dataesr/scientific-paragraphs-categorization huggingface dataset:
2b  and c ), generating 10 protein clusters. In agreement with the principal component analysis correlation graphs (Fig. 2d  and e ), network clustering of the data displayed similar trends with regards to fragmentation of the graph into two localisationdependent networks (Fig. 4 ). The fragmentation of the dataset into 2 distinct groups suggests that significant heterogeneity in protein expression exists between the mitochondrial populations derived from discrete neuronal sub-compartments. Indeed, with further examination we exhibited that proteins with increased abundance in synaptic mitochondria cluster into the distinct network on the left of the graph whereas those with increased abundance in non-synaptic mitochondria are observed in the right network (Fig. 4 ). To identify the functional cascades associated   that appear to demonstrate differential expression between the two mitochondrial subpopulations, the top 3 protein clusters were entered into Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.
Pathway analysis highlights mitochondrial complex I expression differences as potentially conserved regulators in a range of neurodegenerative conditions In order to determine how differential protein expression in mitochondrial subpopulations may synergistically regulate downstream molecular pathways and cellular processes, we performed an in silico analysis using Ingenuity Pathway Analysis (IPA) software. The software promotes the identification of statistically significant functional groups of proteins, based on known protein interactions and biological functions reported in the published literature  [20]. Functional networks generated by IPA software are statistically ranked according to a score calculated via a righttailed Fischer's exact test, taking into account the number of original input proteins and the size of the resulting network.
Methods and results The FIBROTARGETS consortium has merged data from 12 patient cohorts in a common database available to individual consortium partners. The database consists of >12 000 patients with a large spectrum of cardiovascular clinical phenotypes. It integrates community-based population cohorts, cardiovascular risk cohorts, and heart failure cohorts. Conclusions The FIBROTARGETS biomarker programme is aimed at exploring fibrotic pathways allowing the bioprofiling of patients into specific 'fibrotic' phenotypes and identifying new therapeutic targets that will potentially enable the development of novel and tailored anti-fibrotic therapies for heart failure.
Study population Taking advantage of its multinational structure, the FIBROTARGETS consortium has merged data from 12 patient cohorts in a common database available to individual consortium partners. The database consists of >12 000 patients with a large spectrum of cardiovascular clinical phenotypes. 8 It integrates community-based population cohorts (Stanislas  9  and Health ABC  10  ), cardiovascular risk cohorts (REVE-1 and REVE-2, 11 ADELAHYDE, 12 R2C2, HVC, REMI,  13  and STOP-HF  14  ), and heart failure (HF) cohorts (Leitzaran, MEDIA-DHF, and TIME-CHF  15  ). Cohort details are summarized in Table  1  and the Supporting Information, Tables  S1, S2 , and S3. Patients from the merged database were clustered into four main phenotypic groups: (i) 'healthy subjects', including (but not exclusively consisting of) participants without cardiovascular risk factors or renal dysfunction, (ii) participants at 'risk' of developing HF as evidenced by the presence of diabetes and/or hypertension or other cardiovascular risk factors and no overt cardiac dysfunction, (iii) patients with prevalent HF with reduced left ventricular ejection fraction regardless of HF symptoms, and (iv) patients with prevalent HF with preserved left ventricular ejection fraction regardless of HF symptoms.
Data management The FIBROTARGETS database was elaborated and is maintained at the Inserm Clinical Investigation Center (CIC1433), Nancy, France. In order to be included in FIBROTARGETS, participating institutions and investigators must comply with the latest local, national, and international rules on data protection, biomedical research regulations, good clinical practices, and the Helsinki Declaration. All participants from each cohort must have provided written informed consent. Informative data relevant to the FIBROTARGETS consortium have been extracted from each cohort dataset. Thus, the merged dataset retains a maximum of information retrieved from each contributing cohort in order to include baseline characteristics of each participant (anthropometrics, medical history and medication use, routine haematological and biochemical measurements, electrocardiographic and echocardiographic variables) as well as follow-up information, whenever available.
A data dictionary has been elaborated based on agreed common definitions and criteria enabling classification of clinical events and phenotypes with a particular focus on HF events, left ventricular dysfunction, hypertension, diabetes, atrial fibrillation, myocardial infarction, and chronic kidney disease (see Table  1  and Supporting Information, Tables  S1, S2 , and S3). The investigators of each study have also collected followup data. By ensuring a large diversity of phenotypes within the FIBROTARGETS population (in terms of age and presence or absence of co-morbidities), the merged database provides a unique tool to evaluate the determinants and utility of profiling patients according to specific fibrotic pathways using candidate biomarkers.
