dataesr/scientific-paragraphs-categorization huggingface dataset:
After each hourly collection, the tube will be placed in a labeled box in a freezer. The samples will then be stored in a freezer at -80 • C until analysis and treatment by expanded-range enzyme immunoassay. Energy expenditure and physical activity Physical activity will be monitored during the intervention using ActiGraph GT3X accelerometers (Pensacola, FL, USA) at a 1-s epoch setting to ensure that participants keep the same level of physical activity between sessions. This multisensory tool continuously collects various motion parameters through a triaxial accelerometer  [50]. Sex, age, body weight, and height data will be transferred to estimate the level of physical activity and activity energy expenditure using algorithms developed by the manufacturer's software (Actilife). Accelerometers will be secured to belts and distributed to the participants on awakening on Day 1.
, 2009) , and resource availability  (Palumbi, 1994)  can impede gene flow  (Cowman and Bellwood, 2013)  in some marine taxa  (Cowen et al. , 2000; Leis, 2002; Hauser and Carvalho, 2008). Spatial distribution of the genetic variation to establish connectivity patterns among marine taxa has been subject of extensive research in recent years  (Cowen et al. , 2000; Cowen and Sponaugle, 2009; Crandall et al. , 2019) , including sharks (e. g. ,  Momigliano et al. , 2017). However, data is limited when it comes to the quantification of population structure in coastal shark species inhabiting both island and continental coastal waters. Such studies can be vital for the identification of management units and metapopulations. For example, an analysis examining the population structure of the tiger shark on a global scale using a suite of mitochondrial and nuclear (microsatellites) genetic markers revealed population isolation for the Hawaiian Islands  (Bernard et al.
SNP Filtering The dataset was filtered by excluding duplicate SNPs possessing identical Clone IDs and by removing loci with a call rate (proportion of individuals scored for a locus) <99%, read depth >7 and minor allele frequencies (MAF) <2%. Detection of loci under selection was implemented in BayeScan v. 2. 1  (Foll and Gaggiotti, 2008). The most conservative neutral model in BayeScan was used to minimize falsely detected loci under selection  (Lotterhos and Whitlock, 2014). Runs consisted of 100,000 iterations with a burn-in length of 50,000 iterations  (Foll and Gaggiotti, 2008; Foll, 2012). Once probabilities had been calculated for each locus, the BayeScan function plot_R was used in the R v. 3. 2. 0 statistical package  (Venables et al. , 2003)  to identify putative outlier loci. A range of false discovery rate (FDR) values from 0.
01 to 0. 20 were evaluated based on preliminary testing and recommendations by  Gondro et al. (2013). Departure from Hardy-Weinberg Equilibrium was tested for each locus using the software Arlequin v. 3. 5. 2. 2  (Schneider et al. , 2000)  using an exact test with 10,000 steps in the Markov Chain method and 100,000 dememorizations. Tests for linkage disequilibrium was performed using the software PLINK v. 1. 07 (Chang et al. , 2015). Related individuals were detected using TrioML with COANCESTRY v. 1. 0. 1. 7  (Wang, 2011). To determine cut-off r values for categorizing relatedness coefficients produced by the various algorithms into full-sib and less-related brackets, COANCESTRY was again used to simulate 200 dyads in each group based on the same allele frequencies as the original population samples.
Multiple AMOVAs were performed: i) one overall analysis including all sampling locations as separate populations, ii) Fiji versus all other sites grouped as one population, iii) insular (New Caledonia, Fiji) versus continental locations, and iv) Pacific Ocean versus Indian Ocean. Visualization of broad-scale population structure and population assignment was carried out by performing a Discriminant Analysis of Principal Components (DAPC) in the R package adegenet 1. 4. 2 (Jombart, 2008; Jombart and Ahmed, 2011). DAPC was carried out for all neutral loci. An α-score optimization was used to determine the number of principal components to retain. Alpha-score optimization indicated that the first nine PCs (42. 7% of total variation) should be retained for analysis. Given the wide temporal range for sampling in Fiji (2007-2017), separate DAPC analyses were run to determine the temporal stability within Fiji samples.
