dataesr/scientific-paragraphs-categorization huggingface dataset:
We hypothesize that the effort dedicated to Guadeloupe was due to the presence, for years, of a single team of academic marine biologists and to the creation of a Marine National Park, which required fundamental naturalistic studies during the late 1980's. With the exception of a few episodic publications of new records  [29]  or new species (e. g. [33] ), the sparse knowledge of Martinique sponge diversity remained within the so-called "grey literature". Our main objective in the present work was to fill the gap of knowledge concerning sponge diversity of Martinique. We wanted both to promote the study of sponge taxonomy and to provide a first inventory of the sponge diversity of this island. Our baseline datasets were a few unpublished records of 80-90 species from the CORANTILLES and ECORECIF campaigns (1981 and 1986 respectively; J.
Vacelet pers. comm. ) and from environmental studies requested by regional agencies in charge of marine environment management (S. Carteron, pers. comm. ). We decided to train early-career scientists, students and environmental managers willing to work on sponge biodiversity in tropical seas and especially in French islands. By gathering all data from the grey-literature, the preliminary observations after short field trips and the knowledge acquired during student training, we expected to present a first overall picture of Martinique sponge diversity. To achieve this goal, we organized a training course in sponge taxonomy in December 2013, and we took all participants to the field to confirm the occurrence in Martinique of the 200 most common shallow water species of the Caribbean Sea. After the field course, some students and teachers have continued to study collected sponge samples in their home laboratories according to their biological or geographical relevance.
Baseline of Martinique sponge diversity After analysis of all available data, the sponge fauna of Martinique is composed of a minimum of 191 species (Table  1 ) of which 134 have been assigned valid species names. This represents an important increase of 173 additional species to the numbers previously known from the World Porifera Database. Three of the four Porifera classes are represented in our list of species: 164 Demospongiae, 13 Homoscleromorpha and 14 Calcarea (Table  1, Fig 3). Thirty-seven taxa were identified only at the genus level, and two at the family level. One third of the unidentified sponge species are definitely considered new to science (Table  2 ; Fig  4 ), and their description is in progress in several laboratories. The remaining unidentified species still require more thorough examination, molecular analyses, or even further sampling.
Revealing a hotspot of sponge diversity in the French Antilles In the Caribbean Sea, the estimated number of sponge species is about 520  [1] , while in the greater Caribbean (including Florida and Bermuda) this number approaches 760 accepted species  [3]. The highest values of sponge species richness are from Cuba, with 255 species  [1] , a number that includes shallow and deep water (> 100 m deep) species. The least diverse Caribbean region appeared to be the Eastern Caribbean, with a total of 126 species recorded. After accounting for important recent works (e. g. [33, 41] ) and an increased effort in incorporating scattered data, the World Porifera Database now contains 242 species for the Eastern Caribbean marine ecoregion  [30]. Our inventory of Martinique sponge diversity adds 46 species to this biogeographic region.
Once the species new to science are described, the sponge diversity for the region might reach 300 species, representing thus about 40% of the Caribbean sponge diversity. As a result of the present work, gaps of knowledge of the Eastern Caribbean sponge diversity have been significantly reduced, and Martinique can now be compared to other Caribbean areas that have received special attention. The most important sponge inventory in a French island of the Eastern Caribbean prior to this study is from Alcolado & Busutil  [31] , who worked in the National Park of Guadeloupe. However, part of the knowledge acquired in Guadeloupe remained in the so-called grey literature. Alcolado & Busutil  [31]  investigated the reef community and mangroves in only two surveys of one week each, and they then added to their own results some data coming from a report made by Vacelet  [42]  for the University of Antilles-Guyane.
For instance, a total of 18 orders of Demospongiae were recorded in Martinique against 17 in Guadeloupe. In this sponge class, the two most representative orders are the Haplosclerida, with 25 species in Guadeloupe and 41 in Martinique, and Poecilosclerida, with18 and 24 species, respectively. This trait reflects the overall predominance of these two taxa worldwide, with 2314 valid Poecilosclerida and 1114 valid Haplosclerida registered in the World Porifera Database. In both islands, Verongiida, Agelasida and Clionaida were also represented by about 10 species each, which is a usual trait of tropical sponge communities. Regarding the other French islands, Saint Martin and Saint Barthelemy, our knowledge is still too poor to attempt any kind of comparison of sponge diversity patterns. Interesting trends emerge when we compare Martinique sponge diversity with Curacao-Bonaire-Aruba, the three Dutch islands of the southern Caribbean where sponge fauna has been intensively studied since 1975 by van Soest and his co-workers  [18] [19] [20] [43] [44] [45] [46] [47].
