dataesr/scientific-paragraphs-categorization huggingface dataset:
The reason is that the model and observation errors tend to covariate both in space and time (as confirmed by our analysis discussed below in Sect. 3. 5). Thus, on the one hand, the data included in different subsets should be sufficiently separated in time and/or space to avoid co-variation of errors of different partial estimates. On the other hand, the number of the subsets should not be too small to ensure that the standard error estimate is sufficiently reliable (note that statistical inference defined by Eq. (  12 ) is based on n d -1 degrees of freedom). It was also necessary to take into account that the error structure in temporal and spatial domains can be different. In view of these considerations, we opted to divide the original dataset into four subsets in the temporal domain and four subsets in the spatial domain.
Each of the subsets in the temporal domain included data for only one season but for the full spatial domain. The gridded data subsets for winter, spring, summer, and autumn months included 3. 9 × 10 4 , 4. 1 × 10 4 , 5. 4 × 10 4 , and 4. 1 × 10 4 values in the case of NO 2 measurements and 2. 6 × 10 3 , 1. 4 × 10 4 , 2. 5 × 10 4 , and 1. 2 × 10 4 values in the case of CO measurements. The spatial subsets were defined as shown in Fig. 4e , f and each included about 4. 3 × 10 4 and 1. 3 × 10 4 values for the whole year in the cases of NO 2 and CO measurements, respectively. The standard error was estimated in accordance to Eq. (  12 ) independently for both "temporal" and "spatial" subsets (that is, n d was equal 4 in the both cases), and the maximum of the two estimates of σ s c was selected as the final estimate of the standard error.
As contributions of various sources to the CO and (especially) NO 2 columns in the different countries are different, the aggregation error is likely to be manifested as deviations between the different partial estimates. For example, if, in a hypothetical situation, an emission estimate inferred from the full dataset were mostly affected by strong emission sources from only one country, a partial estimate obtained after leaving the measurements over that country out would likely be much less affected by the same sources, at least in the case of emission estimates of such a short-lived species as NO x. The confidence intervals estimated using Eq. (  12 ) are also likely to account for most of estimation errors associated with uncertainties in the diurnal and weekly variations of anthropogenic emissions, as well as with uncertainties due to shortcomings in the model representation of chemical processes (including effects of subgrid-scale chemical interactions).
Using the synthetic data, we obtained uncertain emission estimates which were compared with true emission data specified in the model. Each Monte Carlo experiment included 100 iterations performed with the same covariance matrix and with the same bias, s , but with different samples of random errors. The bias added to C s m in each experiment for any given day was specified by linearly interpolating (in time) the monthly biases shown in Fig. 3c, d , with the magnitude of the monthly bias values scaled (in different experiments) with a factor (δ) ranging from 0 to 1. The uncertainties (expressed as the standard error) in the emission estimates were evaluated both in the "direct" way (as the root mean square difference between the uncertain and true emission estimates) and by averaging squares of σ s c calculated by using the subsampling technique described above.
The results shown in Figs. 8  and 9  demonstrate that the optimal emission estimates obtained with our inversion procedure are likely not significantly biased even if the true emissions are considerably different from the bottom-up emission inventory data. These results also confirm that our inversion procedure enables efficient separation of the uncertainties in the model data due to emission errors from other systematic uncertainties in the model and observation data. Importantly, the fact that the emissions perturbations are retrieved almost perfectly indicates that the effects of chemical interactions (nonlinearities) and changes in NMHC emissions on the relationships between NO x and CO emissions and the NO 2 and Both nondiagonal elements of the error co-variance matrix and the systematic uncertainties were taken into account in the OSSEs (specifically, both ξ and δ were set to be equal to unity; see further details in Sect.
Unlike the EMEP data, the EDGAR v4. 2 data strongly disagree with our estimate for the NO x emissions from the EHI sector. The differences of our estimates with the EDGAR v4. 2 data are also larger than with the EMEP data in particular in the cases of the Ês tot estimates of both NO x and CO emissions and in the case of CO emission estimate for the EHI sector, although smaller in the cases of the CO emission estimates in the TCO sectors. It is noteworthy that the differences between all our NO x emission estimates with the corresponding EDGAR data are statistically significant. In general, our analysis indicates that the NO x and CO emission data provided by the EMEP inventory are more consistent with the NO 2 and CO satellite measurements than those given by the EDGAR v4. 2 inventory. This is an expected result because the methodology used in the EMEP inventory is specific to national statistical data available from European countries, while the EDGAR v4.
