dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Airborne data from the BAe 146 were obtained using the BAe 146-301 Atmospheric Research Aircraft operated by  Directflight Ltd  and managed by  FAAM , which is a joint entity of the  NERC  and the  UK Met Office. ICE-D was supported by  NERC  grant number  NE/M001954/1  and the  Met Office. SAVEX-D flights were funded by  EUFAR TNA (European Union Seventh Framework Programme  grant agreement  312609 ), and other  AER -D flights were funded by the  Met Office. Claire L. Ryder  acknowledges  NERC  support through  Independent Research Fellowship   NE/M018288/1. SAVEX-D was supported by projects  PROMETEUII/2014/058  and  GV/2014/046  from the  Valencia Autonomous Government  and  CGL2015-70432-R  from the  Spanish Ministry of Economy and Competitiveness -European Regional Development Fund.
Data availability. This dataset collection holds datasets from the FAAM BAe-146 aircraft and ground-based measurements taken in Cape Verde off the coast of Senegal, Africa, during 2015 and 2016 in support of the UK Ice in Clouds Experiment -Dust (UK ICE-D) project: http://catalogue. ceda. ac. uk/uuid/ d7e02c75191a4515a28a208c8a069e70  (Bennett, 2015). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-17225-2018-supplement.
A search for new supernova remnants (SNRs) has been conducted using TeV γ-ray data from the H. E. S. S. Galactic plane survey. As an identification criterion, shell morphologies that are characteristic for known resolved TeV SNRs have been used. Three new SNR candidates were identified in the H. E. S. S. data set with this method. Extensive multiwavelength searches for counterparts were conducted. A radio SNR candidate has been identified to be a counterpart to HESS J1534-571. The TeV source is therefore classified as a SNR. For the other two sources, HESS J1614-518 and HESS J1912+101, no identifying counterparts have been found, thus they remain SNR candidates for the time being. TeV-emitting SNRs are key objects in the context of identifying the accelerators of Galactic cosmic rays. The TeV emission of the relativistic particles in the new sources is examined in view of possible leptonic and hadronic emission scenarios, taking the current multiwavelength knowledge into account.
An identification of the source as SNR requires an additional independent measurement such as a spectral signature or an identifying detection from another waveband. This paper presents a systematic search for new Galactic SNR candidates using the TeV γ-ray band for the discovery of these objects. Previous TeV SNR studies, for example, with the HEGRA Cherenkov telescope system  (Aharonian et al. 2001 (Aharonian et al. , 2002) )  or with H. E. S. S. (Aharonian et al. 2004 ; H. E. S. S. Collaboration 2018d), focused on the detection of TeV emission from SNRs (or SNR candidates) that have been known from other wavebands. The data that are used for the search presented here stem from the H. E. S. S. Galactic Plane Survey (HGPS) that is presented in detail elsewhere (H. E. S. S. Collaboration 2018e).
This could be inferred by adopting the arguments that have been made to interpret dark TeV accelerators, that is, TeV sources without X-ray and radio counterparts, as evolved SNRs  (Yamazaki et al. 2006). The search presented in this paper has revealed three SNR candidates, HESS J1534-571  (Pühlhofer et al. 2015) , HESS J1614-518  (Aharonian et al. 2006b) , and HESS J1912+101  (Aharonian et al. 2008b). HESS J1534-571 was additionally classified as a confirmed SNR in the course of the presented study based on its identification with a radio SNR candidate. For HESS J1614-518 and HESS J1912+101, no counterparts have been found yet that would permit firm identification. The paper is organized as follows: In Sect. 2, the TeV SNR candidate search and identification strategies applied to the HGPS data are presented.
A dedicated H. E. S. S. data analysis of the three sources that fulfill the candidate criteria is performed, including a TeV morphological and spectral characterization. For the first time, SNR candidates are established from the TeV band alone. A thorough evaluation of the available multiwavelength (MWL) data toward the sources including nondetections is necessary to permit valid physics conclusions for these TeVselected sources. In Sect. 3, the search for MWL counterparts in the radio continuum and the GeV bands is presented, which led to positive identifications for HESS J1534-571 (radio, GeV) and HESS J1614-518 (GeV). Section 4 deals, to the extent necessary, with MWL data (X-ray, infrared, and radio/sub-mm line data) and catalog searches that have not revealed clear counterparts. In the discussion (Sect.
