dataesr/scientific-paragraphs-categorization huggingface dataset:
High β values imply that the size distribution is dominated by small fires. The results presented below have been computed for each of the eight different fire patch datasets of the FRY database. However, we will further only focus on the results obtained from the MCD64A1-derived fire patch dataset, with a cutoff value of 14 days. The figures obtained for the FireCCI41 fire patch product with a cut-off of 14 days (which spans the years 2005 to 2011) can be found in the Supplement. The same analysis was also performed with a cut-off value of 3 days for both MCD64A1 and FireCCI41: testing another extreme cut-off value allows us to estimate the impact on the results of the temporal threshold parameter used to reconstruct fire patches by  Laurent et al. (2018).
Discussion Following the hypothesis from Rothermel's equation of fire spread, and considering that FRP can be used as a proxy of fire reaction intensity  (Wooster et al. , 2003 (Wooster et al. , , 2005)) , we used the global fire patch database FRY to test if high FRP fires propagate faster and are therefore systematically larger than low FRP fires. We found that this hypothesis is actually verified for low to intermediate FRP in most fire regions and for the three defined vegetation types. We identified biomespecific FRP vs. FS relationships, with FRP leading to maximum FS being higher in temperate and boreal forests, followed by grasslands, savannas and tropical forests. In most fire-prone biomes, the positive relationship between FS and FRP does not hold for larger and more intense fire patches (Fig.
In BOAS the relationship between FS and FRP is different from the one observed in BONA and TENA. This could be a result from the less-flammable vegetation and the highest number of ground fires in BOAS  (Kasischke and Bruhwiler, 2003). Moreover, BA detection of surface fires (and consequently, fire patch characterisation) is known to be difficult in boreal Asia, and numerous discrepancies have been observed between the BA products obtained from different moderation resolution sensors  (Chuvieco et al. , 2016). The median FS is globally lower for the datasets generated from FRY with smaller cut-off values (see Figs. S1 and S2 in the Supplement) because big fire patches tend to be split into smaller patches for lower cut-off values, reducing the average fire patch size. The median FS is also lower for the FireCCI41-derived datasets due to its ability to detect smaller patches from its better spatial resolution.
Changing the survey or the cut-off value does not impact the global distribution of large and small fire patches. Reducing the cutoff to 3 days does not change the observed relationship between FS and FRP. The results obtained from the dataset derived from FireCCI41 follow the same trend, but for some GFED regions (TENA, EURO, NHSA, AUST), the seasonality is shifted 1 month later than for MCD64A1. Reducing the cut-off values lowers the temporal shift observed in Fig. 4  at a global scale (Figs. S3 and S4 in the Supplement), but the global distribution of the shift is conserved. Similarly, FireCCI41 yields smaller shifts than for MCD64A1, but with the same spatial distribution. In the previous section, we hypothesised that FRP can be used as a proxy of fire reaction intensity but the limitations of such an approach should be mentioned.
However, we can expect this effect to be mitigated by the fact that our analysis does not account for very small fires since the FRY database does not provide fire patches smaller than 107 ha for MCD64A1. Finally, a recent study  (Roberts et al. , 2018)  used 3-D radiative transfer simulations to show that the canopy structure intercepts part of the FRP emitted by surface fires. This means that the FRP measured from remote sensing for forested areas and savannas could underestimate the actual FRP. We can also expect this underestimawww. biogeosciences. net/16/275/2019/ Biogeosciences, 16, 275-288, 2019 tion to vary with tree species that are associated with different fire regimes. For example, it is probable that the amount of radiation energy intercepted by the canopy differs strongly between crown fires from highly flammable black spruce and jack pine forests from BONA  (Rogers et al.
2  and could therefore explain the peak of the number of fire patches at intermediate FRP  (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30). The amount of radiative energy reaching the MODIS instruments is much smaller at larger scan angles than at nadir. This means that the MODIS instruments will be less sensitive to low values of FRP at high latitude  (Giglio et al. , 2003; Schroeder et al. , 2005). This could explain the difference of the distribution of FRP associated with fire patches in BONA: the stronger asymmetry of the distribution in this region (i. e. the larger tail toward high FRP values) could arise from missing active fire data from less intense fires in this region. The temporal sampling of FRP also differs with the latitudinal coordinate since the number of satellite overpasses is larger at high latitude than at the Equator (from two observations per day to 15 at the poles;  Giglio et al.
