dataesr/scientific-paragraphs-categorization huggingface dataset:
For each positive PCR product, we also performed a quantitative PCR to obtain parasitemia  28. Briefly, for each individual, we conducted two qPCR in the same run: one targeting the nuclear 18s rDNA gene of Plasmodium and the other targeting the 18s rDNA gene of bird (see ref. 28 for primers and probe sequences). Parasite intensities were calculated as  relative quantification values (RQ) as 2e -(C t 18s Plasmodium -C t 18s bird) using the software SDS 2. 2 (Applied Biosystem). C t represents the number of PCR cycles at which fluorescence is first detected as statistically significant above the baseline, which is inversely correlated with the initial amount of DNA in a sample. RQ can be interpreted as the fold-amount of target gene (Plasmodium 18s rDNA) with respect to the amount of the reference gene (host 18s rDNA).
All qPCR were run on an ABI 7900HT real-time PCR system (Applied Biosystem). Modeling under random forest. We used a set of moderately high-resolution climate and satellite remote sensing variables to characterize the environmental differences among our sampling areas. Variables were re-aggregated from their native resolutions to 1 km resolution. We used 19 bioclimatic variables (representing both temperature and precipitation) from the WorldClim database  14  which are 50-year averages (1950-2000) of annual means, seasonal extremes and degrees of seasonality in temperature and precipitation, and represent biologically meaningful variables for characterizing species range  15, 19, 29  : (http://www. worldclim. org/), and we used elevation measure, downloaded from the Earth Remote Sensing Data Analysis Center (ERSDAC, http://www.
Most statistical procedures and traditional data modeling technique (such as linear regression or ANOVA) measure variable importance indirectly by selecting variables using criteria such as statistical significance and Akaike's Information Criterion. The Random Forest has a different approach; it is a non-parametric algorithm method. We used this method because Random Forest procedures (i) do not require the use of any particular model (which might be difficult to assign given a complex response such as disease prevalence), (ii) do not require normalized data, and (iii) have consistently outperformed traditional regression procedures on a number of datasets  15, [30] [31] [32]. The advantage of random forest models is their ability to predict a continuous (in this case, prevalence) rather than categorical (presence/absence) variable across a landscape, and their ability to model complex interactions among predictor variables.
Acknowledgments We thank all the volunteers from the  National Museum of Natural History of Paris  and from the  University of Bourgogne  for their help on the field and for providing samples:  F. Baroteau ,  J. Besnault J. Birard ,  M. Brenier ,  L. Brucy ,  P. Bulens ,  E. Cellier-Holzem ,  P. Cannesson ,  G. Chaussi ,  B. Faivre ,  P. Fiquet ,  C. Girard ,  R. Guerreiro ,  D. Lavogiez ,  J. Marion ,  F. Martayan ,  G. Massez ,  P. Ollivier ,  V. Ternois , and  M. Zucca. Financial support was provided by the  Region Ile de France  to C. L. , R. J. and G. S. , the  Region Bourgogne  to G. S. , the  CNRS Program MIE  to G. S.
Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially chal-lenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.
g. Karl et al. , 2001; Corno et al. , 2007; Aksnes and Ohman, 2009;  Published by Copernicus Publications on behalf of the European Geosciences Union. C. Beaulieu et al. :  Factors challenging our ability to detect long-term trends in ocean chlorophyll  Lomas et al. , 2010; Saba et al. , 2010; McQuatters-Gollop et al. , 2011). Satellite-derived ocean color and temperature data allow comprehensive estimates of the global distribution of ocean primary productivity, estimated from data provided by the Coastal Zone Color Scanner (CZCS), Ocean Color and Temperature Sensor (OCTS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS) ocean color instruments. These data sets have allowed many scientific advances over the past decades as illustrated, for example, in  McClain (2009).
The CZCS sensor generated the first satellite ocean color data from November 1978 to June 1986; although focused primarily on coastal regions, CZCS also provided a picture of global patterns. However, it is not possible to determine trends from the CZCS record; the mission was a proof-of-concept, and thus the sensor was not continuously validated and suspected to drift after the first year of operation  (Hooker and McClain, 2000; NRC, 2004). Ten years later the OCTS was launched and operated from July 1996 to June 1997. SeaWiFS became operational in September 1997 and remained remarkably stable for a decade, offering new opportunities for ocean biogeochemistry and climate research. Nevertheless, SeaWiFS began having telemetry problems in January 2008 and failed completely in December 2010.
