dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of strains and phylogenetic analysis Identification of strains Marseille-Q5893 and Marseille-Q5883 was carried out using a Microflex LT MALDI-TOF mass spectrometer (Bruker Daltonics, Bremen, Germany)  (Seng et al. 2013). The spectra of the two strains were imported into the MALDI Biotyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings). Interpretation of the scores was performed as previously described  (Hadjadj et al. 2016). The 16S rRNA gene of strains Marseille-Q5893 and Marseille-Q5883 was sequenced using the fD1 and rP2 universal primers (Eurogentec, Angers, France) as previously described  (Drancourt et al. 2000) , using an ABI Prism 3130xl Genetic Analyzer capillary sequencer (Thermo Fisher, Saint-Aubin, France).
The species thresholds for dDDH and OrthoANI are 70% and 95-96%, respectively  (Meier-Kolthoff et al. 2013; Kim et al. 2014). Furthermore, the CRISPRCasFinder program was used to easily detect clustered regularly interspaced short palindromic repeats (CRISPRs) and cas (CRISPR-associated) genes in both strains  (Grissa et al. 2007 ). The PathogenFinder 1. 1 program was also applied to identify gene families that correlate with pathogenicity  (Cosentino et al. 2013).
Strain identification and phylogenetic analysis Strains Marseille-Q5893 and Marseille-Q5883 could not be identified by our MALDI-TOF MS instrument, as the score was lower than 1. 8, suggesting that the corresponding species was not referenced in the database and could be an unknown species (Fig. S1 ). Additionally, strain Marseille-Q5893 revealed a 98. 5% 16S rRNA sequence similarity with Anaerococcus obesiensis strain FDAARGOS_989 (CP067016. 1), the phylogenetically closest species with standing in the nomenclature. As this value was below the 98. 65% threshold for defining a new bacterial species, strain Marseille-Q5893 was considered as a representative of a putatively new species within the family Peptoniphilaceae in the phylum Firmicutes. Similarly, strain Marseille-Q5883 revealed a 90.
Using API 50 CH strips, acid is not produced from glucose or other sugars. All remaining reactions were still negative with API 20E strips. The major fatty acids are hexadecanoic acid (61%), 9-octadecenoic acid (20%), and tetradecanoic acid (6%). The size of genome is 1. 09 Mbp and its G+C content is 29. 4 mol%. The type strain Marseille-Q5893 T (= CSUR Q5893 = CECT 30,496) was isolated from a vaginal sample of a 30-year-old healthy woman at day 16 of the menstrual cycle. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers OM728648 and CAK-MRU010000001, respectively. Description of Mediannikoviicoccus gen. nov. Mediannikoviicoccus (Me. di. an. ni. ko. vi. i. coc'cus. N. L. masc. n. coccus (from Gr. masc. n. kokkos, a grain or seed), a coccus; N. L. masc. n. Mediannikoviicoccus, a coccus named in honor of the clinical microbiologist doctor Oleg Mediannikov).
Acknowledgements We thank  Ludivine Brechard  for sequencing the genome and  Aurelia Caputo  for submitting the genomic sequence to GenBank. We sincerely thank  Takashi Irie ,  Kyoko Imai ,  Taku Sakazume ,  Yusuke Ominami ,  Hisada Akiko  and the  Hitachi team of Japan  ( Hitachi High-Tech, Japan ) for the collaborative study conducted together with  IHU Méditerranée Infection , and for the installation of a SU5000 microscope at  IHU Méditerranée Infection facility.
Out of these studies, only  Ridley and Holme (2016)  co-estimated the secular variation (SV) with the main field (MF) using magnetic field measurements made between 1973 and 2003. However, due to the inhomogeneous temporal and geographical data distribution, most of the selected observations were from the Galileo mission at low latitudes. Ridley and Holme (2016)  computed two models, one with only MF time averaged Gauss coefficients and one with time dependent MF and SV coefficients. The latter model was considered better because of its lower residuals and greater smoothness. Nevertheless, they considered their SV model to be reliable only up to degree 2. None of these spacecrafts provided data near the poles. This was overcome by the recent Juno measurements. Juno space probe was launched on August 5, 2011 and entered Jupiter's orbit in July 2016.
