dataesr/scientific-paragraphs-categorization huggingface dataset:
PCDD/F, PCB, PBDE, PBB, and OC measurements were performed by gas chromatography (Agilent 7890A, Nantes, France) coupled with high-resolution mass spectrometry (GC-HRMS) on electromagnetic sector instruments (JEOL MS 700D or 800D, Nantes, France), operating at a resolution of 10,000 and in the single ion monitoring (SIM) acquisition mode  [32]. HBCD isomers were quantified using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) on a triple quadrupole instrument (Agilent 6410). All data were generated in a manner blind to the control or case status of individuals. QA/QC procedures included systematic analysis of negative (blank, n = 3) and positive (standard reference material, n = 1) control samples in each batch of analyzed samples, and several inter-laboratory assays that were realized both nationally and internationally confirmed the robustness and accuracy of the protocols.
Statistical Analysis Distributions of participants' characteristics and concentration data of POPs in adipose tissue were summarized as medians and interquartile ranges. The distribution of continuous variables and POP concentrations of cases were compared with controls using the Mann-Whitney U test. Chemicals with detection rates below 50% were excluded, and LOD/2 was attributed to non-detected samples. Correlation between POPs was evaluated using Spearman's rank correlation coefficient and displayed with heatmaps. A preliminary exploratory analysis to identify the most relevant POPs contributing to the discrimination between groups was conducted with sparse partial least square analysis (sPLS-A)  [38]. All statistical analyses were conducted with R software v. 3. 5. 0 (Free Software Foundation).
Acknowledgments: The authors greatly thank  Christophe Rousselle  at  ANSES (Agence Nationale de Securité Sanitaire , Environnement et travail,  Maison-Alfort , France;  CRD 2016-02 ). We also thank  INSERM (Institut National de la Santé et de la Recherche Medicale, Paris)  and  CNRS (Centre national de la Recherche Scientifique (MP-A). We thank  Peter Brooks  for assistance in editing the English language of the manuscript.
Study Population and Data Collection The European Prospective Investigation into Cancer and Nutrition (EPIC) is a multicenter prospective cohort study designed to investigate the associations between diet, lifestyle, genetic and environmental factors, and various types of cancer. The rationale and methods of the EPIC design have been published previously  [32, 33]. Participating countries include France, Germany, Greece, Italy, The Netherlands, Spain, the UK, Sweden, Denmark, and Norway. Between 1992 and 1998, standardized dietary and lifestyle/personal history questionnaires, anthropometric data, and blood samples were collected from most participants at recruitment, before disease onset or diagnosis. Diet over the previous one year was measured at baseline by validated country-specific dietary questionnaires developed to ensure high compliance and better measures of local dietary habits.
Serum samples were stored at the International Agency for Research on Cancer (IARC) at -196 • C in liquid nitrogen for all countries except Denmark (-150 • C, nitrogen vapor). Individuals who were eligible for the study were selected from the general population of a specific geographical area, town, or province. Exceptions included the French subcohort, which is based on members of the health insurance system or state-school employees; the Utrecht (The Netherlands) subcohort, which is based on women who underwent screening for breast cancer; and a portion of the Spanish and Italian subcohorts that included blood donors. The present analysis is based on participant data from all centers except for Norway (blood samples only recently collected; few CRCs diagnosed after blood donation), Sweden (no available serum samples), and Greece (excluded due to unforeseen data restriction issues).
Case Ascertainment and Selection Cancer data were coded using ICD-10 and the second revision of the International Classification of Disease for Oncology. CRC cases were selected from participants who developed colon (C18. 0-C18. 7), rectum (C19-C20), and overlapping or unspecified-origin colorectal tumors (C18. 8-C18. 9). CRC is defined as colon and rectal cancer cases. Of 1001 CRC cases with measurements of Se and SELENOP  [12] , 1 was excluded due to stage coded as in situ, and 5 cases were removed for having a follow-up time of 0, resulting in 995 CRC cases.
