dataesr/scientific-paragraphs-categorization huggingface dataset:
g. use 0. 036 for 360 ppm of CO 2 ). The concentration values are C N 2 = 78. 084, C O 2 = 20. 946, C Ar = 0. 934 and C CO 2 = 0. 036. Figure  1  shows calculated King factors in the UV-Vis-NIR. For illustration, F air equals 1. 063 at λ = 250 nm and 1. 047 at λ = 1 µm. The constant value of 1. 06 leads to an error in the Rayleigh cross section of respectively 0. 3 and 1. 2 %; the impact on the retrieval of relatively low aerosol extinction coefficients is significant. The AerGOM algorithm offers the choice to retrieve the neutral air density or to remove the contribution from the measured transmittance T meas by making use of ECMWF air density profiles, as provided in the GOMOS residual extinction files. The resulting transmission T to be used for the data inversion of all other species is given by Figure  1.
Frequently used models Prior to the actual inversion of occultation measurements, little is known about the composition, size distribution and morphology of atmospheric particles. The use of Mie theory to model extinction spectra for data inversion purposes is therefore limited. In practice, it is usually preferred to represent aerosol extinction or optical thickness spectra by a smooth analytical function with a small number of parameters (which are to be fitted). The well-known Ångström empirical power law (β = Aλ -α ) is a prime example. It is however not versatile enough; researchers are often forced to make the coefficients A and α wavelength dependent, an approach that seems rather arbitrary. In the current operational GOMOS Level 2 algorithm (IPFv6. 01), a quadratic polynomial of wavelength (Eq.
After comparison of the fit quality, the second-degree polynomial of inverse wavelength was singled out as a good versatile model for particle extinction spectra for the bulk of GOMOS measurements. As an example, we used the SAGE II-CLAES stratospheric aerosol climatology of  Bauman et al. (2003)  and converted a few of their values for effective radius R eff , mode   3. Furthermore, stratospheric in situ data derived from impactor samples collected onboard an ER-2 aircraft  (Pueschel et al. , 1994)  were used (see Table  4 ). In both cases, we assumed US76 temperatures at the considered altitudes and derived corresponding H 2 SO 4 weight percentages with the method of  Carslaw et al. (1997). Refractive indices were obtained with the method of  Krieger et al. (2000). Finally, we calculated the extinction spectra in Fig.
Transmittance data As mentioned before, the GOMOS IPFv6. 01 processor uses exclusively SPA1 and SPA2 data for the retrieval of O 3 , NO 2 , NO 3 and aerosol extinction data products, while the SPB1 and SPB2 data are reserved for the retrieval of O 2 and H 2 O. With respect to aerosol retrievals, this is regrettable; at longer wavelengths, the relative contribution of aerosol extinction is stronger (in the lower atmosphere) due to weaker air scattering. Furthermore, anticipating future research, particle size distribution retrievals improve if the spectral range is larger  (Fussen et al. , 2002). We therefore studied the possibility of exploiting SPB1 and SPB2 data in the AerGOM processor. Of course, care needs to be taken to avoid the use of wavelengths at which O 2 and H 2 O absorb. Figure  4  shows one way of doing this.
It is intuitively clear that the spectral ranges to the left and the right of the O 2 absorption band in the SPB1 data are useful to extract aerosol extinction. On the other hand, it is far less obvious to define SPB2 spectral pixels that are free of H 2 O absorption lines. The importance of these spectral regions is nevertheless clear when we observe the transmittance altitude profiles in the right panel of Fig. 4 ; in the lower stratosphere and upper troposphere (our main region of interest) a very useful range of transmittance is present in the SPB1 and SPB2 spectral bands while the SPA transmittance values have almost dropped to zero. For flexibility, the AerGOM processor offers the possibility to select SPA/SPB1/SPB2 spectral pixels at will. Due to the difficulty of finding SPB2 spectral pixels without H 2 O absorption, and the fact that Aer-GOM is at present not able to perform H 2 O retrievals, SPB2 data are currently not selected for the retrievals.
AerGOM spectral inversion In comparison with the GOMOS processor, the AerGOM spectral inversion is conceptually much simpler. No separate differential method is used to derive NO 2 and NO 3 SGDs. Instead, contributions from all molecular/particulate species to the optical extinction are included in the Beer-Lambert Table  4. Stratospheric aerosols: bimodal lognormal particle size distribution parameters N 0 and N 1 (aerosol total number density), r m0 and r m1 (mode radius), and σ 0 and σ 1 (mode width), representative of the period just before and after the Pinatubo eruption. The data were obtained from impactor samples on an ER-2 aircraft and were published by  Pueschel et al. (1994)  (Table  1a ). For optical calculations, we used a H 2 SO 4 weight percentage of 78 %, corresponding to a temperature of 217 K.
