dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence assembly and gene prediction and annotation All of the viral DNA genomic libraries, as emPCR products, were sequenced through two duplicated multiplex runs on a Roche GS FLX Titanium sequencer. 454 image and signal processing software v. 2. 3 generated a total of 2,434,736 PassedFilter reads after removing reads under 40 bp in length. The raw data from the 454-pyrosequencing machine were first processed through a quality filter and only saved sequences that met the following criteria: i) contained a complete forward primer and barcode, ii) contained no more than one "N" in a sequence read where N is equivalent to an interrupted and resumed signal from sequential flows, iii) reads were 200 to 500 nts in length, and iv) reads had a average quality score of 20. Using SFF tools implemented in the 454 GS-Assembler 2.
3, each read was trimmed to remove 3' adapter and primer sequences and was parsed by a MID adaptor barcode. The corresponding QUAL file also was updated to remove quality scores from reads not passing quality filters. This procedure allowed the unambiguous assignment of 2,429,860 reads of 384-bp on average to the corresponding genomic libraries Separate assembly for each library was performed by the MIRA assembler version 3. 2. 0 using the following parameters: --job = denovo, genome, accurate, 454 -DP: ure = yes -CL:emrc = yes -AL:mo = 50 -ED:ace = yes. Overall a total of 1557 contigs containing 2,330,493 reads were generated. The resulting contigs were assigned to their corresponding viruses and ordered between each other by alignment against reference viral genomes, e. g. PBCV-1, NY-2A, and AR158 for NC64A viruses [GenBank: JF411744, DQ491002, DQ491003], ATCV-1 for SAG viruses [GenBank:EF101928] and MT325 and FR483 for Pbi viruses [GenBank:DQ491001, DQ890022].
A first list of putative ORFs was constructed using the GeneMarkS program (using the -lo and -op options)  [43]. A list of potential ORFs (size >60 codons) occurring in the intergenic regions between GeneMarkS predicted genes was compiled. These potential ORFs were added to the predicted gene list only if they had a significant match (BLASTP e-value < 1e-5) in the Genbank non-redundant (nr) database, omitting matches in the Chlorovirus genus. Predicted proteins were functionally annotated based on match against multiple sequence databases, including Swissprot, COG, Pfam and nr databases using an e-value threshold of 1e-5 for both BLASTp and HMMer searches. tRNAs genes were predicted using the tRNAscan-SE program, ignoring pseudo-and undetermined-tRNAs.
Phylogenetic analysis Phylogenetic analysis was performed using the following general pipeline: homologous sequences were searched in databases using the BLAST EXPLORER tool  [43]. Multiple-sequence alignments were performed using the MUSCLE program  [44] , followed by manual edition, and removal of gaped sites and poorly aligned regions. Phylogenetic trees were reconstructed using the PHYML program (Maximum likelihood)  [45]  and Mega 4 (Neighbor Joining and maximum parsimony)  [46]. Statistical support for branches was assessed using 1000 bootstrap datasets.
Acknowledgements This research was partially supported by the  French Ministry of Research, CNRS, PACA-Bioinfo platform, NSF-EPSCoR  grant  EPS-1004094  (JLVE),  DOE-DE -EE0003142  (JLVE),  Stanley Medical Research Institute   11R-0001  (JLVE, DDD),  NIH NCRR  grant  5P20RR016469  and  NIH NIGMS  grant  8P20GM103427  (GD),  NIH  Grant  P20 RR15635  from the  COBRE Program  of the  National Center for Research Resources (JLVE)  and a  University of Nebraska Layman  award (MK). We thank  Andrew Benson ,  Jaehyoung Kim  and  Joseph Nietfeldt of the Core for Applied Genomics and Ecology, University of Nebraska-Lincoln for Roche /454 sequencing, signal processing and sequencing quality analyses. We also thank  Mike Nelson  for collecting and initially evaluating several of the viruses used in this study.
Acknowledgements The authors thank  Peter WH Holland ,  ND Holland  and  Nacho Maeso  for critically reading the manuscript and for useful suggestions. JKY was supported by the  National Science Council, Taiwan  ( NSC99-2627-B-001-003 ,  NSC101-2923-B-001-004-MY2 ), and by the  Career Development Award  from  Academia Sinica, Taiwan  ( AS-98-CDA-L06 ). MS was supported by funds from the  ANR  ( ANR-09-BLAN-0262-02  and  ANR-11-JSV2-002-01 ),  CNRS  and  UPMC.