For this, the sampled years that have a sufficiently large number of individuals within each year were split as a separate populations [2010 (n = 28), 2011 (n = 22), and 2017 (n = 18)]. The samples collected in 2007 (n = 6), 2008 (n = 1), and 2009 (n = 3) were considered inadequate for this analysis given the small sample sizes. Furthermore, to determine if the pattern of differentiation obtained when using all Fijian samples simultaneously was stable, the DAPC analyses were also run comparing all Indo-Pacific populations against individual years (2010, 2011, and 2017) from Fiji. The determination of the optimal number of clusters (k) was tested using both fastStructure  (Raj et al. , 2014)  and Admixture  (Alexander et al. , 2009). Both software packages were written for large autosomal SNP data sets and unlike other programs can test the likelihood of a k of one.
Admixture utilizes a crossvalidation procedure when determining the optimal k. While, for fastStructure the full model was selected with a seed of 100. For both programs, k ranging from 1 to 10 was run. Additionally, fastStructure included the python script chooseK. py which was run after the ten k scenarios were finished and the optimal k was selected. Once the optimal k was determined the probability of individual population assignments was calculated and visualized using both Admixture and AssignPOP  (Chen et al. , 2018).
For Admixture the crossvalidation inflection point was at k = 2, while for fastStructure the python script chooseK. py selected a k of 2 based on marginal likelihood. The population assignment analyses for both Admixture and AssignPOP assigned the same individuals to the same populations, with all continental locations and New Caledonia to population A, and Fiji belonging to population B (Figure  3 ). The best performing assignment model in AssignPOP was Support Vector Machine (SVM) with an assignment accuracy of 0. 999 (s. d. = 0. 001) for population A and 1. 0 for population B.
ACKNOWLEDGMENTS Great thanks are owed to all assisting research colleagues, fishermen and the staff from  Beqa Adventure Divers in Fiji. We authors thank  Dr. Will White  ( CSIRO ) for the bull shark tissue samples from  Papua New Guinea  via collaboration with a  PNG-NFA ACIAR study , and the observers from commercial fishing vessels and artisanal surveys who collected the samples. We thank  Crispen Wilson  and  Les Noble  for sharing samples collected in South Africa and Indonesia. KG thanks the  Ausbildungs-Stiftung fuer den Kanton Schwyz und die Bezirke See und Gaster (Kanton St. Gallen) , the  Shark Foundation Switzerland and Pacific Scholarship for Excellence in Research & Innovation of the University of the South Pacific. We thank  Mike Neumann  and  Ian Campbell  for continued advice during the study.
Acknowledgements The authors thank  Steven Allison ,  Rachel Gallery ,  Pierre-Henri Gouyon ,  Moira Hough ,  Laura Meredith ,  Mitch Pavao-Zuckerman  and  Scott Saleska  for discussion. E. A. was supported by fellowships from  Ecole Doctorale Frontières du Vivant and MemoLife Laboratory of Excellence  ( PIA-10-LBX-54 ). H. L. acknowledges support from  CON-ACyT-MEXICO ,  CONACYT  grant  CB2015-01/250590 , the foundation  Sofía Kovalevskaia of SMM  and the Chair " Modélisation Mathématique et Biodiversité " of VEOLIA-Ecole Polytechnique-MNHN-F. X. R. F. acknowledges support from  FACE Partner University Fund, CNRS Mission pour les Initiatives Transverses et Interdisciplinaires (MITI) ,  PSL University (IRIS OCAV  and  PSL University of Arizona Mobility Program) , and a grant from the  Dimensions of Biodiversity program  of the  United States National Science Foundation  ( DEB-1831493 ).
These data were collated from 41 countries and used to compile a specially constructed geo-database. A review of 6297 observation records showed that F. circinatum and the symptoms it causes on conifers occurred in 14 countries, including four in Europe, and is absent in 28 countries. Field observations and experimental data from 138 host species revealed 106 susceptible host species including 85 Pinus species, 6 non-pine tree species and 15 grass and herb species. Our data confirm that susceptibility to F. circinatum varies between different host species, tree ages and environmental characteristics. Knowledge on the geographic distribution, host range and the relative susceptibility of different hosts is essential for disease management, mitigation and containment strategies. The findings reported in this review will support countries that are currently free of F.
