dataesr/scientific-paragraphs-categorization huggingface dataset:
Beads were resuspended in Laemmli buffer (62. 5 mM Tri-HCl (pH 6. 8), 25% glycerol, 5% bmercaptoethanol, 2% SDS and 0. 01% bromophenol blue) and boiled before analysis by western blotting using a rabbit anti-TYRO3 (#5585; Cell Signalling) or anti AXL (ABN275; Millipore) antibodies. Immunoblots were analysed with Bio-Rad ChemiDoc XRS Imaging System and quantified using Image Lab Software (Bio-Rad). Production of shRNA clones. Two TYRO3 short hairpins (shRNA), TRCN0000002178 (sh1) and TRCN0000002179 (sh2), two AXL shRNA, TRCN0000000572 (sh3) and TRCN0000000576 (sh4), and a prion protein (shPRPC) were produced into the retroviral vector pRS. The shPRPC vector encodes a shRNA that engages the endogenous RNAi machinery and targets PRPC mRNA; as human cells do not express those proteins, this vector serve as a non-silencing control.
Immunohistochemistry for TYRO3, AXL and GAS6 was performed in a series of 107 sarcomas (23 LMS, 24 SS, 22 DDLPS and 38 UPS), from nationally certified tumour bank of Centre Leon Berard. The TMAs were constructed from representative tumour cores spotted in triplicate along with 5 control samples. A second series of 251 sarcomas of ATGsarc microarray expression data set (ATGsarc database) from French Sarcoma Groups (FSG) was analysed. The ATGsarc database (http://atgsarc. sarcomabcb. org/) is a web accessible database containing genomic alterations and expression profiles of different sarcoma histological subtypes. This database includes 94 LMS, 94 UPS and 63 DDLPS profiled using Affymetrix U133 Plus 2. 0 (Paris, France). Chips that were normalised altogether with GCRMA algorithm. All histologies were centrally reviewed within FSG group of pathologists.
The percentage of cells at different staining intensities was estimated by visual assessment. The overall (average) staining intensity was scored as 0 (none), 1 (weak), 2 (moderate) or 3 (strong), by visualisation at different magnifications. A minimum of 100 cells was evaluated in calculating the H-score. Statistics. GraphPad Prism (version 5) (Graphpad prism, Paris, France) and IBM SPSS v19 software (IBM, Paris, France) were used to compare samples, conditions and groups of treatment. Survival curves were plotted according to the Kaplan-Meier method and compared using the log rank. Unpaired and paired P-values of Po0. 05 were considered to represent significant difference.
Neither AXL and GAS6 nor TYRO3 expression correlates to age, site, gender or grade. TYRO3, AXL and GAS6 expression was not correlated with relapse-free survival or overall survival in the cohort of sarcoma patients, nor in the small series of LMS patients. The second series of patients constitute the ATGsarc database. In all, 251 sarcomas, including n ¼ 94 LMS, n ¼ 94 UPS and n ¼ 63 DDLPS, were used to study TYRO3, AXL, MERTK, GAS6 and PROS1 gene expression. The primary tumours of LMS had a significantly higher expression level of TYRO3 and GAS6 as compared to UPS (Figure  5C  and F ) but lower levels of MERTK (Figure  5E ). Conversely, UPS had higher expression levels of MERTK (Figure  5E ). Protein S expression level was similar in all three histological subgroups (not shown). The PFS of this series (with a median follow-up of 57 months) was then analysed comparing patients with expression levels above and under the mean for all five genes, TYRO3, AXL, MERTK, GAS6 and PROS1.
Ethics and study approval by competent authorities. The study was approved by the Ethics committee of the Centre Leon Berard in March 2012. The sarcoma tumour banks and databases received authorisations from the Advisory Committee on Information Processing in Material Research in the Field of Health (CCTIRS) and the French Data Protection Authority (CNIL). The CCTIRS approval obtained on 24 November 2009; CNIL No. 909510 obtained on 5 February 2010. Written informed consent was received from participants before inclusion in the study.
