dataesr/scientific-paragraphs-categorization huggingface dataset:
The antitumor activity of lorlatinib was summarized in terms of objective response rate (ORR), intracranial ORR, DOR, and PFS. The Kaplan-Meier method was used to estimate DOR and PFS. The data cutoff date was August 15, 2016. All analyses were performed using SAS version 9•2 (SAS Institute, Inc. , Cary, NC, USA) with the exception of the unpaired t test for DOR, which was performed using GraphPad Prism (GraphPad Software, Inc. , La Jolla, CA, USA). This study is registered with ClinicalTrials. gov, number NCT01970865.
Role of the Funding Source This study was designed jointly by investigators and representatives of Pfizer. Pfizer collected and analyzed the data. Molecular analysis of tumor specimens was conducted in Dr Alice Shaw's laboratory and funded by an RO1 grant from the National Institutes of Health. The first author wrote the first draft of the manuscript, had full access to all the data, and had final responsibility for the decision to submit the manuscript. All authors (ATS, EF, TMB, BB, AN, SP-V, JFG, MJ, JD, LPJ, JSC, JC, J-FM, AA, and BJS), including Pfizer representatives, had access to the raw data, were involved in data analysis, interpretation, and manuscript preparation, made the decision to publish the manuscript, and vouch for the completeness and accuracy of the data and analyses.
At the time of data cutoff, seven (29%) of 24 patients had a confirmed complete response and four (17%) of 24 patients had a confirmed partial response, for an intracranial ORR of 11 (46%) of 24 patients (95% CI, 26-67%). Of the 19 ALK-positive patients, intracranial ORR was eight (42%) of 19 patients (95% CI, 20-67%) (Figure  1B ). Among ROS1-positive patients, three (60%) of five patients had intracranial responses, including two who had failed prior crizotinib. Of the 32 ALK-positive patients with measurable or nonmeasurable CNS disease at baseline, ten had a confirmed complete or partial intracranial response. Five of the ten responding patients had failed two or more different ALK TKIs (including a secondgeneration TKI), four had failed crizotinib only, and one had failed ceritinib only.
These biopsies may have been collected as de novo samples for the study or may have been collected outside of this study. All biopsies were taken from extracranial sites of disease. For ten samples from Massachusetts General Hospital (MGH), sequencing was performed using local sequencing platforms and these data were stored, summarized, and validated by MGH; for the remaining two samples, next-generation sequencing was performed in a central laboratory and additionally summarized and validated by MGH (see Methods). For each patient, the ALK resistance mutation is shown above the bar corresponding to the patient's best percent change in systemic (ie, extracranial) target lesions according to Response Evaluation Criteria in Solid Tumors v1•1. WT (wildtype) indicates that no ALK mutation was identified in the resistant specimen.
Acknowledgments We thank the participating patients and their families, as well as the investigators, subinvestigators, research nurses, study coordinators, and operations staff. We would also like to thank  Karen Klamerus  for her support of the study. This study was supported by  Pfizer Inc. , by grants from the  National Cancer Institute  ( 5R01CA164273  to  Dr. Shaw ) and the  National Foundation for Cancer Research  (to  Dr. Shaw ), by Be a Piece of the Solution, and by  LungStrong. Meghan Sullivan of inScience Communications ,  Springer Healthcare , provided editorial support funded by  Pfizer, Inc.
The aforementioned techniques provide cloud properties typically averaged over 1 km, which may be insufficient to study cloud processes at a microphysical scale (i. e. , measurements of microphysical cloud properties, spatial resolution less or equal to 100 m). In situ observations are based on direct measurement techniques at a higher spatial resolution (generally < 100 m). Numerous previous studies dedicated to the assessment of the in situ microphysical properties of Arctic clouds focused on specific case studies  (Avramov et al. , 2011; Gayet et al. , 2009; Rangno and Hobbs, 2001; Verlinde et al. , 2007). Statistical analysis of mixed-phase cloud properties derived from several in situ datasets or airborne campaigns are very scarce and often focus on the western Arctic region  (McFarquhar et al.
