dataesr/scientific-paragraphs-categorization huggingface dataset:
The Tara Oceans match-up points are combined with particulate absorption spectra and HPLC pigment information from four other expeditions. These expeditions are from the North  Atlantic (19th Atlantic Meridional Transect, 2009, 17 match-up data points; North Atlantic Bloom Experiment, 2008 , 33 match-up data points), Equatorial Pacific (2006, 67 match-up data points), and Subarctic Northeast Pacific (LineP, 2009, 6 match-up data points). These studies, described in  Dall'Olmo et al. (2009)  and  Westberry et al. (2010) , add spatial coverage to the Tara Oceans dataset and increase both the number of points and the dynamic range of absorption measurements and pigment concentrations used for development of the method presented here. The total number of match-up points from all five expeditions used in this study is 210 (Fig.
Using fixed values for center wavelengths and widths greatly reduces the time needed to execute the decomposition method, and standardizes the decomposition to all of the spectra, allowing for application of the method to a much larger spectral absorption dataset (e. g. Tara Oceans, ∼70,000 1 km 2 binned absorption spectra). Absorption by non-algal particles (NAP) is accounted for by the inclusion in Eq. (  2 ) of an exponentially decaying NAP function: a NAP (λ) = a NAP (400 nm) * exp(-0. 01 * (λ -400 nm)). (3) A sensitivity study of the value used for the slope of NAP absorption was conducted using exponent values ranging from -0. 006 nm -1 to -0. 013 nm -1. The values of the relative median error (e median , described in Section 2. 4; values shown in Table  2 ) were found to vary by an average of 3%, with improvements in modeled phytoplankton absorption (a gaus (λ i )) as assessed by increased correlation with match-up data points found for some absorption peaks but not others.
Thus, the value chosen for the NAP absorption slope does not strongly influence the results presented here. We spectrally weighted the decomposition by the inverse of the standard deviation associated with the particulate absorption spectra measurements at each wavelength (σ 2 SD (λ), e. g. AC-S data have higher noise in the blue due to lower lamp output in that part of the spectrum). Note that for the Tara Oceans data, we have uncertainty values for each individual absorption spectrum, which are calculated as the standard deviations of the one-minute binned spectra. However, in the case of the other four expeditions, spectral uncertainty data was not available, so we used a mean uncertainty spectrum calculated from all the Tara Oceans uncertainty spectra (n = 87). This weighting improved the relative median error (e median ; Table  2 ) by an average of 22% across all values of a gaus (λ i ).
Absorption and pigment correlations We found strong correlations when comparing the three major chlorophyll pigments (chlorophylls a, b, and c) with wavelengths of peak absorption for each chlorophyll (Fig. 3 , Table  2 ). e median values range from 30% to 57% for all chlorophyll and carotenoid pigment predictions (Table  2 ). There is no improvement in the prediction of TChl a concentration compared to a prediction of TChl a using the method of line-height magnitude at 676 nm  (Boss et al. , 2013). Significant correlations are also found between the photosynthetic carotenoids (PSC) and absorption coefficients at 523 nm, and between the photoprotective carotenoids (PPC) and absorption coefficients at 492 nm (Fig. 5 , Table  2 ). One pigment that is not reported by HPLC analysis in our dataset is phycoerythrin, which is found in the cyanobacteria Synechococcus and Trichodesmium, as well as in Cryptophytes.
We applied the decomposition method to the entire Tara Oceans dataset (binned to 1 km 2 spatial resolution; 69,944 global data points) and applied Eq. (  5 ) (Section 2. 4) to calculate inverted pigment concentration ratios for TChl b, Chl c, PPC, and PSC, each relative to TChl a (at 675 nm) (Table  3 ; Fig. 6 ).
, 2012; Chekalyuk and Hafez, 2013). The dataset of match-up points used in this study span five years and a wide range of locations throughout the world's oceans, which implies that the a gaus (λ i )/[pigment j ] relationships are relatively well constrained in space and time. In-line systems, such as that used during the Tara Oceans expedition  (Slade et al. , 2010) , can record large quantities of data with relatively low effort, thus providing a more comprehensive view of phytoplankton ecology than is currently available using laboratory-based phytoplankton absorption measurements. If we have a true absorption spectrum given by a true (λ), we then get:  λ max λ min a true (λ ′ )f (λ, λ ′ )dλ ′ = a meas (λ). (A. 1) Note that this is a simple matrix equation that in theory could be inverted directly.