Such nonsymmetrical differences indicate that none of the cases considered represent the seasonal cycles in NO x and CO emissions quite perfectly. The uncertainty levels in our estimates of both NO x and CO emissions using actual data are considerably larger than those obtained above in our OSSEs (see Sect. 3. 5) in which synthetic data were generated using a simplified error model. (Note that to be compared with the confidence intervals discussed in this section the standard errors presented in Sect. 3. 5 should be multiplied with the t score of about 1. 2. ) This result indicates that, as expected, the uncertainties in our emission estimates are caused not only by random uncertainties in the input (measurement and simulation) data but also by other factors -such as the aggregation error and spatial variability of the bias -which could not be adequately taken into account in our tests.
Therefore, our top-down NO x emission estimates can be considered as an independent alternative to bottomup estimates based on emission inventory data alone. Both our NO x and CO emission estimates could formally be combined (in the Bayesian way) with the bottom-up (a priori) estimates; the uncertainties in the combined (a posteriori) estimates would probably be lower than the uncertainties in either the top-down or a priori estimates taken alone. In general, our results confirm the findings of previous studies (see the corresponding references in Introduction) showing that NO 2 and CO retrievals from satellite measurements can provide useful information on NO x and CO emissions over high emission regions. In this regard, it can be noted that while previous inverse modeling studies utilized satellite CO measurements to estimate CO emissions from regions with predominantly anthropogenic sources involved global  CTMs (e.
3 % confidence interval (in %). and ÊCO 2 comb, tot ) are shown in Fig. 11  in comparison with the corresponding data of emission inventories. The same estimates are listed in Table  3 , which, in addition, presents another version of the hybrid estimates of total FF CO 2 emissions, ÊCO 2 s, sum and ÊCO 2 comb, sum (see Sect. 3. 3). Note again (see also Sect. 2. 3) that CO 2 emissions from the cement production are not included in our estimates. Two types of the confidence intervals are provided along with the CO 2 emission estimates based on measurements of one proxy species. The full confidence intervals include the uncertainty in the topdown estimates of the proxy species as well as the uncertainty in the conversion factors. The partial confidence intervals were estimated by taking into account only the uncertainty in the top-down estimates of the NO x and CO emissions.
The differences between the same hybrid estimates and the corresponding estimate (2. 86 Pg CO 2 ) provided by the CDIAC inventory (7 and 5 %) are slightly smaller than the differences with the EDGAR v4. 2 data and are not statistically significant. Therefore, our analysis suggests that the CO 2 emissions in the region considered are likely estimated more accurately by CDIAC than by EDGAR v4. 2; however, the difference between the data of the two inventories in the case considered is small (∼ 6 %). Note that if the conversion factor uncertainties were not taken into account, which is not recommended, the difference between our NO 2 -measurement-based CO 2 emission estimate for the EHI sector and the respective EDGAR v4. 2 estimate would be statistically significant. However, it is not significant with respect to the full confidence interval.
Summary and conclusions We examined feasibility of estimation of FF CO 2 emissions by using NO 2 and CO column retrievals from satellite measurements. FF CO 2 emissions are an important component of the global carbon balance and are believed to be a major contributor to global warming. Although such emissions are usually known with better certainty than CO 2 fluxes associated with the biosphere, there still exist considerable divergences between data of different bottom-up FF CO 2 emission inventories; typically, such data cannot be evaluated by using atmospheric CO 2 measurements and rarely come with a reported uncertainty structure. We followed the concept of proxy species that suggests constraining FF CO 2 emissions by using atmospheric measurements of minor species co-emitted with CO 2.