Moreover, the data were all collected during spring (April and May). For this study, we restricted the measurements to continuous ascent and descent flight sequences into single-layer MPCs at the aircraft speed (between 80 and 100 m s -1 for all campaigns) since our main objective is to study the vertical partitioning of ice and liquid thermodynamical phases. Our dataset consists of 71 cloud profiles (see Table  1 ) representing more than 21 000 measurement points at 1 Hz (350 min of cloud observations), spread out over 18 flights performed above Arctic open sea water.
In situ instrumentation A similar in situ instrumentation was loaded on the three aircraft: the German Polar-2 and Polar-5 and the French ATR-42. The same data processing procedure was used in order to derive the cloud microphysical parameters (at the same scale: i. e. , ∼ 100 m). This consistent cloud dataset is used to achieve a statistically representative description of the properties of Arctic mixed-phase clouds sampled over the Greenland and Norwegian seas during spring. The suite of in situ instruments used to measure the MPC microphysical and optical properties consists of the following: -The cloud particle imager (CPI;  Lawson et al. , 2001)  captures cloud particle images on a 1024 × 1024 pixel CCD camera with a pixel resolution of 2. 3 µm and 256 grey levels. At least 5 pixels are necessary to identify a cloud particle, so the particle sizes derived from the CPI range from 15 µm to approximately 2 mm.
The images are processed using the software developed at the Laboratoire de Météorologie Physique (LaMP;  Lefèvre, 2007)  based on the original CPIView software  (Lawson et al. , 2001; Baker and Lawson, 2006). In par-ticular, it provides particle size distribution (PSD) and derived microphysical parameters such as particle concentration, effective diameter, extinction coefficient and ice water content as well as a particle habit classification. The data processing method used to derive the extinction coefficient (σ ) and the ice water content (IWC) is described in Appendix A. -The forward scattering spectrometer probe (FSSP-100;  Baumgardner et al. , 2002; Knollenberg, 1981)  provides the droplet size distribution from 3 to 45 µm. The derived parameters from the PSD are the droplet concentration, the effective diameter, the extinction coefficient (σ ) and the liquid water content (LWC).
The retrieval method used to determine the liquid water content is described in Appendix A. All these cloud probes were heated in order to avoid icing during the flights. The combination of these probes provides the microphysical properties of cloud particles from a few micrometers (typically 3 µm) to about 2 mm. Data are recorded at 1 Hz frequency, which corresponds to a spatial resolution of about 100 m (according to the aircraft speed). The uncertainties and measurement ranges associated with the derived cloud parameters are summarized in Table  2. However, it should be noted that in situ measurements' accuracy may be hampered by the shattering of large ice crystals on the probe inlets, inducing smaller particle artifact  (Heymsfield, 2007) , leading to an overestimation of small particle concentration.
Thus, an altitude of 1 corresponds to the top-of-the-cloud liquid-containing layer and 0 to its base. Negative values characterize regions of ice precipitation below the cloud layer and the altitude of -1 defines the ground level according to Eq. (2). To obtain representative statistical results, the cloud layers have been stratified in 10 levels with intervals of 0. 2 of normalized altitude, each containing around 2000 observations (i. e. , about 10 % of the dataset). The vertical profiles of MPC microphysical properties presented hereafter are obtained by averaging the in situ measurements over each normalized altitude layer. The profiles are computed for the whole dataset and for each main meteorology situation separately (see Sect. 2. 5) for a better analysis and discussions of the results.
Meteorological situations All the selected situations correspond to low-level singlelayer mixed-phase clouds in the boundary layer during spring. If these criteria ensure the homogeneity of the dataset, weather conditions still vary significantly from one campaign to another or even within a campaign. In order to provide a comprehensive dataset to improve model parameterization, it is of great importance to discriminate and classify the observations depending on environmental conditions. The most trivial classification is the temperature regime. Savre and Ekman (2015) , showed that it is one of the major factors (with cloud top entrainment) controlling the production of new ice crystals and the maintenance of MPCs. In the present study, two temperature regimes have been selected based on the mean cloud top temperature of each situation: the "cold" situations (-22 • C < T Top < -15 • C) and the "warm" situations (-15 • C < T Top < -8 • C).
