dataesr/scientific-paragraphs-categorization huggingface dataset:
Optical analysis All DOM fluorescence signatures were analyzed according to the procedures of  Cory and McKnight (2005). EEMs were collected over an excitation range of 240-450 nm, by 5 nm and 10 nm increments, respectively, while emission was monitored from 300-600 nm and 300-560 nm by 2 nm increments on a Fluoromax-3 fluorometer (Jobin-Yvon Horiba). No inner filter corrections were necessary; absorbance scans (Shimadzu UV-1601 PC) confirmed samples were optically dilute (<0. 02 AU in excitation range 240-450 nm). All EEMs data were corrected for instrument-specific bias using manufacturer-generated correction files for excitation and emission and blank corrected. The blank for the Nalgene bottle control was obtained by storing lab grade water for 24 h and when the EEM for this sample was blank corrected there were no detectable fluorophores (see supplemental information, figure S1 available at stacks.
All media were pre-incubated for at least 72 h to test for contamination before inoculation. All cultures were grown in an illuminated incubator at 4-6 • C until signs of growth appeared (∼3 weeks, with some organisms only appearing after several months). Genomic DNA was extracted from cells that formed isolated colonies on agar plates after several transfers. Samples were sent to Laragen (www. laragen. com) for 16S ribosomal gene sequence analysis using an Applied Biosystems ABI3730 (www. appliedbiosystems. com) automated sequencer. Nearly full-length 16S sequences were obtained with multiple reads (1250-1450 bp). Nucleotide sequences were assembled and aligned in BioEdit  (Hall 1999)  using the ClustalX function with data obtained from GenBank using the BLAST search tool (Version 2. 0; www.
ncbi. nlm. nih. gov/ BLAST. cgi, Altschul et al 1990). Sequences were filed with GenBank under accession numbers FJ152549-FJ152553. A culture independent approach was also employed with stream water (200 ml) from site CG3 filtered onto a 47 mm Supor R -200 0. 2 µm pore size, sterile membrane under low pressure (<7 psi). Filters were transferred to cryovials containing 4 ml TES buffer (100 mM Tris, 100 mM EDTA and 2% SDS), flash frozen in liquid nitrogen, and stored at -80 • C. Biomass collected on the filter was extracted and purified using the UltraClean Soil DNA kit (MO BIO, Carlsbad, CA, USA). Using universal bacterial 16S rRNA primers 9F (5 -GAGTTTGATCCTGGCTCAG) and 1492R (5 -GGTTACCTTGTTACGACTT) (Stackebrandt and Liesack 1993) purified genomic DNA was amplified. Each 50 µl PCR reaction contained approximately 2 ng µl -1 DNA, 0.
Based on DGGE migration patterns, 27 clones from each clone library were selected and sent to Functional Bioscience Inc. on LB agar plates for DNA preparation and sequencing. DNA sequences were determined with a Big-DYE Terminator v3. 1 cycle sequencing kit and sequence reaction products were run on an ABI 3730 DNA sequencer. DNA sequences were aligned and assembled in BioEdit  (Hall 1999). Sequences were analyzed for chimeras with Bellerophon  (Huber et al 2004) , and chimeric sequences were removed. All sequences were deposited in GenBank accession numbers: HQ221513-HQ221542. Gene sequence identification was obtained through a comparison with known sequences in the GenBank data base using BLASTN (www. ncbi. nlm. nih. gov/blast/Blast. cgi, Altschul et al 1990). All 16S Table  1. Water quality of streamwater from the Cotton Glacier, Antarctica.
Nearly full-length 16S rRNA sequences (1250-1450 bp) of these isolates revealed that they were members of the β-Proteobacteria and Bacteroidetes lineages (formerly known as Cytophaga-Flavobacteria-Bacteroides (CFB) phylum). The CG3 16S rRNA clone library sequence lineages were consistent with the dominant lineages found in the Cotton Glacier isolates. For clone library and isolate sequences the closest relatives, according to GenBank, came from cold temperature environments (see figure  4 ). Two strains of fluorescent pseudomonads (CGina-01 and CGina-02) recovered from an R2A enrichment culture were ice nucleation active. However, none of the sequenced isolates showed INA. In pure culture, these strains had INA that could be detected at -4 • C. These strains produced ice nucleation active cells at a rate of 1 cell per 1.
