dataesr/scientific-paragraphs-categorization huggingface dataset:
Taking into account that the effects of chemical nonlinearities in our case were found to be rather small (see the corresponding discussion in Sect. 5), we approximated monthly values of ζ for different years by linearly interpolating between unity and its values calculated (employing Eq. 3 to express α m ) with j = 2008 and k = 1996 by using the EDGAR v4. 2 emission data for the respective years. An impact of seasonal variability of emissions on the NO 2 lifetime is disregarded. Note that in most of previous studies employing satellite NO 2 measurements to infer NO x emissions and their changes (e. g. ,  Martin et al. , 2003; Konovalov et al. , 2006; Wang et al. , 2007; Ghude et al. , 2008; Lin et al. , 2010; Lin, 2012) , the effects of chemical nonlinearities were neglected entirely. To estimate annual combustion CO 2 emissions, E (CO 2 ) t , from the NO x emissions, we define the average NO x -to-CO 2 emission conversion factor (for a given year j ) as F j = E (CO 2 )j t E (NO x )j t , j ∈ {1996.
2 NO x emissions in the period from 1996 to 2001 in the both cases. The difference between the trends derived from satellite data for case II and calculated with the REAS v1. 11 data is almost insignificant. Similar to the estimates of the CO 2 emission changes, all of the considered estimates of the NO x emission changes show large positive trends in the second period. The trends in the bottom-up emission inventories (EDGAR v4. 2 and REAS v2. 1) fall into the range of divergence of different topdown estimates. Our results concerning multiannual changes in NO x emissions in China are qualitatively consistent with the results of earlier studies involving analysis of multiannual satellite measurements over China. In particular, an accelerating and rapid growth of NO 2 column amounts over China in the late 1990s-early 2000s was found by  Richter et al.
The considerable difference in the behaviors of the hybrid vs. the bottom-up CO 2 emission estimates is further demonstrated in Fig. 7. Specifically, we have again fitted our estimates (for case I) and the EDGAR v4. 2 data with exponential curves, but this time without splitting the whole period 1996-2008 into two stages. We considered as criteria for goodness of the fit (i) the uncertainty of the coefficient k (see Sect. 3) and (ii) the sum of squares of deviations of logarithms of the fitted data from the corresponding fits, SS log. The obtained exponential fits are shown in Fig. 7. Both the criteria indicate that the dynamics of our hybrid emission estimates is much more consistent with a smooth exponential trend than the behavior of the EDGAR data. In other words, these results show that, in agreement with a simple visual inspection of the time series of our estimates, the relative rate of emis-sion changes is accelerating much more smoothly in the case of the satellite-based emission estimates than in the case of the emission inventory data between 2000 and 2002.
Therefore, we can conclude that the existence of the discussed sharp bend in emission time series is not corroborated by satellite measurements. The origin of the strong nonlinearity in the CO 2 and NO x emission trends found in the emission inventory data can be better understood after considering the dynamics of individual sectors of the EDGAR v4. 2 emissions for China. The anthropogenic CO 2 and NO x emissions from different sectors and their fractions in the total anthropogenic emissions are presented in Fig. 8  as a function of time. In addition, Fig. 8  also shows the ratio of CO 2 emissions to NO x emissions in different sectors; these ratios are analogous to the NO x -to-CO 2 emission conversion factor shown in Fig. 2. It is evident that according to EDGAR v4. 2 the dominant sectors (at least since 2003) providing up to about 70 % (80 %) of the total CO 2 (NO x ) emissions in China are the sectors "public electricity and heat production" (PEHP) and "manufacturing industries and construction" (MIC), although the "residential and other" sectors also provide an important contribution to the CO 2 emissions.
