dataesr/scientific-paragraphs-categorization huggingface dataset:
Description Level 1 data are raw data expressed in volts. They are the result of time sampling at a frequency of 200 MHz. The raw sampling of the lidar profiles is 0. 75 m along the line of sight, and an average over 50 shots is performed during the acquisition, corresponding to about one recording every 5 s (2. 5 s averaging time and 2. 5 s recording time). The first 2000 points are recorded before the laser emission is triggered. This offset makes it possible to record for each profile the contribution of the background sky radiance (BR) to the lidar signal (scattering of solar radiation in the atmosphere). This contribution must then be corrected during pre-processing. The lidar signal S, for each polarization channel, of Level 1 data is expressed in the measurement configuration adopted for EUREC 4 A as S (x, z) = C x 2 • F (x) • (β m (z) + β a (z) + β c (x, z)) • exp - 2 cos (θ (z)) • (τ m (x) + τ a (x) + τ c (x, z)) + BR(z).
Description of Level 2 cloud products Cloud detection is applied to the lidar data acquired during Phase 2 of the flights (rectangles). It is the basis of the Level 2 cloud dataset. For each lidar ABC profile, it uses a threshold approach as already considered for lidar measurements at nadir  (Chazette et al. , 2001; Shang and Chazette, 2014). The threshold is proportional to the standard deviation of the noise of the cloud-free signal during Phase 2 of a given flight. Although the coefficient of proportionality C e is constant, the threshold varies with the distance from the aircraft owing to the decrease in the signal-to-noise ratio (due to the increase in the clear-sky noise) away from the aircraft. As for the aerosol products, a lidar profile is considered to be cloud-free if the logarithm of the ABC can be considered linear with a relative error of less than 10 % (see Sect.
Description of Level 3 cloud products Level 3 cloud products consist of probability distribution functions (PDFs) of cloud chords along the laser line of slight computed during Phase 2 of the flight. It is worth noting that owing to the integration and acquisition time of the lidar measurement (5 s) and the aircraft speed (100 ms -1 ), we are unable to derive a cloud mask along the direction of aircraft motion (the minimum distance we can resolve along this direction is 500 m, which is roughly the upper bound of the cloud chords measured along the line of sight of the lidar). The cloud mask distributed in the Level 2 and Level 3 datasets thus corresponds to the cloud detection done along the line of sight of the lidar only. If clouds were homogeneously distributed within the field of view of the lidar, and perfectly detected by the lidar, similar PDFs would be inferred whatever the distance from the aircraft.
Overview of available data The ALiAS system has been successfully operated during the 20 flights of the EUREC 4 A field campaign from 23 January to 13 February 2020. The related dataset is summarized in Table  5. Flights where the lidar sampled a significant number of clouds (1000) are highlighted in bold font. The mean value of the AEC and its standard deviation informs on the amount of aerosols encountered during Phase 2 of each flight (Fig. 3 ). Note that Phase 2 was not carried out during the test flight on 23 January 2020.
Files format For each flight, data are available within the database as NetCDF files (version 4) for the four levels of processing described in Sect. 5. The Level 1 NetCDF file contains raw data recorded during the whole duration of the flight. It contains all the scalar and time-dependent parameters needed to properly process the signal recorded by each lidar channel. The Level 1. 5 NetCDF file contains pre-processed lidar profiles of ABC and VDR along the lidar line of sight, as a function of time. Also provided are the distance from the aircraft (time dependent) and flight attitude, localization, and altitude useful for data geo-localization. Level 2 and Level 3 are concatenated into one single NetCDF file, separately for cloud and aerosol products. The aerosol Level 2 and 3 NetCDF file contains cloud-free AEC individual values with the corresponding altitude, time and geo-localization parameters and the mean vertical profile of AEC within the altitude range of the flight, respectively.
Data availability The entire dataset is published open access on the AERIS database (https://en. aeris-data. fr/, last access: 12 November 2020). The digital object identifier (DOI) for Level Table  5. General flight characteristics of ATR-42 when operating ALiAS. The mean, standard deviation and maximum value of the aerosol extinction coefficient (AEC) and the volume depolarization ratio (VDR) for each Phase 2 (Fig. 3 ) of each flight are reported. The flights in bold font are those associated with the detection of many clouds. The italic font is used for values of the VDR. The comment "Strong presence of dusts" corresponds to VDR > 2 % and the comment "Presence of dusts" corresponds to 1 % < VDR < 2 %. Flights with a reduced detection range due to window clogging by dusts and/or sea salt aerosols are indicated by "X".