The microbial assemblage present in the Cotton Glacier stream water represents one that can function in a humic-free environment, with higher exposure to ultraviolet radiation dependent on the solar radiation and the transparency of Cotton Glacier water. Although detailed radiometric data are not available for the Cotton Glacier when the samples were collected, pyranometer data recorded at meteorological stations at nearby (<50 km away) Lake Brownsworth in the McMurdo Dry Valleys suggest a high solar flux. The average solar flux at Lake Brownsworth for the months of November through January from 2000 through 2004 was 299 W m -2 and on 30 December 2004 the average solar flux was 300 W m -2 (with minimum and maximum values of 63. 9 W m -2 and 838 W m -2 , respectively, see metadata at www. mcmlter.
These BGE values are similar to those calculated for ultraoligotrophic freshwater lakes in Antarctica (mean: 0. 039, range: 0. 023-0. Using the BP values in table 2 and conversion factors described for the bacterial production calculations it is possible to calculate the number of cells produced per day (cells d -1 ) (table  2 ). These cell production rates, when expressed per liter, are only 3. 2%, 1. 8% and 22. 4% of the bacterial abundances in CG1, CG2 and CG3, respectively (table  2 ). These data suggest that the calculated BGE estimates may be too low. However, the BGE values from this study are similar to others derived from ecosystems in the same region of Antarctica. An alternative hypothesis would be that there is a persistent release of bacterial cells from the ice as it melts. These cells would be counted in the abundances for this season, but their growth would have occurred in previous seasons.
Acknowledgments Helicopter access was provided by  Petroleum Helicopters Incorporated , with special thanks to  D Black ,  B James  and  J Scott Pentecost. Logistical support was provided by  Raytheon Polar Services , with  B Brunner  providing mountaineering advice. Chris Jaros  was integral to field sampling and coordination. K Welch  provided the ion data. Descriptions of the Cotton Glacier geology were provided by  D Elliot. We thank  D Georgakopoulos  ( Athens Agricultural University ) for kindly providing strain GrX-01, and to  T Wagner  for the opportunity to visit this unique system. Funding for this project came from  NSF   OPP-0338260  to YPC,  OPP-0338299  to DMM,  OPP-0338121  to PLM, and  OPP-0338342  and the  Montana Space Grant Consortium  to CMF. Any opinions, findings, or conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the  National Science Foundation.
Spectral characteristics of TEC  variations were analysed using data for different seasons of 2008. Each dynamical spectrum was calculated using ~3 • 10 7 rows of continuous raw data  (Afraimovich et al. 2009b). The time of the TWP appearance was found out to depend greatly on season. In winter, TWPs in the Northern hemisphere are observed mainly 3 h after the passage of the morning solar terminator, when the TEC derivative is maximum  (Afraimovich et al. 2009b). At equinoxes, TWPs appear after the ST passage without any significant delay or lead. In summer, TWPs are recorded 1. 5-2 h prior to the ST appearance at an observation site (Fig. 4 ). It was found later that seasonal variations of TWP recording time correspond well with the moment of the ST passage over the magneto-conjugate area  (Voeykov et al.
2011). That day coincided with the main phase of magnetic storm (Dst -100 nT). For analysis, we used data from the dense GPS array GEONET (~1000 receivers) deployed over the Japanese Islands. The slips were shown to be caused by scintillations of GPS signals. These scintillations resulted from the scattering on small-scale irregularities being a part of larger-scale ones. Large-scale intense plasma irregularities were generated at the equator and oriented along magnetic field lines  (Anderson & Shtraus 2005; Ishin et al. 2009; Demyanov et al. 2011). Maximum number of phase slips corresponded to the line-of-sight (LOS) direction along and across the magnetic field line at the F-region maximum height. Figure  8  presents dependence of slip density P(c) on the angle between LOS and the magnetic field c.
Three strong TCs which developed along the Atlantic coast of the USA were used: Katrina (from 23 August 2005 to 31 August 2005), Rita (from 18 September 2005 to 26 September 2005) and Wilma (from 15 October 2005 to 25 October 2005). We used phase measurement data from dual-frequency GPS receivers (http://sopac. ucsd. edu) located near the area covered by the said cyclones. To analyse ionospheric disturbances produced by cyclones, we selected the following time intervals: -when geomagnetic conditions were quiet; -when cyclones reached their peak intensity. We picked out time series of TEC variations obtained in the evening or at night (LT) when the equatorial ionization anomaly (EIA) was absent. This can be explained by the fact that ionospheric irregularities are highly probable in the EIA region.
