dataesr/scientific-paragraphs-categorization huggingface dataset:
A further expected difference between the data sets arises from their detection limits. The lowest N ice that can be detected by the in situ instruments is 10 -4 cm -3 ; for the highest N ice there is no limitation. Detection limits for DARDAR-Nice depend on lidar-radar sensitivity but are also influenced by the instrumental resolution, which may cause specific features to remain undetected. This effect, however, is represented in Fig. 13  by the 10 s sampling of 1 Hz in situ PSDs. Indeed, DARDAR-Nice misses the high N ice in the temperature range 210-220 K, but these are young, lineshaped contrails which are too small scale to be detected by satellite. Also, part of the thinnest cirrus are not represented by DARDAR-Nice. This will be further discussed in the next section describing the global N ice distribution from DARDAR-Nice.
Overall, following the abovementioned arguments, an overestimation within a factor of 2 in DARDAR-Nice by comparison to the in situ data set is expected, although there is still some uncertainty as to how this overestimation will propagate globally and over various ice cloud regimes. The ratio of 1. 73 in N ice between DARDAR-Nice and the in situ data set, found from Fig. 13 , should therefore be thought of as a minimum expected bias. Finally, the bias is expected to be stronger at low temperatures than at high temperatures, due to the increased likelihood of empty size bins as well as the higher importance of the representation of the small ice particle mode in the PSDs. A correction that depends on    13 ). The 10th and 90th, 25th and 75th, and 50th percentiles are indicated by dotted, dashed, and plain lines, respectively.
046 cm -3. This is still larger than the average median of 0. 03 cm -3 reported from the N ice in situ climatology (blue line; from Fig. 7 ). Nevertheless, in light of the offset between the data sets, the decreasing detectability of thin cirrus by DARDAR-Nice, and other sources of error in both methods, the agreement between the DARDAR-Nice and the in situ N ice climatology is good. Altogether, both data sets have advantages and disadvantages: DARDAR-Nice has the advantage of the long, global time series where all atmospheric situations above the detection limit showing up at the times of observations are sampled. However, the thinnest cirrus at cold temperatures are not detected. Further, an offset is found between DARDAR-Nice and the in situ observations. On the other hand, the in situ N ice climatology is based on a much smaller data set which is randomly distributed between different atmospheric situations.
15 , where the occurrences of DARDAR-Nice N ice retrievals are indicated by the colored background. It can be noticed that, in regions of high frequency of occurrence (for T 210 K), the agreement between DARDAR-Nice and the in situ data set is best. From this portrayal it can be seen that the cirrus clouds do not spread evenly across all temperatures, but that about 50 % of the cirrus appear at temperatures above about 225 K (see also Table  3 , temperature range of global "most frequent" median N ice ). This clustering of cirrus at warmer temperatures, also reported by  Yang et al. (2010)  from CALIPSO cloud climatologies, is likely because in situ-origin and liquid-origin cirrus clouds are both found in this warmest and lowest cirrus layer, while at colder temperatures and higher altitudes only in situ-origin cirrus are present (see also  Sect.
Note that an analysis of N ice in the IWC-T space, similarly to Fig. 6 , is shown in Fig. 5  of the Supplement. This figure shows a good climatological agreement between the satellite product and the Cirrus Guide II data set, with very similar distribution of N ice in the IWC-T space. Differences could be attributed to lack of statistics in Fig. 6  (noisy patterns). However, one notable difference is the slope of the IWC-T relation, which appears much flatter in the satellite product than in the in situ data, as indicated by the density isolines.
Summary and conclusions The Cirrus Guide II aims to represent cirrus clouds and their environment from the perspective of in situ and satellite remote sensing observations. To this end, an in situ database is created, which is based on measurements with state-ofthe-art instrumentation and extended in comparison to earlier studies  (Schiller et al. , 2008; Krämer et al. , 2009; Luebke et al. , 2013; Krämer et al. , 2016). The in situ database consists now of about 168 h of ice water content IWC, 90 and 84 h of ice crystal number concentration and mass-mean radius N ice and R ice , 96 and 186 h of in-cloud and clear-sky RH ice , and, correspondingly, 186 h of clear-sky water vapor (H 2 O). The measurements span the altitude range between 5 and 20 km and cover the latitude band from 75 • north to 20 • south.