Healthy Lifestyle Index score and Mediterranean diet score were adjusted for in separate models. These variables were chosen based on previous published evidence showing their associations with CRC incidence or survival and/or blood Se concentrations. Information regarding categorization and harmonization of tumor stage data has been previously published  [36]. Confounding assessment was conducted by evaluating whether there was a >10% change in hazard ratios (HRs) after including the variable in the model. Age at diagnosis, sex, stage, location of primary tumor, smoking status, BMI (kg/m 2 ), year of diagnosis, and baseline diabetes status were included in the final analysis.
We explored whether the association between Se or SELENOP and risk for CRC-specific and overall mortality is non-linear using non-parametric restricted cubic splines  [38, 39]  fitted to a Cox proportional hazards model using the SAS macro "lgtphcurv9"  [40]. Tests for non-linearity used the likelihood ratio test, comparing the model with only the linear term to the model with the linear and cubic spline terms  [40]. p-values of non-linearity tests from these models were consistent with a linear response (Supplementary Figures  S1-S4 ). The effect of missing tumor stage information on effect estimates was assessed using several approaches. The first approach reclassified missing tumor stage values into a separate missing category and adjusted for the stage variable in the final model (included in the primary analysis).
Second, a sensitivity analysis was conducted by excluding participants with missing stage information and subsequently by assessing how the results were affected by the missing stage information. Finally, an imputation of missing tumor stage values was conducted using the SAS PROC MI procedure  [41]. The multiple imputation method was based on available data for the other covariates in the model and assumed that the stage data were missing at random. Subgroup analyses by categories of potentially biologically relevant effect modifiers (length of follow-up, sex, age at diagnosis, tumor site, tumor grade, tumor stage, year of diagnosis, smoking status, and BMI) were conducted. We also stratified by geographic region categorized as Northern (Denmark), Central (UK, The Netherlands, Germany, North of France), and Southern (South of France, Italy, Spain).
Stratified multivariableadjusted HRs and 95% CIs were reported per one SD increase in Se or SELENOP. A cross-product of Se or SELENOP as a continuous variable and the covariate of interest as a continuous or categorical variable was included in the model to test for multiplicative statistical interaction; and the likelihood ratios based on the models with and without the interaction terms were used to test for statistical significance. All statistical tests were conducted using SAS version 9. 2 (SAS Institute), and p-values of <0. 05 were considered statistically significant.
Study concept All in all, we considered data collected at 91 out of the 102 permanent monitoring plots of the French monitoring program RENECOFOR (see below under 2. 2) which is part of the UNECE ICP Forests intensive (Level II) monitoring network  (Lorenz and Fischer, 2013). Eleven of the 102 plots were not included in this study because data collection was stopped after severe damage caused by storms in 1999 (nine plots) or because growth measurements were systematically affected by anomalies (removals of bark at measurement height between periodical growth surveys that may have altered the second measurement -two plots) or because of financial reasons. With few exceptions (four oak plots; one spruce plot; and three Silver fir plots), plots were single-species and even-aged. Four steps were undertaken and for each dataset we always considered the mean periodical defoliation (averaged over ten years) and the cumulated periodical growth, i.
Data resources, data quality and measurement methods Data on the indicators defoliation and growth (BAI) were collected at the permanent monitoring plots of the RENECOFOR program in France. Two datasets including paired defoliation and BAI data relevant to growing periods  1995-2004 and 2000-2009  were considered. These two different periods were chosen to maximize the total number of plots for the study (n = 91), with conifer plots only (n = 47) in the 1995-2004 period and both broadleaves (n = 44) and conifers (n = 19) in the 2000-2009 period. In particular, the 1995-2004 dataset includes data from 47 plots (with 16-52 trees each; 2008 trees in total) where the main tree species (MTS) were conifers (Table  2 ): silver fir (Abies alba Mill. ), Scots pine (Pinus sylvestris L. ) and Norway spruce (Picea abies (L.
) H. Karst. ). Plots with broadleaves were also measured in this period, but not included in the present analysis because concerns about "a shift in the severity of the defoliation scoring"  (Ferretti et al. , 2014)  for broadleaves between 1994 and 1997 that may had potentially affected the consistency of defoliation data. In the 2000-2009 dataset there was a large prevalence of broadleaved species (Table  2 ) with a total of 3116 trees in 63 plots: European beech (Fagus sylvatica L. ) and sessile oak (Quercus petraea Liebl. ) were the most frequent species. Nineteen conifer    Top: 1995 Top: -2004;; bottom: 2000 -2009. Codes are the plot ID. See Table  1  for abbreviations. plots were present in both datasets. The rest of the 102 plots were not measured in 2009 because of financial constraints.
