reference Centre for arboviruses (CNR), or the World Reference Center for Emerging Viruses and Arboviruses (WRCEVA). Viruses that could not be obtained but for which full length genome sequences were available, were re-created using the versatile infectious sub-genomic amplicons (ISA) reverse genetics technology  (Aubry et al. , 2015 (Aubry et al. , , 2014)). In order to select representative genotypes, we collected dengue full-length genome sequences from the NCBI database and complemented this database with those of our, still unpublished, "in house" and CNR strains. We performed phylogenetic reconstructions with the maximum likelihood method to assign all available genome sequences to a genotype in a serotype (supplementary material  Fig 1, 2, 3  and 4 ). Within each genotype, we focused on strains that were not subjected to extensive cell passage and were either available as biological isolates in virus collections or as full-length sequences in GenBank.
Six dengue genotypes were available only as complete genome sequences in the NCBI database without any biological strain counterparts in referenced collections (DENV-1 genotype III, DENV-2 genotype sylvatic and Asian II, DENV-3 genotype V and DENV-4 genotype III and sylvatic). Two genotypes were not available at all because of incomplete genome sequence (DENV-1 genotype II and DENV-3 genotype IV). To obtain the biological viruses from the completely sequenced strains, we designed reverse genetics systems based on the ISA technique  (Atieh et al. , 2016; Aubry et al. , 2015 Aubry et al. , , 2014) )  and generated synthetic overlapping DNA fragments that covered each of the entire genome, bordered by a CMV promoter on the 5' end and a Ribozyme and poly-adenylation signal on the 3' end. The overlapping fragments were cotransfected into a mix of human and hamster embryonic kidney cell lines (HEK 293 and BHK-21 purchased from the American Cell Culture Collection).
g. SDM25N), exhibited larger differences in activity towards the various genotypes of DENV. Importantly, these data illustrate the fact that a sound in cellulo evaluation of anti-dengue candidate molecules requires the use of a complete reference virus panel that enables estimates of the antiviral activity against each of the identified DENV genotypes to be obtained. Modern reverse genetics techniques have enabled us to develop such a representative collection, and it has been made available to the scientific community through the European Virus Archive collection (EVA). We believe that the availability of this new tool will enable the independent assessment of pan-serotypic activity of anti-dengue candidates in the future, fulfilling a critical requirement for a successful dengue antiviral small molecule.
Acknowledgments We would thank  Dr Robert Tesh  from  UTMB (Texas)  for providing the dengue 4 Genotype I strain G11337 from India. We thank  Dr Raphaelle Klitting  for her help and expertise regarding the dengue phylogeny. We would thank  Pr E. A Gould  and  Dr Stéphane Priet  for their careful reading of the manuscript. As well as  Magali Gilles , and  Fiona Baudino  from the  UMR UVE (Marseille, France)  for excellent technical help. We also thank  Geraldine Piorkowski  and  Karine Barthelemy  from  UMR UVE (Marseille, France ) for the sequencing.
The SRoll2 solution As in SRoll1, the improved SRoll2 is a global solution algorithm, estimating instrumental systematic effect parameters from the sky data, as described in detail in the LowEll, and L03 papers. It works on the data in a single frequency band and produces Stokes I, Q, and U maps, using all polarization-sensitive bolometers (PSBs) at this frequency. SRoll2 is better at handling systematic effects than SRoll1, in particular the ADCNL effect. The nonlinearity of the analogto-digital convertors (ADC) was measured in the ground tests, as well as in flight (during the warm phase of the Planck HFI when the dilution cooler 3He tank was empty), and corrected in the first module of the Time Ordered Information (TOI) data processing. The accuracy of this correction was not good enough for the CMB channels, and left residuals which were then corrected empirically in the mapmaking process as a time varying linear detector chain gain.
The 353-GHz modulated signals were more spread in analog-to-digital units (ADU) making the correction much smaller. Starting from the same TOIs, but not applying this first-order linear correction, SRoll2 builds a full nonlinear correction per detector and per stable pointing observation (also called "ring") in which, for each ADU of the signal, a nonlinear correction of the ADC transfer function model is fitted iteratively to the difference between the data and the data model at each iteration. As the HFI bolometers have never shown in tests gain variations other than the one associated with the temperature drift of the 0. 1K-plate (which is corrected for), we attribute to the ADC any residual apparent gain variation, and we adopt a detector response stable in time during the mission. This is a major change which should coherently correct several systematic effects associated with the nonlinearity of the ADC residuals.