The STC contributed 30% of the present knowledge of Martinique sponge diversity, and, all together, brought our expertise to the current level of the rest of the eastern Caribbean. Currently, the open reef and rocky habitats seem the most diverse in Martinique, but we expect that a major source of unique species still awaits discovery, particularly under rocks, and under the roofs and crevices of caverns and caves. Several vocations arose among the STC participants who were not truly established in sponge taxonomy, and the Martinique community is now more aware of the usefulness of sponges in a global assessment of ecosystem functioning. There are already a large number of on-going research projects and some peer-reviewed publications that will feed global databases, such as the World Porifera Database.
Acknowledgments We would like to thank the following agencies and programs for funding and various forms of support:  CNRS LIA MARRIO  project,  TOTAL Foundation ,  Region La Martinique ,  DEAL La Martinique ,  CAR/SPAW Guadeloupe, COFECUB, Coordination for the Improvement of Higher Education Personnel (CAPES) ,  National Council of Technological and Scientific Development (CNPq) , and  Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ). For logistic scientific support and microscopes we would like to thank the  IRD , especially  Dr. Patrick Queneherve  ´and  Mr. Yoan Labrousse ,  Universitedes Antilles , particularly  Dr. Juliette Smith-Ravin  and  Dr. Maximilian Hassler , and the  City of Les Anses d'Arlet. The involvement of our friend  Jean Claude Erin , from  La Sucrerie Motel , was essential for the success of the Sponge Training Course, respectively in organizing the diving and the housing of the "sponge camp".
Materials and methods The full description of sample collection from 106 natural Black Pine populations (3154 individuals), population geographic data, DNA extraction, laboratory manipulations and statistical methods used are presented in  Naydenov et al. (2016). To sum up, the sampled area covers the entire range of the Pinus nigra, from 5. 1°N to 48. 1°N latitude (from Morocco to Austria) and 4. 7°W to 39. 1°E longitude (from Morocco to Turkey). The data are from specific hotspot regions of chloroplast genome as described by  Vendramin et al. (1996). Sequence variation in 13 regions was investigated: seven SSRs (single sequence repeats, i. e. , size variations) and six flanking regions with single nucleotide polymorphisms (SNPs); for more information, please see the DATA-S (Electronic supplementary material).
Historical migration rate (M) We obtained coalescent-based estimates of maximum posterior values of historical effective population size and migration rate between the European Black Pine formations by using Bayesian and Maximum likelihood inferences from MIGRATE-n v. 3. 0 program  (Beerli 2006). The time scaling is in "time units," i. e. , in units of mutation rate scaled to the generation (1 unit ≈ 1. 2 million years [hereafter Ma]; number of generations = 1/µ). To discard any misinterpretation, we used the MIGRATE algorithm using the coalescent theory of  Kingman (1982a, b) , but the time scaling in output files of the program was in the "time unit," which is independent of effective population size, and the "time unit" was different from the "coalescent unit. " One "coalescent unit" is equal to 1N e for haploid and 2N e for diploid DNA in generations.
In the present manuscript, we have only used "time unit" as defined by  (Beerli 2006). The MIGRATE program was used for the chloroplast DNA data for a large number of species  (Edh et al. 2007; Clark and Carbone 2008; Bai et al. 2010; Wang et al. 2011; Naydenov et al. 2014). Preliminary tests were performed to adjust the method of analysis to the different groups of populations: AA-1 to AA-5; BB-center, northern, southern, eastern and western; CC-1, 2, 3, center, northern, southern, eastern, western (Table  2 ; see  Naydenov et al. 2016). The initial test with   (Stefanov 1941 (Stefanov /1942 (Stefanov , 1943;; Gorbunov 1958; Dijkstra 1973; Palamarev 1989; Arslan et al. 2012; Ehrendorfer 2013). (Color figure online) 700 cores of high-performance computing (HPC) system suggested the use of a dataset with no more 90-100 individuals for each scenario (i.