9 , and also plotted in Fig. 10  versus the similar ratios calculated with the EDGAR v4. 2 and PKU-CO2 inventories. The scatterplots are presented separately for the two subsets of provinces depicted in Fig. 3. The first subset of provinces (see Fig. 3a ) has been considered above. The provinces in the second subset feature considerably larger threshold magnitudes of mean NO 2 columns ([NO 2 ] c = 5 × 10 15 molecules • cm -2 ) than the first subset ([NO 2 ] c = 1 × 10 15 molecules • cm -2 ). It is evident that any positive correlation between our estimates of the emission ratios for the years 2008/2002, and the EDGAR v4. 2 data ratio is absent. Negligible or negative correlation takes place also between the EDGAR and PKU-CO2 data. These results reflect the fact that according to EDGAR v4. 2, the spatial distribution of the emission changes is almost constant over these six years, while both of our hybrid estimates and the PKU-CO2 bottomup data for different provinces are rather inhomogeneous (see Fig.
9 ). Note that the distribution of the EDGAR v4. 2 emission data is performed with sector-specific spatial proxy datasets that are kept constant over time. As such, Fig. 9b  visualizes only the impact of relative change in fraction of the different sectors, but not yet any urbanization process that might explain the spatial inhomogeneity of the emission changes. The agreement of our estimates with the PKU-CO2 data is noticeably better (although yet far from perfect). Specifically, the correlation coefficient (r) is of 0. 23 and 0. 46 for the first and second subsets of provinces (with the number of provinces included being 28 and 14), respectively. A likely explanation of the larger correlation coefficient for the smaller subset of highly polluted provinces is that smaller magnitudes of NO 2 columns are associated with larger relative uncertainties caused by absolute errors in the retrievals (e.
g. , due to a bias in estimation of a stratospheric part of the measured total NO 2 columns) and in simulations (e. g. , due to uncertainties in biogenic emissions). The fact that our estimates demonstrate much better agreement with the data of the PKU-CO2 inventory than with the EDGAR v4. 2 inventory may be considered as indication that our estimates are sufficiently reasonable. Indeed, if our estimates were completely noisy, they would not be able to manifest a considerable correlation with any independent data. On the other hand, the same fact can be considered as evidence that the spatial structure of real emission changes in the period from 2002 to 2008 is considerably better reproduced in the PKU-CO2 inventory than in the EDGAR v4. 2 inventory. This result could indeed be expected because the PKU-CO2 inventory is based on detailed statistical information for individual counties of China (thus even finer than the provincial level), while the EDGAR v4.
2 inventory operates with the national fuel statistics, which are not all distributed to the point sources, but which uses proxy data for, e. g. , the residential sector. Interestingly, the correlation coefficient between our estimates of CO 2 emission ratios and the PKU-CO2 data increases from 0. 46 to 0. 52 when the conversion factors are assumed to be constant both in time and space (this case is not shown). That is, applying  the conversion factors from EDGAR v4. 2 reduces the correlation rather than increases it. This fact might be considered as indication of uncertainties in the conversion factors, but the difference is rather insignificant to allow for us to make any firm conclusions. A similar analysis was performed for the ratio between the years 1997/2002. Results of this analysis that are not presented here do not contain any evidence that one of the emission inventories is considerably more accurate than the other over that period.
Specifically, correlations with our top-down estimates were found to be almost negligible both with the PKU-CO2 and EDGAR v4. 2 data. It seems unlikely that this result is due to a significantly different level of uncertainties in our top-down estimates. A more likely explanation is that both the PKU-CO2 and EDGAR v4. 2 data for this period are very uncertain because of serious potential flaws in available statistical information about economic activities. Possible major errors in the statistical information used as input data for inventories of emissions in China have already been discussed in previous publications  (Akimoto et al. , 2006; Zhang et al. , 2007; Gregg et al. , 2008; Guan et al. , 2012).
These results suggest that the atmospheric photochemistry in China is, on the whole, predominantly in the so-called high-NO x regime  (Sillmann, 1999). Additional simulations were made to examine a possible effect of changes in emissions of volatile organic compounds (VOC) on our estimates. Specifically, along with the baseline simulations performed with the 1996 and 2008 EDGAR v4. 2 emissions, we performed two model runs where the VOC emissions were swapped between the 1996 and 2008 datasets. The results of these runs indicate that a small nonlinearity in the response of the annual NO 2 columns to the NO x emission changes between 1996 and 2008 is almost completely compensated by the corresponding changes in the VOC emissions, since an increase in VOC emissions decreases the NO 2 lifetime, and vice versa.