6. MODIS (Terra) aerosol optical depth measurements at 550 nm and OMI absorption aerosol optical depth measurements at 500 nm on 23 March 2010 (top) and 8 April 2011 (bottom). Data downloaded from the Giovanni online data system (http://disc. sci. gsfc. nasa. gov/ giovanni/overview). Prata (2009). Traditionally broadband instruments have been used for the detection of volcanic ash and their general small footprint and high revisit time remain a decisive advantage  (Pavolonis and Sieglaff, 2010; Prata and Prata, 2012). Only a few papers deal with ash measurements from high spectral resolution instruments, and these report better sensitivity to thin plumes and better differentiation of ash with other atmospheric parameters (clouds, water vapor, sand and surface emissivity features)  (Gangale et al.
This set of observations was then subjected to a strong ash detection test with stricter detection thresholds. Observations which passed the strong ash test were labeled ash. In addition, all observa-tions within 150 km of these observations and which passed the weak ash detection test were also labeled ash. All other mineral containing aerosols were labeled as sand. While the above method is ad-hoc and can obviously be improved, it was found to have a larger sensitivity and less false detections than the ash-correlation method  (Clarisse et al. , 2010b). We have applied the ash detection algorithm on 4 years of IASI data, and compared with available literature data. This analysis confirmed the robustness of the current algorithm, as no major ash-rich eruption was missed, while excellent agreement was obtained with published data for the eruptions of Eyjafjallajökull and Chaitén.
Monthly averages of ash detection are indicated together with sand detection on Fig. 7. Ash in January can be attributed to eruptions of Kirishima (Japan), Tengger Caldera. Average MODIS (Terra) aerosol optical depth measurements at 550 nm for June 2011. This product is not sensitive to aerosol composition and therefore shows a variety of different aerosols and their transport patterns which can be attributed to natural aerosol (sand mainly between 0 and 30 • N and ash below -30 • N), and anthropogenic (mainly urban/industrial pollution in India and Asia and biomass burning in Central Africa). This figure can be compared with Figure  7  for the minerals and with Figure  10  for ammonium sulfate aerosol. Data downloaded from the Giovanni online data system (http://disc. sci. gsfc. nasa. gov/giovanni/overview).
Fig. 8. Average MODIS (Terra) aerosol optical depth measurements at 550 nm for June 2011. This product is not sensitive to aerosol composition and therefore shows a variety of different aerosols and their transport patterns which can be attributed to natural aerosol (sand mainly between 0 and 30 • N and ash below -30 • N), and anthropogenic (mainly urban/industrial pollution in India and Asia and biomass burning in Central Africa). This figure can be compared with Fig. 7  for the minerals and with Figure  10  for ammonium sulfate aerosol. Data downloaded from the Giovanni online data system (http://disc. sci. gsfc. nasa. gov/giovanni/overview). (Indonesia) and Kizimen/Karymsky (Kamchatka, Russia) and in June to the eruptions of Puyehue-Cordón Caulle (Chile). The eruption of Kirishima illustrates nicely how ash and sand can be differentiated even over areas with significant background sand transport.
Note that our monthly average was constructed using binary data, which was designed to yield almost no false detections. This also means that data near the edges of plumes is lost. The MODIS AOD monthly average on the other hand, benefits a great deal from averaging. Observations that would otherwise never been attributed to dust (be-cause of having an only marginally augmented AOD) do contribute to the monthly average, and help in establishing the harder to detect plume edges. The fact that both maps agree is not-trivial and is a clear demonstration of how well the detection of sand works with the unified method. Also note that the (limited) transport from the Taklamakan and Gobi desert shows up really well in the IASI monthly average. It is harder to discern in the MODIS AOD average, partly because of problems with bright surfaces, partly because of interference with anthropogenic aerosol (which is seen over the whole of Asia and the North Pacific ocean).