High Energy Stereoscopic System (H. E. S. S. ) The TeV data presented in this paper were taken with the H. E. S. S. array in its first phase, in which the array consisted of four identical 12 m imaging atmospheric Cherenkov telescopes, located in the Khomas Highland of Namibia at an altitude of 1800 m above sea level. The telescopes are used to detect very high-energy γ-rays through registering the Cherenkov light that is emitted by a shower of charged particles, initiated by the primary photon entering the atmosphere. The telescopes are equipped with mirrors with a total area of 107 m 2  and cameras of 960 photomultiplier tubes each. The energy threshold of the array is roughly 100 GeV at zenith and increases with zenith angle. The direction and energy of the primary photon can be reconstructed with an accuracy of ≤ 0.
H. E. S. S. data analysis The work presented here is based on two not fully identical data sets that were treated with two different analysis chains. For the TeV shell search on a grid as described in Sect. 2. 3. 3, products from the HGPS data set (Sect. 2. 2. 1) published by the H. E. S. S. Collaboration (2018e) were used. For the individual source analysis described in Sects. 2. 3. 4 (morphological fits), 2. 4 (surface brightness maps), and 2. 5 (energy spectra), a dedicated data selection and refined analysis (Sect. 2. 2. 2) was used.
The HGPS data set Between 2004 and 2013, a survey of the Galactic plane was conducted with the H. E. S. S. telescopes. Initial survey observations  (Aharonian et al. 2006b ) have been followed by deeper observations of regions of interest, and many known objects in the Galactic plane have also been observed individually (e. g. , H. E. S. S. Collaboration 2011 Collaboration , 2016 Collaboration , 2018c,e),e). The H. E. S. S. Galactic Plane Survey (HGPS) data set consists of all data taken in the longitude range between l = 250 • and l = 65 • (including the Galactic center) and |b| 3. 5 • in latitude. The resulting data set does not provide homogeneous sensitivity. The point source sensitivity in the core survey region is 1. 5 % of the Crab nebula integral flux above 1 TeV, but better sensitivities are achieved for many individual regions.
Details of the data set, analysis methods, and the resulting source catalog are reported in H. E. S. S. Collaboration (2018e). For the work presented in this paper, only a morphological analysis of the HGPS sky map has been performed. Therefore, only sky map products of the HGPS have been used, namely a sky map of γ-ray event candidates after image-shape based background rejection, a sky map of estimated remaining background level, and an exposure sky map. The search for SNR candidates has been performed on an equidistant sky grid and not, for example, on preselected HGPS source positions.
E. S. S. Collaboration 2018e). To ensure a homogeneous acceptance for background events, particularly in the Galactic plane with high optical noise, an image amplitude cut of 160 photoelectrons (p. e. ) is applied for sky maps. Together with the energy reconstruction bias cut that is derived from Monte Carlo simulations and discards events with a bias above 10 %, the presented data sets have an energy threshold of about 600 GeV. To derive energy spectra, the image amplitude cut is usually reduced to achieve a wider spectral energy range. For HESS J1534-571 and HESS J1614-518, an amplitude cut of 60 p. e. was set, leading to a spectral energy threshold of around 300 GeV. The spectral background is derived from background control regions that are defined run-wise and are chosen to have the same offset to the camera center as the source region  (Berge et al.
2007) , to ensure a nearly identical spectral response to background events in the source and background control regions. Similar to the process above, sky areas with known sources or with excess events above a certain significance threshold have to be excluded. After lowering the threshold to 60 p. e. for a spectral analysis of HESS J1912+101, this process failed to deliver background estimates with acceptable systematic errors as quoted below. Therefore the 160 p. e. amplitude cut was maintained for the spectral analysis of this source. Possible reasons are that the sky area around HESS J1912+101 contains significant soft subthreshold sources or diffuse emission, or that it suffers from high optical stellar noise. All three data sets contain large portions of runs that were performed under nonoptimal conditions for spectral analysis, i.
2010). The goodness of the fit is not considered as stand-alone criterion for a shell-like source morphology. Because of limited statistics, acceptable fits to the data might be obtained but without any discriminating power. Rather, the shell fit quality is compared to a fit result of a simpler default model (referred to as null hypothesis) that is chosen to represent a typical Galactic TeV source different from known SNR shell sources. The most abundant identified Galactic source class of this character comprises H. E. S. S. Collaboration: A search for new SNRs in the Galactic plane with H. E. S. S. PWNe, which are usually well described by a centrally peaked morphology. As a default model, a two-dimensional symmetric Gaussian with variable width was chosen, which represents typical PWNe and the majority of other known Galactic H.
