dataesr/scientific-paragraphs-categorization huggingface dataset:
To overcome these challenges, observational-based multidisciplinary datasets with resolutions matching spatio-temporal dynamics of ecological processes occurring in the oceans are needed. While monitoring programs are common in coastal areas, the offshore part of the oceans remains poorly monitored due to very large spatial scales, and the associated expense and logistical challenges of sampling the animals and their environment  (Richardson and Poloczanska, 2008). New approaches are being developed to fill observational gaps. Among them, natural biogeochemical tracers present the advantage of recording various parameters on in situ conditions experienced by animals, suitable for later retrospective analysis  (McMahon et al. , 2013a). Spatio-temporal variations of natural abundance stable isotope (SI) ratios of carbon and nitrogen (expressed as δ 13 C and δ 15 N values) have been widely used during the last decades to infer aspects of hydrological conditions  (Kline, 1999) , nutrient sources and limitations  (Rau et al.
Environmental variables A range of variables were considered to produce parameters used to explain zooplankton SI variability. The main constraint was to obtain a consistent dataset covering the whole study area with good spatial resolution, and being available for a time period matching at least  2009 -2018 (SI data) (SI data). Observational data were prioritised. Available variables were: chlorophyll-a concentration (chla), net primary production (NPP), sea surface temperature (SST), wind speed (wind), mixed layer depth (MLD), and distance to the shelf (dist). Data were provided in various resolutions before being projected onto a 0. 25 • grid. All datasets were retrieved from the European Union Copernicus Marine Environmental Monitoring Service (CMEMS). 1 SST were extracted from the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product, provided by the UK's Met Office.
The Eppley-VGPM calculation was adapted from the VGPM approach  (Behrenfeld and Falkowski, 1997) , in which the polynomial description of light-saturated photosynthetic efficiencies as a function of SST is replaced with the exponential relationship described by  Morel (1991)  and based on the curvature of the temperature-dependent growth function described by  Eppley (1972). The script for the calculation was acquired from Oregon State University. 3 Chla and NPP products are dependent on atmospheric conditions, resulting in missing data for some areas due to persistent cloud coverage. The distance to the shelf (dist) was calculated as distance from the centre point of the grid cell to the 200 m isobath, based on ETOPO1 Global Relief Model. Each environmental covariate value was extracted at each sampling location.
Model development Model development and metrics production were conducted using the software environment R v. 4. 1. 3 (R Core Team, 2022). All maps were produced using the m_map package  (Pawlowicz, 2019)  running under Matlab 2020a. To produce isoscapes, we used the following sequence: (1) obtaining georeferenced SI dataset, (2) extracting environmental variables at sampling locations and times, (3) carrying preliminary dataset checks, (4) selecting the best models using a frequentist approach, (5) implementing spatial component using Bayesian framework, and (  6 ) predicting SI values for the whole study area using the modelled relationship and continuously observed environmental variables as predictors. The first two steps were introduced above. We followed the protocol detailed by  Zuur et al.
, 2009) , was demonstrated to be a powerful spatial statistical package to produce isoscapes  (St John Glew et al. , 2019). Compared to the frequentist mixed modelling approach, the Bayesian framework enable uncertainty to be more easily interpretable, allow the inclusion of boundary effects and solve the spatial dependency term in an alternative and faster way. We used the guidelines in  Lindgren and Rue (2015)  to write and structure the code with the R-INLA package, and from  Zuur et al. (2017)  to adapt frequentist GAM smoothers to INLA. This allowed a better control on the number of knots which define the "wiggliness" of the smoothers (i. e. , the amount of smoothing). In ecological studies, where datasets are often sparse and fragmented, it is recommended to apply greater amount of smoothing to dampen wiggliness as high-frequency variations in poorly constrained smooth terms can result in models which are too specific to the dataset and makes difficult to relate smoother shapes and ecological processes.
Outlier grid cells were blanked from maps. Isoscapes were produced over the time period for which environmental variables were available, i. e. , 1998-2020. Several approaches can be used to model seasonal isoscapes using GAM: (1) splitting the dataset and developing independent models for each season, (2) using the global dataset and including season as a fixed effect,or (3) using the global dataset to develop a universal model and making predictions with seasonally explicit sets of predictors. Our dataset did not allow for the first option as not enough data were available for each season, especially for winter. The two other options gave similar results when comparing observation and modelled values at sampling locations (using frequentist models without spatial component) (Supplementary Figure  3 ).