Field change over a 45-year time span was observed and zonal drift was invoked to explain the temporal change of an intense magnetic flux patch near the equator  (Moore et al. , 2018 (Moore et al. , , 2019 ). An updated external magnetodisk field model for Juno is also available  (Connerney et al. , 2020). None of the existing models based on Juno data attempt to model explicitly the current global temporal variation of the field. In this study, we use the high quality Juno measurements to derive a SH model of the Jovian field, simultaneously describing its MF and SV up to SH degrees 16 and 8, respectively. Section 2 details the data and the selection criteria we use for this study. Section 3 describes the method used to derive the models and their spectra that was assessed with a thorough synthetic analysis (Text S1 in Supporting Information S1).
Data Juno has a near polar, highly elliptical orbit with apojove exceeding over 100 times Jupiter's radius. The prime mission lasted 5 years and provided data for 33 orbits with one complete orbit taking about 53 days. The space probe was initially planned to undergo a reduction maneuver for achieving 14-day science orbits but Juno entered safe mode for its second orbit, thereby remaining in its initial 53-day capture orbit for the entire mission. The spacecraft aims to obtain a global coverage of the planet. For the first eight orbits, the shift between successive orbits was 45° in longitude. The subsequent shifts reduce the longitudinal spacing by half to obtain data from the gaps left previously. Juno uses two fluxgate magnetometers, located on one of the three solar arrays to measure the vector magnetic field.
Magnetic field measurements acquired by Juno are available under two versions. The version 1 data provides measurements across the entire orbit, whereas the version 2 data gives only near planet measurements from the orbit, denoted as perijove hereafter. Both version 1 and 2 data are provided in three Cartesian coordinate systems-planetocentric, sun-state, and payload. Since planetocentric system is body-fixed, it is the most appropriate to study the internal field. We use the version 2 one-second data in planetocentric coordinates from the first 28 perijoves (data available for only 27 perijoves, excluding the second one). As discussed later, synthetic tests inversion including the latest perijoves from 29 to 33 leads to an increase in polar gaps that degrades some model coefficients. Perijove 19 was also dismissed because spurious oscillations were later observed.
This results in high degree, low order terms being less resolved (i. e. , zonal and near zonal terms). Figure  S2  in Supporting Information S1 shows the root mean square differences between Juno's dataset and predictions by our model, a model calculated without SV, and the model by  Connerney et al. (2022) , considering different truncation degrees for each model. At SH degree 16, our model and the model by  Connerney et al. (2022)  have a root mean square misfit to data equal to about 800 nT.
This corresponds to a 2. 3% change over the course of 4 years of the dataset used, compared to the 1. 4% change over a similar duration for the Earth's magnetic field. As for Earth's, it should not be ignored when modeling the magnetic field over periods exceeding a few years. The spatial pattern of temporal variation of the field brings further dynamical constraints. The power spectrum of the SV calculated at R sf increases with degree (Figure  1a ). Indeed, the SV reveals intense small scale structures (Figure  4 ). The strong negative radial field patch immediately south of the equator (Figure  4b ) coincides with a pair of SV structures (Figure  4d ), suggesting eastward drift  (Amit, 2014; Livermore et al. , 2017). This is opposite to the westward drifting low-and mid-latitude patches observed with Earth's SV  (Aubert & Finlay, 2019; Bullard et al.
Data Availability Statement All Juno magnetometer data used here are publicly available on NASA's Planetary Data System (PDS) at Planetary Plasma Interactions (PPI) node at https://pds-ppi. igpp. ucla. edu/search/?sc=Juno%26t=Jupiter%26i=FGM. The model coefficients and their standard deviation for the static field to degree 16 and its SV to degree 8 are available at: https://doi. org/10. 5281/zenodo. 6564162.
Methods We used data from the COSEMIL study, the first national sexual health survey in the French military, conducted in 2014-2015  [8]. The survey included a two-stage random sampling strategy. Initially, 18 military units were randomly selected after stratification by location (Mainland France, or overseas bases) and military branch (Army, Navy, Air force), with unequal probabilities of selection based on unit size. Next, 120 individuals per unit were randomly selected with oversampling of women (1 woman for 5 men, as women account for only 16% of the French military workforce). Selected individuals were invited by the unit commander to participate in an information session during on-duty hours and subsequently provided informed consent to participate. All participants were informed about the goals, procedures and voluntary nature of the study before providing consent to participate.