Flight Date Start and end time Altitude range AEC ± SD (km  much better sampling than would have been the case with nadir or zenith measurements. This new dataset will make it possible to analyse the macroscopic properties of shallow cumuli near cloud base for a range of meteorological conditions and mesoscale organizations. It will also offer a baseline measurement to assess the value of future spaceborne missions as the forthcoming Earth Clouds, Aerosols and Radiation Explorer mission (EarthCARE;  Illingworth et al. , 2015)  and to evaluate the realism of the new-generation climate models. Aerosol optical parameters were also derived; biomass burning and dust aerosol plumes were present during the field campaign. The data have been classified according to the level of numerical processing applied: (i) Level 1 data are the raw horizontal lidar profiles, (ii) Level 1.
5 data are the calibrated lidar profiles corrected from system characteristics, (iii) Level 2 data are the geophysical parameters directly derived from the individual profiles and (iv) Level 3 data are the synthesis of these parameters. Level 2 and Level 3 data have been combined in the same NetCDF files. All these data are available on the AERIS database (https://en. aeris-data. fr/, last access: 12 November 2020). Author contributions. PC participated in the field experiment on board ATR-42; analysed the data; developed the algorithms for the Level 1. 5, Level 2 and Level 3 datasets; and wrote the paper. JT, AB and CF participated in the field experiment on board ATR-42 and contributed to the paper writing. SB coordinated the project, participated in the field experiment on board ATR-42 and contributed to the paper writing.
Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Elucidating the role of clouds-circulation coupling in climate: datasets from the 2020 (EUREC4A) field campaign". It is not associated with a conference. Acknowledgements. The idea of trying horizontal lidar measurements to characterize clouds at cloud base was suggested to us by Bjorn Stevens at the outset of the EUREC 4 A project. The authors gratefully acknowledge Jean-Christophe Canonici, Jean-Christophe Desbios, Thierry Perrin, Laurent Guiraud and all the technicians, engineers, pilots and director from SAFIRE, the French facility for airborne research (http://www. safire. fr, last access: 12 November 2020), and airplane delivery, for making the preparation of the ATR and the EUREC 4 A airborne operations possible.
We thank the Caribbean Regional Security System (RSS) for hosting the ATR and the ATR team in Barbados during the experiment; David Farrell and the Caribbean Institute for Meteorology and Hydrology (CIMH) for their logistical and administrative support; and the Department of Civil Aviation in Barbados and Andrea Hausold (from DLR), for their help and support of airborne operations. The authors also thank AERIS for their support during the campaign and for managing the EUREC 4 A database. The authors are thankful to the three anonymous referees whose comments helped improve the overall quality of the paper. Financial support. The EUREC4A project was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no.
During 2000-2017, 38 out of 54 OIC countries with data have witnessed increase in CO2 emissions per capita 13. While the developed countries GHG growth is declining, growth in OIC countries is still very high at a level of 4. 4% during the period 2000-2013. With this rate, GHG emissions are expected to be doubled in 2027. The sector responsible for such GHG emissions is the energy sector, which contributes to 72% of emissions of OIC countries. On the positive note, OIC countries have recorded the largest improvement on the EPI issue category of "Biodiversity and Habitat" since the last decade with its average score increasing from 51. 9 to 58, which corresponds to a 12% improvement 14.
SESRIC also established and updated the OIC Statistics Database (OICStat), which provides data on the environmental status of the OIC Member States. OIC countries are also participating actively at all relevant international forums to strengthen global efforts aimed at environmental protection and sustainable development. Given the continued and effective presence of the OIC at regional and international forums, the OIC as a group has been granted Advisory status with UN organs, such as: Economic and Social Council (ECOSOC), UNIDO, UNICEF and the Department of Public Information (DPA) 35. Most of the OIC Member States recognize the Right to a healthy environment through regional agreements and declarations including some 40 States that have incorporated this right in their constitutions and national legislations 36.
