dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge support from the  European Union   FP7-SPACE-2013-1 programme  for the  HELCATS  project (# 606692 ). The HI instruments on STEREO were developed by a consortium that comprised the  Rutherford Appleton Laboratory  and the  University of Birmingham  (both in the UK),  Centre Spatial de Liège (CSL) ,  Belgium , and the  Naval Research Laboratory  (NRL),  USA. The STEREO/ SECCHI  project, of which HI is a part, is an international consortium led by  NRL. We recognise the support of the  UK Space Agency for funding STEREO/HI  operations in the UK. We also acknowledge use of the CDAW SOHO/LASCO event catalogue (https://cdaw. gsfc. nasa. gov/CME_list/),which is generated and maintained at the CDAW Data Center, by  NASA  and the  Catholic University of America  in cooperation with  NRL , and the STEREO/SECCHI/COR2 CME catalogue (http://solar.
Thus, Figure  3  shows that the single shot lidar SNR is detector NEP limited for a range longer than 0. 6 km. A SNR of 0 dB is estimated at 3 km. It is worth pointing out that the SNR may vary by more than a factor of two during the day just due to the change in the atmospheric conditions and the particles content. The lidar signal is averaged over 9000 shots for visualisation. The resulting SNR is calculated and compared with data estimated single shot SNR. A longer data set was used to verify successfully that the SNR increases with the square root of the number of shots at least up to 4000 averaged shots.
CONCLUSIONS A new direct detection chain at 2 µm using a HgCdTe APD prototype has been implemented and associated with a dual wavelength pulsed Ho:YLF oscillator. The performances of the lidar in terms of signal to noise ratio have been assessed. For the displayed data set, a SNR of 1 is obtained at 3 km mainly limited by the NEP of the detector. Preliminary DIAL measurements have been tested. The error budget on CO2 absorption coefficient estimate shows already a clear advantage of direct detection compared to coherent detection.
By phenotype, labeled cells at the start comprised primarily HSCs; at 1 wk, HSCs plus MkPs and MEPs; and at 2 wk, a variety of Kit + and Kit -progenitors (Fig. S4 A ). In total, 188, 107, and 283 cells from the respective time points passed quality control and were pooled and analyzed by unsupervised clustering and dimensionality reduction using the Seurat software  (Satija et al. , 2015). The resulting cell pool segregated into five broad clusters (Fig. 4  A) that expressed genes characteristic of either HSCs (cluster 0), MkPs (cluster 1), MPPs (cluster 2), MyPs (cluster 3) or EryPs (cluster 4; Fig. 4  B and Fig. S4 B ). The expression of genes encoding key transcription factors (TFs; Fig. 4 C ) and the genes encoding surface markers (Fig. S4 C ) was also consistent with the proposed identities of the clusters.
Data represent mean ± range of duplicate plates for HSCs (average 30 initial colonies per plate) and single plates for non-HSCs and MkPs (34 and 23 initial colonies, respectively). (F) The growth of single Lin -Tom + cells sorted from individual Pdzk1ip1-CreER R26 tdTomato reporter mice at the start, 1 or 2 wk after induction with tamoxifen. Data represent mean ± range of two animals per time point (average 182, 107, and 71 initial colonies per animal at the start, 1, or 2 wk, respectively). (G) Secondary growth of colonies derived from single Lin -Tom + cells. U or U+Mk colonies from Tom + cells at the start, 1, or 2 wk after labeling were harvested on day 7, replated in fresh liquid medium, and their ability to produce secondary U or U+Mk colonies was scored on day 10 after replating as a fraction of replated colonies of each type.
HSCs rapidly generate a broad spectrum of progenitors The data described above showed the emergence of multipotent and lineage-restricted progenitors from labeled HSCs after 2 wk of tracing. To better resolve this earliest stage of multilineage differentiation, we sequenced single Lin -Tom + cells from two additional animals at 2 wk after labeling. The results were merged with the same time point from the first experiment, and the pooled 647 cells were analyzed using Seurat as above. As expected, this analysis revealed four broad cell clusters corresponding to MyP (cluster 0), HSC + MPP (cluster 1), MkP (cluster 2), and EryP (cluster 3;  Fig. 5, A  and B ). These clusters contained cells from both experiments, suggesting that the clustering by transcriptional profiles overcame batch effects (although the latter were detectable within the clusters, particularly clusters 1 and 2; Fig.
