dataesr/scientific-paragraphs-categorization huggingface dataset:
Multi-Channel Seismic Imaging and Chirp Data A grid of Multichannel seismic reflection data was acquired during the 2009 Scan and 2012 Atacames cruises (Figure  1c ). Scan data were recorded using a 4000-in 3 (65. 5 L), 20-to 80-Hz frequency air gun seismic source, and a 640-channel, 8-km-long digital streamer. Shots were fired every 25 m, providing a 160-fold coverage. Scan data were processed by SINOPEC (China Petroleum and Chemical Corporation) for PetroEcuador through classical steps  (Yilmaz, 2001) , including multiple elimination, spherical divergence compensation, predictive deconvolution in the Tau-P domain, prestack noise attenuation, stacking, and prestack time migration. Atacames high-resolution seismic reflection data were recorded using a 72-channels, 450-m-long digital streamer.
The source array consisted of two 13/13 in 3 plus two 24/24 in 3 mini-GI (Generator-Injector) guns, providing a signal with a peak frequency at ∼130 Hz. Shots were fired at 140 bars every 25 m ensuring a nine-fold stack. The seismic lines were processed on board with the Seismic Unix software (Center of Wave Phenomena, Colorado School of Mines) for band pass filtering, spherical divergence correction (water velocity), normal moveout (NMO) velocity analysis and correction, stack and constant velocity-time migration (1,490 m/s). Sub-bottom profiler (Chirp), 1. 8-5. 3 kHz reflection data were acquired with a IXSEA-DELPH system coincidently with most Atacames seismic reflection lines.
Overall, although it is essential to document the ISC coupling by geodesy, and critical to investigate the physical conditions of the plate interface and adjacent rocks, the Quaternary geological evolution of subduction margins provides additional constraints for understanding the current inter-plate coupling and rupture processes. "Earthquakes and Volcanoes in the Northern Andes" and was funded by the Institut de Recherche pour le Développement (IRD), the Institut National des Sciences de l'Univers (INSU), and the Institut Français pour l'Exploitation de la Mer (IFREMER), which provided ship time, equipment, and data processing during the Atacames cruise. We are grateful to the Secretaria de Hydrocarburos del Ecuador (SHE) for providing us with seismic sections from the SCAN-2009 seismic experiment within the framework of the SHE-IRD and SHE-EPN cooperation agreements.
, 2012). Black frame is location of (c). (c) Multibeam bathymetric map with contours every 50 and 25 m on the shelf. White barbed line is the subduction trench; blue dotted line is shelf edge; white lines with tick marks are slope scars; thin red lines are SCAN seismic data; thin blue lines are ATACAMES seismic and chirp data (Michaud et al. , 2015) ; SIS-58 is SISTEUR seismic line from Collot et al. (2004) ; bold segments with figure numbers are the seismic sections presented in the text and supporting information; yellow-orange pattern represents CP and J locked asperities; simplified fault pattern in red (see Figure3). KAT24 to 33 are ATACAMES coring sites.
Data Availability Statement All data from the Atacames cruise used in this work are available at the French National Marine Database SISMER following the link https://doi. org/10. 17600/12010010 (pages are in French and in English). This includes seismic data stored under the folder "Data Managed By SISMER" and « National Bank of Raw Marine Seismic Data ». The first set of files are Chirp data, followed down the list by HR Seismic data. Seismic line number used in this publication refers to the one used in the seismic data base. Data labeled « restricted access » will be made available following a request via an Application Form on the platform after advice of the cruise supervisor conformably to the European and French legislation. Requirement for SCAN-2009 seismic data can be addressed to Dirección de Análisis de Información Estratégica de Hidrocarburos Av República de El Salvador N36-64 y Suecia, Quito 170505.
These surface waves can be obtained by seismic interferometry and then analyzed by ambient noise tomography (ANT). In the last decades, ANT has been successfully applied worldwide, at different scales: Regional scales, such as in the western United States (e. g. Moschetti et al. 2007; Shapiro et al. 2005) , Madeira Island  (Matos et al. 2015) , West of Java, Indonesia  (Pranata et al. 2019)  or Costa Rica  (Nuñez et al. 2019) ; continental scales such as Europe  (Yang et al. 2007; Lu et al. 2018) , North America  (Bensen et al. 2008)  and Australia  (Saygin and Kennett, 2012 ) and even at global scales (e. g. Haned et al. 2016; Nishida et al. 2009). In this study, we analyze continuous seismic data from a total of 38 broadband stations, temporarily deployed across the Cape Verde Islands  (Weber et al.
