dataesr/scientific-paragraphs-categorization huggingface dataset:
This limitation should have a low influence on the estimation of the aerosol direct radiative forcing (ADRF), since interaction of fine particles (such as biomass-burning aerosols) with long-wave radiation is shown to be small  (Ramanathan and Feng, 2009). In this study, an offline and one-way coupling between CHIMERE and WRF has been performed. A detailed description and evaluation of this methodology can be found in  Péré et al. (2011). Meteorological input parameters required by CHIMERE such as 3-D wind, air temperature or relative humidity are provided by WRF. Secondly, aerosol optical properties (AOT, SSA and g) are simulated using CHIMERE for an aerosol core-shell mixing. Finally, meteorological simulations are performed using aerosol optical properties as inputs in the WRF radiative transfer module.
Here we focus our study on the impact of fine aerosols (10 nm ≤ radius ≤ 5 µm) on shortwave solar radiation. To reduce computing time, aerosol optical properties are first calculated at four wavelengths (0. 3, 0. 4, 0. 6 and 0. 99 µm), and then interpolated on the shortwave radiation bands (0. 2-6 µm) of the WRF radiative transfer module. The wavelength-dependent refractive indexes of each chemical species used to perform Mie calculations are reported in Table  1. AOT, SSA and g are estimated for the 15 vertical layers of the CHIMERE model ranging from 40 m to about 6 km above ground level. For higher altitude, a climatology of optical properties for the free troposphere and the stratosphere  (Hess et al. , 1998)  has been used (Table  2 ). To estimate the shortwave ADRF and its feedback on air temperature and atmospheric dynamics, two simulations have been performed for the period 5-12 August 2010: one including the ADRF and the other without.
Evaluation of the modelled aerosol optical properties Simulations of aerosol properties during this specific episode have been evaluated with POLDER, CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) and AERONET (AErosol RObotic NETwork) observations. POLDER is on board the PARASOL satellite of the A-Train constellation launched in December 2004. This sensor is a. wide-field-of-view imaging radiometer providing spectral, directional and polarized radiance measurements over land and ocean for visible wavelengths with an overpass time around 10:30 LT over Europe. In this study, level 2 cloudfree AOT data at 550 nm with a 18 km horizontal resolution and corresponding to fine aerosols (radius less than 0. 3 µm) have been used  (Deuzé et al. , 2001; Tanré et al. , 2011). The CALIOP instrument, on board the CALIPSO satellite of the A-Train constellation since April 2006, is an elastic backscatter lidar providing the vertical distribution of aerosols and clouds.
Summary and perspectives The main objectives of this work were to study the direct radiative forcings of aerosols during the peak of the 2010 Russian wildfires and their feedbacks on air temperature and atmospheric dynamics. We focused on the ADRF in the shortwave solar region as interactions of fine aerosols with longwave radiation are shown to be low. For such objectives, an offline coupling between the chemistry-transport model CHIMERE (associated with an aerosol optical module) and the Weather Research and Forecasting (WRF) model has been performed. To take into account the impact of aerosol solar extinction on the shortwave radiative fluxes, aerosol optical properties have been simulated using CHIMERE up to an altitude of 500 hPa (a climatology has been used for higher altitudes) and then used as input in the WRF radiative transfer module.
Acknowledgements. The authors are grateful to the  CRI at Lille 1  for its technical support. This work was partly funded by the  French ministry in charge of ecology  and the  PRIMEQUAL program  (APIFLAME). We also acknowledge the  University of Wyoming  for providing us the meteorological data. Anthony  Ung (INERIS)  is acknowledged for his help in processing the fire-emissions data. Edited by:  D. Spracklen  The publication of this article is financed by  CNRS-INSU.
