dataesr/scientific-paragraphs-categorization huggingface dataset:
This cluster's sequences were all retrieved from deep waters except for the SAGs reported here. The resulting sequence from the 78 identical partial 23S rRNA genes, when aligned against the NCBI database, showed a lower identity against K. algicida OT-1 (90. 5%). Nevertheless, it was still its closest culture hit and the phylogenic tree supported the assignment of the SAGs to the genus Kordia ITS with high bootstrap values (Supplementary Figure  1 ). The 78 SAGs' ITSs were identical and coded for Ala-tRNA and Ile-tRNA. These tRNAs showed only one polymorphism each when compared to those of K. algicida. The nucleotidic change was located at the next-to-last base, not affecting the structure of the functional molecule. The box A conserved region present at the end of the ITS was identical to that of K.
algicida although the former was located slightly upstream (17 bp) (Supplementary Figure  2 ). Amplification of the beta subunit of RNA polymerase (rpoB) was successful in 18 SAGs, obtaining ∼1,400 bp amplicons with 100% of both amino acid and nucleotide identity among them. Their closest hit in NCBI database was the rpoB gene of K. algicida OT-1 with a 98% amino acid identity. High bootstrap values supported the location of the SAGs' rpoB sequence in the phylogenetic tree, within the Flavobacteriaceae cluster and closest to K. algicida OT-1 (Supplementary Figure  3 ). An amplification summary can be found in Table  1.
Novel Proteorhodopsin Gene The PR gene was amplified in 34 out of the 98 Kordia SAGs. The resulting 167 amino acid sequences turned out to be 100% identical to one another. The sequence was closest to the Flavobacteriaceae Flagellimonas sp. DIK-ALG-169's proteorhodopsin (89% identity, 100% coverage) (AHN13811) and Kordia sp. PC-4  (Yoshizawa et al. , 2012 ) (88% identity, 91% coverage) in the NCBI database. When comparing sequences in the Ocean Microbiome Reference Gene Catalog (OMRGC), the closest hit shared 84. 2% amino acid similarity (unclassified Flavobacteria, NOG136807 ID. v1. 022398661). Phylogenetic reconstruction placed the SAGs' proteorhodopsin in a cluster of Flavobacteriaceae PR genes (Figure  3 ). We tried to amplify the proteorhodopsin gene from the 17 SAGs retrieved from 3,000 m at the North Pacific Gyre (AAA285-F05) but the results were negative.
CONCLUSION This study shows how state-of-the-art single cell genomics can be combined with more traditional techniques such as MLSA to obtain an overview of the genetic composition of the population of study without sequencing whole genomes beforehand. Thus, this can provide both an analysis of population genetics avoiding bias by isolation in culture and an approach to select genomes of interest for further population genomics and biogeography studies. In our case, through MLSA of SAGs we have found that the dominant heterotrophic bacterial taxon in the sample was genetically homogeneous. Moreover, the study of this population in combination with available metagenomic datasets and the oceanographic metadata of the sampling area has helped shedding light on the species distribution, dynamics, and potential ecological niche of a novel Kordia species that contains proteorhodopsin.
FUNDING Funding was provided by the  Spanish Ministry of Economy  and Competitivity grants  GL2011-26848/BOS MicroOcean PANGENOMICS  to SA,  CTM2013-48292-C3  "EcoBGM" to CP-A and JG as well as grant BIOSENSOMICS through " Ayudas Fundación BBVA  a investigadores y creadores culturales" to SA. Additional funding was provided by the  Spanish Ministry of Economy and Competitiveness Science and Innovation  through the  Consolider-Ingenio programme  (project Malaspina 2010 Expedition, ref. CSD2008-00077 ) to  Carlos M. Duarte  and the  US Department of Energy Joint Genome Institute 2011 Microbes program  grant  CSP602  grant to SA (Contract No. DE-AC02-05CH11231 ). Other contributions were the  US NSF  grants  OCE-1232982  and  OCE-1335810  to RS. MR-L held a  Ph. D. Fellowship FPI  ( BES-2014-068285 ) funded by the  Spanish Ministry of Economy and Competitivity.
MATERIALS AND METHODS The is only known locality for C. servadeii is the cave Grotta della Foos (507/229 FR) (total length 2625 m, total depth 161 m), in Tramonti di Sotto (PN), Carnic Prealps, northeastern Italy. Observation sites were chosen where at least two to three individuals per square meter were found. Sites were marked with flags and photographed. Each observation session for each beetle lasted six hours per researcher. Individual beetles under observation were pointed at to the next-shift researcher at the end of an observation period to guarantee continued data recording of the same beetle during the long-term tracking. Using this approach, the same individuals were monitored by different observers in 24-h sessions. Individuals were observed at close distance and were illuminated by a cold LED light to avoid heat-related disturbance.