DATA AND EMPIRICAL GREEN FUNCTIONS We processed continuous seismic waveform data from 38 temporary stations of the 9A network  (Weber et al. , 2007) [Fig. 1b) ]. These stations were equipped with Earth Data PR6-24 data loggers and Guralp CMG-3ESP seismometers and recorded during 10 months from December 2007 until September 2008, with a sampling rate of 100 Hz. Inter-station distances vary from ~22. 5 km to ~295 km, not counting stations on the same island. For each station, continuous vertical-component seismic data were cut into 6-hr length segments. The corresponding 6-hr time series were down sampled from the original 100 Hz to 2 Hz, demeaned and detrended. The instrument response was removed in order to obtain ground velocities and the seismograms were then band-pass filtered in the 0.
ACKNOWLEDGEMENTS This study was supported by  Fundação para a Ciência e a Tecnologia (FCT)  project  SIGHT (Ref. PTDC/CTA-GEF/30264/2017 ) and it is also a contribution to FCT projects  FIRE (Ref. PTDC/GEO-GEO/1123/2014 ) and  RESTLESS (PTDC/CTA-GEF/6674/2020 ). This work was also developed in the framework of FCT UIDB/50029/2020 - Instituto Dom Luiz. Joana Carvalho  was supported by  FCT  under the PhD grant  PD/BD/114480/2016  and under the project  PTDC/CTA-GEF/30264/2017. Ricardo S. Ramalho  acknowledges his former IF/01641/2015 fellowship funded by  FCT. Martin Schimmel  thanks  SANIMS  ( RTI2018-095594-B-I00 ). All figures were made using GMT -Generic Mapping Tools (Wessel et al. , 2013). Data pre-processing was done using SAC -Seismic Analysis.
We estimated the frequency of non-specific influenza-associated clinical endpoints to inform the feasibility of pragmatic randomized controlled trials (RCT) assessing relative vaccine effectiveness (rVE). Hospitalization rates of respiratory, cardiovascular and diabetic events were estimated from Denmark and England's electronic databases and stratified by age, comorbidity and influenza vaccination status. We included a seasonal average of 4. 5 million Danish and 7. 2 million English individuals, 17 and 32% with comorbidities. Annually, approximately 1% of Danish and 0. 5% of English individuals were hospitalized for selected events, ~50% of them respiratory. Hospitalization rates were 40-50-fold and 2-10-fold higher in those >50 years and with comorbidities, respectively. Our findings suggest that a pragmatic RCT using non-specific endpoints is feasible.
Studies comparing two vaccines measure relative VE (rVE) which, using an efficacious comparator, report smaller effect sizes and therefore require even larger sample sizes to demonstrate superior protection from influenza-associated cardiovascular and non-respiratory events. Pragmatic trials, which typically measure outcomes using real world data from existing databases or public health registers, may be a feasible method of randomizing interventions in hundreds of thousands of study participants and therefore increase power to measure rVE against non-specific outcomes in a cost-effective manner  16, 17. Their sample sizes are dictated by the estimated rVE and incidence rate of the outcome under assessment. These studies would be feasible if it is logistically and financially possible to randomize and vaccinate the necessary sample size within a single or multiple healthcare systems from which outcomes could be reliably captured.
Demographics and influenza vaccination coverage rate From Denmark, the study cohort aged ≥18 included a seasonal average of 4,469,268 individuals, 50. 7% female. Overall, 17% of the study population had ≥1 high risk condition, increasing from 5. 7% in those 18-34 years to 43% in those ≥75 years (Table  1 ). From England, the cohort included a seasonal average of 7,212,471 people of whom 50. 5% were female. In the overall population, 32% had ≥1 high risk condition, increasing from 16. 7% in the 18-34 years to 63% in those ≥75 years. Cardiovascular, diabetes, immunocompromised, asthma and other respiratory conditions were the most common high-risk conditions in both countries. Influenza vaccination coverage rates (VCR) captured in these healthcare databases was 13% in Denmark and 24% in England, increasing in the populations aged ≥75 to 55 and 78%, respectively (Fig.
