dataesr/scientific-paragraphs-categorization huggingface dataset:
Library quality control metrics were as follows: 550. 19 pg/µl with an average size ~454 bps. Libraries were sequenced on an Illumina HiSeq4000 platform (Illumina) (one lane, 100 PE). Cell counting, suspension, GEM generation, barcoding, post GEM-RT cleanup, cDNA amplification, library preparation, quality control, and sequencing were performed at the Genomics High Throughput Sequencing facility at the University of California, Irvine. Data processing for 3′-end transcripts. Transcripts were mapped to the mm10 reference genome (GRCm38. 91) using Cell Ranger Version 2. 1. 0. The web summary of Cell Ranger statistics on cells, sequencing and mapping metrics prequality control assessment for downstream bioinformatics analyses were as follows -cell metrics: ~22,322 estimated number of cells, ~84.
Clustering of cells was performed using the Seurat R package  25. In brief, digital gene expression matrices were column-normalized and log-transformed. To identify cell clusters, principle component analysis (PCA) was first performed on the list of highly variable genes. A set of highly variable genes was then identified by binning the average expression of all genes into evenly sized groups and computing the median dispersion (variance divided by the mean) in each bin. Genes were selected for inclusion in PCA with an average expression > 0. 01 and dispersion > 1. 0. To identify significant PCs, we used the Jackstraw method (JackStraw function in Seurat). The top 40 PCs were used for clustering with the Louvain modularity-based community detection algorithm to generate cell clusters (FindClusters function, 13 clusters with resolution = 0.
Acknowledgements M. V. P. is supported by  Pew Charitable Trust  grant and  NIH  grants  R01-AR067273 ,  R01-AR069653 , and  U01-AR073159. G. C. is supported by  NIH  grant  R01-AR055309 ,  NIH Skin Diseases Research Core  grant  P30-AR057217. W. S. P. is supported by  NIH  grant  R01-AI047833. Q. N. is supported by  NIH  grant  U01-AR073159 ,  NSF  grants  DMS1562176 , and  DMS1763272 , and  Simons Foundation  Grant (594598, QN). C. F. G. -J. is supported by  NSF-GRFP  ( DGE-1321846 ), MBRS-IMSD training grant ( GM055246 ), and  Howard Scheidermann Fellowship Award. R. R. -V. is supported by the  University of California President's Postdoctoral Fellowship Program. D. M. is supported by the  Canadian Institutes of Health Research Postdoctoral Fellowship. This project was also funded by the  UCI Stem Cell Research Center  seed grant,  UCI Skin Center pilot grant , and  UCI Center for Complex Biological Systems (CCBS)  opportunity award.
Risk management indicators are used to mitigate the potentially dramatic effects of natural hazards. Local authorities and managers use them in elaborating rescue and urbanism plans, which do not always work, highlighting society's vulnerability in the particular context of global environmental and climate changes. Within this context, the United Nations (Sendai, 2015) advised to construct a series of indicators to better cope with human losses and economic disasters. Actually, the question is whether or not such indicators do constitute successful decision-making tools. In this article, we critically reviewed the recent literature (from 2013 to 2017) using the Web of Science database of Clarivate Analytics to assess how indicators are currently being constructed in risk management, with a focus on risks of inundations, coastal and seismic risks.
Methods : data source and papers selection For the selection of papers to be included in our bibliographic analysis, the multidisciplinary science catalogue Web of Science database was used. In order to obtain as many papers as possible in line with this survey, the following keyword searches were performed based on the topic: "vulnerability" OR "resilience" AND "indicator", with the third alternating entry "natural" AND "risk", "natural" AND "hazard", "natural" AND "disaster". In addition, research with the keyword "indicator", this time based on the article's title, was carried out, in order to particularly analyse papers that critically valuate indicators. To limit the results to peer-reviewed, published works, the search was restricted to scientific articles and reviews. Finally, to evaluate any impact of the UN Sendai conference, we focused only on two years before and after the conference, collecting papers from 2013 to 2017.
