dataesr/scientific-paragraphs-categorization huggingface dataset:
The addition of at least 2 mM Ca 2ϩ in the reaction mixture was essential for the activity (data not shown). AlyA5 showed the highest activity at pH 7. 0 in MOPS buffer (Fig. 3D ) and displayed a narrow range of tolerance. The choice of the buffering molecule was critical, as exemplified by the 50% decrease in activity in Tris-HCl buffer at pH 7. 0 compared with MOPS. To analyze from which end of the substrate the exolytic enzyme attacks, AlyA5 activity was monitored on poly-G blocks that had their reducing ends reduced using sodium borohydride prior to the digestion. Compared with the reaction with classical poly-G substrate, AlyA5 retained 75% of its activity on such reduced substrates, indicating that the enzyme cleaves the unit at the nonreducing end. The 25% decrease in activity can be attributed to loss of material during the successive evaporation/resolubilization steps that are part of the reduction protocol.
6 -2. 6 and 3-4 ppm (see below). Characterization of End Products-The degradation of poly-G was followed by NMR to characterize the end products resulting from AlyA5 activity. Fig. 12  shows that during the course of the reaction, the concentration of the disaccharide ⌬G increased although the concentration of the longer unsaturated oligosaccharides decreased. A small amount of the unstable ⌬-(4,5)-unsaturated pyranose monomer was also identified in the NMR spectra. As the reaction progressed, signals corresponding to protons from CH 2 groups appeared in the NMR spectra (Fig. 12 ). These proton signals have chemical shifts typical for CH 2 groups close to a keto group or hemiketal  (56). Thus, the NMR data confirm that one of the reaction products is DEH resulting from the spontaneous conversion of the unsaturated monosaccharide ⌬.
This versatility is rather rare among described alginate lyases, which are in general specific toward one of the three types of block-structures occurring in alginate. One other endolytic PL7 alginate lyase, A1-IIЈ (accession code Q75WP3, not included in any attributed subfamily, see Fig. 1 ), has been reported to display broad substrate specificity  (68) , although these two enzymes do not fall into the same subfamily  (54). Together with PL14 (61, 69), this is the second example of an alginate lyase family containing both enzymes active on polymer chains and on short oligosaccharides. This is in agreement with the general rule that endo-or exolytic activity is not defined by family membership (25) but rather by specific structural differences in loops surrounding the active site formed by the conserved scaffold.
This study presents the joint H 2 16 O and HDO retrieval from Infrared Atmospheric Sounding Interferometer (IASI) spectra over the Western Siberia. IASI is an instrument on board the MetOp-A European satellite. The global coverage of the instrument and the good signal-to-noise ratio allow us to provide information on δD over this remote region. We show that IASI measurements may be used to estimate integrated δD between the surface and 3 km altitude or from 1 to 5 km depending on the thermal contrast, with observational errors lower than 4 % and 8 %, respectively. The retrieved data are compared to simulations from an isotopic GCM, LMDZ-iso over 2011. The satellite measurements reproduce well the seasonal and day-to-day variations for δD, showing for the latter a good correlation with the model (r up to 0.
, 2007). This work documents for the first time δD retrievals from IASI radiances over land and at high latitudes. While the methodology to retrieve spectra measured above land was already developed in  Lacour et al. (2012) , the present dataset is interesting in that it includes cases with high thermal contrasts (∆T ) which greatly impact the sensitivity of the retrieval. Generally high latitudes retrievals are more challenging than low and mid-latitudes retrievals, mainly due to the cold temperature profiles of the atmosphere and the lower concentration of water vapor that affect the information content present in the measurement. To cope with that difficulty, a specific a priori covariance matrix for high latitudes retrievals was built in order to better constrain the solutions of the inverse problem.
One of the rationales for the present study is to acquire a first IASI δD dataset for the high-latitude regions for comparison with model simulations, which have been suggested to have deficiencies at these high-latitudes  (Risi et al. , 2012a). Furthermore, models also underestimate the role of the continental recycling at mid-latitudes as over Siberia (e. g. Risi et al. , 2013). Thanks to the joint effort from different communities within the Russian-French WSIBISO project (http://wsibiso. ru), the new dataset from IASI retrievals allows us to evaluate the performance of isotopic General Circulation Models (GCMs) over Siberia. Here we use the isotopic version of the LMDZ model, LMDZ-iso  (Risi et al. , 2010). A global description of the IASI mission is given in Sect. 2. Section 3 presents the methodology used and provides a detailed characterization of the retrieved δD, under different thermal contrast conditions.