Conclusion We characterised, for the first time, the actual relationship between fire size and fire intensity using a combination of fire patch size and active fire datasets at a global scale. We found that in most fire-prone ecosystems, fire size increases with fire intensity only at low fire intensity, reaches a threshold at intermediate intensity and then starts to decrease. Conversely, in temperate and boreal forests, FS and FRP are proportional even for high fire intensity. This behaviour is observed with significant differences among land cover types (shrublands/grasslands, savannas and forests) for both MCD64A1 and FireCCI41 products, and for all cut-off values used for fire patch reconstruction. We suggested that the FRP threshold value is influenced by the fragmentation of the landscape and the feedback between fuel connectivity and BA during the fire season.
Acknowledgements. Pierre Laurent's post doc grant was supported by the  ESA   Fire_cci project. Maria Vanesa Moreno 's post doc grant was supported by the  UM Post-Doc program  and  University of Montpellier  (France) and was co-financed by the  ESA   Fire_cci  project. We also thank  OSU OREME  for financial support in the "Fire" observation task and for hosting the dataset (https://data. oreme. org/fire/fire_data). Edited by: Akihiko Ito Reviewed by: three anonymous referees.
Data availability. Data are available on the OSU OREME web site (https://data. oreme. org/fire/fire_data, last access: 17 January 2019) (OSU OREME, 2019) or through the DOI: https://doi. org/10. 15148/0e999ffc-e220-41ac-ac85-76e92ecd0320. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-16-275-2019-supplement. Author contributions. All authors designed the study and interpreted the results. PL performed the data processing and analysis. PL and FM led the writing of the paper and all authors provided their comments and suggestions.
mRNA sequencing and expression profiling Pools of the total RNA extracted from the heterografts, at the two time points sampled, were sent in biological triplicate for deep sequencing by Massive Analysis of 3′-cDNA Ends (MACE) by GenXPro GmbH (Frankfurt Main, Germany). Twelve different libraries were obtained, three biological replicates per each experimental, TN21/110R_21DAG; TN21/110R_80DAG; TN112/ 110R_21DAG; TN112/110R_80DAG. Libraries were and analyzed by GenXPro GmbH as described by Zawada et al. (2014). The raw sequencing data of the 12 libraries were deposit in NCBI under the project PRJNA517111 and it is available in this address http://www. ncbi. nlm. nih. gov/bioproject/517111. True-Quant technology was used to remove duplicate reads from the raw dataset, low-quality sequence bases were trimmed and the poly-(A)-tail was clipped off.
The barcoded samples were sequenced in an Illumina Hiseq2000, with 1 × 100 bps. MACE reads were mapped onto the Vitis vinifera 12X genome assembly. The number of transcripts per gene was normalized by the library size of mapped reads multiplied by 1 million. The resulting contigs of the assembly were annotated by BLASTX to the Swiss-Prot database, CRIBI V1 annotation. The normalization and the analysis of the differentially expressed genes (DEG) were done with DEGSeq R/Bioconductor package  [63]. The analysis was performed between heterografts at each time point and between time-points (from 21DAG to 80DAG), for each heterograft separately. Genes were considered significantly differentially expressed with an FDR value < 0. 05 and a log2 fold change threshold of x ≥ |1|. Only transcripts with more than 100 counts at least in one of the condition were considered.
microRNA sequencing One pooled sample per condition was sent for miRNAs sequencing on an Illumina Hiseq2000 run by Fasteris (Geneve, Switzerland). Polyacrylamide gel was used to select and purify small RNAs from 18 to 30 nucleotids (nt) followed by the bound of 3p and 5p adapters. Subsequently, cDNA was synthesized and amplified generating the libraries for Illumina sequencing. Low-quality reads (FASTq value < 13) were removed and adaptor sequences were trimmed, using the Genome Analyzer Pipeline (Fasteris) as described in Galli et al. [65]  Sequences shorter than 18 nt and longer than 25 nt were excluded from further analysis. To identify phylogenetically conserved miRNAs, sRNA sequences were mapped to a set of all conserved non-redundant Viridiplantae obtained from miRBase database (Release 19, August 2012) using Bowtie v 0.
