dataesr/scientific-paragraphs-categorization huggingface dataset:
9c, d ). To analyze the interactions between PI3Kγ and the heart immune response, a correlation analysis was performed using the transcriptome data set from CCC patients (GSE84796). The expression of the PIK3CG gene positively correlated with the expression of myeloid cells markers (CD11b, CD300a, and CD33) and inflammatory mediators genes (Supplementary Fig. 7a-h ), indicating that the increased influx of leukocytes in chagasic hearts might be the cause of the increase in PI3Kγ expression. To validate these findings, real-time PCR was performed in the heart samples. The expression of pro-inflammatory cytokines (TNF and IFNG) and chemokines (CCL5/RANTES) was higher in heart tissue of CCC patients compared with the DCM or control groups (Supplementary Fig. 8a-c ). In agreement, there was a positive correlation in heart tissue of CCC patients between the expression of the PIK3CG gene and the proinflammatory genes TNF (r 2 = 0.
The following antibodies were used: APC-Cy7 rat anti-mouse CD45 (1: 200, clone 30-F11, BD Biosciences, cat. 557659); FITC armenian hamster anti-mouse CD3 (1:250, clone 145-2C11, eBioscience, cat. 11-0031-63); PercP rat anti-mouse CD4 (1:250, clone RM4-5, BD Biosciences, cat. 553052); PE rat anti-mouse CD8a (1:250, clone 53-6. 7, BD Biosciences, cat. 553033); Alexa Fluor® 647 rat anti-mouse Foxp3 (1:100, clone R16-715, BD Biosciences, cat. 563486); PE rat anti-CD11b (1:250, clone M1/70, BD Biosciences cat. 553311); PE hamster anti-mouse CD152/CTLA-4 Clone (1:150, UC10-4F10-11, BD Bioscience, cat. 553720); PE/Cy7 anti-mouse CD39 (1:250, clone Duha59 BioLegend, cat. 143806). For each sample, a minimum of 1 million events were acquired using a BD FACSVerse cytometer driven by the FACSuite software (BD Biosciences, San Diego, EUA).
The obtained data were analyzed using the software FlowJo version X (Tree Star, Inc. ). Gating strategies for flow cytometry analysis are schematically represented in Supplementary Fig. 11. Heart CD11b + and CD11b -cells isolation. The mouse heart tissue was sliced in small pieces and digested using Liberase TL (Roche) for 1 h at 37 °C/5% CO 2. The obtained cell suspension was filtered through a 40-µm cell strainer (Corning®). Isolated cells were stained with PE rat anti-CD11b (1:300; clone M1-70, BD Biosciences, cat. 553311) and then incubated with anti-PE magnetic beads (Miltenyi Biotec). The cell suspension was separated into CD11b + and CD11b - samples using MACS® cell separation columns (Miltenyi Biotec) according to the manufacturer's instructions. Total genomic DNA extraction and parasitism analysis.
Gene expression data were deposited in the GEO database (GSE84796 and GSE111544). Microarray analyses and signal normalization were performed with GeneSpring software (11. 5. 1, Agilent). For each sample, the P-value was obtained using the Student's t-test and adjusted for the false discovery rate with the Benjamini-Hochberg method. Genes were considered differentially expressed if adjusted P-values were <0. 05 and showed an absolute fold change >2. 0. Statistical analysis. Results are expressed as the mean ± s. e. m. for in vitro and in vivo experiments. The normal distribution of data was analyzed by D'Agostino and Pearson test. The data set obtained by experiments with mice or primary cells assumed a normal distribution. Differences were considered to be statistically significant with P < 0.
05 by unpaired Student's t-test or ANOVA followed by Bonferroni post test. Survival curves were evaluated using the log-rank/ Mantel-Cox test. For analysis of the correlation, Pearson's test was carried out. All statistical analyses were performed using GraphPad Prism Software 5 except for the analysis of correlation, for which SPSS was used. No statistical methods were used to predetermine the sample size. Variation within each data set obtained by experiments with mice or primary cells was assumed to be similar between genotypes since all strains were generated and maintained on the same pure inbred background (C57BL/6).