Statistical and bioinformatics considerations Predictive models (of the aforementioned phenotypic clusters) using biomarker variables will be developed in three ways: (i) using classical statistical methods (as described above) including selection procedures in the second phase, (ii) using machine-learning tools such as regression trees and random forests, and (iii) using system biological or computational models based on available knowledge of myocardial fibrosis. In addition to the prediction of phenotypic clusters, associations between biomarkers and clinical outcomes will be assessed, including all-cause and cardiovascular mortality and HF hospitalizations. Cox models will be used to assess the associations between biomarkers and outcome. Further bioinformatics analysis will integrate the obtained FIBROTARGETS biomarker data in the context of previous knowledge.
Baseline characteristics of the study population The characteristics of the main cohorts are described in Table  1  ('merged' dataset) and Supporting Information, Tables S1 (population/'healthy' cohorts), S2 (cardiovascular risk cohorts), and S3 (HF cohorts). The FIBROTARGETS population consists of 12 922 individuals, 49% of whom are of female sex. The mean ± standard deviation (SD) age is 54 ± 23 years with a mean ± SD body mass index of 25. 7 ± 5. 6 kg/m 2. Sixty-six per cent of the individuals present a diagnosis of hypertension, 19% had diabetes mellitus, and 29% had HF. Additionally, individuals were classified based on the four selected risk factors (hypertension, obesity, diabetes, and history of HF), allowing the establishment of subgroups based on disease/risk factors, as represented in Figure  1.
Acknowledgements The authors wish to express their gratitude to all the partners of the FIBROTARGETS consortium:  Faiez Zannad  and  Frédéric Jaisser ,  Institut National de la Santé et de la Recherche Médicale, France ;  Catherine Clusel  and  Marie-Alix Fauvel ,  Institut National de la Santé et de la Recherche Médicale-Transfert SA, France ;  Javier Díez  and  Arantxa González ,  Centro de Investigación Médica Aplicada, Spain ;  Kenneth Mac Donald ,  University College Dublin, National University of Ireland, Ireland ;  Stephane Heymans  and  Anna Papageorgiou ,  Universiteit Maastricht , The Netherlands;  Thomas Thum ,  Medizinische Hochschule Hannover ,  Germany ;  Mariann Gyöngyösi  and  Johannes Winkler ,  Medical University of Vienna, Austria, Austria; Quoc-Tuan Do, Greenpharma S. A.
, 1998). In this study, we aimed to decipher the respective contributions of type I and type II IFNs to shed light on their respective influence in HIV/SIV infection and pathogenesis. We analyzed IFN and ISG transcriptomic profiles and their associated functional signatures in the cynomolgus macaque (macaca fascicularis) pathogenic model of SIV infection, taking advantage of longitudinal access to blood and lymphoid tissues. We elucidated the respective contribution of type I and type II IFNs in determining ISG expression levels, patterns, and associated functions in both acute and chronic SIV infection, by classifying the ISGs into either type I or type II, based on information obtained from the Interferome database  (Samarajiwa et al. , 2009)  or by using an innovative approach to classify ISGs based on the presence of ISRE or GAS conserved consensus sequences in their respective promoters.
The main biological functions associated with D9-ISGs and M3-ISGs and their FC are listed in Expanded View Table  2. We annotated the ISGs using the Interferome database  (Samarajiwa et al. , 2009)  to understand the contribution of type I IFNs, type II IFN, or both to the ISG expression pattern and identified type-I ISGs (inducible by type I IFNs but not type II IFN), type II ISGs (inducible by type II IFN but not type I IFNs), and TypeI&II ISGs (inducible by both IFN species). We also used a novel approach to restrict our analysis to genes directly targeted by interferon signaling and to exclude any genes described in the Interferome database that may be induced by secondary mechanisms. It consisted of annotating the ISGs based on the presence of ISRE and/or GAS consensus sequences in the promoter regions of all ISGs upstream of the 5'UTR, as these sequences are targets of ISGF3 (STAT1/STAT2/IRF9) and was not certified by peer review) is the author/funder.