Comparison of the non-nested models Adopting the assumption that the chosen morphological models for shell (H 1 ) and null hypothesis (H 0 ) represent the true TeV source populations sufficiently well, the improvement in the fit quality (i. e. , the likelihood that the shell model describes the given data set better than the Gaussian model) can be interpreted in a numerically meaningful way. However, there is the (purely analytical) issue that the two compared models are non-nested, i. e. , one cannot smoothly go from H 0 to H 1 with a continuous variation of the parameters (for a rigorous definition see  Eadie et al. 1971). In this case, a likelihood ratio test (LRT) cannot be applied  (Protassov et al. 2002). One way to overcome this problem is to adopt the Akaike Information Criterion (AIC)  (Akaike 1974).
For a given model, AIC is computed as AIC = 2k -2 ln (L ML ) , ( 2 ) where k is the number of parameters and L ML is the maximum likelihood value for that model. Testing a set of models on the same data set, L AIC,i = C • exp - AIC i -AIC min 2 (3) gives a likelihood or relative strength of model i with respect to the best available model, i. e. , the model found to have the minimum AIC (AIC min )  (Burnham & Anderson 2002). In order to quantify if and how L AIC,H 0 translates into a probability that the improvement obtained with the shell fit over the Gaussian model is due to statistical fluctuations, a limited number of simulations have been performed using the parameters of the H. E. S. S. data set of HESS J1534-571 (the source with the lowest shell over Gaussian likelihood; see Table  3 ).
Morphology fitting procedure and results from individual source analyses To overcome the limitations of the grid search (see  Sect. 2. 3. 3)  and from the survey data analysis (cf. Sect. 2. 2), final morphological parameters and shell identification likelihoods were derived using the H. E. S. S. data sets introduced in Sect. 2. 2. 2 and the analysis as described in the following. 5 orphological fits were performed on uncorrelated oncounts (i. e. , γ-candidates after γ-hadron separation, not background-subtracted or flatfielded) sky maps with 5 • × 5 • size and with 0. 01 • × 0. 01 • bin size. Entries in these maps have pure Poissonian statistical errors. The model fit function On i is constructed as On i = A × Bkg i + psf * M i × N ref,i. ( 4 ) Bkg i is the estimated background event map derived from the ring-background method  (Berge et al.
Acknowledgements. The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  Alexander von Humboldt Foundation , the  Deutsche Forschungsgemeinschaft , the  French Ministry for Research , the  CNRS-IN2P3  and the  Astroparticle Interdisciplinary Programme of the CNRS , the  U. K. Science and Technology Facilities Council (STFC) , the  IPNP of the Charles University , the  Czech Science Foundation , the  Polish National Science Centre , the  South African Department of Science and Technology and National Research Foundation , the  University of Namibia , the  National Commission on Research, Science & Technology of Namibia (NCRST) , the  Innsbruck University , the  Austrian Science Fund (FWF) , and the  Austrian Federal Ministry for Science, Research and Economy , the  University of Adelaide  and the  Australian Research Council , the  Japan Society for the Promotion of Science , and by the  University of Amsterdam.
We appreciate the excellent work of the technical support staff in  Berlin, Durham, Hamburg, Heidelberg, Palaiseau, Paris, Saclay , and in Namibia in the construction and operation of the equipment. This work benefited from services provided by the  H. E. S. S. Virtual Organisation , supported by the national resource providers of the  EGI Federation. The shell search performed on the entire HGPS data set as presented in Section 2. 3 employed Gammapy routines as presented in Donath et al. (2015). This research has made use of the VizieR catalog access tool,  CDS, Strasbourg, France. The original description of the VizieR service was published in Ochsenbein et al. (2000). This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France  (Wenger et al. 2000).
multiplication of gravitational forces by a factor of 8 , valid only when located out of the galaxy (or at the edge of it) will of course increase its stability. This has been confirmed by simulations (under the EPJ Web of Conferences 182, 03006 (2018) https://doi. org/10. 1051/epjconf/201818203006 ICNFP 2017 0 h h  hypothesis) and might be in accordance with experimental data [24]. In this  IGM 0  case, after its slow dissolution, a giant galaxy yields a very faint large galaxy, which can be easily compared with a LSB galaxy. But this does not occur under the  IGM 7    u 0 supposition, in which case this galaxy is only dissolving faster, without any remaining structure. The computational flexibility is low and the liberty degrees are in the galaxy characteristics input. The first characteristic is the simulated matter density with respect to 0  , in other words NbP with respect to 0 , that IGM matter density is equal to  IGM 0  .
