dataesr/scientific-paragraphs-categorization huggingface dataset:
In December 2000, a fully automatic NO y instrument was installed on one of the five Airbus A340 aircraft used in the MOZAIC project (Measurement of Ozone and Water Vapour by Airbus in-service Aircraft) for measurements of O 3 and H 2 O since 1994. This long-range aircraft was operated by Lufthansa, mainly out of Frankfurt and Munich. After an initial testing period, regular data collection started in May 2001. Until May 2005, 1533 flights have been recorded, corresponding to 8500 flight hours of NO y measurements. Concurrent data of NO y and O 3 are available from 1433 flights and concurrent data for CO, O 3 and NO y exist from 1125 flights since 2002. The paper describes the data availability in terms of geographical, vertical and seasonal distribution and discusses the quality and limitations of the data, including interference by HCN.
Other than research aircraft, commercial aircraft fly basically every day and hence provide data in much larger quantities than can be obtained with research aircraft. An important aspect is that the cruise altitude of commercial long-range aircraft of the type Airbus A340 as used for MOZAIC coincides with the average tropopause (TP) height at mid-latitudes. The measurements made in MOZAIC thus help to gain a better understanding of the downward transport of O 3 from the stratosphere (cf. Murphy et al. , 1993; Murphy and Fahey, 1994) , and, together with measurements of O 3 and CO, to discriminate the impact of lightning and aircraft emissions on the UT/LS from the influence of convective transport of surface emissions into the UT/LS (cf. Gressent et al. , 2014). Drawbacks of using commercial aircraft for atmospheric research are significant limitations in size, weight and accessibility of the instruments (see below).
, 2005)  and a CO instrument was added in 2002 on all five MOZAIC aircraft  (Ne´de´lec et al. , 2003). In this paper, we describe the installation of the MOZAIC NO y instrument and present the data obtained in the troposphere and lower stratosphere during 1533 flights (8500 h) between May 2001 and May 2005. Besides a discussion of the data quality and the statistics of the data set in terms of geographical coverage and vertical distribu-tion, examples are presented on the distribution of NO y in the UT/LS, including the correlation of NO y with O 3.
Calculation of the net NO signal S NOðtÞ ¼ S MMðtÞ À S ZMðtÞ (2) where S_ZM(t) is the interpolated zero mode signal at the time of S_MM(t). 3. Determination of the artefact NO y signal (S_BG(t)) by interpolation of the measurements made by overflowing the inlet line with oxygen. 4. Conversion of the net ambient NO y signal (S_NO(t) Á S_BG(t)) to mixing ratio with the sensitivity of the CLD (S) and the converter efficiency (E) determined from the calibrations before and after deployment, and from the in-flight calibration checks. m NO y ðtÞ ¼ fS NOðtÞ À BGðtÞg=fS Ã Eg (3) 5. Calculation of 4 s averages of m_NO y and synchronisation of the time of the NO y data to that of the main MOZAIC data set. The latter is achieved by adjusting the time of the NO y data to match distinct changes in the pressure measured in the inlet line of the NO y instrument, that is, when the aircraft changes altitude, to the pressure changes recorded by the MOZAIC data acquisition system.
Performance During MOZAIC operation, the CLD had a sensitivity of 200Á600 cps/ppb and the gold converter usually had a conversion efficiency of !95% for NO 2 and HNO 3. During some periods, however, the conversion efficiency was found to deteriorate significantly. In these cases, the data were not sent to the database (see grey-shaded areas in Supplementary Fig. A1 ). The conversion efficiency of the converter for NO 2 and HNO 3 was found to be independent of pressure between 150 and 1000 hPa. This is due to the fact that the converter is longer than theoretically required for the flow rate applied and the relatively homogeneous temperature profile of the converter (cf. Pa¨tz et al. , 2006). As detailed in Volz-Thomas et al. (  2005 ), conversion of HNO 3 was always found to be equal or better than that for NO 2.