Flow cytometry analysis Conventional flow cytometry analysis was performed using the FlowJo software (FlowJo, LLC). Cell populations in the PB were defined as previously described  (Sawai et al. , 2016). Cell populations in the BM were defined as follows: HSC, LSK CD150 + CD48 -; ST-HSC, LSK CD150 -CD48 -; MPP2, LSK CD150 + CD48 + ; LSK CD150 -CD48 + ; MyP, Lin -c-Kit + Sca-1 -CD150 - CD41 -; MEP, Lin -c-Kit + Sca-1 -CD150 + CD41 -; MkP, Lin -c-Kit + Sca-1 -CD150 + CD41 + ; CLP, Lin -Kit lo/-Sca-1 -Flt3 + CD127 + Ly6D -; and CLP', Lin -Kit lo/-Sca-1 -Flt3 + CD127 + Ly6D +. High-dimensional flow cytometry data were analyzed using the viSNE algorithm  (Amir et al. , 2013)  as implemented in FCS Express (De Novo Software). To facilitate the analysis of rare labeled cells, all Tom + cells from each acquired BM sample (4-6 × 10 6 cells) were gated, and the entire sample was then downsampled to 5 × 10 4 cells.
Before sequencing with Hiseq 2500, the libraries were quantified using Qubit and BioAnalyzer to check their quality. For scRNA-Seq, cells were sorted into 96-well plates containing a mix with 5× Maxima Transcription Buffer, dNTPs, and NXGen RNase inhibitor (Lucigen). Once sorted in wells, plates were spun down and put on dry ice for 5 min to lyse the cells. A modified version of the SMA RTseq2 protocol  (Picelli et al. , 2014)  was performed using SuperScript II Reverse transcription and KAPA HotStart Ready Mix to generate and amplify the cDNA for each cell  (Bracken, 2018). Each cell/transcript was assigned a unique 12-bp barcode that facilitated individual cell cDNA pooling at the Nextera XT library preparation step. QC analysis was done using Qubit and BioAnalyzer kits.
low. cutoff = 0. 16, x. high. cutoff = 3, and y. cutoff = 0. 7. The resulting variable genes were subjected to principal component analysis, and top principal components were selected based on the inflection point of the elbow plot of P values and used for cell clustering. The dataset in Fig. 4  yielded 1,990 variable genes out of total 22,971 detected genes across 578 cells, and principal components 1-6 were used for clustering. The dataset in Fig. 5  yielded 2,073 variable genes out of total 34,615 genes across 647 cells, and principal components 1-9 were used for clustering. Further software-based subclustering was precluded by insufficient numbers of cells in each cluster. Therefore, the subclusters within the myeloid and erythroid clusters (Fig. 6  B and Fig. 7 D ) were identified manually based on the heterogeneous expression of key subset-specific genes including TFs.
We focused our investigations on how nucleobases assemble themselves to form small-molecule crystals. In the present work, we show the results of our research on cytosinium chloride (CC), adeninium chloride hemihydrate (ACH) and guaninium dichloride (GDC) salts in the crystalline state for which it was possible to determine crystal charge densities experimentally. The crystal structures of CC, ACH and GDC were reported previously  (Matkovic-C ˇalogovic& Sankovic, 1999; Broomhead, 1948; Cochran, 1951; Kistenmacher & Shigematsu, 1974; Cunane & Taylor, 1993; Mandel, 1977; Nieger, 2006). In addition,  Cunane & Taylor (1993)  published high-resolution X-ray diffraction data (sin/ = 1. 32 A ˚À1 at 123 K) for a single crystal of ACH. This first charge-density study mainly presented the refinement strategy and procedure of the studied system as well as its molecular electrostatic potential features, which was regarded as a great success at the time.
Experimental and computational methods For a detailed description of the methods used, please see the supporting information. 2. 1. Experimental charge-density models 2. 1. 1. Crystallization, data collection and processing. The nucleobases were each added to a mixture of distilled water with a few drops of 38% HCl, and the mixtures were stirred and heated until the compounds had dissolved completely. The solutions were left to evaporate at room temperature in the case of cytosinium chloride (CC), and at 310 K in the cases of adeninium chloride hemihydrate (ACH) and guaninium dichloride (GDC). Crystals were obtained after one month. For CC and ACH, single-crystal X-ray measurements were performed at 90 K on an Agilent Technologies SuperNova four-circle diffractometer equipped with a low-temperature nitrogen gas-flow device (Oxford Cryosystems Cryostream Plus).