Trajectory analysis and isoscape metrics The stable isotope trajectory analysis (SITA) framework and its tools devoted to isoscape data sets were used to assess the long-term spatio-temporal dynamics of isoscape at year and seasonal scales  (Sturbois et al. , 2021b). The Space of analysis was defined by δ 13 C and δ 15 N, and is called here after δ. In this study, SITA was used to evidence the broad spatial and temporal structure captured by the modelled isoscapes, aiming (1) to identify different eco-regions characterised by different SI compositions and temporal trends; (2) to represent the magnitude and the nature of changes at different temporal scales. Analysis has been performed with the R-package "ecotraj"  (De Cáceres et al. , 2019; Sturbois et al. , 2021a). Modelled δ 13 C and δ 15 N values of a 1 • × 1 • spatial grid were subset from the original output model (0.
, 2019-2020)  as input for a trajectory heatmap. A similar analysis was performed at the finest temporal scale of the 69 successive isoscapes including all seasons * years from winter 1998 to fall 2020 for the 471 1 • × 1 • grid cells for which no isoscape value was missing at any dates for both isotopes. Net changes values were calculated and compared among trajectory clusters to look for isoscapes cycles and dynamics temporality. Differences in δ 13 C/δ 15 N values and length-based SITA metrics were tested with one-factor ANOVAs by permutation using the function "aovp" of the package "lmPerm. " Directionbased metrics were tested using circular statistics  (Landler et al. , 2019). The Hermans-Rasson test was used to verify if there was unimodal bias in the distribution of angle α values for each cluster, i.
We aimed to find a compromise between isoscape resolution and data uncertainties as to keep the latter in reasonable range (ca 1. 5 for δ 13 C and 3 for δ 15 N). Extreme predicator values are a problem toward the limits of the domain (e. g. , warm water in the south border) and this problem is amplified within the more data-limited seasonal isoscapes. To reduce the uncertainties more observational should be added to the model, ideally coming from undersampled regions and/or during extreme events. The models we developed could easily be updated in the future by integrating other datasets, still keeping a control on the accuracy of the predictions. Building a larger data base (see for example  Verwega et al. , 2021)  would enable us to develop further the seasonal isoscape, in terms of coverage but also of model structures (e.
Funding The  CPR Survey  would not be possible without the support of the shipping industry, nor the dedication of the past and present team. Current funding includes the  UK Natural Environment Research Council , Grant/Award Numbers:  NE/R002738/1  and  NE/M007855/1 ; EMFF; Climate Linked Atlantic Sector Science, Grant/Award Number:  NE/R015953/1 ,  DEFRA UK ME-5308 ,  NSF USA OCE-1657887 , DFO CA  F5955-150026/001/HAL, NERC UK NC-R8/H12/100 ,  Horizon 2020: 862428 Mission Atlantic and AtlantECO 862923 ,  IMR Norway  and the  French Ministry of Environment, Energy , and the  Sea (MEEM). Stable isotope measurements were funded by a  National Environmental Isotope Facility  grant-in-kind (no. 2323 ) to CT and BE. BE was funded from the  European Union 's  Horizon 2020 MSCA program  under Grant agreement no.
Background: Previous studies had limited power to assess the associations of circulating insulin-like growth factors (IGFs) and IGF-binding proteins (IGFBPs) with clinically relevant prostate cancer as a primary endpoint, and the association of genetically predicted IGF-I with aggressive prostate cancer is not known. We aimed to investigate the associations of IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3 concentrations with overall, aggressive and early-onset prostate cancer. Methods: Prospective analysis of biomarkers using the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group dataset (up to 20 studies, 17 009 prostate cancer cases, including 2332 aggressive cases). Odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression.