High content studies that profile mouse and human embryonic stem cells (m/hESCs) using various genome-wide technologies such as transcriptomics and proteomics are constantly being published. However, efforts to integrate such data to obtain a global view of the molecular circuitry in m/hESCs are lagging behind. Here, we present an m/hESC-centered database called Embryonic Stem Cell Atlas from Pluripotency Evidence integrating data from many recent diverse highthroughput studies including chromatin immunoprecipitation followed by deep sequencing, genome-wide inhibitory RNA screens, gene expression microarrays or RNA-seq after knockdown (KD) or overexpression of critical factors, immunoprecipitation followed by mass spectrometry proteomics and phosphoproteomics. The database provides web-based interactive search and visualization tools that can be used to build subnetworks and to identify known and novel regulatory interactions across various regulatory layers.
Introduction Embryonic stem cells (ESCs) are pluripotent cells characterized by their capability to self-renew and differentiate into all adult cell types. Recent efforts in systematically profiling ESCs have yielded a wealth of high-throughput data. Highthroughput technologies including gene expression microarrays, RNA sequencing, chromatin immunoprecipitation followed by deep sequencing (ChIP-chip/seq), genomewide inhibitory RNA (RNAi) screens, immunoprecipitation followed by mass spectrometry (IP-MS) proteomics and phosphoproteomics, as well as other emerging technologies have been applied to profile the same cell type by many laboratories across the world in the past decade. Several databases and tools have been published to facilitate the integration of such data  (1) (2) (3) (4) (5) (6) (7) (8) (9) , and such efforts pave the way toward an in silico reconstruction of the gene and protein regulatory networks that regulate selfrenewal and pluripotency of these important cells.
For example, Plurinet  (2) , FunGenES (4), StemBase  (5) , SyStemmCell  (10) , iScMiD  (9)  and PluriNetWork  (1)  incorporate stem cell data from several studies and provide web-based interfaces for data query and visualization. However, in general, these databases contain information from a single regulatory layer, mostly transcriptome measurements, and thus overlook other important layers as well as cross-layer interactions. To address the need for further data integration in the field, we constructed a more inclusive database called Embryonic Stem Cell Atlas from Pluripotency Evidence (ESCAPE). This database integrates numerous additional types of data ranging from epigenetics, transcriptomics, to proteomics and phosphoproteomics. These data sets are processed into gene lists, gene-gene and protein-protein interactions, and data tables for easy download and manipulation.
Results A comprehensive embryonic stem cell database constructed from published high-throughput studies Results from numerous published mouse and human embryonic stem cells (m/hESC) genome-wide profiling studies, as well as loss-of-function/gain-of-function (LOF/GOF) studies, were systematically collected and processed to construct the ESCAPE database. Most data sets are from mouse with several from human embryonic stem cells. In its current version, ESCAPE contains (i) 206 521 documented protein/DNA interactions from ChIP-chip/seq studies, connecting 61 transcription factors (TFs) to their putative target genes; (ii) 153 920 LOF/GOF interactions connecting 28 TFs from LOF KD/knockout studies followed by genomewide expression, and 55 TFs from GOF overexpression studies followed by genome-wide expression.
The ESCAPE database descriptive statistics are provided in Table  1. The references are also listed in Table  2. The entity relationship diagram of the database design is shown in Figure  1. Data sets to construct the ESCAPE database are freely downloadable and searchable online. The ESCAPE database is stored as a MySQL relational database. The web interface is implemented as a set of PHP scripts running under Apache as well as a set of Java Servlets running under Tomcat all interacting with the database using SQL. The network viewer used in the network generator page is Cytoscape Web  (11)  implemented in Flash. The canvas visualization within the enrichment analysis page is implemented with the JavaScript library D3  (12). JavaScript and AJAX are implemented throughout the site for improving user experience (UX)  (13).
The web interface contains several modules: (i) an interface to browse and query the data; (ii) an interface to download the data; (iii) a tool to generate subnetworks from an input list of genes using background networks generated from the database; (iv) a tool to perform enrichment analysis on user entered gene lists using background lists of genes generated from the database and visualized on a canvas, as well as enrichment analysis of user inputted lists using Enrichr, a tool to visualize enrichment results against 35 gene set libraries  (14) ; (v) an interface to predict lineage commitment on gene KDs or overexpressions; (vi) a downloadable MATLAB software with a graphical user interface for learning Boolean functions and simulating subnetwork dynamics given a prior subnetwork topology and experimental measurements of subnetwork node expression levels across many conditions (Figure  2 ).