Figure2. Left panel: temperature vs. altitude inside of cirrus clouds (adopted from Schiller et al. , 2008). Middle panel: temperature vs. potential temperature. Right panel: clear-sky vs. altitude. Plotted are 28 flights from the database, with blueish colors for Arctic, greenish colors for midlatitude, and reddish colors for tropical observations. Magenta lines: range of the TTL (tropical tropopause layer) and tropical CPT (cold point tropopause) (after Fueglistaler et al. , 2009).
Acknowledgements. This paper is dedicated to our colleague  Cornelius Schiller , who passed away much too early in 2012. He was the initiator of the  StratoClim  project, including the tropical aircraft campaign. Through this campaign, he intended to complement his work on hydration and dehydration in the tropical tropopause layer, based on observations over Brazil, Australia, and  Africa  (Schiller et al. , 2009) with measurements over Asia. The campaign took place successfully out of Kathmandu, Nepal, in summer 2017, after several years of planning and many hurdles that had to be overcome. The  StratoClim  project is funded by the  European Commission  within  Framework program 7 under ENV. 2013. 6. 1-2 ,grant agreement no. 603557. Funding for Cirrus Guide II is partly provided by the  DFG HALO-SPP  project  ACIS  ( KR 2957/1-1 ).
Data availability. The Cirrus Guide II in-situ data set is accessible under https://doi. org/10. 34730/266ca2a41f4946ff97d874bfa458254c (Krämer et al. , 2020) , the DARDAR-Nice data set under https://doi. org/10. 25326/09 (Sourdeval et al. , 2018b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-12569-2020-supplement.
Materials and Methods This manuscript presents findings from pairwise and network meta-analyses carried out in accordance with the PRISMA recommendations  [31] , and meeting the PRISMA extension statement requirements for reporting of systematic reviews incorporating network meta-analyses of healthcare interventions  [41]. For further details of our methods and their prospective character, our registered systematic review protocol (International prospective register of systematic reviews PROSPERO reference ID: CRD42020148155) is available for readers by clicking on the following link: https: //www. crd. york. ac. uk/prospero/display_record. php?ID=CRD42020148155. By using database-specific search strategies being developed with search terms related to participants/population, interventions, exposures, and the type of study to be included, MEDLINE via PubMed, Ovid and Web of Science, EMBASE via Elsevier's Scopus, the Cochrane Controlled Register of Trials (CENTRAL), and other important databases of published studies via Web of Science, were searched up to September 2019.
In addition, ClinicalTrials. gov, the EU Clinical Trials Register and the United Kingdoms' ISRCTN registry, and relevant gray literature sources were searched. The reference lists of the included studies were scanned to identify all relevant studies cited by the included studies. The literature search was limited to the English language. Our full search strategy and search results are available for readers online (Panel S1). As previously described  [40] , our strategy was focused on the identification of RCTs including their extension follow-up studies, and all varieties of post-hoc analysis that assessed individuals with normal kidney function (NKF)/CKD into KDIGO GFR categories G1 and G2, CKD patients into the KDIGO GFR categories G3A to G5, chronic dialysis patients, and kidney transplant patients.
Review Manager software (RevMan) version 5. 3 (Cochrane Collaboration) and META-analysis package FOr R (METAFOR) version 2. 4 (R project) were used for calculations at the pairwise level. Thereafter, OR with their corresponding 95% CrI for the combined outcome of non-response to iron supplementation were calculated via Bayesian network meta-analysis (Markov chain Monte Carlo simulation on vague priors random-effect method for 'bad' outcomes and zero values correction), with calculation of the value of SUCRA corresponding to each of the iron supplements described in studies eligible, and with verification of convergence (Brooks-Gelman-Rubin method) and inconsistency. NetMetaXL software (Canadian Agency for Drugs and Technologies in Health and Cornerstone Research Group)  [43]  was used for performing network meta-analysis.
