dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIALS AND METHODS Mycoplasma strains and culture conditions. The genome sequences of M. agalactiae strains PG2 T , 5632, and 14628 and M. mycoides subsp. capri serovar capri strain PG3 T are available in databases (GenBank accession numbers CU179680, FP671138, and AJPR00000000. 1 and JFAE00000000. 1, respectively). Other M. agalactiae clinical isolates came from a collection maintained in our laboratory. Most isolates were collected within the framework of the Vigimyc national surveillance network  (20)  or during specific epidemiological studies  (19). Strains were grown in PPLO medium supplemented as previously described  (21). Tet- DNA extractions and PCR amplifications. Mycoplasma genomic DNA was extracted from logarithmic-phase cultures (2 ml) using a Blood & Cell Culture DNA kit from Qiagen.
(12, 13). Genes are designated by their locus tags. b Glycosyltransferases in PG2 were predicted using the CAZy database, and homologs in strains 5632 and 14628 were determined by BLASTP analysis using PG2 T sequences (identity percentages are indicated in parentheses). aa, amino acids. Some enzymes were not detected (ND) using BLASTP searches. c Polysaccharide synthases were identified in this study by analyzing their secondary structure predicted by TMHMM 2. 0 and their similarity with other known synthases using Phyre. The length of the predicted synthases and the number of transmembrane domains (TMDs) are indicated. c Concentrations were below the detection limit (0. 8 g/ml). nation was done three times and the results were expressed as means Ϯ standard deviations (SD). The limit of quantification (LOQ) was calculated using the following equation: LOQ ϭ Mbs ϩ 10 ϫ SDbs, where Mbs is the mean of 10 blank samples (without sugar) and SDbs is the standard deviation of the blank samples.
Samples were then passed through a Propac PA1 precolumn (Dionex) (4 by 50 mm) followed by a CarboPak PA 1 column at 30°C. Gradient elution was performed at a flow rate of 1 ml/min with a multistep gradient as follows: 0 to 25 min, 90% H 2 O and 10% NaOH (160 mM); 25 to 34 min, 100% NaOH (200 mM); 35 to 50 min, 90% H 2 O and 10% NaOH (160 mM). Peak analysis was performed using Chromeleon software, version 7. 0. For nuclear magnetic resonance (NMR) spectroscopy, CPS were exchanged twice with 99. 9% D 2 O (Euriso-top), dried under vacuum, and dissolved in 99. 96% D 2 O (3 mg/0. 5 ml). 1 H NMR spectra were recorded, at 80°C, on a Bruker Avance 500 spectrometer equipped with a BBI probe (5-mm sample diameter) and Topspin 1. 3 software. 1 H NMR spectra were accumulated using a 30°pulse angle, a recycle time of 1 s, and an acquisition time of 2 s for a spectral width of 3,000 Hz for 32-K data points after presaturation of the HOD signal using an experimental sequence provided by Bruker.
BLASTP analyses were conducted in the Molligen database  (29)  to search for enzymes potentially involved in glucose uptake and phosphorylation and for the synthesis of UDP-sugar in M. agalactiae genomes, using the proteins described for members of the M. mycoides cluster  (12, 13). Homologs of enzymes described in Fig. 1  for M. mycoides subsp. mycoides PG1 and M. capricolum subsp. capripneumoniae strain Ambosa were first searched using BLASTP. Glycosyltransferases from M. agalactiae strain PG2 T were retrieved from the CAZy databases  (30) , and the homologous enzymes in strains 14628 and 5632 were identified using BLASTP analyses and the Molligen database  (29). Glycosyltransferases showing (i) several TMDs predicted by TMHMM2  (31)  with a large cytoplasmic loop bearing DXD and R/QXXRW-like motifs (identified by alignment with the galactan synthase MSC_0108 with Clustal Omega  [32] ) and (ii) homology with the cellulose synthase of Rhodobacter sphaeroides cellulose synthase subunit a (Uniprot accession number Q3J125) as shown using Phyre  (33)  were classified as synthases.
Transmembrane proteins (see Fig. S1  in the supplemental material) were represented by using the TOPO2 software (http://www. sacs. ucsf. edu/TOPO2/). Phylogenetic analyses of synthases among mycoplasmas. Glycosyltransferase sequences of diverse mycoplasma species were obtained from the CAZy database or from the nonredundant protein database of the National Center for Biotechnology Information by carrying out BLASTP searches against the synthase sequences identified in this study or previously described  (13). Among the putative glycosyltransferases, synthases were specifically identified as described above and selected to construct a phylogenetic tree using the www. phylogeny. fr website  (34, 35)  and the methodology of Ulvskov et al. (36). Briefly, the amino acid sequences were aligned with Muscle v3.
