dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We are grateful to  Anna Yunnie ,  Christine Wood ,  Laurent Lévêque , and the divers of the  Marine Operations department at the Roscoff Biological Station  for their help in sampling and preliminary sorting of the samples in EC. We also thank the marina operators for their permission to conduct the experiment. We are grateful to the following people for helping with the Mediterranean samples or sending specimens to be used as reference:  Maria Casso ,  Franck Gentil ,  Gretchen Lambert ,  Adam Langenbacher ,  Julia Nunn ,  Mari Carmen Pineda Mark Rius ,  Kirsty Smith Paolo Sordino , and  Davide Tagliapietra. Susanna López-Legentil  sequenced the specimen from  Miseno Lagoon.
The age of the participants (68% females) at the time of the experience was 31 ± 15 years (7 missing values), and the age at the time of the study was 52 ± 13 years (2 missing values). Loss of consciousness at time of the NDE was attributed to head injury in 13% of the cases, to anesthesia/drug use in 30% of the cases, and to cardiac arrest in 10% of the cases (228 missing values). Severity of physical trauma/closeness to death was rated retrospectively by the experiencers as "not serious" in 27% of the cases, as "serious" in 37% of the cases and as involving loss of vital signs in 35% of the cases (209 missing values). The NDEs were rated as "pleasant" in 82% of cases, as "neutral" in 8% of cases and as "unpleasant" in 9% of cases (279 missing values). The NDE corpus metadata can be visualized in Fig.
This resulted in 165 substances that were manually classified into the following classes: serotonergic psychedelics, dissociative psychedelics, entactogens, deliriants, depressants/sedatives, stimulants, antipsychotics/antidepressants, oneirogens and others. Some substances were also given a secondary category due to the complex nature of their effects. The data in the Erowid corpus is summarized with detail in the supplementary material. Table  S1  presents the following information for serotonergic psychedelics: generic drug names/botanical species, PubChem CID (for molecular compounds)  (Hähnke, Kim, & Bolton, 2018) , number of reports and a brief description of the known mechanism of action and the geographic distribution (for naturally occurring plants, mushrooms and cacti)  (Rätsch, 2005).
Corpora preprocessing The preprocessing of the NDE and Erowid corpora was performed using the Natural Language Toolkit (NLTK, http://www. nltk. org/) in Python 3. 4. 6. We applied the following steps: (1) separation into individual words after discarding all punctuation marks (word repetitions allowed); (2) word lemmatization using NLTK (i. e. conversion to the root from which the word is inflicted); (3) conversion to lowercase; (4) discarding words containing less than three characters (after lemmatization)  (Sanz et al. , 2018). Since texts from the Erowid corpus are likely to be influenced by the nature of the substance being reported, we manually compiled a list of words including substance names, different slang variations (e. g. "weed", "coke"), and words relating to the possible routes of administration (e.
Principal component analysis and automated component labeling We applied principal component analysis (PCA) to reduce the term-document matrix into a smaller number of components capturing topics that recurrently appear in the corpora (using an algorithm based on SVD as implemented in MATLAB 2014). We considered only the topics represented by the first five components, in terms of explained variance in the tf-idf rank-reduced termdocument matrix. Each component was associated with one coefficient per term in the vocabulary. These coefficients were used to represent the topics in word cloud format using the website https://wordart. com/. To avoid the arbitrary naming of the components, for each of them we selected the 20 terms with the largest coefficients, and queried a large space for the words with the highest semantic proximity.
Similarity between NDE and drug-induced experiences We performed a SVD of the term-document matrix, grouping as single documents all NDE narratives and all reports associated with the 165 substances in the Erowid corpus. We retained the first 20 singular values; this choice was based on the number of drug categories included in the corpus, as well as on previous research showing adequate results using the same dataset  (Sanz et al. , 2018). The robustness of the results against the choice of this parameter was evaluated and is presented in a latter section. We obtained the correlation matrix derived from computing all pairwise correlations between the columns of the rank-reduced term-document matrix * A (Fig. 2A ). The narratives of substances within the same category tended to display a higher semantic similarity when compared to those of other categories; this was especially clear for antipsychotics and deliriants.