The performance of the MOZAIC NO y instrument was investigated by comparison to an ECO Physics Model 790 SR instrument operated by ETH Zu¨rich and MPI Mainz aboard a Learjet A35 during the German project SPURT  (Engel et al. , 2006; Hegglin et al. , 2006). As detailed in  Pa¨tz et al. (2006) , the comparison confirmed the characteristics of the MOZAIC instrument (see above) in terms of precision and accuracy. The overall uncertainty was 96. 5% (2 sigma) from calibration errors and 9100 ppt (2 sigma) from instrumental background. The offset of 4392 ppt in the correlation between the data of the two instruments also gave an, albeit weak, indication for the presence of interferences by atmospheric HCN for the MOZAIC instrument. The memory for HNO 3 caused by the inlet line of the MOZAIC instrument (20 s for 67% recovery of a step change; 90Á150 s for 90% recovery; see  Volz-Thomas et al.
, 2005)  was also confirmed by the comparison. The MOZAIC instrument exhibited a symmetric memory when entering or leaving the stratosphere, as seen from the correlation with O 3. The SPURT instrument, which had the gold converter mounted outside of the fuselage, exhibited basically no memory when entering the stratosphere. When leaving the stratosphere, however, the memory was similar to that of the MOZAIC instrument. The explanation for this observation was the accumulation of HNO 3 at the (cold) tip of the converter during the time spent in the stratosphere (see  Pa¨tz et al. , 2006  for details). The conclusion was that the memory of the inlet line of the MOZAIC instrument, on average, does not bias the NO y data, whereas instrumental configurations with the converter mounted outside of the aircraft can indeed lead to a positive bias of the tropospheric data due to artificial transport of HNO 3 from the stratosphere by the instrument itself.
Stratmann (  2013 ) compare the NO y data collected in MOZAIC with those collected in the CARIBIC project. They find good agreement between the seasonal mixing ratios over Europe, both in terms of median and width of the distributions. The deviations between the medians of the two data sets are B10%, except for the fall season, where the MOZAIC data set exhibits 20% lower mixing ratios than the CARIBIC data set. 2 , which shows the number of NO y data for each month. Reasons for data gaps were failures of the NO y instrument or inadequate performance due to contamination as described above, and sometimes missing information on aircraft position due to failure of the MOZAIC data acquisition system. After 2005, the instrument was operated for several periods without converter and was then used for testing of a new version with a photolytic converter in the European FP6 project IAGOS-DS (Integration of routine Aircraft measurements into a Global Observing System; Thomas and Volz-Thomas, 2010).
Data availability The MOZAIC data have been included in the database of the new research infrastructure IAGOS (In-service Aircraft for a Global Observing System) and are accessible at www. iagos. fr. The database contains the data at two different time resolutions, (1) the 4 s data as provided by the principal investigators (see above) and (2) 1 min averages calculated from the 4 s data. In the following, we are using the 1 min averaged data. The 10 Hz data recorded by the NO y instrument are not available in the database. The annual data availability is summarised in Table  1. In total, valid NO y data have been gathered during 1533 flights, comprising 8500 h of data. Coincidental measurements of NO y and O 3 are available from 1433 flights. Coincidental data of NO y , O 3 and CO data are available from 1125 flights between January 2002 and May 2005.
Data quality Each NO y value in the MOZAIC database has associated a 1 sigma uncertainty (in ppb), which has been calculated by error propagation of the individual contributions from the following: (1) Precision of the PMT signal calculated from the counting statistics of the difference between measure and zero mode (S_MM(t) Á S_ZM(t)) of the CLD. This contribution is only significant for the raw data recorded at 10 Hz resolution. It is insignificant, however, for the data in the database, which are averaged over 4 s or 1 min. Figure  3  shows the relevant contributions to the overall uncertainty of the NOy data in the form of frequency distributions calculated from all flights in the MOZAIC database. The upper panel shows the contribution arising from DBG, which is independent of the prevailing NO y mixing ratio.
e. NO and its atmospheric oxidation products), also adds to the overall uncertainty of the MOZAIC NO y data. Since the first measurements of HCN in the atmosphere by  Coffey et al. (1981) , HCN measurements have been made at many different locations and from different platforms. Most data sets represent total columns measured by infrared spectroscopy. The in-situ measurements  (Spreng and Arnold, 1994; Schneider et al. , 1997; Singh et al. , 2003; Hornbrook et al. , 2011; Simpson et al. , 2011; Le Breton et al. , 2013)  made in unpolluted air show, on average, HCN mixing ratios between 150 and 250 ppt without significant vertical gradient. The HCN column densities from spectroscopic measurements at, for example, Jungfraujoch, Switzerland  (Rinsland et al. , 2002) , and Lauder, New Zealand  (Zeng et al.