Three other ocean color sensors, MODIS-Terra, MERIS and MODIS-Aqua have been operating since December 1999, March and July 2002, respectively, but MERIS failed in May 2012. Several years of overlap between SeaWIFS and MODIS-Aqua allowed successful cross-calibrations to merge data from the two sensors (e. g. Fargion and McClain, 2003; Maritorena and Siegel, 2005; Pottier et al. , 2006; Meister et al. , 2012) , increasing our potential for the detection of secular trends in ocean chlorophyll. However, MODIS Aqua and Terra are now beyond their operational lifetimes. Another ocean color instrument, the Visible Infrared Imager Radiometer Suite (VIIRS), is currently operational, but the data quality is, as of yet, undetermined. Several authors have studied these satellite records in order to investigate trends in global ocean chlorophyll concentration and primary productivity  (Gregg et al.
9 and a significance level of 5 %)  (Henson et al. , 2010). This time frame assumes no interruption in satellite data -an unlikely scenario given the age of the current ocean color satellites (MODIS) and the unproven potential for VIIRS to provide the necessary data quality. The European Space Agency and the European Organisation for the Exploitation of Meteorological Satellites are planning the launch of the Ocean and Land Colour Instrument (OLCI) in 2013. If this is unsuccessful or MODIS fails before OLCI is operational, a discontinuity due to the change of instrument in the time series could seriously inhibit our ability to detect trends in ocean chlorophyll and productivity. Similarly, when OLCI exceeds its lifetime, a gap before launching a future satellite would again affect our ability to detect trends.
Data and models We use monthly mean chlorophyll concentration data covering the January 1998-December 2007 period collected by SeaWiFS (version R2010. 0; available at http://oceancolor. gsfc. nasa. gov/) averaged globally and in 14 biomes (shown in Fig. 1 ). The biome definition separates the regions where phytoplankton growth is seasonally light limited (for mid to high latitudes), regions where the ocean is gaining heat (equatorial regions) and oligotrophic regions  (Henson et al. , 2010). Observations taken after 2007 were not used as they are not continuous due to intermittent problems with the SeaWiFS instrument. The seasonal cycle was removed from the monthly means by subtracting from each month the mean of all observations taken during the same month for all years. To estimate long-term trends in surface ocean chlorophyll, we use the same three coupled physical-biogeochemical models as presented in  Henson et al.
Estimation of trends and noise in ocean color satellite data and biogeochemical models To test for the presence of global and regional trends in ocean chlorophyll in SeaWiFS data, we fit the regression model presented in Eq. (  1 ) to deseasonalized monthly mean SeaWiFS observations for 1998-2007, globally and in the 14 biomes presented in Fig. 1. We also estimate the first-order autocorrelation and standard deviation (Eqs. 2-3) in the ocean chlorophyll concentration from the deseasonalized monthly mean SeaWiFS observations. We estimate the trends predicted by the three ocean biogeochemical models also using the regression model presented in Eq. (  1 ). For all the regression analyses, we verify the underlying assumptions of independently distributed normal errors with a constant variance using the Anderson-Darling normality test, the Breusch-Pagan homoscedasticity test and the Durbin-Watson independence test.
Region Chlorophyll concentration linear trends IPSL-PISCES and NCAR-CCSM3 models project decreasing trends, while the GFDL-TOPAZ model projects an increasing trend. In the Southern Ocean Pacific, the trends projected by the GFDL-TOPAZ and NCAR-CCSM3 models are increasing. The IPSL-PISCES model projects a decreasing trend for the Southern Ocean Pacific. Whether the trends detected in the high-latitude North Atlantic and in the Southern Ocean Pacific in the SeaWiFS data might represent climate change or decadal variability cannot be answered without a detection and attribution study. Answering this question is even more challenging since the models often do not agree on the sign of the projected trends. Other regions where the three biogeochemical models do not all agree on the sign of the trend are Oligotrophic South Atlantic, Equatorial Pacific, Oligotrophic South Pacific and the Southern Ocean Indian.