Participation was anonymous, and participants incurred no sanctions if they declined participation. Altogether, 1268 men and 232 women aged 18-57 years were included. The participation rate was 76% (14% did not attend the study information session, 9% refused participation and 1% were excluded due to software deficiency during data collection). The COSEMIL study was approved by the French government oversight agency (Commission Nationale Informatique et Liberté, N° 2014-100) and the Comité de Protections des Personnes Sud Méditarranéen II (N° 2013-AOOO47-38). Participants were asked to complete a 37-minute questionnaire on laptops, providing information on a range of topics including sociodemographic characteristics, sexual attitudes and behaviours as well as sexual health outcomes: sexually transmitted infections (STI), sexual violence, unintended pregnancy, and sexual dysfunction.
Mechanistic models describe forest dynamics based on the dynamics of plant carbon and water exchange at a high temporal resolution. Ten of the models describe the ecosystem-atmosphere exchange of carbon, and nine of them describe the exchange of water in forest stands at a daily to annual time step. All 13 models have been applied as research tools to study climate impacts on managed forests. The simulations followed the ISIMIP phase 2a simulation protocol (https://www. isimip. org/proto col/), which provides a consistent simulation setup based on common, harmonized data for initializing, driving and evaluating models from the PROFOUND database  (Reyer et al. , 2020a (Reyer et al. , , 2020b)). The models were initialized with observed stand characteristics (e. g. , stem diameter at breast height, tree height, stand density, stand age) and then driven with locally observed weather data (e.
| Evaluation data The PROFOUND database  (Reyer et al. , 2020a (Reyer et al. , , 2020b ) hosts observed data from nine boreal and temperate forest stands located across Europe (Table  2 ). The database provides measurements of forest structure including basal area (BA), arithmetic mean diameter at breast height (DBH) and arithmetic mean tree height (H). On a subset and actual evapotranspiration (AET). For the carbon flux data, there are multiple products available for the same variable due to varying underlying estimation techniques  (Pastorello et al. , 2020). We used the data derived with constant friction velocity (USTAR) threshold where the reference is selected based on model efficiency for processing NEE (NEE_CUT_REF; https:// fluxn et. org/data/fluxn et201 5-datas et/data-proce ssing/,  Pastorello et al.
, 2020)  and the daytime (DT) method  (Lasslop et al. , 2010)  for partitioning NEE into GPP and Reco. The first year of carbon flux measurements at each site was discarded since the majority of data points had a quality flag of "poor". Daily AET was derived from measured latent heat flux (LE) to the atmosphere by AET = LE ∕ , with = 2. 501 -0. 00237 × T air × 10 6 , where T air is the mean daily temperature  (Foken, 2008). Annual AET was aggregated as the sum of daily AET derived from the measured daily latent heat flux.
| General applicability We interpreted the general applicability of the models as the application range across tree species. As opposed to the accuracy of local predictions and the realism of environmental responses, this quantification was independent of the actual simulations and solely based upon the tree species represented in the models. We computed the share of European forests covered by dominant tree species each model is currently parameterized for. Data on tree species group coverage across Europe were derived from Brus et al. (2011). In case a model covered only subsets of a tree species group (e. g. , only Larix decidua and not L. kaempferi for genus Larix), we assumed the forest area of that species group to be covered fully by the model. We only expect a minor overestimation of the area covered by a model because the tree species groups with many species are the ones that are less dominant in Europe.
Furthermore, the understory contribution to the carbon balance was not assessed in any of the models but contributes to the measured carbon balance  (Dirnböck et al. , 2020). Additionally, uncertainties in model forcing data may contribute to model-data mismatches. For example, the climate data used to drive the simulations was sometimes observed at or close to the forest stand, but in some cases only inferred from the nearest climate station  (Reyer et al. , 2020b) , which may introduce additional uncertainties, for example, due to orographic effects. Likewise, even though the stands are managed using standard silvicultural treatments  (Reyer et al. , 2020b) , specific, local forest management actions may not be perfectly covered by the models' approximation of the management. Overall, we find that simpler models, like SALEM, SIBYLA, 3PG, BASFOR and PREBAS did not necessarily perform worse than more complex models like 3D-CMCC-FEM BGC, 3D-CMCC-FEM LUE, 4C, Landscape-DNDC or GOTILWA+.
