dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Model The RCD map was constructed using a methodology previously described  [22, 27]. The maps are drawn using Process Description (PD) language of Systems Biology Graphical Notation (SBGN) syntax. The CellDesigner format represents a proprietary extension of the Systems Biology Markup Language (SBML)  [28]. The most basic unit of RCD map is a biochemical reaction, formed by reactants, products and various types of regulators (Figure  1  and Figure  S1 ). The data model includes the following molecular objects: proteins, genes, RNAs, antisense RNAs, simple molecules, ions, drugs, phenotypes, complexes. These objects can play a role of reactants, products and regulators in a connected reaction network. The objects "phenotypes" play a role in biological process outcome or readout. Edges on the maps represent relations between biochemical reactions and chemical species or reaction regulations of various types.
Map Annotation Format Each entity and reaction on the map is annotated using the NaviCell annotation format (Figure  S2 ). The annotation includes three sections namely "Identifiers", "Maps_Modules" and "References". "Identifiers" section provides links to the corresponding entity descriptions in HGNC, UniProt, Entrez, GeneCards, REACTOME, KEGG, and Wikipedia databases. "Maps_Modules" section includes links to modules where the entity is found. "References" section includes notes added by the map manager and links to relevant publications in PubMed. Annotations can be associated with a molecular entity (such as protein) as well as to its particular modification (such as a particular post-translational modification). NaviCell (described below) provides mechanisms of cross-referencing RCD map with other molecular and pathway databases, such as KEGG PATHWAY, Reactome, Atlas of Genetics and Cytogenetics in Oncology and Haematology, GeneCards, and Wiki Genes.
Generation of NaviCell Map with NaviCell Factory CellDesigner map annotated in the NaviCell format is converted into the NaviCell web-based front-end, which is a set of html pages with integrated JavaScript code that can be launched in a web browser for online use. HUGO identifiers in the annotation form allow using NaviCell tool for visualization of omics data. A detailed guide of using the NaviCell factory embedded in the BiNoM Cytoscape plugin  [70]  is provided at https://navicell. curie. fr/doc/NaviCellMapperAdminGuide. pdf.
Data Acquisition and Groups Definition Gene expression datasets from lung cancer and Alzheimer's disease (GSE36980, GSE48350, GSE5281, GSE19188, GSE19804, GSE33532) were downloaded from the Gene Expression Omnibus (GEO, https://www. ncbi. nlm. nih. gov/geo/). For the testing, we grouped the cases of each disease and compared them to their respective controls. The ovarian cancer data was obtained from The Cancer Genome Atlas (TCGA)  [80, 81]. The group definitions for the ovarian cancer cases were used as previously described by using a non-negative matrix factorization approach  [80].
Module Scores Calculation and Visualization The Representation of Module Activity (ROMA) analysis  [55]  was performed using the "rRoma" and "rRomaDash" R packages (available at https://github. com/sysbio-curie/rRoma and https://github. com/sysbio-curie/rRomaDash). ROMA quantifies the activity of a group of genes biologically related, referred to as a module in the RCD map (see section" Structure of the Regulated Cell Death map"), by calculating the largest amount of one-dimensional variance in the expression of the genes that constitute a module across the samples. With the analysis provided by ROMA, we identified the over-dispersed modules in RCD map. Further, one-tailed pairwise t-tests using the ROMA scores from all modules was done, to determine module activity directionality. Then the calculation of the top contributing genes was performed per module using Pearson's correlation, selecting those with an absolute correlation coefficient of 0.
Supplementary Materials: The following are available online at http://www. mdpi. com/2072-6694/12/4/990/s1, Figure  S1 : Data model, Figure  S2 : Annotation page on an entity on RCD map using NaviCell format, Figure  S3 : Annotation page on reaction edges on RCD map using NaviCell format, Figure  S4 : Representation of regulated cell death modes in a form of activity flow diagrams including inputs (in green), key players and outputs (in red), Figure  S5 : Comparison of the Regulated Cell Death Map with other pathway resources, Figure  S6 : Boxplots corresponding to the ROMA scores in different modules across the six studies for the comparisons of Alzheimer's disease and lung cancer, Figure  S7 : Heatmap representing ROMA scores averaged between the three studies, separating disease groups and controls, Figure  S8 : Heatmap representing the Enrichment scores, from Gene Set Enrichment Analysis, averaged between the three studies, Table  S1 : List of key input and output players in RCD map modules, Table  S2 : Comparison of gene content between pathways from KEGG and REACTOME databases to the Regulated Cell Death map, Table  S3 : Genes corresponding to copy number average equal or larger than 4 in the groups of ovarian cancer cases, Table  S4 : Gene set enrichment analysis scores, Supplementary text: Description of the RCD content.
