dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics Statistical data analysis was carried out using SPSS software version 22 (IBM SPSS Statistics 22. 0, IBM, Armonk, NY). Data are expressed as the mean of five replicates. p < 0. 05 was considered as statisti-cally significant using Fisher's exact test and t-test if appropriate. The mean inhibitory concentration 50% (IC 50 ) of HDACi was calculated using GraphPad Prism 4. 02 software (GraphPad Software, Inc. ). Graphs were illustrated using the same software.
# According to Miettinen et al. [47] , percentages are indicated in brackets. Note that information on tumour location, mutational status and risk classification were not available for all GIST cases; wildtype was classified by no mutation in KIT exons 9, 11, 13, 17 and PDGFRA exons 12, 14 and 18. (p 5 0. 5778) or entinostat (p 5 0. 7616; t-test, Figure  2B ). To confirm these results, ApoTox-Glo TM Triplex Assay was performed. The evaluated HDACi again decreased proliferation in sarcoma cell lines, correlating with HR23b expression (data not shown). Furthermore, analysing the GIST cell line GIST882, the WDLS cell line T778, the DDLS cell line Fu-DDLS-1, the SS cell line 1273/99 and the EWS cell line SK-N-MC compared to the CTCL cell line HUT78, we could show that the evaluated HDACi showed apoptosis induction (Figure  1B ).
HR23b in sarcomas 67 chemotherapy and irradiation in patients with relapsed or metastasized tumours is limited and, therefore, new treatment options are needed to move this field forward. In vitro studies have shown that HDACi are active in a wide range of sarcomas including rhabdomyosarcoma, osteosarcoma, chondrosarcoma, GIST and synovial sarcoma and that they induce cell cycle arrest and apoptosis or reduce tumour growth  [21, 22, [28] [29] [30] [31] [32] [33]. Despite these promising results, HDACi have not generally been effective in clinical trials in solid tumours to date. One reason for this so far limited clinical success of HDACi might be the lack of appropriate biomarkers for the selection of patients. Almost 200 clinical trials with HDACi are currently underway according to the database ClinicalTrials.
Above 2400 m, the internal averaging is 30 s for all reporting intervals. In firmware 1. 72 and 2. 03, the internal averaging interval is 30 s for the entire profile and does not change with reporting interval (Table  1 ). If a reporting interval is selected that is shorter than the internal averaging interval, consecutive profiles overlap in time and are hence not completely independent. Observations from three ceilometer networks (Table  2 ) are used in this study to illustrate aspects of the data   2 ). Sensors A and B are the old hardware generation with the CLE311 board, as is the Met Office sensor W, while LUMO sensors C and D and the SIRTA sensor S have engine boards CLE321. For both sensors A and B the transmitter has been upgraded from CLT311 to CLT321 during their operation, and for sensor S the transmitter CLT321 was replaced by a spare part of the same generation.
4. 2). Data acquired with version 1. 72 or 2. 03 are more consistent across all range gates as the whole profile is treated equally with an internal averaging interval of 30 s. The range histograms (Fig. 1a ) show the impact of the incomplete background correction; i. e. instrument-related background signal and cosmetic shift are not accounted for. Both cause a systematic pattern in the observed profiles, illustrating the range dependence of the background signal. The cosmetic shift is particularly strong for version 1. 71. To capture both background effects, profiles are analysed during times when atmospheric variations are expected to be small and instrument conditions are stable (Fig. 2 ). P profiles are extracted and averaged hourly when noise induced by solar radiation is absent (4 h around midnight), cloud cover is low (< 10 % of the hour), no fog is present, the window trans- mission is reasonable (> 80 % on average), laser pulse energy is high (> 98 % of nominal energy), and sufficient data are available (> 90 % of the hour; data gaps may occur due to maintenance or problems with data acquisition such as power cuts).