Relationship between semantic similarity and NDE metadata We understand as "NDE metadata" all the information that is not contained in the NDE narratives themselves, but informs on the surrounding circumstances and demographics. In Fig. 8A , we show the mean rating (in terms of NDE narrative semantic similarity) for each drug category, when comparing to NDEs associated with loss of consciousness due to head injury, anesthesia/drug use, and cardiac arrest. The mean ranking vs. drug category remained stable across conditions, and resembled that of Fig. 4A. A similar result is shown in Fig. 8B  (proximity to death) and Fig. 8C  (emotional valence of the experience). As shown in the pie charts, ketamine emerged in almost all cases as the drug whose reports presented the highest semantic similarity to NDE narratives.
The time between the occurrence of the NDEs and participation in the study averages approximately two decades  (Greyson, 2007a (Greyson, ,2007b)). Reports of subjective effects from Erowid Experience Vaults are likely closer to the time of substance use, but present several limitations of their own. While substantial relevant data was available for the participants of the NDE study, demographical information is not commonly available for Erowid users. Reports could be contaminated by expectation effects, and most lack laboratory verification of the identity and dose of the consumed substances, which may be even unknown to the users reporting the experiences. Dose could bias the results if subjects consumed higher doses of ketamine compared to those of serotonergic psychedelics such as LSD and psilocybin, resulting in dissociation and feelings of disembodiment only for the latter.
Acknowledgments We acknowledge insightful discussions with  David Luke ,  Facundo Carrillo ,  Mariano Sigman ,  Diego Fernandez Slezak ,  Robin Carhart-Harris ,  Raphaël Millière ,  Chris Timmermann  and  Hannes Kettner. We also thank the founders, curators, contributors and volunteers of Erowid Center for sharing their data and for their decades of work on the experience report collection. This study was supported by the  University  and  University Hospital of Liege , the  Belgian National Funds for Scientific Research (FRS-FNRS) , the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 785907  ( Human Brain  Project  SGA2 ), the  Luminous  project ( EU-H2020-fetopenga686764 ), the  European Space Agency (ESA) ,  Fonds Léon Frédéricq , the  Bial Foundation , the  Mind Science Foundation , the  European Commission  and the  National Scientific and Technical Research Council (CONICET).
Code and data availability The source code for the MARS and GETM model used in this study are available at https://wwz. ifremer. fr/mars3d/ and https://getm. eu/. Research data is archived on 4TU. Research Data (https://data. 4tu. nl/) with the doi https://doi. org/10. 4121/uuid:c6753aa0-d501-4cbe-9476-a2833d47bfc6. https://doi. org/10. 5194/os-2019-50 Preprint. Discussion started: 11 June 2019 c Author(s) 2019. CC BY 4. 0 License.
-The atomic physics for the spectral lines has to be well known. Examples of some of the problems that one might encounter are an underestimation of the observed flux in the 50-130 Å wavelength range  (Testa et al. 2012b)  or anomalous behaviour for ions of the Li and Na isoelectronic sequences for which the atomic physics models tend to underestimate line radiance  (Del Zanna et al. 2001; Sect. 7. 4 of Del Zanna & Mason 2018 ). The quality of the atomic data will depend on the database chosen. For the purpose of this paper, we use CHIANTI (version 9. 0, previous versions described in  Dere et al. 1997 , Del Zanna et al. 2015)  through the ChiantiPy Python package (version 0. 8. 5), but in principle another database can be used. -The maximums of the contribution functions of all lines should be at similar temperatures so that we do not mix abundances at various heights.
Computation of the contribution functions We use the CHIANTI atomic physics database to compute the contribution functions. We also use it to retrieve information about each spectral line, such as typical photospheric abundance, FIP of the element, and upper and lower levels of the transition. Furthermore, for the cases when density maps can be obtained, for example when using a radiance ratio between a pair of lines with different density sensitivities, we compute these contribution functions on a grid of temperatures and densities.
Synthetic radiance maps We start by computing Emission Measure 2 (EM) maps using the  Cheung et al. (2015)  code (version 1. 001), which is available in the SDO/AIA package in SolarSoft. This particular EM inversion method finds a sparse solution, that is, it uses a minimum number of basis functions to produce an EM(T ) compatible with the observations. We chose an observation of the Atmospheric Imaging Assembly (AIA;  Lemen et al. 2012 ) instrument aboard the Solar Dynamics Observatory (SDO;  Pesnell et al. 2012) , on June 3, 2012, close to the maximum of solar activity, so that the coronal emission is inhomogeneous. The Sun in this particular day presented various ARs and a large CH at the centre. As shown in Fig. 1 , we select two separate regions of interest, one being centred on an AR, and the other centred on a CH.
