dataesr/scientific-paragraphs-categorization huggingface dataset:
Data management and statistical analysis Study data were collected and managed using the REDCap (Research Electronic Data Capture) application hosted at the University Hospital of Clermont-Ferrand  [14]. Statistical analysis was performed using Stata 13 software (StataCorp LP, College Station, USA). The tests were twosided, with a type I error set at α = 0. 05. It seemed difficult to propose a sample size estimation according to lack of knowledge on literature about the main objective and associated outcome in this population. Also, the sample size was fixed according recruitment ability and according to sequential design without type I error inflation  [15]. However, a posteriori statistical power calculation was proposed. These aspects were clarified in discussion. Baseline characteristics were presented as mean (± standard-deviation) or median [interquartile range] for continuous data (assumption of normality assessed using the Shapiro-Wilk test) and as the number of patients and associated percentages for categorical parameters.
Discussion OCT1, OCT2, OCT3, CTR1 and ATP7B were detected by immunohistochemistry at various levels in CRC tissue samples after surgery and before adjuvant chemotherapy. Despite the small number of patients, high expression of OCT3 in tumor samples remained an independent factor of CRC non-response to adjuvant FOLFOX-4 chemotherapy. The expression of OCT1, OCT2, CTR1 and ATP7B were not related to FOLFOX-4 response. As it was mentioned in the statistical considerations (see above), due to difficulties to propose sample size estimation, a posteriori statistical power calculation was proposed according to results observed for primary outcome. With n = 15 responders and n = 16 non-responder patients, the statistical power was greater than 95% to show a difference of 118 points for OCT3 expression in tumors (H-score: 5 [0-60] vs 123 [70-178]), sampsi command of Stata software considering non-normal distribution  [17].
The measurements use the full Run-1 pp collision dataset recorded at centre-of-mass energies of 7 and 8 TeV. The details of the selections and the results are given for the data collected in 2012 at √ s = 8 TeV. The same algorithms are applied with minor differences to the √ s = 7 TeV data collected in 2011. To overcome the difficulties arising from the absence of a single, pure control sample of prompt photons over a large E T range, three different data-driven techniques are used. A first method selects photons from radiative decays of the Z boson, i. e. Z → ℓℓγ (Radiative Z method). A second one extrapolates photon properties from electrons and positrons from Z boson decays by exploiting the similarity of the photon and electron interactions with the ATLAS electromagnetic calorimeter (Electron Extrapolation method).
5 < |η| < 3. 2) and forward (3. 1 < |η| < 4. 9) regions. The muon spectrometer surrounds the calorimeters. It consists of three large superconducting air-core toroid magnets, each with eight coils, a system of precision tracking chambers (|η| < 2. 7), and fast tracking chambers (|η| < 2. 4) for triggering. A three-level trigger system selects events to be recorded for offline analysis. A coarser readout granularity (corresponding to the "towers" of Fig. 1 ) is used by the first-level trigger, while the full detector granularity is exploited by the higher-level trigger. To reduce the data acquisition rate of low-threshold triggers, used for collecting various control samples, prescale factors (N) can be applied to each trigger, such that only 1 in N events passing the trigger causes an event to be accepted at that trigger level.
Data and Monte Carlo samples The data used in this study consist of the 7 TeV and 8 TeV proton-proton collisions recorded by the ATLAS detector during 2011 and 2012 in LHC Run 1. They correspond respectively to 4. 9 fb -1 and 20. 3 fb -1 of integrated luminosity after requiring good data quality. The mean number of interactions per bunch crossing, µ, was 9 and 21 on average in the √ s = 7 and 8 TeV datasets, respectively. The Z boson radiative decay and the electron extrapolation methods use data collected with the lowestthreshold lepton triggers with prescale factors equal to one and thus exploit the full luminosity of Run 1. For the data collected in 2012 at √ s = 8 TeV, the transverse momentum thresholds for single-lepton triggers are 25  (24)  GeV for ℓ = e (µ), while those for dilepton triggers are 12  (13)  GeV.