RESULTS A complete polysaccharide biosynthetic pathway is encoded by M. agalactiae. Metabolic pathways involved in the biosynthesis of ␤-(1¡2)-glucopyranose and ␤-(1¡6)-galactofuranose polysaccharides have already been predicted for several mycoplasma species within the M. mycoides cluster (Fig. 1 ). These data served as a basis to identify putative polysaccharide biosynthetic pathways in M. agalactiae. BLASTP analyses were conducted using whole-genome sequences of M. agalactiae available in databases to detect sequence similarity with enzymes of the M. mycoides cluster putatively involved in glucose uptake, transformation, and activation as UDP monomers. Homologs of Pgm and GalU were predicted in each M. agalactiae genome tested (similarity of Ͼ55% with MSC_0829 and MSC_0110, respectively), but no sequence similarity was evidenced for phosphotransferase system (PTS) permeases, Glk, GalE, or Glf (Table  2 ).
Putative glycosyltransferase genes were further explored using the CAZy database  (30). For strain PG2 T , the only M. agalactiae strain available in the CAZy database, three genes were predicted to encode glycosyltransferases, namely, MAG4860, MAG0570, and MAG3010 (Table  2 ). Homologs of the corresponding glycosyltransferases in strains 14628 and 5632 were retrieved by conducting BLASTP searches (Table  2 ). Remarkably, only the MAGb_1260 product (strain 14628) was found to display structural similarity to the previously described galactan synthase encoded by M. mycoides subsp. mycoides PG1 T MSC_0108  (13). Both are transmembrane proteins carrying a cytoplasmic domain with DXD and R/QXXRW motifs of the GT-A glycosyltransferase family  (37)  (see Fig. S1  in the supplemental material). However, the MAGb_1260 product is composed of 7 TMDs (see Fig.
(12)  failed to react with purified CPS, suggesting a different structure for the polysaccharide secreted by M. agalactiae 14628 (data not shown). The chemical composition and structure of the CPS in M. agalactiae were therefore elucidated by HPAEC analysis and NMR spectra, as described in Materials and Methods. Both analyses revealed that the CPS produced by strain 14628 was composed solely of glucose. Indeed, the correlations in the 2D NMR COSY (Fig. 3A ) and TOCSY spectra made it possible to attribute various chemical shifts to protons of the glucosyl residues (4. 481   (38, 39). This linkage was confirmed by the presence of connectivities between the H-1 of glucose (␦ 4. 481 ppm) and the signal at ␦ 4. 168 ppm, which corresponded to the H-6= of glucose in the NOESY spectrum. Similar connectivities were also observed in the 1 H/ 13 C HMBC spectrum.
ACKNOWLEDGMENTS We thank  Monika Gjorgjieva ,  Marie Guillot ,  Patrice Cuchet , and  Véronique Lefriand  for their excellent technical assistance. We are grateful to  François Poumarat , who first observed mmc4 antigenic phase variation in M. agalactiae isolates many years ago. Special thanks to all members of the  VIGIMYC network  for supplying several M. agalactiae isolates with documented clinical histories. P. G. , F. T. , E. B. , and C. C. were responsible for the conception and design of the experiments; C. P. -R. was responsible for the monomer composition and NMR analysis; P. G. , E. B. , and E. S. performed all of the other experiments; P. G. , F. T. , C. P. -R. , E. B. , and C. C. performed the data analysis; F. T. , P. G. , C. P. -R. , E. B. , and C. C. were responsible for the draft of the manuscript; all of us were responsible for critical review of the manuscript and approval of the final version.
Models We use data from nine LSMs participating in the PCN, including CLM4. 5, CoLM, ISBA, JULES, LPJ-GUESS, MIROC-ESM, ORCHIDEE, UVic and UW-VIC. For detailed information about the models and simulations we refer to  Rawlins et al. (2015) ,  Peng et al. (2015)  and  McGuire et al. (2016). The total soil depth for soil thermal calculations ranges from 3 m (divided into 8 layers) in LPJ-GUESS to 250 m (divided into 14 layers) in UVic. The physical properties of the soil differ among the models as well, and four of them (CLM4. 5, ISBA, UVic, UW-VIC) include organic horizons. Three models (ISBA, LPJ-GUESS, UW-VIC) do not archive soil subgrid results and provide only area-weighted ground temperature (i. e. averaged over wetlands and vegetated areas, and in some cases lake fractions). Table  1  lists relevant snow model details.