12. 7. The frequency of identified miRNAs was obtained by aligning the conserved precursors identified in this study and the sRNA library using Bowtie v 0. 12. 7 with the default parameters. For target prediction, psRNA target database (http://plantgrn. noble. org/ psRNATarget/) and literature review were used. qPCR Three independent samples of each experimental condition were used for cDNA synthesis according to the manufacturer's instructions of qScript™ microRNA cDNA Synthesis Kit (Quanta, Bioscience). Briefly, miRNAs were first polyadenylated in a poly(A) polymerase reaction and then qScript Reverse Transcriptase was used to convert the poly(A) tailed miRNAs into cDNA using an oligo-dT adapter primer. The qPCR amplification reactions were performed using the PerfeCTa Universal PCR Primer (specific to the unique sequence of the oligo-dT adapter primer) and the PerfeCTa® SYBR® Green SuperMix for iQ™, as forward primers the exact mature miRNA sequences were used (Additional file 6).
[67]. After analysing their variance between samples using geNorm and NormFinder  [68]  in the Genex software (MultiD, Goteborg, Sweden) the siRNA4 and siRNA41 of Medicago truncatula recently discovered and published by Formey et al. [69] , revealed to be the best reference genes. Statistical analysis was performed with Statistica software (Statsoft Inc. , Tulsa, USA). To evaluate the between combinations, one-way analysis of variance (ANOVA) was performed (p < 0. 05).
Digital PCR QuantStudio™ 3D digital PCR (QS3D) System (Life Technologies) was used for the absolute quantification of TFs expression. Three biological samples at 21 and 80DAG cDNA was synthesized using 200 ng of total RNA previously treated by RNase-free DNase and then reverse transcribed using ImProm-II™ Reverse Transcription System following the manufacturer's instructions. The dPCR reaction solutions were prepared with 20 ng of cDNA, the QS3D reaction mix, 0,9 μM of primer forward and reverse and 0,25 μM of each FAM and VIC labeled probes (Additional file 7). Digital PCR was performed according to Santos et al. (2017). Data analysis and management were performed using QuantStudio™ 3D Analysis Suite™ software (https://apps. lifetechnologies. com/ quantstudio3d/). Confidence level was set to 95% and desired precision to 10%, in the Poisson Plus algorithm version 4.
Acknowledgments The authors thank  Teresa Roque ,  José Valadas  and  João Carvalho  from  Plansel Nursery  for the acquisition and management of the plants, as well as the monitoring and maintenance of plants in the nursery;  Margarida Teixeira  for her collaboration in the field work and harvesting of plants. The authors also acknowledge  Susana Araújo  for her contribution in the revision of the final manuscript.
Availability of data and material All relevant information is in Additional files 1, 2, 3, 4, 5, 6, 7. All raw RNA sequencing data used in this research have been submitted to NCBI Sequence Read Archive (SRA) under the BioProject PRJNA517111. The submission id is SUB5084389, with the SRR attribution of SRR8487047, SRR8487050, SRR8487049 to TN21/110R at 21 DAG triplicates; SRR8487041, SRR8487042, SRR8487043 to TN21/110R at 80 DAG triplicates; SRR8487045, SRR8487046, SRR8487048 to TN112/110R at 21 DAG triplicates, and SRR8487044, SRR8487051, SRR8487052 to TN112/110R at 80 DAG triplicates. This data is accessible at https://www. ncbi. nlm. nih. gov/sra/PRJNA517111.
17 After sowing, the preparations were incubated in a stove for 48 h at 37 °C. The estimation of the propagation of the biofilm of P. aeruginosa was determined by applying the digital photogrammetric method, a technique that allows to extract from images quantitative and qualitative information of an object. According to Kellett et al. ,  16  the growth rate was estimated according to the propagation of the biofilm density calculated from the Petri disks by the surface occupied between the Petri dishes of 0% hypertonic salts and all Petri dishes of hypertonic salts after 48 h of culture. Therefore, the method used to calculate the surface is based on the application of a multimedia software (NIJ ImageJ. 16. 0) in order to obtain surfaces in pixels. The number of pixels corresponds to the density of the surface of each biofilm.
The density of the eradication of the biofilm on Petri dishes was calculated by means of a smartphone of type Condor C8 device of dimension (7. 7 × 15. 4 × 1. 0 mm) with a screen resolution of 1280 × 720 pixels. 24 All collected data were treated by SPSS. 20, Excel 2016, Origin Pro. 8, and NIJ ImageJ. 16. 0 softwares. 30, 31 he monitoring of the kinetic in the liquid medium was done by measuring the optical density between the initial and the final time of 48 h at 600 nm. 33 The sowing of all tubes was performed with an inoculum volume of 0. 1 mL (1% v/v) of a new obtained bacterial suspension after 18 h of culture in a selective medium "cetrimide agar" and incubated in a horizontal position to increase the contact area with the air oxygen at 37 °C. At the end of the culture, 4 mL samples were taken in each tube to determine the viable biomass against concentration.