Number Date Latitude Longitude Altitude N 0 r m0 σ 0 N 1 r m1 σ 1 in Fig. 3  (km) (cm -3 ) (µm) (cm -3 ) (µm)  and in situ impactor measurements (green). Also shown is the fit with the second-order polynomial of inverse wavelength (black solid lines). The numbers on the plots correspond to the data in Tables  3  and 4. (27 September 2004, 21. 17 • S, 48. 62 • E) for SPB1 (left) and SPB2 (middle) at tangent altitudes of (roughly)  15, 20, 25, 30, 35, 40 and 45 km (from bottom to top). Right panel: tangent altitude profiles of GOMOS transmittance for spectrometer A: 300 (magenta), 400 (green), 500 (blue), 600 nm (cyan); spectrometer B1 (black); B2 (red); the B1 and B2 profiles are very rough estimates of what can be expected; they are median values of the wings left and right of the B1 oxygen band, and the entire B2 spectrum respectively.
AerGOM processing The entire 10-year GOMOS data set has been processed with the AerGOM algorithm. The specific configuration that was chosen, taking into account the required data quality and processing speed, is presented in Table  5. Notice specifically that for this first tentative processing the FCM method was not used because it is computationally expensive. Furthermore, the Rayleigh contribution was not retrieved but computed and removed, using meteorological data together with the Rayleigh cross section (Eq. 2). Finally, SPB2 data were not used (since all wavelengths are affected by water vapour, a species that is currently not retrieved by AerGOM), while only the SPB1 spectral pixels outside the O 2 absorption band were exploited. By launching several batch processes in parallel, we were able to process the entire dark limb GOMOS data set in two days.
A first look at the AerGOM results A detailed validation will be presented in a companion paper  (Robert et al. , 2016). Here, we will present a qualitative evaluation of the obtained AerGOM data by comparison with the IPFv6. 01 products; visual inspection is sufficient to demonstrate the improvement. Figure  5  shows an ensemble of 115 randomly chosen aerosol extinction profiles (in a window from April 2002 to April 2005, between 60 • S and 60 • N), evaluated at three wavelengths (386, 452 and 525 nm) using the assumed quadratic law. Clearly visible are the IPFv6. 01 spurious oscillations, which increase in amplitude for wavelengths farther away from the reference wavelength of 500 nm. As was anticipated, the situation improves dramatically for the Aer-GOM data. We also observe a larger spread of the aerosol extinction profiles at lower altitudes (below about 20 km) for both retrieval algorithms.
0 data set. Notice the increased variability of extinction values at short wavelengths (below 400 nm), reflecting the larger retrieval errors due to the small aerosol-molecular extinction ratio at these wavelengths. In particular, the spectral maxima between 300 and 400 nm should not be considered as physical features but result from the lack of instrument sensitivity to aerosols. The correspondence between the IPFv6. 01 and AerGOM data sets with SAGE II results is illustrated in Fig. 7. Shown are chronologically ordered aerosol extinction values at 386 nm for collocated GOMOS-SAGE II occultation events (within a window of 500 km and 12 h), at three different altitudes spanning the middle stratosphere. On average, the IPFv6. 01 data follow the SAGE II values closely but are very noisy. The amplitude of this noise decreases strongly in the AerGOM v1.
The extinction retrieval errors were taken into account. Results are shown in Fig. 8  at three different altitudes. We immediately see that the variability of the AE is for the most part buried in the experimental error and is therefore statistically not significant. The histograms in the figure for the full GOMOS-SAGE II collocation data set confirm this finding: the statistical spread of the AE distributions falls largely within the limits of the experimental error. The findings are valid for AerGOM as well as SAGE II.
Conclusions The GOMOS aerosol extinction profiles produced by the official IPFv6. 01 algorithm are of good quality around the 500 nm reference wavelength, but they show pathological behaviour in other spectral regions. This finding hinted at a conceptual error in the algorithm, instead of a lack of information in the GOMOS data. Within the framework of the Aer-GOM project, a new algorithm was developed that has some similarities with the IPF code but is equipped with two fundamentally different concepts: an improved aerosol spectral law and a full spatial inversion that does not discard retrieval covariances between species. Additionally, a more accurate Rayleigh scattering cross section and air refractive index has been implemented. The spectral range has been increased by the possibility to use SPB1 and SPB2 spectral measurements (although only SPB1 data have been selected for the first data processing presented in this paper).