Acknowledgments Thanks to the scientists and crew of the Tara Oceans expeditions, in particular  M. Picheral  and  S. Searson  for their efforts in the collection of quality AC-S spectra. Thanks to  W. Slade  and  J. Loftin  for installation of the instrument and management of logistics, and  L. Taylor  and  T. Leeuw  for the processing of all raw Tara Oceans AC-S data. Thanks to  M. Ouhssain  for the extraction and HPLC analysis of Tara Oceans pigment samples. We sincerely thank the commitment of the people and the following institutions and sponsors who made this singular expedition possible:  CNRS, EMBL, Genoscope/CEA ,  UPMC VIB ,  Stazione Zoologica Anton Dohrn ,  UNIMIB ,  ANR ,  FWO ,  BIO5 , Biosphere 2, agnès b. , the  Veolia Environment Foundation ,  Region Bretagne ,  World Courier ,  Cap L'Orient , the  Foundation EDF Diversiterre, FRB , the  Prince Albert II de Monaco Foundation ,  Etienne Bourgois , the  Tara Foundation  teams and crew.
Tara Oceans  would not exist without the continuous support of the participating institutes (see http://dx. doi. org/10. 1371/journal. pbio. 1001177). This is contribution no. 12  of the Tara Oceans Expedition 2009-2012. Funding for the collection and processing of the Tara Oceans AC-S dataset was provided by  NASA   Ocean Biology and Biogeochemistry program  under grants  NNX11AQ14G  and  NNX09AU43G  to the  University of Maine.
Acknowledgments Work in our laboratories is funded by the  ANR/Investissements d'Avenir program  by means of the  Oceanomics  project (grant No. ANR-11-BTBR-0008 ). CB and CdV also acknowledge the  EU Micro B3  project for financial support. CB additionally acknowledges funding from the  ERC  " Diatomite" and ANR "DiaDomOil " projects. CB thanks  André Abreu  for advice on governance issues; SR and SB thank  Sté phane La Barre  and Benoî t Sé rive for helpful discussions, as well as Cancé ropôle Grand-Ouest and notably  Sylvain Routier  (axis: Natural sea products in cancer treatment) for continuing support. This article is contribution number  0010  of the Tara-Oceans Expedition  2009-2012.
Along the green lineage (Chlorophyta and Streptophyta), mitochondria and chloroplast are mainly uniparentally transmitted and their evolution is thus clonal. The mode of organellar inheritance in their ancestor is less certain. The inability to make clear phylogenetic inference is partly due to a lack of information for deep branching organisms in this lineage. Here, we investigate organellar evolution in the early branching green alga Ostreococcus tauri using population genomics data from the complete mitochondrial and chloroplast genomes. The haplotype structure is consistent with clonal evolution in mitochondria, while we find evidence for recombination in the chloroplast genome. The number of recombination events in the genealogy of the chloroplast suggests that recombination, and thus biparental inheritance, is not rare.
Data We analyzed data from 13 O. tauri strains that had been sampled from the North-West Mediterranean sea and have been described previously  (Grimsley et al. 2010 ). These strains were established from clonal culture and identified as O. tauri based on 100% identity in the 18S encoding DNA. Their whole nuclear and cytoplasmic genomes have been sequenced using Illumina GA at the Joint Genome Institute (http://www. jgi. doe. gov/, Community Sequencing Program CSP-129). We aligned the PE-reads data sets (76 bp for the strains RCC1108, RCC1114, RCC1115, RCC1116, RCC1558, RCC1559, and RCC745 and 101 bp for the strains RCC1110, RCC1112, RCC1117, RCC1118, RCC1123, and RCC1561) against the reference chloroplast (GenBank accession: CR954199. 2) and mitochondrial (CR954200. 2) genome sequences using the Burrow-Wheeler Aligner (BWA) with parameters n ¼ 6, l ¼ 35, k ¼ 3, and e ¼ 3  (Li and Durbin 2009).
De Novo Assembly of Organelles Genomes Genomes were assembled de novo from the PE-reads data sets of each strain using Abyss  (Simpson et al. 2009 ) with kmer ¼ 41, 10 PE-reads to join two contigs into one scaffold, and additional parameters were kept as default. To extract organellar contigs, we blasted them against the chloroplast and mitochondrial genomes, using thresholds of 95% nucleotide identity over a minimum of 500 bp. We ended with 2 to 10 contigs for the mitochondria and with 2 to 7 contigs for the chloroplast (supplementary table S1, Supplementary Material online). We then aligned de novo contigs onto the reference using nucmer from the mummer v3. 20 package  (Kurtz et al. 2004). By using nucmer with options "-maxmatch -l 30 -banded -D 5" and the other options set to default we required a minimum exact-match anchor size of 30 bp and a minimum combined anchor length of 65 bp per cluster.