The Geographic Distribution of F. circinatum The occurrence of F. circinatum is well known for some countries, while information regarding its distribution globally or within many countries is scattered or poorly documented, and in some cases records are erroneous. To present the current distribution of F. circinatum, a geo-database for the pathogen was developed and used to generate an interactive map (see Supplementary Materials and interactive map: http://bit. do/phytoportal). The geo-database contains geographic coordinates of 6297 sampling or observation records from 106 different hosts in 41 countries (including states): Africa (1 country), North and South America (7 countries), Asia (5 countries, including the Asian part of the Russian Federation and the Asian part of Turkey), Europe (28 countries including the European part of the Russian Federation and the European part of Turkey), and Oceania (2 countries).
circinatum and thus indicate a need for better understanding of intraspecific variation within the species. An overlap of the current European distribution of F. circinatum with the original CLIMEX parameters of Ganley et al. [28] , but using newer high-resolution climate data  [29]  (Figures  3  and 4 ), shows that some of points fall in areas that were hitherto considered unsuitable for the pathogen. These mainly represented more recent points that were only included in the current study (nursery records were again not considered in the calculations). This finding suggests that the models could be improved in the light of new records of PPC in the last few years and updated with more recent climate data sets. The F. circinatum geo-database and web platform developed as part of this study aims to provide a suitable platform and resource to improve future modelling studies, as done for Dothistroma species  [132].
This possibly should be taken into account when assessing the risk to nurseries in these areas, and it is important to recognise that infected nursery plants represent a source of infection for trees in both forests and plantations. [28]  using higher resolution climatic data  [133]. The European distribution of non-nursery F. circinatum findings is shown as red dots in the main figure, while the inset displays the dataset used by Ganley et al. [28]  in the original CLIMEX modelling. Table  2. Minimum, average and maximum values of climatic and topographical variables from the dataset of Hijmans et al. [134]  and new observations of current distribution of Fusarium circinatum from the geo-database presented in the current study.
Host Range Knowledge of the host range of F. circinatum has been growing steadily, and recently reports of non-pine hosts have increased. For the purpose of this review, all known hosts of F. circinatum and their susceptibility ratings were compiled and assessed (Tables  3 4 5 6 ). The host list and susceptibility ratings were based on results of both field observations and experimental inoculations reported in peer-reviewed and "grey" literature, as well as from unpublished studies and the geo-database records compiled in this study. Such an extensive and integrated list has not previously been published because the information is scattered throughout numerous sources. We summarized these results and included data for 138 hosts (including 18 Pinus hybrids) tested in growth chamber, greenhouse, nursery and field inoculations or survey data from the wider environment.
Analyses of Geo-Database Data and Host Range Globally, 138 plant hosts (96 Pinus, 24 non-Pinus woody species, and 18 grass and herb species) were surveyed for susceptibility to F. circinatum using reports involving visual and molecular detection. In terms of geography, our study reports F. circinatum from 14 countries (including four European countries) and considers the pathogen absent in 28 countries. The interactive map (http://bit. do/ phytoportal) presents 6297 observational records, which represent reports from 87 plant species and shows the status of F. circinatum in the 41 countries for which there are data. The interactive map does not include data for 16 herbaceous plant species and 35 pine hosts (including 18 Pinus hybrids) because the material was experimentally tested in growth chambers/greenhouses and precise locations are unknown, but records for these hosts are reported in Tables  3 4 5.
The pathogen is present only in nurseries in Brazil, Chile, and Uruguay and has not yet been found on mature trees in the wider environment. The inclusion of sample information from poorly studied areas has provided improved knowledge of the distribution of F. circinatum worldwide. All records are available in the geo-database (http://bit. do/phytoportal), which will be active in the future, and updated information regarding the pathogen will be included. The current distribution of F. circinatum was summarized using a number of fine scale climatic and topological variables. Only data points from the open environment were analysed, excluding nursery records. The moisture dependent F. circinatum has been detected inland far from the coast, where the pathogen may survive and potentially cause disease from -14.