Initial time series of TEC variations were filtered for two periods: 02-20 and 20-60 min. To determine spatial localisation of the disturbances observed, we mapped the TEC variation intensity in the region covered by cyclones. The obtained distributions of TEC disturbances were compared to maps of near-ground meteorological parameters (pressure, meridional and zonal wind speeds). Maps of meteorological parameters were constructed from NCEP/NCAR Reanalysis data (http://www. esrl. noaa. gov/psd). Figure  11  demonstrates spatial and temporal dynamics of TEC variations with the period of 20-60 min, and variations in near-ground pressure during Katrina. Black lines with variable line weights depict trajectories of ionospheric points for satellites PRN02, PRN04, PRN07, which were observed at 10 GPS stations in the evening/at night (LT).
The application of GPS data allowed us to validate this model experimentally for the first time  (Afraimovich et al. 2006a ). The wave propagation during the 25 September 2003 Tokachi-oki is schematically presented in Figure  12  as an example. Using the analysis of data after the destructive earthquake of 12 May 2008 in Wenchuan Province (China) as an example,  Afraimovich et al. (2010a)  have shown that the plane front of TEC disturbances in the ionosphere (at about 500 km from the source) is parallel to the fault line. World's densest network of GPS receivers GEONET (Japan) allowed us to analyse the evolution of CID propagating at more than 2000 km. CID was generated by the earthquake of 4 October 1994 near the Kuril Islands and was detected by the nearest GPS receivers ~10 min after the earthquake.
2010). This is quite convenient when making comparison with other instruments. G ¼ X I ij AV S ij ð3Þ The advantage of using GEC is that local features of ionospheric parameters are smoothed. As a result, regularities that describe state and dynamic regime of the ionosphere can be revealed. GEC values and variations are determined by three main factors: solar activity, magnetospheric processes and processes in the neutral atmosphere. This generalised ionospheric parameter can be thus used as an index which reflects state of near-Earth space. Besides, GEC data can be used to solve inverse problems (to obtain quantitative characteristics of flux of solar UV radiation, etc. ). GEC dynamics during the 23rd solar cycle (1998-2011) is represented by the black curve in Figure  17. One can see significant GEC variations (from 0.
Acknowledgements. We are very thankful to  I. F. Gamayunov ,  V. A. Karachentsev ,  V. V. Kiryushkin ,  T. N. Kondakova ,  A. V. Plotnikov ,  P. V. Tatarinov ,  I. I. Ushakov  for their active assistance. We are thankful to the referees who have enabled us to improve the manuscript through their valuable suggestions. The study was partially supported by  RFBR  (under Grants Nos. 12-05-33032  a,  12-05-31279  a and  12-05-31069  a), by the  Ministry of Education and Science of the Russian Federation  (under agreement Nos. 8699 ,  8388  and  14. 518. 11. 7065 ), and by the  Russian Federation President Grant   MK-3771. 2013. 5. Fig. 17. Dynamics of GEC, F10. 7 index and GEC model values in 1998-2010. J. Space Weather Space Clim. 3 (2013)  A27.
State of salinization of French aquifers The approach is based on recent data (2005-2010) on the mineralization of the French groundwater bodies as defined by the European Water Framework Directive, extracted from the ADES data base (http://www. ades. eaufrance. fr) and integrated in a GIS. A classification of water bodies was undertaken based on the number of observation points impacted by average chloride concentrations above a threshold (100 mg/L) to identify those with salinities significantly above the mean geochemical background values in the French context without taking into account specific water uses. Based on literature, hypotheses on the origin and vectors of salinization are formulated for most of the concerned water bodies and a general typology of French continental and overseas groundwater bodies is established.
Zhou et al. (2009) reported global soil respiration sensitivity (including root respiration and SOC decomposition), estimated by an empirical model with a global soil respiration dataset, of 3. 3 Pg C yr -1 ∆ • C -1 and 3. 21 Pg C yr -1 ∆ • C -1 , respectively. Although these values are not directly comparable to SOC decomposition responsiveness, but to SOC decomposition response in the almost biome models in this study, their sensitivity could be underestimated. There is still a lack of observation-based estimation of global SOC response intensity to ∆T. Both global SOC stocks and a data-oriented parameter such as this may be important information for the constraint and validation of global SOC dynamicsDiscussion Paper | Discussion Paper | Discussion Paper |.