1 , which shows a sampling example around the city of Yekaterinburg for one day. This area is referred hereafter as YEK. O and HDO are the three main isotopologues of water having large absorption bands in the thermal infrared region. Spectral signatures of these species are well detected by IASI. However, as explained by  Herbin et al. (2009)  O is very challenging as its variation in the atmosphere is small. This is not the case for the HDO/H 2 16 O ratio for which IASI was shown earlier to provide results of sufficient accuracy. In the following, we therefore focus on the retrievals of HDO and H As there are numerous IASI data (more than one million observations per day) and as the retrieval procedure for δD currently relies on a line-by-line approach that is computationally expensive, we have chosen to process only 1 pixel out of 4.
Furthermore, only spectra with a cloud fraction (see IASI Level 2 Product Guide, 2008) below or equal to 10 % are considered and the few unrealistic positive retrieved δD values are filtered out. Overall this restricts the dataset to 9 % of the observations (15 234 out of 175 528) over the Siberia area for the year 2011. Despite these strict criterions and as shown in Fig. 1  for one day, the YEK area is still well covered by IASI.
General aspects of the retrieval methodology In this study we use for the retrieval the line-by-line radiative transfer code "Atmosphit" developed at Université Libre de Bruxelles to retrieve HDO/H 2 O ratios from IASI spectra, using the HITRAN database for spectroscopic parameters  (Rothman et al. , 2003). It relies on the OEM described by  Rodgers (2000)  and has been adapted to perform the retrieval on a natural logarithmic scale and to optimally retrieve HDO/H 2 O ratios  (Lacour et al. , 2012). A forward model F needs to describe the physics of the measurement. An atmospheric state vector can be related analytically to F by: y = F(x, b) + ε = K(x -x a ) + ε (2) where y corresponds to the measurement vector (IASI radiances), ε is the instrumental noise, x the state vector that contains the parameters to be retrieved, and b the vector containing all other model parameters impacting the measurement (e.
Acknowledgements. M. Pommier was supported by a fellowship grant from WSIBISO project (http://wsibiso. ru). This research was partly supported by a grant of the Russian government under the contract 11. G34. 31. 0064. P. -F. Coheur is Senior Research Associate with FRS-FNRS. IASI was developed and built under the responsibility of Centre National des Etudes Spatiales (CNES) and flies onboard the MetOp satellite as part of the Eumetsat Polar system. The authors acknowledge the Ether French atmospheric database 11073 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (http://www. pole-ether. fr/etherTypo/index. php?id=1450&L=1) for providing the IASI L1C data. The publication of this article is financed by CNRS-INSU. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | pospheric vertical resolution from Aura TES radiances, Atmos.
In contrast, the space-based processing, such as for GOSAT data, is often contaminated by atmospheric light scattering and hence GOSAT-TCCON comparisons should include close analysis of these effects. [7] In our recent companion paper  [Oshchepkov et al. , 2012]  (hereafter referred to as Part I), we have applied the PPDF-based method to evaluate the light path modification from GOSAT observations. For a representative statistical GOSAT-TCCON comparison, we collected weekly mean GOSAT data within a rather large 15 latitude by 45 longitude grid box (both over land and ocean) centered at each TCCON station. To constrain the natural variability in CO 2 within each sampling domain, we excluded observations for which the NIES atmospheric transport model (NIES-TM)  [Belikov et al. , 2012]  showed >1 ppm difference in CO 2 column abundance from that of the TCCON.
In particular, the results revealed the effects of optical path lengthening over Northern Hemispheric sites, essentially from May to September of each year, and of light path shortening for the GOSAT sun-glint observations in tropical regions. We showed that these effects compromise accurate gas retrievals. [8] This study is the second in a series of companion papers describing the effects of atmospheric light scattering on the observations of atmospheric carbon dioxide from space. The paper introduces a comparative analysis of six retrieval algorithms for the GOSAT SWIR data processing during 22 months of GOSAT operations from June 2009 to March 2011. We include an algorithm comparison against TCCON measurements, an algorithm cross-comparison, and analysis of the impact of atmospheric light scattering on the gas retrieval products.