Some of the studies included several countries at the same time or were at a broader regional level. However, our focus was on those case studies dealing with more than one risk. [8] , and our database reflects this general current situation. There is an overlap of the terms "indicator" and "index", as well as terms that serve in constructing indicators, such as "variable", "factor", and "parameter" (Table  2 ). For example, in the study  [95]  the authors construct composite drought vulnerability index/indicator using seventeen variables/factors/indicators, which noticeably demonstrates the overlapping of these terms. The absence of universal definition of indicators is theoretically discussed in  [34] , with a proposed definition of indicators from  [32]  being "quantifiable variables that represent a selected characteristic of resilience […]", with the equivalence between "indicator" and "concept".
More natural and improved combination of natural and social indicators needed The indicators used in the papers included in our dataset are predominately related to vulnerability and not to hazards, that is to social and not to natural or environmental components of risk (Figure  2 ). Additionally, social vulnerability indicators do not always include risk perception and coping capacity  [84]  and the justifications for their usage are often limited  [20, 87]. Since risk is the product of hazard and vulnerability, it may be that one should not be considered without the other. We claim that corresponding indicators used from both hazard and vulnerability should be included and valued in a balanced way, as it is the case of studies from Brazil  [90] , Argentina  [55]  and the USA  [77]  (Figure  3 ).
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement  No713750. Also, it has been carried out with the financial support of the  Regional Council of Provence-Alpes-Côte d'Azur  and with the financial support of the A* MIDEX  (n°  ANR-11-IDEX-0001-02 ), funded by the  Investissements d' Avenir  project funded by the  French Government , managed by the  French National Research Agency (ANR). This work has also been funded by the  Labex OT-Med  ( ANR-11-LABEX-0061 ) supported by the  Investissements d'Avenir, French Government  project of the  French National Research Agency (ANR)  through the A* Midex  project ( ANR-11-IDEX-0001-02 ). The authors highly appreciate the comments, critics and suggestions of four anonymous referees on an earlier draft of this paper.
HBc Production and Preparation HBc was produced in plants via a transient expression system based on agroinfiltration. HBc expression vector was constructed on the basis of pEAQ-HT plasmid, developed by Peyret and Lomonossoff  [33]. The coding sequence of HBcAg of 552 bp in length derived from HBV subtype adw4 (GenBank: Z35717), was cloned into the vector Age I and Xho I restriction sites using sites Age I and compatible ends of Sal I, respectively, introduced by PCR using the following primers: Forward: AACCGGTATGGACATTGACCCTTATAAAGAATTTG Reverse: TGTCGACTGCAGTTAACATTGAGATTCCCGAGATTGAG Complete vector pEAQ-HBc was introduced into Agrobacterium tumefaciens EHA105 and LBA4404 strains via electroporation. Agroinfection was performed with Agrobacterium strains grown overnight on selective liquid LB medium supplemented with kanamycin (50 mg/l) and used to infiltrate leaves of 5-7 week-old Nicotiana benthamiana plants, cultivated in growth chamber under 5-6 klx light intensity, 16/8 h photoperiod and at a 22/16 • C temperature regime.
Characterization Methods Transmission electron microscopy (TEM) images were acquired with Hitachi TEM HT7700 microscope (Tokyo, Japan). Grids were made of copper coated with a carbon film, mesh 300. Samples were prepared by placing 15 µL drop on the grid and draining the excess solution with blotting paper and left for 15 min. to dry. Subsequently, samples were negatively stained with 10 µL of 2% uranyl acetate. Particle size analysis was performed with free ImageJ software version 1. 51w (NIH, Bethesda, MD, USA). Dynamic light scattering (DLS) and ζ-potential measurements were performed on Malvern Zetasizer Nano ZS90 (Worcestershire, UK) in a Folded Capillary Zeta Cell DTS1070. Prior to measurement, samples were briefly sonicated, diluted to optimal concentration and filtered with a 0. 2 µm syringe filter (Merck Millipore, Burlington, MA, USA).
Acknowledgments: We are grateful to:  Monika Kręcisz , CAT,  UAM , for her contribution;  Aleksandra Gryciuk ,  IPG PAS , for construction of vector for plant transient expression. In addition, we are grateful to  Professor George Lomonossoff ,  John Innes Centre, Norwich, UK , for kindly providing the pEAQ-HT vector for transient expression of HBcAg in plants and to  Plant Bioscience Ltd  for allowing the material to be used.
