dataesr/scientific-paragraphs-categorization huggingface dataset:
(2019). Here, we recall the main processing steps for the estimation of the effective resolution: 1) the SSHmap data are interpolated to the locations of independent SSHalongtrack data, 2) the along-track and interpolated data are divided into overlapping segments of 1500 km length every 300 km, 3) each segment is stored in a database and referenced by its median coordinates (longitude, latitude), 4) finally, between latitudes 90°N-90°S and longitudes 0°-360°E, we consider 10° × 10° longitude × latitude boxes for the global products every 1° increment. All available segments referenced in the 10° × 10° box are selected to compute the mean power spectral densities of the independent signal (SSHalongtrack) and the mapping error 330 (SSHmap-SSHalongtrack). Before the spectral calculation, the signals are detrended and a Hanning window is applied.
These SSH variabilities for time scales smaller than 10 days are filtered in the DUACS mapping approach (Figure  7a ). In contrast, the MIOST multiscale mapping approach https://doi. org/10. 5194/essd-2022-181 Open Access (EXP03) resolves spectral peaks near 4 days, 5 days, and 7 days for wavelengths > 20° in longitude (Figure  7b ). We show in the next section that parameterizing these equatorial wave modes in MIOST also contributes to significantly reduce the mapping error in this region. For time scale > 10 days, each dataset has relatively similar spectral contents, particularly the energetic westward propagation of equatorial Rossby waves for negative wavenumbers. Gain/loss error variance MIOST vs DUACS [%] Arctic 0,0023 0,0023 0,0% 0,0007 0,0007 -3,3% Antarctic 0,0033 0,0031 -5,9% 0,0008 0,0008 -2,6% Equatorial band 0,0014 0,0014 -0,8% 0,0005 0,0005 0,3% Low variabilityoffshore 0,0013 0,0012 -5,8% 0,0004 0,0004 -4,1% Earth System Science Data Discussions Preprint.
Geostrophic current quality Figures 9a and 9b show the validation against the independent drifter velocity data in terms of mapping error 𝜎 𝑒𝑟𝑟 for the zonal and meridional velocities. The largest mapping error 𝜎 𝑒𝑟𝑟 in DUACS reaches 300 to 400cm 2. s -2 in the western boundary surface current (e. g. , the Gulfstream, the Kuroshio, Mozambique, and Agulhas currents). In offshore low variability region, the error variance is < 80cm 2. s -2. The differences in mapping error between MIOST and DUACS are shown in Figures  9c  and 9d  for zonal and meridional velocities, respectively. Mapping errors are smaller in MIOST than in DUACS mainly in the core of the ocean gyres. In the intertropical region, the DUACS maps appear to be closer to the independent drifter velocities than MIOST. 425.
Data Availability The MIOST gridded products (https://doi. org/10. 24400/527896/a01-2022. 009,  Ballarotta et al. , 2022)  are hosted on the AVISO+ website at the following repository: https://data. aviso. altimetry. fr/aviso-gateway/data/SLA_MIOST_alti_drifters/. The reference DUACS maps are hosted on the E. U. Copernicus Marine Service portal (https://doi. org/10. 48670/moi-00146).
If the participant responded correctly, the contrast was reduced in the subsequent trial by increasing the optotype luminance. In the visual acuity component, the target size changed with every trial. Indeed, the target shrunk across trials when the subject responded correctly. At the end of each 30-trial test run, the participant's visual acuity and contrast sensitivity thresholds were recorded for a data analysis as LogMAR and LogCS units. To standardize the testing conditions, the assessments were conducted in similar examination rooms. The subject viewed the stimuli binocularly on a monitor driven by an Apple MacBook Pro (13-inch, Mid 2009). The monitor was placed at a distance of 1. 75 m from the participant.
Color Discrimination Threshold The Ishihara test was first used to screen participants with congenital color deficiencies. The test consists of 17 plates, each illustrating a different color-defined number embedded within other colored dots. The numbers are correctly recognized by people with normal color vision but are not discernible to color-deficient observers. The Ishihara plates were viewed at a distance of approximately 70 cm. The remaining participants were tested using an adapted version of the Cambridge Color Test (CCT; Cambridge Research Systems Ltd. , Rochester, UK), which was designed using Psykinematix software version 1. 5 (KyberVision Japan LLC, Sendai, Japan). The stimuli were presented using an Apple MacBook Pro (13-inch). Gamma correction was performed using a Spyder5-Pro display calibrator (Datacolor, Trenton, NJ, USA).