4. 1. Any direct evidence of changes in biogenic NO x emissions in China during the considered period is not yet available. Nonetheless, the changes in biogenic NO x emissions are unlikely to exceed the changes in fertilizer consumption, which, according to the World Databank (http://databank. worldbank. org/ddp/home. do), has increased in China in the period from 2002 to 2008 (similar data for an earlier period have not been available) by about 25 % (from 373 up to 468 kg per hectare of arable land). This is three times less than the supposed increase in anthropogenic NO x emissions (see Fig. 2 ). Taking into account that the total annual soil NO x emissions specified in our model for China are from 8 to 15 times smaller than the total Chinese anthropogenic emissions NO x emissions in the 1996-2008 period, the impact of a possible trend in soil NO x emissions on our estimates of changes in anthropogenic NO x and CO 2 emissions is indeed likely to be rather negligible (less than 10 %).
An upper limit for the uncertainty associated with a possible trend in soil emissions is assessed above by means of a special case (case II) of our estimation procedure (see Figs. 4  and 5a ). To make sure that our estimates of NO x and CO 2 emission changes are indeed insensitive to the interannual meteorological variability (which was disregarded in our estimation procedure), we have performed test simulations with meteorological conditions for the years 1997 and 2007. These simulations were performed at an early stage of this study by using the EDGAR v4. 1 data for 2005. The years 1997 and 2007 were chosen taking into account the ENSO precipitation index (ESPI)  (Curtis and Adler, 2000)  values that were predominantly positive during 1997 but was mostly negative during 2007. The ESPI index characterizes the long-term temporal variability of precipitation in the eastern Asia region; it seems reasonable to assume that fluctuations of precipitation patterns are related to variability of atmospheric circulation driving air pollution transport processes.
1 and 0. 3 % in the periods from 1996 to 2001 and from 2001 to 2008, respectively. It is important that using different estimates of conversion factors cannot "reconcile" our CO 2 and emission trend estimates with the corresponding EDGAR v4. 2 and GCP data for the first period. Moreover, possible uncertainties in the NO x -to-CO 2 emission conversion factors can hardly explain the sharp bend in the evolution of both CO 2 and NO x emissions between 2001 and 2003, whose existence is not supported by observational evidence. It is noteworthy that the agreement of both our hybrid estimates of CO 2 emission trends and top-down estimates of NO x emissions trends is considerably better with the corresponding data of REAS inventory than it is with the data of the EDGAR inventory. This is an expected result taking into account that the regional REAS inventory is based on more detailed information about emission processes in China than the global EDGAR inventory.
Summary and conclusions This study explores the idea of verification of CO 2 emissions data by means of satellite measurements of tropospheric NO 2 column amounts. Indirect estimates of CO 2 emissions are obtained from the measurement-based estimates of NO x emissions by applying the emission factors from the EDGAR v4. 2 and REAS (v1. 11 or v2. 1) emission inventories. Using a simple inverse modeling method, we evaluate the multiannual changes (trends) in both CO 2 and NO x anthropogenic emissions in China in the period from 1996 to 2008. The method is based on a linear relationship between NO x emissions and the NO 2 columns, and it involves the simulations of the "background" NO 2 content in the troposphere and of a part of the seasonal cycle of NO 2 columns, which are not related to the anthropogenic NO x emissions in China and their seasonal variations, respectively.
The performed analysis involving several variants of our estimation procedure indicates that these differences cannot be explained by systematic uncertainties in our baseline estimates. In contrast, some quantitative differences between the strong positive trends in the bottom-up and indirect topdown estimates for the period from 2001 to 2008 are found to be in the range of possible systematic uncertainties in our estimates. It is noteworthy that the differences of our estimates with the REAS (v1. 11 and v2. 1) inventory data are considerably smaller than those with the EDGAR v4. 2 data (for both the periods considered). Along with the estimates of changes in total national CO 2 emissions in China, we considered the spatial structure of these changes. Specifically, using satellite measurements of tropospheric NO 2 we have estimated the ratios of emission changes for the years 2008 and 2002 in individual provinces of China, and compared them with corresponding ratios obtained with the EDGAR v4.