The data are fit to a sum of the photon and background contributions. The photon and background m ℓℓγ distributions ("templates") are extracted from the Z → ℓℓγ and Z +jet simulations, corrected to take into account known data-MC differences in the photon and lepton energy scales and resolution and in the lepton efficiencies. The signal and background yields are determined from the data by maximising the likelihood. Due to the small number of selected events in data and simulation, these fits are performed only for two photon transverse momentum intervals, 10 GeV < E T < 15 GeV and E T > 15 GeV, and integrated over the photon pseudorapidity, since the signal purity is found to be similar in the four photon |η| intervals within statistical uncertainties. Figure  4  shows the result of the fit for unconverted photon candidates with transverse momenta between 10 GeV and 15 GeV.
The efficiency of the identification criteria is determined from the extrapolated photon distributions of the discriminating variables. The following three sources of systematic uncertainty are considered for this analysis: • As the Smirnov transformations are obtained independently for each shower shape, the estimated photon identification efficiency can be biased if the correlations among the discriminating variables are significantly different between electrons and photons. Non-closure tests are performed on the simulation, comparing the identification efficiency of true prompt photons with the efficiency extrapolated from electron probes selected with the same requirement as in data and applying the extrapolation procedure. The differences between the true and extrapolated efficiencies are at the level of 1% or less, with a few exceptions for unconverted photons, for which maximum differences of 2% are found.
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ;  EPLANET ,  ERC ,  FP7 ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Région Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF; BSF , The ATLAS Collaboration.
Dust Devil Terminology Table  1  summarizes the terms used for dust devils in different languages. Clearly some terms are simply descriptive of the phenomenon ('dust whirl(wind)'), some are onomatopoeic, and Fig. 2  The cumulative growth of the corpus of dust devil work in refereed journals in the ADS abstract database (http://adsabs. harvard. edu/). Note the slope change around 2003, and the jump circa 1970 some invoke the ephemeral/aethereal nature of them with reference to spirits or devils. It is of interest that the shape-inspired term 'trombe' appears in early French and German literature (sharing a root with the English 'trombone') but has largely fallen into disuse.
Scientific Significance of Dust Devil Studies It is the general impression of the authors that the public finds dust devils 'interesting', a qualitative endorsement of the utility of their study. However, the scientific level of interest can be at least crudely quantified. An informal bibliometric study, searching for refereed journal papers with "Dust devil" in the title in the Smithsonian Physics/Astronomy database found some 131 papers 1930-2015. It is recognized that this database will not capture all the literature (particularly that in languages other than English), but it can be considered representative. Figure  2  shows the cumulative number of papers versus time-it is seen that papers accumulate at the rate of about 1. 5/yr until the early 21st century when there is an abrupt change in slope-essentially reflecting studies of dust devils on Mars as an ongoing enterprise-to ∼4/yr.
Vortices figure prominently in his thinking, and his idealized model of a vortex (which like a temperature scale, and an engine cycle, bear his name) is often used in dust devil studies. Little further British work on dust devils is of note until an important contribution by  Flower (1936) , a meteorologist working in Egypt for the Air Ministry (it is tempting to speculate whether Flower ever met or worked with Ralph Bagnold, in the same part of the empire at the same time; Bagnold was a desert soldier and pioneer in aeolian studies, his 1941 book 'Physics of Blown Sand and Desert Dunes' remains required reading today). Flower's tabulations of dust devil diameters, heights, longevity etc. remain one of the most comprehensive visual datasets, and were recently used to develop analytic correlations of diameter and longevity  (Lorenz 2013).
This survey lasted May-September, using a linear array of six recording stations. When a human operator saw a dust devil, chart recorders were activated. Only 21 events were recorded in the five months of operation, a rather modest haul. Lambeth concludes with the suggestion that "dust devils can be measured in adequate numbers only by using mobile sensors". Sinclair had by this time started to make many encounters using an impressive jeepborne meteorology tower (Fig. 9 ), as well as measurements in a sailplane of the dust plumes atop dust devil. Sinclair's papers (1965 Sinclair's papers ( , 1969 Sinclair's papers ( , 1973) )  and thesis are well worth reading even today, noting such challenges as the high-speed data acquisition required on a hot, dusty, bouncing jeep in the desert (a cine camera recorded film of instrument readings!).