Statistical data treatments A linear regression was performed between the number of Perkinsus olseni in the gills and in the total body. A 1-way ANOVA was used to compare the intensity of infection over time. Maximum Type I error rates were set at α = 0. 05. Prior to ANOVA, homogeneity of variance was confirmed using Cochran's test. Data were log-transformed to follow homogeneity requirements. Where ANOVA was significant, differences between treatments were separated by a Tukey test of mean comparison. When variances were not homogeneous, the non-parametric Mann-Whitney U-test was used. The impact of the tidal level on the GR was assessed by a 1-way ANOVA. To assess the association between CI, Perkinsus spp. infection and GR, multiple regressions were performed for each site. CI data represented the condition of the clam only at the experiment completion.
Acknowledgements. C. D. was financed by Fonds Communs Aquitaine-Euskadi and Conseil Général de la Gironde. Many thanks to F. Prince, P. Lebleu, M. Basterextea for their valuable help in clam collecting. Authors are grateful to A. C. Barnes for English editing. The study was partly financed by ANR Multistress. We are grateful to SEPANSO for allowing us to work at the National Reserve of Banc d'Arguin (France). Andrews JD (1991) Effects of Perkinsus marinus infection in the eastern oyster, Crassostrea virginica: I. Susceptibility of native and MSX-resistant stocks. J Shellfish Res 10: 417-423 Burreson EM, Ragone Calvo LM (1996) Epizootiology of Perkinsus marinus disease of oysters in Chesapeake Bay, with emphasis on data since 1985. J Shellfish Res 15: 17-34 Burreson EM, Alvarez RS, Martinez VV, Macedo LA (1994) Perkinsus marinus (Apicomplexa) as a potential source of oyster Crassostrea virginica mortality in coastal lagoons of Tabasco, Mexico.
In preparation of the first ignition attempts on the Laser Mégajoule (LMJ), an experimental program is being pursued on OMEGA to investigate LMJ-relevant hohlraums. First, radiation temperature levels close to 300 eV were recently achieved in reduced-scale hohlraums with modest backscatter losses. Regarding the baseline target design for fusion experiments on LMJ, an extensive experimental database has also been collected for scaled implosions experiments in both empty and gas-filled rugby-shaped hohlraums. We acquired a full picture of hohlraum energetics and implosion dynamics. Not only did the rugby hohlraums show significantly higher x-ray drive energy over the cylindrical hohlraums, but symmetry control by power balance was demonstrated, as well as high-performance D 2 implosions enabling the use of a complete suite of neutrons diagnostics.
Theory Recently the LTE opacity code bigbart  [13]  was extended to treat Non-LTE plasmas through a collisional-radiative model. This model includes the processes of spontaneous emission and resonant photoabsorption, collisional excitation and de-excitation, radiative recombination and photoionization, collisional ionization and three-body recombination, autoionization and dielectronic capture. The linear system to be solved is dn¡ ~át M M E"A i = 1, ,JVÍ (1) where n¡ is the population density of atomic state in = (nv • • ,TIM), Rij and Rji are rates connecting different atomic states and M is the total number of atomic states considered. The steady-state solver, i. e. , (dn¡ dt = 0), uses as input an electronic temperature t e and density n e to compute rates between atomic states. Rates of certain processes and their inverses are obtained through the detailed balance principle with the relativistic atomic package FAC  [14].
Models We have used data from 14 models, which performed the future ACCMIP simulations (see Appendix A). Most of the models are CCMs, with the exception of CICERO-OsloCTM2, MOCAGE and STOC-HadAM3. The two former are chemistry-transport models (CTMs), while STOC-HadAM3 produces its own meteorology, but without any interactions with climate. The CCMs were run with an atmosphere-only configuration, with sea-surface temperature and sea-ice data coming either from coupled oceanatmosphere model simulations or from observations. The models that are linked to coupled climate models that participate in CMIP5 (either because they share the same atmospheric component, or because they use SSTs/SICE generated by CMIP5 models, or both) are: (1) CESM-CAMsuperfast (atmosphere-only version of CESM-CAM, using sea surface temperatures (SSTs) and sea-ice (SI) from the latter's CMIP5 simulations), (  2 ) CMAM (based on the preceding generation GCM, but using SSTs/SI from CanESM2 CMIP5 simulations), (3) EMAC (used SSTs/SI from a CMIP5 simulation with the CMCC model, which is, like EMAC, based on ECHAM5, but with differences in the A.