Vertical distribution of the NO y data 3. 4. 1. Tropospheric profiles. The vertical distribution of NO y in the troposphere over Frankfurt, where the vast majority of the profiles have been collected, is shown in Fig. 5. Displayed are all data obtained during the last 25 min of descent and at altitudes below 8 km. The first condition is necessary in order to limit the horizontal extent of the profiles to 200 km in longitude and 100 km in latitude. Each profile is displayed as a vertical bar with the NO y concentration given by the colour coding. It is obvious that the cut-off altitude of 8 km is not reached in all flights, because the aircraft sometimes start the descent far away from the destination. The absence of profiles after 2004 is due to the fact that the aircraft was then operated out of Munich.
Individual data were collected at potential vorticities up to 15 pvu. The middle panel shows the PV dependence of the mixing ratio of NO y and O 3 in bins of 1 pvu for the four seasons. The NO y mixing ratios show, like O 3 , a gradual increase between 2 and 8 pvu in the lower stratosphere. The highest mixing ratios are observed in spring and summer. The data above 10 pvu are not representative because of the limited sampling statistics. In the troposphere (PV B2 pvu, see below), O 3 mixing ratios tend to further decrease, whereas NO y mixing ratios show a distinct maximum in the troposphere around (2 pvu, which is indicative of NO y sources from surface emissions, in addition to lightning (cf. Gressent et al. , 2014)  and aircraft emissions. This is corroborated by  Separation of the MOZAIC data into samples of primarily tropospheric or stratospheric origin has been made by several authors using different criteria.
The simplest criterion used in the early days of MOZAIC was a threshold of approx. 100 ppb O 3. Later, a dynamic threshold was used, based on PV, either by simply using a local threshold of 2 pvu for the separation, or by implementing more complex schemes, depending on how rigid a criterion was required for the question of interest. For example,  Thouret et al. (2006)  defined the TP region as a 30 hPa thick layer around the local TP defined as the 2 pvu surface. Gressent et al. (2014)  used a local criterion of PV B2 pvu for selecting tropospheric air masses and, in addition, discarded all data for which PV values of !2 pvu had been encountered by the sampled air masses during the previous 3 days, based on back-trajectories calculated with FLEXPART  (Stohl et al. , 2005). The additional criterion served to remove a small but significant fraction of data with potentially stratospheric signature.
(1993). In the MOZAIC data, the largest dynamic range is observed in winter and spring, when the aircraft fly higher into the stratosphere because of the lower TP. The smallest dynamic range is observed in fall. In winter, separation between the two branches is most distinct, indicating little exchange between LS and TP, whereas significant mixing is observed in the other seasons, particularly in summer and fall. There are a few very high NO y mixing ratios observed in the lower stratosphere (indicated by ellipses) which are indicative of pyro-convective plumes influencing the lower stratosphere as discussed by, e. g. Fromm et al. (2000) ;  Fromm and Servranckx (2003). When confining the MOZAIC data to the lower stratosphere (LS see above), the tropospheric branch disappears and the slope of the NO y -O 3 correlation ranges between 2.
Conclusions and outlook The MOZAIC NO y instrument, flown between 2001 and 2005 on board of an Airbus A340 operated by Lufthansa, provided data of known quality from more than 1500 long distance flights, comprising 8500 h of NO y measurements in the troposphere and lower stratosphere. We have summarised the quality and shortcomings in the NO y data set and gave examples of the distribution of NO y in order to demonstrate the potential of the data for future scientific applications. In the lower stratosphere, NO y shows a strong increase with altitude and is highly correlated with O 3 , whereas NO y mixing ratios in the UT are highly variable, particularly in spring and summer, where very large NO y mixing ratios are frequently observed over Northern Canada, Siberia, Europe and the North American East Coast.