We made the choice of only two temperature regimes and two main air mass origin in order to ensure representative and statistically significant datasets for each class. Table  5  shows that all the cold situations are correlated with a north origin air mass (blue in Table  5 ). Among the 12 warm situations, 7 correspond to air masses originating from the north (green in Table  5 ) and 5 from the south/east (red in Table  5 ). So, at the end, this classification leads to only three types of situations: (i) cold cloud top temperature situations with air masses originating always from the north (hereafter referred to as COLD cases); warmer situations with air masses which originate either (ii) in the north (hereafter WARM_NO cases) or (iii) from the continent: south and east (hereafter WARM_SO cases).
(2011)  or  Rangno and Hobbs (2001)  who showed that "polluted" MPCs exhibit higher droplet concentrations and smaller ice-precipitating particles compared to "clean" MPCs. A large number of droplets are expected to reduce the riming process and thus contribute to the large observed values of LWC (as liquid droplets are not consumed by the ice crystals). To go further into the analysis of our microphysical dataset, additional measurements of key parameters are necessary. In particular, quantifying the mechanisms responsible for the formation and growth of droplets and ice crystals within MPCs by measuring the numbers of IN and cloud condensation nuclei (CCN) is needed. It would enable us to perform an accurate ice closure and to quantify, for example, the possible impact of secondary ice production processes).
Conclusions and outlook In this study, a characterization of Arctic boundary-layer mixed-phase clouds microphysical properties has been performed. In situ data from four airborne campaigns over the Greenland Sea and the Svalbard region are compiled and analyzed. The dataset represents in total 18 flights and 71 vertical profiles in MPCs (more than 350 min of cloud in situ observations). Cloud phase discrimination is achieved and vertical profiles of the number, size, mass and shape of ice crystals and liquid droplets within MPCs are determined. The main conclusions of the present work are summarized as follows: i. Liquid phase is mainly present in the upper part of the MPCs with high concentration of small droplets (N w ∼ 120 cm -3 , D eff,w ∼ 15 µm) and averaged LWC around 0. 2 g m -3. Ice crystals are present everywhere in the MPCs with no significant vertical variability (N i ∼ 3 L -1 , D eff,i ∼ 100 µm, IWC ∼ 0.
) parameterization are more similar to the present work; they used measurements in Arctic MPCs over the Greenland Sea. The dataset was collected during spring and summer, above open sea, ice sheet and transitions. This variability in the seasons and surface conditions may explain the differences observed compared to the present work. Above all, Young et al. (2017) displayed an averaged concentration, whereas the maximum ice number is presented here. However, even by taking the averaged ice concentrations in the present work, the parameterization does not match with that of Young et al. (2017) (not shown here). Finally, the detailed time series displayed in the Young et al. (2016) and Lloyd et al. (2015) works which present the cases used for the determination of the parameterization of Young et al.
Acknowledgements. This research was funded by the  Centre National de la Recherche Scientifique -Institut National des Sciences de l'Univers (CNRS-INSU)  and the  Expecting EarthCare Learning from A-Train (EECLAT)  project. We thank the  Alfred Wegener Institute (AWI)  and the  Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE)  for the organization of the campaigns and for providing research aircrafts. The authors acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and READY website (http://www. arl. noaa. gov/ready. html)used in this publication. We thank  Peter Tunved  from the  Stockholm University  for providing the aerosol data from Mount Zeppelin station via the EBAS database. We thank the anonymous reviewers who made very useful comments that strengthened the manuscript.
Results The Gräfenberg array was installed between 1976 and 1980, and continuous records are available for a period of forty years. We analyzed vertical component records of the first four stations to be installed for which the instrumentation was identical over 30 years, i. e. with the longest time series (GRA1, GRA2, GRA3 and GRA4; see Fig. 1 ). The data recorded after instrument replacement (end 2006) was not included, to exclude instrument related bias. The interstation distance is either around 10 km or 20 km (Fig. 1 ). The scope of the data processing was to obtain the average long-term evolution in the study area of the relative changes dv/v of the coda of fundamental mode Rayleigh waves using all 6 station pairs, based on correlation of seismic noise. As the target of this study are groundwater systems, we focus the analysis on seismic noise in the second microseismic peak (around 7 s period), which in northern Europe is dominated by seismic waves created by wave interaction with the sea floor in the North Atlantic and adjoining coasts  [20] [21] [22].