For guaninium dichloride GDC, X-ray measurements were carried out on a Bruker APEXII ULTRA single-crystal diffractometer with a TXS rotating anode (Mo K) equipped with a CCD-type area detector, multilayer optics and an Oxford Cryostream low-temperature attachment set to 100 K. For CC and ACH, the determination of unit-cell parameters, integration of reflection intensities and data reduction, including multi-scan absorption corrections, were performed using CrysAlis PRO  (Version 1. 171. 36. 32; Agilent, 2013). Finally, reflection merging was carried out with the SORTAV program  (Blessing, 1987 (Blessing, , 1989 (Blessing, , 1995 (Blessing, , 1997)). For GDC, the determination of unit-cell parameters, integration of reflection intensities and data reduction were performed with the APEX2 suite of programs (integration was carried out with SAINT, Version 8.
27B;  Bruker, 2013) , and the multi scan absorption correction, scaling and merging of reflection data were carried out with the SORTAV program. 2. 1. 2. Structure solution and refinement. Using OLEX2  (Dolomanov et al. , 2009) , the structures were solved with SHELXS  (Sheldrick, 2008 (Sheldrick, , 2015) )  with direct methods and refined with olex2. refine using the independent-atom model (IAM). Multipole refinements were performed in MoPro  (Guillot et al. , 2001; Jelsch et al. , 2005)  with the use of the Stewart-Hansen-Coppens multipolar model  (Stewart et al. , 1975; Hansen & Coppens, 1978). Refinements were performed against structure factor amplitudes (F) with the I ! 2(I) cutoff. The initial atomic coordinates, x, y and z, for all atoms, the anisotropic atomic displacement parameters (U ij ) for non-hydrogen atoms and the isotropic atomic displacement parameters for hydrogen atoms were taken from the IAM refinement.
S1-S4 in the supporting information). PLATON diagrams  (Spek, 2009)  with the atom-labelling schemes are shown in Fig. 1. The CIF files can be retrieved from the Cambridge Structural Database (CSD)  (Groom et al. , 2016)  (deposition numbers 1539172-1539174). Sets of raw diffraction frames and the associated data are accessible online under the following DOIs: https://doi. org/10. 18150/ repod. 7313736, https://doi. org/10. 18150/repod. 0481200 and https://doi. org/10. 18150/repod. 8020589 (Repository for Open Data, ICM, University of Warsaw, Poland).
Periodic quantum mechanical calculations Periodic quantum mechanical calculations were applied to compute theoretical crystal charge densities, theoretical structure factors and crystal cohesive energies (E coh ). The CRYSTAL14 package  (Dovesi, Orlando et al. , 2014; Dovesi, Saunders et al. , 2014)  was used at the DFT-B3LYP/pVDZ level of theory  (Becke, 1988; Perdew, 1986; Lee et al. , 1988; Dunning, 1989). The computations were done in two versions, for experimental and optimized geometries. During optimization, the cell parameters were fixed while the atom positions were allowed to vary. The Grimme dispersion correction  (Grimme, 2006)  was applied for all calculations, whereas a correction for basis set superposition error (BSSE)  (Civalleri et al. , 2008)  was applied for the computation of cohesive energy following the supermolecular approach  (Maschio et al.
Hirshfeld surface and QTAIM analyses Hirshfeld surface analyses  (Spackman & McKinnon, 2002; McKinnon et al. , 2007)  were performed for experimental geometries using Crystal Explorer (Version 3. 3;  Wolff et al. , 2012). Wavefunction calculations were done for each molecule separately, applying their formal charge, at the HF/6-31G** level  (Hehre et al. , 1972)  using GAUSSIAN09  (Frisch et al. , 2016). A QTAIM (quantum theory of atoms in molecules;  Bader, 1990)  analysis was carried out on all electron-density models of CC, ACH and GDC crystals. For models in the multipole representation (experimental, UBDB and the one fitted to the theoretical structure factors), integrated charges were computed using the WinXPRO program  (Stash & Tsirelson, 2002 , 2007). For exact (not approximated by the multipolar model) theoretical crystal electron densities, integrated charges were calculated for experimental and optimized geometries using TOPOND14  (Gatti et al.
