dataesr/scientific-paragraphs-categorization huggingface dataset:
We would like to be able to identify the presence or absence of a CME from "Sun-as-a-star" observations, noting that stellar observations of CMEs are difficult  (Leitzinger et al. , 2014)  but important for the question of exoplanet habitability and long-term stellar angular momentum loss. This event list generally has the best observational database available, since it is recent, and the X-class flares are the most powerful solar events, approaching in terms of energy the low end of the energy range for which stellar flares are detectable. At present, there is much discussion relating the morphology of flares and CMEs to emerging flux, sheared fields, magnetic cancellation, interacting coronal loops, sigmoid structures, etc. , but all of these properties tend to be qualitative in nature. As such, the aim of the present study is to question the possibility of a statistical study on the largest flares recorded at high spatial and temporal resolution with the Solar Dynamics Observatory (SDO;  Pesnell, Thompson, and Chamberlin, 2012)  to provide more clues to constrain a generic model.
The Hinode EUV Imaging Spectrometer (EIS) The Hinode/EIS  (Culhane et al. , 2007)  is a scanning slit spectrometer observing in two wave bands in the EUV: 170 -210 Å and 250 -290 Å. The spectral resolution is 0. 0223 Å pixel -1 , which allows velocity measurements of a few km s -1. We analyzed data from EIS to explore the temperature at which the flows change from red-shifted to blue-shifted during a flare. The standard calibration was used through the routine eis_prep. Additionally, the slit tilt and the orbital variation of the line position were corrected. For each pixel we fitted the line using a single Gaussian profile producing an intensity and Doppler-velocity "sparse image" for each raster scan.
The Flares We choose a time period from when SDO/AIA data were available in May 2010 until November 2014. During this period 42 X-flares occurred and nine had no CMEs of any significance. Table  1  summarizes the X-flares. The CMEs were determined from the LASCO catalog. The events were thus described as either eruptive or non-eruptive. Note, however, that this is an extreme case of small-number statistics, since 6/9 of the non-eruptive flares in our sample came from the single region NOAA 12192. We also determined whether the events had solar energetic particles (SEPs) related to them or not. It is generally accepted that SEPs can be accelerated through two methods  (Masson, Antiochos, and DeVore, 2013)  -through impulsive flare that inject particles in the heliosphere via open field lines and through CMEs through which shock fronts play an important role in the acceleration process  (Reames, 2013).
He found that CME onsets are associated with flares of any duration with ≈ 6 % of flares having less than 1 hour duration and 60 % under two hours. The peak GOES intensities of CME-associated flares could be of any value, but they are systematically larger than for the average solar flare. Andrews (2003)  also carried out a statistical study of 311 M and X-class flares and their SOHO/LASCO and EIT datasets. This study found that 40 % of the M-class flares did not have an associated CME. This also supports the work of  (Harrison, 1995)  where the intensity and duration of flares were found not to guarantee a CME association. More recent statistical work also making use of the SOHO/LASCO data was carried out by  (Yashiro et al. , 2005)  who analyzed 1301 X-ray flare events. These authors found that the CME-associated flares increased monotonically with flare GOES class from about 20 % for C-class flares to 100 % above the X3 level.
Does Spot Area Correlate with Flare Magnitude? Figure  1  shows that no spot area-flare magnitude correlation exists within our database, yet it is widely believed that active regions with the most complex and largest sunspot groups are more likely to produce more powerful flares  (Sammis, Tang, and Zirin, 2000). Such Figure  4  GOES soft X-ray light curves for four non-eruptive flares, with the same time scale as that of Figure  3. characteristics are measured in flares and used as input into space weather flare forecasts, and so it is important to understand clearly what is meant -our result seems to contradict the conclusions of  Sammis, Tang, and Zirin (2000). The explanation probably lies in the fact that  Sammis, Tang, and Zirin (2000)  used the maximal flare and maximal area for a given region, ignoring the time variation; their resulting correlation presumably relates more to the availability of a magnetic field than to its organization into sunspots per se.