Sinclair also performed a couple of visual surveys from fixed stations, data still referred to today. It is of note that Sinclair's work was funded by the Office of Naval Research (ONR), although it is not obvious whether this had any connection with nuclear testing, or was merely one of many areas of fundamental research supported by the US Defense Department. At this point space exploration and dust devil meteorology begin to intersect. J. A. Ryan, at the Missile and Space Systems Division of the Douglas Aircraft Corporation, speculated  (Ryan 1964 ) that transient winds associated with 'a cyclonic system (somewhat analogous to a terrestrial dust devil)' might be responsible for the yellow clouds, presumed to be of dust, that had been seen telescopically on Mars. Later, Ryan and J. Carroll (then of the 'Space Sciences Division' of McDonnell-Douglas: the space program now allowed the progressively-consolidating aerospace industry to employ their own scientists, a situation that unfortunately is not widespread today) with NASA funding published several papers on field observations of dust devils in the Mojave desert in California, with a fixed meteorological station (e.
Japan Although there are no deserts or arid regions in the Japanese islands, dust devils are sometimes reported on fine days especially in warm seasons. They commonly occur over sandpaved playgrounds in schools and in sports fields, because strong surface heating under fine-weather conditions generates intense thermals locally. An example was reported in  Tachikawa and Deguchi (1987)  who showed that a whirlwind blew away a tent over the playground in an elementary school during a sports festival at the school in October 1986. The Japan Meteorological Agency (JMA) archives the information on tornados and other gust events that occur in Japan on the web (http://www. data. jma. go. jp/obd/stats/data/bosai/ tornado/index. html). Based on an inspection of this database for the present paper, it is found that there were 18 recorded occurrences of dust devils from 1961 to 2009 in Japan.
14 ;  Edgett and Malin 2000). It was recently noted (Lorenz and Zimbelman 2014see also chapter in this volume by  Reiss et al. )  that in fact dust devil tracks can be clearly seen-but were not recognized at the time-in Mariner 9 images of Proctor crater in the Hellespontus region of Mars, images in which dunes on Mars were first reported. It was assumed (J. Cutts, personal communication to RL) that these dark markings were simply fissures on the ground filled with dark material. The MOC dataset also provided the first opportunity to study dust devils as a population-the extensive coverage in time and space of both active dust devils and their tracks leading to several key conclusions, including (i) martian dust devils are, like on Earth, most active in spring and summer (e. g. Whelley and Greeley 2008) , (ii) dust devils do not appear to trigger global dust storms on Mars (e.
g. ,  Balme et al. 2003a Balme et al. , 2003b) )  but do contribute to the persisting haziness of Mars' atmosphere  (Cantor et al. 2006 ), (iii) dust devils are more common in some parts of Mars than in others, especially in the Amazonis Planitia region  (Cantor et al. 2006). Further discussion on orbital observations of dust devils at Mars is given in the Chapter by  Fenton et al. (2016). In the last 15 years, martian dust devil data collection has focused on three areas: observations of dust devils tracks from orbit, observations of active dust devils from orbit, and observations and measurements of dust devils from the surface. Recent advances include the first measurements from orbit of horizontal forward velocity of martian dust devils (tens of meters per second, based on multi-image acquisitions from the ESA Mars Express mission;  Stanzel et al.
Dust Devil Hazard Damage and discomfort due to dust devils has been noted since Greek and Roman times earlier in this paper. In the modern world, dust devils, and dry convection generally, can present a hazard to aviation. Typically this is due to sudden changes in wind speed and direction, although sometimes reduced visibility is a factor too. As discussed earlier, some studies in this direction were undertaken in Australia. Lorenz and Myers (2005)  review US air accident reports, finding around 100 incidents in the NTSB Air Accident database 1995-2005 pointing to dust devils as a contributing factor, and four fatal accidents. An informal survey of news media and the Internet reveals a number of reports of injury due to dust devils. Usually these are associated with lightweight structures-barns, tents, children's bouncy castles and parachutes are often the mechanism by which a sudden wind can cause injury.
Specifically, the main objectives are (i) to parameterise the hydrographs in order to analyse the level and the dynamics of phage fluxes during rainfall-runoff events (rising and falling limbs), (ii) to investigate the possible origins of phage pollution wave arrivals in an aquatic environment and (iii) to give new data for risk assessment by detection of infectious phage particles during rainfall-runoff events. For this study, surface water was sampled from the Alzette River, located in the south of Luxembourg, downstream and upstream from a wastewater treatment plant. The effect of rainfall on water quality was estimated by infectious FRNAPH enumeration and molecular biology tools.