The entire GOMOS 10-year mission data set has been processed, and the resulting Level 2 product files (containing altitude profiles for aerosol extinction and gas densities, with error estimates) have been stored as the AerGOM v1. 0 data set. An initial inspection of the obtained results shows that the pathological behaviour of the aerosol profiles at wavelengths far from the 500 nm reference is severely reduced. Furthermore, a coarse comparison of GOMOS-SAGE II colocations shows much better agreement for AerGOMv1. 0 than for IPFv6. 01 at these wavelengths. Since algorithm development forms the subject of this paper, a detailed validation study of the aerosol extinction product has been presented in a separate companion paper  (Robert et al. , 2016). Validation of the other products (O 3 , NO 2 , NO 3 ) will be carried out in the future.
Acknowledgements. The AerGOM project was financed by the  European Space Agency  (contract number  22022/OP/I-OL ). Additional research was supported by a  Marie Curie Career Integration Grant  within the  7th European Community Framework Programme  under grant agreement no. 293560 , the  European Space Agency  within the  Aerosol_CCI project of the Climate Change Initiative  and the  Belgian Space Science Office (BELSPO)  through the  "Chercheur Supplémentaire" programme. Edited by: O. Torres Reviewed by: two anonymous referees.
The radio flux density is directly proportional to the dust opacity and hence to the total mass of gas if one supposes that the dust-to-gas mass ratio is constant (typically one takes 1/100). An array of bolometers installed at the focus of a large dish was used by Johnstone et al. [11]  to map with 7 to 14 arcsecond spatial resolution more than half a degree of the BN/KL region. The bolometric data reveal a chain of compact sources embedded in a faint filamentary structure extended north-south. Recently, a catalog of dense cores has been established from 1. 3 mm continuum observations made in the direction of BN/KL with various single dish antennas (see Shimajiri et al. [12] ). The most massive cores are found to lie along, and not outside, the north-south filaments observed over nearly two degrees in the direction of Orion BN/KL.
Interferometric observations of complex molecules We briefly review below the results that we have recently obtained with the IRAM and ALMA arrays and concentrate our discussion on two molecules, the methyl formate and ethylene glycol, observed with both arrays. The ALMA data were taken from the public data acquired during the Science Verification campaign, where only a small subset of all ALMA antennas had been used while the array was still in construction. Methyl formate (HCOOCH 3 ). A few tens of lines of the methyl formate molecule (MF) have been identified by Favre et al. [1]  with the IRAM array. Fig. 4  shows an example of the spatial distribution for three of these lines around the 'Hot Core' and 'Compact Ridge'. Several molecular 'cores', labeled MF1, 2, etc. , are identified and nearly coincide or lie in the vicinity of the dust emission peaks seen in the continuum map (Fig.
To this end, (i) a fast and simple chromatographic separation for Zn was introduced and calibrated; (ii) mass bias effects related to the analytedopant ratios (Zn/Cu) were assessed critically; (iii) effects of spectral and non-spectral interferences on Zn isotopic compositions were quantified; and (iv) new isotopic values for RMs of soils, sediments and plant matrices (2709 San Joaquin soil, 1646a estuarine sediment and 1573a tomato leaves) extending the data base for δ 66/64 Zn in materials relevant for environmental studies were determined. The database for previously analyzed RMs like the BHVO-2 basalt, BCR-2 basalt and AGV-2 andesite was further expanded.
As demonstrated by  and non-spectral interferences in matrix-doped standards identified the formation of Cr and Ti oxides and hydroxide ionic species in the plasma as the main interferences on Zn isotope ratios. Other elements such Fe, Ca, Mg do not produce large interferences, with shifts commonly lower than 0. 1‰. Six certified reference materials (USGS and NIST materials) were analyzed: BHVO-2 basalt, BCR-2 basalt, AGV-2 andesite, San Joaquin soil SRM 2709, 1646a estuarine sediment and 1573a tomato leaves. In the case of rock RMs (BHVO-2 basalt, BCR-2 basalt and AGV-2 andesite), the δ 66/64 Zn JMC values were in agreement with values reported in the literature. This work contributed with a simplified chromatographic protocol for Zn separation, additional insights about the mass bias processes in the MC-ICP-MS instruments and a new dataset of Zn isotopic compositions for environmental reference materials in order to fill a gap in the metrological traceability and analytical control of Zn isotopic data, and stimulate future inter-laboratorial calibrations.