Molecular  electron densities of molecules bearing their formal charges were obtained using GAUSSIAN09 at the MP2/6-31** level  (Hariharan & Pople, 1973). The electron densities were then analysed using the PIXELc module  (Gavezzotti, 2003a,b,c,d)  of the Coulomb-London-Pauli (CLP) program  (Gavezzotti, 2011)  which allows the calculation of lattice energies. The total intermolecular interaction energy was defined in PIXEL as follows E tot ¼ E es þ E pol þ E disp þ E rep ; ð2Þ where E es is the electrostatic interaction energy, E pol is the polarization energy, E disp is the dispersion energy and E rep is the repulsion energy between interacting molecules. Due to the limitations of the program, i. e. a maximum of two molecules are allowed in the asymmetric unit, the crystal cohesive energy was only computed for CC.
Electrostatic contributions to intermolecular interaction energies and to crystal cohesive energies were also computed from experimental, UBDB and theoretical multipolar models of charge densities with the XDPROP module of the XD2016 package  (Volkov et al. , 2016). The exact electrostatic energy (E es ) was computed with the use of the EPMM method  (Volkov, Koritsanszky & Coppens, 2004). The electrostatic energies from molecular monopole moments (point charges, E Coul ) were obtained by extracting monopole-monopole contributions from calculations with the mMM option or, alternatively, computed directly from Coulomb's law, E Coul = q 1 q 2 /4" 0 r. To differentiate between results for dimers and results for the whole crystal, energies for the latter will be abbreviated as E coh,es and E coh,Coul.
Conclusions and outlook The present work contributes to our understanding of intermolecular interactions among protonated nucleobases and chloride counter-ions surrounding them in the solid state. For single crystals of cytosinium chloride (CC), adeninium chloride hemihydrate (ACH) and guaninium dichloride (GDC), we collected high-resolution X-ray diffraction data to determine crystal charge densities experimentally. On the basis of these, we have extensively analysed the molecular charges, electrostatic potentials and electrostatic interaction energies for molecular dimers and whole crystals. These experimental results were further supported by extensive theoretical calculations, including UBDB charge-density modelling, dimer interaction energy computations based on perturbation theory (PIXEL, DFT-SAPT) and periodic quantum mechanical computations (CRYSTAL) based on the variational principle.
Funding -Funding was provided by the  Spanish Ministerio de Economia y Competitividad and European funds (FEDER)  ( CGL2013-48193-C3-1-P ,  CGL2013-48193-C3-2-P ,  CGL2012-30759 ,  CGL2015-65055-P ). JMP, GT and MRR were supported by  Juan de la Cierva ,  Ramón & Cajal  and Talentia postdoctoral contracts, respectively. JMP was partially supported by a 2017 Leonardo Grant for Researchers and Cultural Creators,  BBVA Foundation. The Foundation accepts no responsibility for the opinions, statements and contents included in the project and/ or the results thereof, which are entirely the responsibility of the authors. Author contributions -JJS, GT, and JF designed the study with considerable assistance from JMP and  CRC. CRC performed all molecular analyses with considerable assistance from JMP.
4. An elastic-plastic correction for determining a material's R-curve using fracture test data from a residually-stressed specimen has been demonstrated. This method requires the use of inelastic finite element analysis in addition to characterisation of the specimen's initial residual stress field and strain hardening state. 5. Elastic strains measured at the tips of growing cracks in residuallystressed and non-residually stressed material were observed to be the same, even when the surrounding stress field differed. This supports the concept of a near-tip stress field that can be characterised using a very limited number of parameters.
We used reef-forming coral fossil records, which are good indicators of tropical climates since they mostly develop in water temperature greater than 25°C. We collected all reef-forming coral fossil occurrences from the Fossilworks database  [46]  and reconstructed the tropical border latitude for each 0. 5 Ma step on an equal area grid at a resolution of 0. 5°using the 95th percentile of palaeo-latitudinal limits. As older fossils became scarcer and present a higher dating uncertainty, we considered a dating uncertainty of 3 Ma for fossils younger than 10 Ma, 5 Ma for fossils between 10 and 50 Ma and 10 Ma for fossils older than 50 Ma. Following Gotelli et al. [16] , the diversification model keeps track of the distribution of each species in each cell of a grid of suitable habitats (here landmasses) as well as phylogenetic relationships between species at any single point in time.