This paper presents the first results about the assimilation of CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) extinction coefficient measurements on-board the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite in the chemistry transport model MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle) of Météo-France. This assimilation module is an extension of the Aerosol Optical Depth (AOD) assimilation system already presented by  Sič et al. (2016). We focus on the period of TRAQA (TRAnsport à longue distance et Qualité de l'Air dans le bassin méditerranéen) field campaign that took place during the summer 2012. This period offers the opportunity to have access to a large set of aerosol observations from instrumented aircraft, balloons, satellite and ground-based stations.
Moreover, the assimilation of lidar profiles has the advantage of constraining the vertical structure of the model in a much more realistic and direct way. Consequently, the assimilation of lidar information would serve to reduce the influence of diffusion and better constrain the vertical structure  (Campbell et al. , 2010). In this study, we present the assimilation module of lidar measurements in the CTM of Météo-France, MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). The assimilation system coupled to the MOCAGE CTM was initially developed and used for atmospheric gases, predominately ozone (O 3 ) (e. g. Semane et al. , 2007; El Amraoui et al. , 2008b; Rabier et al. , 2010; Bencherif et al. , 2011; Barré et al. , 2013; Emili et al. , 2014; Hache et al. , 2014; Abida et al. , 2016) , carbon monoxide (CO) (e.
g. El  Amraoui et al. , 2010; Claeyman et al. , 2010 Claeyman et al. , , 2011a;; El Amraoui et al. , 2014)  and water vapour (H 2 O) (e. g. Payra et al. , 2016). The assimilation of aerosol components within the MOCAGE CTM is more recent compared to that of chemical species. The first work related to the aerosol assimilation in the MOCAGE model concerned the MODIS AOD assimilation. The assimilation module presented in this work is an extension of the AOD assimilation system already described by  Sič et al. (2016). We consider the extinction coefficient measurements from the CALIOP lidar on-board the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite. We focus on the African dust event occurred in late June-early July 2012 over the Mediterranean Basin (MB) during the TRAQA (TRAnsport à longue distance et Qualité de l'Air dans le bassin méditerranéen) field campaign held between June 26 and July 11, 2012 (see Section 3.
3 later for more information about the TRAQA campaign). During this dust outbreak event, several aerosol profiles within the dust plume have been measured by the PCASP (Passive Cavity Aerosol Spectrometer Probe ; see Section 3. 3. 1) instrument on-board the instrumented aircraft. This study aims principally to: 1. Present the lidar assimilation module as well as the first results dealing with the assimilation of CALIOP observations in terms of extinction coefficient into the MOCAGE CTM. 2. Evaluate the impact of lidar assimilation on the 3D tropospheric aerosol distribution at regional scale during this large scale event. The lidar measurements from the CALIOP instrument are assimilated into the MOCAGE CTM using the variational 3D-FGAT (First Guess at Appropriate Time) method. The impact of the CALIOP extinction coefficient assimilation on the aerosol distribution has been evaluated using a set of independent data including AERONET (AErosol RObotic NETwork), MODIS, aircraft as well as balloon measurements.
MODIS The MODIS instruments on-board the two EOS (Earth Observing System) satellites Terra (since 2000) and Aqua (since 2002), observe atmospheric aerosols and provide information about aerosol distribution on global coverage at horizontal resolutions of 10 and 3 km. The evaluation of CALIPSO analyses in terms of MODIS AOD is done by using Collection C61 retrievals at 550 nm from both Terra and Aqua. The MODIS data concern both the deep blue and the dark target products. For more information about the improvements of the C61 collection in comparison to the C6 collection, the reader is referred to "https://modis-atmosphere. gsfc. nasa. gov/sites/default/files/ModAtmo/C061_Aerosol_Dark_Target_v2. pdf" for the deep-blue product, and to  Gupta et al. (2016)  for the dark target product. The MODIS C61 version used in this comparison has a resolution of 10 km × 10 km.