Acknowledgments CAPES and  CNPq  provided scholarships for  Daniel Araújo  that supported this work. We thank all the laboratory personnel of the  Institute of Geosciences at UnB  and  USP , especially,  Erico Zacchi ,  Myller Tonhá ,  Fernando Cavalcante  and  Jeane Chaves  for their collaborations in this study. We thank  Timothy Mulholland  for his substantial support on the English grammar correction. We also thank the two anonymous reviewers for their valuable suggestions and comments.
Among the stilbenes, we compared transresveratrol (the most abundant form in foods and beverages) to pterostilbene, one of its hydroxylated derivatives, since both of them have been reported to repress adipogenesis in fat cells  [24] [25] [26]  (see below). Caffeic acid was chosen as representative of the phenolic acids since it has also been demonstrated to be inhibitory on adipogenesis  [27]. We also included quercetin in our comparative study, since it is quantitatively the most important phenolic compound present in foodstuffs (as indicated by the phenolic compounds database: http://www. phenol-explorer. eu/ [28] ). The following results obtained on human adipose tissue are not only relevant for obesity research but also valuable for oxidative stress aspects because AOs are ROS-generating enzymes and because phenolic compounds, though being among the most important low molecular weight molecules absorbed from foods capable of eliciting nonenzymatic antioxidant actions in consumers, are scarcely described to impair MAO  [2] [3] [4].
These data confirmed that benzylamine is predominantly oxidized by SSAO in hAT, as previously reported  [34]. Phenolic compounds dose-dependently inhibited benzylamine-induced H 2 O 2 release (Figure  3(b) ), indicating that they apparently behave as SSAO inhibitors. In this case, the IC 50 values (M) for caffeic acid, resveratrol, quercetin, and pterostilbene were 36, 45, 69, and 136, respectively. However, resveratrol and caffeic acid abolished benzylamineinduced signal at 1 mM, almost to a deeper extent than the reference inhibitor semicarbazide, while quercetin and pterostilbene impaired partially the benzylamine-induced signal. This dissimilarity prompted us to further verify whether the phenolic compounds were impairing the generation of hydrogen peroxide or whether they blunted its detection by the chromogenic mixture used for fluorometric detection.
At the same doses, quercetin has been reported already to inhibit MAO-A in a complete manner, though via a peroxidase-based detection of hydrogen peroxide production, therefore probably with a substantial overestimation of its inhibitory properties  [45]. Hence, we propose here that resveratrol and quercetin have to be considered as poorly potent MAO inhibitors since they hamper [ 14 C]tyramine oxidation (mainly catalyzed by MAO-A, predominant in human adipocytes) at doses that are closer to the millimolar than to the micromolar range, that is, levels that cannot be reached by nutritional intake. Indeed, maximal plasma resveratrol levels are estimated to reach 3-4 M after ingestion of 1 mg/kg, according to the freely available database http://www. phenol-explorer. eu/ [28]. Nevertheless, it is important to take into account the possibility that a full mixture of natural phenolic compounds may be effective at lower doses, due to potential synergistic effects.
Acknowledgments The authors express gratitude to all the members of the  Consortium for Trans-Pyrenean Investigations on Obesity and Diabetes  for valuable discussions and to  Keith Tipton  ( Dublin, Ireland ) for his knowledge about AO-interacting drugs and elixir of life. The authors would like to thank the staff of Plastic Surgery Dpt. of  Rangueil Hospital (Toulouse, France ) for facilitating access to postsurgical wastes. This work is partly supported by  DIOMED & REF-BIO/POLYFrEsNOL projects ,  Instituto de Salud Carlos III (CIBERobn), Government of the Basque Country  ( IT-512-13 ), and  University of the Basque Country (UPV/EHU)  ( ELDUNANOTEK UFI11/32 ).
Regarding the RR component within the phosphatase complex, the three DesR-REC molecules in the ASU display identical 'active-like' structures  (Trajtenberg et al. , 2014). BeF 3 -moieties mimicking phosphoryl groups are observed bonded to each phosphorylatable Asp 54(RR) , and coordinated to Mg 2+ cations, consistent with this crystal form being a snapshot of the phosphatase reaction at its pre-dephosphorylation step. The DesKC H188E :DesR-REC complex Wild-type phosphorylated DesKC (wt P~DesKC) in complex with DesR variants resisted crystallization. The crystal structure was eventually solved by using the phosphomimetic DesKC H188E mutant in complex with DesR-REC. Three X ray diffraction datasets were obtained from DesKC H188E :DesR-REC crystals soaked with different concentrations of MgCl 2 and BeF 3 -(Table  1 ).
