dataesr/scientific-paragraphs-categorization huggingface dataset:
SEQUENCE ANALYSIS The fasta sequences obtained were filtered for errors using cd-hit, blasted against the NCBI-NR database using the BLASTX default settings  (Altschul et al. , 1990; Benson et al. , 2005)  and analyzed using MEGAN4  (Huson et al. , 2011). In parallel, metagenomic datasets were analyzed using the Metagenome Rapid Annotation with Subsystem Technology (MG-RAST;  Miteva, 2008). Reads were taxonomically and functionally annotated by similarity searching against SEED database  (Overbeek et al. , 2005)  with a maximum e-value of 10 -5. Annotated data were analyzed at a broad taxonomical level (Phylum/classes) and at the second level of hierarchical functional subsystems classification from SEED database including 477 subsystems representing the collection of functional roles that make up a metabolic pathway, complex, or a class of proteins.
We compared the different snow samples taken during springtime for the global community composition. The relative abundance of reads in the fifty most represented functional subsystems in the snow datasets were tested for their correlation with chemical parameters (pH, Hg, methylmercury, etc. , see Table  1 ). The resulting Pearson correlation matrix was then visualized in a heatmap from the R-Package "gplot"  (Ihaka and Gentleman, 1996). We compared functional subsystem distributions from snow metagenomes with other metagenomes from different ecosystems publically available on the MG-RAST platform (listed in Table  S1 ;  Miteva, 2008; DeAngelis et al. , 2011; Delmont et al. , 2011b; Varin et al. , 2012a). Read distributions among the different functional subsystems for each ecosystem were then analyzed with the statistical software STAMP  (Parks and Beiko, 2010)  using analysis of variance as the statistical test parameter.
SNOW METAGENOMES CHARACTERISTICS Snow metagenomic datasets harbored on average 27,000 sequences with a length of 330 nucleotides. The smallest and the biggest datasets were obtained from the samples SVN65 (12,181 sequences) and SVN7 (42,989 sequences), respectively. Taxonomic annotation efficiency at a broad taxonomic level (phylum/classes) was high; with a proportion of unannotated reads varying between 5 and 12% of the total sequences. However, functional annotation efficiency was low; the percentage of reads with no occurrence with genes with known functions in the database varied between 60 and 88% (for SVN8 and SVN40, respectively). Detailed relative abundances of each functional or taxonomical group are directly available on MG-RAST software under the accession number indicated in Table  S2.
Reads related to Fungi were dominant in surface snow metagenomes sampled between the 25th of April (SVN18) and the 27th of May (SVN56) and reached up to 70% of annotated reads in the sequenced sample from the 13th of May (SVN40). We also observed differences in community composition between surface and basal snow (SVN8 and SVN65), where reads annotated to bacteria from Proteobacteria, Bacteroidetes/Chlorobi, and Cyanobacteria were dominant relative to Fungi that were in relatively low annotated read abundance. Sequence reads from the snow metagenomes were classified into metabolic functions using the SEED database Of the annotated reads, most were classified as carbohydrate metabolism genes (10-19%), followed by virulence, amino acid, protein, DNA, cell wall, cofactors, and respiration. The functional profile varied among snow samples.
FUNCTIONAL SIGNATURE OF SNOWPACK MICROBIAL COMMUNITY The relative abundance of annotated reads of functional subsystems was compared among different ecosystem metagenomic datasets referenced in Table  S2 : snowpack, polar microbial mats for samples corresponding to the cryosphere and soil (forest and grassland) together with oceans (coastal and open oceans) for mesophilic environments. All of the snow samples grouped together in the PCA of the functional read distributions and were separated from the other ecosystems (Figure  4 ). However, different snow samples from the same sampling site in Svalbard and from the same sampling season were more dispersed than samples collected for other environmental groups even those that included sequences from different sampling sites, time periods, extraction protocols, and sequencing technologies.
DISCUSSION Snow is increasingly considered to be a diverse and active ecosystem, but the microbial community inhabiting this environment remains poorly understood  (Larose et al. , 2013a). High throughput sequencing was used to determine the composition and potential functional capability of the microbial community in the snow. We sequenced nine snow samples taken over a Spring sampling season. The resulting metagenomic datasets were analyzed for both their taxonomic and their functional profiles based on comparing the sequences to known microorganisms and proteins. Bacterial community composition of our snow samples had been analyzed in detail previously using a 16S rRNA gene phylogenetic microarray  (Larose et al. , 2013c). Despite differences inherent to the methodologies used, the bacterial taxa detected were similar with both techniques (microarray and high throughput sequencing) and consistent with previous studies on snow in the Arctic and Antarctic  (Carpenter et al.