Acknowledgements The authors gratefully acknowledge the technical assistance of  Cristiane Maria Milanezi ,  Wander Ribeiro ,  Ieda R. Schivo ,  Giuliana Bertozi , and  Sérgio Rosa. We also thank  Júlio Anselmo Siqueira  for supervising the animal facility as well as providing all animals used in this study. The research leading to these results received funding from the  São Paulo Research Foundation (FAPESP)  under grant agreements no. 2012/14524-9  ( Thematic Project  to J. S. S. ),  2013/08216-2  ( Center for Research in Inflammatory Disease ), from the  University of São Paulo NAP-DIN  under grant agreement no. 11. 1. 21625. 01. 0 ; and a  CAPES   Visiting Professor Grant  no. 88881. 068044/2014-01. M. C. S. was supported by doctoral fellowship from the  São Paulo Research Foundation  Grant  2011/09488-0.
C. C. was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Aix-Marseille University  ( Direction des Relations Internationales ), the  USP-COFECUB program , the  ARCUS II PACA Brésil program , and from the  Laboratoire d'Excellence (LabEx) ParaFrap   ANR-11-LABX-0024. E. C. N. and C. C. were recipient for.
Aerosol Mass Spectrometry Characterization of the atmospheric PM was obtained using a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (hereafter AMS; Aerodyne, Inc. , Billerica, Massachusetts, USA;  DeCarlo et al. , 2006; Canagaratna et al. , 2007). Detailed aspects of the AMS operation in GoAmazon2014/5 were presented in de  Sá et al. (2017). In brief, the instrument was housed within a temperature-controlled research container, and the inlet to the instrument sampled from 5 m above ground level. Ambient measurements for this study were obtained every other 4 min. The other 4 min were used for analysis of output from an oxidation flow reactor as presented in  Palm et al. (2018). Data analysis was performed using SQUIRREL (1. 56D) and PIKA (1. 14G) of the AMS software suite  (Sueper and collaborators; DeCarlo et al.
Auxiliary measurements and datasets In complement to the AMS data set, the analysis herein incorporated auxiliary gas and particle measurements from T3  (Martin et al. , 2016). Mass concentrations of molecular and tracer organic species in the gas and particle phases were measured by a Semi-Volatile Thermal Desorption Aerosol Gas Chromatograph (SV-TAG) at a time resolution of one hour  (Isaacman-VanWertz et al. , 2016). Concentrations of volatile organic compounds (VOCs) were measured by a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometer (PTR-ToF-MS;  Liu et al. , 2016b). In the Mobile Aerosol Observing System (MAOS) of the ARM Climate Research Facility (ACRF;  Martin et al. , 2016) , measurements of NOy were made using a chemiluminescence-based instrument (Air Quality Design). The raw NOy measurements (10-s resolution) were averaged across 30-min intervals to dampen the influence of brief local events.
Particle Soot Photometer (SP2; Droplet Measurement Techniques). The two datasets differed by a factor of up to three in absolute mass concentrations, as observed in other studies  (Subramanian et al. , 2007; Cappa et al. , 2008; Lack et al. , 2008) , but they agreed well in the temporal trend. The analysis herein for BC is thus restricted to the temporal trends. Wind direction, solar irradiance, and precipitation rate were measured by the ARM Mobile Facility (AMF-1), which was also part of the ACRF. Additional measurements from T0a, T0t, and T2 were also used in the analysis. At T2, non-refractory particle composition and concentration were measured by an Aerosol Chemical Speciation Monitor (ACSM;  Brito et al. , in preparation). ACSM measurements were also made at T0a during the wet season of 2015  (Carbone et al.
, in preparation). Further datasets collected by AMS at T0t during the wet season of 2008 (AMAZE-08 campaign) were used in the analysis  (Chen et al. , 2009; Schneider et al. , 2011). AMS measurements made onboard the G-1 aircraft of the ARM Aerial Facility (AAF) during IOP1 also supported the analysis herein  (Shilling et al. , in preparation).
Air mass backtrajectories and precipitation rates Simulations of two-day backward air mass trajectories, starting at 100 m above T3, were made using HYSPLIT4  (Draxler and Hess, 1998). Input meteorological data were obtained from the Global Data Assimilation System (GDAS), provided by the NOAA Air Resources Laboratory (ARL), on a regular grid of 0. 5° × 0. 5°, 18 pressure levels, and 3-h intervals. Trajectory steps were calculated for every 12 min. Information on precipitation along the trajectories was obtained from the S-band radar of the System for Amazon Protection (SIPAM) in Manaus  (Machado et al. , 2014). The radar had a beam width of 1. 8°, and it scanned 17 elevation angles every 12 min. Data were recorded to a range of 240 km at 500-m gate spacing. The reflectivity fields were quality controlled to remove non-meteorological echo and calibrated against the satellite precipitation radar of the Tropical Rainfall Measuring Mission and Global Precipitation Measurement (TRMM-GPM;  Kummerow et al.