White cells indicate cases with no evaluation data available whereas grey cells indicate cases that are not provided by the model. The model coverage of sites and variables depends on the model application range. norm. LC, normalized lack of correlation; norm. , 2022, 23, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/gcb. 16384by Inrae -Dipso, Wiley Online Library on [12/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons LicenseGPP and Landscape-DNDC overestimated GPP at Bily Kriz. Most models underestimated AET in Le Bray, while overestimation was evident at Bily Kriz (Figures S10-S17). Predicted-observed offsets from linear patterns in the residuals (NU) were generally low except for BA and DBHinc simulated by FORMIND, DBHinc simulated by ForClim v.
Because already multiple models exist to study climate impacts on forests, we expect that our study will provide a common benchmark to test whether new modelling efforts outperform the models presented here to add value to the existing set of tools. MM, MG, PV and RA acknowledge financial support from I-Maestro (Innovative forest management strategies for a resilient bioeconomy under climate change and disturbances, grant nos. 773324 and 22035418, 2019-2022) funded by the ERA-NET Cofund ForestValue. AC, CB, CT, GM and DD thank Foundation Euro-Mediterranean Centre on Climate Change and the ALForLab (PON03PE_00024_1) project co-funded by the Italian National Operational Program for Research and Competitiveness (PON R&C) 2007-2013, through the European Regional Development Fund (ERDF) and national resource (Revolving Fund-Cohesion Action Plan [PAC]) MIUR, for their support in model development and data analysis.
DATA AVA I L A B I L I T Y S TAT E M E N T The reference data that support the findings of this study are publicly available in GFZ Data Services at https://doi. org/10. 5880/ PIK. 2020. 006. The simulation data that support the findings of this study are publicly available in the ISIMIP repository at https://doi. org/10. 48364/ ISIMIP. 169780.
For example, a metabarcoding attempt on sea turtle epi ora  [29]  failed to recover some of the diatom taxa identi ed in microscopical surveys, including the dominant POE taxon Labellicula lecohuiana Majewska, De Stefano & Van de Vijver. The authors acknowledged that this failure was likely due to the lack of any relevant reference sequences for the genus Labellicula. Further, the position of Labellicula in the molecular phylogeny of diatoms is unknown. This uncertainty signi cantly hinders any bioinformatic efforts to nd sequence data even closely related to Labellicula among both the metabarcoding reads and the reference databases. Many other POE taxa have uncertain phylogenetic a nities within the raphid diatoms, including the "Tripterion complex" (Tripterion Holmes To address the aforementioned issues, the goal of the present study was to generate additional DNA sequence data from POE diatom taxa on sea turtles and sirenians.
Discussion This study helps to lay the groundwork for developing a database of DNA sequence data from POE diatoms. The use of DNA sequence data enables application of these epizoic diatom assemblages as proxies for host and ecology without the need for an extensive knowledge of benthic diatom taxonomy. We generated DNA sequence data from 16 of the known POE diatom species for sea turtles and maintained 12 of these taxa in culture. This opens the door for future studies on the molecular, genomic and physiological nature of the epizoic diatoms. Moreover, our ability to culture POE diatoms without their hosts invites several questions about diatom-host interactions. Speci cally, POE diatoms evidently do not require the host to survive and so we propose four non-exclusive hypotheses that could explain the speci city of POE to particular hosts in the wild: H1: The epizoic environment creates a unique nutrient supply regime affecting bottom-up competition, with POE taxa being better competitors in the epizoic environment and/or worse competitors in a nonepizoic environment.
Permanent mounts for the LM slides were made with Naphrax® mounting medium (Brunel Microscopes, www. brunelmicroscopessecure. co. uk) and micrographs were taken with a Zeiss Axioskop. Coverslips for SEM were coated with iridium by a Cressington 208 Bench Top Sputter Coater (Cressington Scienti c Instruments, Watford, UK) and micrographs taken with a Zeiss SUPRA 40 VP scanning electron microscope (Carl Zeiss Microscopy, Thornwood, NY, USA). Sequence data were added to a dataset of raphid and araphid pennate diatoms, with Asterionellopsis glacialis used as an outgroup (see Table  S1  for GenBank accession numbers). SSU data were aligned by the SSUalign program, using the covariance model outlined in  Lobban et al. [67]. Data were initially partitioned by gene, by paired and unpaired sites in SSU secondary structure and codon position in rbcL and psbC.