Frontiers in Microbiology | Extreme Microbiology The relative abundance of functionally unannotated reads was very high in our snow metagenomes, illustrating the lack of environmental representatives in databases, especially from largely unexplored environments such as snowpacks. However, our preliminary data indicate that the Arctic snowpack harbors a specific functional signature based on gene annotation of sequenced DNA and that this functional potential is correlated to the varying environmental conditions during the spring season and with depth in the snowpack. We observed a correlation between specific functional gene abundance and chemical parameters. For example, Hg and bio-Hg were correlated to the subsystem tetrapyrroles in which most of the reads are associated with heme and siroheme biosynthesis and cobalamin and coenzyme B12 biosynthesis.
Fungi using effective adaptation mechanisms might be able to grow and develop in such habitats and are not just windblown contaminant spores  (Gunde-Cimerman et al. , 2003). If this is the case, then Fungi might be carrying out several different metabolic activities in the snow. Unfortunately, the large majority of Fungi affiliated reads in our metagenomic datasets (up to 86%) were not functionally annotated due to a lack of fungal genomic and protein data, and therefore, we were unable to clearly characterize the functional potential of the fungal microbial community. Some bacterial adaptation mechanisms have been described for how microorganisms in culture deal with these cold habitats  (Deming, 2002; Rodrigues and Tiedje, 2008). These consist of polyunsaturated fatty acid biosynthesis involved in maintenance of membrane fluidity, production of exopolysachharides, cold shock protein, DNA gyrase maintaining DNA topology or choline betain uptake.
CONCLUSION This study explored the microbial community functional genes in the Arctic snowpack. This microbial community, including representative members associated with cold environments, underwent major changes during the Spring season. Functional data that correlated with chemical parameters supported the hypothesis that this variation in microbial community structure and function could be explained by fluctuations in environmental conditions. Moreover, in this study, we tested the occurrence of a specific functional signature from the snowpack microbial community. Intense UV-light irradiation might be a critical factor in defining the microbial ecology of the Arctic snowpack ecosystem. Further sampling during the dark period as well as metatranscriptomic and atmosphere comparison studies year round would help establish how microorganisms are selected in snowpack and the role of light as a major driver of snowpack microbial community structure and function.
ACKNOWLEDGMENTS Lorrie Maccario was supported in part by  Region Rhone-Alpes. The authors would like to acknowledge the financial and logistic support provided by the  Institut Polaire Emile Victor (IPEV). We also would like to thank  Aurélien Dommergue  from the  glaciology laboratory (Grenoble -France ) for the chemical analyses,  Emmanuel Prestat  and  Sebastien Cécillon  ( Laboratoire Ampère Ecole Centrale de Lyon ) for their help in bioinformatics analyses.
, x n ). Note that V ν , Var[ Ŷ (x)] and Var  [∂ Ŷ (x) /∂ν] all depend on ξ. In contrast to designs that simply minimize the kriging variance, EK-optimal designs are typically not space-filling, in particular for small numbers of observations. Unfortunately, maximization of the EK-criterion is computationally demanding, since evaluation of (1) requires the evaluation of the target function for all points in the candidate set, being unfeasible for high dimensional design spaces as it is often the case for computer experiments. It would thus be useful to have an alternative criterion that can substitute (1) in the optimization procedure while still closely reflecting the actual prediction uncertainty. The paper is organized as follows. In Section 2 we motivate our approach, exploiting the intimate link that should exist between the precision of predictions of the values of the field from a given dataset and the accuracy of the estimates of the process parameters based on the same observations.
Application to a real oceanographic field This section presents the application of the design algorithm described in section 3. 1 to a real oceanographic dataset. The data used in this study was made available through a collaboration with the institute MUMM, a department of the Royal Belgian Institute of Natural Sciences. The data is the output of the biogeochemical oceanographic model MIRO&CO  Lacroix et al. (2007). MIRO&CO-3D is run to simulate the annual cycle of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton with realistic forcing conditions. The model covers the entire water column of the Southern Bight of the North Sea, while in the study presented here we concentrate on an horizontal (sea surface) grid of 21 × 21 points corresponding to the Belgian Coastal Zone (BCZ).