Browsing and querying data sets within the ESCAPE database The ESCAPE database provides web-based user interface to allow easy browsing and querying. From the Browse page of the web interface, users can click on one of the tables listed on the left, and then the contents of the selected table are displayed in the center of the page. The contents of the table can be sorted by clicking the name of the column. In addition, information about the methods used to generate the table and the number of entries are displayed above each table. There are two ways to search the ESCAPE database: (i) a general search for a gene using the search bar displayed on top of any web page of the ESCAPE web interface or (ii) a detailed search within a selected table. The detailed search is provided under the Browse section of the website.
(2) Logic operator AND, and operator = and where '1' is written in the Relationship column. (3) Logic operator AND, and operator = and where 'GOF' is written in the PerturbType column. The query will be automatically converted to the SQL statement: 'SELECT * FROM logof WHERE (sourceName=''srrb'' OR sourceName=''Nanog'' OR sourceName=''Sox2'') AND (relationship=''1'') AND (perturbType=''GOF'')'. The results will be formatted and displayed in a results page. All the tables of the ESCAPE database can be freely downloaded from the Download page of the website. The tables are provided as either flat tab-separated text files or as mySQL files.
Enrichment analysis with ESCAPE Another function of the ESCAPE web interface is the ability to perform enrichment analyses (Figure  4A ). The enrichment analysis tool performs gene list enrichment analysis using the various experimental modalities that produced gene lists. These include candidate genes from RNAi screens, protein lists from IP-MS pull-downs, genes differentially expressed after KD or overexpression, and target genes for TFs and histone modifications as determined by ChIP-seq/chip. In this web application portion of the site, users can query their own gene lists for overlap with gene lists from the ESCAPE database or analyze their gene list with another external gene list enrichment analysis tool called Enrichr  (14). On the left, users can cut and paste lists of Entrez gene symbols and then press Submit to perform the enrichment analysis.
In the middle, most of the lists from the ESCAPE database are visualized as a canvas. Each square represents a list. The color indicates the experiment type, and the brightness indicates the level of local similarity among the lists. We use simulated annealing to arrange the lists from the ESCAPE database by their gene content similarity using the Sets2Networks algorithm  (17). The enriched terms appear as circles on top of the colored squares representing the gene lists from the ESCAPE database on the canvas: the brighter the circle, the more significant the overlap with the input list. The results are also available in a table with the associated p-values on the right. To compute statistical enrichment, the Fisher exact test is implemented. The resulting lists of enriched experiments only show the enriched terms determined by a cutoff threshold P-value of P < 0.
Lineage specification prediction with ESCAPE The next function of the ESCAPE web interface is a tool to predict lineage-propensity differentiation outcome on single or combinatorial KD of multiple pluripotency factors (Figure  5 ). The tool considers the target genes of knocked-down pluripotency factors and predicts the additive expression of lineage markers based on the combinatorial additive predicted levels of these factors. Specifically, effects of gene KDs on lineage commitment are dynamically computed by enrichment analysis for targets of knocked-down factors against lists of lineage-specific marker genes using the Fisher's exact test. Targets of KD factors were first identified from the LOF/GOF table, and lineage specific components were assembled manually from literature as follows: (i) Trophectoderm: the gene expression data set (GSE11523) reported trophectodermlike state after depletion of Oct4/Pou5f1 in mESCs.
Gene expression was profiled at six time points. Genes were sorted according to average fold change of expression on differentiation related to time point 0. The top 5% of genes with an average fold change of at least two and with a monotone increase in expression at each time point upon differentiation were considered as trophectoderm markers. (ii) Primitive endoderm: the same set of experiments and data processing as described for (i) were conduct after overexpression of Gata6 in mESCs. (iii) Neuroectoderm: the gene expression dataset (GSE12982) isolated Sox1-GFP positive cells from mESCs where Ezh1 and Ezh2 were knocked-down. Genes were sorted according to fold change increase in expression comparing differentiated cells to mESCs. The top 10% genes with a monotonic increase and fold change of at least 1.