Model testing and grouping of partitions were performed by PartitionFinder 2 [68] using all nucleotide substitution models, linked branches, and rcluster search [69] settings for trees inferred by RAxML 8  [70]. The best model was chosen using the corrected Akaike information criterion (AICc). Maximum Likelihood and Bayesian Inference based phylogenies were inferred using IQ-TREE version 1. 6. 12 for Linux  [71]  with partitioned models  [72]  and multi-threaded MPI hybrid variant of ExaBayes version 1. 5 [73], respectively. Nodal support for the maximum likelihood phylogeny was assessed using 1,000 bootstrap replicates via IQ-TREE. ExaBayes analyses included four independent runs with two coupled chains where branch lengths were linked. Convergence parameters included an average deviation of split frequencies (ASDSF) of less than or equal to 5% with a minimum of 10,000,000 generations.
Bayesian nodal support was assessed using posterior probabilities, with the rst 25% of the trees removed as "burn-in". Maximum likelihood phylogenetic tree derived from a concatenated 3-gene DNA sequence dataset, representing the Achnanthes, Craspedostauros and Staurotropis clades (complete tree shown in Figure  S1 ). Support values (ML bootstrap support/BI posterior probability) shown above nodes; "*" = nodes with 100%/1. 0 values. Taxon name followed by DNA extraction voucher number or strain ID. Taxa isolated from epizoic habitats followed by a diagrammatic representation of the host from which the strain was isolated, and metadata on the location and setting in which the host was sampled (A = aquarium, R = rehabilitation facility, W = wild).
DNA isolation, ampli cation and sequencing Figure 2 Maximum likelihood phylogenetic tree derived from a concatenated 3-gene DNA sequence dataset, representing the clade containing the Tripterion complex, Tursiocola and Proschkinia clades (complete tree shown in Figure  S1 ). Support values (ML bootstrap support/BI posterior probability) shown above nodes; "*" = nodes with 100%/1. 0 values. Taxon name followed by DNA extraction voucher number or strain ID. Taxa isolated from epizoic habitats followed by a diagrammatic representation of the host from which the strain was isolated, and metadata on the location and setting in which the host was sampled.
Acknowledgments Financial support for sequencing and SEM comes from the  Jane  and the  Roland Blumberg Centennial Professorship in Molecular Evolution  at  UT Austin  and the  US Department of Defense  (grant number  W911NF-17-2-0091 ). Sampling in South Africa was done with partial nancial support from  The Systematics Association (UK)  through the  Systematics Research Fund Award  granted to RM (2017 and  2020 ). Work in the Adriatic Sea was supported by  Croatian Science Foundation , project  UIP-05-2017-5635  (TurtleBIOME). KF has been fully supported by the " Young researchers' career development projecttraining of doctoral students " of the  CSF  funded by the  EU  from the  European Social Fund. The authors would like to thank  Bob and Cathy Bonde ,  Martine DeWit  and  Jim Powell , the  UF Aquatic Animal Health Program, Sea  to  Shore Alliance , and the entire manatee capture team, as well as  Richie Moretti  and  Bette Zirkelbach  of  the Turtle Hospital in Marathon, Florida  for access and the  Cape Eleuthera Institute  in The Bahamas assistance with epizoic collection.
Acknowledgements Financial support from the  National Natural Science Foundation of China  ( 51472189 ;  62004129; 22005202 ) is gratefully acknowledged. This work was supported by the  Scientific Research Startup Fund for Shenzhen High-Caliber Personnel of Shenzhen Polytechnic , nos. 6022310038k  and  6022310049k ;  Shenzhen Science and Technology Innovation Commission  (project no. JCYJ20200109105003940 ); and also supported by the  Post-Doctoral Foundation Project of Shenzhen Polytechnic   6021330007K. L. L. and P. N. thank  New York University AbuDhabi  for financial support.