RESULTS scRNA-Seq Profiling of Splenic and Blood Human and Mouse NK Cells Identifies a Species-Specific NK Cell Gene Signature We investigated the NK cell profiles in the paired blood and spleen of brain-dead human donors and mice, with a Drop-Seq-based scRNA-seq technology  (103 Genomics, Figures S1A and S1B and STAR Methods)   (Zheng et al. , 2017). We analyzed $4,000 NK cells from each of the mouse blood and spleen and from human spleen samples, and $3,000 human blood NK cells. scRNA-seq detected a mean of 914 genes per cell in the human dataset and 919 in the mouse dataset, resulting in a total of 16,806 human genes and 15,144 mouse genes detected in all cells. We analyzed the specific NK cell signature of the cells recovered. NKp46 is a cell surface receptor detected on subsets of ILC1s, ILC3s, and T cells, but overall recognized as a marker of the NK cell lineage in humans and mice  (Walzer et al.
, 2007). We generated with BioGPS a list of genes with an expression pattern strongly correlated (correlation coefficient R 0. 9) with that of the gene encoding NKp46 (NCR1 or Ncr1). For each species, we cross-checked this list of genes against the known NK cell-specific genes from available datasets  (Bezman et al. , 2012; Bjo ¨rklund et al. , 2016). The intersect between the resulting list for each species and our own scRNA-seq dataset was then used to define NK cell-specific transcriptomic signatures. This approach identified 13 genes as defining a robust consensus NK cell signature in mice and humans (Figure  S1C ). In mice, the 13 NK celldefining genes encoded activating receptors (Klrb1c [NK1. 1], Klrk1 [NKG2D], and Ncr1 [NKp46]), inhibitory receptors (Klrg1 [KLRG1] and Klrb1b), the transcription factor eomesodermin (Eomes), the cytolytic proteins perforin (Prf1) and granzyme A (Gzma), the IL-18 coreceptor (Il18rap), and a sphingosine 1-phosphate receptor promoting NK cell egress from lymph nodes and bone marrow (S1pr5 [S1P5]) (Figure  S1C ).
High-Throughput scRNA-Seq Identifies Three Subsets of Mouse Splenic NK Cells To assess mouse NK cell heterogeneity within the spleen, we performed unsupervised hierarchical clustering on the 4,182 mouse splenic NK cells (data not shown). NK cells did not cluster on the basis of sample, but into three different subsets for each sample, which we named mNK_Sp1 to 3. A representative t-SNE plot of 1,439 cells from one sample shows these three subsets (Figure  2A ). We identified 159 genes with significantly different expression patterns between mouse splenic NK cell subsets (Table  S2 ), and a heatmap of these genes clearly separated these subsets on the basis of differences in their transcriptomic profiles (Figure  2B ). We identified the sets of genes driving the segregation of the mouse spleen NK cell subsets by performing PC analysis (PCA) (Figure  2C ).
QUANTIFICATION AND STATISTICAL ANALYSIS Preprocessing of mouse and human samples Raw FASTQ files were processed with CellRanger software (v1. 3. 1), which performs alignment, filtering, barcode counting and UMI counting. Reads were first simultaneously aligned with the mm10 and GRch38 genomes, with CellRanger software (v1. 3. 1), for their assignment as murine or human sequences. Species assignments were improved by individual alignment with the mm10 and GRch38 sequences, resulting in two clusters, one for each species. Finally, we took the intersection between the corresponding clusters of the two alignments and we removed cells identified as doublets (within a range of 1. 5 to 3. 8%). Low-quality cells were excluded in an initial quality-control (QC) step, by removing genes expressed in fewer than three cells, cells with less than 200 genes expressed, and cells expressing more than 2500 genes.
ACKNOWLEDGMENTS The E. V. laboratory is supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( TILC , grant agreement no. 694502 ), the  Agence Nationale de la Recherche  (convention no. ANR-15-CE15-0016-01 ),  Equipe Labellisee ''La Ligue ,''  Ligue Nationale contre le Cancer  (convention no. EL2017. LNCC/ ErV ), MSDAvenir (convention no. 161204A50 ),  Innate Pharma  (convention no. 10406A20/V ), and institutional grants to the  CIML (INSERM, CNRS , and  Aix-Marseille University)  and to  Marseille Immunopo  ˆle. We thank  S. Carpentier ,  S. Guia ,  C. Bernat , and the cytometry platform (CIML) for help and advice.