In order to appreciate the details of the trophic and cleaning activities, and to identify the sex of the beetles, a 30X magnifying lens was used. Sex determination in C. servadeii was possible due to external morphological sexual dimorphism. Beetle behaviour was recorded on a standardized data collection form prepared before sampling and based on prior observations  (Paoletti et al. 2011). Photographs and video were also taken with a 550D Canon camera. Each behavioural activity lasting more than 1 min, as well as shorter activities that were considered significant, were recorded. To simplify the recording process, five different behavioural categories were distinguished: trophic activity, cleaning or preening, site movement, interaction with other individuals, and other activities. As a trophic activity, the action of a beetle, with head facing down, in contact with the substrate, moving jaws and galeae and seemingly browsing the moonmilk surface was considered.
Although additional observational data will be needed for verification, our findings may be extrapolated to other species of both genera, i. e. , Cansiliella tonielloi  Paoletti, 1972 , C. montisceseni Piva, 2000 , and hadesia vasiceki Müller, 1911 , h. lakotai Perreau & Pavićević, 2008 , and h. zetae Delić, Polak & Trontelj, 2016. entific. The metal composition of substrate, including moonmilk, was detected with atomic absorption spectrophotometry using a graphite cooker SMEWW 3113 B (APHA 2012). Mouthparts and tarsal segments of C. servadeii, h. asamo, and h. weiratheri were prepared and compared using a scanning electron microscope (SEM), using methods as described by  Paoletti et al. (2011).
ACKNOWLEDGEMENTS We thank all members of the " Gruppo Speleologico Pradis " and members of the  "Circolo Speleologico Idrologico Friulano "(CSIF)  for their help during in cave field activities in Italy. We also thank the speleologists of  Zelena Brda  speleological group in Trebinje for their collaboration for field work in Bosnia and Herzegovina. The research and previous manuscript drafts benefited from ideas and contributions from  R. Caicci ,  O. Coppellotti ,  L. Deharveng ,  A. Faille ,  B. Jalžić ,  L. Latella ,  R. Mazzaro ,  O. Moldovan ,  M. Simonetti ,  U. Sauro ,  M. Schilthuizen  and  B. Sket. We thank two anonymous referees for their valuable comments and suggestions. L. DORIGO ,  A. SqUARTINI ,  V. TONIELLO ,  A. L. DREON ,  A. PAMIO ,  G. CONCINA ,  V. SIMONUTTI ,  E. RUZZIER ,  M.
Furthermore, "RCR initiators" belonging to another family (Rep_1) are also involved not only in the maintenance of three plasmids from Firmicutes but also in their mobilization by ICEBs1  [98]. Therefore, the classical distinction between RCR initiators and relaxases is probably not relevant. Unexpectedly, an extensive analysis, very recently performed on 124 genomes of Streptococcus, revealed that 118 among the 144 IMEs detected in these genomes encode a putative non-canonical relaxase related to rolling circle replication initiators  [26]. Among these 118 IMEs, 45 encode RCR initiators/relaxases belonging to the MobT/Rep_Trans family. The other 73 IMEs encode putative relaxases related to other RCR initiator families [PF01719/Rep_2 (35 elements), PHA00330 (21 elements), PF01719/Rep_2 associated with a helicase domain (15 elements) and PF02407/Viral-Rep (2 elements)].
Among these 118 IMEs, 35 do not encode any CP. Unexpectedly, the 83 other IMEs encoding a RCR initiator-related relaxase (24 with MobT, 34 with PF01719, 19 with PHA00330, 4 PF01719 associated with a helicase domain, 2 with PF02407) also encode a non-canonical CP (called TcpA). TcpA CPs are distantly related to FtsK, a protein involved in dsDNA translocation during cell division and to TraB, a protein involved in dsDNA conjugative systems from Actinobacteria. It should be mentioned that all the very few known events of mobilization in trans of plasmids involving a RCR initiator-related relaxase also involve a TcpA CP encoded by the helper ICE  [98] [99] [100]. This suggests that the putative IMEs encoding relaxases related to RCR initiators can only hijack conjugative elements that encode TcpA.