2 and PKU-CO2 inventory data. The comparison has revealed that our estimates agree better with the PKU-CO2 inventory than with the EDGAR inventory. This result can be considered as evidence that the accuracy of our measurement-based estimates of emission changes is at least comparable with the accuracy of the corresponding data of emission inventories. Indeed, since the PKU-CO2 inventory was elaborated using the regional (provincial) energy production and consumption statistics, it is expected to more accurately reflect the spatial structure of emissions than the EDGAR inventory, which is based on total national statistical information. Although we have attempted to "bracket" our estimates with regard to possible systematic uncertainties, and have concluded that the major discrepancies between our estimates and the data of the global emission inventories are unlikely due to possible uncertainties associated with our method and measurement data, it should be kept in mind that some possible systematic biases are difficult or even impossible to evaluate accurately.
Acknowledgements. This study was supported by the  Russian Government  under the contract Nos. 11. G34. 31. 0014  and  11. G34. 31. 0048. The simulations were performed on the computer cluster of  Siberian Federal University. I. B. Konovalov acknowledges support from the  Russian Foundation for Basic Research  (grant No. 11-05-91061 ), and  M. Beekmann  acknowledges support from the  French PICS (Programmes Internationaux de Coopération Scientifique)  grant No. 5826. Edited by:  J. Ma.
We propose a novel fragment assembly method for low-resolution modeling of RNA and show how it may be used along with small-angle X-ray solution scattering (SAXS) data to model low-resolution structures of particles having as many as 12 independent secondary structure elements. We assessed this model-building procedure by using both artificial data on a previously proposed benchmark and publicly available data. With the artificial data, SAXS-guided models show better similarity to native structures than ROSETTA decoys. The publicly available data showed that SAXS-guided models can be used to reinterpret RNA structures previously deposited in the Protein Data Bank. Our approach allows for fast and efficient building of de novo models of RNA using approximate secondary structures that can be readily obtained from existing bioinformatic approaches.
We propose a computational procedure that uses small-angle Xray solution scattering (SAXS) data to obtain low-resolution approximations of RNA structures. This process can be used as a diagnostic tool to help confirm predicted secondary structures with a higher degree of certainty than chemical footprinting approaches alone  [10, 11]. We use the most popular metrics to verify our approach and compare our results obtained with SAXS data with those from other approaches to RNA modeling. Our test targets are stem loop 3 (HP3) and stem loop 4 (HP4) of 7SK RNA, which is one of the most abundant regulatory RNA in mammals  [12, 13].
A fragment database may occasionally lack coverage; if it does, we resort to alternative strategies for subdividing secondary structure elements into strands and perform fine sampling of these subdivided flexible parts. Because these parts represent minor portions of the considered structures, we still substantially reduce the number of necessary sampling steps, while still sampling the difficult parts for which our database may lack coverage. [31]  Theoretical analysis of the information content of a single SAXS experiment performed on early-generation beamlines has suggested that there are no less than 17 to 20 degrees of freedom within acquired data  [32] , thus justifying the hypothesis that judicious use of SAXS information alone may be used to validate the orientation of helices in structures with no less than 8-10 junctions.
RNA Database We started our study with the RNAJunction database  [33] , which contains only junction structures, and idealized helices produced by X3DNA  [34]. This is not unlike the approach in  [35] , where RDC data were used to determine the overall orientation of the helices to create a coarse-grained model of the molecule. The results of the modeling were much more accurate for the full database extracted from the Protein Data Bank (PDB) containing the conformations of both helices and junctions (data not shown). We enriched this database with information about junctions from RNAJunction database, in order to mitigate omissions in our extraction procedure(see  [33]  for an analysis of the completeness of the RNAJunction database). To further ensure a complete database, we also added fragments from the LIR database  [36, 37]  that our procedure had not included.