e. , for two admixing edges AB and BC). This calibration is important to determine the reasonable time-consuming calculation versus stable results (at SE ≤ 5% for 20 independent replicates). Similar samples sizes, in agreement with the MIGRATE-n v. 3. 0 program manual recommendations, have been used for chloroplasts and mitochondrial DNA in other plant (see above) and animal case studies  (Carreras et al. 2007; Juste et al. 2009; and Lin et al. 2009 ). The MIGRATE-n v. 3. 0 program  (Beerli 2006)  was used in more than 500 similar studies for large numbers of organisms (please see the program credits from http://popgen. sc. fsu. edu/Migrate/ Info. html). After that, we used two sets of data (AA/BB and BB/ CC). Each set of data has two representative populations of 36 individuals for a total of 72 individuals.
To avoid imbalance between sample size of formations and to examine the most recent migration patterns, we used the most frequent haplotypes with the minimum frequency of 5% for the simulation, using initial "theta" and migration parameters derived from F st -like calculations, as described by  Beerli and Felsenstein (1999) , a free-to-vary custom migration model without predetermined geographic distances, 5 9 10 5 burn-in interactions and 5 9 10 4  sample outputs, one long chain (long chain ε = infinity) and four heating temperatures (between 1 and 50), and sum-Gelman convergence for the MCMC search strategy. The haplotype choice for the MIGRATE program is very important and depends on the objective of analysis. There are two principal strategies: (a) to use the most frequent haplotypes; and (b) to use haplotypes related to the MRCA.
The first two methods are more adapted for populations under the assumptions of constant population size and non-migration (or low migration). The third method is better for panmictic populations. Historically important changes in population size were studied by Tajima's D/Fu's Fs test and the Rogers and Harpending test (i. e. , the Harpending's Raggedness index, H-R), using the ARLEQUIN v. 3. 1  (Excoffier et al. 2002)  and DNAsp v. 5. 10. 1  (Rozas et al. 2003)  software. The test computed the difference between the mean number of pairwise differences (Theta-Pi) and the number of segregated sites (Theta-k), i. e. , the test determines the difference between DNA sequences evolving randomly (i. e. , neutrally), or under a non-random process. We used Fu's simulation from Fu's Fs test (similar to Tajima's D) because it was a more sensitive indicator of population  t f = t a /N o ; r = N o /N 1 ; N 1 -present effective population size; r-rate of effective population change; t f -time in generation; N o -ancestral effective population size; the number of years for one generation is 68 from  Naydenov et al.
The algorithm used to identify apparent bottlenecks at specific loci is described in  Griffiths and Tavare (1994a, b). The prior population size (theta), time of occurrence of the bottleneck (T) and coalescence pressure (S, strength of bottleneck) was set at 0. 1/10. 0 units (for mono-parental DNA one unit of T is equal to N e ). The same algorithm was used for chloroplast data by  Taylor and Keller (2007)  and  Brooks (2012). All N e historical fluctuation methods (i. e. , Tajima's D/Fu's Fs; R-H; and G-T tests) and historical migration pattern from MIGRATE program assumed neutral mutation/selection model from the Neutral Theory of Molecular Evolution  (Kimura 1983). The correlations of the parameter from the five sub-groups of demographic fluctuations from Tables 1S-5S versus population longitude and latitude data are according to the method of  Naydenov and Alexandrov (1999)  and  Naydenov et al.
Pure bottleneck group The high-probability sub-group with p ≤ 0. 01 showed normal distribution for biological data after removing the population AT-03 (S-W-statistic = 0. 909 at p \ 0. 05). This sub-group is named the pure bottleneck group with average N e-1 = 3200 (SD = 1. 484). Bottlenecks were detected, with statistical support L-ratio (p ≤ 0. 01), in 24. 5% of the populations (i. e. , 26 populations; Table  1S  from Appendix, Electronic supplementary material), generally a long time ago (according to the time limit in the SweepBOOT software). Evidence of a strong bottleneck was found for 5 populations, with no indications of a specific geographic location, such as Iberian Peninsula, Balkan Peninsula or Asia Minor (Fig. 1S ; Table  1S  from Appendix, Electronic supplementary material). In this sub-group, the past reductions in effective population size were not limited to specific parts of the natural distribution area of P.