Participants were allowed to smoke as they would normally before the session, but smoking was not permitted during testing. In accordance with the Declaration of Helsinki, all participants provided written informed consent at the start of the study, and the study was approved by the Brighton and Sussex Medical School Research Governance & Ethics Committee in the UK and by the French national regulatory authority (ethics committee CCP Nord Ouest-II: ID-RCB: 2011-A01071-40). Participants were compensated in cash for their participation. Due to a technical fault, data from one participant from the UK and eight participants from France were not included in any analyses. Data from one UK participant who fell asleep during scanning were also excluded. Following preprocessing of the fMRI data, an additional two participants (one from the UK sample and one from the French cohort) were excluded from subsequent analyses due to excessive head movements (see below for details.
MRI methods fMRI data at both UK and French sites were acquired on a Siemens Avanto 1. 5 T (32 channel head coil, T2*-weighted echo planar images, repetition time = 3300 ms, echo time = 50 ms, 36 interleaved 3 mm slices, 0. 75 mm slice gap, in-plane resolution 3x3 mm, 255 volumes total). The first six volumes were discarded for steady-state magnetization. A T1 was acquired for co-registration in fMRI preprocessing (repetition time = 1160 ms, echo time = 4. 24 ms, 0. 9x0. 9x0. 9 mm resolution).
non-binge drinkers) as between subjects factors. Perspective (self vs. other) and pain (pain vs. no-pain) were included as within subject factors. Post-hoc analyses of significant interactions were all corrected for multiple comparisons using Bonferroni correction. Participants' subjective sense of how successful they had been in adopting the perspectives of self and other during the in-scanner task were analyzed using a repeated measures ANOVA with binge group as a between-, and perspective condition as within-, participant factor. 2. 6. fMRI analyses 2. 6. 1. fMRI preprocessing fMRI data were preprocessed and analyzed using SPM12 (v6225, www. fil. ion. ucl. ac. uk/spm), including realignment to the mean image, slice-time correction to the (anatomically) middle slice number 18, coregistration with T1 structural and MNI normalization, and 8 mm smoothing, with default settings applied for all options.
It could be that in our case the perspective self versus other may not have been reliably or successfully adopted by the participants. However, no difference was found between the two groups regarding their ratings of how successfully they adopted the self versus the other perspectives; all participants tested reported that they were more successful in adopting the perspective of self than that of other. Note, however, that these data were obtained only from the UK subgroup. Nevertheless, binge drinkers showed, increased activation during the 'pain for other' condition when compared to non-binge drinkers, albeit in an area within the fusiform gyrus and not in more classic affective pain regions (aMCC and insula). It is also worth noting, that inspection of the distribution of activations in both the insula and, in particular, aMCC (extending superiorly to preSMA) indicated overall higher activations in binge drinkers compared to non-binge drinkers.
Acknowledgements This research was supported by The  European Regional Development Fund ,  European Commission   INTERREG IVA program  No. 4096  "AlcoBinge" awarded to MN and TD. The present study was also supported in part by the  European Union 's  Horizon 2020 program  (668863-SyBil-AA; awarded to TD and HDC). Kathleen W. Smith was funded by  Le Conseil régional de Picardie, Amiens, France. The funding bodies had no role in study design, data collection and analysis, interpretation of the findings or decision to publish.
Materials and methods (a) Sampling, sequencing and data filtering Whole-genome sequencing data for 26 accessions of A. semialata and one of A. paniculata were sequenced here and added to 48 accessions retrieved from previous studies (electronic supplementary material, table  S1 ). For herbarium samples, genomic DNA (gDNA) was isolated using the BioSprint 15 DNA Plant Kit (Qiagen). Libraries were prepared with 22-157 ng of gDNA using the Illumina TruSeq Nano DNA LT Sample Prep kit (Illumina, San Diego, CA, USA). Each sample was sequenced at the GenoToul-GeT-PlaGE platform (Toulouse, France) as pairedend reads on 1/24th of an Illumina HiSeq3000 lane. For fresh or silica gel dried leaves, gDNA was isolated using the Plant DNeasy Extraction kit (Qiagen). Libraries were constructed by the respective sequencing centres and paired-end sequenced on full, 1/6th or 1/12th lane of an Illumina HiSeq2500 at the Sheffield Diagnostic Genetics Service (UK) and the Edinburgh Genomics facility (UK; electronic supplementary material, table  S1 ).