5 (atmosphere-only version of the model used for CMIP5, but using SSTs/SICE from CMIP3, with the best possible correspondence between RCPs and SRES scenarios), (  12 ) STOC-HadAM3 and 13) UM-CAM (both using SSTs/SI from HadGEM2 coupled CMIP5 simulations). CICERO-OsloCTM2 used ECMWF IFS model forecast data for 2006 for all simulations. Detailed model descriptions are provided in the ACCMIP overview paper of  Lamarque et al. (2013). Below we present some of the main features of the models, with an emphasis on those that are important for OH and methane lifetime. Surface methane concentrations were prescribed in all models (but then methane is allowed to undergo loss processes in the rest of the atmosphere, thus methane burdens will not be exactly identical), except for (a) LMDzORINCA, in which specified emission fluxes were used (see  Szopa et al.
, 2012) , and (b) GISS-E2-R, in which interactive emissions for wetlands and prescribed emissions from other sectors were used (see  Shindell et al. , 2004 Shindell et al. , , 2012b)). For prescribing methane concentrations at the surface, most models used data from the database of  Meinshausen et al. (2011) , except for CICERO-OsloCTM2 and EMAC that used present day methane values from IPCC TAR and from AGAGE  (Prinn et al. , 2000) , respectively, scaled to match the evolution in  Meinshausen et al. (2011)  in the future. UM-CAM and STOC-HadAM3 are the only models having a globally constant concentration of methane. Note that methane concentrations vary between different time periods in all models. Anthropogenic and biomass burning emissions of NO x , CO, and aerosols used in the simulations were identical in all models  (Lamarque et al.
The proposed simulation length for each timeslice was 4-10 yr (after spin-up) using prescribed monthly SSTs (no interannual variations), valid for each timeslice and averaged over 10 yr (see number of simulated years for each model in  Lamarque et al. , 2013). There are certain gaps in the data provided (e. g. missing variables for some models), but overall the dataset is fairly consistent. In addition to the above-mentioned simulations, sensitivity experiments were conducted by some modelling teams: a) simulations with present day emissions but climatic conditions set to 2030 or 2100 levels, and b) simulations with perturbed methane concentrations. While these experiments were only performed by a sub-set of the ACCMIP models, in combination with some further tests performed solely with GISS-E2-R they provide further valuable insight into the processes controlling OH and methane lifetime (see Sect.
We note that NCAR-CAM3. 5 uses the results from a CCSM3 Commitment simulation as an equivalent for RCP2. 6, which leads to an underestimate of the climate effect (see  Lamarque et al. , 2013)  on methane lifetime. The overall change between 2000 and 2100 for RCP2. 6 is minimal though (-4. 5 % on average). For RCP4. 5, for which data from fewer models (7) are available, there is generally a tendency for methane lifetime decreases, especially after 2030. The exception is the GISS-E2-R model, where we find the opposite trend, although with smaller increases than for RCP2. 6. Overall, RCP4. 5 reveals the largest negative change in global methane lifetime levels relative to 2000 (-8. 4 % on average). In RCP6. 0 (6 models), there is a mix of positive were only plotted for timeslices with data from at least 7 models, after removing results from CICERO-OsloCTM2, HadGEM2 and UM-CAM.
Of the models that simulate stratospheric ozone, those with the fastest ozone recovery are GISS-E2-R, HadGEM2 and MOCAGE. LMD-zORINCA shows no stratospheric ozone changes, due to the fact that an offline ozone climatology was used  (Li and Shine, 1995). Data for photolysis rates of ozone to yield O 1 D (JO 1 D) were only provided by a fraction of the models. In most cases there is a detectable decrease in J (O 1 D), and it relates to the increase of overhead ozone. However, only one model (GISS-E2-R) shows global J (O 1 D) decreases that are sizeable (6-15 %) in all RCPs. The GISS-E2-R results on stratospheric ozone and J (O 1 D) may partly be an overestimate, as this model has been found to have an ozone hole that extends slightly too far equatorward in September-October and persists about one month too long in the polar region  (Shindell et al.
Also, low stratospheric ozone columns in this model may be causing higher UV radiation resulting in greater tropospheric OH production, although it is difficult to diagnose this as photolysis rates are not available from MOCAGE  (Naik et al. , 2012a). When we exclude HadGEM2 and UM-CAM, the relationship becomes weaker and less significant. To examine what drives the inter-model differences in OH in the future, we performed a similar analysis, using the difference between 2100 and 2000 values of the variables as quantities of interest. For 2100, we used data from the RCP8. 5 scenario, due to the fact that it is expected to have the strongest signals. From our analysis, the strongest relationship is with changes in temperature and humidity (see Fig. 10a  for temperature), two factors that strongly depend on each other.