Acknowledgement: I am greatly indebted to  Prof. Reidun Twarock  ( University of York, UK ) for numerous discussions, inspiring remarks and useful suggestions which in uenced the nal form of this paper. Tom Keef helped me to nd the examples of capsid symmetries realized in nature. It is a pleasure to express my thanks to the  Organizers of the programme "Topological Dynamics in the Physical and Biological Sciences ", in particular  Keith Mo  att and  Mikhail Monastyrski , for inviting me to the  Newton Institute of Mathematics , where part of this paper was written.
Acknowledgments PR was supported by a  Université Blaise Pascal postdoctoral fellowship , with experimental work funded by a grant from the volcanology group of the  Laboratoire Magmas et Volcans. This is Laboratory of Excellence ClerVolc  ( ANR-10-LABX-0006 ) contribution number  106. We thank the reviewers  B. Andrews  and  C. Wilson , whose comments and suggestions significantly improved this manuscript.
Acknowledgements. This work is a contribution to the  HyMeX  programme supported by the  CNRS, Météo-France ,  CNES ,  IRSTEA  and  INRA , through the meta-programme  MISTRALS (Mediterranean Integrated STudies at Regional And Local Scales)  dedicated to the understanding of the Mediterranean Basin environmental processes (http://www. mistrals-home. org). This work was also carried out within the framework of the  DRIHM  project. The authors acknowledge  Météo France  and the  HyMeX database  for supplying the observational data. Computational resources were allocated by  Grand Equipement National de Calcul Intensif  ( GENCI  project  2013010569 ) and  Calcul en Midi Pyrenees  (CALMIP, project  P1247 ). Edited by: C. Ramis Reviewed by: two anonymous referees The publication of this article is financed by  CNRS-INSU.
e. 305, 313, 320, 340, 380, and 400-700 nm)  for the UVBR, UVAR, and PAR were obtained every day at local noon (~15:15 h) with a profiling radiometer (PUV-542T; Biospherical Instruments). After each profile, the PUV was rinsed first with nanopure water and subsequently with seawater from the next mesocosm to be sampled, to avoid contamination among the experi-  mental units. A 3°C increase in temperature (2100 trend,  Christensen et al. 2007 [IPCC 2007] ) was applied to the high temperature and high tempera-ture+UVBR treatments 1 d before the experiment, and temperature in the mesocosms was then maintained constant from 10 February (Day 0) to 19 February (Day 9). Temperature in the mesocosms was controlled using a N480D electronic controller placed 1 m below the water surface, and data were recorded every 60 s.
088 g KNO 3 , 0. 6805 g KH 2 PO 4 and a 20. 35 ml solution of 30 g l -1 NaSiO 3 was prepared and 0. 125 ml of this solution was added to each mesocosm (on Day 1) to increase nitrogen, phosphorus, and silicium concentrations by 5, 0. 31 and 0. 39 µM, respectively. Seawater within the mesocosms was continuously mixed from the bottom to the surface with a pump at a turnover rate of 1000 l h -1. To ensure that mixing was performed correctly, vertical profiles of water column properties (pH, dissolved oxygen, temperature, and salinity; data not shown) were measured 3 times a day with a U-10 HORIBA multiparameter probe. These profiles confirmed the homogeneity of the water column. At night, the mesocosm openings were sealed with plastic covers to avoid external contamination. One of the control mesocosms presented technical difficulties and the results are not presented here.
(2000). The relative contribution of major algal groups to total chlorophyll a (chl a) was estimated using the CHEMTAX 195 program developed by  Mackey et al. (1996). The initial matrix of accessory pigments to chl a ratios is presented in Table  1  and includes algal groups identified by microscopy during this study as well as pigmentbased types of phytoplankton from  Jeffrey & Wright (2006). For the analyses of nutrients (nitrate plus nitrite and phosphate), 2 replicate 60 ml samples were taken in each mesocosm every day at 08:00 h. Samples were filtered onto precombusted Whatman GF/F filters and kept frozen at -20°C until analysis within a month after the end of the experiment. The analysis of nutrients was performed using a Bran Luebbe Auto Analyzer 3 system following the method of  Grasshof et al.