Acknowledgements We would like to thank  Irena M. Naydenova , T&T for their technical assistance; the two anonymous organizations for their financial support;  Ph. D. Z. Kaya  ( Turkey ),  Ph. D. M. Kostadinovski  ( Macedonia ),  M. Topac  ( Turkey ) and  Ph. D. C. Varelides  (Greece), who all made direct (and indirect) logistical help in supplying some samples. We would also like to thank the  Ministers of Forestry, Education and Science of  all the countries with participant persons for providing the funding for sample collection and fruitful collaboration. We also wish to thank the  Nature Publishing Group  Language Editing-NPGLE (www. languageediting. nature. com) for the careful English revision of this manuscript.
ACKNOWLEDGMENTS The KCC ice core was collected by a joint team effort from Institut für Umweltphysik, Universität Heidelberg, the Climate Change Institute at the University of Maine and the Climate and Environmental Physics Institute, University of Bern. We thank the 2013 drilling and processing team for their efforts, in particular the drillers Remo Walther and Samuel Marending. Financial support for the recovery of the core was provided by the Helmholtz Climate Initiative REKLIM and the Arcadia Foundation. Special thanks go to Hubertus Fischer and his group at the Climate and Environmental Physics Institute, University of Bern, for providing invaluable support for the field campaign and ice core analysis. We also thank the Alfred-Wegener-Institute, Bremerhaven for additional support in ice core processing.
For pb6, formation of tubes occurs even when this domain is absent (see below), and some T5-like TTP lack it (Supplementary Fig. 1a ). A closer examination of the N-terminal domain structure reveals subdomain duplication, which is confirmed by DALI pairwise comparison  27  (Fig. 1a , b and Supplementary Table  1 ). The common core shared by both subdomains consists of a βsandwich flanked by an α-helix, and a long loop. This loop is not resolved in subdomain 1 (loop β3-β4), whereas it is stabilised by crystal contacts in subdomain 2 (loop β13-β14; Fig. 1b  and Supplementary Fig. 2 ). On a sequence level, T5-like TTPs have no homologues in the databases according to the PSIBlast and HHPRED software tools. However, both subdomains display high structural homology with TTPs of other sipho-and myophages, distal tail proteins of siphophages, T6SS tube proteins, the tube protein of R-pyocin, baseplate hub proteins from myophages and spike proteins from T6SS (Fig.
2 M HEPES pH 7. 5, 0. 3 M LiSO 4 , 0. 1 mM MnCl 2. Selenomethionine-pb6 crystals were obtained with a protein solution at 7 mg mL -1 and a reservoir buffer containing 11% 3350 PEG, 0. 2 M HEPES pH 7. 5, 0. 1 M LiSO 4 , 40 mM MnCl 2. Crystals typically grew within 2 days. All crystals were cryoprotected with 25% PEG 400 and flash-frozen in liquid nitrogen using nylon loops (CryoLoop TM , Hampton Research). Data collection and crystallography. X-ray data sets were collected at the European Synchrotron Radiation Facility (French beamline for Investigation of Proteins -BM30A). A native data set was collected on a native crystal up to 2. 2 Å and a MAD data set was collected on a single selenomethionine-pb6 crystal up to 3 Å. Diffraction frames were integrated using the programme XDS  40  and the integrated intensities were scaled and merged using the CCP4 programmes 41 SCALA and TRUNCATE, respectively.
A summary of the processing statistics is given in Table  1. pb6 was solved de novo by combining the MAD and the native data sets. Selenium positions were determined within the asymmetric unit using the programme SHELXD  42. Heavy-atom refinement and initial phasing were performed using the programme  SHARP 43. Phases from SHARP were improved by density modification using the programme SOLOMON  44. The figure of merit after density modification was 0. 82. The first steps of model building were performed automatically using BUC- CANEER  45  and completed manually with  COOT 46. Model refinement was performed with PHENIX  47  and BUSTER (2016 BUSTER version 2. 10. 3. Cambridge, UK: Global Phasing Ltd). The model was optimised through iterative rounds of refinement and model building. Automated non-crystallographic symmetry and translation/libration/screw were applied, and at the end stages of the refinement hydrogen were added (except for solvent molecules).