11 The inoculums were inoculated on a solid medium culture to obtain a bactericidal effect of KH 2 PO 4 and NaCl for 24 h of incubation at 37 °C. 4. 3. Data Treatment. This study used the macroscopic focal photogrammetry method described by Broderick et al. ,  30  Saimmai et al. ,  31  and Slama  32  to estimate the density of the biofilm as a function of the focal length between 0. 01 and 0. 1 m. The pictures of all the Petri dishes were taken by considering the same calibration parameters. 7] [28] [29] [30] [31] [32]  The photogrammetric method cannot reconstruct visible surfaces because the method uses an approach of an uncalibrated camera. Dimensional references are necessary to convert the reconstruction into real dimensions. The quality of the reconstruction depends on the resolution of the image, the target, and the number of images.
05 was considered significant. 39  The data were analyzed using SPSS. 20, Or in Pro. 8, and Excel 2016. For the reproducibility of the obtained results, all the experimental assays were carried out three times and all duplication was carried out in double samples in the same conditions. The objective of this replication was to constitute a statistical cohort in order to carry the aberrant data using SPSS. 20-Data Mining (K-nearest neighbor analysis and predictive correlated modeling modules). This statistical method showed a guarantee of the reproducibility of the obtained results. Thus, on all three corrected assays and when consigned by the K-nearest neighbor analysis, an ANOVA statistical analysis was carried out using Origin Pro 8 software. All data were modeled by the predictive correlation module of SPSS-Data-Miner.
Th statistical analysis parameters such as sum of square, mean square, distribution function F value, error of distribution function Prob > F, correlation data R 2 , and Adj. R 2 in confidence interval CI ≥ 95% were studied according to Bassirou et al. 40  ■ ASSOCIATED CONTENT * S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsomega. 8b02733. Comparatives results of NaCl and KH 2 PO 4 according to the concentration of the cetrimide agar medium; statistical analysis and ANOVA of all biofilm densities from the solid and liquid medium; results of the Knearest neighbor analysis (CPS: case processing summary); and K-nearest neighbor analysis and predictive correlated modeling (characteristic "central", type "learning and retention" and K = 3) applied from KH 2 PO 4 and NaCl in the solid and liquid environment, respectively (PDF).
3‰ from 1960 to 2012 (see observations from the Scripps CO 2 Program; http://scrippsco2. ucsd. edu/; data compiled in ref. 15  ) due to the increasing combustion of fossil carbon. This trend can cause the synchronized decrease of δ 13 C in plants  16. Therefore, in addition to the shifting C4-C3 diet composition, the trend of δ 13 C in both C4 and C3 feeds will affect the temporal trends of livestock δ 13 C CH4-ruminant. In this study, we establish a global, time-dependent dataset at national scale of the C3-C4 diet composition of domestic ruminants, the enteric methane emissions (F CH4-ruminant ), and the flux weighted isotopic signature of the methane emissions (δ  13  C CH4-ruminant ) over the period between 1961 and 2012. First, we separate the crop concentrate feeds consumed by ruminant vs.
18  (1131 Mt). But our estimates have large uncertainty ranging from 477 to 1976 Mt (95% confidence interval) due to the large uncertainty in digestibility of feeds. In summary, the simple feed model and assumptions on feed digestibility of ruminants' feed can generally reproduce livestock feed consumptions at global scale well compared with ref. 18. In this study, annual F CH4-ruminant was calculated based on IPCC Tier 2 methods. Compared with Tier 1 method that used the livestock population data and default emission factors, Tier 2 approach uses a more detailed country-specific data on gross energy intake and methane conversion factors for specific livestock categories, allowing the consideration of diet quantity and quality  13. Table  2  shows the comparison of the global F CH4-ruminant estimated in this study and those from previous studies for the contemporary period (1980-2012; see also Fig.
The EDGAR v4. 3. 2 inventory 10 applies IPCC Tier 1 methods, but uses country-specific milk yield and carcass weight trend for cattle emissions (not for other animal types like sheep and goats; i. e. , a hybrid Tier 1 method). This study and ref. 11  both account for country-specific milk yield and carcass weight trend for all ruminants. Second, ruminant livestock numbers used in this study and EDGAR v4. 3. 2 10 are all from FAOSTAT 9 , while ref. 11 used additional data from subnational administrative regions in several countries, including the United States, Australia, Brazil, Canada, China, and Mongolia. This will affect the F CH4-ruminant and its trend. Third, the method used to calculate F CH4-ruminant can affect the trend estimate. Time invariant default emission factors of IPCC Tier 1 method were used by FAOSTAT 9 and EDGAR v4.