, 1998; Hou et al. , 2014). Ground clutter was used to analyze the stability of the calibration. The data were gridded at 2 km × 2 km in the horizontal and 0. 5 km in the vertical using the NCAR Radx software. The reflectivity at 2. 5 km in altitude was converted to rain rates based on the data sets of a Joss-Waldvogel disdrometer  (Joss and Waldvogel, 1967) , located at T3, 70 km downwind of the radar.
Among all sites, the T2 observations had both the highest average organic mass concentrations and the largest variability. These characteristics of the T2 dataset can be explained by a combination of (i) the proximity of the site to Manaus, (ii) the rapid and 180° changes in wind direction caused by the intersection of the trade winds with a local river breeze  (dos Santos et al. , 2014) , and (iii) possible contributions of emissions from brick kilns, located mostly southwest of the site, especially during night time  (Martin et al. , 2016; Cirino et al. , submitted). The diel trends of the sulfate mass concentrations were in large part similar to those of the organic mass concentrations. One distinction in the case of sulfate, however, is that the variability at the T0 sites is similar to that at the T3 site.
The explanation is that the background sources of sulfate, including not only in-basin emissions but also out-of-basin long-range transport, are variable and significant enough to make the variability at the background sites similar to that at the T3 site  (de Sá et al. , 2017). Overall, the organic PM1 at T3 was highly oxidized, as indicated by the position of gray markers in the plot of Figure  3. By contrast, the blue markers represent the dataset collected at T2 during the same period. The datasets encompass all times of days and all conditions at both sites. Datasets from background sites collected in different years are shown in Figure  S2. Points to the upper left represent more oxidized material, and points to the lower right represent less oxidized material  (Ng et al. , 2011a). The comparison depicted in Figure  3  illustrates the effects of the plume over the 4 h of transport from T2 to T3  (Cirino et al.
The inset of Figure  4a  shows the mean fractional loading contribution of each factor during the analysis period. The correlations of factor loadings with co-located measurements of gas-and particlephase species are shown in Figure  5. The scientific interpretation of each factor was based on a combination of (i) the characteristics of the factor profile (i. e. , "mass spectrum"), as referenced to a worldwide database of AMS spectra and PMF analyses  (Ulbrich et al. , 2009b; Ulbrich et al. , 2009a, 2009c), and (ii)  the temporal correlations between the factor loading and other co-located measurements. Three factors interpreted as primary emissions of organic PM were resolved: an anthropogenicdominated factor (hereafter, "ADOA"), a biomass burning factor ("BBOA"), and a fossil-fuel hydrocarbon-like factor ("HOA").
They were characterized by NOy < 1 ppb, ozone < 20 ppb, and particle number < 1200 cm -3 (Table  2 ; Figure  6 ). The two clusters differed especially in that Bkgd-2 had significantly larger concentrations of sulfate and black carbon. A comparison of the datasets of Feb 13 (predominantly Bkgd-1) and Feb 16 (predominantly Bkgd-2) in Figure  6  highlights these differences. Concentrations of sulfate and black carbon were 0. 15 and 0. 10 µg m -3 , respectively, on Feb 13, compared to 0. 40 and 0. 15 µg m -3 on Feb 16. The backtrajectories associated with Bkgd-1 had both northeasterly and southeasterly components. The wind fields, out of line with the trade winds, may suggest passage through recent weather systems and may imply wet deposition, which in turn might explain lower gas and particle concentrations (Table  2 ).
These recent weather systems might not have been excluded from the scoped dataset because of inaccuracies in the intersections of the backtrajectories with precipitation data, as discussed above, or because they were more distant than captured by the 10-h backtrajectories. Consistent with this hypothesis, the centroid value calculated for the 4-h averaged solar irradiance at T3 (Section 3. 3. 2) was lower for Bkgd-1 (400 W m -2 ) compared to the other clusters (600 W m -2 ), suggesting an association of Bkgd-1 with overcast conditions. By comparison, the backtrajectories associated with Bkgd-2 were predominantly from the northeast, coming from the direction of the T0t and T0a sites (Figure  7 ), in line with the dominant trade winds of the wet season. The air masses of Bkgd-2 may have experienced less wet deposition and may represent more extensive atmospheric oxidation than those of Bkgd-1.