2) were included as covariates in the statistical analyses. As a result, sex, head circumference at birth, and BMI measured at the time of testing were included as covariates. Weekly physical activity time was also found to be correlated with both screen time at the age of 6 and at least one of the outcomes. However, as the data for weekly physical activity were limited (available for only 261 participants), we conducted a sensitivity analysis to assess the potential influence of this variable on the results instead of directly incorporating it into the regression models. The same set of covariates was used in all statistical models.
Statistical Analyses Two-step hierarchical linear regression models (utilizing standardized beta coefficients) were employed to assess the relationship between screen time and children's FrACT and color discrimination scores. The protan, deutan, and tritan color vision axes, visual acuity scores, and contrast sensitivity scores were considered in separate regression models. Screen time at the 6-year follow up was first entered into the model (unadjusted model). Given that they were found to be potential covariates, sex, head circumference, and BMI were entered into the second block (adjusted model). All analyses were conducted using IBM SPSS software, version 27. 0. 1. 0. (IBM, Armonk, NY, USA). Visual inspection of Q-Q plots of the residuals confirmed that the assumptions of linearity and normality of the residuals were respected.
Acknowledgments: We are grateful for the physicians, nurses, and all families who participated in this study (and continue to do so). We wish to express our sincerest thanks to  PR Fabrice Lainé  and the  UIC  health personnel,  Stéphanie Métayé  and  Cécile Réminiac , for their rigorous work in examining the children, as well as  Ronan Garlantézec ,  Sylvaine Cordier ,  Florence Rouget ,  Charline Warembourg ,  Nathalie Costet ,  Véronique Villalon , and  Isabelle Coiffec  who contributed to the PELAGIE cohort in general. We would also like to thank  Rebecca Cernik  for her help with the linguistic revision and editing of the manuscript.
BMI. Other limitations involve excluding individuals without BMI information (increasing the risk of selection bias), having limited sample sizes that preclude the analysis of a wider range of cancers, or relying on self-reported and recalled weight and height, which could increase the likelihood of exposure misclassification. A study with BMI data measured by health professionals, capturing incident cancer cases from a large and representative population, and using advanced multiple imputation techniques to BMI for all eligible participants could help gain understanding of the adiposity-cancer association through a life-course perspective. We investigated the association between duration of years lived with a BMI ≥ 25 and ≥30 kg/m 2 , cumulative exposure to a BMI ≥ 25 and ≥30 kg/m 2 , age of onset of a BMI ≥ 25 and ≥30 kg/m 2 during early adulthood (18-40 years) and BMI at baseline in relation to the risk of 26 cancer types.
Study design, setting, and data sources We conducted a population-based cohort study from January 1st, 2009 (index date or baseline date) to December 31st, 2018, using prospectively collected primary care records from the Information System for Research in Primary Care (SIDIAP; www. sidiap. org) in Catalonia, Spain. SIDIAP contains pseudo-anonymized records for >8 million people since 2006  25. It covers >75% of the population of Catalonia and is representative of the general population of Catalonia by age, sex, and geographic distribution  25. SIDIAP contains longitudinal data on anthropometric measurements, disease diagnoses (International Classification for Diseases, 10th revision [ICD-10]), sociodemographic and lifestyle information, among others. SIDIAP can be linked to the Minimum Basic Dataset (CMBD), a national population-based registry that includes hospital discharge information of mandatory registration  29.