Meteorological, hydrological and physico-chemical data Daily rainfall data of the closest meteorological station (6 km from the studied site) were provided by the "Administration des Services Techniques de l'Agriculture" (ASTA, http://www. agrimeteo. lu). The water discharge (m 3 /sec) of the Alzette River was estimated using the rating curve and the water level data provided by the "Administration de la Gestion de l'Eau". Turbidity of each water sample was measured in nephelometric units (NTU) with a portable turbidimeter (HACH 2100Q).
Discussion The objective of this study was to provide new comprehensive information on the origin, dynamics and transport of viral particles in surface water during rainfall-runoff events. To this purpose, Fspecific RNA phages, selected as faecal and viral pollution indicators, were detected and quantified using both culture and molecular assays. Data was then analysed in an innovative way integrating hydrological, climatological and microbiological parameters. In low flow conditions, infectious FRNAPH were detected in the Alzette River with concentrations ranging from 2. 1 to 3. 2 log 10 PFU/ 100 mL, which was comparable to the results of previous studies  (Lucena et al. , 2003; Ogorzaly et al. , 2009; Skraber et al. , 2009a; Stewart-Pullaro et al. , 2006). It corresponded to the initial faecal pollution of the river and correlatively suggested possible viral pollution.
Acknowledgements The authors would like to thank  D. Collard ,  A. Tavares-Furtado  and  C. Walczak  for their excellent technical assistance in sampling campaigns and lab experiments. We thank  N. Martinez Carreras ,  C. Hissler  and  C. Tailliez  for their valuable assistance in understanding the rainfall-runoff events. We also thank  L. L'Hoste  for his help in the production of the Alzette watershed map and  J. F. Iffly  for the installation of automatic sampling in the studied area. Blandine Fauvel  received an AFR grant from the  National Research Fund  (AFR code  6035344 ), Luxembourg.
Experimental Design This observational study involved 106 sows from six commercial Danish sow herds. Sow herds were included based on the distance to one of the larger Danish slaughterhouses  [13, 14]. Within four distance categories, a random postal code was selected, and randomly chosen farmers within the postal code, sending more than an average of 8 sows per load, were contacted by phone. Herds could be included if they were willing to participate, and if they sent sows to the specific slaughterhouse. For the present dataset, inclusion further required the use of a transfer vehicle. Data was collected during 2015 and included data from 11 loads visiting a total of six Danish sow herds. Selection of animals to be slaughtered and thus included in the dataset was done by the farmers. For ethical and legal reasons, unfit sows (as described in Chapter 1 of Annex 1 of  [4] ) could not be included in the dataset.
Data Collection The clinical examination of the sows before loading them onto the transfer vehicles consisted of several parameters. For the present study, recordings of lameness score (on a scale from 0-3, where 0: normal gait; 1: abnormal gait, all legs are weight bearing; 2: lame to a degree where the affected limb can be recognised, the use of the limb is limited; and 3: seriously lame, does not bear weight on the affected limb or avoids walking), body condition score (using a 1 /2-point scale from 1-4 focusing on the visibility of ribs, spine and hips and 1: skinny (can be recognised visually); 2: slim (requires light pressure to recognise); 3: intermediate (requires hard pressure to recognise) and 4: fat (cannot be seen or felt)) and presence of milk in the udder (assessed as 0/1 by udder palpation) were made.
Before the sows were loaded onto a transfer vehicle, one temperature logger (iButton DS1923, Maxim Integrated, San Jose, CA, USA; resolution ≤ 0. 5 • C) was placed inside each vehicle at a height of approximately 1. 1 m. The temperature inside the vehicle was logged every minute. After arrival of the commercial pig truck, the logger was taken down and data collected. During the stay in the transfer vehicle, behaviour of the sows was recorded by a hand-held video camera (Canon Legria HF R56, Canon, Diegem, Belgium), from outside the vehicle and at a distance of at least 1. 2 m from the animals. The person filming took care not to disturb the animals. The behaviour of the sows was analysed by behaviour sampling and continuous recording  [15]. One observer, blind to the experimental hypotheses, performed all behavioural analyses.