Phylogenetic Reconstruction We used three different methods to reconstruct the phylogenies of the mitochondrial and chloroplast genomes of our samples. Evolutionary models with appropriate parameters were searched out of 24 models using Mega5 tool  (Tamura et al. 2011). It came out with the TN93  (Tamura and Nei 1993)  substitution model with a proportion of invariant sites for cpDNA and the HKY  (Hasegawa et al. 1985)  model for the mtDNA. Transitions to transversions ratio were estimated to be 1. 43 for the cpDNA and 4. 92 for mtDNA. Three different methods were used for the phylogeny reconstructions: the neighbor-joining (NJ) method with the above substitution models for each data set, the maximum parsimony (MP) method with SPR search method and the maximum likelihood approach (PhyML trees)  (Guindon and Gascuel 2003)  with the above mentioned substitution models for each data set as implemented in Geneious (Geneious version 6.
16). To quantify the level of recombination in the chloroplast, we used a coalescent-based maximum likelihood method as implemented in LDhat  (McVean et al. 2002). We estimated the rate of recombination r LDhat ¼ 0. 96 in the genealogy of the cpDNA. As expected, the mitochondrial data set showed no evidence of recombination using this method. If chloroplast and mitochondria are inherited together clonally, we would expect their genomes to contain congruent phylogenetic signals  (Olson and McCauley 2000). We constructed the phylogenies for each organelle (fig. 4 ) and tested for congruence using KH and SH tests implemented in PAUP 4. 0 beta 10 Win  (Swofford 2002). Both tests rejected congruence (KH, P value < 10 À4 ; SH, P value < 10 À4 ) between cpDNA and mtDNA phylogenies. In agreement with the absence of recombination in the mitochondrial genome, we observed two distinct mitochondrial haplotypes in our population.
The presence of recombination between the alleles segregating in the population is the consequence of homologous recombination between sequences carrying different mutations from the two parental cells. This implies chloroplasts are, at least occasionally, biparentally inherited in O. tauri. To estimate the relative prevalence of recombination in cpDNA as compared with nuclear DNA, we compared the estimated recombination rate in cpDNA and nuclear DNA for the same 13 strains  (Grimsley et al. 2010 ) using the LDhat software  (McVean et al. 2002). The number of recombination events in the genealogy of the cpDNA is 0. 96 while an estimation from nuclear markers of the same strains is 2. 9  (Grimsley et al. 2010). This suggests that recombination in cpDNA is approximately one-third as frequent as recombination in the nuclear genome and that chloroplast biparental inheritance is not exceptional.
Acknowledgments The authors acknowledge the Genotoul bioinformatic platform (http://bioinfo. genotoul. fr/)for access to sequence analysis tools and computer cluster. They thank the  Genomic of Phytoplankton group , especially  Nigel Grimsley  and  Herve Ḿoreau  for support. The Illumina sequence data analyzed in this study were produced by the  US Department of Energy Joint Genome Institute  http://www. jgi. doe. gov/within the  Community Sequencing Program   CSP-129. This work was supported by the  European Community 's  7th Framework program FP7  under grant agreement no. 254619  to G. P and A. E. W. and the  AAP-FRB2009-PICOPOP  (http://www. fondationbiodiversite. fr/) to G. P.
Acknowledgements This work was supported by  "Education and Lifelong Learning" Operational Program  funded by  EU-European Social Fund and Greek Secretariat for Research and Technology , the  Spanish MEC  ( MAT2009-08024 ),  CONSOLIDER  ( CSD2007-00041 ), and  FEDER program. Financial support by the  Access to Research Infrastructures  activity in the 7th Framework Program of the  EU  ( SFERA Grant  Agreement n. 228296) is gratefully acknowledged. C. M. Boubeta  thanks the " Ramón y Cajal" Spanish program.
Acknowledgements The authors thank four anonymous reviewers for insightful comments and suggestions. Special thanks go to  Sean Healy  and  Jan Kersting  for their assistance on earlier scenario analyses,  Guido Knoche  for feedback on earlier drafts of the paper as well as  Greg Molecke  for proofreading. The usual disclaimer applies. This work was supported by the  German Federal Environment Agency (UBA)  under the project titled " Ausgestaltung des neuen Klimaschutzabkommens: Analyse und Vorschläge für Verpflichtungen der Industriestaaten " ( FKZ 3710 41 132 ). The opinions expressed in this document are the sole responsibility of the authors and do not necessarily reflect the official opinion of the  German Federal Environment Agency.
