dataesr/scientific-paragraphs-categorization huggingface dataset:
Taro Co (Fig. 15i ) and Ngangla Ringco (Fig. 15j ) started to shrink after 2010, significantly for the former, and slightly for the latter. Lei et al. (2013a, b)  analyzed the water storage and water balance variations of lakes Ziling, Namco, Pung Co and Zigetangco from 1976 to 2010 using a combination of satellite imagery and bathymetry of these lakes. Their results are in very good agreement with the water volume calculated in the Hydroweb database and reproduced in Fig. 16. However, time sampling of the water volume variations in  Lei et al. (2013a, b)  is rather irregular. This can be refined using satellite altimetry results which provide quite interesting results between 1994 and 2000 for these four lakes. It was mentioned above that lakes Namco and Pung Co (for example) grew at a reasonably steady rate from 1996 to 2005, while Lake Zigetangco started to grow around 1997-1998.
Methodologies for calculating water level and volume variations using satellite data are not straightforward and require the development of specific approaches in order to correct the measurements for some effects like geoid slope errors. Satellite imagery processing also leads to algorithm development to extract the water contour of lakes that are mostly based on normalized indexes. The Hydroweb database provides the water level for about 230 lakes worldwide to scientists who do not want to process the altimetry data themselves. For some specific regions or individual lakes, the Hydroweb also produces areal extent and volume variations of lakes, as is done for many lakes over the Tibetan Plateau. The accuracy of satellite altimetry can be calculated using in situ measurements as ground truth.
Acknowledgments The authors acknowledge the  Centre National d'Etudes Spatiales (CNES)  for financing our project through the  TOSCA  project. The authors are also grateful to the  Centre for Topographic Studies of the Oceans and Hydrosphere  (CTOH, www. legos. obs-mip. fr/observations/ctoh)at  LEGOS (Toulouse, France)  for providing altimetry data in a standard and useful form. The Landsat data have been downloaded from the Earth explorer system of  United States Geological Survey (USGS)  from their web site: http://earthexplorer. usgs. gov/. We are grateful for this service. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
ATLID receiver chain design The receiver chain includes the receiver telescope, the receiver optics, the science channels detection chain, the ATLID Control and Data Management Unit (ACDM). The ATLID telescope is an afocal Cassegrain with 620mm primary mirror diameter, made of Silicon Carbide to ensure high stability. The signal is transported to the detectors by means of fibre couplers, allowing deporting the whole detection chain on the anti-sun wall for passive cooling. Part of the flux is split at focal plane assembly entrance and imaged on the CAS which provides laser spot position information. The detection chain, composed by the three Memory CCDs and the Instrument Detection Electronics, is able to measure single photon events to meet the worst case radiometric performance requirements.
The Fibre Coupler Assemblies, developed by Bertin Technologies, have completed their successful qualification at QM level, and FMs assembly is ongoing. The Co-Alignment Sensor (CAS), developed by CRISA and aiming at measuring the retro-reflected laser spot with better than 1/10 pixel accuracy, completed its mechanical and thermal qualification sequence and already initiated the FM manufacturing. The ATLID Control Data and Management unit successfully passed its manufacturing review and it is now near its integration completion.
Canonical correspondence analysis (CCA) was used to characterize the relationships between bacterial community profiles and physicochemical parameters using the R package FactoMineR  [30]. Ordination and PERMANOVA (adonis) testing was performed using the vegan package 2. 2-1 in R  [37]. Putative identifications of selected T-RFs were performed by in silico restriction of the data using TRiFLe  [25]. Last, mantel tests (1000 permutations, Spearman correlations) were performed (vegan package 2. 2-1 in R) in order to test correlations physicochemical parameters and the relative abundance of specific terminal-restriction fragment, based on Bray Curtis distances dissimilarity matrices. dsrAB Clone Library and Sequence Analysis A dsrAB clone library was performed using surface waters of the estuary plume stations (PC1 and PD1 samples) and marine water collected at 240 m depth (MA240 sample).