Background: Rates of adverse events with sorafenib were higher in the DECISION trial in radioactive iodinerefractory, advanced differentiated thyroid cancer (DTC) than in trials of sorafenib for other tumor types. One possible explanation is that sarcopenia, a known predictive factor of toxicity in patients with cancer, is more common in patients with DTC due to hormone suppressive therapy. Methods: This retrospective exploratory analysis was performed to assess whether the risk of early toxicity leading to dose modification (DMT) with sorafenib was higher in patients with sarcopenia compared with those without sarcopenia. The data set comprised patients from the phase III DECISION trial with a computed tomography scan available to determine muscle mass. The skeletal muscle (SM) cross-sectional area was used to determine the SM index and define sarcopenia.
Study population DECISION was a multicenter, randomized, double-blind, placebo-controlled phase III trial (NCT00984282; EudraCT 2009-012007-25) evaluating sorafenib (400 mg twice daily) versus placebo in patients with locally advanced or metastatic radioactive iodine-refractory DTC. Details of the DECISION trial have been previously published  (16). The analysis data set comprised patients in the DECISION trial with a lumbar computed tomography (CT) scan taken ‡30 days before the start of treatment that could be analyzed to determine muscle mass. The reasons for exclusion from the analysis set were a lack of an appropriate lumbar CT scan or the presence of prosthetic metal that prevented analysis of the scan.
Assessments Skeletal muscle mass and lean body mass (LBM) were determined using methods previously described  (10) (11) (12) (13) 21). CT images taken for diagnostic and follow-up purposes ‡30 days before the initiation of sorafenib were used to assess regional muscle mass. Images were analyzed using ImageJ software, v1. 42q (National Institutes of Health). The third lumbar vertebra (L3) was used as a standard landmark; the cross-sectional areas (cm 2 ) of the sum of all muscles were computed for each image, and the mean values for two consecutive images were computed for each patient. The total lumbar SM cross-sectional area (SM area; cm 2 ) was normalized for stature and expressed in cm 2 /m 2. The SM index (cm 2 /m 2 ) was calculated using the formula: SM index (cm 2 /m 2 ) = SM area (cm 2 )/(0.
Consequently, every effort should be made to prevent muscle wasting through nutritional support and appropriate exercise programs  (25) (26) (27). Conversely, other targeted agents may be associated with gain in muscle mass over time. In a study of patients with medullary thyroid carcinoma, those treated with vandetanib (a VEGFR2 and RET inhibitor) gained body weight and muscle mass at 3 months compared with the placebo group; nevertheless, those with low muscle mass had a higher probability of DMTs  (28). Muscle gain was also observed in patients with cholangiocarcinoma treated with the MEK inhibitor selumetinib  (29). The appropriate threshold for defining sarcopenia may vary between different populations defined by age, tumor type, or ethnicity. The widely used definitions are based on data from North American or European populations and may not be adapted to other populations.
Data Sharing Statement Availability of the data underlying this publication will be determined according to Bayer's commitment to the EFPIA/ PhRMA ''Principles for responsible clinical trial data sharing''. This pertains to scope, time point, and process of data access. As such, Bayer commits to sharing upon request from qualified scientific and medical researchers patient-level clinical trial data, study-level clinical trial data, and protocols from clinical trials in patients for medicines and indications approved in the United States and European Union as FIG. 3. Cumulative rate (Kaplan-Meier) of toxicity leading to dose modification (A) and severe toxic events (B) in patients treated with sorafenib with and without sarcopenia. necessary for conducting legitimate research on request from qualified scientific and medical researchers.
Calculations using the Mob2 database from Thermocalc software  [34]  suggested that a treatment at 1380 • C (γ phase) during 72 h should result in gradients width of 600-900 µm depending on the diffusing element. Controlling the atmosphere during this step is of paramount importance to prevent oxidation or decarburization of the sample. Diffusion treatments were conducted under primary vacuum with a continuous flow of argon (of high purity-oxygen less than 5 ppb). The pressure of the furnace chamber was 0. 5 mbar with an argon flow of 0. 5 L/min. Titanium shavings were added to further reduce oxygen content in the furnace and prevent unwanted oxidation or decarburization. At the end of this step, diffusion multiples were cooled in the furnace and held at 650 • C for 8 h to obtain a sufficiently ductile microstructure for the subsequent plastic deformation step.