AERONET The AERONET project is a federation of ground-based remote sensing aerosol networks. It uses CIMEL sun/sky radiometers that make measurements within the 340-1020 nm for the direct sun radiation  (Holben et al. , 1998). For more than 25 years, the project has provided long-term, continuous and readily accessible public domain database of aerosol optical, microphysical and radiative properties for aerosol research and characterization, validation of satellite retrievals, and synergism with other databases. AERONET measurements are available at three levels: Level 1 (unscreened), Level 1. 5 (cloud screened), and Level 2 (cloud screened and quality assured). The network imposes standardization of instruments, calibration, processing and distribution. For more information about the AERONET project, the reader could be referred to "https://aeronet.
For all the comparison days, the statistics of the assimilated product are significantly improved compared to the free run field. For example, during the dust outbreak event of June 29, 2012, the correlation si improved from 0. 47 to 0. 52, whereas the bias (RMSE) is reduced from -0. 18 (0. 36) to -0. 02 (0. 30) for MODIS versus the direct free run and MODIS versus the assimilated field, respectively. In figure  2  Many previous studies have highlighted the existence of biases between the CALIOP and MODIS observations (e. g. ,  Kittaka et al. , 2011; Redemann et al. , 2012; Shikwambana and Sivakumar, 2018). The comparison between both datasets shows that MODIS AOD is generally higher than CALIOP-derived AOD  (Oo and Holz, 2011). Ma et al. (2013)  reported that the largest differences between CALIPSO and MODIS occurs during the active dust seasons over the major dust regions.
Comparison to AERONET observations In this Section, we exploit the AOD in-situ observations from AERONET to quantify the added value of the CALIOP assimilated field in comparison to the model free run. We therefore use all available AERONET AOD L2 data collected during the period of study from different stations located within the assimilation domain. Figure  3  shows the location as well as the number of the AOD observations for each measurement station used for the comparison during the considered period (from June 20 to July 11, 2012). In total, we consider measurements from 47 AERONET stations which are located in the domain of study. Most of the stations have a number of measurements greater than 300 (only 4 of the 47 used stations have a number of measurements less than 300 over the whole period of comparison).
(2016)  for AOD assimilation, and Figure  7  of this study for CALIOP assimilation) seems to show an underestimation of the field resulting from the assimilation of the extinction coefficient of the CALIOP lidar. An explanation may be due to the fact that both MODIS AOD and LOAC measurements generally show an overestimation of aerosol concentrations compared to independent observations. Indeed, the study conducted by  Shikwambana and Sivakumar (2018)  highlights the overestimation of MODIS AOD compared to several datasets (e. g. , CALIPSO, MERRA-2 and MISR). On the other hand, the validation of LOAC measurements conducted by  Renard et al. (2016)  shows that the retrieved concentrations of the largest particles could be overestimated by up to 50% for particles above about 2 µm. Consequently, almost the total concentration of desert aerosols is affected by this overestimation since the majority of desert dust bins are greater than 2 µm (see Table  1 ).
We will particularly focus on the advantages and the limitations of each approach during a desert dust outbreak event. https://doi. org/10. 5194/amt-2019-482 Preprint. Discussion started: 28 January 2020 c Author(s) 2020. CC BY 4. 0 License. We also plan to study the added value of measurements from passive and active probes during volcanic eruption events. This is a very important theme for Météo-France since it is one of the VAAC (Volcanic Ash Advisory Center) whose responsibility extends over a large part of Europe, Asia and Africa. As a perspective of this work, we will consider simultaneously assimilating the observations from passive and active sensors by carrying out an initial de-biasing of both observation datasets. A much more ambitious solution will consist to assimilate satellite radiances directly in a global model using an integrated approach.