Amplicons were ligated into the PCRTOPO 2. 1 TA cloning vector (Invitrogen, The Netherlands) and transformed into competent E s c h e r i c h i a c o l i T O P 1 0 F ' c e l l s ( I n v i t r o g e n , The Netherlands) according to the manufacturer's instructions. Positive transformants were randomly chosen and their inserts were PCR amplified using the M13F and M13R primers. Amplified gene fragments were checked on 0. 8 % agarose gel and were then sequenced. The dsrAB sequences were assembled by using the software Sequencher v4. 1. 4 and compared with those present in databank using the BLAST tool at NCBI. Clone sequences were deposited in the European Nucleotide Archive (ENA) with accession numbers HG932608-HG932652 and LT575364-LT575375.
All samples were diluted 10-and 100-fold with culture medium (40 mL final volume) and were dispensed in 384-well microplates (1 microplate per dilution, 100 μL per well). Microplates were then placed in a BD GasPak™ EZ Gas Generating Systems at 20 °C. After an incubation time of 3 weeks, the concentration of viable sulfidogenic bacteria was estimated according to the dilution and the number of positive wells (black iron sulfide) per microplate. Then, isolates were recultured and purified in fresh media by successive dilutions. For strains identification based on 16S rRNA sequences, direct PCR amplifications were achieved with the 63F/1387R primer set as described above. Sequences were assembled by using the software Sequencher v4. 1. 4 and compared with those present in databank using the BLAST tool at NCBI.
dsrAB Genes Cloning Based on dsrAB T-RFLP profiles and physico-chemical parameters, PC1, PD1, and MA240 samples were reported to differ significantly from each other. Thus, these later were used to further assess the diversity of the dissimilatory (bi)sulfite reductase genes using dsrAB cloning and sequencing. A total of 57 dsrAB clones were recovered and rarefaction analysis indicated that the saturation plateau was not reached (data not shown). Taxonomic assignment of dsrAB clones revealed that sequences detected in PC1 sample were mainly related to the Desulfobacteraceae and Desulfobulbaceae families (Fig. 4 ). Notably, several clone sequences from PC1 sample shared high sequence similarities with Desulfobulbus propionicus (Table  3 ). Further in the gradient, PD1 sample was mainly represented by non-affiliated clones.
This made the sample sizes of spring and autumn samples similar (Table  1 ), and allowed us to perform unbiased comparisons. Five hundred subsamples of N = 26 were randomly drawn for each spring sample, and these bootstrapped samples were randomly associated in triads containing one sample of each location: MA, TH and SL. Each triad was then associated to the real (i. e. non bootstrapped) autumn samples, and population differentiation indices were estimated as described above. Patterns of linkage disequilibrium in each sample were investigated as proposed by  Weir (1979). Corrections for multiple testing were performed according to  Narum (2006)  when necessary, for both real and bootstrapped data to maintain the significance level at α = 0. 05. We estimated genetic relatedness among individuals within each sample (i.
e. kin aggregation) by estimating the coefficient of relatedness r xy  (Queller & Goodnight 1989 ) using IDENTIX  (Belkhir et al. 2002). Briefly, to test whether individuals within each sample were genetically more related than expected by chance, the mean identity index of all sea bream pairs was compared with the null distribution assuming no relatedness. The null distribution was obtained by calculating identity indices for randomly generated samples of similar size (Table  1 ). Finally, we searched for putatively selected loci using the F ST -outlier detection method by  Beaumont & Nichols (1996)  implemented in the software LOSITAN  (Antao et al. 2008). Outlier loci were searched using three different levels of comparison that were performed among (i) all six samples from Mauguio (MA), Thau (TH) and Salses-Leucate (SL) lagoons, (ii) three entering (E) and (iii) three migrating (M) juvenile samples.