They showed that three families of IMEs have a high prevalence either in S. agalactiae  [37, 48]  or in larger set of species from Firmicutes  [124]. None was performed in another bacterial division. Furthermore, an exhaustive search for conjugation modules based on hidden Markov model profiles deduced from known relaxases (including canonical ones and MobT), CPs and MPFs, i. e. , essentially conjugation modules of proteobacterial plasmids, was performed on 1124 archaeal and bacterial genomes. It detects 402 chromosomal relaxase genes that are not associated with CPs or MPFs  [1]  and therefore are probably carried by IMEs. This analysis also suggests that IMEs are the most frequent class of elements that transfer by conjugation. The authors of this study also suggested that the strong bias in the initial dataset for proteobacterial plasmids did not allow the detection of very distant or unrelated genes, precluding the detection of many elements in other bacterial divisions and archaea.
) MobV None IncP plasmids (VirD4, T) [50] Tn6215 Clostridioides difficile (fir. ) MobV ND [90] Tn4451 e Clostridium perfringens (fir. ) MobV IncP plasmids (VirD4, T) [91] IMESag-rpsI f,g Streptococcus agalactiae HRC (fir. ) MobV pAMβ1 plasmid (VirD4, FATA) [37] tet(O) fragment Streptococcus pyogenes (fir. ) MobV Proposed: Tn5252 superfamily (VirD4, FATA) [31] IME-oriT e,f,h Streptococcus agalactiae (fir. ) MobT Proposed: Tn916 and ICESt3 (TcpA, FA) [63] IME_Sag2603_tRNAlys e Streptococcus agalactiae (fir. ) MobT Proposed: helpers with TcpA and FA [26] IME_SsuBM407_tRNAleu e Streptococcus suis (fir. ) MobT TcpA Proposed: helpers with TcpA and FA [26] IME_SdyRE378_ebfC e Streptococcus dysgalactiae (fir. ) PF01719 Proposed: helpers with TcpA and FA [26] IME_SsalJIM8777_rpmG e Streptococcus salivarius (fir.
) PF01719 TcpA Proposed: helpers with TcpA and FA [26] IME_SsuTL13_rpsI e Streptococcus suis (fir. ) PF01719-helicase Proposed: helpers with TcpA and FA [26] IME_Seq35246_rpsI e Streptococcus equi (fir. ) PF01719-helicase TcpA Proposed: helpers with TcpA and FA [26] IME_SanC238_tatD Streptococcus anginosus (fir. ) PHA00330 Proposed: helpers with TcpA and FA [26] IME_SpnA45_tRNAleu e Streptococcus pneumoniae (fir. ) PHA00330 TcpA Proposed: helpers with TcpA and FA [26] IME_SiniSF1_ebfC Streptococcus iniae (fir. ) PF02407 TcpA Proposed: helpers with TcpA and FA [26] a This table includes all types of IMEs that were found to be mobilized in trans (generally one per type) and some putative ones. The activity of transfer of underlined elements has not been demonstrated. b α, alphaproteobacteria; β, betaproteobacteria; γ, gammaproteobacteria; act.
Field recording An embankment was constructed at Hericourt in Franche-Comté region in the northeast of France. This region is characterized by a continental climate influenced by ocean. The embankment was constructed with lime/cement treated silty soil. A weather station was installed on the top of embankment, recording meteorological data such as solar radiation, air temperature and air relative humidity at 1. 5 m height, wind speed and direction, rainfall and atmosphere pressure. The soil surface temperature was also monitored. All these data were recorded half-hourly. The studied period is 30 d, starting from 14:42:52, 06/07/2011 to 14:42:52, 05/08/2011. Solar radiation R s , air temperature and soil surface temperature are used for the analysis in this study. shows the variations of solar radiation data, and the details for 24 h on 07/07/2011 are depicted in Fig.
Acknowledgements The authors wish to acknowledge the support of the  European Commission  by the  Marie Curie   IRSES Project GREAT e Geotechnical and Geological Responses to Climate Change: Exchanging Approaches and Technologies on a World-wide Scale  ( FP7-PEOPLE-2013-IRSES-612665 ). The authors also wish to thank the  China Scholarship Council (CSC)  and  Ecole des Ponts Paris Tech  for their financial supports.