ACKNOWLEDGEMENTS We thank  Christophe Caux  for valuable contribution. We are thankful to  Rodrigo Nalio  and  Isabelle Durand  for flow cytometry support. This work was supported by the  National Cancer Institute: LYric  ( DGOS-INCA 4664 ),  NetSARC INTERSARC, RREPS ,  Agence Nationale de la Recherche: LabEx DEvweCAN , ( ANR-10-LABX-0061 ),  Eurosarc  ( FP7-278742 ),  La Ligue  contre le  Cancer de L'Ardeche  and Association pour la recherche sur le cancer.
That it is "flat" for percomorphs with a polytomy of orders is a naked acknowledgement that they lacked evidence for the relationships among these groups. The Perciformesthe largest vertebrate order, long regarded as a polyphyletic taxonomic wastebasket (e. g. ,  [41, 42, 50, 51, [57] [58] [59] )was circumscribed to include families not placed in other orders and tagged as a group without synapomorphies. EO Wiley and GD Johnson  [57]  could not create structure where no anatomical evidence for structure existed. Starting around the mid-1970s (and before the era of internet), the most influential source for fish classification has been JS Nelson's Fishes of the World  [38] [39] [40] [41] [42] , receiving more than 9300 citations (Google Scholar, as of March 2017). Another monumental effort that synthesizes knowledge on systematic ichthyology is Eschmeyer's Catalog of Fishes [60], an authoritative reference for taxonomic fish names, featuring a searchable on-line database (http://www.
calacademy. org/scientists/ projects/catalog-of-fishes), with a print version published in 1998  [61]  and a recent list of family-level names  [62]. This database also indicates carefully curated valid names and their synonyms under the classification of JS Nelson's Fishes of the World with modifications. It has been constantly updated since the 1980s and gradually became another obligatory reference facilitated by the pervasive influence of the internet. Only JS Nelson's Fishes of the World uses explicit criteria to justify the taxonomic arrangements, while Eschmeyer's Catalog of Fishes is mostly intended for nomenclatural purposes. The phylogenetic criteria used by JS Nelson to update his classifications, however, have been based mostly on his personal views of the value of morphological evidence to define phylogenetic hypotheses  [63] , resulting in often poorly justified combinations of previous hypotheses in order to achieve a perceived "community consensus" view of phylogeny.
g. ,  [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [86] [87] ). Steady progress towards acquisition of larger molecular datasets via PCR and Sanger-sequencing technology in subsequent years rapidly produced multigene phylogenies (up to 20 gene fragments) that significantly improved our knowledge of fish relationships. The most recent large-scale analyses included hundreds to thousands of species across the Tree of Life of fishes  [8, 10, 11, [88] [89] [90] , many of which contributed to the resolution of the percomorph bush  [54]  into nine well-supported supra-ordinal clades (see below)  [8, 27, 91, 92]. These large-scale studies also provided, for the first time, a monophyletic definition of Perciformes. Most recently, massively parallel ("next generation") sequencing technologies, in combination with efficient methods to capture thousands of markers in a single reaction (e.
g. , target enrichment  [93, 94] ), has ushered in a promising future to tackle difficult phylogenetic questions by analyzing hundreds or thousands of gene fragments  [95]. However, genome-scale comparisons among fishes based on hundreds of loci have been limited so far to studies including a few dozen  [96] [97] [98] [99] [100]  or a couple hundred fish taxa  [101] [102] [103] , largely supporting previous studies based on smaller number of genes (but see  [101] ). Compilation of genome-scale databases to enable large-scale phylogenomic studies of fishes is actively underway  [104] [105] [106] [107] [108]. Here, we present a revised phylogenetic classification for bony fishes based on multi-locus trees inferred for nearly 2000 species. The classification is an update of the three previous versions (including two online updates posted on www.
deepfin. org), originally published by R Betancur-R. , RE Broughton, EO Wiley, K Carpenter, JA Lopez, C Li, NI Holcroft, D Arcila, M Sanciangco, J Cureton, et al. [8]  and built on the Linnean scheme proposed by EO Wiley and GD Johnson  [57]. Our phylogenetic classification has been adopted by several public databases and documentation resources, including NCBI (www. ncbi. nlm. nih. gov/Taxonomy), the Paleobiology Database (www. paleobiodb. org), FishBase (www. fishbase. org), Catalogue of Life (www. catalogueoflife. org [109] ), and OneZoom (www. onezoom. org). The new version presented here incorporates phylogenetic results from recent studies and fixes involuntary errors and omissions. We also highlight and comment all cases where taxonomic decisions made by JS Nelson, T Grande and MVH Wilson  [42]  are in conflict with current phylogenetic hypotheses supporting this classification, as well as the differences with  WN Eschmeyer [60]  and R Van Der Laan, WN Eschmeyer and R Fricke  [62].