Increased expression of CCL3 may be a beneficial response of the host against HIV/SIV infection, due to its ability to block HIV entry into CCR5 + T cells  (Cocchi et al. , 1995) , but it can also be was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted September 22, 2017. ; https://doi. org/10. 1101/192021 doi: bioRxiv preprint deleterious, through the recruitment of new target cells to inflamed lymph nodes. The CCL3 gene is reported to be type I IFN-inducible in the Interferome database. However, it bears a GAS sequence, but no ISRE, in its promoter and could thus be sustained by IFN- during the chronic phase or low undetectable type I IFN expression through alternative type I IFN signaling.
Transcriptomic analysis was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted September 22, 2017. ; https://doi. org/10. 1101/192021 doi: bioRxiv preprint Microarray data were processed using R/Bioconductor. Gene expression values were scaled and normalized using the limma package. Differentially expressed genes were identified using two-sample t-tests (p < 0. 05) and based on a 2-fold-change (FC) threshold. Functional Enrichment analysis was performed using Ingenuity® Pathway Analysis (IPA, version n° 31813283) and the right tailed-Fisher Exact test. P-values were corrected for multiple comparisons using the Benjamini-Hochberg Multiple Testing Correction. Genes were annotated using DAVID Bioinformatics Resources 6.
ISG annotations Differentially expressed interferon-induced genes were annotated using two approaches as described in Expanded View Figure  1. The first approach was based on the Interferome database (http://www. interferome. org), an open-access database of type I, II, and III Interferon-regulated genes collected from analyzing expression datasets of cells and organisms treated with IFNs. We used this database to identify and classify genes depending on their expression following induction by either type I IFN, type II IFN, or both IFN species. We also used a complementary approach in which the genes were annotated depending on the presence of either GAS, ISRE, or both consensus sequence motifs in gene promoters within the 2,000 bp upstream of the 5'UTR after locating them using Geneious software (http://www.
Grants This work was supported by the  Agence Nationale de Recherches sur le SIDA  et les hépatites virales (ANRS). NE, SD and CPBP benefited from  ANRS  post-doctoral fellowships. This work was also supported by  Agence Nationale de Recherches sur le SIDA et les hépatites virales (ANRS)  through research project grant  11360  attributed to BV, by the  French government  " Programme d'Investissements d'Avenir" (PIA)  under Grant  ANR-11-INBS-0008 , that funds the  Infectious Disease Models and Innovative Therapies (IDMIT, Fontenay-aux-Roses, France)  infrastructure, and  PIA  grant  ANR-10-EQPX-02-01  that funds the  FlowCyTech facility  (project investigator RLG). Cell Cycle IFNs type Consensus  BCL2L14   I/II Gas IFI27 I/II Gas/Isre FASLG I Gas/Isre PRF1 I Gas/Isre PMAIP1 I/II Gas/Isre BIK I/II Gas/Isre NLRP3 I Gas CASP4 I/II Gas/Isre CASP5 I/II Gas MLKL I/II Gas CASP1 I/II Gas GZMB I/II Gas/Isre TNFSF10 I/II Gas/Isre PIM1 I/II Isre BAK1 I/II Gas/Isre NAIP I/II Gas FAS I/II Gas/Isre.
Conflict of interest The authors declare that they have no conflict of interest. GAS-1 TTCCnnnAAA GAS-2 TTACnnnAAA GAS-3 TTCCnnnAAG GAS-4 TTACnnnAAG EV Figure  1 : ISG annotations. Differentially expressed interferon-induced genes were annotated using two approaches: the Interferome database helped to identify and classify genes depending on their induction by either type I IFN (α, β), type II IFN (γ), or both IFN types. In addition, Geneious software was used to annotate ISGs depending on the presence of either GAS, ISRE, or both sequence motifs within the 2,000 bp upstream of the 5'UTR of each gene. (A) Schematic diagram of annotations. (B) ISRE and GAS sequences. EV Figure 1.