Vaisala introduced a correction for the near-range artefacts that proves efficient in dry conditions (Fig. 6d ); however, if attenuation is increased due to hygroscopic growth, the peak at the fifth range gate is still evident in the normalised RCS profile (Fig. 7vii ). Applying the near-range correction proposed for observations from earlier firmware version (as used for Fig. 7ii , iv, vi), the artefacts could still be removed (Fig. 7viii, ceilometers A  and B ), but it could also result in an overcorrection (Fig. 7viii, ceilometers C  and D ). Note that this approach can only be tested on sensors for which a historic dataset of measurements with older firmware versions is available to calculate the respective correction   2 ) over the first 200 m range: (i, iii, v, vii) logarithm of the range-corrected signal reported RCS (a.
BLview (Vaisala's boundary layer detection software, e. g. used by  Tang et al. , 2016)  has range-variant smoothing windows. β smooth (t, r) = (2w t + 1) -1 • (2w r + 1) -1 (12) The quality of range-corrected attenuated backscatter can be evaluated by comparison to the noise floor. The latter represents variations associated with electronic and optical noise and noise introduced by the solar background light. If no high cirrus clouds are present, it is assumed the signal from the very highest range gates contains only noise (i. e. atmospheric signal contribution is negligible). In this case, the noise floor F can be defined as the mean β plus standard deviation σ β of the attenuated backscatter β (i. e. before range correction) across a certain number of gates from the top of the profile.
Data availability Data of Vaisala CL31 ceilometers used for this study can be accessed from the different sources: -Data from the LUMO network can be requested online  (Grimmond, 2016). -Data from the Meteo France ceilometer at the SIRTA site can be downloaded from SIRTA (2016), using the "Download tool" menu, selecting "search by instrument", "backscatter LIDAR" category and finally "Ceilometer CL31". -Met Office data are available from the Centre for Environmental Data Analysis (Met Office, 2015).
Acknowledgements. This study was funded by  H2020 UR-BANFLUXES, NERC ClearfLo  ( H003231/1 ),  NERC Airpro  ( NE/N00700X/1 ),  NERC//Belmont TRUC  ( NE/L008971/1 G8MUREFU3FP-2201-075 ),  European Cooperation in Science and Technology (COST)  action "TOPROF":  ES1303 ,  King's College London , and  University of Reading. We thank  KCL Directorate of Estates and Facilities ,  ERG/LAQN , and  RGS/IBG  for providing sites and other support and the many staff and students at University of Reading and KCL who contributed to data collection. Met Office,  Meteo France , and the SIRTA observatory are kindly acknowledged for providing observations. We would like to acknowledge the useful discussions within the  TOPROF community  (especially with  Mariana Adam ,  Met Office ;  Frank Wagner , DWD; and  Maxime Hervo ,  Meteo Swiss ).
This is done to satisfy our reflection selection criteria described earlier and choose reflections that are of approximately the same age as at Dome C. Fig. 6. Radar reflection age differences between Vostok and Dome C ice cores using the combined HiCARS and UT/TUD radar systems, as a function of their age at the Dome C ice core site using HiCARSdetermined reflection ages. Each data point represents each of the thirteen radar reflections traced. The vertical black dashed line separates reflections belonging to the last glacial from the penultimate glacial. Error bars are displayed for each reflection representing the total age uncertainty for each system. Both age uncertainties are displayed, red and blue corresponding to HiCARS and UT/TUD, respectively. Three reflections fall well outside their respective total age uncertainty bounds: we attribute this to the presence of aeolian terranes in the vicinity of Lake Vostok.
The repeatability of the radar stratigraphy obtained from two independent radars supports our assumption that our error estimates are conservative. This comparison increases our confidence that the radar reflections traced are isochronal, as the two radar systems not only use different frequencies and processing steps, but are flown over different parts of the ice surface. We note that our third reflection selected at Dome C for its brightness is dated at 73. 10 ± 2. 00 ka, and may originate from the acidity peaks associated with the Toba event(s)  (Svensson and others, 2012). Although the MCoRDS data have a finer vertical resolution and use coherent processing, they are only a single line, and, therefore, stratigraphically complex areas cannot be avoided. The UT/TUD Vostok survey avoids this issue, through its gridded survey design.