Ortholog-based comparison of human and mouse transcriptomic signatures We compared human and mouse organ-specific or subset-specific transcriptomic signatures by manually transforming mouse data into human orthologs according to the information in public databases (NCBI). Genes on the gene lists of both species were identified by pairwise comparison. Assessment of the robustness of human NK cell subsets in a machine-learning protocol A machine-learning approach proposed by Valentine Svensson was used (http://www. nxn. se/valent/2018/3/5/actionable-scrnaseq-clusters). We performed the analysis by logistic regression, using 50% of our cells for training and 50% for testing. Receiver operating characteristic (ROC) curves were constructed, using the false-positive rate (x axis) and the true positive rate (y axis) for all possible thresholds of probabilities given by the logistic regression.
5:0. 9:1. 0. -19. 0 (c = 0. 5, MeOH) obtained using also DERA Ecoli as catalyst). f ee determined by chiral GC after formation of the methyl glycoside derivative against a racemic sample obtained by aldol addition of 1b (80% v/v) to 2a catalyzed by pyrrolidine. g α:β 1:1. h Hemiketal:acyclic adduct ratio was 85:15. i α:β 4:1. j ee determined by chiral HPLC analysis. Racemic mixture obtained by chemical synthesis using LDA methodology. Nu: nucleophile; E: electrophile. stereocenters, which resulted to be R-configured, in 4a,b and 5a,b, and R,R in 4e-f. Derivatives of lactol 5a are extensively used as the chiral side chain for the synthesis of statin drugs. 37 he spectral data of 5a matched those obtained from the trimerization of acetaldehyde catalyzed by DERA Ecoli. Lactol 4a is the 5-epimer of 5a and may constitute also an important building block for stereochemical analogs of statins.
Methods Sample preparation. Research-grade nitrogen (>99. 9995% purity, obtained from BOC) was condensed at 77 K and loaded into symmetric DACs equipped with built-in graphite resistive-heaters. Wide opening seats with Boehler-Almax diamonds were used to allow for maximal scattering angle in XRD experiments. Culet sizes were 200 μm and rhenium gaskets were pre-indented to 20 μm prior to loading. Raman spectroscopy was conducted using the 514 nm emission line of an Ar + ion laser. Pressure was controlled via a gas-membrane and pressure was determined using the diamond edge scale  32. The samples were recovered to ambient-temperature and transported by applying the load of the gas-membrane onto the DAC screws. XRD and structural refinement procedure. Single-crystal XRD data used for refinement were collected at the ID15B beamline at ESRF (Grenoble, France) using a monochromatic beam λ = 0.
411 Å focused to a spot size of 10 × 10 μm. Data were recorded on a MAR555 flat panel detector. The sample was rotated about ω over a range of 60°in 0. 5°increments. The image-plate shown in Fig. 2   ). The structure was solved by dual-space methods using SHELXT and refinement of the crystal structure was carried out against |F 2 | with the SHELXL refinement package  34, 35. Refinement was limited to an isotropic treatment for all nitrogen atoms, an anisotropic treatment led to refinement instabilities. Rigid bond restraints were applied to all molecular bonds with default estimated standard deviations of 0. 004 Å 2. Intramolecular bond distance restraints do not improve the fit to the data. DFT calculations. DFT calculations of energetics and vibrational spectra were performed with CASTEP  36  18.
Acknowledgements R. T. acknowledges studentship funding from  EPSRC  Grants No. EP/L504956/1  and No. EP/M506515/1. M. F. acknowledges  DOE FES  funding number  FWP100182. M. M. acknowledges support from the  ERC  grant Hecate. M. M. C. and M. M. are grateful for computational support from the  UK  national high performance computing service,  ARCHER , and the  UK Materials  and  Molecular Modelling Hub , which is partially funded by  EPSRC  ( EP/P020194 ), for which access was obtained via the  UKCP consortium  and funded by  EPSRC  grant ref  EP/P022561/1. We acknowledge the  European Synchrotron Radiation Facility  for provision of synchrotron radiation facilities at the ID15B beamline. Parts of this research were carried out at the light source PETRA III at  DESY , a member of the  Helmholtz Association (HGF) , under proposal I-20160268 EC.