5 only since the results are symmetric. For example, the number of years necessary to detect a trend of the same magnitude will be the same if the discontinuity occurs after 25 % or 75 % of the data were collected. essary to detect a trend signal. In addition, we showed that a discontinuity in the satellite data measurements could have a large negative impact on our ability to understand ocean productivity's response to climate change. We estimate approximately 27 yr of continuous observations are required to detect global trends in surface chlorophyll concentration. The above presents an idealized scenario. In reality, the scenario could be that the remaining ocean color sensors, MODIS Aqua and Terra, fail during the present year (2012) or next year (2013) before a new satellite is launched, and VIIRS fails to achieve the necessary data quality.
If the discontinuity occurs during   2 ). We present the fraction of data before the discontinuity between 0 and 0. 5 only since the results are symmetric. For example, the number of years necessary to detect a trend of the same magnitude will be the same if the discontinuity occurs after 25 % or 75 % of the data were collected. 2018 instead, we would need 43 yr of observations, and the trend would not be detectable before 2040. In this case, the discontinuity occurs at a time when approximately 50 % of the data were collected. Overall, it could take an additional 13-16 yr of observations to detect a trend in satellite ocean chlorophyll under the idealized scenario if the two MODIS sensors fail and VIIRS remains of questionable reliability before OLCI is launched. Of course, this could be reduced if a cross-calibration was possible with another instrument that made overlapping measurements for a few years, so that a consistent, continuous time series was available.
Other satellites have been launched or are planned, but if the data is not available for cross-calibration, the same problem will occur. Several assumptions were made in this study and our results are valid only if they are reasonable. These assumptions include: 1. Following  Henson et al. (2010) , we made the assumption that the trends in ocean chlorophyll concentration are linear since the biogeochemical models used projected trends that are approximately linear over time. More development would be necessary in order to detect spatio-temporal trends, nonlinear trends or step-like behavior changes or to assess the number of years of observations required to do so. 2. To assess n * , we used the trends projected by three ocean biogeochemical models coupled with climate models. We used several models to represent the uncertainty associated with the trend magnitude.
, 2004). If real observed trends are greater than or less than the range predicted by the ocean biogeochemical models, n * may be fewer or more than our estimates, respectively. For example, the trends could be different if other coupled climate-ocean biogeochemistry models were used. Coupled climate-ocean biogeochemistry models projections from the Coupled Model Intercomparison Project Phase 5 database that are or will be made available should be considered in future work. C. Beaulieu et al. : Factors challenging our ability to detect long-term trends in ocean chlorophyll 3. Similarly, n * could vary if a different biome definition was used and if we assumed the biomes were also expanding as suggested in  Polovina et al. (2008 Polovina et al. ( , 2011)). For example, if we were using a different biome definition exhibiting smaller internal variability and/or larger signal, n * would decrease.
5. The results are also based on the assumption that the red noise estimated by the standard deviation and first-order autocorrelation from ten years of satellite data observations is representative of long-term internal variability. We assumed that these statistical properties are stationary, but the results could vary slightly if these properties change in time or if it takes longer to estimate internal variability. 6. We have used SeaWiFS satellite data only to make sure that we analyze a self-consistent time series. However, the results could vary if longer merged time series from different satellites (e. g. SeaWiFS and MODIS-Aqua) were used. Furthermore, we ignore the possible utility of other platforms such as ships, moorings, floats, gliders, and aircraft for estimating long-term trends in ocean chlorophyll and the use of these platforms to eliminate or reduce potential discontinuities when merging satellite data records.
Acknowledgements. The authors thank  GSFC/NASA  for providing the SeaWiFS data that was used in accordance with the SeaWiFS Research Data Use Terms and Conditions Agreement. The authors also thank  V. Saba  and two anonymous reviewers for useful comments on the manuscript. CB and JLS acknowledge financial support from the  Carbon Mitigation Initiative  with support from  BP. JLS and RRR were partly supported by the  NF-UBC Nereus Program. SAH was supported by  NERC  grant  NE/G013055/1. SCD acknowledges support from  NSF  grant  EF-0424599. Edited by:  G. Herndl.