We suspect this is mostly due to the pervasive presence of buried aeolian terranes, which affect all depths of the ice column. These terranes are more difficult to recognize, due to the incoherent processing and low bandwidth of the UT/ TUD data (Fig. 7c ). In addition, the UT/TUD grid used to trace the reflections around Vostok is connected to the HiCARS lines via a single ∼200 km UT/TUD line (Fig. 1 ). This means no crossovers can be used as checkpoints on the integrity of the reflections used for 200 km, which we Fig. 9. Radar reflection age differences between Vostok and Dome C ice cores using the MCoRDS radar system, as a function of their age at the Dome C ice core site using MCoRDS-determined reflection ages. Each data point represents one of the thirteen radar reflections traced. The vertical black dashed line separates reflections belonging to the last glacial from the penultimate glacial.
Lake Vostok also affects layer geometries as the ice flows over it, due to anomalous accumulation effects and flow over steep bedrock topography  (Studinger and others, 2003; Leonard and others, 2004) , and can contribute to the complex stratigraphy observed in the area. Despite these natural hurdles, most reflections could be successfully traced along the Byrd-Totten Glacier divide, connecting the two ice core sites. It is still well within our reach to obtain a more complete and direct chrono-stratigraphic link between the ice cores. It simply requires the collection of further radar data with gridded survey designs to get better alternative routes to link the Dome C and Vostok areas. Our investigation informs us that the only impediment to connecting the two cores more completely was the design of these particular surveys, which was ultimately not well suited to the particular challenges of the East Antarctic plateau.
We encourage new linkages with grid spacings appropriate for the regional distribution of erosion-related unconformities. This spacing should be estimated from the distribution of aeolian and strain rate anomalies extracted from the existing surveys at the ice core districts, needed for accurate ice core extrapolation objectives. On the condition that further data can be collected, we are confident that the two deep ice cores can be more fully connected, thus providing additional information, and perhaps most usefully depth uncertainties, in existing synchronizations.
ACKNOWLEDGEMENTS This work was supported by  NSF  grants  ANT-0733025  and  ARC-0941678 ,  NASA  grants  NNX08AN68G ,  NNX09AR52G , and  NNX11AD33G  ( Operation Ice Bridge ) to Texas, the  Jackson School of Geosciences , the  G. Unger Vetlesen Foundation ,  NERC  grant  NE/D003733/1  and the  Global Innovation Initiative  award from the  British Council. We acknowledge the use of data products from  CReSIS  generated with support from  NSF  grant  ANT-0424589  and  NASA Operation IceBridge  grant  NNX13AD53A. A portion of this work was carried out by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with the  NASA. Operational support was provided by the  U. S. Antarctic Program  and by the  Institut Polaire Français Paul-Emile Victor (IPEV)  and the  Italian Antarctic Program (PNRA and ENEA ).
In the cheese habitat, arylsulfatase may be involved in the release of molecules conjugated with sulfate, such as alkylphenols, which contribute to sheep-like flavors of the cheeses manufactured from sheep's milk  (18). Thus, this specificity found in the genome of C. variabile Mu292 might be of interest for understanding sulfur metabolism in cheese, which is of great importance for the cheese-making process  (19). This second genome sequence of Corynebacterium variabile will allow deeper comparative genomic studies among Corynebacterium species and other Actinobacteria, provides new elements for understanding the adaptation strategies of cheese bacteria to the cheese habitat, and potentially aids in discovering novel technological properties for the food industry. Nucleotide sequence accession numbers.
Physical spaces have been shown to be more suitable for their entrepreneurs at the early stages of development, due to the high costs and long waiting time required with the majority of web-based Fab-spaces (Mortara and Parisot, in press). Community-run physical Fab-spaces are in general less costly than the others, however anecdotal evidence indicates that they are also less well appointed and maintained  (Mortara and Parisot, in press). A particularity is cluster 5, composed of networks of web-connected, home-based 3D printer owners through which users could find a local expert-machine. Whilst data regarding the real impact of these new forms of design/ manufacturing networks on industrial economies are not yet available, it can be observed that cluster 5's costing models and resources positioning might compete with some of the mid-range physical Fab-spaces (cluster 9) and hence could provide an opportunity for accessing competences for the entrepreneurs more distant from a physical Fab-space.