We sequentially parametrized the model on 24 zones and projected the model to the remaining two zones. We used the collated vector of independently projected diversity to compute the mean square error and estimated the reliability of the speciation and dispersal distance values. While this does not provide a fully independent evaluation of the models as is normally recommended (e. g. [47] ), it is the best available approach given that an independent dataset is unavailable. We further reported the following performance indicators: Spearman rank correlation among observed and modelled richness, error rate in β-diversity as well as the Spearman rank correlation among observed and modelled β-diversity (using a Mantel test). Dissimilarity among regions was expressed by the pairwise Sorensen dissimilarity index, which is a linear transformation of a metric of β-diversity sensu stricto  [48].
Funding. L. P. was supported by the  FNS  project  REEFISH  no. 310030E-164294. J. C. S. was supported by the  European Research Council  ( ERC-2012-StG-310886-HISTFUNC ) and also considers this work a contribution to his VILLUM Investigator project ' Biodiversity Dynamics in a Changing World ' funded by  VILLUM FONDEN  (grant no. 16549 ). N. E. Z. was supported by the  SNSF  grants  31003A_149508/1  and  310030L_170059.
Membership is totally free to encourage participation, resulting in a truly worldwide, global group of scientists. Figure  1  shows the membership by country and continent as of July 2017. The IPWG is led by two co-chairs who oversee the managerial activities of the group, such as the interaction (together with a rapporteur) with the CGMS. The two co-chairs are selected for a 2-year term and remain as "outgoing" chairs over the following 2 years to ensure a smooth transition with the new "incoming" co-chairs. The IPWG web site (http://ipwg. isac. cnr. it, last accessed September 21, 2017) contains information on the group including datasets, meeting material, reports, access to validation results, and activities of the working groups. Links to relevant IPWG web site pages are listed in Table  1.
, 2010)  and the future US Joint Polar Satellite System (JPSS:  Goldberg, Kilcoyne, Cikanek, & Mehta, 2013)  series. The IPWG contributed to two major National Academy of Sciences (NAS) reports (for the TRMM and GPM missions), while an assessment of global precipitation products (Gruber &  Levizzani, 2008)  was conducted by the IPWG community and the results made available to WCRP. In addition, the group has provided support for the inclusion of the high-frequency channels (166 and 183 GHz) on the GPM Microwave Imager (GMI), for the implementation of a second Megha-Tropiques ground station to facilitate better data collection, and for efforts to protect PMW frequency allocations for use in Earth Observation.
To address this issue, IPWG has supported and endorsed the IWSSM since its inception in 2005  (Bennartz et al. , 2011; Bennartz & Ferraro, 2005 ). IWSSM's main objective is the coordination of international efforts to retrieve falling snow using active and passive microwave instruments. A large component of IWSSM has historically been the evaluation and consolidation of individual ice particle scattering models. At the IWSSM-5/IPWG-8 joint meeting in Bologna, Italy, the efforts culminated in the "scattering working group" being established. The main outcomes of the working group were: (a) need for creating a single scattering focus group to discuss formats, share databases and exchange information; (b) single-scattering properties are just the start of a process towards knowing the bulk scattering and other physical properties of hydrometeors for a better representation of cloud and precipitation physics in models; (c) develop a common scattering database for use in radiative transfer operators; and (d) support full exploitation of precipitation missions through better representation of particle scattering and improved microphysical knowledge.
IPWG WORKING GROUPS AND OUTCOMES At the biennial workshops, scientists participate in one (or more) of the working groups that have been established by the IPWG to concentrate upon key activities. These working groups currently are: 1 Validation working group focuses on the evaluation of a number of research and operational satellite-based precipitation datasets (further discussed in Section 4). A key activity of this group is sustaining and expanding the intercomparisons of these data with regional verification datasets. While the IPWG has a number of established intercomparison regions (Australia, Europe, Japan, South Africa, South America, USA, see http://ipwg. isac. cnr. it/ calval. html, last accessed September 26, 2017) there is a desire to expand these regions to include those with under-represented meteorological/climatological regimes.
Furthermore, the validation of snowfall products will be necessary as these become increasingly available. Large-scale validation should also be encouraged through the use of existing ground validation datasets, such as the Global Precipitation Climatology Centre (GPCC:  Becker et al. , 2013; Schneider et al. , 2016 ) gauge analysis at daily and monthly scales. The IPWG validation working group reaffirmed the need to maintain in situ observations and to develop access to hitherto unavailable surface datasets to ensure the comprehensive validation of satellite precipitation products. 2 Applications working group addresses the links between the developers/providers of the precipitation products and the user community. Key issues identified included making links to data and processing/analysis tools available on the IPWG website to better equip users to exploit the satellite-based precipitation products.