Acknowledgements MOZAIC was co-funded by the European Commission between 1993 and 2003. The authors gratefully acknowledge the strong support by Deutsche Lufthansa AG for free transportation of the instruments and the technical support by Lufthansa Technik AG and enviscope GmbH during installation and operation. The authors also thank Fernand Karcher and his colleagues of Me´te´o France, Toulouse, for hosting the MOZAIC database and for providing information on potential vorticity.
Though other ministries are actually involved in water resources management, MoWR and later MoI and MoE continue to be the most powerful ministries in water resources management. For this reason, WECS is also located under MoWR and later under MoE. WECS is the permanent secretariat of the Water and Energy Commission (WEC), which was established by His Majesty's Government of Nepal in 1975 with the objective of "developing the water and energy resources in an integrated and accelerated manner"  (ADB, 2004, p. 4 ). The establishment of WEC was largely driven by the government's objective to be well equipped to negotiate transboundary water and energy issues with India, notably through the creation of "an integrated and centralized environmental database system for information related to water resources development and management"  (WECS, 2003).
Methods for calculating the acaricidal efficacy Efficacy against ticks was calculated from the total count of live ticks counted on the dogs 48 h after each infestation, or removed. Efficacy calculations based on arithmetic and geometric means of the tick counts was calculated using Abbott's formula: Efficacy (%) = 100 × (C -T) / C, whereby: C = Mean live tick count on the control group; T = Mean live tick count on the treated group. Statistical analysis were carried out using the chi-square test or Fisher's exact test as applicable using software package SAS ® version 9. 3. The level of significance of the tests was set at 5 %.
By contrast, PC and 1-CV distributions show trends similar to the other proteins from the dataset. This analysis revealed that evolutionary information may not be helpful for detecting protein binding sites in peculiar cases such as antibody-antigen interfaces. ) and we used it to detect putative PPI sites. In this model, we consider only surface residues and ignore completely or almost completely buried residues (asa<5%) that are generally highly conserved and would not contribute significantly to the interface (S3 Table ). Predicted protein interfaces are defined as residue clusters composed of a seed, forming the core of the predicted site, several concentric layers forming an extension, and an additional outer layer. Intuitively, seed/extension/outer layer approximate support/core/rim, although we do not seek a perfect match between the individual components of the two models.
Detecting lowly conserved interface residues based on the protein surface local geometry To describe the local geometry of the protein surface, we use the measure of circular variance (CV) that evaluates the density of protein around an atom. This simple geometric descriptor captures the structural properties of interacting residues. To properly assess the predictive role of CV, we compared JET 2 and its iterative version iJET 2 (see Materials and Methods) to JET/ iJET, which use only sequence information. We applied both methods to two testing sets, namely PPDBv4 and the Huang dataset of 62 protein complexes  [20]  (S1 Table ). One should notice that PPDBv4 was also used for the analysis of the signals encoded in experimental interfaces (see above). Although this analysis conceptually inspired the detection strategies implemented in JET 2 , the method was not trained on PPDBv4 and no JET 2 parameter was set based on PPDBv4 analysis.
For all proteins, the three scoring schemes were systematically used and the best patch or combination of patches was retained. The performance values obtained when running the complete automated clustering procedure of the program (JET 2 AutoComplete ) are also given. Over all proteins, the most exposed interface residues, constituting the rim, are generally less conserved than the rest of the protein (Fig  1A ). JET 2 greatly improves the detection of these residues for both testing sets (Fig  4C ). Sensitivity values obtained for the rim residues over 10 iterative runs of iJET 2 are increased by 20 to 32 compared to iJET. These results show that JET 2 , by exploiting the local geometry of the protein surface and combining it it with amino acids physico-chemical properties, is able to specifically detect lowly conserved protruding interacting residues, as those forming the rim of the interface or those involved in e.