The expected sequencing depth ranged from 0. 6 to 58. 7 × (median = 4. 9×; electronic supplementary material, table  S1 ). Raw Illumina datasets were filtered before analysis using the NGSQC Toolkit v. 2. 3. 3  [14]  to remove reads with less than 80% of the bases with Phred score above 20, reads containing ambiguous bases or adaptor contamination. The retained reads were further trimmed from the 3 0 end to remove bases with Phred score below 20. The quality of the filtered datasets was assessed using FastQC v. 0. 11. 9  [15].
(c) Assembly of organelle genomes and molecular dating Plastid and mitochondrial genome sequences were assembled here using a reference-guided approach, except for 15 plastid genomes retrieved from previous studies (electronic supplementary material, table  S1 ). The reference dataset consisted of the chromosome-level nuclear, mitochondrial and plastid genomes of an Australian individual of A. semialata (AUS1-01  [10, 18] ). Pairedend genomic reads were mapped to this reference using Bowtie2 v. 2. 3. 5  [19]  with default parameters. Variant sites from the reads uniquely mapped to each organelle were incorporated into a majority consensus sequence using the mpileup function of Samtools v. 1. 9  [20]  implemented in a bash-scripted pipeline  [12]. Only sites covered by more than five times the expected sequencing depth of the nuclear genome (electronic supplementary material, table  S1 ) were called, discarding potential organelle-nuclear transfers.
This approach produced sequences that were already aligned to the organelle references. The plastid alignment was manually combined with the 15 previous sequences using Aliview v. 1. 17. 1  [21] , after the latter were aligned using MAFFT v. 7. 427  [22] , and the second inverted repeat was removed. Both organelle alignments were then trimmed to remove sites covered by less than 90% of individuals using trimAl v. 1. 4  [23]. The resulting plastid and mitochondrial alignment lengths were 84 587 bp and 139 916 bp, respectively (available on Dryad: https://dx. doi. org/ 10. 5061/dryad. zs7h44j6v [24] ). A time-calibrated phylogeny was obtained independently on plastid and mitochondrial alignments using BEAST v. 1. 8. 4  [25]. royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 287: 20201960 The median ages estimated in  [10]  were used for secondary calibration of the genus Alloteropsis (11.
(e) Genetic structure Principal component and individual-based admixture analyses were performed on reads mapped to the whole nuclear genome. Reads were sorted and indexed using Samtools v. 1. 9, and duplicates were removed using the function MarkDuplicates from Picard tools v. 2. 13. 2 (http://broadinstitute. github. io/ picard/). Genotype likelihoods were estimated using ANGSD v. 0. 929  [35]. Sites covered by 70% or more of individuals and with mapping and base quality scores of 30 or above were retained, resulting in 11 439 variable sites. A covariance matrix was estimated from the genotype likelihoods using PCAngsd v. 0. 98  [36]. Eigenvector decomposition was carried out using the eigen function in R v. 3. 4. 4 to recover the principal components of genetic variation. Individual admixture proportions were estimated from genotype likelihoods using a maximum likelihood approach with NGSadmix v.
(f ) Genome composition The multigene coalescence approach implemented above allows for different histories of loci, but not for different histories of alleles at each locus, as expected in diploid hybrids or allopolyploids. Likewise, the population genomics tools used above are not tailored for mixed ploidy datasets  [40] , and do not explicitly consider known ploidy differences. We consequently established the history of polyploids with phylogenetic trees of phased alleles obtained with a newly developed pipeline. To reduce risks of paralogy, we considered only near-universally singlecopy orthologues in land plants according to BUSCO v. 3. 0. 2 [41]. Out of 1202 such genes identified in A. semialata, 473 had at least one exon longer than the average insert size of the reads used here. The longest exon of each of these genes was used.
Sequences were then generated for both alleles using custom bash scripts in which different depth filters were applied for resequencing and high coverage (20× or above) datasets (at least 3 and 10 reads covering each position and at least 2 and 3 reads covering each variant for polymorphic sites, respectively). Phased sequences shorter than 200 bp were discarded. We only retained genes that: (i) included at least one sequence for each of the two congeners; (ii) had at least four sequences in each of the four nuclear clades of A. semialata (see Results); and (iii) had at least 30 sequences in total (50% of the possible maximum). A maximum likelihood tree was inferred for each of the 300 genes that matched these criteria using PhyML v. 20120412  [42]  with a GTR + G + I model. The resulting trees were rooted on A.