We developed a general inverse modeling method aimed at estimation of the budgets of FF CO 2 emissions from different sectors of economy in a given region by using satellite measurements of proxy species. The method involves (1) obtaining top-down estimates of anthropogenic emissions for a proxy species from the satellite measurements and simulations performed with a mesoscale CTM, (2) using bottom-up emission inventories to relate CO 2 emissions with emissions of the proxy species, and (3) combining CO 2 emission estimates derived from measurements of different proxy species. Important parts of our method are robust techniques to estimate systematic differences between the measured and simulated data, as well as uncertainties in top-down estimates of the proxy species. Considering NO 2 and CO as the proxy species, the method was applied to a western European region including 12 countries by using the NO 2 and CO column amounts retrieved from, respectively, the OMI and IASI satellite measurements along with the simulated data from the CHIMERE CTM.
The study region was selected by taking into account that uncertainties in available bottom-up emission inventory data for the EU countries with well-developed statistics are likely rather low, compared to potential uncertainties in FF CO 2 emission data for countries with less developed statistical infrastructure, although such uncertainties are likely not quite negligible even in the study region. The relationship between FF CO 2 emission and the NO x and CO emissions was represented by the NO x -to-CO 2 and CO-to-CO 2 emission conversion factors evaluated with the EDGAR v4. 2 emission inventory. The estimates were obtained for the total FF CO 2 emissions from the region considered as well as individually for FF CO 2 emissions aggregated into two different source categories (sectors), such that the first category (EHI included the emissions associated mostly with energy and heat production and heavy industries, and the second category (TCO) comprised transport, chemical industry, and all other anthropogenic sources.
Nonetheless, in spite of the large uncertainties (which may be overestimated by our procedure), the differences between our top-down estimates of CO emissions and respective EMEP data are rather small (less than 7 %). Similar to our NO x emission estimates, the top-down CO emission estimates differ more considerably from the EDGAR v4. 2 data. The top-down estimates of the NO x and CO emissions were used to obtain different hybrid estimates (combining different information coming from measurements and bottom-up inventories) of CO 2 emissions. The NO 2measurement-based hybrid estimate of total CO 2 emissions is about 12 % smaller than the respective estimates based on the EDGAR v4. 2; the difference exceeds the estimated uncertainty range (∼ 11 %) of our estimate, although only marginally. In contrast, the difference between the same hybrid estimate and the corresponding estimate provided by the CDIAC inventory (∼ 7 %) is not statistically significant.
OMI and IASI retrievals are available at http://www. temis. nl/. ipsl. jussieu. fr/ether/pubipsl/iasi_CO_uk. jsp (ES-PRI, 2016), respectively. The EMEP, EDGAR v. 4. 2 and CDIAC emission inventory data are available at http://www. ceip. at/ms/ceip_home1/ceip_home/webdab_ emepdatabase/emissions_emepmodels/ (EMEP/CEIP, 2014), http://edgar. jrc. ec. europa. eu/overview. php?v= 42 (EC-JRC/PBL, 2011) and http://cdiac. ornl. gov/trends/ emis/tre_coun. html (CDIAC.
Acknowledgements. The study was supported by the  Russian Science Foundation  (grant no. 15-17-10024 ). We acknowledge the free use of tropospheric NO 2 column data from the OMI sensor from www. temis. nland the use of CO column data from http://ether. ipsl. jussieu. fr/ether/pubipsl/iasi_CO_uk. jsp. Edited by: Q. Zhang Reviewed by: two anonymous referees.
OMI and IASI retrievals are available at http://www. temis. nl/. ipsl. jussieu. fr/ether/pubipsl/iasi_CO_uk. jsp (ES-PRI, 2016), respectively. The EMEP, EDGAR v. 4. 2 and CDIAC emission inventory data are available at http://www. ceip. at/ms/ceip_home1/ceip_home/webdab_ emepdatabase/emissions_emepmodels/ (EMEP/CEIP, 2014), http://edgar. jrc. ec. europa. eu/overview. php?v= 42 (EC-JRC/PBL, 2011) and http://cdiac. ornl. gov/trends/ emis/tre_coun. html (CDIAC (TEMIS, 2016) and http: //ether.