Construction and content The phylogenetic framework for this version of the classification (version 4) is based on a recent update of the fish Tree of Life  [27]  with the addition of four clades obtained by large-scale phylogenetic studies: cypriniforms  [102] , non-cypriniform otophysans (i. e. , Characiformes, Siluriformes and Gymnotiformes;  [101] ), percomorphs  [92] , and syngnatharians  [103]. Input subtrees were time-scaled using the R  [110]  package Ape ("chronos" function  [111] ) and grafted to the backbone tree using custom R code (see Additional files 1 and 2) based on secondary age calibrations and functions implemented in the R package phytools  [112]. The secondary calibrations were obtained from a Bayesian analysis of a subset of 201 taxa with 61 fossil age constraints (primary calibrations).
Introduction Gliomas consist of more than 80% of brain tumors. Consequently, the descriptive epidemiology of gliomas is often treated in a wider context of brain tumors. The incidence estimation of brain tumors is often difficult due to the incompleteness of the data collected and the lack of reproducibility of the current classifications. In United States, data collection is centralized by specialized register called Cerebral Brain Tumor Registry of the United States (CBTRUS). In France, two major sources of data are available: the specialized registry of central nervous system tumors of the Gironde existing since 1999 and French Brain Tumor DataBase (FBTDB) existing since 2005. The incidence of central nervous system tumors is currently around 18/100000 in France  [1] [2] [3]  and 21. 42/100,000 in the USA  [4].
Therefore, developing new molecular biomarkers was an urgent requirement. Indeed, the identification of these biomarkers is beneficial for managing and monitoring glioblastoma patients. The purpose of this work is to determine the prevalence of IDH mutations and EGFR amplifications in the northeast region of Morocco population. The results obtained will be integrated as a molecular approach in the diagnostic process and management of glioblastoma in Morocco. The combination of new markers with histological criteria allows establishing a histomolecular classification of these heterogeneous tumors, therefore overcoming the 2007 WHO classification limitations. The magnetic resonance imaging (MRI) data of all patients were reviewed by a senior radiologist to confirm the diagnosis of glioblastoma.
Representing about 30 % of the Earth's total cloud cover, upper tropospheric clouds play a crucial role in the climate system by modulating the Earth's energy budget and heat transport. When originating from convection, they often form organized systems. The high spectral resolution of the Atmospheric Infrared Sounder (AIRS) allows reliable cirrus identification, both from day and nighttime observations. Tropical upper tropospheric cloud systems have been analyzed by using a spatial composite technique on the retrieved cloud pressure of AIRS data. Cloud emissivity is used to distinguish convective core, cirrus and thin cirrus anvil within these systems. A comparison with simultaneous precipitation data from the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) shows that, for tropical upper tropospheric clouds, a cloud emissivity close to 1 is strongly linked to a high rain rate, leading to a proxy to identify convective cores.
Combining AIRS cloud data with this cloud system approach, using physical variables, provides a new opportunity to relate the properties of the anvils, including also the thinner cirrus, to the convective cores. It also distinguishes convective cloud systems from isolated cirrus systems. Deep convective cloud systems, covering 15 % of the tropics, are further distinguished into single-core and multicore systems. Though AIRS samples the tropics only twice per day, the evolution of longer-living convective systems can be still statistically captured, and we were able to select relatively mature single-core convective systems by using the fraction of convective core area within the cloud systems as a proxy for maturity. For these systems, we have demonstrated that the physical properties of the anvils are related to convective depth, indicated by the minimum retrieved cloud temper-ature within the convective core.
g. ,  Luo et al. , 2010; Yuan and Houze Jr. , 2010; Igel et al. , 2014; Takahashi and Luo, 2014; Deng et al. , 2016). They are, however, hampered by the very narrow track and thus are missing the horizontal extent of the system. In this article, we use infrared (IR) sounder data to study mesoscale deep convective systems and, more specifically, their horizontal extent and IR emissivity distribution. The high spectral resolution of IR sounders, in particular the Atmospheric Infrared Sounder (AIRS) aboard Aqua since 2002 and the Infrared Atmospheric Sounding Interferometers (IASIs) aboard MetOp since 2006, allows reliable cirrus identification, both from day and nighttime observations (e. g. ,  Stubenrauch et al. , 2010 Stubenrauch et al. , , 2013)). Using the AIRS physical variables of pressure and emissivity, we reconstruct cloud systems.