"Time domain errors" can be inferred by time series analysis of Wolf number records. This is possible only when these records are sufficiently long and uninterrupted. Differences between observers, called here "dispersion errors", on the contrary require a substantial sample of simultaneous observations, with no need for continuity in time. Thanks to the large number of stations that offer long and almost uninterrupted records of their Wolf number, we are now in a unique position of inferring and comparing these two types of errors from several decades of observations. To do so, we consider a dataset of 52 quasi-continuous observations, most of which are used for compiling the international Sunspot Number at SILSO. More details on these observations can be found in This article is structured as follows: Section 2 presents different estimators of the error, while Section 3 compares their results.
Autoregressive Sunspot Number Model The idea behind autoregressive (AR) modelling is to reproduce the dynamics of the sunspot number time series with a linear parametric model, and then use that model to forecast the sunspot number one day ahead. The innovation, or difference between the observed and the predicted sunspot number then provides us with an estimate of the residual noise η(t). Linear parametric models are widely used to model systems that can be described by linear differential equations with a stochastic forcing  [Chatfield, 2003] , and AR models have been shown to properly capture coloured and white noise in climate data  [Mann and Lee, 1996, Schulz and Mudelsee, 2002]. We model the daily-valued sunspot number time series x(t) as x(t i ) = a 1 x(t i-1 ) + a 2 x(t i-2 ) + • • • + a p x(t i-p ) (4) η(t i ) = x(t i ) -x(t i ) , (5) where x(t) stands for the predicted sunspot number.
We find that models of order p = 8 yield the best compromise between model parsimony and predictive capacity. High-order models tend to become unstable, and, conversely, the predictive capacity drops for small orders. Note that the time-average of x(t) must be subtracted before fitting the AR model. This estimator of the noise level requires time series that are regularly sampled, and devoid of data gaps. To meet this requirement, we first interpolate all data gaps by expectationmaximization [Dudok de  Wit, 2011]. This powerful technique relies on the high correlation between simultaneous observations of the sunspot number to replace missing values by conserving the values of the covariance matrix between observations, regardless of whether there are observations or not. Expectation-maximization has been widely used in climate data analysis [e.
Cases have indeed been reported wherein the sunspot-counting procedure of specific stations has been drifting in time. The use of a scaling factor k (see Eq. 1) is likely to have a major impact on these long-range correlations as it represents a feedback loop in the calibration procedure. The lack of crisp and fully traceable procedure for determining k is one of the main challenges that awaits the estimation of long-term errors in sunspot number records. The dispersion error also reveals some unexpected spectral peaks: the one at a one-year period suggests that the seasonal variations in the observations (the network is mainly located in the northern hemisphere) might introduce a small but statistically noticeable error. Likewise, there is a small peak at seven days, which we might associate with the way data are collected on a weekly basis by some observers.
Ethics and dissemination: The study project has been approved by the appropriate ethics committee (CPP Sud-Méditerranée). Informed consent is required. If combined application of HFNC and NIV for preoxygenation of patients with hypoxaemia in the ICU proves superior to NIV preoxygenation, its use will become standard practice, thereby decreasing hypoxaemia during the intubation procedure and potential complications related to intubation. Trial registration number: NCT02530957.
METHODS: DATA COLLECTION, MANAGEMENT AND ANALYSIS Data collection and management Data will be collected and recorded on case report forms by trained local research coordinators or residents blinded to the randomised intervention. Patients will receive standard ICU monitoring consisting of ECG analysis, SpO2 and a non-invasive blood pressure cuff. Prior to orotracheal intubation, the nurse will set the time intervals on the non-invasive blood pressure cuff monitoring and electronic medical record in the patient's room to run every minute until 15 min after successful intubation. Continuously throughout the procedure (from preoxygenation to 1 hour following intubation), vital parameters will be collected by software on an external laptop called S5/Collect by General Electric, on the General Electric CARESCAPE Monitors.
The following data will be collected and registered before intubation: demographic and epidemiological data: age, sex, weight, height, date and hour of intubation, on-call procedure, severity scores (Simplified Acute Physiologic Score (SAPS) II at admission, Sequential Organ Failure Assessment (SOFA) score on the day of the procedure), type of admission, reason for ICU admission, indication of intubation, comorbidities. The following parameters will be recorded during the 4 hours before intubation: nature and number of operators, and their training, arterial pressure and lowest saturation, arterial blood gases with calculated PaO 2 /FiO 2 ratio, delay between the time where the intubation is decided and its realisation, presence of vasopressor drugs, prior NIV use, existence of predictive criteria of difficult intubation evaluated by the MACOCHA score.