Diagnostics SMPS measurements are performed to obtain d m using a TSI 3091 SMPS that consists of a TSI 3080 DMA upstream a TSI 3776 CPC operated with 0. 3 L min -1 aerosol flow rate and 1 : 10 sample/sheath flow ratio. Charge neutralization is provided by a TSI 3088 soft X-ray neutralizer. TEM measurements are performed to obtain d pp and N pp from which D f is calculated (Sect. 4. 2). All parameters are estimated from the TEM images by using ImageJ freeware software. TEM measurements are performed on the FEI Tec-nai G2 20 microscope (200 kV acceleration voltage) available at the center for electron microscopy of Lille University. All samples are deposited on Lacey carbon meshes.
The mass spectrometer was operated in data-dependent mode, automatically switching between MS and MS/MS acquisition for the five most abundant ion peaks per MS spectrum. Full-scan MS spectra (300-1700 m/z) were acquired at a resolution of 70,000 after accumulation to a target value of 3,000,000 with a maximum fill time of 20 ms. The five most intense ions above a threshold value of 170,000 were isolated (window of 1. 6 Th) for fragmentation at a normalized collision energy of 27% after filling the trap at a target value of 1,000,000 for maximum 60 ms with an underfill ratio of 1%. The S-lens RF level was set at 60 and we excluded precursor ions with single, unassigned and charge states above six from fragmentation selection. protein identification and quantification. Data analysis was performed with MaxQuant (version 1.
5. 6. 5)  45 using the Andromeda search engine with default search settings including a false discovery rate set at 1% on both the peptide and protein level. Spectra were searched against the human proteins in the UniProt/ Swiss-Prot database (database release version of August 2016 containing 20,198 human protein sequences) supplemented with the sequence of GFP. The mass tolerance for precursor and fragment ions were set to 4. 5 and 20 ppm, respectively, during the main search. Enzyme specificity was set as C-terminal to arginine and lysine, also allowing cleavage at proline bonds with a maximum of two missed cleavages. Variable modifications were set to oxidation of methionine residues, acetylation of protein N-termini and phosphorylation of serine and threonine residues. Only proteins with at least one unique or razor peptide were retained and quantified by the MaxLFQ algorithm integrated in the MaxQuant software  46.
A minimum ratio count of two unique or razor peptides was required for quantification. Further data analysis was performed with the Perseus software (version 1. 5. 5. 3)  47  after loading the protein groups file from MaxQuant. Proteins only identified by site and reverse database hits were removed, and the LFQ protein intensity values were log2 transformed. Technical replicate samples of SIRT2-GFP and the GFP negative control sample were grouped, proteins with less than three valid values in at least one group were removed and missing values were imputed from a normal distribution around the detection limit. Then, a t-test was performed (FDR = 0. 01 and S0 = 2) to compare the SIRT2-GFP sample with the GFP negative control sample and to generate the volcano plots depicted in Fig. 1B  (Table  S1 ).
Immunofluorescences microscopy. For immunofluorescence HeLa cells were plated onto coverslips prior to treatments. Following treatments cells were washed three times in PBS and fixed using 4% PFA for 10 mins. Cells were then permeabilised for 10 mins in 0. 1% Trition X-100 PBS. Nuclei were stained with 300 nM (100 ng. mL -1 ) DAPI for 15 mins, then washed three times in PBS before being mounted using Fluoromount-G ® Mounting Medium (INTERCHIM). For live imaging HeLa cells were plated onto 35 mm glass bottom dishes (MatTek, P35G-1. 5-10-C) in FluoroBrite ™ DMEM (Gibco) supplemented with 10% FCS. Cells were treated with 20 nM LMB and images were acquired every 5 mins in an atmospheric chamber at 37 °C. All images were acquired using a Zeiss Axio Observer spinning-disk confocal microscope equipped driven by the MetaMorph software.
Acknowledgements We would like to acknowledge and thank  Pascale Cossart  for her valuable insight and support throughout the project and for her contribution towards the acquisition of financial support. We also thank  Michael G. Connor  for his help in the quantification and analysis of microscopy data. MJGE is supported by a fellowship from the  French Government 's  Investissement d' Avenir program , the  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ). Work in the M. A. H. laboratory received financial support from the  Institut Pasteur and the National Research Agency  ( ANR-EPIBACTIN ).