Cell carbon content of microzooplankton was calculated from the C:V ratio of  Putt & Stoecker (1989) , where C:V = 0. 19 pg µm -3 for ciliates and using the carbon conversion factor of 220 fg C µm -3 for heterotrophic flagellates  (Børsheim & Bratbak 1987). It is worth noting that the fixation of samples with Lugol's solution may cause an underestimation of coccolithophorids in micros copy. However, as described below, the tendencies in the abundance of prymnesiophytes observed under the microscope were additionally confirmed by other techniques such as flow cytometry and HPLC. To test the significance of the observed differences between treatments, repeated measures ANOVA tests were run. If differences between treatments were found, post-hoc Tukey tests were performed. If normality and/or homoscedasticity were not verified, data were normalized with a ranked transformation (i.
Acknowledgements. This research is part of the project ' Combined Effects of Ultraviolet-B Radiation, Increased CO2 and Climate Warming on the Biological Pump: a Temporal and Latitudinal Study ', led by S. D. and supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC, SRO  Grant#  334876-2005 ), and by a grant from the  Instituto Antártico Argentino (IAA)  to G. A. F. in the frame of the project ' Research on Ultraviolet and Global warming effects on Biological pump Yields ' (RUGBY). We thank  Pascal Rioux  and  Prof. Émilien Pelletier  for providing the data for nutrients and POC-PON. We also gratefully thank  Dr. Mariano Mémolli , director of the  Dirección Nacional del Antártico , as well as the authorities of the  Centro de Investigaciones Australes (CADIC)  from  Ushuaia , particularly  G.
In this paper the mass m (in gram) is presented as a power law relation of D max (in centimeters): m(D max ) = α • D β max. (1) In other studies the mass (and thus density) of hydrometeors was determined following various principles. For example, ice crystals were collected on a sheet of plastic or a Petri dish. After taking microphotographs of the crystals, these were melted to deduce their mass from the resulting hemispherical drops  (Locatelli and Hobbs 1974; Mitchell et al. , 1990). Another method used is to classify hydrometeors according to their crystal habits associated to specific m(D) relations for individual habits, following the scheme given by Magono and Lee  (1966). Furthermore, when the CWC mass concentration is measured directly with simultaneously 2-D images of hydrometeors, m(D) relationships are derived from integral CWC data and corresponding particle size distributions (hereafter PSD) extracted from the images.
In the following Sect. 2 the MT aircraft campaigns and corresponding in situ microphysical measurements are described. The methodology to retrieve m(D) coefficients for CWC calculations from the variability of the hydrometeor images and corresponding radar reflectivity measurements is presented in Sect. 3. Section 4 then presents results on the variability (every 5 s during flight) of calculated m(D) coefficients. Then the coefficients are parameterized (as a function of temperature, also mean value) and compared with existing m(D) relations. All these m(D) relations are applied to data sets of the two MT flight campaigns, thus calculating CWC, in order to study their impact on Z-CWC and Z-CWC-T parameterizations fitted to corresponding measured reflectivity data.
Cloud data from the Megha-Tropiques flight campaigns Two airborne measurement campaigns were conducted with the French Falcon 20 research aircraft from SAFIRE (Service des Avions Français Instrumentés pour la Recherche en Environnement). During the first campaign, The Falcon20 was flown out from Niamey (Niger) in August 2010 (hereafter MT2010) during the monsoon season above the West African continent. The second campaign was conducted above the Indian Ocean between November and December 2011 (hereafter MT2011) at Gan (Maldives). While African continental MCS in the monsoon seasons are due to the convergence of wet colder air masses from the ocean with dry warmer air masses, the convection over the Indian Ocean is due to the buoyancy of wet air masses leading to weaker convection in our case.
In the two first levels (-1 and -5 • C) hydrometeors are similar with one exception where at -5 • C a dendrite shows up for MT2011. For other levels, ice crystal shapes are generally different. Besides aggregates, significant amounts of individual large pristine ice crystals such as dendrites (typically due to water vapor diffusion only) could be observed for MT2011, whereas 2-D images for MT2010 generally look more like aggregates (more or less rimed) and sometimes graupels. In order to derive particle size distributions and aspect ratios from the 2-D images, standard corrections of the OAP data were performed. In particular, rejection of 2-D images   due to shattering  (Field et al. , 2006 , Korolev and Isaac, 2005 , Field et al. , 2003)  and also rejection of splashes were applied. The inter-arrival time based shattering analysis is performed continuously to packages of 2000 particles along the flight track and 2-D image data are corrected accordingly as a function of time.