This event shows prompt dimming and had an accompanying CME. • Is the occurrence of CMEs related to the fraction of active-region area involved? The hypothesis here is that larger flare ribbons would indicate higher altitude reconnection, which might make it easier for eruptions to escape. However, we found no difference between those with and without CMEs. • Do X-flares with no eruptions have greater non-thermal energy? For those flares where non-thermal energy was estimated from the FERMI data there was no systematic difference between the flares with and without CMEs. • Is the temperature dependence of evaporation different in eruptive and non-eruptive flares? Once again, we found no difference between flares with or without CMEs. • Is EUV dimming only seen in eruptive flares? Yes -EUV dimming is seen for all eruptive X-flares and the dimming will start within an hour after the peak of the flare.
The only characteristic that seems to show a consistent difference between flares with and without CMEs is that of coronal dimming. This can be observed distinctly in the spatially unresolved EUV light curves from EVE, in principle providing an ideal candidate for comparison with the stellar case. However, it is difficult to have similar spectral coverage for the stellar observations. The NASA Extreme Ultraviolet Explorer (EUVE;  Bowyer, Malina, and Marshall, 1988) , which stopped observing in 2001, had the closest overlap in wavebands covering 90 -150 Å, 180 -350 Å. To our knowledge EUVE data provided no evidence for coronal dimming events. Modeling and theory should be developed to understand the dimming process not only for the Sun, but also for the case of larger sunspot groups, and on faster-rotating stars.
Acknowledgements The authors thank  ISSI  for the support of the solar-stellar team. This CME catalog is generated and maintained at the CDAW Data Center by  NASA  and  The Catholic University of America  in cooperation with the  Naval Research Laboratory. SOHO is a project of international cooperation between  ESA  and  NASA. The  Solar Dynamics Observatory  is a mission in the  NASA Living With a Star program. Hinode is a Japanese mission developed and launched by  ISAS/JAXA , with  NAOJ  as domestic partner and  NASA  and  STFC (UK)  as international partners. It is operated by these agencies in cooperation with  ESA  and  NSC (Norway). TL acknowledges funding of the  Austrian FFG  within  ASAP11  and the  FWF   NFN  project  S116601-N16. MJ thanks  V. Bommier  for providing the UNNOFIT-inverted maps used in the active-region area calculations.
We aim to provide an innovative, comprehensive way of mapping the profusion of stem cell-based clinical trials registered at ClinicalTrials. gov to explore the diversity of the fields of application and the temporal complexity of the domain. We used a chord diagram and phylogenetic-like tree visualizations to assist in data mining and knowledge discovery. The search strategy used the following terms: "stromal OR stem OR mesenchymal OR progenitor. " The Medical Subject Headings (MeSH) thesaurus was used to more finely classify diseases treated by stem cells, from large fields of application to specific diseases. Of the 5,788 trials screened, 939 were included, 51. 1% of which were related to mesenchymal stem cells (MSCs). No real specificity emerged as to the therapeutic uses of the different types of stem cells.
One requirement for knowledge discovery in databases (KDD) is to work with relevant, up-to-date databases associated with low noise and errors  [23]. Different sources of data can be used. Analyses of published studies take advantage of the scientific peerreview process, but publication is often delayed and negative trials are less likely to be published. This leads to a retrospective, and potentially biased, point of view. During the 1990s, clinical trial registers were strongly promoted in biomedical research, with the aim of revealing the existence of all trials and eliminating publication bias  [24]. The International Committee of Medical Journal Editors required registration of all trials starting enrollment after July 1, 2005, and of ongoing clinical trials that began enrolling patients before that date  [24].
Exploration of these registers gives a more up-to-date and representative snapshot of the complexity of the young and constantly evolving field of stem cells  [25, 26]. Among the different registers, the ClinicalTrials. gov database (CTD) is one of the best designed for aggregation and analyses  [27]. Launched in 2000, concomitantly with the setting up of stem cell related trials, this worldwide register contains detailed, standardized characteristics of each trial, particularly keywords describing disease conditions using the Medical Subject Headings (MeSH) thesaurus, as furnished by the U. S. National Library of Medicine. This provides an in-depth classification and enables computer tools to be used for interpretation at both low (fields of application) and high (specific diseases) granularity.