A minimum of 50 000 reads per cell were sequenced and analyzed with Cell Ranger Single Cell Software Suite 3. 0. 2 by 10X Genomics. Raw base call files from the Nextseq 500 were demultiplexed with the cellranger mkfastq pipeline into library specific FASTQ files. The FASTQ files for each library were then processed independently with the cellranger count pipeline. This pipeline used STAR21 to align cDNA reads to the Gallus gallus genome (Sequence: GRCg6a, Gallus gallus reference). The used sample-size in scRNAseq allowed us to robustly identify cell populations as small as 18 cells at E4, 6 cells at E6 and 66 cells at E10. The Seurat package (v3. 0) was used to perform downstream clustering analysis on scRNAseq data  43. Cells went through a classical Quality Control using the number of detected genes per cell (nFeatures), the number of mRNA molecules per cell (nCounts) and the percentage of expression of mitochondrial genes (pMito) as cut-offs.
Outliers on a nFeature vs nCount plot were manually identified and removed from the dataset. Most importantly for this study, potential doublets were identified by running the Scrublet algorithm  44  and then removed from the dataset. Gene counts for cells that passed the above selections were normalized to the total expression and Log-transformed with the NormalizeData function of Seurat using the nCount median as scale factor. Highly variable genes were detected with the FindVariableFeatures function (default parameters). Cell cycle effect was regressed out using the ScaleData function. Using highly variable genes as input, principal component analysis was performed on the scaled data in order to reduce dimensionality. Statistically significant principal components were determined by using the JackStrawPlot and the ElbowPlot functions.
e gene Log-normalized count > 0) within a cell of at least one of the CT markers (PRRX1, TWIST2, PDGFRA, OSR1, SCX) with at least one of muscle markers (PAX7, MYF5, MYOD1, MYOG). CT identity is conferred to all cells allocated to a CT cluster, except the CT/M cells. M identity is conferred to all cells allocated to a muscle cluster, except the CT/M cells. « Other » identity is conferred to all cells to clusters that are neither CT nor muscle, except for the CT/M cells. The scRNAseq datasets were then analysed with the angle of these 4 identities using Seurat tools such as Feature plots, Heatmaps and Violin plots.
Quantitative real-time PCR qPCR was performed as described in  22. Total RNAs were extracted from SMAD6 or control myoblasts. 500 ng of RNA was reverse-transcribed. qPCR was performed using SYBR Green PCR master Mix (Aplied Biosystems) with primers listed in Extended Ressources Table  2. The relative mRNA levels were calculated using the 2^-ΔΔCt method  46. The ΔCt were obtained from Ct normalized with GAPDGH and RPS17 levels in each sample. Statistics. GraphPad Prism 6 software was used for statistical analysis. Non-parametric tests were used to determine statistical significance, which was set at p values <0. 05.
GEO data accession number. Raw sequencing data will be deposited in the NCBI Gene Expression Omnibus database (https://www. ncbi. nlm. nih. gov/geo/) upon acceptance. with betaGal (Osr1-lineage-derived cells, green) and PAX7 (red) antibodies, combined with DAPI (blue) (f), and with betaGal (Osr1-lineage-derived cells, green) and MYOD/MYOG (red) antibodies combined with DAPI (h). (g,i) High magnifications. (g) Arrows point to the betaGal+ nuclei (Osr1-lineage-derived, green) and PAX7+ (red) that are located in between myotubes, while arrowheads point to the betaGal+ nuclei (green) that are PAX7-and located inside myotubes. These betaGal+ PAX7-nuclei correspond to cells in which PAX7 has been donwregulated and betaGal expression kept after cell incorporation into myotubes. (i) Arrows point to the betaGal+nuclei (Osr1-lineage-derived, green) that are MYOD/MYOG+ (red).
Acknowledgements: We thank  Sophie Gournet  for illustration. This work was supported by the  CNRS, Inserm, SU, AFM  and  FRM. We thank the  Roslin Intitute  ( Prof Helen Sang  and  Dr. Adrian Sherman ) for providing us with GFP + chicken eggs. The production of the GFP + chicken embryos was supported by grants from  BBSRC  and the  Wellcome Trust. ARTbio was supported by the  CNRS, SU , the  Institut Français de Bioinformatique (IFB)  and by a grant from the  SIRIC CURAMUS.