This ADCNL determination method converges if the initial estimated frequency map used is close enough to the exact solution. This is why the ADCNL computation is always done after a computation of corrections of all other systematic effects, including the calibrated response, to produce an estimated frequency map based on the last minimization of the difference between the data rings (called HPRs for HEALPix  (Górski et al. 2005)  rings) and the model, providing the best correction available. The ADCNL residual semi-empirical correction is fitted as a function of the demodulated signal (not going through a full model of the ADC and the modulation electronic signal). The ADCNL correction is degenerate with the calibrated response variation. Therefore, it is not possible to fit the ADCNL correction simultaneously with the calibration which is the result of the frequency map solution from the previous step.
Simulations have verified that one Step#1 iteration before computing the ADCNL correction is enough to get convergence. The second step (called Step#2) is new: it starts from the polarized map generated in Step#1 and refines, for each bolometer, the ADCNL parameters which are the spline function weights used to model the ADC correction. This is contained in the central box of Fig. 1. In about 20 iterations, the ADCNL converges, and the final maps are obtained by returning to Step#1. This section details the SRoll2 algorithm: the first subsection explains the data model hypotheses, the second subsection details the Step#1 that computes the calibrated detector chain response, but also the systematic correction parameters at the exception of the ADCNL ones, the third subsection describes the Step#2 that fits the ADCNL model parameters.
The last subsections address map projection, future improvements, and simulations. Step#1: mapmaking parameters computation Step#1: mapmaking parameters computation Step#1: mapmaking parameters computation Step#1 algorithm uses data HPRs to consistently compute the maps, the detector response, and the systematic effects correction parameters other than ADCNL. Step#2 computes the ADCNL correction that is propagated to the subsequent iteration. In the middle box, Step#1 produces maps used as input for Step#2 that computes the ADCNL correction. This whole process is then iterated 19 times. The bottom box is again a Step#1 to build the final maps.
Improved scheme Similarly to the SRoll1 data model (see equation 1 of L03), the SRoll2 data model for bolometer signal is given by Eq. 1 where indices are: b for the bolometer, up to n bolo ; r for the stable pointing period (ring), up to n ring ; p for the sky pixel; h for bins of spin frequency harmonics (up to n harm ), labeled as bin h=1 for the first harmonic, bin h=2 for harmonics 2 and 3, bin h=3 for harmonics 4 to 7, and bin h=4 for harmonics 8 to 15; f for the polarized foreground, up to n comp (dust and freefree). In this paper, "response" refers to the detector chain response of a given bolometer to a CMB signal (in ADU/µK), calibrated on the orbital dipole signal. g b M b,r,p = A b,r,p • S p + n harm h=1 γ b,h V b,r,p,h + n comp f =1 δL b, f C p, f + D tot r,p + F dip b,r,p + F gal b,r,p + O b,r + g b N b,r,p + g b ℵ b,r , (1) where: g b is the calibrated response of a bolometer chain; -M b,r,p is the measured bolometer total signal; -A b,r,p = 1, ρ b cos(2φ b,r,p ), ρ b sin(2φ b,r,p ) is the pointing vector giving the observed pixel for a given bolometer in a given ring; -S p = I p , Q p , U p is the sky signal in pixel p after subtraction of the orbital dipole assumed to be known with an amplitude invariant in time.
As demonstrated by simulations, the ADCNL correction for the demodulated signal can be fitted on a limited number of spline bases in ADU level (called 1D spline). When all other systematic effects are removed, the residuals between the sky signal and the data model are attributed to the ADCNL residuals averaged over all observations with this bolometer for which the demodulated signal is in the same range of ADUs. The modulation signal varies slowly and is stable on one ring. When needed, an additional time-dependant description (called 2D spline) with a limited number of stable correction ranges of rings is introduced. Figure  3  illustrates that, for the bolometer 100-4b, the 1D spline catches all patterns of the ADCNL residuals. On the contrary, the 2D spline catches small time-dependant effects for a few bolometers, like the bolometer 143-1a, especially at the beginning of the mission.
Õb,i changes because the ADCNL correction based on the ADU level that follows the very slow focal plane thermal evolution is degenerate with the very low frequency noise. After this Step#2, a new iteration, with a new gain and a new ADCNL model, can start. Figure  4  shows the residual between the simulated HPR data and the model given the number of iterations for a simulation without noise. It demonstrates that ADCNL effect can be cleaned very well, as the convergence, measured by the residual spectrum at = 3, falls below the noise for 10 gain iteration. We therefore limit the number of iterations to 20. Increasing the number of iterations does not improve the results. These ADCNL residuals describe the level of uncertainty reached if one ignores all other systematics at low multipoles which are shown to be smaller in Sect.