Statistical Analyses Descriptive statistics was used to describe the behaviour of the sows during the stays in the transfer vehicles. In addition, possible correlations between behavioural variables, the duration of the stay and the temperature in the vehicles were calculated using Spearman correlations. All statistical analyses were performed with SAS Enterprise Guide software (version 5. 1, SAS Institute Inc. , Cary, NC, USA). Results are presented per load and as medians across loads, except for temperature in the transfer vehicles, which is presented as mean ± STD per load. A probability level of p < 0. 05 was considered statistically significant, whereas 0. 05 < p < 0. 10 was considered a tendency.
Streptococci sRNAs: A LARGE SPECIES PANEL Currently, 67 different Streptococcus species are reported on the NCBI database and their genome sequenced and annotated. Nevertheless, only a few of them have been the subject of studies pointed toward the characterization of sRNAs. Bioinformatics approaches have been widely used to detect sRNAs in bacterial genomes. Tesorero et al. (2013)  reported that among the 67 available genomes, 23 different Streptococcus genomes harbor at least one sRNA homolog to the sRNAs detected for S. pyogenes MGAS315 strain. Moreover, species as Streptococcus suis, Streptococcus uberis, Streptococcus mitis, Streptococcus oralis, Streptococcus sanguinis, Streptococcus dysgalactiae were also reported to harbor more than two csRNAs  (Marx et al. , 2010). For S. suis, a pig pathogen, a differential RNA-sequencing approach was conducted on the strain P1/7 grown in rich medium and biological fluids (pig blood and cerebrospinal fluid) and allowed the identification of 29 sRNAs  (Wu et al.
FRET measurements of PKA activity PKA activity was measured with the FRET-based A-kinase activity reporter 3 (AKAR3)  3  exhibiting a specific cytoplasmic localization (NES, for nuclear export signal) to avoid nucleus localization. 4 After 2h of culture in MEM containing FBS, the medium was replaced by MEM without FBS containing an adenovirus encoding AKAR3-NES at a multiplicity of infection of 200 pfu/cell and myocytes were kept for 48h at 37°C (0. 5% CO2). Prior to the experiments, cardiomyocytes were maintained in a Ringer solution at room temperature (20-25°C) for at least 5 min. Images were captured every 5 s using the 40x oil immersion objective of an inverted microscope (Nikon) connected to a software-controlled (Metafluor, Molecular Devices) cooled charge coupled camera (Cool SNAP HQ2).
Dual emission imaging of CFP and YFP was performed using a Dualview emission splitter equipped with a 510LP dichroic mirror and BP filters 480/30 nm and 535/25 nm. Cyan Fluorescent Protein (CFP) was excited during 300 ms by a Xenon lamp (Nikon) using a 440/20BP filter and a 455LP dichroic mirror. Dual-emission imaging of CFP and Yellow Fluorescent Protein (YFP) was performed using a Dual-View emission splitter equipped with a 510 LP dichroic mirror and BP filters 480/30 and 535/25 nm, respectively. Average fluorescence intensity measured with AKAR3-NES was measured in the entire cell. Background was subtracted and YFP intensity was corrected for CFP bleed through before calculating the ratio. Ratio images were obtained with the ImageJ software.
Data analysis Ca 2+ transient amplitude was measured by dividing the twitch amplitude (difference between the end-diastolic and the peak systolic ratios) by the end-diastolic ratio, thus corresponding to the percentage of variation in the Fura-2 ratio. Similarly, sarcomere shortening was assessed by its percentage variation, which is obtained by dividing the twitch amplitude (difference between the end-diastolic and the peak systolic sarcomere length) by the end- diastolic.
Funding This work was supported by the  Fondation de France  (to GV), the  Agence Nationale de la Recherche   2010 BLAN 1139-01  (to GV) and the  Investment for the Future program   ANR-11-IDEX-0003-01  within the LABEX  ANR-10-LABX-0033  (to RF and PB). PB was a recipient of doctoral grants from the  CORDDIM program of Région Ile-de-France , from the  Fondation pour la Recherche Médicale.