2  During preoxygenation, the following data will be recorded: the length of preoxygenation, the vital parameters (oxygen saturation at the beginning and at the end of the preoxygenation, lowest oxygen saturation, lowest and highest arterial pressure and heart rate). During the intubation procedure, the following parameters will be collected: doses of hypnotic and neuromuscular blocker used, oxygen saturation at the beginning and at the end, lowest oxygen saturation, mild (<90%), moderate (<85%) or severe (<80%) hypoxaemia, total duration of the intubation procedure, number of operators, number of attempts, Cormack-Lehane grade, traction force on the laryngoscope, Sellick manoeuvre, difficult intubation (more than 2 attempts), modified Intubation Difficulty Scale (IDS score)  28  and occurrence of complications related to intubation.
Statistical analysis A predefined statistical analysis plan will be followed. The statistical analysis will incorporate all the elements required by the CONSORT statement for nonpharmacological interventions. Statistical analysis will be performed in an intention-to-treat population, including all the randomised patients except patients who withdraw their consent, do not meet the inclusion criteria or improve before intubation. All analyses will be conducted by the medical statistical department of the Montpellier University Hospital using statistical software (SAS, V. 9. 3; SAS Institute; Cary, North Carolina, USA, and R V. 2. 14. 1). A two-sided p value of <0. 05 will be considered to indicate statistical significance.
Nevertheless, in the course of our studies, a novel class of eco1D suppressor mutations has been discovered, namely smc1D1164E, which alters a key amino acid within the Smc1 ATPase's D loop, and smc1L1129V, which alters its signature motif (Figure  S4C ). These mutations cause a severe reduction in ATPase activity (without affecting ATP binding) but cause only a mild reduction in the amount of cohesin loaded throughout the genome  (Elbatsh et al. , 2016, in  this issue of Molecular Cell). To test whether smc1D1164E or smc1L1129 affects dissociation of NScc1 from Smc3, we measured their effects on crosslinking between Smc3 S1043C and C56 from a version of Scc1 whose N terminus was tagged with multiple myc epitopes. We used this method instead of using an epitope tag on Smc3's C terminus (as performed in all previous experiments), because tetrad analysis revealed that the latter was synthetic lethal with both smc1D1164E and smc1L1129V (data not shown).
, 2012) , even under glacial climatic conditions. Third, we explore if inter-site differences in surface albedo could explain differences in the energy input for surface snow metamorphism  (Picard et al. , 2012)  and hence differences in δO 2 /N 2 mean levels of the three sites. Surface albedo is currently measured over East Antarctica with MODIS multispectral imager on board TERRA and AQUA satellites. Data collected since 2001 enable to compare the albedo of our three sites of interest (Table  1 ). For this purpose, White Sky broadband albedo data (surface albedo under perfectly diffuse illumination conditions) were extracted from MCD43A3 products (http://www. umb. edu/spectralmass/terra_aqua_modis/v006). Only values for which local solar noon sun zenith angle is less than 65 • and high-quality flags (QA = 0 in MCD43A2 products) are considered  (Schaaf et al.
, 2011). They show similar values at Vostok and EDC (0. 83), and significantly lower values at Dome F (0. 80). This implies that, today, about 15 % more incoming solar radiations are absorbed by Dome F surface snow and can act on its metamorphism. However, surface metamorphism is not simply related to surface albedo. This can be investigated using the grain index time series developed by  Picard et al. (2012). The amplitude of diurnal cycles and grain size near the surface indicate more metamorphism at Dome C than at Dome F. While present-day data provide a hint for possible differences in surface snow metamorphism, further studies are needed to better understand how the surface energy budget controls the surface and subsurface snow metamorphism, and how it can explain the differences in δO 2 /N 2 mean level and phasing between δO 2 /N 2 and insolation forcing at different sites.