Sampling Algorithm Secondary structure definition provided as an input, is used to split predicted 3D structure into a tree of separate secondary structure elements. Thus, replacement of an element in any point of the tree with a 3D structure would only affect the placement of elements below it in the tree hierarchy (see Figures  1,  and 2 ). For each of the secondary structure elements, we perform a database search to find fragments matching the number and lengths of the strands. These fragments are then scored in terms of sequence similarity (see below). In the rare event when there are not enough fragments found, we mark this element for sampling using variant B of the algorithm, and search the database by strands for each strand within the element. Then we loop the annealing protocol through about 20 stages of decreasing temperature (to 80% of the previous value after each stage), making 100 fragment exchange attempts within each stage.
Each replacement step consists of superimposing boundary atoms onto the boundary atoms of the previous element in the topology of the secondary structures, and then minimizing the 3 last dihedral angles of those boundary elements to assure a contiguous backbone. In the case of insufficient fragment coverage, version B of the sampling scheme replaces all strands within an element instead of replacing fragments. After such a replacement, an additional term that corresponds to the consistency of the different strands (contiguity) is added to the score. The main contribution to the scoring function E score is the fit to the SAXS data valued as x 2 fit. Additional components just increase bias towards the high-resolution integrity of the structure, while still permitting some variation in physically plausible geometry, in case the fragment coverage does not suffice for accurate modeling.
Experimental data are acquired by SAXS experiments and then fitted by scaling intensity so that the best x 2 measure is obtained using the CRYSOL program  [39]. E exp ~x2 ~1 N p X Np i~1 ½ I e (s i ){c : I(s i ,r,dr) d(s i ) Where N p is the number of points of experimental data; I e (s) is the experimental data curve; I(s,r,dr), a theoretical curve parameterized by r&1:0+0:04, is the volume displaced by each atomic group; and dr is the average excess electron density of the solvation shell layer. This algorithm benefits from relatively few degrees of freedom and thus enjoys faster convergence than a conventional fixedfragment-size algorithm like ROSETTA for RNA  [40].
Scoring Function The scoring function is a sum of heuristic terms corresponding to the similarity of database fragment sequences to modeled sequences, geometric model quality terms, and fit to experimental data: E score ~wseqsim E seqsim zw geom E geom zw exp E exp E seqsim is a fragment match score that takes into account consistency of the sequence between the fragment and a modeled sequence. E geom is a geometric quality score that is a weighted sum of the contiguity score E integr (measured as a sum of excess bond lengths in the covalently bound backbone) and a steric clash count E clash (measured as a count of clashing atoms, and then multiplying clashes between phosphorus and backbone by 5 to increase their contribution). E exp is fit to experimental small-angle scattering data. w seqsim , w geom , and w exp are weights optimized by regression and corresponding to the given energy functions.
SAXS Experiments Samples at concentrations of 2. 2-3. 8 mg/ml for stem loop 3 of 7SK RNA (HP3) and 0. 7 mg/ml to 1. 2 mg/ml for stem loop 4 of 7SK RNA (HP4), both in 50 mM cacodylate buffer at pH 6. 5, were measured on the X33 beamline  [44]  operated by European Molecular Biology Laboratory at DORIS III storage ring. Each sample was exposed for 8 frames of 15 seconds each to 1. 5 A ˚Xray wavelength at 10uC. Scattered radiation was recorded with a Pilatus 1M photon-counting detector. Data gathered for all samples was checked for presence of expanded or disordered conformations using a Kratky plot. All of these seem to be compact, monodisperse conformations as indicated in the Figure  4  and 5. Data parameters are given in table  1. The SAXS data for the concentrations used for structural modeling are included in File S4 for HP3 and File S5 for HP4.
SAXS Envelope Modeling Consistency checks between frames were performed automatically by X33 automated processing system  [45]  using ATSAS software  [46]. The expected molecular masses of the solutes were estimated from intensity extrapolated to zero angle, and were found to be consistent with the expected masses for the monomers (see table 1. The maximum diameter of each particle was estimated by indirect Fourier transform using GNOM software  [47]. Ab initio structures of the monomers were obtained using the program DAMMIF  [48] , which uses Monte Carlo simulated annealing to build the compact and contiguous bead model of uniform scattering length density that has the least discrepancy between experimental and forward-calculated scattering curves. Calculations for HP4 were performed in the volume of a sphere with a diameter of 80.