2 • C (mean temperature of the coldest month) to +36. 5 • C (mean temperature of the warmest month), indicating that F. circinatum may establish in a wider range of areas than previously thought. This finding suggests that F. circinatum could thrive and cause considerable damage in nurseries from high latitudes or areas generally not considered suitable for PPC. This possibility should be taken into account when assessing the risk of nurseries in these areas. This review summarizes data for 138 host species tested in growth chamber experiments, greenhouse, nursery, or field inoculations; or survey data from the wider environment. The species from which data were gathered include 96 species in the genus Pinus, 24 other tree species in fifteen genera, and 18 grass and herb species. Fusarium circinatum has been reported to infect 106 different host species, including 67 Pinus species, 18 Pinus hybrids, 6 non-pine tree species, and 15 grass and herb species.
Levels of susceptibility to F. circinatum were analysed separately for different age classes of pine and non-pine hosts, classified as either seedlings (recently emerged seedlings and plants) or mature trees, due to the different disease cycle of F. circinatum in nursery and wider environmental conditions. Only one Pinus species, P. koraiensis, is considered resistant to F. circinatum based on greenhouse inoculations. Tree age clearly plays an important role in susceptibility to F. circinatum, and much work needs to be done to fully understand the interaction between host, environment and pathogen and their interactive influences on susceptibility and severity of infection. Thus, the results in this study clearly support the need for standard protocols to be applied in pathogenicity tests to compare data in different conditions (including laboratories) and increase knowledge on the biology and epidemiology of F.
Funding: This study was financially supported by  COST Action FP1406 (PINESTRENGTH) , the  Estonian Science Foundation  grant  PSG136 , the  Forestry Commission ,  United Kingdom , the  Phytophthora Research Centre Reg. No. CZ. 02. 1. 01/0. 0/0. 0/15_003/0000453 , a project co-financed by the  European Regional Development Fund. ANSES is supported by a grant managed by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" programme  ( ANR-11-LABX-0002-01 ,  Laboratory of ExcellenceARBRE ). SW was partly supported by  BBSRC  Grant  reference BB/L012251/1  "Promoting resilience of UK tree species to novel pests & pathogens: ecological & evolutionary solutions (PROTREE)" jointly funded by  BBSRC, Defra ,  ESRC , the  Forestry Commission ,  NERC  and the  Scottish Government , under the  Tree Health and Plant Biosecurity Initiative.
The aim of this work is then to focus on the spectral anisotropy properties and magnetic compressibility at small scales, by exploiting the detailed comparison of in situ observations and highresolution kinetic numerical simulations. The paper is organised as follow: in Sec. 2 we introduce the spacecraft and numerical dataset used and in Sec. 3 we describe their spectral properties. In Sec. 4 we discuss the spectral anisotropy at sub-ion scales and test, for the first time, the Saur and Bieber model in numerical kinetic simulations; in Sec. 5 we address properties of the magnetic compressibility and its dependence on the plasma beta. Finally, in Sec. 6 we discuss our conclusions and the implications of our finding for the interpretation of solar wind observations and simulations.
Cluster STAFF spectra For our analysis we use the dataset discussed by  Alexandrova et al. (2012) , when Cluster was in the free solar wind, i. e. , not magnetically connected to the Earth's bow shock. Details have been described also in  Lacombe et al. (2017)  and we recall here the main aspects. Magnetic field fluctuations are measured by the STAFF (Spatio-Temporal Analysis of Field Fluctuation) instrument, composed by a wave form unit (SC) and a Spectral Analizer (SA). Power spectra are computed on board in a magnetic field aligned system of coordinates (MFA), based on the 4s magnetic field measured by the FGM (Fluxgate Magnetometer) experiment. A selection of 112 spectra has been performed, retaining in each spectrum only measurements above 3 times    1  and the coloured region indicates the sub-ion range that can be directly compared with the analogous region in the observations.
Note that a selection criterium is imposed on the angle θ BV , the angle between the local 4s magnetic field and the flow velocity, i. e. , that θ BV is large enough to avoid a connection with the Earth bow shock during the sampled interval; θ BV in the dataset has an average value of ∼ 80 degrees. This implies that for each spectrum, the mean magnetic field makes a big angle with respect to the sampling direction; moreover, we have checked that θ BV does not vary significantly during the 10 minutes over which spectra are averaged. As a consequence, this procedure selects intervals in which Cluster observed highly oblique k-vectors and, to a good approximation, the component B x corresponds also to the sampling direction (radial) and is orthogonal to B 0 ; B y corresponds to the other perpendicular component and B z is identified as the compressive component B.