4 Assimilation of CALIOP Lidar measurements during the TRAQA field campaign We assimilate the extinction coefficient measurements from the CALIOP instrument into MOCAGE during the TRAQA campaign period. The objective is to assess the added value of CALIOP analyses compared to the model free run. Both fields are compared to different datasets presented in Section 3 including AOD observations from MODIS and AERONET as well as the in-situ measurements collected during the TRAQA field campaign. The extinction coefficient observations from the CALIOP lidar are assimilated in the period between June 20 and July 11, 2012. For this assimilation experiment, the domain in which the assimilation takes place, called the control domain, is defined with a resolution of 0. 2 • × 0. 2 •. It spatially covers the MB and the African Saharan desert.
https://doi. org/10. 5194/amt-2019-482 Preprint. Discussion started: 28 January 2020 c Author(s) 2020. CC BY 4. 0 License. In this study, we have evaluated the added value of the assimilation of the CALIOP extinction coefficient observations to better document a desert dust transport event compared to the model free run. The CALIOP assimilation product has been evaluated against different independent datasets : AOD from MODIS (Moderate-resolution Imaging Spectroradiometer) and AERONET (AErosol RObotic NETwork), aerosol concentration from both the PCASP (Passive Cavity Aerosol Spectrometer Probe) instrument on-board the ATR-42 aircraft and the LOAC (Light Optical Particle Counter) on-board the balloon during the TRAQA field campaign. The results show that CALIOP analyses improve the model compared to the AOD AERONET independent observations.
Acknowledgements. This work is funded in France by the  Centre National de Recherches Météorologiques (CNRM) of Météo-France  and the  Centre National de Recherches Scientifiques (CNRS). We would also like to thank  NASA  and the  ICARE , the  French atmospheric composition database (CNES  and  CNRS-INSU)  for providing the AERONET data. ḧttps://doi. org/10. 5194/amt-2019-482 Preprint. Discussion started: 28 January 2020 c Author(s)  2020. CC BY 4. 0 License.
2  (Jónsson et al. , 2013) , which can be used as a sign of historic authenticity over modern contamination (Fig. S1 ). Post-mortem damage signals were also obtained for each read using pmdtools  (Skoglund et al. , 2014)  (Fig. S2 ). Mapping statistics including the depth of coverage were recorded using Qualimap  (García-Alcalde et al. , 2012). Due to the low coverage of the human sample, we performed a pseudo-haploid calling approach, common to the processing of aDNA, using the SAMtools Pileup tool  (Li et al. , 2009). This data was then merged with the Human Origins dataset for its use in population genetics analyses  (Lazaridis et al. , 2014; Lazaridis et al. , 2016).
Modern DNA contamination Schmutzi was used to estimate the amount of modern DNA contamination in the mitochondrial (mtDNA) genome  (Renaud et al. , 2015)  likely deriving from the DNA of those who have handled the newspaper in the years following Marat's death. We identified mitochondrial contamination based on the inferred deamination patterns as 52. 5% +/-4. 5% with the full haplogroup profiles provided in Table  S2. This allowed the modern DNA sequences to be delineated from the ancient DNA sequences using Jvarkit and a custom script  (Pierre, 2015)  by selecting the human reads with mismatches in their first or last three nucleotides. This reduced the amount of modern mtDNA contamination to 0-0. 1%. The depth of coverage was recorded We also independently estimated the amount of contamination based on the heterozygous sites in the X chromosome using angsd v0.
Uniparental markers and sex determination analyses The mtDNA haplogroup was determined using SAMtools pileup tool calling the positions defined in the Phylotree database  (van Oven, 2015). We used a genome browser  (IGV. v2. 4. 14)  to study the genomic context of each possible SNP  (Robinson et al. , 2011). Only those SNPs that were present in two or more reads, and those which were not located at the ends of the reads, were considered. The contamination was estimated by calculating the ratio of discordant reads at haplogroup-diagnostic positions. Molecular sex was assigned with Ry_compute  (Skoglund et al. , 2013) , a script designed for the sex identification of low coverage individuals (Fig. S3 ).