Assessment of variables To calculate BMI trajectories we extracted data on valid BMI measurements (before applying multiple imputations) assessed from January 1st, 2006 until December 31st, 2018. These were calculated using the weight and height of individuals assessed in a standardized manner by general practitioners or nurses in clinical practice  28. To be considered valid BMIs, the BMI measurements had to be (i) comprised between 15 kg/m 2 and 60 kg/m 2 (ie, extremely low values could be indicative of an underlying disease, and extremely low/high values could be due to data entry errors in medical records); (ii) measured at or after 18 years; (iii) not measured during pregnancy (from the 3rd month of pregnancy until 2 months after delivery). The outcomes were incident diagnoses of 26 cancer types (head and neck; esophagus; stomach; colorectal; liver; gallbladder and biliary tract; pancreas; larynx; trachea, bronchus, and lung; bone and articular cartilage; malignant melanoma of skin; breast [categorized into pre and postmenopausal due to well-established evidence indicating different relations with BMI]; 14 cervix uteri; corpus uteri; ovary; prostate; testis; kidney; bladder; brain and central nervous system [CNS]; thyroid; Hodgkin lymphoma; non-Hodgkin lymphoma; multiple myeloma; and leukemia) that have been previously validated in SIDIAP (including the CMBD)  26.
We identified cancer diagnoses using ICD-10 and ICD-9 codes recorded in the SIDIAP and CMBD databases, respectively (Supplementary Table  S4 ). Potential confounding variables that we were able to consider were age (in 5-year categories) at index date, sex (female, male, as it appears registered in the Catalan public health system), geographic region of nationality (Spanish, Global North, or Global South)  30  , the Mortalidad en áreas pequeñas Españolas y Desigualdades Socioeconómicas y urban census tracts, categorized into quintiles by SIDIAP to which we added a rural category since the index was unavailable for participants living in those areas)  31  , smoking status (never, former, or current smoker), and alcohol intake (no, low, or high risk) which is constructed based on type of alcoholic drink, amount, situation, and frequency of consumption  32.
Statistical analyses We used a two-step approach for the statistical analyses. Firstly, we estimated life-course BMI trajectories among individuals aged ≥18 years (we excluded those without 1 year of history or follow-up before and after, respectively, their entry into SIDIAP, n = 5,279,567). Secondly, we used these trajectories to construct longitudinal BMIderived exposures among the study participants and we investigated their association with cancer risk using survival models. Calculation of BMI trajectories. We applied multilevel time raster multiple imputation to BMI to obtain the BMI trajectories (five imputed trajectories per individual)  33. This approach allows to impute irregularly spaced longitudinal data (such as unbalanced BMI assessments in an electronic health records database) relying on within-and betweenpatient information.
The multilevel component of this approach refers to the imputation model which was a linear mixed model. The cluster variable of this model was each individual's identifier. Level 1 variables (ie, that can vary within individuals) were the age at the BMI measurement and indicator variables of cancer, cardiometabolic conditions (ie, hypertension, type 2 diabetes, and cardiovascular diseases), and bariatric surgery, for which the value was 1 if the individual had been diagnosed with the condition/disease or had the procedure before the BMI assessment (for cancer, 1 year prior), 0 if otherwise (as the diagnosis of these conditions and procedures can lead to changes in BMI). These variables were arbitrarily chosen based on clinical knowledge and due to the quality of the variables' registry in the SIDIAP database.
Level 2 variables (ie, that vary between individuals) were sex, the MEDEA deprivation index, smoking status, alcohol intake, geographic region of nationality, the Charlson Comorbidity index (an indicator of multimorbidity composed of 19 items), diagnosis of different cancer types, and follow-up time. These variables were selected based on the multiple imputation literature stating that one should include exposures, covariates, and time of follow up/outcomes (when using a time-to-event analysis) as well as auxiliary variables to impute variables with missing data  34, 35. The time raster component of this approach was used to homogenize the times of BMI measurement. We imputed BMI at six notequally-spaced age points (18, 30, 40, 55, 70, and 90+ years of age). We used a B-spline of degree 1 to discretize the time of BMI measurement: considering all valid available BMI measurements in an individual's health record, we attributed weights to the spline according to whether the age at measurement of the real BMI measurement coincided with one of the age points of interest.