Hybrid 2-D numerical simulations In situ observations are directly compared with numerical simulations performed with the hybrid-PIC code CAMELIA  (Matthews, 1994; Franci et al. , 2018a). The hybrid model captures well the transition from fluid to kinetic regime around ion scales. Moreover, it reproduces successfully many of the main properties of solar wind turbulence observed by spacecraft at sub-ion scales  (Franci et al. , 2015b,a). It is then a suitable tool to investigate the turbulent regime probed by STAFF/Cluster data. We use here 2D simulations -computationally more affordable than 3D-in order to explore the parameter space observed in situ; in particular we focus on the effects associated to variations in the proton and electron plasma beta β p and β e. Franci et al. (2016)  have shown that 2D hybrid simulations are able to capture the ion-break scale behaviour in different beta regimes observed in solar wind turbulence  (Chen et al.
With this approach and within the observational conditions previously described, we can perform a direct comparison of simulations and in situ data. The numerical dataset used was originally presented in  Franci et al. (2016)  and is available online. It is constituted by a set of different 2048 2 2-D simulations of decaying turbulence with different beta conditions covering the range of variations observed in situ, and β p = β e ; runs are initiated with random perpendicular Alfvénic fluctuations with vanishing cross-helicity and equipartition in magnetic and kinetic energies. Spectra are computed at the maximum of the turbulent activity.
FUNDING This work was supported by the Programme National PNST of CNRS/INSU co-funded by CNES. It has also been funded by Fondazione Cassa di Risparmio di Firenze through the project HY-PERCRHEL. We acknowledge PRACE for awarding us access to resource Cartesius based in the Netherlands at SURFsara through the DECI-13 (Distributed European Computing Initiative) call (project "HybTurb3D"), and CINECA for the availability of high performance computing resources and support under the ISCRA initiative (grants HP10C877C4 and HP10BUUOJM) and the program Accordo Quadro  INAF-CINECA 2017-2019 (grants C4A26 and C3A22a). L. F. was supported by Fondazione Cassa di Risparmio di Firenze, through the project "Giovani Ricercatori Protagonisti", and by the UK Science and Technology Facilities Council (STFC) grants ST/P000622/1 and ST/T00018X/1.
DATA AVAILABILITY STATEMENT The in situ observations analyzed for this study can be found in the main ESA Cluster archive, https://csa. esac. esa. int. The numerical simulations dataset analyzed for this study can be found in the Data deposit by the EUDAT infrastructure (https://www. eudat. eu/), through a persistent identifier (pid): https://b2share. eudat. eu/records/ a58135af9c9d429f92c15ce88bdfdd55.
The case study SSS datasets are obtained from the temporal series of hourly observations and predicted tide levels (the astronomical tide), collected at a total of 67 sites located on the Spanish, French (Atlantic and English Channel), and British coasts (see Fig. 2 ). The French tide gauges are managed by the French oceanographic service (SHOM -Service Hydro graphique et Océanographique de la Marine), while Span ish and British ones are managed by IEO (Instituto Espanol de Oceanograffa, Spain) and the British Oceanographic Data Centre (BODC), respectively. For convenience, the same observation periods as those used by  Weiss (2014)  were used in the present study. They range from 1846 for Brest (in France) to 2011 (for almost all the sites), and they show an average effective duration of observations of 31 years.
Acknowledgements. The permission to publish the results of this ongoing research study was granted by the  EDF (Electricité De France). The results in this paper should, of course, be considered to be R & D exercises without any significance or embedded commitments for the real behavior of the EDF power facilities or its regulatory control and licensing. The authors would like to acknowledge the  SHOM (Service Hydrographique et Océanographique de la Ma rine, France) , the  BODC (British Oceanographic Data Centre, UK) , and the  IEO (Instituto Espanol de Oceanografta, Spain)  for providing the data used in this study. Review statement. This paper was edited by  Ira Didenkulova  and reviewed by  Andreas Sterl  and one anonymous referee. Nat. Hazards Earth Syst. Sci. , 20, 1705-1717 ,2020.