Reconstruction and analysis of individual haplotypes Individual raw sequencing data were quality-filtered using TRIMMOMATIC  (Bolger et al. 2014) , to remove adapters and low quality reads. We used GH and Prl gene sequences retrieved from GenBank to perform a reference assembly for each individual using GENEIOUS 7. 1. 5 (Kearse et al. 2012). Five iterations were used for alignment, after each of which the reference sequence was corrected using the previous consensus alignment. Heterozygote positions were identified for each individual and encoded using the IUPAC ambiguity code, and individual consensus sequences were aligned for each gene using CLUSTALW  (Thompson et al. 1994). Alignments were edited by hand to remove ambiguities within regions of low complexities (mini-and microsatellites).
Microsatellite spatiotemporal variation patterns Real datasets -The level of genetic differentiation estimated using all 23 markers over the whole data set was not significant (θ = 0. 0020 ± 0. 0160), nor was genetic differentiation among E and M samples (θ = 0. 0049 ± 0. 0108, and θ = -0. 0035 ± 0. 0094; respectively). However, these multilocus average values did not reflect the variance among loci (Fig. 2 ). The GH and Prl loci showed a significant global genetic differentiation (Fig. 2A ; θ GH = 0. 0192, θ Prl = 0. 0188; P's<0. 001). Surprisingly, genetic differentiation at GH and Prl markers was significant among the E samples but not among the M samples (Fig. 2B&C ). All pairwise differentiation values calculated between spring E samples were significant for both GH and Prl loci (GH: θ SL-E/TH-E = 0.
Fish sampled in TH and SL were showed to likely belong to a demographic unit that undergone a similar life-history throughout the year (see "Materials and Methods -Sampling", and Electronic Suppl. Fig. S1 ), suggesting that these demographic units inhabiting ecologically similar lagoons undergone similar allele frequency variation between spring and autumn. Bootstrapped datasets -Because spring and autumn sample sizes differed, we performed bootstrap resampling to investigate the consistency of patterns of genetic differentiation reported in the previous subsection. Sample sizes of E samples were standardized to N = 26 individuals to match the sample size of M samples (Table  1 ). The level of overall genetic differentiation (i. e. among the 6 samples) estimated using all 23 markers was found significant in only 7 (1.
4%) out of 500 replicates. Triads of bootstrapped E samples were also compared and significant multilocus genetic differentiation was detected in only 13 cases (2. 6%) out of 500 bootstrap resamplings. Such patterns are expected by chance alone, and therefore multilocus genetic homogeneity detected with the real dataset among all 6 samples or among the E samples were likely not affected by unequal sampling sizes between spring and autumn juveniles. When looking specifically at patterns of genetic differentiation at the GH and Prl loci in bootstrapped samples, results reported significant overall genetic differentiation in 448 (89. 6%) and 465 (93. 0%) cases out of 500 bootstrap resamplings for Prl and GH, respectively. These numbers reached 463 (92. 6%) and 484 (96. 8%) for Prl and GH, respectively, when considering only the triads of bootstrapped E samples.
Although our analysis revealed haplotype groups for each gene, close similarities among groups indicated that any possible signature of selection had largely been erased by recombination. This is consistent with the prediction that the chromosomal signature left by local (i. e. habitat-based) selection in panmixia typically affect narrow genomic regions, especially if spatially varying selection has been maintaining polymorphism for a long time  (Gagnaire et al. 2015). Acknowledgements: This research was funded by IFREMER (Project SpartaGen of the GDR MarCo), by the CNRS (Project EC2CO METASPAR), and the TOTAL Foundation Archiving of data: Data will be deposited on a public database upon final acceptance of the current submission. 1 ).
Acknowledgments This paper is the result of ongoing discussions within a multidisciplinary team, where sharing and refining our points of view enriched our understanding and our work. This is fostered by the research activities developed under the  EU   FP7 projects MARS  (grant agreement no. 603378 ) and  OpenNESS  (grant agreement no. 308428 ) and, consequently, by all the partners and colleagues we have the pleasure to collaborate with.