After choosing the lines and computing their contribution functions, we determine the optimal linear combination of these lines for the LCR method (Sect. 3. 3. 3). The reference EMs that we use for the optimisation are plotted on Figs. 2  and 3. These are available in the CHIANTI database and correspond to typical EMs for a coronal hole, an active region, and the quiet sun. The resulting coefficients are included in Table  1. For the 2LR method, we use the inverse of the maximum of the contribution functions of the Si x and of the S x lines as values for the (single) α and β coefficients, respectively, therefore allowing the use of the same formalism as for the LCR method. In particular, we can compute the cost function defined in Eq. (  21 ) for both the LCR method (following optimisation) and the 2LR method, as shown in Table  3.
Acknowledgements. The authors thank  Mark Cheung  for the SDO/AIA DEM inversion code,  Deborah Baker  for providing the FIP bias map data, and  Giulio Del Zanna ,  Susanna Parenti , and  Karine Bocchialini  for their comments. The idea for this work came following a presentation by  Hardi Peter  at the Solar Orbiter joint SPICE-EPD-SWA meeting in Orsay in November 2015. AIA is an instrument on board SDO, a mission for NASA's  Living With a Star program. NZP thanks  ISSI  for support and participants to the  ISSI  team n. 418 "Linking the  Sun  to the Heliosphere using Composition Data and Modelling" led by  Susanna Parenti. Hinode  is a Japanese mission developed and launched by  ISAS/JAXA , with  NAOJ  as domestic partner and  NASA  and  STFC (UK)  as international partners. It is operated by these agencies in cooperation with  ESA  and  NSC (Norway).
CHIANTI is a collaborative project involving  George Mason University , the  University of Michigan (USA)  and the  University of Cambridge (UK). This work used data provided by the MEDOC data and operations centre (CNES /  CNRS / Univ. Paris-Sud ), http://medoc. ias. u-psud. fr/. Python modules used include: numpy, matplotlib, astropy, scipy, sunpy, chiantipy and colorblind (available at https://github. com/volodia99/colorblind).
1A ) and the RNA-seq data showed that eRF3A and UPF1 mRNA levels were reduced to 22% and 37% of control levels, respectively (Fig. 1B ). Then, each cell lysate was used to prepare both ribosomal footprint and mRNA libraries and thus obtain matched data. Two biological replicates of ribosomal footprint and mRNA libraries were generated and subjected to deep sequencing to obtain the Ribo-seq and the RNA-seq data sets, respectively. Ribo-seq and RNA-seq reads were aligned against the human genome assembly hg38 (Genome Reference Consortium Human  Build 38). After discarding rRNA and multiple aligned reads, we obtained ~1. 8x10 7 and ~1. 2x10 8 unique mapped reads per sample for RNA-seq and Ribo-seq data, respectively (see Supplemental Figure  S1  for details). Our Ribo-seq and RNA-seq data sets were highly reproducible across deep-sequencing replicates as well as across biological replicates, with Pearson correlation coefficient (R) values > 0.
97 (Supplemental Figure  S2, A  and B ). Reads data were therefore pooled over replicates for further analysis. For Ribo-seq data, ribosome-protected fragment-length assays generated a peak of 29-30 nucleotides for all samples, as expected for mammalian ribosomal footprints (Supplemental Figure  S2C ). Moreover, providing additional evidence for footprint of ribosomes decoding the message, a 3-bp periodicity at the 5ʹ end of the read coverage was apparent as a peak at 0. 33 Hertz on the periodogram (Periodicity = 1/Frequency) for the coding sequences (Supplemental Figure  S2D , CDS panel), and not for the 5ʹUTRs (Supplemental Figure  S2D , 5ʹUTR panel). Then, we used the RNA-seq and Ribo-seq data sets to establish a transcriptome map of HCT 116 cells (see Material and Methods section for details).
Then, RT-qPCR assays were used to verify the results of the gene expression changes in RNA-seq data, with particular focus on the transcription modulators mentioned above. The mRNA for β2-microglobulin (B2M) was used as the endogenous reference mRNA in the qRT-PCR assays. eRF3A and UPF1 mRNAs were used as controls to assess knockdown efficiencies. As shown in Fig. 3A , RT-qPCR data correlated well with the RNA-seq results (Fig. 2  and Supplemental File S1). To validate our differential expression analysis of Ribo-seq data, we also examined the protein level of ATF4 and IFRD1 together with the transcriptional regulator C/EBPβ whose translation is controlled by an uORF located within the long activating form (LAP*) coding sequence, downstream of the main CDS start codon (Fig. 3B ). The expression of the short inhibitory form (LIP) of C/EBPβ is regulated at the translation level by this internal uORF  [44, 45].