More details on this approach can be consulted in Appendix 2. To construct the life-course trajectories, we joined two contiguous BMI measurements (ie, between two consecutive age points) with a straight line. This method has previously been used to assess longitudinal changes in BMI in SIDIAP and elsewhere  27, 33. To implement the multilevel time raster multiple imputations we used the library MICE 3. 13. 0 available for the software R version 4. 0. 3. Calculation of exposures. We used the BMI trajectories to calculate the exposures and we subsequently analyzed their associations with cancer risk (time-to-event analysis). The window to capture longitudinal exposures was between the ages of 18 and 40 years and was separated from the time-to-event window, which extended from the age of an individual ( ≥ 40 years for everyone) one year after index date until the age at end of follow-up (Supplementary Fig.
Association between BMI-derived exposures and cancer risk. We investigated the association between each of the exposures with the risk of the 26 cancer types by running Cox proportional hazard models with age as the underlying time metric in each of the five imputed datasets and pooling the results using Rubin's rule  38, 39. The minimallyadjusted models included one exposure at a time and were adjusted for sex and stratified by age (5-year categories). The fully adjusted models were further adjusted for the geographic region of nationality, MEDEA deprivation index, smoking status, and alcohol intake. We guided our decisions on the control for confounding by using a directed acyclic graph (Supplementary Fig. S15 )  40. We multiply imputed covariates with missing data at baseline (using predictive mean matching, with 5 imputations drawn) (Appendix 2) and we checked the proportional hazard assumptions for the variables included in the models by visual inspection of survival curves.
Acknowledgements We would like to thank all healthcare professionals of Catalonia who daily register information in the populations' electronic health records. Funding was obtained from  Wereld Kanker Onderzoek Fonds  (WKOF grant number:  2017/1630 , awarded to HF), as part of the international grants from the  World Cancer Research Fund. MR's salary was also funded by  World Cancer Research Fund (UK)  (grant number: IIG_2019_1978, awarded to TDS), as part of the  World Cancer Research Fund International  grant program. TDS acknowledges receiving finan-cial support from the  Instituto de Salud Carlos III  (ISCIII; Miguel Servet 2021:  CP21/00023 ). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. Where authors are identified as personnel of the  International Agency for Research on Cancer  and  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  and  World Health Organization.
Data availability The main data supporting the findings of this study are available within the article and its Supplementary information. The source data underlying figures and Supplementary Figures are provided as Source Data File. Additional details on datasets (such as aggregated data) and protocols that support findings of this study will be made available by the corresponding authors upon request. The authors will give feedback within 30 days. The raw data used in this study cannot be deposited in an online database and are only available for the researchers participating in this study in accordance with the data extraction agreement with SIDIAP and with current European and national law. The General Data Protection Regulation (GDPR) is a key piece of legislation in Europe that governs the collection, processing, and storage of personal data, including electronic health records (EHRs) (such as SIDIAP, the database underlying this study).
Models for ovary, cervix, and corpus uteri cancers were only computed in females, for breast pre-menopausal only in premenopausal females, for breast post-menopausal only in post-menopausal females, and for prostate and testis only computed in males. Brain and CNS includes pituitary gland and pineal gland tumors. Abbreviations: BMI Body Mass Index, CI Confidence Interval, CNS central nervous system, Cum Cumulative, Lymph lymphoma. 2 | Hazard ratios of 26 cancer types related to duration of BMI ≥ 25 and ≥ 30 kg/m 2 in years, respectively, with 95% CIs, allowing for non-linearity. Notes: Data are presented as HRs with the respective 95% CIs. Source data are provided as a Source Data file. Green lines are for the duration of BMI ≥ 25 exposure and yellow for ≥30 kg/m 2. Models are adjusted for geographic region of nationality, the MEDEA deprivation index, smoking status, and alcohol intake and stratified by age (5-year categories).
Cancer types are ordered by descending ranking of the HRs for BMI at index date of Fig. 1. Models for ovary, cervix, and corpus uteri cancers were only computed in females, for breast pre-menopausal only in premenopausal females, for breast post-menopausal only in post-menopausal females, and for prostate and testis only computed in males. Brain and CNS includes pituitary gland and pineal gland tumors. Abbreviations: BMI Body mass index, CI confidence interval, CNS central nervous system; Lymph: Lymphoma. ratios of 26 cancer types related to age of onset of BMI ≥ 25 and ≥ 30 kg/m 2 (among people who ever had a BMI ≥ 25 and ≥ 30 kg/m 2 , respectively) in years, respectively, with 95% CIs, allowing for non-linearity. Notes: Data are presented as HRs with the respective 95% CIs. Source data are provided as a Source Data file.