, 1976; Earle et al. , 2006; Segal-Rosenheimer et al. , 2009)  and aqueous form  (Remsberg, 1973; Downing et al. , 1977; Boer et al. , 2007). For the calculation of the Jacobian, we use the recent publicly available dataset from  Earle et al. (2006)  assuming a logarithmic size distribution with an effective radius of 0. 2 µm. Because we expect ammonium sulfate aerosols close to the surface, for this type of aerosol we only used cloud filtered spectra. Covariance matrices were constructed separately for land and ocean on global data of 24 days covering all months. As we have a priori no easy way of filtering out spectra with an ammonium sulfate signature, the idea of using a global covariance matrix is to reduce the weight of such spectra. Ammonium sulfate was unambiguously identified over East China for May-July 2010, see Fig.
Acknowledgements. IASI has been developed and built under the responsibility of the  Centre National d'Etudes Spatiales (CNES, France). It is flown onboard the Metop satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near real time data distribution service. We also acknowledge the MODIS and OMI mission scientists and associated  NASA  personnel for the production of the data used in this research effort. MODIS and OMI optical depth data was downloaded from the  Giovanni  online data system, developed and maintained by the  NASA GES DISC. L. Clarisse and P. -F. Coheur are respectively  Postdoctoral Researcher  ( Chargé de Recherches ) and  Research Associate (Chercheur Qualifié)  with  F. R. S. -FNRS. C. Clerbaux  is grateful to  CNES for scientific collaboration  and financial support.
The research in Belgium was funded by the  F. R. S. -FNRS  ( M. I. S. nF. 4511. 08 ), the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA-Prodex  arrangements and the  Support to Aviation Control Service (SACS+)  project). We also acknowledge financial support from the  "Actions de Recherche Concertées" (Communauté Francaise de Belgique). Edited by:  E. Highwood.
01 K s -1 artificial cold pool in the low levels of a small domain for 10 min. Measurements available from the AMMA campaign  (Stone et al. , 2010)  are used to build the initial profiles for chemical species. For unavailable species, the initial profiles are estimated either from simulation by the MOCAGE CTM model  (Bousserez et al. , 2007; Teyssédre et al. , 2007)  at the location corresponding to the COPT campaign and for June; or from measurements averaged over June and July for the years 1998 to 2007 and taken from the IDAF database  (Adon et al. , 2010 , http://idaf. sedoo. fr) at Lamto, Ivory Coast. In order to allow short-lived species with unknown concentrations to form and adjust their mixing ratios to an equilibrium state, a simulation with a box model corresponding to 24 h of spin-up was performed using the set of data composed by AMMA and IDAF measurements as initial conditions, as well as MOCAGE simulation results.
Acknowledgements. Computer resources were provided by  I. D. R. I. S. ( Institut du Développement et des Ressources en Informatique Scientifique , project no. 90005 ). We acknowledge the  IDAF  project, which is an initiative of the  IGBP (International Geosphere Biosphere Program) IGAC (International Global Atmospheric Chemistry) project and forms part of its core DEBITS (DEposition of Biogeochemically Important Trace Species ) task. IDAF is supported by  CNRS-INSU  ( Centre National de la Recherche Scientifique -Institut National des Sciences de l'Univers ) and the  OMP (Midi-Pyrénées Observatory ) and also the  IRD (Institut de Recherche pour le Développement). We wish to acknowledge the use of the  NCAR Command Language (NCL , http://dx. doi. org/10. 5065/D6WD3XH5,Boulder, Colorado) version 6.
Previous systems approaches in ecology were unsuccessful An integrated ecological systems approach was attempted in the 1960s  [29, 30] , most notably in the International Biological Programme (IBP)  [31]. The systems ecology of the 1960s foundered on at least two grounds: practically-the computing power available in the 1960s was insufficient to run the necessary models; and philosophically-the approach attempted to measure as many parameters of a system as possible without using theory beyond its use as a tool for informing which function might best fit a given dataset  [32, 33]. Our use of the term 'systems ecology' is distinct from the approach used in the IBP, instead it is analogous to the approach used in climatology and in systems biology where process-based models are derived from observational and experimental data.