When JET 2 was run on the entire PPDBv4 (except for antibodies) and Huang datasets, it automatically chose SC2 for 126 proteins-90 from PPDBv4 and 36 from Huang. 62 of those proteins have a bound small molecule in their PDB structure (complexed conformational state). For 67% of the remaining proteins, we could find experimental evidence reported in the literature that the proteins do interact with a small molecule (see S17 Table for the list of proteins along with manually collected information about their ligands). Overall, this analysis revealed that (i) exploiting the local geometry of the protein surface in combination with evolutionary information often permits to better define protein-binding patches and segregate them from small ligand-binding pockets, (ii) JET 2 /iJET 2 automated clustering procedure is efficient for selecting the most appropriate scoring strategy (SC2).
The regulatory protein RGS9 terminates the signal by binding to G α (1FQJ, PPDBv4). By using SC2, iJET 2 predicted the G βγand RGS9-binding patches of G α with high sensitivity (Sens = 58% and 62%) and high precision (PPV = 78% and 54%). The two patches predicted over the two structures share 73% of their residues (Fig  7A ). Among the true positives, 64% (9/14) of the seed residues are involved in both interactions whereas most if not all residues from the extension and the outer layer are specific of the interaction with one partner (S4 Table ). Thioredoxin (Trx) from E. coli facilitates the reduction of other proteins such as PAPS reductase (2O8V, PPDBv4). Trx is itself reduced by Trx reductase (1F6M, PPDBv4). In addition to its anti-oxidant function, Trx also plays a structural role upon bacteriophage T7 infection by associating with T7 DNA polymerase (PDB code: 1X9M)  [45].
Thanks to the experimental knowledge on the two proteins studied here, we could validate JET 2 predictions. This further suggests that JET 2 could be useful for identifying moonlighting proteins and help to learn about a protein's partners by looking at the characteristics of the interaction site. For instance, given a protein for which we know a partner and the localisation of its binding site, the presence of a significant number of false positives located in the extension and/or outer layer of a patch predicted by JET 2 may indicate that this patch is involved in the association with another partner-and that the false positive residues actually interact with this other partner. To test this hypothesis, we selected the proteins from PPDBv4 whose interaction site was predicted by SC1 with high sensitivity (> 80%) but rather low precision (< 60%) and looked for additional structural data in the Protein Data Bank (PDB).
Comparison with other tools The strategy employed in JET 2 combines three physically and biologically meaningful residuebased descriptors in a rational way to predict protein-protein interfaces at large scale. To evaluate the relevance of our approach, we compared the results of iJET 2 to those of three state-ofthe-art methods that use machine learning, namely VORFFIP, PredUs and eFindSite PPI. The VORFFIP method  [36]  integrates a broad set of residue descriptors-including solvent accessibility, energy terms, sequence conservation, crystallographic B-factors and Voronoi Diagrams-derived contact density-in a two-steps random forest ensemble classifier. We applied VORFFIP on the Huang dataset and PPDBv4, after having removed the proteins that were used for training the method (Table  1  and Fig   8).
VORFFIP predictions were defined from residues having a normalized score (or probability) greater than 0. 5. The overall performance of iJET 2 (2 or 8 runs out of 10) is comparable to that of VORFFIP on the Huang dataset (S6 Table ). On PPDBv4, iJET 2 achieved higher sensitivity and higher precision than VORFFIP, with similar specificity and accuracy values (S7 Table ). iJET 2 performed particularly better on the antibody-antigen and bound antibody-antigen complexes and on the proteins with other function from PPDBv4. Fig  3  shows examples where iJET 2 single-patch predictions give a better coverage (1TMQ:R), better overall localization (1K5D:L) or more precise definition (1E4K: L) of the experimental protein interfaces than VORFFIP predictions. iJET 2 also detects more experimental interacting residues in the multi-patch predictions reported on Fig  6.
Then, the algorithm combines the information from the templates with four residue-based descriptors (relative accessible area, generic and position-specific interface propensities, sequence entropy) to compute interfacial probability scores for every residues of the query protein, by using support vector machines and a naive Bayes classifier. The method was shown to outperform PredUs when protein models are given as inputs instead of experimentally determined structures  [48]. eFindSite PPI was applied to the proteins from the Huang dataset, for which it showed better performance values than iJET  [38]. On average, eFindSite PPI achieved a sensitivity of 62% and a precision of 62% (see Table  5  in  [38] ). iJET 2 displays a lower sensitivity of 48% (resp. 42%) but a higher precision of 66% (resp.