2016)  showed a connection between humanmediated wild boar movement and the observed genetic structure at local scale. We analyzed genome-wide SNP data of European wild boar to (a) assess to what extent European wild boar populations are subject to pig hybridization, translocation and inbreeding, and to (b) delineate the continental wide spatial genetic structure of wild boar. In so doing, we ultimately aimed to infer (i) to what extent anthropogenic influences have altered the natural spatial genetic structure, and (ii) how and to what extent the spatial genetic structure still holds signatures of the LGM. Given the historic and contemporary intense human influence on European wild boar, we predicted to detect inbreeding, hybridization and translocation events in numerous wild boar populations.
MATERIALS AND METHODS Sample collection and genotyping To obtain a good coverage of the European continent, we combined publicly available SNP data on European mainland wild boar  (Iacolina et al. 2016)  with newly collected samples from previously underrepresented regions, and with genetically distinct populations as reference (Table  S2 ). All samples were collected within the frameworks of national game management and population control programs according to national laws. For detection of pig introgression, we incorporated 140 pig samples, which were collected within the framework of the PigBioDiv project (see  Megens et al. 2008 ). These pig samples came from four standardized domestic breeds (Large White, Landrace, Angler Sattle and Pietrain), three Spanish (Negro Iberico, Manchado and Retinto) and three Italian indigenous breeds (Calabrese, Casertana and Cinta Senese).
We isolated DNA following the Gentra Puregene Blood kit protocol (Qiagen, Venlo, the Netherlands). Samples were genotyped using the PorcineSNP60 DNA Analysis Kit beadchip (https://emea. illumina. com/ products/by-type/microarray-kits/porcine-snp60. html). After excluding duplicates and individuals with call rates below 0. 95, we retained 464 animals from 23 different countries in mainland Europe (Table  S2 ). Additionally, we had 15 wild boar from Israel and the Greek island of Samos (Near East lineage) and 33 samples from the East-Asian lineage (Korea, Japan, eastern Russia and China) for comparison. Since two versions of the PorcineSNP60 beadchip are available (v1 and v2), and some of the previously published data were genotyped with version v1, we verified that genotypes of both beadchip versions aligned and were hence compatible (see Fig.
Quality control We focused our analyses on the 26,342 autosomal SNPs that occurred on both PorcineSNP60 beadchip v1 and v2, had call rates above 0. 95 and minor allele frequency (MAF) above 0. 025. Finally, to adhere to the assumption of neutrality, for all analyses except runs of homozygosity (ROH), we excluded intragenic SNPs (remaining: 15,296 SNPs). We used PLINK  (Purcell et al. 2007; Chang et al. 2015)  to remove SNPs with strong dependency (LD) (function indep-pairwise, window of 50 SNPs, step size 5). Two filtering procedures were used, a more relaxed r 2 < 0. 5 for ROH analyses (21,261 intra-and intergenic SNPs remaining) and a more stringent r 2 < 0. 2 (9761 intergenic SNPs remaining) to investigate genetic variation and structure. Additionally, we used the same software to calculate, separately for each sampling location, relatedness among individuals as an identity-by-descent score using subsets of SNPs with MAF > 0.
S2 ). To subsequently estimate genetic distances among pigs, the simulated pure wild boar and the observed wild boar samples, we calculated Hamming pairwise genetic distances using the R package poppr 2. 8. 0  (Kamvar et al. 2014). Subsequently, we did a PCoA analysis using the R package ape 3. 0  (Paradis et al. 2004 ). In accordance with  McVean (2009) , the projected ordination positions of the observed wild boar samples towards the pig source population were then taken as a proxy for percentage of pig ancestry. Furthermore, following  Goedbloed et al. (2013a) , we further explored pig introgression by examining the distribution of alleles that are uncommon in European wild boar (MAF < 0. 025), but abundant in pigs (MAF > 0. 225). We studied whether these putative pig alleles had a higher frequency in the hybrids detected by PCoA projection.