We will increasingly require ensembles of model runs with different initial conditions, parameter sets, or even processes (cf. [48] ) in order both to scope out the variability of outcomes, and to quantify context-dependent predictability of future states. This approach is routinely followed in weather forecasting  [49] , and similar needs apply to ecological forecasting. The sources of uncertainty in the modelling process need classification  [50] [51] [52] [53]. At the very least, there are uncertainties in model parameters, in the specification of processes that are included, and in the datasets that inform the models; there will be processes that we know to be operating in principle, but about which we lack enough knowledge, or data, to make sensible model specifications, and there will be real-world processes about which we are currently ignorant.
(b) Complexity Predictive systems ecology is 'big science'. It requires large amounts of data and complex models. To avoid the fate of classical systems ecology, model complexity must be decided carefully, and standardized approaches for describing individual organisms and their interactions and model structure are required. Just as big data analysis requires advanced computational statistics, predictive systems ecology will require models as complex as necessary to realistically represent ecosystems. In a complex system with multiple patterns, there may be trade-offs between agreement with different datasets or advantages in some cases to including particular processes that are not required for others. In such cases, a multi-model approach with replaceable components allows for different model purposes to be accommodated within an overarching scheme  [58].
Still we may have to accept that high process accuracy may not, in all cases, lead to high agreement between model and past observation  [64] , and that multiple process pathways can potentially give rise to similar output patterns, so that model selection may not be straightforward  [65]. Systems ecology will need to develop methods to deal with the availability of data-a significant advance could be made in ecology if more scientists working in the discipline adopted the habit of data sharing (www. datadryad. org), and funders made free data access a condition for funding, as is the norm in some in other areas of science, and is being actively encouraged by funders in most countries. Successful models will also suggest key gaps in data and hopefully inspire new and relevant observations and experiments.
The development of land process models has burgeoned, with each model incorporating the same fundamental processes to predict the same global phenomena, such as the dynamics of terrestrial carbon. Now, the focus is on why the different models make different predictions which are resulting in modelintercomparison  [72 -74] , benchmarking models against standard datasets  [75, 76] , model-data fusion  [77, 78] , and characterizing the expected behaviour of the terrestrial carbon cycle  [79]. The development of terrestrial vegetation models has benefited from the existence of, often rich, datasets collected by foresters in many parts of the world. These allow the growth, development and reproduction of trees to be well constrained by data and the increasing use of Bayesian methods to infer probability distributions for model parameters allows estimation of the likely error in both parameters and output (for a recent example, see  [78] ).
A key aspect of this work is to link to human activities, focusing mainly on fisheries activities, and a range of models of food webs and fisheries are being linked to biogeochemical and physical models  [98, 99]. Some key features of ocean ecosystem models are that they frequently operate at large scales (sometimes a global scale) and that they integrate the physical environment into models more extensively than is typical for terrestrial models. The low availability of datasets for many parts of the world's oceans can limit the ability to constrain these models with data (for a discussion of this issue in the Southern Ocean, see  [97] ).
Along this path, the act of developing and constructing new models, in which assumptions about ecosystem dynamics must be made explicit and shown to be operational, has multiple potential benefits  [133] , not least to identify theoretical and data collection gaps and opportunities, and to confront existing ideas with new datasets in a meaningful way. With a suite of these models, we might hope to get to grips with the multiple issues of policy relevance that ecosystems present  [134] , although there will remain many difficulties in the translation from model to policy  [135, 136]. However, relevance to policy should not be the sole driver of our interest: developing predictive systems ecology should also inspire us through new and surprising discoveries. To truly forecast the future of Darwin's '.
Four pCO 2 levels, chosen according to best practices  [50]  and IPCC projections  [48] , were used: (1) present day, pCO 2 = 390 matm (control), (2) optimistic scenario, pCO 2 = 550 matm, (3) realistic scenario (close to what expected for 2100), pCO 2 = 750 matm and (4) pessimistic scenario, pCO 2 = 1000 matm. Unfiltered seawater, pumped from a depth of 10 m in the Bay of Villefranche, was continuously supplied to four 200 l header tanks. The chosen pCO 2 levels in the experimental tanks were obtained by bubbling pure-CO 2 using a continuous pH-stat system (IKS, Karlsbad, Aquastar). In the control tank, pCO 2 was maintained at 390 matm by bubbling CO 2 -free air produced by stripping CO 2 from ambient using soda lime, and adjusting to the appropriate level through the IKS system. pH values corresponding to each pCO 2 level were estimated based on desired pCO 2 , total alkalinity (A T ), temperature and salinity using the R package seacarb  [51].