Specific data on amphibious boats were collected from the main shipbuilding company. The end of life of rope nets and material was not included in the study. After harvest, mussels are placed in an oxygenated/ aerated purification tank for at least 24 h, at the technical base. Water from the bay is pumped into a tank shared by all shellfish producers in the area. Afterwards, mussels go through a declumper, which separates the mussels and shreds the ropes and nets. Mussels are then cleaned in a brushing machine and calibrated, first mechanically, then by hand, to remove other waste (crabs, algae, broken mussels, etc. ). Organic waste from cleaning and processing stages and all other types of waste are buried at the local landfill. Wastewater is returned to the sea without treatment. At this point, 20-30% of the mussels have been discarded, mainly due to undersized mussels.
Life cycle impact assessment Impact categories were selected based on previous studies and guidelines in aquacultural LCA  (Aubin et al. 2013 , Pelletier et al. 2007 ). Climate change (kg CO 2 -eq), acidification (kg SO 2 -eq), and eutrophication (kg PO 4 -eq) were calculated using characterization factors of CML2 baseline 2000 v2. 03  (Guinée et al. 2002). Energy demand (MJ) was calculated using the Total Cumulative Energy Demand method, v1. 03  (Frischknecht et al. 2004 ), excluding growth energy content in biotic resources. Water dependence (m 3 ) was based on freshwater resource use, as developed by  Aubin et al. (2009). Calculations were performed with SimaPro® v8. 0. 5 software (PRé Consultants, Amersfoot, Netherlands).
(2010a) a n d  Lourguioui et al. (2017) ( 4 7 2a n d4 0 4k gC O 2 -eq ton -1 ,r espectively), without C sequestration in shells and bouchots (Table  5 ), our result is only 20% lower. With the main hypotheses, climate change is 50% lower than the C footprint calculated by Fry (2012)(252kgCO 2 -eq ton -1 ) which is near from our results without C sequestration in bouchots. Differences with results of previous studies can be explained by differences in the production practice studied or LCA characteristics (e. g. , analysis, boundaries, databases, models).
4 , Table  5 ). The C sequestration in shells was not accounted in the studies of  Iribarren et al. (2010a)  and  Lourguioui et al. (2017) , but was in  Fry (2012)  which explain huge differences in results. Estimates of C sequestration in bouchots are sensitive to the C sequestration model used, the tree species from which the wood comes, the lifespan of the bouchots, and their final fate. Inventories from the ecoinvent database used to model exotic wood production were standard ones, and estimates of a wood's C sink effect are debatable. In the ecoinvent database, C sequestration by alata wood is based on its C content (49. 4% C by mass). This is consistent with ILCD recommendations (Joint Research Center 2010); however, other approaches can be used, such as dynamic LCA  (Levasseur et al.
For this reason, it was not used in the present study. The b-factors were obtained solely from the raw theoretical harmonic frequencies computed within DFT. A different source of uncertainty affecting the comparison of the theoretical data and experimental measurements arises from the strategy used to model the carbonate defects in the apatite crystal host. Although the theoretical carbonate concentration ($4. 4 wt% of CO 2 ) is consistent with the concentrations observed in biogenic samples (4. 8 wt% of CO 2 in bone,  Elliott, 2002) , the periodic models do not account for the random character of substitutions affecting equivalent sites in minerals. A relaxation of the cellparameters of the defect model would prejudicially increase this character by constraining the full system symmetry to that locally imposed by the defect.
ACKNOWLEDGMENTS This work was performed using HPC resources from  GENCI-IDRIS  (Grants  i2106041519 ). This work was supported by  French  state funds within the framework of the Cluster of Excellence MATISSE led by  Sorbonne Universite  ´s and also by  CNRS   TelluS INSU 2017 INTERRVIE. We thank three anonymous reviewers and associate editor  Ruth E. Blake  for their thoughtful comments.
INTRODUCTION IUCN's World Database on Protected Areas (WDPA 2014) records over 100,000 protected areas worldwide, covering over 12% of the Earth's land surface. Protected areas are recognized as the most important core units for in situ conservation  (Gaines et al. 2010; Game et al. 2009; Gray 2010; Lester et al. 2009; Lubchenco et al. 2003 Lubchenco et al. , 2007;; Pimm et al. 2001). Conservationists and protected area managers around the world spend millions of dollars each year to conserve biodiversity and create new protected zones  (Castro and Locker 2000). However, measuring the number and extent of protected areas provides only a unidimensional indicator of political and national commitments to biodiversity conservation  (Chape et al. 2005). Protected areas play a vital role in biodiversity conservation.