The number of trees per plot was in general greater than 40 and trees were haphazardly selected from those belonging to the dominant and codominant layer. Raw data consisted of tree defoliation (in 5% steps) and tree circumference (in cm). "Defoliation" is used to describe deviation of foliage density on a tree in comparison with a reference tree assumed with full density. Here, the reference tree adopted was always a local one and this implies that reported defoliation values account for the site condition. While it is known to be prone to observer errors (e. g. Innes et al. , 1993) , the quality of defoliation assessments can be controlled by adequate training and field checks  (Ferretti et al. , 1999; Bussotti et al. , 2009; Eickenscheidt & Wellbrock, 2014). The quality of defoliation data used in this study was evaluated by  Ferretti et al.
The time period of ten years was chosen to reduce the relative importance of the uncertainties in circumference measurements in the calculated growth, especially for slowly-growing trees and/or at high elevation sites. Original circumference data were inspected to evaluate possible measurements anomalies. All in all, the overall frequency of reported anomalies for individual tree measurements was 1. 1% (Fig. A1 ) and these measurements were discarded. Circumference data were subsequently transformed into basal area, BA, and then into periodical (ten years) Basal Area Increment (BAI period ) for the examined growth periods. A summary of data resources for both growth periods is reported in Tables  1  and 2.
Moreover it is easy to interpret as it is expressed in the same unit as the response variable of the model itself (BAI period ). The results of the correlation analysis between defoliation and BAI carried out for individual plots were summarized by means of metaanalysis, with Spearman Rho adopted as effect size  (Harrison, 2011; Stewart, 2010; Hillebrand, 2008). We used both fixed-and random/ mixed-effects models  (Berkey et al. , 1995; van Houwelingen et al. , 2002)  after the adopted effect sizes were tested with respect to normality by using the Shapiro-Wilk test  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) : W = 0. 96, P = 0. 108; 2000-2009: W = 0. 98, p = 0. 572). See  Ferretti et al. (2014)  for details. Under the R statistical environment (R Core Team, 2020), metaanalyses were conducted by using the metafor package  (Viechtbauer, 2010) , the package ggplot2  (Wickham, 2016)  was used for graphing, while the R base package was used for linear modelling and correlation analyses.
Defoliation and growth of the whole tree population examined We calculated the relative growth of the tree population examined in 2000-2009 (the most complete of the two datasets examined) as percent of the growth of a theoretical population of trees with null defoliation: with all trees in defoliation category 0%, the growth of the entire population is assumed to be 100%. This population is purely theoretical, but is convenient for comparison purposes. In our hypothesis (increasing defoliation associated to increasing growth reduction), we expect a progressive and negative deviation from 100% relative growth as soon as the distribution of trees shift towards higher defoliation categories. The relative growth of the population is directly related to the observed proportion of trees in the various defoliation categories (from 0 to 100%) and on their BAI rel d (see above).
The percent contribution of trees in each defoliation category d to the relative growth of the entire tree population can be calculated as follows (Equation  2 ): Contribution to Relative Growth d = 1 N ( n d BAI reld ) ⋅100 where N is the total number of trees of the population examined. For the 2000-2009 dataset it amounts to 3116 (Table  4 B). The relative growth of the entire population (P) over the period examined is then the sum of contributions of each defoliation category d (0, 5, 10, …100) and is expressed in percent of the growth of a theoretical population of trees with null defoliation. It can be written as follows (Equation  3 ): Relative Growth P = ∑ d=100 d=0 1 N ( n d BAI reld ) •100 Notation are the same as in Equation  2.