For IGF-I, two-sample Mendelian randomization (MR) analysis was undertaken using instruments identified using UK Biobank (158 444 men) and outcome data from PRACTICAL (up to 85 554 cases, including 15 167 aggressive cases). Additionally, we used colocalization to rule out confounding by linkage disequilibrium. Results: In observational analyses, IGF-I was positively associated with risks of overall (OR per 1 SD ¼ 1. 09: 95% CI 1. 07, 1. 11), aggressive (1. 09: 1. 03, 1. 16) and possibly early-onset disease (1. 11: 1. 00, 1. 24); associations were similar in MR analyses (OR per 1 SD ¼ 1. 07: 1. 00, 1. 15; 1. 10: 1. 01, 1. 20; and 1. 13; 0. 98, 1. 30, respectively). Colocalization also indicated a shared signal for IGF-I and prostate cancer (PP4: 99%). Men with higher IGF-II (1. 06: 1. 02, 1.
6, 7  The Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group (EHNBPCCG) is a pooled individual participant nested case-control dataset of prospective studies of hormonal and nutritional factors and prostate cancer risk, which previously reported positive associations of IGF-I, IGF-II, IGFBP-2 and IGFBP-3 with overall prostate cancer risk and an inverse association with IGFBP-1. 8  However, in this previous study it was unclear whether IGF-II or the IGFBPs are associated with prostate cancer independently of IGF-I, and the analyses of associations with aggressive disease subtypes were underpowered to provide strong evidence of an effect. 8  The EHNBPCCG dataset has since been expanded to include more than double the number of prostate cancer cases (up to 17 000 prostate cancer cases, including 2300 aggressive cases).
In blood-based observational analyses it is difficult to rule out the possibility of biases including residual confounding or reverse causality. Mendelian randomization (MR) uses germline genetic variants as proxies of putative risk factors and estimates their associations with disease risk. These germline genetic variants are randomly allocated and fixed at conception, and therefore MR is less likely to be affected by these biases and so is potentially a more robust method for causal inference. 9 ] [12]  Using these genetic datasets, we also ran colocalization analyses to investigate whether the IGF1 gene region and prostate cancer share the same genetic signal to exclude the possibility of confounding by linkage disequilibrium. 13 sing these two international consortia and UK Biobank, we aimed to assess the associations of circulating IGF-I with overall, aggressive and early-onset prostate cancer risk, using observational and genetic methods.
Further analyses We examined heterogeneity in the associations of each biomarker with prostate cancer by participant characteristics, with subgroups defined a priori based on the availability of data and previous analyses using this dataset  8, 16  ; heterogeneity in the associations by study was also examined (Supplementary Methods). We additionally investigated unadjusted matched associations, associations in tenths, and estimates per 80th percentile increase. Associations were also examined following mutual adjustment for other biomarkers (IGF-I, IGF-II, IGFBP-1,-2,-3, free and total testosterone and sex hormone-binding globulin [SHBG]), and we tested for interactions between these biomarkers; further details are available in the Supplementary Methods. Stratified analyses and associations in tenths were not investigated for earlyonset disease due to the limited number of cases.
26 To rule out reverse causality, analyses were repeated after applying Steiger filtering which excludes variants with larger effects on prostate cancer risk than on IGF-I. 27 he associations of the IGF-I cis-SNP, defined as the lead SNP on the biomarker gene coding region identified from the exposure datasets, with prostate cancer were investigated using the Wald ratio. This cis-SNP is less likely than trans-SNPs to be affected by horizontal pleiotropy. 28.
Colocalization analysis Colocalization was used to investigate whether the associations of variation in the IGF1 gene region with both circulating IGF-I concentration and prostate cancer risk, share the same genetic signal or whether the associations identified by our MR analysis may be confounded by linkage disequilibrium. 13 Analyses were conducted for a 75kb region surrounding the lead IGF-I cis-SNP (rs5742653) using the UK Biobank and PRACTICAL datasets. 12, 17 Colocalization was assessed using three approaches: conventional colocalization,  13  which tests for the presence of a single shared genetic signal; as well as the sum of single effects (SuSiE) regression framework  29  ; and conditional iterative colocalization. 30 The latter two methods allow for the possibility of multiple independent (but partially correlated) causal variants in proximity.