5 were considered as neuroectoderm markers. (iv) Mesendoderm: the same set of experiments and data processing as described for (iii) were conduct after isolation of T-GFP positive cells (T stands for the gene brachyury). By sliding the bars on the web interface, users can choose the components and level of knockdown of 14 pluripotency factors. Corresponding positive and negative targets of each specific pluripotency factor were extracted from the LOF table within the ESCAPE database. As a result, the enrichment P-values reflecting the significance of differentiation potential toward each specific lineage on knockdown(s) are displayed on top. In addition, the up and down genes are provided in two text boxes below the lineage prediction display. Such lists can be further analyzed using the external enrichment analysis tool Enrichr  (14)  or any other tool available within ESCAPE or beyond.
Functional associations among 15 pluripotency regulators and 15 lineage markers The aggregated data in ESCAPE can be used to elucidate functional associations among pluripotency and differentiation components across various regulatory layers. Specifically, to demonstrate the usefulness of the compiled ESCAPE database to dissect the pluripotency machinery, we examined functional correlations among 15 pluripotency factors and 15 differentiation markers selected  (20). Heatmaps of degree of similarity were constructed (Figure  6 ) where we scored pair-wise similarity distance between the components as follows: (i) Shared targets from the ChIP-chip/seq experiments; (ii) Co-expression similarities based on global mRNA measurements after pluripotency TF LOF or GOF; (iii) Histone modification target gene similarities analyzed in mESCs and differentiated cells; (iv) Protein co-occurrence measured after pull-downs of pluripotency TFs followed by MS proteomics; (v) Similarities of miR targets predicted computationally and limited to miRs preferentially expressed in mESCs; and (vi) co-expression similarities during embryoid body differentiation.
Conclusions ESCAPE is a freely available online resource that integrates current genome-wide data encompassing several regulatory layers and data types. Through the web interface, the data sets can be browsed, searched and downloaded. Additionally, a set of web-based tools were implemented to interact with the information in the database. Given a set of seed genes, users can perform network expansion, and upstream regulatory factors and downstream targets enrichment analyses, as well as perform combinatorial lineage predictions. Organizing the experimental data into a coherent and interactive framework can potentially enable better utilization of such data for systems-level analyses and construction of dynamical models. ESCAPE has been proven useful already to at least one research study  (26).
Based on this, a complete picture on genome organization evolution clearly requires considering the information coming from both core gene order (backbone stability) and insertions/deletions of accessory genes (genome organization stability). To fulfill this task, we implemented a graph-based framework to study in depth the stability of prokaryotic genomes and applied it to a selected dataset of genomes. We improved Eq. 1 for backbone stability in time, and then we compared the fit of the new and several other models to the data. Using the fitted model, we studied genome backbone stability within and between bacterial species to better understand genome organization dynamics on the short and the long evolutionary time. The relationship between backbone stability (BS) and genome organization stability (GOS) provided information about the importance of genome organization rearrangements and gene content dynamics for genome evolution in different species.
The diameter can be calculated in different ways; we use Johnson's method  [34]  implemented in the Matlab library MatlabBGL  [35]. We propose to use the diameter as an alternative stability measure because it allows to consider accessory genes and to convey additional information to the previous measures. As shown by Watts and Strogatz  [36] , the simple rewiring of a small fraction of the links in a regular lattice results in a sudden lowering of the diameter of the graph; similarly, when the position of a gene changes between different genomes, the diameter of the corresponding GGN shrinks (Figure  2B ). It follows that the diameter is inversely related to the stability of the genomes. The GGN can be obtained summing any number of adjacency matrices: if the edge values of the GGN are normalized by the number of genomes under comparison we obtain a weighted network with edge values corresponding to the fraction of times a given gene is found close to another one in these genomes.