The abrB2 and abrB2 C34S mRNAs were quantified with quantitative RT-PCR using with the same abrB2-specific primers (data not shown). In parallel experiments, antibodies specifically recognizing the AbrB2, not the AbrB1 paralog protein (Supplementary Fig. 5 ), were generated and used for western blot analyses, which showed equal abundance of the AbrB2 and AbrB2 C34S proteins (Fig. 1 , panel B). Furthermore, circular dichroism analysis of the AbrB2 and AbrB2 C34S proteins confirmed that the C34S mutation did not alter the overall AbrB2 protein structure (Supplementary Fig. 2 ).
3 ). AbrB2 glutathionylation was further confirmed by direct visualization with MALDI-TOF mass spectrometry. Indeed, a clear increase in the AbrB2 mass of approximately 306 Da, consistent with the formation of one glutathione adduct per AbrB2 monomer, was observed (Supplemental Fig. 3 ). The area corresponding to the shifted peak increased with treatment duration and reached more than 80% after 2 h incubation with GSSG. We again verified that AbrB2 glutathionylation could be reversed with DTT. Furthermore, the absence of glutathionylation of the cysteine-less AbrB2 C34S protein was also verified (data not shown). These data showing that the unique cysteine of AbrB2 can be glutathionylated are significant because many Fig. 3  e Analysis of the glutathionylation of AbrB1 and AbrB2 with biotinylated glutathione BioGSSG.
Acknowledgements This work was supported by the  Agence Nationale de la Recherche  Grants  ANR-09-BIOE-002-01  ( EngineeringH2cyano ) and  ANR   Blanc08-0153  (GLUTAPHOTO), and  CNRS  ( Centre National de la Recherche Scientifique )  Programme Interdisciplinaire Energie PIE2 (Reprogramhydrogen ). J. D. , S. S. , P. S. , C. L. and S. S were recipients of fellowships from the  CEA  (J. D. ) and  ANR  (S. S. ; C. L. ; M. O-R; V. C. and P. S. ). We also thank  Cecile Fairhead  for access to the «ArraySud transcriptome plateau technique».
Paleoclimate Temperature Major progress in quantitative understanding of climate change has occurred recently by use of the combination of data from high resolution ice cores covering time scales of order several hundred thousand years  [48] [49]  and ocean cores for time scales of order one hundred million years  [50]. Quantitative insights on global temperature sensitivity to external forcings  [51] [52]  and sea level sensitivity to global temperature  [52] [53]  are crucial to our analyses. Paleoclimate data also provide quantitative information about how nominally slow feedback processes amplify climate sensitivity  [51] [52] [54] [55] [56] , which also is important to our analyses. Earth's surface temperature prior to instrumental measurements is estimated via proxy data. We will refer to the surface temperature record in Fig.
Acknowledgments We greatly appreciate the assistance of editor  Juan A. An  ˜el in achieving requisite form and clarity for publication. The paper is dedicated to  Paul Epstein , a fervent defender of the health of humans and the environment, who graciously provided important inputs to this paper while battling late stages of non-Hodgkin's lymphoma. We thank  David Archer ,  Inez Fung ,  Charles Komanoff  and two anonymous referees for perceptive helpful reviews and  Mark Chandler ,  Bishop Dansby ,  Ian Dunlop ,  Dian Gaffen Seidel ,  Edward Greisch ,  Fred Hendrick ,  Tim Mock ,  Ana Prados ,  Stefan Rahmstorf ,  Rob Socolow  and  George Stanford  for helpful suggestions on a draft of the paper.
In general, they carry a chloroplast-targeting sequence and share the structural features of membrane transporters, namely a hydrophobic structure, a pore-forming sequence, and molecule-binding sites. Using bioinformatics tools, these specific features can be used to predict homologues of membrane transporters from other organisms, as targeted to the chloroplast. Following sequencing of the Arabidopsis thaliana (L. ) Heynh (Arabidopsis) genome  [28] , bioinformatic analyses have estimated that 4. 5 % of its nuclear-encoded proteins represent membrane channels and transporters  [29, 30]. According to the Transport Classification system (TC) available at the TCDB database  [31]  (http://www. tcdb. org/), transport proteins found in biological membranes belong to three major categories: channels/porins (TC #1), secondary transporters (TC #2), and primary transporters/ pumps (TC #3).
Apart from TCDB, there are several other excellent databases of membrane transporters, including PlantsT for functional genomics of plant transporters (  [32]  (http://plantst. genomics. purdue. edu/) and ARAMEMNON for sequence analysis of plant membrane proteins  [33]  (http://aramemnon. botanik. uni-koeln. de/). In this review, we provide an update on channels and transporters from the thylakoid membrane of the cyanobacterium Synechocystis sp. PCC 6803 (Synechocystis) and Arabidopsis, that belong to the three major TC categories. Table  2  presents a summary of the current knowledge about the biochemical activity and physiological role of these types of transporters. We also perform a comprehensive phylogenetic analysis to reveal their origins and evolutionary history. Mass-spectrometry-based proteomics has been a very successful approach in identifying a tremendous number of chloroplast envelope channels and transporters  [34, 35].