After the cold rolling step, the new composition profiles were measured using EPMA. Figure  4a  compares the composition gradients after diffusion treatment and after cold rolling for a diffusion couple between Fe-C-Mn and Fe-C-Mo. The concentration profiles went from 950 µm for Mn to 6. 6 mm and from 600 µm for Mo to 4. 2 mm after cold rolling. Another example of diffusion couple between Fe-C and Fe-C-Ni is shown in Figure  4b. One can notice that the carbon content is constant across the diffusion couple. In some cases, due to the influence of substitutional content on the chemical potential of carbon, a gradient of carbon concentration exists; however, it remains of small magnitude, at most 0. 025 %wt•mm -1 in case of Cr containing couples. This gradient does not interfere with the interpretation of the data since any further modeling compared against it takes into account the carbon content measured locally by EPMA.
Samples were then rapidly cooled down at 80 • C/s to an inter-critical temperature (730 • C, 750 • C, 762 • C, and 775 • C) and held 20 min at this temperature to follow austenite-to-ferrite phase transformation. Finally, samples were quenched to room temperature at 80 • C/s. The obtained Debye-Scherer diffraction rings (Figure  6a ) were converted to classical intensity -2θ diffraction spectra (Figure  6b ) by circular integration using pyFAI software. Rietveld refinement was used to calculate phase fractions using the FullProf software. A pseudo-Voigt function was used to model the experimental diffraction peaks and a total of 22 parameters were used for Rietveld adjustment, such as scale and shape factors, temperature effect, lattice parameters, and asymmetry factors. However, the obtained 2D diffraction patterns contained some saturated pixels due to the presence of some coarse grains in the microstructure.
). Figure  10  illustrates an isothermal section of the Fe-C-Mn at 760 • C calculated using TCFE9 database of ThermoCalc, showing the different growth modes for the composition range of the diffusion couple. It can be seen that, for the whole composition gradient, both LENP or PE are theoretically possible transformation modes. The predicted ferrite fraction using PE and LENP modes after 20 min at 760 • C are represented in Figure  9. LENP and PE calculations were carried using DICTRA software and TCFE9 and Mob2 databases. A spherical 50 µm grain size was used, which is representative of the real microstructure. At low Mn content, the measured ferrite fraction is comparable to the predicted one using both PE and LENP models, which differs little from the equilibrium fraction. As Mn concentration increases, the measured ferrite fraction becomes lower than both PE and LENP predictions.
If they happen to be in such a position, they contribute spots that saturate the detector locally, leading in turn to shouldering on the integrated profiles. The last two examples illustrate the effect of composition on the formed ferrite fraction at a given temperature and time. The use of HEXRD gives access to time-and space-resolved kinetics of ferrite growth during precipitation experiments. Figure  12  shows the evolution of ferrite fraction measured using HEXRD as a function of time and position (composition) along a diffusion couple between Fe-0. 16C-1Mn and Fe-0. 16C-0. 2Mo (%wt. ). The composition variation within the X-ray beam in this case was ±0. 02Mn and ±0. 004Mo (%wt. ). The effects of Mn and Mo content on ferrite growth kinetics are clearly illustrated by the obtained dataset using HEXRD experiments.
Ferrite growth rates, as well as the ferrite fraction reached at the plateau, increase with increasing Mo content (and decreasing Mn content). This dependency of ferrite growth rate on Mn and Mo compositions was obtained using one single experiment, which illustrates the importance of high-throughput methods on providing rich databases that can be used to understand phase transformation kinetics in steels. To have a better understanding of the effect of Mn and Mo contents on ferrite growth kinetics, comparisons were made between the measured fractions using HEXRD and the predicted kinetics using both PE and LENP models for a series of compositions (Figure  13a-d ). The following compositions were chosen: Fe-0. 16C-0. 3Mn-0. 14Mo, Fe-0. 16C-0. 65Mn-0. 06Mo, Fe-0. 16C-0. 88Mn-0. 015Mo, and Fe-0.
• The last and most important complication was related to the through-thickness gradient of composition generated during cold rolling, due to a deformation disparity between the surfaces and the center of samples, which prevented measuring the transformation at constant composition for a given X-ray beam position. The through-thickness composition profile was measured by EPMA, and only datasets where there was no through-thickness gradient are presented here. A significant number of gradient samples had to be discarded based on this criterion. The many low-reduction passes are suspected to have amplified the through-thickness inhomogeneity of deformation normally expected from rolling. However, we are not able at this point to precisely determine the conditions on the alloy's rheology, sample geometry, and rolling schedule that would minimize this effect.