[9] The paper is organized as follows. Section 2 briefly outlines the data (GOSAT, TCCON, atmospheric transport model) used in this study. We give an overview of the inversion schemes and basic specifications of each GOSAT retrieval algorithm in section 3. Section 4 compares the results of the GOSAT validation study from different algorithms in terms of CO 2 seasonal variability (section 4. 1) and a pairwise GOSAT-TCCON X CO2 statistical comparison (section 4. 2). Section 5 analyzes the contribution of atmospheric light scattering to GOSAT data products represented by different algorithms over TCCON sites. We analyze algorithm cross-comparison in section 6 and summarize the results in section 7.
GOSAT [11] We analyzed L2 data (X CO2 ) derived from 22 months of GOSAT operation from June 2009 to March 2011. In this period, the following versions of L1B (radiance spectra) data derived from the thermal and near-infrared sensor for carbon observation-Fourier transform spectrometer (TANSO-FTS)  [Kuze et al. , 2009]  have been applied to L2 data processing: V050. 050, V080. 080, V100. 100, and V110. 110. Suto et al. [2011]  give details of the updates applied through these versions. [12] When comparing against TCCON measurements only GOSAT observations over land were considered in this study and these were selected within a 5 radius latitude/longitude circle centered over each TCCON site. To increase its dynamic range, the GOSAT TANSO-FTS instrument collects data over land in one of two gain states.
TCCON [13] Eleven TCCON sites have been selected for the GOSAT validations study, namely, eight sites in the Northern Hemisphere: Bialystok, Poland (53. 2 N, 23. 1 E); Bremen, Germany (53. 1 N, 8. 85 E); Garmisch, Germany (47. 5 N, 11. 1 E); Lamont, USA (36. 6 N, 97. 5 W); Orleans, France (48. 0 N, 2. 11 E); Park Falls, USA (45. 9 N, 90. 3 W);  Sodankyla, Finland (67. 4 N, 26. 6 E) ; and Tsukuba, Japan (36. 0 N, 140. 2 E); and three sites from the Southern Hemisphere: Darwin, Australia (12. 4 S, 130. 9 E); Lauder, New Zealand (45. 0 S, 169. 7 E); and  Wollongong, Australia (34. 4 S, 150. 9 E). The groundbased TCCON data considered here are mean values measured within AE1 h of the GOSAT overpass time (around 13:00 local time).
NIES TM [14] The NIES global atmospheric tracer transport model was used in this study, primarily to fill gaps in the seasonal variability of X CO2 when TCCON data were not available. The present version (NIES-08. 1i) of the NIES TM  [Belikov et al. , 2012]  has a horizontal resolution of 2. 5 Â 2. 5 and implements a 32-level flexible hybrid sigma-isentropic (s-θ) vertical coordinate system. This combines both terrain-following and isentropic levels switched smoothly near the tropopause. The vertical transport in the stratosphere is calculated from the climatological heating rate derived from JRA-25/JCDAS reanalysis  [Onogi et al. , 2007]. The heating rate was adjusted to fit to the observed mean age of air in the stratosphere as described by  Belikov et al. [2012]. [15] The model incorporates fossil fuel CO 2 fluxes derived from the Emission Database for Global Atmospheric Research (EDGAR) 1998 distribution  [Olivier and Berdowski, 2001]  scaled by the growth rate obtained from the Carbon Dioxide Information Analysis Center (CDIAC)  [Boden et al.
, 2009]. All biospheric source/sink distributions over land and ocean are represented by the climatological inversion flux derived using inverse modeling with 12 TransCom3 models and observational data obtained from GLOBALVIEW-CO2 at 87 sites during 1999-2001  [Miyazaki et al. , 2008]. [16] The X CO2 calculated from NIES TM tracer distributions was compared with measurements acquired at 12 TCCON ground-based FTS sites for the period from January 2009 to January 2011. The model was able to reproduce the seasonal and inter-annual variability of X CO2 with correlation coefficients of 0. 8-0. 9. A comparison of modeled data with TCCON observations revealed model biases of AE0. 8 ppm for X CO2 except at the Sodankylä site, for which the model showed a larger bias of 1. 21 ppm  [Belikov et al. , 2012].