(c) Nuclear phylogeny A multigene coalescent phylogeny was estimated based on 3553 nuclear markers. The four nuclear clades previously defined within A. semialata  [11]  were retrieved with high support (figure  2 ), and similar relationships were obtained with different thresholds for missing data (electronic supplementary material, figure  S3 ), and when only diploid samples were included (electronic supplementary material, figure  S4 ). The monophyly of nuclear clade I, which corresponds to organelle lineage A and is associated with C 3 photosynthesis, is supported here by almost all quartet trees. A lower proportion of quartet trees (∼75%) support the monophyly of nuclear clade II, which contains non-C 4 accessions from the Central Zambezian region (C 3 + C 4 ; organelle lineages B and C).
healthy arteries, using transcriptomic and proteomic datasets. By immunohistochemistry, PCSK6 was localised to fibrous cap SMA+ cells and neovessels in plaques. In human, rat, and mouse intimal hyperplasia, PCSK6 was expressed by proliferating SMA+ cells and upregulated after 5 days in rat carotid balloon injury model, with positive correlation to PDGFB and MMP2/MMP14. Here, PCSK6 was shown to co-localise and co-interact with MMP2/MMP14 by in situ proximity ligation assay. Microarrays of carotid arteries from Pcsk6 -/-vs. control mice revealed suppression of contractile SMC markers, ECM remodeling enzymes and cytokines/receptors. Pcsk6 -/-mice showed reduced intimal hyperplasia response upon carotid ligation in vivo, accompanied by decreased MMP14 activation and impaired SMC outgrowth from aortic rings ex vivo.
Human material and cohorts Patients undergoing surgery for symptomatic (S) or asymptomatic (AS), high-grade (>50% NASCET)  19  carotid stenosis at the Department of Vascular Surgery, Karolinska University Hospital and Department of Surgery, Vascular section, Södersjukhuset, Stockholm, Sweden, were enrolled in the study and clinical data recorded on admission. Symptoms of plaque instability were defined as transitory ischemic attack (TIA), minor stroke (MS) and amaurosis fugax (AF). Patients without qualifying symptoms within 6 months prior to surgery were categorized as AS and indication for carotid endarterectomy (CEA) based on results from the Asymptomatic Carotid Surgery Trial (ACST)  20. Carotid endarterectomies (carotid plaques, CP) and blood samples were collected at surgery and retained within the Biobank of Karolinska Endarterectomies (BiKE).
The BiKE study cohort demographics, details of sample collection, processing and large-scale analyses (genotyping, transcriptomic and proteomic profiling) were as previously described  21, 22. Briefly, peripheral blood mononuclear cells were used for preparation of genomic DNA and high-density genotyping of n=127 BiKE patients was performed by Ilumina 610w-QuadBead SNP-chips. For microarrays, plaques were divided transversally at the most stenotic part, the proximal half of the lesion used for RNA preparation while the distal half was fixed in 4% Zn-formaldehyde and processed for histology. Normal artery controls (NA) were obtained from nine macroscopically disease-free iliac arteries and one aorta from organ donors without history of cardiovascular disease. The microarray dataset is available from Gene Expression Omnibus (GSE21545).
For LC-MS/MS proteomic analyses, atherosclerotic plaques from n=18 patients (n=9S+9AS; matched for gender, age and statin medication) were collected and processed as previously described  22. A central portion of the plaque corresponding to the maximum stenosis was separated from the respective downstream peripheral end (adjacent tissue) of the plaque and used in comparisons  23. For immunohistochemistry additional tissues were used: normal radial arteries obtained at coronary by-pass surgery, one internal carotid artery from a 61-year-old male treated for a neck tumor, and in-stent stenosis (intimal hyperplasia) tissue obtained from a patient after treatment of a traumatic aortic transection with a stent graft. The database of IMPROVE, a large, multicenter, European longitudinal cohort study (acronym: Carotid Intima Media Thickness (IMT) and IMT-PRogression as Predictors of Vascular Events in a High-Risk European Population) was used for studying single nucleotide polymorphism (SNP) associations with various cIMT measures.