From this database, we screened 5788 trials and, using computer tools, comprehensively mapped human stem cell-based clinical trials using a chord diagram and phylogenetic-like tree visualizations. This revealed no real specificity in the uses of stem cells, including MSCs, in the different fields of application. The lack of an apparent global strategy despite the regulations (e. g. , requirements for advanced-therapy medicinal products in Europe and good tissue practice in the United States  [28] ) and the costs associated with good manufacturing practices may be hypothesized and are probably linked to the field's strong intention to reach the market. Increasing interest in cultured and allogeneic cells compared with the heterogeneous fraction and autologous uses, respectively, was revealed.
Dataset Selection, Data Cleaning, and Preprocessing The search strategy used the keywords "stromal OR stem OR mesenchymal OR progenitor. " All trials were exported into XML format, then parsed into local database. Trials were included if (a) cell therapy was based on enriched/purified/sorted stem cells or expanded stem cells and (b) efficacy or safety of stem cell therapy was stated as an objective of the trial. For each trial included, the original tissue for cells and the donor (autologous or allogeneic) were recorded. Only the "Diseases" branch (C) of the MeSH classification is used by the CTD to describe study conditions. This branch contains 26 sub-branches, corresponding to major disease groupings. If the CTD had failed to attribute MeSH keywords to a trial, two authors (P. M. and J.
Data Mining A phylogenetic-like representation was obtained by using the Interactive Tree of Life (http://itol. embl. de) [30]. Because phylogenetic trees were developed to schematically represent kinship relationships between groups of living beings, we used this type of representation to visualize relationships between diseases described in the MeSH classification. All MeSH descriptor terms extracted from included trials (disease conditions) were represented around a rooted tree. Summary statistics for each term, together with individual lists of the related trials, were provided. Internal divisions of the tree were represented by sublevels of these terms (ancestors). Two major datasets were represented around the tree (online data): trial phases and start dates. Chord diagrams  [31]  were used to analyze double-entry tables (http:// mkweb.
The limitations of this study concerning the representativeness of this dataset should be considered. Other sources could have been consulted, such as the International Clinical Trials Registry Platform (ICTRP), which provides access to trials from several worldwide databases, including CTD (which accounts for about 60%-80%  [52, 53] ). Unfortunately, ICTRP displays great disparities in data quality among the different registries, particularly for important elements, such as primary outcomes and intervention details; this makes it difficult to conduct a systematic review  [52]. CTD was chosen because it has more detailed and standardized exportable characteristics and registers a majority of trials. For instance, a search performed in ICTRP with the keywords "mesenchymal stem cells" indicated that at least 408 of 562 trials (73%) were registered in CTD.
Furthermore, disease misclassification, although variable according to clinical specialties, is low  [27] , and some registered data even better reflect reality than the final publications  [54]. Another limitation was that registered trials make up only a part of all existing trials  [24]. It has been suggested that only 50% of clinical studies indexed in PubMed that involved administration of cells for regenerative medicine indicated any clinical trial identifier  [53]. In fact, the final dataset is only the tip of the iceberg, and the explosion of cell therapy outside the framework of academic research strongly concerns the scientific community. All around the world, private clinics already offer stem cell tourism, routinely injecting MSCs provided by some companies with nonvalidated or unproven procedures  [55] [56] [57] [58].
Because these clinics often use registries to gain legitimacy, an important implication of this work would be to carefully analyze final publications, especially from these companies, to assess the quality of and transparency of cell culture/procedures, cell controls, study design, reporting, placebo-controlled efficacy, and transparency about cell culture/processing and ethical considerations  [25, 59, 60]. Another limitation is semantic. The frequent confusion between expanded cells and heterogeneous fraction (particularly the term "stem" for CVD) does not facilitate analysis of this field. For instance, in NCT01788059, "injection [of] mesenchymal stem cell" was used, whereas the authors described the isolation of BMMNCs using Ficoll gradient. Similarly, the term "adiposederived stem cells" has been used by some authors to describe the injection of stromal vascular fraction (e.