The simulations were generally run for the period 1960-2009, although some simulations were stopped a few years earlier. Each model team was free to choose appropriate driving data sets for weather and climate, atmospheric CO 2 , nitrogen deposition, disturbance, land cover, soil texture, etc. However, the climate forcing data (surface pressure, surface incident longwave and shortwave radiation, near-surface air temperature, wind and specific humidity, rain and snowfall rates) are from gridded observational data sets (e. g. CRUN-CEP, WATC; Table  S1  in the Supplement). The exception is MIROC-ESM, which was run as a fully coupled model, forced by its own simulated climate. Mean annual air temperature simulated by MIROC-ESM for the permafrost region was within the range (-7. 2 to 2. 2 • C) of the other forcing data sets used in this study and the trend in near-surface air temperature (+0.
03 • C yr -1 ) was the same for all forcing data sets. However, MIROC-ESM had both the highest annual precipitation (range 433 to 686 mm) and the highest trend in annual precipitation (range -2. 1 to +0. 8 mm yr -1 ) among the forcing data sets. The spatial domain of interest is the Northern Hemisphere permafrost land regions. Our analysis is based on the 0. 5 • × 0. 5 • -resolution gridded driving and modelled data for winter (DJF) 1980-2000.
g. Anisimov and Sherstiukov, 2016; Decharme et al. , 2016; Park et al. , 2014; Brun et al. , 2013; Pavlov and Malkova, 2009; PaiMazumder et al. , 2008). The soil temperature data set was run through four independent methods of quality control  (Sherstiukov, 2012b). However, some soil temperature www. the-cryosphere. net/10/1721/2016/ The Cryosphere, 10, 1721-1737, 2016 observations could be disturbed by grass cutting during the warm season and the removal of organic materials, mainly at agricultural sites, which may affect the trend in the warm season  (Park et al. , 2014) , but this does not affect our results on the air-upper soil temperature relationship in winter. Precipitation station data have been compiled from the GSOD data set produced by the National Climatic Data Center (NCDC; http://www.
ncdc. noaa. gov) for all of the stations that are included in the RIHMI-WDC data set. In addition to the station's ground snow depth observations we use gridded snow water equivalent (SWE) data from the GlobSnow-2 product (http://www. globsnow. info/swe/), which have been produced using a combination of passive microwave radiometer and ground-based weather station data  (Takala et al. , 2011). Orographic complexity, vegetation cover and snow state (e. g. wet snow) affect the accuracy of this product. When compared with ground measurements in Eurasia, the GlobSnow product shows root mean square error (RMSE) values of 30 to 40 mm for SWE values below 150 mm, with retrieval uncertainty increases when SWE is above this threshold (e. g. Takala et al. , 2011; Muskett, 2012; Klehemet et al. , 2013).
In order to be compared with station data, snow depth was then calculated from SWE using a snow density of 250 kg m - 3  , which is a median observed value in winter. Zhong et al. (2014)  report snow density values of 180-250 kg m -3 for tundra/taiga and 156-193 kg m -3 for alpine snow classes. Woo et al. (1983)  report snow density values of 250-400 kg m -3 for various terrain types. Choice of density does not materially affect the results. All these data have been compiled for winter (DJF) and the same time period of 1980-2000. This period was chosen because soil temperature data are sparse before 1980 and the JULES simulation stopped in the year 2000. Comparison of the simulations with the station data was done using a weighted bilinear interpolation from the four surrounding model grid points onto the station locations.
By limiting our analysis to winter only, we are able to attribute the cross-model and model-to-observation differences in T primarily to snow insulation effects. In winter, the effects of other factors (e. g. soil moisture, texture) on T are much smaller than that of snow. Ground surface temperatures were not recorded in the Russian data set, but 20 cm soil depth temperatures were. To test how sensitive results are using 20 cm depth temperatures instead of ground surface temperatures, we also analysed model-simulated temperature differences between ground surface and T air , and found no qualitative differences, hence justifying the use of 20 cm observations. We use the Pearson product-moment correlation coefficient and its significance  (von Storch and Zwiers, 1999)  to investigate the covariability between T and d snow as well as between T soil and its two forcing factors (T air and d snow ).