3 mg/mL protein prepared in 5 mM AMP-PCP, 20 mM MgCl 2 ) crystallized in mother liquor 18% (w/v) PEG 3350, 0. 3 M tri-potassium citrate. Protein drops were set up by mixing 2 mL protein + 2 mL mother liquor. Cryo-protection was achieved by quick soaking in 20% (w/v) PEG 3350, 0. 3 M tri-potassium citrate, 5 mM AMP-PCP, 25% (v/v) glycerol, and 20 to 150 mM MgCl 2 + 0 to 5 mM BeF 3 -. wt P~DesKC was crystallized as described  (Albanesi et al. , 2009) , except that 5 mM ATP was used throughout instead of AMP-PCP. X ray diffraction data processing, crystal structure solution, refinement and analysis Single crystal X ray diffraction was performed with a copper rotating anode home-source (Protein Crystallography Facility, Institut Pasteur de Montevideo) or synchrotron radiation (Soleil, France).
Software for data processing, structure determination and analysis was provided by the SBGrid Consortium  (Morin et al. , 2013). Small angle X-ray scattering data acquisition and analysis SEC-SAXS experiments were carried out at beamline SWING (Soleil synchrotron, France). Purified DesK-DesR complexes samples were brought to 10 mg/mL in 50 mM Tris. HCl pH 8. 0, 300 mM NaCl, and injected into a Superdex 75 5/150 GL column (GE Healthcare Biosciences) equilibrated in the same buffer. Samples were eluted at constant flow (0. 15 mL/min) and loaded into capillary cell for X-ray exposure. Recorded frames were processed with Foxtrot (David and Perez, 2009) following standard procedures. Subsequent analyses of the scattering data were performed with tools from the ATSAS  (Konarev et al. , 2006). Forward scattering I (0) and the particle's radius of gyration (R g ) were estimated using the Guinier approximation.
Isothermal titration calorimetry Isothermal titration calorimetry (ITC) assays were performed on a VP-ITC (MicroCal Inc. , Northampton, MA). Titrations consisted of an initial injection (1 mL), followed by 25-30 injections of 10 mL of DesR-REC (ligand) on the cell containing the different DesKC variants (partner). Assays were carried out at 15 ˚C in a buffer containing 20 mM Tris. HCl pH 8, 0. 3 M NaCl, 10 mM MgCl 2 and 0. 5 mM AMP-PNP. The heat released by the dilution of the ligand was determined injecting the ligand, on the cell containing the working solution without partner, using the same sequence of injections. The concentrations used for the experiments were between 25-30 mM for DesKC and 350-400 mM of DesR-REC and each titration was done in duplicate. The data were analyzed with MicroCal Origin version 7 software (MicroCal Software Inc.
), after manual baseline correction, and subtraction of heat due to ligand dilution. Binding isotherms were fitted to a two independent sequential site model. Sequence-based direct coupling analysis of the HK:RR complex Sequences of HK and RR pairs, with the same architecture as DesK and DesR (families HisKA_3 and NarL, respectively), belonging to the same operon, were selected from the Uniprot database using hmmsearch  (Eddy, 1998). Hidden Markov model-profiles for each domain were obtained from Pfam  (Finn et al. , 2016). Redundancy was filtered using a cutoff of 90%  (Li and Godzik, 2006)  resulting in a total of 3318 sequences including HKs (DHp+CA) and RRs (REC+HTH). The concatenated HK:RR sequences were aligned with hmmalign  (Eddy, 1998)  and manually curated, removing sequences with large insertions.
DI (direct information) of each pair of residues of the alignment was calculated using mfDCA matlab script  (Morcos et al. , 2011)  and we only take in consideration coevolving pairs of residues farther than five positions apart in sequence. The same procedure was followed for the HisKA and PhoB families for comparison purposes. A similar trend in equivalent 3D positions for covariant residue pairs among different HK and RR families was thus confirmed  (Podgornaia et al. , 2013; Skerker et al. , 2008; Weigt et al. , 2009). In silico modeling and molecular dynamics simulations The phosphotransferase complex was built by replacing the DesKC H188E dimer of 5IUK with wt P~DesKC (PDB 5IUM). This replacement resulted in model with barely no clashes, except for P~His 188(HK) slightly bumping into Phe 82(RR) and Arg 84(RR).