For both systems, the obtained fractions deviated from the predicted ones using LENP and PE models. These results highlight the importance of high-throughput methods in studying the effect of composition on austenite to ferrite phase transformation. The large datasets generated using this method can be used to validate and optimize the existing models describing phase transformation kinetics in steels. However, some experimental issues were encountered during this study making the quantitative interpretation of the obtained results more difficult. Some solutions are being studied to overcome these complexities.
2. 5 days old plant were counted from the day when plates were taken out from the 4 °C and put into the growth chamber, with the 16 h. (light): 8 h. (dark) long-day conditions was set. Expression patterns of the suberisation reporter line pGPAT5::mCITRINE-SYP122 were observed using an epifluorescence microscope with GFP filter as described for FY 088 staining  6. The length of the root and the length of the continuous suberisation zone were measured with the ImageJ software. Real time quantitative RT-PCR analysis. qRT-PCR was done for the analysis of the transcript level of suberin biosynthesis genes and ABA-related genes in wild-type Col-0, esb1-1, esb1-1pCASP1::CDEF1, pCASP1::C-DEF1, sgn3-3, esb1-1sgn3-3, pSCR::abi1, pSCR::abi1esb1-1, aba1, and aba1esb1-1. Two reactions were done per biological sample and 4 independent replicate samples per genotype were used to evaluate the transcript abundance of each genotype.
light/14 h. dark). Leaves were harvested 3 to 5 h after lights were on. To determine stomatal aperture, we modified the protocol described in  31. Nail polish was applied to the abaxial surface of leaves, when fully hardened a piece of transparent scotch tape was used to get out the film of clear nail polish from the surface of the leaf, the tape with the stomatal impression in the nail polish was placed onto a slide and observed under a light microscope (Zeiss Axioskop 40) at 20x objective and images were recorded digitally by ProgRes ® CapturePro 2. 9 Image Capture Software. Data were collected by measuring the maximum inner width of the stomatal pore from the captured photographs of a minimum of 50 stomata from 5 individual leaves per genotype and condition. Three independent measurements were done for the comparison between Col-0 and esb1-1, two independent measurements were done for the other mutant lines.
, Milford, USA), conditioned with 1 ml methanol, 1 ml water, and 0. 5 ml sodium phosphate buffer (pH 2. 7). After sample application, the column was washed with 2 ml 5% methanol and then eluted with 2 ml 80% methanol. Eluates were evaporated to dryness and dissolved in 30 ul of mobile phase prior to mass analysis using a 1290 Infinity Binary LC System coupled to the 6490 Triple Quad LC/MS System with Jet Stream and Dual Ion Funnel technologies (Agilent Technologies)  35. Statistical Analyses. The data were analysed using either analysis of variance (ANOVA) with the MIXED procedure or by using pair t-test in SAS (version 9. 2, SAS Institute Inc. , NC, USA). For ANOVA analyses, Tukey's honest significant difference (HSD) or Duncan's multiple range test (MRT) post-hoc adjustments were used to determine significant differences between treatments means at α = 0.
Acknowledgements We acknowledge support from the  ERA-NET   Coordinating Action in Plant Sciences program  project  ERACAPS13. 089_RootBarriers ,with support from  Biotechnology and Biological Sciences Research Council  (grant no. BB/N023927/1  to D. E. S. ), the  German Research Foundation  (DFG; grant no. FR 1721/2-1  to R. B. F. and the AgreenSkills+ fellowship programme to MC-P which has received funding from the  EU's Seventh Framework Programme  under grant agreement  N° FP7-609398  (AgreenSkills+ contract). This work was also funded by the  Ministry of Education, Youth and Sports of the Czech Republic (National Program for Sustainability I , grant no. LO1204 ), the  Swedish Governmental Agency for Innovation Systems (Vinnova)  and the  Swedish Research Council  (VR). We thank  Kevin Mackenzie  ( University of Aberdeen-Microscopy Histology Facility ) and  Carine Alcon  ( BPMP-PHIV microscopy platform ) for assistance using the confocal microscope and stereo microscope for observing the root samples, and the  Swedish Metabolomics Centre  (http://www.