Product basis set The eigenstates entering the response function (  2 ) are sought within LCAO Ψ n (r) = a X n a f a (r), (7) where the expansion coefficients X n a are determined in a diagonalization procedure. The atomic orbitals f a (r) are given by a product of radial functions and spherical harmonics. The LCAO solution is setup and solved within the DFT package SIESTA  [44, 45]. The products of eigenstates Ψ * n (r)Ψ n (r) in equation  (2)  give rise to products of atomic orbitals f a * (r)f b (r). Furthermore, we aim at solving the integral equation (3) for the interacting response function. In order to turn this equation into an algebraic equation that is easily solved, one has to use a basis set of functions that are capable to span the space of atomic-orbital products. This set of basis functions should be as small as possible and contain preferably localized functions.
With respect to electronic structure, it is well known from the literature that LDA and GGA predict a too low onset of the d-bands in the solid and this reduces the intensity of the low-frequency plasmonic resonance produced mainly by the s-electrons  [79]. This deficiency of LDA and GGA can be corrected by using so-called long-rangecorrected (LRC) xc functionals which contain a portion of Fock exchange  [41, 37, 74] , the van-Leeuwen-Baerends explicit ansatz with the correct asymptotic behavior  [80] , or orbital-dependent functionals  [81, 11]. Unfortunately, these functionals are not available within the publicly-available version of the SIESTA package  [44]. Moreover, the LRC functionals referenced above give rise to a non-local two-point TDDFT kernel, which is either not known or computationally too expensive to treat the large systems addressed here.
Choice of atomic orbital basis set The choice of the atomic-orbital basis set determines the quality of LCAO calculations to a large extend. The numerical orbitals used in the SIESTA package are capable to approach the results of plane-wave calculations for bulk systems  [86, 87] , at least for ground state properties and the description of the low lying unoccupied states. At the same time, it was found that semi-infinite systems (surface properties) need special care when being described using confined NAO  [88]. Namely, it was found that adding a single layer of floating orbitals can considerably improve the surface properties with only a small impact on the computational performance. Because the surface-to-volume ratio of our target clusters is rather large  [1] , we assess the quality of the default SIESTA basis set by augmenting it with an extra layer of floating orbitals.
Only valence electrons (5s 1 4d 10 ) are represented in the atomic orbitals set, which give rise to 15 atomic orbitals per atom in the DZP basis set. Moreover, we added a layer of floating orbitals (see subsection 2. 6) in the cluster calculations and two layers of floating orbitals-inner and outer-in the case of shell geometries. Coordinates of these "ghost atoms" were kept fixed during relaxations. The core electrons are removed by using the pseudo-potential (PP) of Troullier-Martins type. The PPs were generated with the ATOM program, part of the SIESTA distribution. The parameters for PP generation were taken from the SIESTA database [93] except for the use of Wu and Cohen functional. It is interesting to note that the optimized PP described in Ref. [94] , which is supposed to provide a band structure in better agreement with all-electron calculations, failed to satisfactorily describe the optical absorption cross section.
Acknowledgments This work is supported, in part, by the  ORGAVOLT (ORGAnic solar cell VOLTage by numerical computation ) Grant  ANR-12-MONU-0014-02  of the  French Agence Nationale de la Recherche  ( ANR) 2012 Programme Modèles Numériques. Federico Marchesin ,  Peter Koval  and  Daniel Sánchez-Portal  acknowledge support from the  Deutsche Forschungsgemeinschaft (DFG)  through the  SFB1083  project, the  Spanish   MINECO MAT2013-46593-C6-2-P project , the  Euroregion Aquitaine-Euskadi program  and from the  Basque Departamento de Educación, UPV/EHU  (Grant No. IT-756-13 ). Peter Koval acknowledges financial support from the  Fellows Gipuzkoa program  of the  Gipuzkoako Foru Aldundia through the FEDER funding scheme of the European Union.
Then, gene expression data from resting CD19 þ human tonsillar B cells and human SMZL samples were used to define a human SMZL transcriptional signature (B40; FDRo0. 03; Supplementary Data 2), which could then be compared to the mouse lymphomas. Unsupervised clustering analysis grouped human SMZL and murine 18-month-old lymphoma samples together (Supplementary Fig. 3b ). Accordingly, the human SMZL transcriptional signature appeared overrepresented in the murine 18-month-old lymphoma transcriptional datasets, but not in the 12-month-old lymphoma datasets (hypergeometric test, P ¼ 0. 0098, and P ¼ 0. 51, respectively). Likewise, comparison of the NKX2-3 mouse lymphoma transcriptional signature with two previously published human SMZL gene expression data sets revealed a significant overlap (P ¼ 0.