Introduction The first clue that aquatic viruses play a major role in their environment came from investigations in the early 1990s that revealed their high abundance  [1, 2]. For example, viruses are the most abundant organisms in the marine environment  [3]. It was estimated that each milliliter of seawater contained millions of viral particles  [4]  and that viral particles accounted for about 94% of nucleic acid-containing particles  [5, 6]. Some environmental studies on aquatic viral communities have shown that the "unknown fraction" (i. e. , without homology to any sequence available in the databases) is substantial, and may range from 30 to 99%  [7, 8]. A recent study focusing on the analysis of virus open reading frames (ORFs) showed that a large fraction had no homologs in databases.
However, ensuring accurate identification of short sequences (singletons and/or contigs) is difficult at best when no homologs are available in databases. Classification tools such as CLARCK or GOTTCHA can overcome these hurdles with their target-specific or unique signature approach  [81, 82]. However, unique signature models can be altered depending on how big the genome is and how sequence divergence is distributed across the genome. Consistently unique or specific k-mers will fail to be recognized in targeted reads if divergence is dispersed instead of being grouped throughout the genome. In parallel, standard mapping tools (e. g. , Bowtie  [83]  ) are not suitable for this situation, because they fail to correctly assign some reads related to similar virus types. Most commonly used mapping tools assume low divergence between datasets and references, and only some are more flexible regarding error rates, insertion/deletions (indels), or substitution  [84].
New tools have been developed to overcome this issue and can correctly classify reads displaying high divergence with reference sequences: MaxSSmap and NexGenMap (both graphics processing unit programs) or Stampy (hash-based program) tolerate divergence up to 30, 15, and 10%, respectively  [85] [86] [87]. Lack of Dedicated Databases and Tools to Limit the Amount of Unknown Data Metagenomic sequencing gives a clear and sharp view of the microbial community present in a given sample. With its ability to detect uncultured microorganisms, this sequencing method can characterize the abundance and diversity of microorganisms that compose a sample and lead to a better understanding of the biological interactions in a community. Identifying new sequences relies heavily on the availability and quality of databases.
There-fore, database curation is of prime importance. Bioinformatics tools and databases dedicated to viral metagenomics have been reviewed elsewhere  [88, 89]. In terms of virus discovery, the purpose of these tools is to help identify viral species and characterize their diversity and their function. In this regard, there are 2 basic approaches to analyze metagenomics samples after sequence filtering. The first is a taxonomic classification based on reads to determine the diversity of species present followed by the assembly of organisms of the selected taxa  [88]. The second involves de novo assembly based on the entire preprocessed set of reads where resulting contigs are classified  [90, 91]. In both cases, once a full genome has been covered, its characterization usually involves the prediction of possible ORFs and the comparison with those found in protein databases.
Although efforts have been made to develop tools for viral discovery pipelines, namely ViromeScan and MetaVir2  [92, 93]  , database integration still requires improvements. The size of viral aquatic databases is growing and has the potential for tremendous increases because a vast number of viral organisms have yet to be discovered. However, these databases should not expand as a single structure isolated from other biological databases. Accordingly, Vinithkumar  [94]  proposed a multilinked approach, where databases could have their own internal compartmentalized structure while being integrated with one another. No matter how well curated, sequences without any similarities to any species recorded in the available databases remain unresolved. Due to the limits of similarity-based methods, new strategies have been developed to decipher this "unknown fraction.
Hurwitz et al. [95, 97]  recently defined "core" PCs as the ones found in every sample of the Pacific Ocean virome and demonstrated that those "core" PCs were more abundant in photic zones than in aphotic zones, assuming gene flow between surface waters and the depths. These examples demonstrate the type of information that clustering methods can provide. Another approach relies on artificial neural networks  [98]  for the identification of viral structural proteins, which are present in all viruses. These networks correctly classified major capsid and tail proteins of phages. The classification has been validated experimentally and turns out to be more sensitive than a commonly used pattern analysis tool, MEME  [99]. However, this procedure is not straightforward because results may be distorted by network architecture (number of neurons and hidden layers), data complexity, or the training and validation datasets.