Acknowledgements We thank all participants, researchers and support staff who made the study possible. CHDS would like to acknowledge the support of  Alice Whittemore  and  David Feldman. CLUE thank  Kathy J Helzlsouer  for her contributions to the cohort and thank the staff and participants of the CLUE study for their important contributions. Cancer data were provided by the  Maryland Cancer Registry, Center for Cancer Prevention and Control, Maryland Department of Health , with funding from the  State of Maryland  and the  Maryland Cigarette Restitution Fund. The collection and availability of cancer registry data are also supported by the  Cooperative Agreement   NU58DP006333 , funded by the  Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the  Centers for Disease Control and Prevention  or the  Department of Health and Human Services.
The authors would like to thank  Paul Appleby  for past contributions to establishing the EHNBPCCG dataset and related coding. We additionally thank investigators from  BUPA, CHS, EPIC Norfolk and MMAS  for contributing data. DISCLAIMER: where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the  International Agency for Research on Cancer/World Health Organization.
Funding This work was supported by  Cancer Research UK  (grant numbers  C8221/A19170  and  C8221/A29017 ) to fund the centralized pooling, checking and data analysis. E. L. W. was supported by the  Nuffield Department of Population Health Early Career Research Fellowship. A. P-C. is supported by a  Cancer Research UK   Population Research Fellowship  ( C60192/A28516 ) and by the  World Cancer Research Fund (WCRF UK) , as part of the  World Cancer Research Fund   International grant programme  ( 2019/1953 ). A. T. B. C. was supported in part by the  Intramural Research Program  of the  National Institutes of Health  and the  National Cancer Institute. CLUE is funded by the  American Institute for Cancer Research, National Institutes of Health Grants  ( IU01AG18033  and  IU01CA86308 ). H. I.
M. S. was supported by research grants from the  National Health and Medical Research Council of Australia. J. A. C. C. was supported by  Grants-in-Aid for Scientific Research  from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. M. E. C. is supported by the  US National Institutes of Health  (Grant  U01 CA164973 ). R. M. M. was supported by the  NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust  and the  University of Bristol. R. M. M. was also supported by a  Cancer Research UK  ( C18281/A29019 ) programme grant (the  Integrative Cancer Epidemiology Programme). The views expressed are those of the author(s) and not necessarily those of the  NIHR  or the  Department of Health and Social Care.
The assessment of shark tooth form differences is successfully achieved with geometric morphometrics  (Whitenack & Gottfried, 2010; Soda, Slice & Naylor, 2017; Cullen & Marshall, 2019; Berio et al. , 2020). Linear Discriminant Analysis (LDA) is a frequently used machine learning algorithm to discriminate between groups based on geometric morphometric data  (Mitteroecker & Bookstein, 2011; MacLeod, 2017; Doyle, Gammell & Nash, 2018). The performances of LDA depend on a much higher number of items (e. g. , teeth) as compared to the number of features (e. g. , aligned coordinates). Yet, these conditions are difficult to meet in biological datasets, which often implies a feature reduction step through a Principal Component Analysis (PCA)  (Fort & Lambert-Lacroix, 2005; Pechenizkiy, Puuronen & Tsymbal, 2006; Sheets et al.
In addition, we estimated the approximate age of the specimens using the von Bertalanffy growth parameters for the Atlantic and Mediterranean populations provided by  Ivory et al. (2004)  and  Bendiab, Mouffok & Boutiba (2012)  respectively because these studies provide sex-specific growth curves. Ivory et al. (2004)  estimated von Bertalanffy parameters based on vertebral growth increment counts, whereas Bendiab, Mouffok & Boutiba (2012) estimated these parameters by analyzing length-frequency distributions. We used the growth parameters (K, coefficient of growth; L1, asymptotic length; Lt, length at age t; t0, theoretical age at which the size is zero) from literature data  (Ivory et al. , 2004; Bendiab, Mouffok & Boutiba, 2012)  (Fig. 1 ) to compute von Bertalanffy growth curves with the following growth equation: Lt ¼ L1½1 À e ÀKðtÀt0Þ.