Formic acid (HCOOH) concentrations are often underestimated by models, and its chemistry is highly uncertain. HCOOH is, however, among the most abundant atmospheric volatile organic compounds, and it is potentially responsible for rain acidity in remote areas. HCOOH data from the Infrared Atmospheric Sounding Interferometer (IASI) are analyzed from 2008 to 2014 to estimate enhancement ratios from biomass burning emissions over seven regions. Fire-affected HCOOH and CO total columns are defined by combining total columns from IASI, geographic location of the fires from Moderate Resolution Imaging Spectroradiometer (MODIS), and the surface wind speed field from the European Centre for Medium-Range Weather Forecasts (ECMWF). Robust correlations are found between these fire-affected HCOOH and CO total columns over the selected biomass burning regions, allowing the calculation of enhancement ratios equal to 7.
The CO retrieval characteristics The CO concentrations are retrieved from IASI using the FORLI-CO software  (Hurtmans et al. , 2012) , which uses an optimal estimation method based on  Rodgers (2000). The spectral range used for the retrieval is between 2143 and 2181. 25 cm -1. The CO total columns have been validated for different locations and atmospheric conditions (e. g. , De  Wachter et al. , 2012; Kerzenmacher et al. , 2012) , and the comparisons with other data have shown good overall agreement, even if some discrepancies were found within COenriched plumes (reaching 12 % over the Arctic in summer, see  Pommier et al. , 2010; and reaching 17 % in comparison  with other IR sounders, see  George et al. , 2009). These data were also used previously to study biomass burning plumes (e. g.
The FRP provides a measure of fire intensity that is linked to the fire fuel consumption rate (e. g. ,  Wooster et al. , 2005). Only data presenting a high confidence percentage are used, i. e. , higher than or equal to 80 % as recommended in the MODIS user's guide  (Giglio, 2013). To characterize each MODIS hotspot by the type of fuel burned, the Global Mosaics of the standard MODIS landcover-type data product (MCD12Q1) in the IGBP landcover-type classification  (Friedl et al. , 2010; Channan et al. , 2014)  with a 0. 5 • × 0. 5 • horizontal resolution has also been used (http://glcf. umd. edu/data/lc/). As the annual variability in this product is limited (not shown) and since the period available (from 2001 to 2012) does not fully match the period of the IASI mission, only the data for 2012 have been used.
g. ,  Paris et al. , 2008)  or from Europe (e. g. ,  Pochanart et al. , 2003). These external influences interfere with the CO plumes originating from forest fires measured over this region. Despite the overall good match between the number of fires and the variation in HCOOH and CO, we are not certain that HCOOH and the CO were emitted solely by fires, and the discrimination between a natural and an anthropogenic origin for each compound is challenging. This assessment is particularly obvious for IND and SIB. To isolate the HCOOH and CO signals measured by IASI, potentially emitted by a fire, we decided to only use the data in the vicinity of each MODIS hotspot. To do so, we co-located the IASI data at 50 km around each MODIS pixel and between 0 and 5 h from the time registered by MODIS for each detected fire, so that each MODIS pixel is associated with a mean value of HCOOH and CO total columns from IASI.
Importance of the meteorological conditions As shown in earlier studies, the wind speed can have a large influence on the detection of tropospheric plumes of trace gases from space (e. g. , NO 2 :  Beirle et al. , 2011; CO: Pommier et al. , 2013; SO 2 : Fioletov et al. , 2015). We have chosen to assign a surface wind speed value for each MODIS hotspot. These meteorological fields were taken from the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data (http://apps. ecmwf. int/datasets/ data/interim-full-daily/levtype=sfc/;  Dee et al. , 2011). The horizontal resolution of these fields is 0. 125 • on longitude and latitude with a 6 h time step. As shown in Fig. 3 , the three regions where the HCOOH : CO correlations are found to be high (r close to 0. 8) correspond to the regions where the surface wind speed was lower, i.
To perform a proper comparison with emission ratios, our enhancement ratios should be converted to emission ratios. To do so, it would be essential to take into account the decay of the compounds during the transport of the plume. However, due to the methodology used, i. e. , averaging the data collected during a few hours (between 0 and 5 h from the time registered by MODIS for each detected fire), the calculation of the decay of each compound is not possible. We therefore have compared our enhancement ratios to emission ratios, and the comparison presented hereafter is mostly illustrative. For both IND and SEA regions, the emission ratios have been calculated from the emission factors provided in  Akagi et al. (2011). For the other regions, in addition to the values from  Akagi et al. (2011) , emission ratios were similarly calculated from emission factors given in other studies (listed in Table  3 ).