Given that sequences populating those sets come from available databases, the whole process and its accuracy still depend, to a certain extent, on what is accessible. Considerable efforts have been made to label the unknown fraction and the new tools and methods are paving the way to a clearer picture of the aquatic viral world. Including the Host in the Analysis Likewise, another major challenge arises when viruses share homologies with their hosts. Here, recoverable viral data is hampered, because, insofar as possible, viral sequences must be distinguished from cellular signals. For example, the increasing number of complete genomes from marine viruses has led to the identification of auxiliary metabolic genes in cyanophages  [93]. In addition to bona fide viral genes, viral genomes may also contain genes coding for proteins involved in photosynthesis  [100]  , overturning the idea that viruses only carry genes responsible for viral reproduction.
Acknowledgements We acknowledge  Heïdi Lançon  and  Dr. Carolyn Engel-Gautier  for the revision of English usage. The CIRMF is supported by the government of Gabon,  Total-Fina-Elf Gabon , and the  Ministère de la Coopération Française. This review article was also made possible by the  ERA-NET   EMIDA -funded project  MOLTRAQ  (molecular tracing of viral pathogens in aquaculture).
The mass spectra collected during the descent show that the medium and low stratosphere and the troposphere are poor in volatile organic species, with the exception of methane  (Niemann et al. , 2005). GCMS detected on the surface not only methane, but also a host of other condensed material among which were C 2 H 6 , CO 2 , C 2 N 2 , and C 6 H 6 (Figure  5 ). The aerosols, for which no direct data on the chemical composition were previously available, were analyzed by the Aerosol Collector Pyrolyzer (ACP) instrument. ACP results show that the aerosol particles are made of refractory organics which release HCN and NH 3 during pyrolysis  (Israël et al. , 2005 , supporting the tholin hypothesis).
Dendrochronological Datasets This study presents a preliminary exploratory analysis, which is part of a large framework aiming at assessing P. pinaster's plasticity response to contrasting climatic conditions in Portugal. Companhia das Lezirias is one of eleven selected sites in the framework. For this reason the sampling size in each location was limited to ten cores and only one core by tree. Dendrochronological series considered here are the average of ten 60-year old maritime pine trees with a mean diameter at breast height of 36. 9 cm and a mean height of 21. 3 m. The material was collected at breast height (1. 3 m), extracting one increment core per tree, from bark to pith. Using a twin-blade circular saw, a 2 mm-thick radial strip segment was sawn from each increment core and then conditioned at 12% moisture content.
Tree-ring cores were dated and synchronized against the reference of two characteristic wet years  (1990 and 1998)  and two characteristic dry years  (1995, 2005)  (Supplementary Figure  S1 ). Age-related trends were removed from dendrochronological series through the application of an appropriate standardization methodology. This was achieved by fitting a negative exponential or polynomial smoothing spline functions on tree-ring width time-series and linear functions on density time-series  (Cook and Peters, 1981; Cook, 1987)  for each tree-ring core (see example of tree n • 7 in Supplementary Figure  S2 ). Standardized indices of tree-rings were obtained by dividing the original observed data of width or density by the best fitted exponential or linear function for each core. The final indices used to evaluate the impact of recent climate change on P.
Meteorological Datasets Meteorological datasets covering the period 1950-2012 were retrieved from the public database of the Observational station data of the ECA&D European Climate Assessment & Dataset  (Klein Tank et al. , 2002)  and the Observations gridded dataset form the EU-FP6 project ENSEMBLES (E-OBS,  Haylock et al. , 2008). Daily temperature and precipitation were extracted from the closest grid point 9. 7 km away from the sampling location. Further climate variables and indexes were calculated from the E-OBS retrieved datasets on a monthly basis. In order to maintain the analysis within a reasonable length we restricted the wide range of potential meteorological variables to the most interesting plant growth climate factors including the following precipitation-based monthly variables: the accumulated precipitation (P), the maximum daily precipitation (Pmax), the number of dry days (Dryd) and the maximum of consecutive dry days or dryspell (Drys).