Reference  12  recently reported U. S. enteric emission of 6. 6 ± 1. 0 Tg CH 4 in 2012 using revised emission factors, which is similar to  Values from this study are shown as mean ± 1-sigma standard deviation a U. S. EPA dataset is based on IPCC Tier 1 calculations supplemented with country-reported inventory data (ref. 19 , pp. 1), with most of the enteric CH4 emissions being from country-reported inventory data (Appendices of ref. 19  , pp. G-8 to G-9). Given the fact that a majority of the reported data were derived from the UNFCCC flexible query system using higher IPCC Tiers, we called the method used by U. S. EPA data Mixed IPCC Tiers that of ref. 26  (6. 2 Tg CH 4 ) and our estimate (7. 4 ± 0. 9 Tg CH 4 ). Using subnational livestock data and IPCC Tier 2 method, ref. 11  reported emissions larger than other estimates (9.
Methods Data. FAOSTAT-Live Animals and Livestock Primary 9 provides annual national statistics on live animal stocks, numbers of slaughtered animal and laying poultry, milking animals (producing milk) and slaughtered animals (producing meat), and correspondent milk yield for milking animals (dairy cows, sheep, goats, and buffaloes for milk), meat yield (carcass weight) for meat animals (i. e. , poultry, pigs, beef cattle, sheep, goats, and buffaloes for meat), and egg yield for laying poultry. In this study, poultry species include chickens, ducks, geese, turkeys, and other birds from FAOSTAT. Data of the period 1961-2012 were used in this study. Concentrate feeds for livestock over the period 1961-2012 were derived from FAOSTAT-Commodity Balances 9. In total, 74 feed commodities were presented in FAOSTAT (Supplementary Table  4 ).
f dressing,poultry = 70% and f dressing,pig = 60% 50 are used in this study. Reconstructing the feeds for ruminants. After having allocated concentrate feeds to poultry and pigs, ruminants are assumed to receive the remaining quantities of feed commodities (Q concentrates ). In addition to feed commodities (concentrate feeds), ruminant livestock are mainly fed on grass but they also receive crop byproducts (stover) and occasional feeds  18. For grass-biomass consumed by ruminants (Q grass ), we used the global livestock production dataset of gridded grassbiomass use for the year 2000 from ref. 18  extrapolated by ref. 51  backward and forward in time during 1961-2012 using metabolisable energy (ME) requirement of ruminants in each country. The rest of the consumption of biomass by ruminant livestock is assumed to be met by local stover and occasional feeds (hereafter, as other feeds), following ref.
e. , Q C3grass,i,j : Q C4grass,i,j ) following a similar geographical C3 vs. C4 distribution  38  (see below). For grass-biomass consumed by ruminants, we used the global livestock production dataset of gridded grass-biomass use for the year 2000 from ref. 18  extrapolated by ref. 51  at the resolution of 0. 5°× 0. 5°backward and forward in time during 1961-2012 using ME requirement of ruminants in each country. To distinguish between C3 and C4 grass-biomass consumed, we used the (0. 5°× 0. 5°) maps prepared for the MsTMIP model intercomparison  52  of the relative fraction of C3 and C4 grasses. The MsTMIP gridded C3-C4 grass distribution is derived from the approach described in ref. 38  based on growing season temperature (see Sect. 3. 6 of ref. 52  for more details). Decadal maps of the relative fraction of C3 and C4 grasses were obtained using gridded CRU-NCEP mean monthly precipitation and temperature data  53  from 1960s to 2000s.
e. , 10 12 g). In this study, Y m = 6. 5% ± 1. 0% (the ± values represent the 95% confidence interval range) is used to represent widespread ruminant fed diets for cattle and mature sheep following IPCC guidelines  13  (Vol. 4, Chapter 10, Tables 10. 12 and 10. 13), given the fact that globally, only little cattle in some developed countries is feedlot cattle with feed diets contain 90% or more concentrates. GE feed,i,j is calculated as the gross energy content of different ruminant feeds (concentrates, grasses and other feeds): GE feed;i;j ¼ Q feed;i;j E GEÀfeed ð16Þ δ  13  C of diet and δ 13 C of enteric methane emissions. We collected published documents with δ 13 C CH4-ruminant observations and, at the same time, with data on δ 13 C diet or information of diet composition (quantities of specific feed categories or proportion of C3 and C4 feeds).