This approach distinguishes isolated cirrus from systems having convective core(s), using the IR emissivity as a proxy; an IR emissivity close to 1 has been verified to be closely related to larger rain rate and large-scale vertical ascent (Sect. 2. 2). This provides a unique opportunity to relate the properties of the anvils, including also the thinner cirrus, to those of the convective cores. One of the World Climate Research Programme grand challenges is to determine the role of convection in cloud feedbacks  (Bony et al. , 2015). Compared to databases of tropical mesoscale convective systems from radar and visible-infrared satellite imagery, this database of upper tropospheric (UT) cloud systems from AIRS cloud properties includes cirrus, reliably identified down to an IR emissivity of 0.
1 (corresponding to a visible optical depth of 0. 2). The motivation of this article is to present this database, which, coupled with other data, will provide an observational metric for a better understanding of the interconnection between tropical convection and the heating induced by the outflowing anvils. Proxies of convective intensity/strength or convective depth may be given by vertical updraft (e. g. ,  Liu et al. , 2007; Takahashi and Luo, 2014) , lightning flash rate (e. g. ,  Zipser et al. , 2006) , level of neutral buoyancy (e. g. ,  Takahashi and Luo, 2012) , area of heavy rainfall (e. g. ,  Yuan and Houze Jr. , 2010) , width of convective core (e. g. ,  Igel et al. , 2014) , cold cloud top temperature or height (e. g. ,  Machado and Rossow, 1993; Fiolleau and Roca, 2013)  and mass flux (e.
noveltis. com/) separately for each satellite viewing zenith angle and for about 2000 representative clear sky atmospheric temperature and humidity profiles of the Thermodynamic Initial Guess Retrieval (TIGR) database  (Chevallier et al. , 1998; Chédin et al. , 2003). Since IR sounders, in combination with microwave sounders, were originally designed for the retrieval of atmospheric temperature and humidity profiles, the atmospheric clear sky situation can then be directly described by simultaneously retrieved AIRS L2 atmospheric profiles  (Susskind et al. , 2014)  of good quality (when the situation is not too cloudy) provided by NASA (version 6 available at Goddard Earth Sciences Data and Information Services Center). In the other case, the instantaneous profiles and surface skin temperature are replaced by those of good quality, averaged over 1 • latitude × 1 • longitude or interpolated in time.
, 2013) , these cloud data are very similar for high-level clouds and slightly improved for low-level clouds  (Stubenrauch et al. , 2017). Cloud types can then be defined according to p cld and cld. In this analysis, to facilitate the reconstruction of the UT cloud systems from the AIRS cloud properties, it is convenient to grid the data, keeping the statistics and occurrence of the individual cloud types inside the grid, while the physical parameters (T , p, ) are averaged inside the grid cell. The grid cell size should not be greater than the average size of the smallest cloud system. A good compromise was found by introducing grid cells of 0. 5 • in latitude and longitude.
Cloud type distributions in empty grid cells are obtained from the probability density function (PDF) of the neighboring grid cells. The PDF of an empty grid cell is built as the sum of the neighboring PDFs, normalized to 1, weighted by the inverse squared root of the distance between the grid cells. Similarly, the physical properties of each cloud type in the interpolated grid, such as temperature, pressure and emissivity, are computed using the same weighted average method. In the course of the study, several questions emerged, such as how many neighbors to use and what should be the shape of the region for the neighbors to be included in the interpolation. The reason we draw readers' attention to these details is due to the irregular gap area shape and size which vary with latitude. The optimal filling configuration was deduced by statistically comparing the fractions of each of the UT cloud types in the grid cells with real data and those with interpolated data, but also by visually examining geographical maps of cloud types, such as that in the top panel of Fig.