Before we compute the correlations, we detrended the data by removing a least squares regression line. The calculated correlation maps (i. e. spatial distributions of correlation coefficients) based on model and observation data, allow comparison of the spatial patterns of these relationships. To further examine the functional behaviour between the key variables, we present relation diagrams between pairs of variables (e. g. variation of T with change of d snow ). To evaluate the performance of the individual LSMs we calculate the RMSE between the observed and modelled relationships. We illustrate the dependence of T vs. d snow and T soil vs. d snow relations for three T air ranges. To distinguish dry snow pack regimes from those where sporadic melt may occur even in winter, we split T air into three regimes: the coldest conditions (T air ≤ -25 • C, representing 24 % of observations), the intermediate temperature conditions (-25 • C < T air ≤ -15 • C, representing 42 % of the observations), and the warmest conditions (-15 • C < T air ≤ -5 • C, representing 34 % of observations).
The scatter in the modelled relationships, indicated by the interquartile range, is of the same order as in the observations, except for ISBA and MIROC-ESM, which produce noticeably smaller variations. Figure  2a  views the T vs. d snow relationship in a complementary form using the PDFs of T for different snow depth regimes. This analysis allows for a detailed evaluation of the snow-regime-dependent T separation by quantify-ing and comparing the modal value and width of the different conditional PDFs. Since the Russian snow depths are clearly non-normal in distribution (Fig. S1  in the Supplement, with a median d snow of 30 cm), we divide the data into "shallow" (d snow ≤ 20 cm) and "thick" (d snow ≥ 45 cm) regimes to separate two snow depth regimes. The modal value of the station-based T PDF is 5 • C for shallow snow and 14 • C for thick snow; that is, thick snow is a better insulator than thin snow.
Roles of atmospheric forcing and model processes The cross-model differences in the snow insulation effect, presented by the air temperature-snow depth-soil temperature relationships described above, are partially due to the differences in the atmospheric forcing data and also due to differences in the snow and soil physics used in the LSMs. However, because the climate forcing data sets utilised with each model are observation-based (except for MIROC-ESM), obvious outliers in individual model performance likely indicate poor or deficient physical descriptions of the air-snow-soil relations in that specific LSM.
Summary and conclusions The aim of this work was to evaluate how state-of-the-art LSMs capture the observed relationship between winter nearsurface soil and air temperatures (T soil , T air ) and their modulation by snow depth (d snow ) and climate regime. We presented some benchmarks to evaluate model performance. The presented relation diagrams of T soil and the difference between T soil and T air regarding snow depth allow for a much better assessment, used to reveal structural issues of the models, than a direct point-by-point comparison with station observations. The results are based on the comparison of LSMs with a comprehensive Russian station data set. We see large differences across the models in their mean air-soil temperature difference ( T ) of 3 to 14 • C, in the sensitivity of near-surface soil temperature to air temperature (T soil vs.
Data availability The data will be made available through the National Snow and Ice Data Center (NSIDC; http://nsidc. org); the contact person is Kevin Schaefer (kevin. schaefer@nsidc. org). The Supplement related to this article is available online at doi:10. 5194/tc-10-1721-2016-supplement. Author contributions. Annette Rinke developed the idea that lead to this paper, Wenli Wang and Annette Rinke carried out research, John C. Moore contributed ideas, Annette Rinke, John C. Moore, Wenli Wang, and Duoying Ji wrote the paper and all authors discussed the results and commented on the paper at all stages.
Acknowledgements. This study was supported by the  Permafrost Carbon Vulnerability Research Coordination Network , which is funded by the  US National Science Foundation (NSF). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  US Government. Eleanor J. Burke  was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Program  ( GA01101 ). Eleanor J. Burke ,  Shushi Peng ,  Philippe Ciais  and  Gerhard Krinner  were supported by the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no. 282700. Theodore J. Bohn  was supported by grant  1216037  from the  NSF Science, Engineering and Education for Sustainability (SEES)   Post-Doctoral Fellowship program. Bertrand Decharme ,  Ramdane Alkama  and  Christine Delire  were supported by the  French Agence Nationale de la Recherche  under agreement  ANR-10-CEPL-012-03.