Besides the inputs, the most significant improvement with respect to the FFP10 simulations is the use of the SRoll2 algorithm at the mapmaking step. As the sky simulations do not perfectly mimic data, a final post processing is needed to add a small correlated white noise between detectors. Having no correlated noise between bolometers in the timeline simulation allows for a much greater level of computational parallelization. Figure  5  illustrates the quality of those simulations compared to the data for the half-mission null test. Overall, simulations show good consistency with the data. We see a low frequency noise rise, much reduced with respect to figure 17 of L03 below = 50, as expected. At 143 GHz, at low multipoles, there is a small discrepancy related to the mask used to compute the spectra.
SRoll2 frequency maps Figure  6  presents the SRoll2 "full mission" maps at 100, 143, 217, and 353 GHz maps, the total dipole being removed. Along with those full mission maps, we build half data maps by splitting the data in two sets for which both give a complete set of frequency maps. We can then build null tests with differences of the two sets of maps in a pair to obtain residuals in which the uncorrelated noise and the signal distortion due to systematic effects not identical in the pair will appear, providing information about the noise and systematic effect residuals. The half-mission (hm) null test is sensitive to the slow temporal variation of instrumental effects (e. g. , the ADCNL effect). The detector-set (detset) null test separates the four PSB detectors, at 100, 143, 217, and 353 GHz, into two detector sets.
Acknowledgements. This work is part of the  Bware  project, partially supported by  CNES. The program was granted access to the  HPC resources of CINES  (http://www. cines. fr)under the allocation 2017-A0030410267 made by  GENCI  (http://www. genci. fr). The authors acknowledge the heritage of the Planck HFI consortium regarding data, software, knowledge, and the IAP hosted HFI DPC computing facility. LP acknowledges the support of the  CNES postdoctoral program.
Results Observation of high-energy phenomena in winter thunderstorms. The Gamma-ray Observation of Winter Thunderclouds (GROWTH) collaboration  31, 32, 35, 43  has been engaged with a multi-point observation campaign of atmospheric high-energy phenomena in coastal areas of Japan Sea  14, 44. Winter thunderstorms in Japan are ideal targets to observe this type of phenomena due to their unique characteristics; most notably typical altitude of clouds is significantly lower than ordinary  38, 45, 46  , which makes sea-level observations of gamma-ray glows viable. We have developed portable radiation detectors dedicated to the multi-point observation. They have a 25 cm × 8 cm × 2. 5 cm Bi 4 Ge 3 O 12 (BGO) scintillation crystal coupled with two photomultiplier tubes (PMTs; HAMAMATSU R1924A). Outputs from the PMTs are amplified, and then read out by a 50 MHz digitiser onboard a data acquisition system.
The data acquisition system stores 20-μs waveforms of the amplified analogue outputs once a pulse is detected, and extracts the maximum and minimum value as well as the timing of the pulse (see also Detector calibration). The maximum value corresponds to the energies of the pulse, and the minimum the analogue baseline voltage. The data acquisition system also records counts of discarded photon events due to buffer overflow, which are used for dead-time correction. Three detectors were deployed at three observation sites in Kanazawa City, the capital of Ishikawa Prefecture, by the Japan Sea coast (Fig. 1 ) and have been operated since October 2016. Lightning discharges were monitored by a broadband lowfrequency (LF: 0. 8-500 kHz) lightning mapping network (hereafter LF network), for which detectors are installed along Toyama Bay and in Noto peninsula.
Another receiver in the extreme-lowfrequency band (ELF: 1-100 Hz) is installed at Kuju, as summarised in the section Radio observations. We also utilise lightning location data of Japanese Lightning Detection Network (JLDN) operated by Franklin Japan Co. , Ltd. Detection of gamma-ray glow and downward TGF. On 9 January 2018, two of our detectors shown in Fig. 1  recorded gamma-ray glows. Figure  2a , b shows long-term count-rate histories of detectors A and B, respectively. At around 17:54 in coordinated universal time (UTC), detector A at Kanazawa Izumigaoka High School (36. 538°N, 136. 649°E) recorded a radiation increase for ~60 s. Then, ~30 s later, detector B at Kanazawa University High School (36. 539°N, 136. 664°E, 1. 3 km east from detector A) also recorded a gamma-ray glow. No radiation enhancements were observed by detector C at Kanazawa University Kakuma Campus (36.