For the MT data set it was calculated that the mean reflectivity difference between 300 m above and below the plane is in the order of 3 dBZ, which means a reflectivity difference between flight level and 300 m above or below of about 1. 5 dBZ. This is less than the calibration and measurement error. Furthermore, the uncertainty in the measured concentrations of hydrometeors is estimated by the probe suppliers to be 20 %. This uncertainty stems mainly from the calculations of the sampling volume. Optical array probes (OAPs) measure the shadow projection of 3-D cloud particles on a 2-D plan. Theoretical simulations of 3-D crystals were processed, and are described in more detail in Appendix A. The corresponding 2-D projections are based on the principle of randomly oriented 3-D particles projected on a 2-D plan.
In general, we consider hydrometeors as a homogeneous mixture of ice and/or air. In order to identify cases where the mixed phase (ice and water) was present, signals of the Rosemount ice detector (RICE) were analyzed. The RICE probe is in fact a supercooled water detector. Few and extremely short cases were identified where the RICE probe showed supercooled water. Data containing possible supercooled water were excluded from m(D) calculations for ice. The dielectric properties of ice particles are linked to the mass-diameter relationship characterized by the fraction of ice f ice (Eq. 10) in the hydrometeors. Equation (10) explains how the ice fraction of the solid hydrometeors are calculated, with ρ ice = 0. 917 g cm -3. The ice fraction f ice cannot exceed 1. f ice = min 1, α • D β max π 6 • ρ ice • D 3 max.
03 for MT2010 (Fig. 10a ) and 1. 01 for MT2011 (Fig. 10b ). For MT2010 CWC(α σ , β σ ) is 14 % larger than CWC B&L , whereas for MT2011 CWC(α σ , β σ ) are close to CWC B&L. The data set of hydrometeors establishing the above B&L scheme stems from winter storms in the central Sierra Nevada in the western part of the North American continent. The crystals were collected at the ground, and subsequently fitted to build the B&L scheme. The B&L scheme seems more appropriated to the MT2011 data set than the MT2010 data set. Comparisons between the two CWC retrieval methods also exhibit good correlation coefficients of 0. 89 for MT2010 and 0. 91 for MT2011, respectively. In addition, error bars for all analyzed CWC are given in Fig. 10  representing their minimum and maximum values which were determined using Eq.
5 K temperature level, since the T-matrix retrieval method does not take into account the liquid water at the surface of melting ice crystals. This should have an effect of increased reflectivity of ice crystals, leading to an underestimation of m(D) coefficients. On average, mean β σ coefficients (Fig. 13a ) are larger for MT2010 than for MT2011, whereas mean α σ coefficients (Fig. 13b ) are more similar and in the same order between the level 260-245 K. The mean profiles show a decrease of mean α σ and β σ coefficients with decreasing temperature described by Eqs. (  21 ) and (  22  reflectivies and temperatures for MT2010 and MT2011 datasets. Z is given in dBZ on the x 3 axis and CWC in g m -3 on the y axis. a) CWC are calculated from α σ and β σ for MT2010 4 dataset. b) as for a) but for MT2011.
c) CWC are calculated using α β,T and β σ for MT2010 5 dataset. d) as for c) but for MT2011. e) CWC are calculated as a function of α T and β T for 6 MT2010 (equation 21). f) as for e) but for MT2011 (equation 22). g) CWC are calculated 7 from average coefficient of MT2010 (equation 19). h) as for g) but for MT2011 (equation 20). 8 i) CWC are calculated using H10 m(D) coefficients of NAMMA for MT2010. j) CWC are 9 calculated using H10 m(D) coefficients of clouds convectively generated for MT2011. k) 10 CWC are calculated using the BF95 parametrisation for MT2010 dataset. l) as for k) but for 11 scale uplift above the North American continent. Globally, β σ of MT2010 are similar to β of CRYSTAL-FACE, and β σ of MT2011 are similar to β of ARM given in SH2010.