They may also have carried PM contributions from out-of-basin sources, which would be consistent with the higher sulfate and black carbon concentrations of Bkgd-2 compared to Bkgd-1  (Chen et al. , 2009; Pöhlker et al. , 2017). Two other clusters were interpreted as "polluted" and labeled "Pol-1" and "Pol-2". They were characterized by concentrations of NOy > 1 ppb, ozone > 20 ppb, and particle number > 1200 cm -3 (Table  2 ; Figure  6 ). The dataset of the afternoon of Mar 9 illustrates a shift in dominance from Pol-2 to Pol-1 (Figure  6 ). Although Pol-1 and Pol-2 both have high concentrations of sulfate and other pollutants, they differ in the extent of those high concentrations. The explanation may be that these clusters represent different source regions. Pol-1 may be associated with emissions from the northern region of Manaus, and Pol-2 may be associated with emissions from the southern region of Manaus.
Comparison of PM among clusters The characteristic PM composition associated with each cluster was determined by calculating the centroid coordinates of the clusters for the AMS species and PMF factors (Section S3). The centroid coordinate of a cluster for a given variable is defined as a weighted mean of that variable across all points in time, where the weight is the degree of membership of each data point to that cluster. A comparison of PM1 concentrations and compositions for the four clusters is shown in Figure  8. Values are listed in Table  2. The NR-PM1 mass concentrations increased by 25% to 200% in clusters Pol-1 and Pol-2 compared to clusters Bkgd-1 and Bkgd-2 (Figure  8a ). Increases in sulfate and associated ammonium concentrations had a smaller yet non-negligible role in the increased PM1 mass concentrations.
g. , LO-OOA and MO-OOA). The clustering analysis contextualized the PMF results and demonstrated that the effects of the urban pollution were neither limited to nor captured by a single PMF factor. Instead, the urban plume influenced several PMF factors in different ways and to different extents. The implication is that changes in the AMS spectral signature of the organic PM caused by polluted conditions may not be sufficiently unique to allow for its complete separation by PMF analysis alone, especially in respect to the production of secondary organic PM. In this context, the Fuzzy c-means analysis served herein as a useful tool to incorporate auxiliary datasets and thereby to further understand anthropogenic influences on PM production and characteristics.
The right side depicts anthropogenic emissions of gas species and particulate matter that can alter natural atmospheric concentrations and processes. There are primary organic PM1 emissions from traffic, cooking, and industrial Table  1. Characteristics of the PMF factors derived from the AMS datasets. Listed are signal fractions fCO 2 + at nominal m/z 44 and oxygen-to-carbon (O:C) and hydrogen-tocarbon (H:C) ratios. Values and associated uncertainties were calculated by running PMF in "bootstrap mode"  (Ulbrich et al. , 2009b). Elemental ratios were calibrated by the "improved-ambient" method, which has an estimated uncertainty of 12% for O:C and 4% for H:C  (Canagaratna et al. , 2015).
Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-172 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 20 February 2018 c Author(s) 2018. CC BY 4. 0 License. confounding processes, such as boundary layer dynamics and wet deposition. The elimination of trajectories having precipitation, however, should not be regarded as fully accurate given the uncertainties in the HYSPLIT trajectories. The scoped dataset spanned 24 afternoons. Four clusters were identified based on minimization of the FCM objective function as well as a subjective assessment of meaningful interpretation of the set of clusters (Section S3).
Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-172 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 20 February 2018 c Author(s) 2018. CC BY 4. 0 License. Positive-matrix factorization and Fuzzy c-means clustering were applied to the datasets to obtain a composite analysis of the shifts in PM1 concentrations and composition under polluted conditions. Based on the FCM clustering, every point in time at T3 was interpreted as being affected by a combination of four influences, as represented by four clusters. Two background (Bkgd-1 and Bkgd-2) and two polluted (Pol-1 and Pol-2) clusters were identified. Particle mass concentrations were double for polluted compared to background conditions. Contributions from secondary processes dominated (> 80%) for both background and polluted conditions.