In parallel, for each transcript, we processed the Ribo-seq data to detect translation signals (ribosome protected fragments) in the 5ʹUTRs, regardless of the sequence information. We then compared the Ribo-seq detected signals with the 'predicted uORFs' to determine truly translated uORFs, i. e. , a predicted uORF containing Ribo-seq signals, within HCT 116 transcriptome. This task was achieved using the algorithm presented in Supplemental Figure  S4  and homemade Python 3 scripts. However, assigning a codon as an initiation site is also dependent on the ribosome footprint coverage threshold that is used to distinguish starts from non-starts. Thus, we set the threshold of read coverage per nucleotide to 10. The overall results mapped 2,726 mRNAs carrying ~7,000 translated uORFs. One can note that a high number (4,179) of transcripts with predicted uORFs were not selected through our algorithm (Supplemental Figure  S5 ).
Discussion Whereas several genome-wide RNA-seq and RNA half-life analyses have previously identified the transcripts that are dysregulated in response to UPF1 knockdown  [20, 21, 23] , the effect of eRF3A depletion on mammalian transcriptome has not been documented previously. In the present study, we established the genome-wide landscape of gene expression in human cells depleted either in the translation termination factor eRF3A or in UPF1, a key actor of the NMD pathway, using RNA sequencing and ribosome profiling  [49, 50]. The RNA-seq and Ribo-seq data sets allowed us to compare for both, eRF3A and UPF1 knockdowns, the modifications of mRNA and translation levels for the ~13,000 transcripts expressed in HCT 116 cells. Our comparison highlights a number of interesting features on the role of translation termination and NMD in physiological gene expression.
Data analysis Reads from transcriptome (RNA-seq) and ribosome profiling (Ribo-seq) libraries were trimmed using Cutadapt  [77] , to remove the 3ʹ adapter of the reads 5ʹTGGAATTCTCGGGT-GCCAAGGAACTCCAGTCA3ʹ. Reads derived from rRNA were removed using Bowtie  [78]  and by aligning to the rRNA reference downloaded from the Ensembl BIOMART database  [79]. The remaining non-ribosomal reads were then aligned to the human reference genome hg38 using TopHat2  [80] , allowing 2 mismatches and keeping only uniquely mapped reads. At the end, we obtained ~1. 2x10 8 RPFs mapping principally to the CDS. These reads were used for subsequent analysis. In parallel to Ribo-seq data, we obtained transcriptomic data, generating ~8x10 7 reads from total RNA. As for ribosome profiling, we retained only uniquely mapped reads excluding rRNA sequences.
Differential gene expression Raw counts were obtained using FeatureCounts  [81]  based on hg38 human annotations. The counts were realized on the exons (feature) and regrouped by transcripts (metafeature) keeping only stranded counts. Then, the counts were normalized using DESeq2  [37]  through the SARTools package using R 3. 3. 1  [82]. RNA-seq and Ribo-seq counts and their normalizations were computed separately. P-value threshold for significant changes was set to 5 × 10 -2. Samples correlations were computed using the SERE statistics  [83]. Gene Ontology analysis  [38]  was carried out using the DAVID gene ontology functional annotation tool (https://david. ncifcrf. gov/) with default parameters. We ranked terms according to the p-value.
All reactions were performed in triplicate. Serial dilutions of a mixture of all cDNAs were used to generate a PCR standard curve. A negative control without cDNA template was run with every assay to assess the overall specificity. Absence of genomic DNA contamination was assessed using enzyme lacking reverse transcriptase reactions as template (no-RT). Data were analysed using Bio-Rad CFX Manager software v3. 1. The amount of the target mRNA was normalized to the B2M (β2-microglobulin) reference gene mRNA.
Acknowledgments This work benefited from the facilities and expertise of the Platform of I2BC for NGS library preparation. We thank  Ammara Mohammad  and  Stéphane Le Crom  from the  Genomic Paris Centre  for assistance with sequencing. This work was supported by the  France Génomique national infrastructure , funded as part of the  'Investissements d'Avenir' program  managed by the  ANR  (Contract  ANR-10-INBS-0009 ).