3. A general inversion scheme was applied to calculate average dv/v k for the whole array, then smoothed to obtain the long-term variations  25. The measured dv/v characterizes the medium in an area larger than that covered by the GRA1-GRA4 network, as the analyzed coda waves are formed by locally scattered waves. Even though the sensitivity kernel fits inside a large circular shape of 135 km radius, the use of several station pairs means that the overlapping part of the sensitivity kernels will reinforce the weight of the local structure. Note that the observed dv/v is bound within ± 0. 01%, i. e. an order of magnitude smaller than velocity variations associated with volcanic eruptions and earthquakes  10, 25, 26. The stability of the results has been tested using two tests (see Methods: Stability testing): by removing one station (and therefore half of the station pairs) from the analysis, and by randomly selecting data points (bootstrap) for the analysis.
While the details may differ between tests, all main conclusions hold. Figure  3  shows the velocity variations using the whole dataset as well as results of the test where data from individual stations are removed in turn. All the curves show consistent velocity variations with large interannual variability over the 30 years of data. The general trend is an increase of dv/v over the whole time period at a rate of approximately 10 -5 per year, with small additional oscillations and with a large bulge during the 1989-1996 period. The differences between the curves of Fig. 3  indicate that it may be possible, as for volcanic eruptions and earthquakes  [27] [28] [29]  , to extract information about laterally varying processes within the subsurface from such noise correlations. Indeed, the lateral variability could be explained by the 3-D geometry of the aquifers, as subsurface water storage is constrained by the natural heterogeneity of geological layers.
Conclusions We demonstrate the ability of long-term monitoring using ambient seismic noise to highlight modifications in mechanical properties in the near subsurface and link them to environmental and climatic external origin. The 30-year seismic observations on the Gräfenberg array are an exceptional long-term dataset to investigate long-term changes in subsurface mechanical state. Careful processing of seismic velocity variations on the 1. 2 s to 10 s frequency band highlights significant changes at interannual time scales, in the order of 0. 01%. The observed velocity variations can be generated equivalently by large velocity changes in localized subsurface layers (typically groundwater) or by a diffuse change throughout the subsurface layers (typically from thermo-elasticity, but also for groundwater at long time scales).
Knowledge of interannual temperature variations is key to estimate long-term seismic velocity changes, for two reasons. First, temperature evolution is known to be non-stationary, generating long-term effects on mechanical properties of the uppermost layers of the Earth. Second, the heat wave penetration depth is increasing with increasing period of this wave, leading to amplified strains in the long-term. The modeling, which aims at providing an approximate estimate of the effect of long-term temperature variations on seismic velocities in a form comparable to the observed dv/v, has 4 steps: Monthly temperature variations for the Gräfenberg area. Surface temperature variations T s (t) are extracted in a 100 km region surrounding the Gräfenberg array, from the CRU TS3. 23 temperature dataset  36  , which provides ½ degree temperature analysis globally at monthly time scale for 1901-2014.
13 ). Discharge data is available on the website of the Bavarian State office for Environment (http://www. gkd. bayern. de). Considering the large integration of seismic velocity variations, ground water processes are expected to dominate over the strongly time variable surface processes. Therefore, we use data from the station, Bärenthal, which best represents groundwater storage changes, i. e. with the smallest temporal variability. The model is validated using long-term groundwater data monitored in station Kunreuth (no 5179), outside the modeled catchment. Precipitation data is provided by E-OBS  35  , which is available at a ¼ degree resolution at daily time scales from 1950 to 2015. Potential evapotranspiration is extracted from CRU TS 3. 23 dataset  36. The Atlantic Multidecadal Oscillation index is downloaded from http://www.