Data acquisition The jaws were microCT scanned using a Phoenix Nanotom S or an EasyTom 150 with voxel sizes ranging from 6. 0 to 30. 8 µm and 3D volumes were reconstructed using the phoenix datos x2 (v2. 3. 0) reconstruction or xact softwares (v11025). The surfaces are available in  Berio et al. (2022). 3D surfaces of right palatoquadrate (upper) and Meckelian (lower) teeth were generated with the Amira software (v6. 5), extracted using the ContourTreeSegmentation module from the AmiraZIBEdition software (v2018. 28) (Stalling, Westerhoff & Hege, 2005) , and were labelled according to their mesio-distal position along the jaw (N Atlantic = 1,757 and N Mediterranean = 1,542, Fig. 2A ). Seven landmarks (respectively numbered  1, 13, 15, 17, 19, 21, and 33  in Fig. 2 , Supplemental Materials 1 and 2) and 31 semilandmarks were placed on each 3D tooth surface.
Data analyses A Generalised Procrustes Analysis was performed on 3D coordinates that were formerly preprocessed following  Berio & Bayle (2020). Semilandmarks were allowed to slide based on minimised bending energy  (Bookstein, 1991). The structure of the dataset was first investigated through a PCA and centroid size patterns, and tooth centroid sizes were used as a proxy for tooth size. The centroid size is computed as the square root of the sum of squared distances between all landmarks and semilandmarks and the centroid of a form  (Webster & Sheets, 2010; Klingenberg, 2016). The slopes of allometric patterns between populations of same sex was assessed with ANCOVAs and the interaction between shape data and size was tested using linear regression models. The shape data used to test for allometry were PC axes, whose number was determined following the procedure described by  Evin et al.
(2013). The relationship between shape data and size was tested using centroid size values of teeth and the specimens TL. Allometric vectors were computed with linear models and pairwise comparisons allowed to compare lengths and angles between the vectors of populations of same sex. Supervised classification of teeth from Atlantic and Mediterranean populations was first achieved with LDA on the same PC axes used for testing allometry, without and with tooth centroid sizes [respectively tooth shapes and forms  (Klingenberg, 2016) ]. The results from the LDA model were compared with those obtained with Random Forests on tooth shape (Procrustes coordinates) and form (Procrustes coordinates + tooth centroid sizes)  (Breiman, 2001). A five-fold cross-validation was performed in both methods (train set = 80% and test set = 20%).
1. 1) and supervised classification was computed using the MASS package (v7. 3. 53) for LDA and the randomForest package (v4. 6. 14) for Random Forests with R software (v4. 0. 3)  (Liaw & Wiener, 2002; Venables & Ripley, 2002; Adams, Collyer & Kaliontzopoulou, 2019; R Core Team, 2020).
Visual description The tooth diversity presented in Fig. 3  is a selection of examples among the whole variation observed in the dataset. It provides a broad overview of the main morphological differences and associated factors in S. canicula tooth forms. Differences in mesio-distal location of a tooth are usually linked with an addition of accessory cusps (Figs. 3A  and 3B ) and an increase of the main cusp bending. Gynandric heterodonty at sexually mature stage is characterised by more accessory cusps in females as compared to males (Figs. 3C  and 3D ). Along the ontogeny, new teeth undergo size increase, as well as the addition of accessory cusps, except after sexual maturation (Figs. 3E  and 3F ), when males experience a decrease in number of accessory cusps, as opposed to females. The interpopulational tooth form differences involve less accessory cusps and blunter teeth in the Atlantic population than in the Mediterranean population (Figs.
Geometric morphometrics The first two principal components gather 61% of the total variation in the dataset. The Atlantic and Mediterranean populations are not visually discriminated in the morphospace (Fig. 4 ). Extreme shapes for PC1 and PC2 suggest that most variation of the dataset relates to the number of accessory cusps, to their relative size compared to the main cusp, and to the mesio-distal bending of the main cusp (Fig. 4 ). In addition, shape variation along PC1 might also involve the relative width of the crown base, as compared to the main cusp height (Fig. 4 ). The tooth centroid size patterns in the S. canicula dataset are exemplified for male palatoquadrate teeth (Fig. 5 ), but similar trends are observed for both jaws and sexes. In hatchlings, the tooth centroid size patterns display no variation along the jaw nor visual differences between populations (Fig.