Mating position does not change with epandrial lobe length We tested whether variation in mating position within treatment groups could be explained by differences in lobe lengths or body size, by testing whether mating angle or tilting index after 10-15 min of copulation was correlated with a) left or right epandrial lobe length, b) the sum of both epandrial lobe lengths, c) the ratio of left and right epandrial lobe lengths and d) tibia length (Table  3 ). In mating couples with unmodified males, neither mating angle nor tilting index was correlated with left epandrial lobe length, the sum of left and right lobe lengths, the ratio of lobe lengths or tibia length (Table  3 ). In a dataset containing the present data and previously published data on wild-type D. pachea mating angles and lobe lengths  [43] , there was no significant correlation between left or right lobe length and mating angle (Table  3 ).
Flies were observed and bristles were laser-ablated at 400x magnification. Acquisition and laser parameters were controlled by MetaMorph software (Molecular Devices) and iLas 2 software (Roper Scientific). Laser parameters were set to 15-23 % laser power and 500-1000 spots. Removal of all the bristles of an epandrial lobe required about 30 laser pulses (one pulse per bristle) and took 5-10 min. All males survived the laser surgery treatment. After laser surgery, males were isolated in vials and left to recover for at least 4 days prior to the mating experiments.
Measures of mating behaviour To measure the mating position angle, screenshots of the video recordings were extracted approximately every 2. 5 min from the start of copulation until the end of copulation, but only when the female was viewed exactly on its dorsal side. On each screenshot, a line was drawn between the bases of the wings in both the male and the female, and the angle between the two lines was measured using tpsDig 2. 17 software  [70]. The resulting angle is equivalent to the angle between the male's and the female's antero-posterior body axis as measured in our previous study  [43]  (Fig. 3a ). This new method makes it possible to estimate the angle even when the male is tilted towards one side of the female whereas the previous method does not. On each screenshot, the degree to which the male was tilted towards the female's right side was assessed through an index defined as the tilting index.
We tested whether mating angle after 10-15 min of copulation was correlated with tilting index after 10-15 min of copulation, using correlation tests for each treatment (left lobe cut, left lobe shaved, and unmodified male) separately. We also tested whether mating angle and tilting index were correlated with tibia length (as a proxy for body size), left epandrial lobe length, right epandrial lobe length, the sum of left and right lobe lengths or the ratio of lobe lengths. We used Spearman's rank correlation tests rather than Pearson's product-moment correlation tests, as the latter are sensitive to outliers in small datasets.
Genitalia dissection for light microscopy After video recording, the males were stored in absolute ethanol at -20 °C and then rehydrated for 5 min in phosphate buffer saline (PBS). Subsequently, the epandrium and the left foreleg were dissected using fine needles under a Zeiss Stemi 2000 stereo microscope. The dissected epandria and the legs were then washed twice in 100 % glycerol and mounted in 100 % glycerol. Dissections were imaged at 200x magnification using a Keyence VHX2000 microscope with a 100-1000× VH-Z100UR/W lens. Measurements of left and right epandrial lobe lengths (Fig. 1a ) and left foreleg tibia length were done using tpsDig 2. 17 software  [70].
Each mating couple was SEM-imaged with a JSM-7500 F field emission scanning electron microscope (Jeol) in female ventral view at 37×, 75×, 110×, 150×, 190×, 250× and 350× magnification, and in lateral view (left and right) at 75× and at 190× magnification. For each mating couple, the positions of the epandrial lobes and lateral spines on the female was observed. We measured the angle between the male's and the female's antero-posterior body axis using tpsDig 2. 17 software  [70] , by assessing the angle between the epandrial dorsoventral axis and a straight line going through the midline points of the female's sternites (Fig. 10 ). During copulation, female external genital structures are partially covered by the male genitalia. To dislodge the mating male and thus completely visualize female structures, three critical-point dried and previously SEMimaged mating couples were dissected.
Classification and features The strain FF12 T was isolated from the mouth of a West African lungfish (Protopterus annectens) in Senegal in June 2014 (Table  1 ). A sterile swab was introduced in the mouth of this fish. The sample was inoculated on a 5% sheep's bloodenriched Columbia agar (bioMérieux, Marcy L'Etoile, France) and incubated at 37°C during 48 hours. First identification of this strain by MALDI-TOF in Dakar was attempted  [30]. Then in Marseille MALDI-TOF protein analysis was performed using a Microflex LT (Bruker Daltonics, Leipzig, Germany) as previously reported  [31]. An isolated colony was deposited in duplicate on a MALDI-TOF target for analysis. Scores ranging from 1. 23 to 1. 47 were obtained for FF12 T , suggesting that this strain was not a member of any known species in the MALDI-TOF database.