Then, a spiral scan over the neighbors is performed for a number of cycles which increases linearly with the gap distance. From case studies, we observed that obtaining realistic cloud system shapes requires the scan to be bound vertically to ±3 grid cells, while allowing to scan the horizontal direction freely. As an example, the top panel in Fig. 4  presents a geographic map of cloud types for 1 day in July after the data gap filling. Once the gaps are filled, we apply a composite technique to reconstruct the upper tropospheric cloud systems; adjacent grid cells containing UT clouds and sharing a common side are grouped. The grid cells must contain more than 70 % of UT cloud types within all AIRS measurements in order to be considered in the procedure. For interpolated grid cells, the threshold is set slightly lower, to 65 %, as this 5 % difference corrects for an observed bias in the UT cloud amount of the interpolated areas.
CTH and ETH are also positively correlated, suggesting that convective intensity and convective depth are related. Unlike the relationship between CTH and LNB, larger correlations between CTH and ETH are found over land than over the ocean. CTH is linked to cloud top temperature T cld through the atmospheric temperature profile. Therefore, with AIRS data alone, we are able to explore the anvil properties with respect to CTH by using the T cld of the convective part of the cloud systems. In the following, we will investigate convective depth only for mature convective systems, defined according to Fig. 9  as systems for which the fraction of convective core area varies between 0. 1 and 0. 3. It should be stressed that all the correlations obtained in this section are very well reproduced if a tighter convective fraction interval is used as maturity proxy.
g. ,  Altaratz et al. , 2014). It has been shown that the rain rate decreases during the development and most probably also the strength of the vertical updraft. This means that one needs to undergo time-lag studies or Lagrangian transport studies which closely follow the convective systems and their atmospheric environment to advance further. While these detailed studies will be subject of a follow-on study, which will include additional variables from other data sets, such as ERA5, we can still go one step further with AIRS data alone by investigating the next question: will there also be a difference in the anvil horizontal structure with increasing convective depth? The top panel of Fig. 12  shows the ratio of thin cirrus anvil area over total anvil size as a function of T cb min for mature single-core convective systems separately over land and over the ocean.
Conclusions and outlook We have built upper tropospheric cloud systems, using cloud pressure and emissivity retrieved from 13 years of AIRS observations. These data have been used to investigate the properties of tropical UT cloud systems and, in particular, relationships between the convective depth given by cloud top temperature in the mature stage of a convective cloud system and the properties of the surrounding cirrus anvils. The benefits of the present UT cloud system database compared to other data and methods are that (1) IR sounder data have a large instantaneous coverage and are sensitive to thin cirrus down to an emissivity of 0. 1 (0. 2 in visible optical depth) during day and night, and (2) our cloud retrieval provides the physical properties of altitude and emissivity decoupled, allowing us to reconstruct the horizontal extent of the UT cloud systems and then to distinguish between deep convective cloud systems and isolated systems and to resolve their emissivity structure, which is essential for determining the radiative feedback of the anvils on convection.
This might have important implications for the radiative effects of these systems, in relation to a convection intensity increase in a warming climate. The above findings are very promising and the observed relationships might provide observational metrics for studying detrainment processes with cloud-resolving models or even climate models, if their spatial resolution is similar to the one of our database, and for constraining parameterizations related to convection and detrainment. Combined with variables derived from other data sets, such as vertical cloud structure and corresponding heating rates, atmospheric humidity, surface temperature, level of neutral buoyancy, vertical and horizontal winds, this database will be the basis to address questions on feedbacks between anvils and convection and on their modulation of the atmospheric circulation, in particular with respect to climate change.
Acknowledgements. This research was supported by the  Centre National d'Etudes Spatiales (CNES) , the  Centre National de la Recherche Scientifique (CNRS)  and the  European Space Agency (ESA)  within its framework of  ESA Climate Change Initiative. The authors thank the Earth Observing System AIRS and AMSR-E teams for providing the data. The calculations have been performed at the ClimServ IPSL center. The authors also thank two anonymous reviewers who helped to improve this paper through their thoughtful comments. Edited by: G. Feingold Reviewed by: two anonymous referees.