Cropland fuel type was also used, since large agricultural biomass burning is occurring in this region (e. g. ,  Duc et al. , 2016). In addition to the EmR (HCOOH/CO) calculated from the EF HCOOH given in the literature, a classification for our ER (HCOOH/CO) has also been done, based on the data from the MCD12Q1 product. As each hotspot is associated with a land cover value defined by the MCD12Q1 product, enhancement ratios by biome have been calculated. The limitations of this dataset are its coarse resolution (0. 5 • × 0. 5 • ) and the lack of seasonal variation. It gives supplementary information on the type of fuel burned identified by MODIS. The corresponding ER (HCOOH/CO) values are provided in Table  3. Only the values calculated from a scatterplot with a correlation coefficient higher than 0.
Conclusions A total of 7 years of HCOOH data measured by IASI over seven different fire regions around the world were analyzed (AMA, Amazonia; AUS, Australia; IND, India; SEA, Southeast Asia; NAF, northern Africa; SAF, southern Africa; SIB, Siberia). By taking into account the surface wind speed and by characterizing each MODIS fire hotspot with a value of HCOOH and CO total columns, this work established en-hancement ratios for the seven biomass burning areas and compared them to previously reported values found in literature. The difficulties in performing such a comparison are associated with the difference in locations, altitude of the sampling, and age of each fire plume studied in these previous publications. However, a fair agreement was found for the enhancement ratios calculated in this work, in comparison with other studies, using satellite, airborne, or FTIR measurements.
The values found over northern Africa were more difficult to interpret as this region is characterized by a poorer correlation between our fire-affected HCOOH and CO total columns. Finally, the estimation of the ER (HCOOH/CO) calculated by the type of vegetation burned, as referenced in the MODIS product, varies with the regions. This shows that other parameters than the type of fuel burned also influence the ER (HCOOH/CO). With these findings and by updating the enhancement ratios, an interesting modeling study could be performed to estimate a new tropospheric budget for HCOOH. This IASI dataset may also be used in the future to study a single plume at different times to inform on the loss during transport. Further insight into the transport and chemistry may be gained by using IASI's capability to measure several fire species simultaneously, such as HCN or C 2 H 2 (e.
Acknowledgements. The IASI mission is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The IASI L1 data are distributed in near real time by  EUMETSAT  through the EUMETCast system distribution. We thank the  MODIS team  for providing public access to fire products MCD14ML and the land-cover-type data product MCD12Q1. This MCD14ML MODIS dataset was provided by the  University of Maryland  and  NASA   FIRMS  operated by  NASA/GSFC/ESDIS  with funding provided by  NASA/HQ. The authors thank  Simon Whitburn  ( ULB ) for his help on the MODIS files. They also thank  Juliette Hadji-Lazaro  (LATMOS) and  Lieven Clarisse  ( ULB ) for preparing the IASI T b dataset. The authors also acknowledge  ECMWF  for free access to the meteorological data. Edited by: Paul Monks Reviewed by: two anonymous referees.
, 2010; 2011 )  or Computed Tomography  ( Hoogendoorn et al. , 2013 ). To date, most atlases have been built from less than 100 subjects  ( Chandrashekara et al. , 2003; Rougon et al. , 2004; Perperidis et al. , 2005a; Duchateau et al. , 2010; 2011; De Craene et al. , 2012; Lu et al. , 2009; Garcia-Barnes et al. , 2010; Peressutti et al. , 2017 ). In a few cases, larger databases have been used to construct motion atlases, such as the Cardiac Atlas Project that contains 30 0 0 subjects, consisting of 2864 asymptomatic volunteers from the MESA cohort and 470 patients with myocardial infarction from the DETERMINE cohort  ( Fonseca et al. , 2011; Medrano-Gracia et al. , 2013b; 2014a )  and the Hammersmith data set  ( Bai et al. , 2015b  ) that contains 1093 healthy subjects. In general, motion atlas construction entails the following steps: (1) geometry estimation, commonly achieved by segmenting the anatomy of interest and generating a 3D mesh that will represent it; (2) motion estimation using a motion tracking algorithm; (3) temporal normalisation that aims to ensure temporal correspondence across the cardiac cycle; (4) spatial normalisation that aims to remove bias towards subject-specific geometries; and (5) motion reorientation, which transports motion estimates from subjectspecific to atlas coordinate systems.