Participants and Procedures We conducted a large-scale study from April 2012 to November 2014. A total of 13,403 heterosexual participants completed an online survey about their attitudes toward SSM and LGP. The number of participants per country was as follows: Belgium (n = 4628), France (n = 2267), Greece (n = 293), Italy (n = 3833), Poland (n = 644), Portugal (n = 1106), and Spain (n = 632). The survey was presented in the participants' native language. The participants (M age = 24. 65, SD = 7. 63) were university students, a third of whom were enrolled in social science faculties (Table  2 ). They were recruited online across different European universities. We emailed the research description, SurveyMonkey link, and inclusion criteria to the universities and requested dissemination via email to the enrolled students.
To examine whether the differences in attitudes toward SSM and LGP between these two clusters was mediated by religiosity, contact with LG people, and attitudes toward LG people, we performed a structural equation modeling analysis using the Lavaan package  (Rosseel, 2012)  in R. Table  5  and Fig. 2  report the standardized path coefficients for all of these variables. The model had a satisfactory fit (X 2 [293] = 4432. 39, p = 0, CFI = 0. 93, RMSEA = 0. 06). An examination of the coefficients suggested that country predicted religiosity and gender role traditionalism (but not contact with LG people), which predicted attitudes toward LG people, whereas the latter predicted attitudes toward SSM and LGP. When the indirect effects are compared, it is the path through religiosity that most strongly explains the difference between countries (see Table  5 ).
Greater religiosity predicted more negative attitudes toward LG, which predicted additional negative attitudes toward SSM and SSP. Attitudes toward LG did not fully mediate the relationship; that means that more religious people hold negative attitudes toward SSM and SSP because they hold more negative attitudes toward LG. It is likely that many religious teachings emphasize heterosexual norms for people's identities, families, and marriages-creating a system of unacceptance. Lastly, we found that religiosity and attitudes toward LG people (but not contact satisfaction with LG people) mediated the differences in attitudes toward SSM and LGP between countries. Across the seven countries, participants who reported having more contact satisfaction with LG people displayed greater support for SSM and LGP, as well as more 8 Data of these studies were collected between 2012 and 2014 in the framework of the present European Research on Attitudes toward Same-Sex Marriage and LG parenting coordinated by the first and the third author.
Limitations The first limitation of this study is the composition of the sample. The study sample encompassed university students who are more educated and have potentially more contact with other cultures compared with the general population  (Henrich, Heine, & Norenzayan, 2010). These students were not randomly selected from all colleges/universities in each country, so the sample is not representative of the general population. A second limitation point to consider is that our data was collected between 2012 and 2014. Since 2014, France and Portugal have approved same-sex marriage and adoption. In Italy, same-sex registered partnership was approved. These legal advancements may have strengthened the trends that we found for these countries although social change takes a long time to become noticeable and do not necessarily accompany legislative changes.
Instrumentation and Method We use data from the Ion Mass Analyzer (IMA), a part of the Analyser of Space Plasma and Energetic Atoms (ASPERA-4) instrument package on board VEx. IMA uses a top-hat electrostatic analyzer to differentiate the energy of the incoming ions in the range 0. 01-36 keV with the energy resolution E/E = 7%. The flying direction of the ion in the 360°× 90°(~2π sr) field of view is resolved by 16 azimuthal sectors of 22. 5°each and elevation deflector plates scanning the elevation plane over 16 (5. 6°wide) steps. Each full ion distribution is sampled over angle and energy every 192 s. The mass per charge is differentiated for M/Q = 1-44 amu through an assembly of permanent magnets. The instrument is described in further detail by  Barabash et al. (2007). All measurements of IMA obtained from April 2006 to November 2014 are used to calculate the escape rate to estimate the O + outflow.
, 2009) , which is the closest observation to a glass transition temperature reported in the literature, if one assumes that PZA crystallizes immediately once it reaches T g on heating the glass. Cooling crystallization from solution specifically targeting the form has been published by Hermanto et al. (Hermanto et al. , 2015). Solubility of , and in water and solubility of in four organic solvents and one mixture of solvents have been determined. The results showed that the solubility of PZA increases with increasing temperature and the solubility data in water from about 287 K to 316 K indicated the following ranking x( ) > x( ) > x( ). Solubility of in various solvents have been obtained by  Zhang et al. (Zhang et al. , 2017). A number of modelling studies involving PZA have been published.