Population genetics analysis Principal Component Analysis (PCA) was performed using SmartPCA in EIG v6. 0. 1 with a subset of modern individuals from the Human Origins dataset  (Patterson et al. , 2006). This subset contained 434 present-day Europeans and 616,938 autosomal SNPs, plus our sample (Fig. 2 ). The Marat sample was projected using the option lsqproject. We also considered the Marat sample projected into an expanded dataset of West Eurasian populations (Fig. S4 ). As projected individuals' components tend to 0, we also carried out a control analysis using Han Chinese, French and Marat (Fig. S5 ). The results were visualised using the R package GGplot2  (Wickham, 2016). This dataset confirmed that Marat is not artefactually placed at the centre of the plot. To formally test the relationship of the Marat sample to relevant geographic regions we calculated f4 statistics of the form f4(Mbuti, Marat; X.
Y) where X and Y are tested for combinations of possible ancestral sources: Sardinian, French, English, Italian_North, Basque, Spanish. f4 values were calculated in qpDstat of AdmixTools v. 5. 0  (Patterson et al. , 2012)  with statistical significance assessed through Zscores following jack-knife resampling (Table  S3 ). This statistic tests the covariance in allele frequency differences between an African outgroup (Mbuti) and Marat relative to the clade formed by X and Y. Positive values of f4 indicate a closer affinity of Marat to Y relative to X, with negative values indicating a closer relationship of Marat to X relative to Y. We additionally ran an unsupervised clustering analysis using ADMIXTURE v1. 3 and another subset of the Human Origins dataset  (Alexander et al. , 2009). This subset included 881 individuals from Europe, West Asia and North Africa typed over 616,938 shared autosomal SNPs.
We filtered the dataset by removing SNPs in high linkage disequilibrium using PLINK. v1. 9  (Purcell et al. , 2007) , removing all SNPs with a r 2 threshold of 0. 4 within a 200 SNP sliding window, advancing by 50 SNPs each time. We performed the clustering analysis using K values ranging from 1 to 10, with 10 replicates for each value of K. We selected K according to the lowest cross-validation error value (K = 4). The ADMIXTURE results at K = 4 were visualised using Pong  (Behr et al. , 2016)  (Fig. 2 ).
, 2018)  against the Kraken database compiled in  Lassalle et al. 2018 (Lassalle et al. , 2018)  and metaMix  (Morfopoulou and Plagnol, 2015)  using megaBLAST and a local custom database consisting of the RefSeq sequences of bacteria, viruses, parasites, fungi and human, as of July 2019. KrakenUniq was run with default parameters. The metaMix-nucleotide mode was run with the default read support parameter of 10 reads was used (Table  S5 ) and the default number of 12 MCMC chains. The number of the MCMC iterations is automatically calculated by metaMix based on the number of species to explore for each dataset, resulting in 10,000 iterations for the blood sample and 3230 iterations for the paper swab. The relative proportion of reads assigned to different species by KrakenUniq and metaMix was highly correlated; R 2 = 0.
, 2019). Our approach allows limiting the number of species to be tested to a small list of plausible candidates. Second, the lack of detection of even one read from a focal microbial species by direct mapping falsifies the null hypothesis that it was not involved in the disease. We did not identify a single sequencing read in either the blood stain or the unstained paper for the agents of syphilis, leprosy, scrofula (tuberculosis) and diabetic candidiasis (thrush) (Table  1 , Table  S5 ). We additionally tested for scabies, which is caused by burrowing of the mite Sarcoptes scabiei under the skin. Since the metagenomic reference database did not include arthropod genomes, this was tested separately by blasting all the non-human reads against the Sarcoptes scabiei genome (GCA_000828355. 1).
Again, we detected not a single read matching to Sarcoptes scabiei, which makes scabies an implausible cause for Marat's skin disease (Table  1 , Table  S5 ). Conversely, metaMix recovered 15,926 and 83 filtered DNA reads from the blood stain and the unstained paper respectively, assigned to Malassezia restricta a fungal pathogen causing seborrheic dermatitis, which has been previously hypothesised as one of the most plausible causes for Marat's condition  (Bayon, 1945; Cohen and Cohen, 1958; Dotz, 1979; Jelinek, 1979). Direct mapping of all reads to M. restricta (GCA_003290485. 1) resulted in 19,194 reads from the blood stain dataset mapping over 17. 17% of the reference genome. KrakenUniq failed to identify M. restricta, instead assigning 627 reads sequenced from the blood stain to M. sympodialis.