Refinement statistics are summarised in Table  1 , and a stereo image of a portion of the electron density map is presented in Supplementary Fig. 7. The atomic coordinates and measured structure factor amplitudes for pb6 were deposited in the Protein Data Bank with accession code 5NGJ. Negative stain electron microscopy. Negative-staining EM allowed purity and concentration assessment, as well as adapting the T5 tail:FhuA ratio prior to cryogrid preparation. Negative-stain grids of all samples were prepared using the micacarbon flotation technique over a 2% sodium silicotungstate solution and were observed with an FEI/Thermo Fisher Scientific Tecnai 12 microscope (LaB 6 electron source, 120 kV) equipped with a Gatan Orius 1000 CCD camera. Cryo-electron microscopy. pb6 tubes and purified T5 tails incubated with or without FhuA were prepared following the same procedure.
This initial model was refined using a classical projection matching method as described  49. Briefly, the contrast transfer function (CTF) corrected tail images were boxed into 128 × 128 pixel windows with an overlap of ¾ of the box. The generated stacked images (1000 images) were aligned against the projections of the model and averaged into classes. The classes were then back projected to obtain a new threefold symmetrised model. After 10 cycles of refinement, we obtained a map at ~10 Å resolution in which the crystal structure of pb6 could be fitted without ambiguity. In order to further improve the resolution, a larger data set was processed as described below. Images were first motion corrected either with Digital Micrograph (Gatan Inc. , USA) for T5 tails or with MotionCorr 50 for tails incubated with FhuA and pb6 tubes.
A new set of starting points was built by combining the best-looking models of all mobile elements (typically twisted or straight the loops). These new starting points were subjected to five iterations of MD refinement. The convergence of the models generated from different starting points lowered the uncertainty of the orientation of the polypeptide chain. Data availability. Coordinates and structure factors for pb6 have been deposited in the Protein Data Bank under accession number 5NGJ. Electron microscopy maps have been deposited in the Electron Microscopy Data Bank under accession codes EMD-3689 (full tail tube), EMD-3689 (empty tail tube), EMD-3691 (pb6 tube) and EMD-3692 (combined tail tube). Other data supporting the findings of this study are available from the corresponding author on reasonable request.
Acknowledgements We thank  Flavien Grégoire ,  Alice Labaronne  and  Valentin Sencio  for help at different stages of the project,  Daphna Fenel  for help with negative-stain EM,  Christine Ebel  for support and  James Conway  for proof reading of the manuscript. We also acknowledge access to the  European Synchrotron Radiation Facility (ESRF)  and SOLEIL beamlines for crystal screening, and thank scientists of the FIP-BM30A at ESFR, in particular  Michel Pirrochi  for help with data collection. This work used the electron microscopy and the high-throughput crystallography platforms from the  Grenoble Instruct Center  (ISBG:  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
3 used to assess relationships between each oxidative status marker and the following predictor variables: sex, age class, living condition (free-ranging vs. captive), sampling date, time of day of blood sampling, restraint duration (minutes elapsed from capture to blood sampling), minutes elapsed from blood sampling to storage and storage duration. The factor individual was included as a random factor because from one individual we had three repeated measurements and from three individuals we had two repeated measurements. We also had three more individuals with two repeated measurements, but these were not included in the mixed models because of missing information on other variables such as restraint duration. These data were, however, considered in the analysis of within-individual changes over time.
Acknowledgements We thank the  Ministry of Environment and Tourism in Namibia  for permission to conduct this study, the  Namibian  farmers for their collaboration and help,  AfriCat Foundation  for the permission to work with their cheetahs,  Dirk Bockmühl ,  Sonja Heinrich ,  Ivan Palmegiani ,  Ruben Portas  and  Bernd Wasiolka  for their valuable help in the field to capture and sample the cheetahs, two anonymous reviewers that provided comments that stimulated us to improve the presentation of the work.