Candidate Reference Gene Selection and qPCR Primer Design Fifteen candidate reference genes were identified from the literature and the sequences of their corresponding homologs were extracted from the NCBI and the Phytozome databases (Table  1 ). The selected candidate reference genes included GAPDH, UBC1, MACP, ACP2, α-TUB, EF-P, 6PGDH, SAMDC, CYP, MDH, ADPRF, β-TUB, PP2A, EIF4α, and PP2C that were earlier reported to be stably expressed in monocots such as rice, pearl millet, barley, foxtail millet, wheat, and maize  (Jain et al. , 2006; Paolacci et al. , 2009; Manoli et al. , 2012; Kumar et al. , 2013; Reddy et al. , 2015b; Zmienko et al. , 2015). Primers were designed using the Primer3 software  (Untergasser et al. , 2012)  with the following parameters: 59-62 • C annealing temperature, 20-22 bp primer length, 45-55% GC contents, and 90-150 bp amplicon length (Table  1 ).
Pooled and diluted cDNA samples were used for qPCR and 2% agarose gel electrophoresis was used to check primer pair specificity prior to sequencing. The sequences amplified by each primer combination were compared with GenBank sequences using the BLASTN and BLASTX algorithms to verify amplicon specificity. Quantitative Real-Time RT-PCR (qPCR) The qPCR reactions were performed using a Realplex Real-Time PCR system (Eppendorf, Germany) and SYBR Green mix (Bioline) in 96 well optical reaction plates (Axygen, USA) sealed with ultra-clear sealing film (Platemax). The reactions were performed in a 10 µl total volume containing 5 µl of 2x SensiMix SYBR No ROX mix (Bioline), 400 nM of each primer, 1. 0 µl of diluted cDNA and nuclease-free water. The reaction conditions were 95 • C for 2 min, followed by 40 cycles of 15 s at 95 • C and 30 s at 62 • C with fluorescent signal recording.
Data Analysis The quantitative cycle (Cq) values were recorded using the RT-PCR system default settings in which the baseline was automatically corrected and threshold values were estimated using the noise band mode. Statistical analysis (mean and CV) of the Cq values was performed using a Microsoft Excel 2010 spreadsheet. PCR efficiencies (E) for candidate genes were evaluated using the dilution series method and pooled cDNA samples. The 12-fold diluted pooled cDNA sample was used for 2-fold serial dilutions. Five serially diluted cDNA samples were used as templates for the construction of standard curves for each primer pair using the above PCR composition and conditions. Standard curves were generated using linear regression based on the quantitative cycle (Cq) values for the dilution series.
Statistical Tools for Normalization The genEX Professional software geNorm and NormFinder algorithms (MultiD Analyses AB, Sweden) were used to identify and analyze the stably expressed gene (s) in diverse experimental samples. The raw Cq values for each gene were corrected according to their PCR efficiencies and then converted into relative quantities. The mean values for the biological replicates were used as the input data for the geNorm and NormFinder analyses. The geNorm program calculates the candidate gene expression stability (M value) using pairwise comparisons and stepwise exclusion  (Vandesompele et al. , 2002). Pairwise variation analysis was performed to identify the optimal number of reference genes required for normalization in each sample set using geNorm and qBase plus software (v: 2.
Primer Specificity and PCR Efficiency Calculations The candidate reference genes selected for this study represent various functional classes and gene families. Primer pairs for 15 candidate reference genes were used for qPCR amplification of sorghum cDNA and yielded single PCR products of the expected sizes (Figure  1A ), as well as single melting curve peaks (Figure  1B ). The amplification products were sequenced, and verification using the NCBI database BLASTN algorithm revealed 100% matches, demonstrating the gene specificity of the qPCR primer pairs. While the amplification efficiencies (E) of the candidate reference genes ranged from 0. 94 (PP2C) to 1. 03 (β-TUB), the regression coefficient values (R 2 ) ranged from 0. 972 (UBC1) to 0. 999 (FE-P and β-TUB) (Table  1 ).
GeNorm Analysis GeNorm software was used to determine the expression stability of the 15 candidate sorghum reference genes; for this analysis, lower M values indicate greater gene expression stability. The software recommends a cutoff M value of 1. 5 to identify sets of reference genes that are stably expressed. Figure  3  displays the expression stability rankings for the tested candidate genes; the lowest M value was calculated for the PP2A and EIF4α pair (M = 0. 76) and corresponded to the most stable expression in the all sample group, whereas the M values for GAPDH and UBC1 were considerably higher than for the remaining candidate genes (Figure  3 ). For the abiotic stress treatments, PP2A and CYP were the most stable reference genes, while UBC1 and β-TUB were the least stable (Figure  3 ).