5 versus 28 kDa). BlastP searches using the bovine AAC sequence in the Arabidopsis database revealed two chloroplast candidates for proteins responsible for this transthylakoid activity, encoded by the At3g51870 and At5g1500 genes and having a theoretical mass of 38. 5 kDa for the processed forms. Based on findings of its initial localization and functional characterization in E. coli, the latter one was annotated as the thylakoid ATP/ADP carrier (TAAC)  [78]. Arabidopsis TAAC shares about 30 % identity with classic AACs, which is concentrated in the six putative transmembrane helices and to a lower degree in the connecting loops  [78]. However, the amino acid sequence of the processed form is 80 residues longer than that of a typical AAC, explaining the difference in the reported size in SDSgels  [77].
No orthologues of PAA1 or PAA2 were found by BlastP searches in either red algae or glaucophytes, suggesting that these lineages may have lost the genes that encode these proteins. The thylakoid K +/ H + and Na +/ H + antiporters KEAs resemble the bacterial K + /H + antiporters KefB and KefC  [116]. Interestingly, detailed phylogenetic analyses of KEAs indicated that the chloroplast-predicted KEAs (KEA1, KEA2 and KEA3) share a most recent common ancestor with the CHX subfamily of CPA2, including CHX23  [94]. Our phylogenetic analysis does not include the CHX subfamily, but a broader analysis (not shown) adds all CHX proteins as an additional clade sister to the rest of those proteins shown in Supplemental Fig. S2. Although at first glance this would appear to be contradicting the earlier result  [94] , the mid-point rooting used in our trees should not be taken for granted.
Sequence selection We began with sequences of thylakoid-located transport proteins from Arabidopsis and representative cyanobacteria for each of the proteins in Table  2. Sequences from curated gene families were drawn from the following databases: ARAMEMNON, Gramene (www. gramene. org/) and Cyanobase (http://genome. microbedb. jp/cyanobase/). The thylakoid-located members were used as queries in BlastP searches  [120]  to NCBI and Cyanobase. BlastP searches were made to other (1) green plants, to get land plant and green alga representatives, (2) non-green plant members of Archaeplastida (includes other chloroplast-containing eukaryotes, such as glaucophytes and red algae), (3) eukaryotes and to (4) cyanobacteria, to provide context for understanding the evolution of the proteins. In some cases we also included the closest non-photosynthetic bacteria matches.
By using BlastP alone, we may have failed to find sequences present only in nucleotide databases, i. e. , without hypothesized or confirmed translations into amino acid sequences. Thus, our findings are necessarily limited by this caveat. For example, it is possible that the closest relatives to the green plant transport proteins have not been found in the other taxa presented in Fig. 5. Although our conclusions are subject to revision in light of new genome sequences not yet available, the existing genomes for members of critical taxa to test the hypothesis of the origin of each transport protein have been available for several years and, in some cases, for more than a decade  [121] [122] [123]. Therefore, the use of BlastP alone is unlikely to affect our findings.
Acknowledgments This work was supported by grants from the  Swedish Research Council and Olle Engkvist Byggmästare Foundation  to C. S. , the  French Ministère de l'Education Nationale de l'Enseignement Supérieur et de la Recherche , and from  University of Le Mans  to B. S. , and grants from the  Swedish Research Council , the  Royal Swedish Academy of Sciences ,  Lars Hiertas Minne  fund, The  Royal Physiographic Society in Lund ,  Helge Ax:son Johnsons  fund and the Lundgrenska fund to B. E. P.
The last opinion was adopted on 24 November 2011. EFSA concluded in its opinion that for 26 substances additional toxicological data are. The requested information on one representative material,  5, 4, 6, 3, ] has now been submitted by the European Flavour Association. This information is intended to cover the re-evaluation of this substance and the re-evaluation of the following five substances in FGE. 21:  4, 3, 5(4H) -dithiazine. 054] 2,4-Dimethyl(4H)pyrrolidino[l,2e]-  1, 3,. 055], 4, 3,. 057], 3,. 079], 2-methyl-2-thiazoline. 086] and ethyl thialdine  In addition, requested information on another representative material, 2-acetyl-2-thiazoline. 010], has now been submitted by the European Flavour Association. This information is intended to cover the re-evaluation of 2-methyl-2-thiazoline.