Although our model recapitulates many aspects of human marginal-zone lymphoma biology, we think it has limitations that include the long tumour latency (which on the other hand reflects the slow development of SMZL observed in patients), and the fact that tumours are triggered by the activation of NKX2-3 in B cells, which is only observed in a fraction of patients with MALT lymphoma and SMZL. In summary, our study reveals that oncogenic NKX2-3 promotes B-cell lymphomagenesis by activating BCR signalling and disturbing lymphocyte dynamics, and provides an experimental mouse model that recapitulates the major features of human SMZL. Finally, our data suggest that targeting the BCR with inhibitors of Lyn and Syk kinases may be of therapeutic value in patients with marginal-zone lymphomas.
When indicated, the quantifications of bands from western blot was performed by densitometry, and subsequent analysis using Quantity One software (Bio-Rad). Generation and characterization of NKX2-3-deficient mice and El-NKX2-3 transgenic mice. Knock-out mice for Nkx2. 3 gene have been previously reported  11. Human NKX2-3 cDNA was cloned into the HindIII SacI of the pEmSR vector (kindly provided by Jerry Adams), to obtain the pEmSR-NKX2-3 vector. A 4. 455-bp PciI AatII fragment containing the NKX2-3 gene was isolated from the pEmSR-NKX2-3 vector, purified and injected into fertilized oocytes of C57BL/6 mice background. Mice were crossed onto C57BL/6 genetic background and housed in the specific pathogen-free animal facilities of our centre. All the experiments were conducted with protocols approved by the Ethical Committee of Animal Experimentation of the University of Navarra.
For each age group, at least eight mice were analysed. The sagital and longitudinal spleen lengths were measured by micro-ultrasound imaging using the Vevo770 high-resolution in vivo micro imaging system (VisualSonics), and data were analysed using the Vevo770 v. 3. 0 software. Flow cytometry analyses. BM, PB, spleen, thymus and lymph node nucleated cells were extracted from Nkx2-3 À / À , Em-NKX2-3 and matched healthy C57BL/6 mice. Preparation of the cells for flow cytometry and staining studies was performed according to the standard procedures  44, 50. Cells were acquired in a cytometer FACScalibur (BD Biosciences), and further analysed using the FlowJo 7. 6. 3 software. Monoclonal antibodies were obtained from BD Biosciences, except for anti-MadCAM-1 antibody (Santacruz Biotechnology), for anti-mouse CCR7 antibody, anti-mouse CD22 and anti-mouse CD279 (PD-1; BioLegend), and for anti-mouse IgM (Jackson ImmunoResearch), and are listed in Supplementary Table  9.
Gene expression microarray data analysis. Samples were processed according to reported methods  44, 50. The cDNAs were hybridized to the Affymetrix HG-U133 Plus2 arrays in the case of human samples. Mouse samples were similarly processed and hybridized to the Affymetrix GeneChip Mouse Gene 1. 0 ST arrays. Raw gene expression microarray data files were submitted to GEO and are available under the accession number GSE49356. Both background correction and normalization were done using RMA (Robust Multichip Average) algorithm. Then, a filtering process was performed to eliminate low-expression probe sets. R/Bioconductor was used for preprocessing and statistical analysis. Linear Models for Microarray Data were used to find out the probe sets that showed significant differential expression between experimental conditions.
Genes were selected as significant using a B statistic cutoff B40. Functional and pathway analysis of gene expression signatures. These analyses were performed according to standard procedures  44, 50. Briefly, functional enrichment analysis of Gene Ontology categories was carried out using standard hypergeometric test. The biological knowledge extraction was complemented through the use of Ingenuity Pathway Analysis (Ingenuity Systems, www. ingenuity. com), which database includes manually curated and fully traceable data derived from literature sources. To study the biological meaning of the results, enrichment tests with respect to MsigDB gene sets was carried out. To this end, the non-parametric Kolmogorov-Smirnoff rank test was used as implemented in the Gene Set Enrichment Analysis software.