ACKNOWLEDGMENTS PS acknowledges  INSPIRE Faculty Award  ( IFA11-LSPA-06 ) and the  Young Scientist Scheme  ( SB/YS/LS-12/2013 ) from the  Department of Science and Technology, Government of India, New Delhi. This work was performed as part of the  CGIAR Research Program on Dryland Cereals. ICRISAT is a member of the  CGIAR Consortium.
We present a method for the systematic identification of picogram quantities of new lipids in total extracts of tissues and fluids. It relies on the modularity of lipid structures and applies all-ions fragmentation LC-MS/MS and Arcadiate software to recognize individual modules originating from the same lipid precursor of known or assumed structure. In this way it alleviates the need to recognize and fragment very low abundant precursors of novel molecules in complex lipid extracts. In a single analysis of rat kidney extract the method identified 58 known and discovered 74 novel endogenous endocannabinoids and endocannabinoid-related molecules, including a novel class of N-acylaspartates that inhibit Hedgehog signaling while having no impact on endocannabinoid receptors. Cells produce a palette of lipid bioregulators, e.
g. steroid hormones, eicosanoids or endocannabinoids, to mention only a few. Known molecules may be identified by probing MS/MS spectra against a reference spectra database 1 or by their direct interpretation relying upon common fragmentation mechanisms and lipid-class specific compositional constraints 2. In principle, these approaches could be extended to the identification of putative lipid structures by relaxing spectra matching requirements  3, 4. However, in crude extracts the abundance of new molecules is often too low compared to other lipids. Therefore their precursor ions are not selected for fragmentation in data-dependent LC-MS/MS experiments and in shotgun experiments they render non-interpretable MS/MS spectra dominated by product ions of co-fragmented lipids and chemical noise.
Chromatographic peaks of anticipated precursors (XIC [f j h i ] + ) and fragments (XIC [h i ] + ) are then aligned by the in-house developed Arcadiate software. They should neatly overlap if [h i ] + is produced from [f j h i ] + (see aligned XIC trace in Fig. 1A ), while no or partial match indicates that this pair of ions is unrelated. The same scheme also applies for more complex fragmentation patterns since peaks alignment specificity increases if XICs of several fragments are required to match. A dataset produced by a single AIF LC-MS/MS experiment suffices for in silico screening for an unrestricted number of combinations of modules. If the structure of candidate molecules remains ambiguous, AIF LC-MS/MS should be followed by the targeted acquisition of full high resolution MS/MS spectra of their precursors, chemical derivatization  11  and, ultimately, by chemical synthesis.
ERC are structurally diverse, however no method afforded a comprehensive overview of their composition: although in total more than 80 ERC have been identified this list is certainly not exhaustive  16, 19, 20. We first benchmarked our method by comparing the number of N-acylethanolamines (NAE) identified by AIF and, independently, by the two targeted LC-MS/MS methods  17, 21  -multiple reaction monitoring (MRM) on a triple quadrupole mass spectrometer and by parallel reaction monitoring (PRM) on a hybrid high resolution tandem mass spectrometer Q Exactive (Fig. 1B ). Both MRM and PRM rely upon an inclusion list of targeted precursors compiled prior the analysis. Contrary, AIF profiling is unbiased since a full dataset of MS and MS/MS spectra is acquired first and then XIC traces for any desired combination of precursor and fragment masses are retrieved and aligned.
In principle, amino-and hydroxyl groups of any endogenous small molecule are plausible targets for N-acylation, O-esterification or -etherification, respectively. We therefore selected 52 polar head groups including amino acids, nucleobases and biogenic amines, each of which was conjugated in silico to 39 fatty acid/fatty alcohol moieties (Supplementary Dataset 1), altogether representing over 2000 putative molecules. A single AIF LC-MS/MS run of rat kidney extract recognized 132 ERC of 18 individual classes, out of which 74 molecules were novel (Supplementary Table  S2 ). Furthermore, we identified 3 molecules of a novel class of ERC: N-acylaspartates (NAAsp) having 16:0, 18:2 and 20:4 fatty acid moieties attached to the amino group of aspartic acid (Fig. 2 ). Considering an extended list of uncommon fatty acids (e.