ACKNOWLEDGEMENTS This paper is part of  Mrs. El-Hajj 's PhD thesis funded by a grant from the  National Council for Scientific Research -CNRS (Lebanon)  and the  CEDRE program  (project number  13 E/L4 ). The authors would like to duly thank  M. Julien Viglione  from the French consulting company "EcoMed s. a. r. l. " for his guidance and contribution in the identification of indicators. This is a LIA O-LiFE contribution number  SA 16-2015.
Selection of articles for inclusion Data were extracted from the main study text, tables and forest plots. A number of articles reported several meta-analyses. We included all of these providing that they dealt with distinct parameters or risk factors. If an article reported several meta-analyses on the same parameter and the same outcome, we selected the most comprehensive one (i. e. the one containing the most datasets) and the one reporting the fixed effects rather than the random effects model. We selected the result from the fixed effects model because this is less strongly influenced by small (i. e. imprecise) studies than the result from the random effects model, which may produce inflated point estimates in the presence of bias (e. g. publication bias)  [10]. When two or more articles dealt with the same parameter and the same outcome, the most recent one was used.
Data extraction Data were extracted independently by two authors (E. D. -M. and F. G. ), and discrepancies resolved by mutual consent. A third author (K. S. B. ) conducted a further 10% data check, which resulted in a small number of errors being detected and corrected. The following data were extracted from the included articles: year of publication, first author, research domain (neurological, psychiatric, somatic disease), research methodology (cognitive/behavioural, genetic, brain imaging, other), number of datasets included in the meta-analysis, summary effect size estimate of the meta-analysis and its statistical significance (based on p < 0. 05), and number of patients and controls included in the meta-analysis. When the number of patients and controls was not given, they were identified from individual studies where possible.
If the effect size was reported as a mean difference, it was converted to Cohen's d using the software OpenMetaAnalyst  [11]. Meta-analyses were excluded if data were incomplete or if the reporting was unclear. For each meta-analysis, we calculated the average number of patients and controls (i. e. number of patients or controls included in the meta-analysis divided by the number of datasets included in the meta-analysis). In order to compare the effect sizes across all meta-analyses and diseases, effect sizes were natural logarithm transformed. The effect sizes expressed as Cohen's d were converted to ln(OR) using the formula ln(OR) = d × π / √ 3. For each disease, the average effect size and the median number of patients and controls in the contributing studies were calculated (see electronic supplementary material, table  S1 ).
The average number of patients and healthy controls was calculated by dividing the number of patients and controls included in the meta-analysis by the number of individual contributing studies. Finally, the number of events in controls was identified from the meta-analysis. Where this was not available, it was calculated from the data provided in the meta-analysis. Where the meta-analysis did not provide the number of events in controls, we identified the contributing study with the largest control  sample size and extracted the number of events in controls from the corresponding original publication. The achieved statistical power of individual contributing studies to reject the null hypothesis of no effect given the effect size reported in the meta-analysis (i. e. the best estimate of the likely population effect size) was calculated using G*Power 3.
Centre of Excellence. Funding from  British Heart Foundation ,  Cancer Research UK ,  Economic and Social Research Council, Medical Research Council  and the  National Institute for Health Research , under the auspices of the  UK Clinical Research Collaboration , is gratefully acknowledged. K. S. B. was funded by the  National Institute for Health Research School for Primary Care Research (NIHR SPCR ). The NIHR SPCR is a partnership between the  Universities of Birmingham, Bristol, Keele, Manchester, Nottingham, Oxford, Southampton  and  University College London. E. D. -M. was funded by a postdoctoral fellowship from the  Region Aquitaine  ( C2SM  research programme). This study was supported, in part, by the  Medical Research Council  and the  University of Bristol  ( MC_UU_12013/6 ). Disclaimer.
We compare these originality indices related to QE to a range of existing indices introduced in the literature in their ability to discriminate species in terms of their functional originality (Table  1 ;  May, 1990; Eiswerth and Haney, 1992; Redding, 2003; Ricotta, 2004; Redding and Mooers, 2006 ; see also  Redding et al. , 2014  for a review). We selected originality indices amongst the most used in the literature. We use numerical simulations and a case study to evaluate the strengths and differences across the range of originality indices. In particular, we evaluate 1) whether measures of phylogenetic originality could serve as a proxy for measures of functional originality in conservation and ecological studies; 2) whether the transformation of functional data into functional trees modifies the way species are ranked according to their measured originalities compared to approaches that directly rely on pairwise functional dissimilarities among species; 3) more generally, whether different indices provide different views on how original species are from each other, hence reflecting the range of ecological and evolutionary processes that generated patterns of originality.