These results from climate model simulations do not directly provide physical explanations, however, and require validation against observations. The positive LW cloud altitude feedback is generally thought to be robust as it is persistently found in climate model simulations since the very first models  3, [6] [7] [8] [9] [10] [11] [12] [13]  and is backed by a plausible physical explanation: high clouds should rise in a warming climate such that cloud temperatures remain nearly constant  13. However, it still needs to be better verified against observations. To directly observe an altitude trend a long enough dataset is needed, highly stable in time, to detect the small changes due to anthropogenic forcing imposed on top of natural variability  14. Spaceborne passive instrument datasets are currently the longest records available  15, 16  but have shown limited accuracy due to LW surface radiation influence through thin clouds  [17] [18] [19] [20]  and limited calibration stability over decadal timescales mainly due to calibration drifts  21, 22  , which significantly increases the time required to detect climate trends.
(  4 ) and estimated at the mean of the two states + t t 2 1  2. In this last equation, a non-linear residual term remains but is negligible  27. Uncertainties are defined as the 95% confidence interval of the regression. Figure  4  shows the regression of the global monthly mean total LWCRE anomaly against the global monthly mean surface temperature anomaly in observations with its 95% confidence interval. It suggests that cloud feedback is negative over this time period with a value of -1. 85 ± 0. 93 W m -2 K -1 (reported in first bar of Fig. 2 ). We estimate the relative contribution of each cloud property to the short-term LW cloud feedback independently in both observations and the model. In the observations, the five cloud properties come from the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) dataset  29, 51  between 2008 and 2014.
Observations before 2008 are not considered because the CALIPSO lidar view angle was changed from 0. 3 to 3° in November 2007, so the cloud properties observed before November 2007 are not fully consistent with those observed after. In the model, the cloud properties come from an Atmospheric Model Intercomparison Project (AMIP) simulation  52  which is forced by monthly mean surface temperature between 2008 and 2014 from ERA-Interim reanalysis  38. The AMIP simulation is performed using the CFMIP Observation Simulator Package (COSP)  32  lidar simulator  29, 33, 53  , so that the five cloud properties output from the model simulation are fully consistent with the observed five cloud properties in the GOCCP dataset. ε ∆ = ∂ ∂ ∆ + ∂ ∂ ∆ + ∂ ∂ε ∆ + C C Z Z LWCRE LWCRE L WCRE LWCRE NL, (7).
Acknowledgements We would like to thank  NASA/CNES  for access to the CALIPSO level 1 data and the  ClimServ center  for the use of the computing cluster. We also thank  CNES  for supporting the development of CALIPSO-GOCCP. We thank  David Cugnet  and  Ionela Musat  for help with the LMDZ simulations. We also greatly thank the anonymous reviewers who helped to significantly improve this manuscript.
BP's data set explicitly covers about 70 countries (96 % of global emissions), and for the remaining countries we use growth rates from the subregion the country belongs to. For the most recent years, flaring is assumed constant from the most recent available year of data (2016 for countries that report to the UNFCCC, 2014 for the remainder). -Cement. Estimates of emissions from cement production are taken directly from  Andrew (2018). Additional calcination and carbonation processes are not included explicitly here, except in national inventories provided by UNFCCC, but are discussed in Sect. 2. 8. 2. -Country mappings. The published CDIAC data set includes 256 countries and regions. This list includes countries that no longer exist, such as the USSR and Yugoslavia. We reduce the list to 213 countries by reallocating emissions to the currently defined territories, using mass-preserving aggregation or disaggregation.
From 2012, the budget year (Carbon Budget 2012) refers to the initial publication year. b The CDIAC database has about 250 countries, but we show data for 213 countries since we aggregate and disaggregate some countries to be consistent with current country definitions (see Sect. 2. 1. 1 for more details). c E LUC is still estimated based on bookkeeping models as in 2017, but the number of DGVMs used to characterise the uncertainty has changed. ment emissions. This is greater than the sum of emissions from all countries. This is largely attributable to emissions that occur in international territory, in particular, the combustion of fuels used in international shipping and aviation (bunker fuels). The emissions from international bunker fuels are calculated based on where the fuels were loaded, but we do not include them in the national emission estimates.
, 2015) , and we make detailed estimates for the years 1997  (GTAP version 5), 2001 (GTAP6), and 2004 , 2007 , and 2011 (GTAP9. 2), covering 57 sectors and 141 countries and regions. The detailed results are then extended into an annual time series from 1990 to the latest year of the gross domestic product (GDP) data (2016 in this budget), using GDP data by expenditure in the current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregrates Database; UN, 2017a) and time series of trade data from GTAP (based on the methodology in  Peters et al. , 2011b). We estimate the sector-level CO 2 emissions using the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match the emission estimates from the carbon budget.