Defoliation and Basal Area Increment (BAI) The 1995-2004 dataset included only conifers (Table  2 ). Mean species defoliation was in general low over the period examined and varied between 7. 8% (Norway spruce) and 18. 1% (Maritime pine, Pinus pinaster Ait. ) (Table  2 ). Periodical BAI was larger for Douglas fir (Pseudotsuga menziesii (Mirb. ) Franco) (535 cm 2 on average, ca. + 64% with respect to the beginning of the growth period examined) than for the other species (200-330 cm 2 , ca. + 20-40%). The 2000-2009 dataset included both conifers and broadleaves. For conifers, defoliation was below 20%, with the exception of Maritime pine and Corsican pine (Pinus nigra ssp. laricio). Periodical BAI was again larger for Douglas fir (603 cm 2 on average, ca. + 62% with respect to the beginning of the period).
9 -1. 5% (conifers, two growing periods), 0. 7% (broadleaves) and 0. 9% (all species). This pattern remains valid when all defoliation categories are considered, although with some changes in slope values for conifers (-0. 7; -0. 8) and broadleaves (-0. 9) (Fig. 2 , insets). This applies also for the majority of the species considered (see Appendix A, Tables  A3, A4 , A5). Table  5  summarizes our main findings for the two datasets, individual species, functional groups and in total. 4  B), we calculated that such a distribution (modal defoliation: 25%; 82% of trees with defoliation ≤ 30%) resulted into an overall relative growth of 58% as compared to a theoretical population of undefoliated (defoliation 0%) trees (Fig. 3 ), i. e. a 42% reduction of relative growth. Fig. 3  shows three additional arbitrary simulated defoliation scenarios and shows that, when the the same BAI rel d is assumed, shifts of tree distribution towards higher defoliation levels (C, moderate increase: mode: 50%; 82% of trees with defoliation ≤55%; D, strong increase: mode: 75%; 86% of trees with defoliation ≤85%) will result into further reduction of relative BAI, up to 70% as compared to the reference (theoretical) population of undefoliated trees.
Conversely, a shift towards lower defoliation (B, moderate decrease: mode: 0%; 82% of trees with defoliation ≤20%) will limit the reduction of relative BAI to 29%. While simulations reported in Fig. 3  are arbitrary in terms of distribution shifts and based on median BAI deviations observed in our study, signals that a shift in defoliation is actually associated to change in growth can be detected in our dataset. Fig. 4  presents results from the 19 coniferous plots for which data were available for both  1995-2004 and 2000-2009  periods: despite the partial overlap of the two periods, we observed a significant negative relationship between changes in defoliation and relative BAI, especially when defoliation change is >5% in absolute values (Fig. 4 ).
g. Ferretti, 1997 Ferretti, , 1998;; Redfern & Boswell, 2004 ) and all solutions have pros and cons. At the RENECOFOR plots, defoliation was always assessed in relation to local, real reference tree, thus accounting for local site and stand characteristics. The adoption of local reference tree permits to interpret increasing defoliation levels as progressive deviation from the local optimum canopy foliage status. As the comparison between BAI and defoliation in our study was always tree-wise, this permits to have always fully comparable data adjusted for site characteristics. Stand density and competition among trees are key drivers for tree growth. It is worth noting that our data refers for the most part to evenaged, single species and relatively homogeneous forest plots and were collected on trees of the dominant and co-dominant storey only.
Conclusions We examined two datasets referring to two 10-year growing periods  (1995-2004 and 2000-2009)  and including 2008 and 3116 trees (respectively) for which paired defoliation (an indicator of forest health) and BAI (and indicator of forest growth) data were available from longterm monitoring in France. We observed that growth, expressed in terms of periodical BAI, is negatively and significantly related to mean defoliation regardless of the model adopted to describe such a relationship. This pattern occurred consistently for the growing periods examined, the geographical domain considered, across functional groups and for most of the individual tree species considered. Over the tree population examined, it resulted in an overall significant average reduction in relative BAI of 0. 9% per unit increase of defoliation.