Carbonate chemistry and specimens measurements Seawater samples for total alkalinity (A T ) measurements were collected twice a week, filtered on GF/F membranes and immediately analyzed. A T was determined potentiometrically using a Metrohm titrator (Titrando 80) and a glass electrode (Metrohm, electrode plus) calibrated using first NBS buffers (pH 4. 0 and pH 7. 0, to check that the slope was Nernstian) and then using TRIS buffer solutions. Triplicate titrations were performed on 50 ml sub-samples at 25uC and A T was calculated as described by Dickson et al. (2007). Titrations of standard seawater provided by A. G. Dickson (batch 106) yielded A T values within 2. 4 mmol kg 21 of the nominal value (standard deviation = 4. 6 mmol kg 21 ). All parameters of the carbonate chemistry were determined from pH T , A T , temperature and salinity using the R package seacarb  [51].
Test robustness was measured on another sub-set of 24 sea urchin specimens using a custom-made device designed to measure the static force required to crush sea urchin tests (adapted from  [53] ). Sea urchins were positioned upside down (in order to mimic fish predator attack) in a glass column. Then, a hollow piston, built to fit and run within the column, was inserted inside the column and progressively filled with lead pellets in order to increase the pressure, until the crushing of the urchin test. The static force required to crush sea urchin tests was measured as the weight (g) of piston and lead added: data were normalized by the diameter of the test. Finally, scanning electron microscopy (SEM) images of different parts of sea urchins (apical disc, spines and jaw pyramids) were taken using an Environmental Scanning Electron Microscope (E-SEM VEGA3, TESCAN) at the University of Genoa.
Acknowledgments Sea urchin juveniles were reared in the  CNR-IBF marine facility in Camogli (Italy ), in the framework of the European Community's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement nu  222492  project (ENRICH); authors are thankful to  Davide Privitera  and  Enrico Cantamessa  for the sea urchins juveniles rearing and to  Laura Negretti  for the SEM images. Authors are particularly thankful to the Academic Editor,  Tasman Crowe , and also to the anonymous reviewers, for their accurate review of the manuscript and precious suggestions for its improvement.
This work is a contribution to the '' European Project on Ocean Acidification '' (EPOCA) which received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement nu  211384  and to the '' Mediterranean Sea Acidification under a changing climate'' project (MedSeA)  under grant agreement nu  265103. VA mobility was funded by ''UniversitaItalo-francese'' (Bando Vinci 2010 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Results We determined the prevalence and parasitemia of Plasmodium relictum of 1750 individuals sampled in 24 different house sparrow populations in France (Table  1 ). We found 24% of infected sparrows with the parasite Plasmodium relictum (lineages Plasmodium SGS1, GenBank accession number JN164729 and Plasmodium GRW11 GenBank accession number JN164731). Prevalence varied from 0% to 78% and parasitemia (relative quantification, see methods) from 0 to 3. 7 204 between populations (Table  1 ). In addition to SGS1 and GRW11, we also found seven other parasite lineages, referring to other Plasmodium morphospecies or to Haemoproteus sp. The very low prevalence of these lineages (ranging from 1% to 8%) prevented us to reliably test the association with climatic variables, and we therefore decided to focus on Plasmodium relictum.
The characteristics of each site were determined using a set of environmental variables that included nineteen bioclimatic metrics at 1 km from the WorldClim dataset  14  (http://www. worldclim. org) and elevation measure. We constructed classification trees using a Random Forest model  21  under the R framework  22  , with climate variables (extracted for each site) as predictors, and both parasitemia and prevalence as response variables. Bioclimatic variables and elevation did little to explain parasitemia, and optimized models never explained more than 2% of variation in parasitemia. In contrast, models attempting to explain variation in malaria prevalence performed well, with up to 83% of all variation explained by only bioclimatic and elevation variables. Of these variables, under both criteria used to evaluate performance (percentage of increase of mean squared error and increase of purity; Fig.