1 hPa. The a priori gas profiles are calculated for every day of observation by the NIES TM  [Maksyutov et al. , 2008]. Prior variance-covariance matrices of gas profiles were evaluated by  Eguchi et al. [2010]  by comparing 2008 NIES TM with observation-based reference data  (GLOBAL VIEW 2008). To avoid unexpected strong constraints on the a priori values and to gain as much information from the observed spectra as possible, the original variance-covariance matrices were multiplied by a factor of 100. [25] The version 01. xx NIES algorithm retrieves aerosol optical depth assuming that it is uniformly distributed from the surface to 2 km altitude. The aerosol optical properties are calculated from an offline three-dimensional aerosol transport model, the Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS)  [Takemura et al.
6 mm (AOD) ≤ 0. 5, and the signal-to-noise ratio of polarization synthesized spectra (SNR) > 100. [26] An improved NIES algorithm, version 02. xx, has been recently developed  [Yoshida et al. , 2012a; 2012b]  to avoid the large negative bias and scatter seen in the retrieved X CO2 with the version 01. xx algorithm (section 4). First of all, in version 02. xx, the solar irradiance database was updated to the G. C. Toon line list because the remaining terrestrial absorption structure in the solar spectrum of R. Kurucz introduced a non-negligible bias  [Uchino et al. , 2012]. To remove the surface pressure bias, the absorption cross section of the O 2 A-band was scaled by a factor of 1. 01. Aerosols simulated by SPRINTARS are categorized in NIES 02. xx into fine and coarse-mode particles. The logarithms of the vertical profile of the number density for both components are retrieved instead of retrievals of the total aerosol optical depth in NIES 01.
Each of six satellite retrieval products is shown in a separate panel. We display the number of weekly mean observations (N a ), average bias (Bias), standard deviation (s), and Pearson's correlation coefficient (r) between GOSAT and TCCON weekly mean X CO2 in each panel. These characteristics are derived from a weighted least squares fit by minimizing the perpendicular offset between GOSAT-TCCON coincident observations  [York et al. , 2004; Oshchepkov et al. , 2012]. This derivation makes it possible to account for uncertainties in both the GOSAT and TCCON datasets. The weights are inversely proportional to the error variance of the data. The variances are defined by the GOSAT a posteriori retrieval error and by uncertainties in the TCCON coincident measurements. [41] As evident from Figures  1, 2 , all GOSAT X CO2 products except NIES 01.
3). [47] Figure  4  and Table  2  demonstrate the substantial improvements in the NIES 02. xx data product compared with the previous version, NIES 01. xx (top of left and middle columns for weekly mean data). Both the negative bias (Bias = À 7. 90 ppm) and scatter (s = 3. 97 ppm) are significantly decreased in the version NIES 02. xx to À1. 07 and 1. 68 ppm, respectively (insets of the panels). Morino et al. [2011]  derived similar values of the statistical characteristics for NIES 01. xx data product using 10 latitude Â 10 longitude grid box over TCCON stations and even poorer values using a smaller sample size. [48]  Uchino et al. [2012]  found that the root cause of the negative bias correction in NIES 02. xx is the solar irradiance database. The solar irradiance line list for NIES 01.
xx retrieval was derived from high-resolution solar FTS measurements at Kitt Peak National Observatory by removing the terrestrial atmospheric absorption structure (R. Kurucz, http://kurucz. harvard. edu/sun/irradiance2008/). However, a small residual CO 2 absorption structure was found in the solar irradiance database that is probably due to insufficient atmospheric correction. This bias showed an airmass dependency, i. e. , large negative bias for small air-mass and small negative bias for large air-mass. In NIES 02. xx, the Kurucz model of solar irradiance spectra was replaced by the model of G. C. Toon, based on balloon-borne and ground-based spectra, which is utilized in all other algorithms (section 3). [49] There are several other reasons why NIES 01. xx has a large negative bias and scatter.
project. We acknowledge funding to support Darwin and Wollongong from the  Australian Research Council , Projects  LE0668470 ,  DP0879468 ,  DP110103118 , and  LP0562346. Lauder TCCON measurements are funded by  New Zealand Foundation of Research Science and Technology  contracts  C01X0204  and  CO1X0406. We acknowledge financial support of the Białystok and Orléans TCCON sites from the  Senate of Bremen  and  EU  projects IMECC, GEOMON and InGOS as well as maintenance and logistical work provided by  AeroMeteo Service (Białystok)  and the RAMCES team at  LSCE (Gif-sur-Yvette, France ) and additional operational funding from the  NIES   GOSAT  project. The Garmisch TCCON team acknowledges funding by the  EC-INGOS  project. Development of RemoTeC was partly funded by  ESA  through the  GHG-CCI  project (S.