, 2007). Atmospheric measurements on the Antarctic ice-cap will also be useful to improve our interpretation of ice-core data  (Jitaru et al. , 2009 , Vandal et al. , 1993). To further develop our understanding of the physico-chemical processes involving Hg over this large area of the planet, we began atmospheric monitoring Hg(0) at Dome Concordia (DC) Station (75°S, 123°E, 3320 m above sea level) in December 2011. This monitoring program is an important contributor to the Global Monitoring Observation System (GMOS) ground-based monitoring network.
Acknowledgements This work contributes to the  EU-FP7  project  Global Mercury Observation System (GMOS ). Logistical support and financial support were also provided by the  French Polar Institute IPEV  ( Program 1028 , GMOStral). We want to acknowledge the Project  ANR-07-VULN-013  VANISH that provided technical support. We are very grateful to  Gregory Teste  and  Laurent Arnaud. We thank the winterovering technical staff ( Sébastien Aubin ). AD is grateful to the  Institut Universitaire de France.
The sample was then dried under vacuum and resuspended in 1 drop of 17. 5 M acetic acid and 1 ml of methanol. Solvent was evaporated under a nitrogen flux. The amount of reducing ends was estimated by the ferricyanide assay (30) using a calibration curve with 20 -400 g⅐ml Ϫ1 glu- cose. Before the treatment, the ferricyanide assay measured 29. 4 g⅐ml Ϫ1 eq⅐glucose in the oligoguluronate solution. No reducing ends could be detected by the same assay after the reduction. Phylogenetic Analyses-Members of the PL7 family were selected in the CAZY database  (31) , and their sequences were aligned using MAFFT with the L-INS-i algorithm and the scoring matrix Blosum62  (32). The alignment was manually edited in MEGA 4. 1  (33). 180 sites were used to derive a phylogenetic tree using a maximum likelihood method conducted with the PhyML program  (34)  implemented online on Phylogeny.
fr (35). Bootstrap values were calculated from 100 resamplings of the dataset. Cloning of Two Genes Coding for PL7 Alginate Lyases-Primers were designed to amplify the coding region corresponding to the mature protein for AlyA5 and for the catalytic module of AlyA1 only, hereafter called AlyA1 PL7. The genes were amplified by PCR from Z. galactanivorans genomic DNA using the oligonucleotide primers as follows: forward ggggggGGATCCtgtaaagacaaacctaaggccactacg and reverse ccccccGAATTCtcactgggcttctggggcgcttc for AlyA5; forward ggggggAGATCTtccggtggttcgtccacccttc and reverse ccccccGAATTCttaattgtgggttacgcttaggtttttg for the catalytic domain of AlyA1 PL7. Using the restriction sites EcoRI and BamHI for AlyA5, and EcoRI and BglII for AlyA1 PL7 , the PCR products were then ligated into the expression vector pFO4, resulting in a recombinant protein with an N-terminal hexa-histidine tag.
The pure fractions were lyophilized and analyzed by ESI-MS using a Bruker Esquire-LC mass spectrometer (Bruker Daltonics Inc. , Billerica, MA). The samples were dissolved in 1:1 MeOH-H 2 O and were directly injected into the electrospray with a flow of 2 l⅐min Ϫ1. The mass range scanned was from 100 to 1100 atomic mass units. All acquisitions were recorded in both negative and positive mode and treated by Bruker Daltonics Esquire LC 4. 5, data analysis version 3. 0. To determine the structure of the end products, the oligosaccharide signals were assigned from one-dimensional proton and selective CSSF TOCSY with Keeler filter (selcssfdizs. 2) from Bruker library NMR spectra and from two-dimensional 1 H-1 H TOCSY NMR experiments using a mixing time of 90 ms. The NMR spectra were referenced at 30 °C using acetone (␦ H 2.
43 Å resolution for AlyA1 PL7 and 1. 75 Å for AlyA5 on beamline PROXIMA-1 equipped with an ADSC Quantum Q315r detector (SOLEIL, St Aubin, France) and a wavelength of 0. 98 Å. All data sets were treated, scaled, and converted with the program suite XDS  (39). The three-dimensional structures were solved by the molecular replacement method with ALY-1 from Corynebacterium sp. (PDB 1UAI) for the crystals of AlyA1 PL7 and A1-IIЈ from Sphingomonas sp. A1 (PDB 2Z42) as initial model, in the case of AlyA5. The starting phases were optimized using the programs ARP/wARP  (40)  and REFMAC 5  (41)  as part of the CCP4 suite  (42). The refinement was carried out with REFMAC 5 and the final model building with Coot  (43). Water molecules were added automatically with REFMAC-ARP/wARP and visually verified.