Intra-species stability analysis In Figure  4  we plot the backbone stability for all intra-species comparisons; genomes above the model predictions are more stable than expected: Sulcia muelleri, Buchnera aphidicola and Prochlorococcus marinus are the most evident cases. In the case of Sulcia, and despite the large phylogenetic distance dividing these genomes, the backbone is almost completely conserved. The age of the symbiosis between Sulcia and its host (260 Mya  [37] ) might explain this high stability. Moreover, the fact that these strains have completely conserved gene orders suggests that they maintained an intact chromosome structure since the time of their separation. In agreement with available information, we also detected increased genome stability for the other endosymbiont of our dataset, B.
marinus and five other Cyanobacteria. This discrepancy derives from the different phylogenetic ranges of the comparisons here and in  [32]. By looking more in detail at the behavior of two additional Cyanobacteria present in our genus dataset (see Methods and Additional file 1), we found that the P. marinus genomes have indeed higher stability (Additional file 2). This reinforces the idea that by considering multiple genomes of the same species we can define the behavior of each group of organisms in a better way. Our analysis moreover shows that P. marinus is relatively unstable on the long-term, in agreement with  [32]. The intra-species analysis shows that all species have more or less stable gene organization. To identify aberrant genomes within a species, we obtained Z-scores for the residuals of each genome with respect to its conspecific and the model predictions (Figure  4C ); genomes with the largest deviation from the mean are discussed below in the light of previous reports about genome organization.
Some of the genomes that were stable following the intra-species analysis showed here instability. We observed that 8/9 of the genomes with highest instability in these comparisons belong to animal pathogens, whereas this category represents about 62% of the genomes in our dataset; however, there is a significant association of instability with the taxonomic affiliation of the genomes, since 6 of these genomes come from the Firmicutes. We checked if Firmicutes tend to be less stable than other genomes and this is indeed the case (Figure  5 , p = 0. 0022 in a Wilcoxon rank sum test), therefore the observed difference between pathogens and nonpathogens may be related to the Firmicutes in our dataset being mostly pathogens. To clarify this point, we focused on Proteobacteria by testing for equality of the median residuals of pathogens and non-pathogens; we obtained a weak but significant difference (Figure  5 , p = 0.
042, when Buchnera is not included in the non-pathogens). Our analysis therefore indicates that pathogens tend to be less stable than non-pathogens and that they experienced past periods of rearrangements, plausibly during adaptation to their new life style. Since these genomes are not particularly unstable on the short evolutionary time, instability was transitory, highlighting the importance of considering multiple time-scales for these comparisons. It should be noticed however that these signals may be related to other taxonomic effects, as also noticed before  [32] , and that can be avoided only with much larger and balanced datasets.
Most non-pathogens are grouped at the bottom of the plot, corresponding to larger coefficients. The probability of sampling 6 nonpathogens species in our dataset can be calculated, giving p = 2. 8E -04 (even by excluding Buchnera). The only pathogen within these species is Clostridium botulinum for which however there are two groups of points biasing the regression estimate (Additional file 3); even by including this species the result is still highly significant (p = 0. 001). This suggests that at short phylogenetic distances, non-pathogens have slower gene content dynamics than pathogens, with rearrangements playing a major role in genome organization evolution.
The relationship between GOS (Eq. 4) and σ (Eq. 6) is therefore informative about the typical length of accessory gene clusters. The relationship between the log transformed variables is linear (Additional file 4), allowing an easy comparison of the behavior of each species and the whole dataset by focusing on the regression coefficients (β) of the model GOS = α + βσ. In particular, given e. g. β = x for the whole intra-species dataset, the species with β < x are characterized by larger accessory gene clusters whereas those with β > x by smaller accessory clusters. Our results (Figure  8 ) suggest that there are wide differences in the size of accessory gene clusters, with Chlamydophila pneumoniae, Mycobacterium tubercolosis and Yersinia pestis having the smaller accessory gene clusters, whereas Ralstonia solanacearum, Burkholderia pseudomallei, Shewanella baltica and Bifidobacterium longum have the largest ones.