Introduction Marfan syndrome (MFS; Online Mendelian Inheritance in Man database (OMIM) #154700) is an autosomal dominant connective tissue disorder with an estimated prevalence of one in every 5000 individuals. It is a multisystemic disease that affects the ocular, skeletal, and cardiovascular systems, as well as lung, skin, and dura. In most cases, MFS is due to mutations in the FBN1 gene encoding fibrillin-1, an extra-cellular matrix protein. To date, over 3000 mutations were reported in the FBN1 database  [1]. The syndrome displays great clinical variability both between and within families, with respect to the age of onset, the presence or the absence, and the number and the severity of the Genes 2019, 10, 128 2 of 10 symptoms. This variability is taken into account in the last nosology for MFS with which the diagnosis can be made with or without ectopia lentis or aortic aneurysm  [2].
Indeed, lower FBN1 gene expression levels were found to be significantly associated with ectopia lentis. Alternative splicing is an essential biological process, and differential expression of alternative transcripts can be involved in specific biological or pathological roles. A published study on alternative splicing of the FBN1 gene showed that one FBN1 isoform's (no longer referenced in the Ensembl database) expression represented a significant proportion of total FBN1 gene expression, and that this proportion varied in a tissue-and development-specific manner  [13]. Therefore, alternative splicing of the FBN1 gene could be a potential mechanism that would explain part of the clinical variability observed in MFS. To investigate this hypothesis, the expression profiles of FBN1 isoforms were investigated firstly in skin fibroblasts from control subjects to establish reference patterns.
Statistical Analysis Statistical analysis was performed using GraphPad Prism version 7. 0 software and Jump 7. 0. 1 software (SAS Institute Inc. , Cary, NC, USA). The statistical difference between two groups was tested using an unpaired Student's t-test, while that between three groups was tested using one-way ANOVA or a Kruskal-Wallis test. The coefficient of determination (R 2 ) was calculated by linear regression, and the statistical significance was determined with Pearson correlation. The significance was set at a p-value < 0. 05 for all tests.
Expression of FBN1 Isoforms in Skin Fibroblasts from Controls In silico analysis of FBN1 isoforms showed the existence of reports for eight isoforms listed in the Ensembl database  [15]. However, none of these isoforms are reported in the literature. In the Genotype-Tissue Expression (GTEx) consortium database  [16] , the expressions of three of the eight isoforms are reported in transformed fibroblasts and in the aorta: FBN1_001 (ENST00000316623. 5, NM_000138), FBN1_004 (ENST00000559133. 1), and FBN1_009 (ENST00000561429. 1) (Figure  1a ). The existence of all eight isoforms in control skin fibroblasts was investigated using RT-qPCR and specific primer pairs. Only isoforms FBN1_001, FBN1_004, and FBN1_009 were found. The FBN1_001 isoform was by far the major isoform found. Furthermore, in our model, FBN1_009 expression was higher than that of FBN1_004 (Figure  1b ), unlike what is observed in the GTEx database.
Discussion This study investigated, for the first time, the expression profile of FBN1 isoforms not only in a large number of control subjects, but also in skin and adventitial fibroblast pairs from patients. Previously, Burchett et al. (2011) studied FBN1 isoform expression profiles in human brain and skeletal muscle, as well as total RNA from human fetal skeletal muscle, brain, liver, aorta, lung, skin, and heart. They detected two splice variants resulting from the inclusion of cryptic exons in intron 54 or 57, but only the latter isoform was further studied. Although this isoform is no longer referenced in the Ensembl database, it could possibly contain the cryptic exon found in FBN1_004. However,  Burchett et al. (2011)  did not identify the 5′ truncation of exons 1 to 37 that leads to isoform FBN1_004.
Discussion This study investigated, for the first time, the expression profile of FBN1 isoforms not only in a large number of control subjects, but also in skin and adventitial fibroblast pairs from patients. Previously,  Burchett et al. (2011)  studied FBN1 isoform expression profiles in human brain and skeletal muscle, as well as total RNA from human fetal skeletal muscle, brain, liver, aorta, lung, skin, and heart. They detected two splice variants resulting from the inclusion of cryptic exons in intron 54 or 57, but only the latter isoform was further studied. Although this isoform is no longer referenced in the Ensembl database, it could possibly contain the cryptic exon found in FBN1_004. However,  Burchett et al. (2011)  did not identify the 5 truncation of exons 1 to 37 that leads to isoform FBN1_004.