(25)  when creating subfamilies was that the group contained at least five members. Thus, we propose that AlyA2 and the four other enzymes in the same group belong to a new subfamily of PL7. This new subfamily, which we hereby define as SF6, appears to be conserved only in marine representatives of the Flavobacteriaceae, possibly representing a nichespecific evolution of PL7 enzymes. As subfamilies correlate in general with substrate specificities, they can help predict the preferred substrate of new enzymes  (25). To date, two alginate lyases have been characterized in SF3 and seven in SF5 (CAZY database, December 2012). They all are classified as poly(␣-Lguluronate) lyases (EC 4. 2. 2. 11). Thus, we hypothesize that AlyA1 and AlyA5 should preferentially cleave G motifs (Fig. 2 ). No prediction can be made on AlyA2 specificity, because none of the proteins belonging to this new subfamily SF6 has yet been characterized.
The apparent molecular mass was between 26. 5 and 29 kDa (estimated from the calibration of the size exclusion column and the SDS-PAGE, respectively). This is concordant with the theoretical molecular mass of 27. 5 kDa calculated for the protein sequence of the catalytic domain using ProtParam implemented in the ExPASy Proteomics Server  (45). The recombinant AlyA5 eluted from the final size exclusion chromatography as two distinct peaks (data not shown), corresponding to apparent molecular masses of 69. 5 and 38. 3 kDa. SDS-PAGE analyses showed that these two populations contained only one protein form under denaturing conditions, with an apparent molecular mass of 42 kDa. Compared with the theoretical molecular mass of 38. 3 kDa, the proteins eluted in the first and second peaks correspond roughly to a dimer and a monomer of AlyA5, respectively.
0 compared with Tris-HCl. Addition of the chelating agents, EDTA or EGTA, in the reaction mixture reduced the enzyme activity by 90 and 95%, respectively (data not shown). This inhibition was only incomplete when the chelating agents were incubated with the enzyme overnight but not added to the reaction mixture (43 and 58%, respectively). This suggests that the interaction between the enzyme and divalent cations takes place during substrate fixation and not before. To determine the mode of action of AlyA1 PL7 , the products resulting from the degradation of alginate were sampled periodically and analyzed by C-PAGE. After 2 min, the polysaccharide fraction was already largely degraded (Fig. 3E ). Following the reaction from 2 min to 14 h, the size of the detected oligosaccharides decreased with time, rapidly reaching the smallest DP of 2.
The appearance of intermediate larger oligosaccharides, DP4 to DP20, at early stages demonstrates that AlyA1 PL7 was acting with an endolytic mode on alginate. Substrate Specificity of AlyA1 PL7 -The enzyme activity was measured on three polymeric alginate substrates, chosen for their difference in the M/G ratio. The kinetic parameters were clearly correlated with the guluronate content of the substrate. The K m value determined on G-rich alginate samples was 4-fold lower than on M-rich alginate samples, whereas the inverse relation is true for k cat /K m (Table  1 ). This suggests that AlyA1 PL7 preferentially cleaves G stretches. The substrate specificity was then determined by carrying out the enzymatic reaction directly in the NMR tube. The 1 H NMR chemical shifts were assigned according to previously published data on alginate poly-and oligosaccharides  (47) (48) (49) (50) (51).
The separation profile of the oligosaccharides showed three major peaks (data not shown), and the molar fractions of the different products were estimated from the size exclusion chromatography (Table  2 ). The ESI-MS analysis showed m/z of 351. 0, 526. 8, and 703. 0 [M-H] indicating di-, tri-, and tetrasaccharide fragments, respectively. The lyase produced mainly trisaccharide and tetrasaccharide oligomers with a total content of 41 and 36%, respectively. Around 19% of the isolated oligomers were disaccharide and only a small amount of pentamers and hexamers were observed. The uniformly sized fractions were not separated further, and the structure and amount of the oligosaccharides present in each fraction were determined by NMR. The NMR spectrum of the disaccharide fraction showed that only ⌬G was produced.