Phylogenetic Analysis The character-taxon matrix used in the phylogenetic analysis is mainly based on a published phylogenetic analysis  [7] , with the addition of Kataria and 2 new characters. Thus, the reported analysis results in a data matrix of 156 characters scored across 23 taxa (Text S1, Dataset S1). All characters were treated as unordered, as in the original phylogenetic analysis  [7]. We analysed our dataset using TNT  [18, 19]  with a heuristic search of 1000 replicates of Wagner trees followed by tree bisection-reconnection (TBR) branch swapping. All characters were equally weighted. Zero length branches were collapsed if they lack support under any of the most parsimonious reconstructions. Also, two alternative support measures (Bremer support  [20]  and bootstrap resampling) were used to evaluate the robustness of the nodes of the obtained most parsimonious trees (see Text S1).
Additionally, the morphological dataset was analyzed with the 13 extant terminal taxa constrained with a backbone formed by the topology derived from the molecular analysis performed by Wiens and colleagues  [21]. This analysis served to test the effect of a molecular topology on our dataset and, more specifically, on the phylogenetic position of Kataria. The nine fossil taxa in the dataset were pruned (using ''pruntax'' command in TNT) and allowed to insert freely into their optimal positions during the constrained analysis. The molecular tree was constructed using the ''edit'' command in TNT. The constrained analysis was performed using a heuristic tree search with the molecular tree enforced as a backbone with ''force'' and ''cons'' commands in TNT.
e. excluding Xenopeltis and Loxocemus): 1) a clade composed by Ungaliophiidae+Erycinae+Boinae+Pythoninae, with a weak bootstrap support of 61% but moderate Bremer value of 5; and 2) a well-supported clade composed by bolyeriids, Kataria, tropidophiids, and caenophidian snakes, which received a strong bootstrap support of 82% but a low Bremer value of 3. This last group is supported by three unambiguous synapomorphies, all of which represent traits of the palatomaxillary arch: internal articulation of palatine with pterygoid short (70-. 0), ectopterygoid contact with the pterygoid is expanded Our additional constrained analysis conducted to test the effect of a molecular topology in the phylogenetic dataset resulted in two most parsimonious topologies. The strict consensus tree (Fig. 5 ) shows that all fossil taxa, except Kataria, were nested inside the enforced clade formed by the extant taxa Anilius and Tropidophiidae.
Acknowledgments We thank  Darrell Frost ,  Ivan Ineich ,  Julian Faivovich ,  Jorge Williams  and  Francisco L. Franco  for loan of specimens. Gratitude is also due to  Manuel Sosa  for the skillful drawings and  Camilo Arredondo  for the photographs. Jack Conrad  and an anonymous reviewer gave thorough and constructive criticisms. We are grateful to  Diego Pol  for his advice on the use of the T. N. T. program package. Finally, we are grateful to  Anjan Bhullar  for the revision of English grammar. The phylogenetic analysis was performed with the program TNT that is freely available through the  Willi Hennig Society.
Research was funded by  Museo de Historia Natural de Cochabamba ''Alcide dOrbigny'' ,  National Geographic Society  (grant  7163-01 ),  Conicet  (grant  PIP 5153 ),  Agencia Nacional de Promocion Cientı ´fica y Tecnologica  (grants  PICT 13803  and  53 ). HZ is supported by grants from  Fundaca ˜o de Amparo aPesquisa do Estado de Sa ˜o Paulo  ( 2011/50206-9 ) and  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico  ( 303545/2010-0  and  565046/2010-1 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2008). During Prehistory and Antiquity, palms of these arid regions also played an important role in the economies of the Near and Middle East. However, palm remains have mostly been identified at the family level -usually as coming from date palm -but little attempt  (Greiss 1957 , Fahn 1986 , Neumann et al. 2000)  has been made to distinguish: (i) the different genera; and (ii) the stem tissue from the woody parts of the leaf. The identification of the different parts of the palm (petiole vs. stem) is important in order to understand practices of date palm exploitation and management of date palm gardens in the past  (Tengberg 2012 ). In the Results section I provide anatomical descriptors for distinguishing between the different palm genera and different parts of the palms. Stem anatomical descriptors as well as anatomical descriptions are compiled in the computeraided identification software Xper 2 available on Internet (Palm-ID database: Thomas 2011b,  Thomas & De Franceschi 2013).