Acknowledgements The whole processing workflow done in Python using MSNoise which heavily relies on the ObsPy toolbox and NumPy. The map was drawn using QGIS and most figures were drawn using Matplotlib. Developments of new tools for the exploration of the critical zone is part of the  CRITEX  project  ANR-11-EQPX-0011. A. Somerhausen ,  D. Hollis  and  J. Rabinowitsh  are acknowledged for proofing the manuscript.
As pre-specified  23  we present functional outcome (mRS) at 3-6 months, but mortality at 90 days. All estimates of treatment effect are provided with their 95% CIs with p values that are deemed conventionally significant, without allowance for multiple testing, at the 5% significance level. Analyses were done using SAS version 9. 3 (SAS Institute, Cary) and R version 2. 11. 1 (https://www. R-project. org).
Discussion The regulations that govern medicinal products seek to ensure that only products that meet standards of quality and safety, and have reasonable evidence of efficacy, are marketed. However, there are important differences between patient eligibility for intravenous alteplase treatment according to scientific guidelines and to marketing authorizations in both US and EU. The present analyses from the STT Collaboration indicate that patients who would have met the criteria of the AHA or ESO guidelines but would have been excluded by the regulatory authorization derived significant and clinically useful benefit by 90 days, with an early increased risk of death from intracerebral hemorrhage completely offset by a later improvement in stroke survival. Within the STT dataset, only two-fifths of patients (mean treatment delay 3.
4 h) would have met the current criteria of the EU label, but this rose to over half (57%; mean delay 3. 3 h) when the age criterion was removed (in accordance with ESO guidelines). For this extended group, alteplase yielded an excellent functional outcome with an OR of 1. 43 (1. 23À1. 65), without any excess risk of 90-day mortality (16. 1% with alteplase and 16. 5% with control: HR 1. 01, 0. 86-1. 19). The current US label suggests a shorter time window for treatment than the EU label, and only 19% (mean delay 2. 3 h) of the individuals in our dataset would have been included by it. A 4. 5-h-revised version of the US label, however, would have been met by 54% of individuals (mean delay 3. 3 h), and among these patients the effects of alteplase closely matched those of the age-revised EU label, with an odds ratio for achieving excellent outcome of 1.
The validity of our analyses is enhanced by having access to all the individual patient data, common definitions for most data items, and stratification of the analyses to retain any influence of individual trials on the outcome. Alignment of the US and EU labels on age and time window may not increase uptake of thrombolysis in routine practice by the proportions suggested from our dataset. The case mix of patients enrolled to the thrombolysis trials represents a combination of various eligibility criteria applied to a population case mix which itself will have changed over the 20 years since some of these trials were Based on a snapshot survey of 10,633 recent thrombolysis treatment registrations in the United Kingdom (UK) undertaken by one author (KRL, unpublished data), we estimate that 42% more patients would meet a 4.
5-h-revised US label than the current US label, and 36% more patients would meet an age-revised EU label than the current EU label. These are likely to be conservative estimates, since some stroke physicians in UK may presently restrict their use of thrombolysis to the current EU label criteria. A recent analysis of 56,689 patients' data from 597 sites registered to the SITS international registry over 6. 5 years reported that if all patients were treated by using ESO guidelines, an additional 17,031 would receive alteplase, which translates into 1922 more patients with favorable three-month outcomes. 26.
JMO has received honoraria from  Astra Zeneca ,  Boehringer Ingelheim ,  Bristol Myers Squibb ,  Pfizer , and Servier. PAGS declares that he was the Chief Investigator of the IST-3 trial, funded by the  Medical Research Council , the  Stroke Association  and the  Health Foundation , the  IST-3 pilot study was supported by a donation of drug and placebo from Boehringer Ingelheim , and PS has received Honoraria and Travel expenses paid to his Department from  Boehringer Ingelheim. DT reports honoraria from  Boehringer Ingelheim ,  Bayer , and  Pfizer. KT has received research grant support from the  Japan Agency for Medical Research and Development  and fees from  Mitsubishi Tanabe Pharma. JMW declares trial funding from the  Medical Research Council, Efficacy and Mechanism Evaluation Programme ,  Stroke Association , and  Health Foundation.