Acknowledgement This study was financed as a part of the  RENECOFOR monitoring program , which has been funded by the  French National Forest Office (ONF) , the  French Ministry in charge of Agriculture , the  French Ministry in charge of Ecology , the  French Agency for Environment (ADEME)  and also, until  2006 , by the  European Union. The data were collected thanks to the contribution of hundreds of ONF's employees since 1992: foresters locally involved in plot maintenance and growth surveys, R&D technicians and crown condition observers regularly trained by the  Department for Forest Health of the French Ministry in charge of Agriculture. We are grateful to  Elisa Santi  for preparing Fig. 1 and to  Francesca Giorgolo  for preparing Figs. 2 and4. Background picture for the graphical abstract is from Swiss  NFI/WSL , Swiss Forest in Pictures, www.
compared to cyanobacterial diazotrophs  [11]. With such divergent physiologies, it is unlikely that different diazotrophs respond to fine scale physical forcing in the same way. Resolving these ambiguities needs coupling fine scale structure and diazotroph activity/abundance measurements at high spatiotemporal resolution. The vast majority of diazotroph activity/abundance data available in the literature up to 2012 stemmed from locations situated ~160 km apart (median distance between stations in the database compiled by Luo et al  [12] ). Recent methodological developments have provided underway N2 fixation and diazotroph abundance data at a spatial resolution of ~18 km  [13, 14]. While such resolution may be appropriate to resolve fine scale processes, previous studies have not sought to explain diazotroph distributions through the lens of fine scale oceanography.
008). Low N2 fixation rates in this region are thought to be caused by the scarcity of iron east of the Tonga volcanic arc  [19]. Nutrients and temperature typically used to define the biogeography of diazotrophs on regional to global scales  [12]. Fig. 1 : Fine scale resolution abundances of diazotrophs along three selected sampling zones. The central image shows a chlorophyll MODIS composite averaged for November 2019 at a resolution of 4 km and the location of the three selected sampling zones. Zoom-out panels (in dotted squares) show the abundance of diazotrophs in each selected zone as nifH gene copies per liter of seawater. Diazotroph abundances (nifH gene copies L -1 ) are superimposed on absolute dynamic topography (ADT, color scale) and geostrophic velocity (arrows). ADT data were retrieved for each zone on 2 nd , 4 th and 22 nd November 2019 for zones 1, 2 and 3, respectively.
Acknowledgements This study was funded by the projects TONGA (ANR-18-CE01-0016, LEFE-CyBER, Fondation A-Midex, Flotte océanographique française) to S. Bonnet and C. Guieu, DEFINE (LEFE-CyBER) to Mar Benavides), and OASIS (Thomas Jefferson Fund) to S. T. Wilson and M. Benavides). The development of SPASSO is supported by TOSCA/CNES and by EU program Copernicus Academy. L. Conradt was funded by a Master degree internship from TONGA A-MIDEX. The authors would like to thank the crew and technical staff of R/V L'Atalante as well as the scientists that participated in sample acquisition and equipment installation onboard (C. Lory, K. Sellegri and F. Gazeau), and sample analyses in the lab (S. Nunige, O. Grosso, A. Torremocha). The MODIS chlorophyll image used in Fig. 1  is a courtesy of J. Uitz.
Narrative evidence contained within historical documents and inscriptions provides an important record of climate variability for periods prior to the onset of systematic meteorological data collection. A common approach used by historical climatologists to convert such qualitative information into continuous quantitative proxy data is through the generation of ordinal-scale climate indices. There is, however, considerable variability in the types of phenomena reconstructed using an index approach and the practice of index development in different parts of the world. This review, written by members of the PAGES (Past Global Changes) CRIAS working group -a collective of climate historians and historical climatologists researching Climate Reconstructions and Impacts from the Archives of Societies -provides the first global synthesis of the use of the index approach in climate reconstruction.
For more information, see  Ge et al. (2018)  and a database of local gazettes at http://lcd. ccnu. edu. cn/#/index (last access: 3 June 2021). In the 1980s, the Central Meteorological Bureau of China initiated a massive project for the compilation of weatherand climate-related records. The work resulted in the most influential publication in contemporary Chinese climate literature, the Compendium of Chinese Meteorological Records of the Last 3,000 Years edited by Zhang  De'er (2004) ; this contains more than 150 000 records quoted from 7930 historical documents, mostly local gazettes. To maximise the availability of the compendium,  Wang et al. (2018)  have digitised the records into the Reconstructed East Asian Climate Historical Encoded Series (REACHES) database (Fig. 3 ). The quantity of records peaks in the last 600 years, during the Ming and Qing dynasties.