This study was conducted in large databases capturing comprehensive healthcare outcomes with a long history of use for medical research, but databases are not perfect. VCRs are under-estimated because influenza vaccinations delivered at nonmedical settings such as pharmacies or workplaces may not always be captured. Reassuringly, the VCR we captured from both Denmark and England are similar to those reported in routine national statistics  29, 30. Trends were consistent by country, though overall incidence rates were higher in Denmark, probably a result of differential healthcare investments or health systems specificities, healthcare seeking behavior or clinical thresholds for hospitalization  31. These findings may not be generalizable to other healthcare settings or countries. Our study did not collect individual-level data so could not describe the effect modification of age on high-risk or vaccination status or intra-season correlations due to repeated observations of the same participants in multiple seasons.
We included slightly different ICD-10 codes than some other researchers, differences which should be considered when interpreting the public health implications of a given rVE value  8, 32. Importantly, high-risk conditions in England were based on primary care consultations rather than the hospital contact data used in Denmark, likely explaining the higher prevalence of some conditions, notably asthma and kidney disorders, in England. Focusing on specificity, we captured only the primary/main reason for hospitalization and therefore may underestimate influenza: due to laboratory confirmation and coding practices, the full influenza burden in the US, for example, has been shown to be around 3-fold higher if codes relating to "any" rather than the primary diagnostic position are included  33, 34.
Study design and population We conducted a retrospective cohort study in the 2010/11-2017/18 influenza seasons from Denmark and the 2010/11-2018/19 seasons from England using large healthcare databases in each country. Populations aged ≥18 years on December 1st each year were included in seasonal cohorts and the number of hospitalized events occurring between December 1st and May 31st (defined as the influenza season) was divided by these denominators to calculate seasonal incidence rates of various outcomes stratified by age (18-34 years [yrs]; 35-49 yrs; 50-64 yrs; 65-74 yrs; ≥75 yrs), influenza vaccination status and the presence of clinical high risk (hereafter "high risk") conditions. In both countries, influenza vaccination is recommended and provided free of charge for high-risk adults and adults aged ≥65 yrs  35, 36.
Data sources All Danish citizens are assigned a unique personal identification number which allows for exact linkage of nationwide administrative registers at the individual level. The Danish Civil Registration System, which records date of birth, emigration status and vital status for persons residing in Denmark, was used define cohorts  37. The Danish National Patient Registry (DNPR) has shown high validity of cardiovascular diagnoses and captures all inpatient and outpatient hospital contacts coded in International Classification of Diseases 10 (ICD-10)  22, 38. The DNPR was used to count hospitalized events and to define high risk conditions. Influenza vaccination status was captured from the Danish National General Practitioners Reimbursement registry. Analyses were conducted by Danish researchers with access to raw, de-identified nationwide registry data in accordance with Danish law.
The UK Clinical Practice Research Datalink (CPRD) is a longitudinal and representative primary care database from a network of over 1,800 primary care practices and includes 16 million currently registered active patients  [39] [40] [41] [42]. This analysis used data from the CPRD GOLD and CPRD Aurum primary care databases to define vaccination and high risk status, linked to secondary care data from Hospital Episode Statistics Admitted Patient Care database to capture hospitalized outcomes rates of specified events  43. Influenza vaccinations administered in GP practices or community pharmacies are captured in these electronic health records. Analysis of the CPRD data was conducted internally by CPRD researchers using databases of pseudonymized patient EHRs, therefore individual participant consent is not required.
Definition of high-risk conditions Clinical high-risk conditions corresponding to eligibility for free annual influenza vaccination were modified from definitions used by the UK National Health Service and Danish Statens Serum Institute. They included cardiovascular disorders (including arrhythmias, congestive heart failure, ischemic heart disease and congenital heart disease), respiratory conditions (including asthma), hepatic and renal disorders, neurologic/neuromuscular disorders, blood disorders, metabolic/endocrine conditions including diabetes and conditions compromising the immune system  35, 36. For each condition, a list of ICD10 codes or prescription medication representing these diagnoses (for diabetes only) was defined (Supplementary Table  2 ). In the UK, primary care events coded with the SNOMED-CT architecture were mapped to these ICD-10 codes following review by a medical doctor (linked codes in Supplementary Data 1).
Statistical methods The total number of incident outcome events experienced by the study population was summed for each season. Incidence rates of included outcomes, expressed as rates per 100,000 population, were calculated per season for populations overall and stratified by age group, high-risk condition, and influenza vaccination status. Average seasonal incidence rates over included seasons and their 95% confidence intervals (CIs) were estimated using a Poisson model with the number of events as the dependent variable, no independent variables, and the log of the population size as an offset, with Stata's 'glm' command. In this parameterization, the exponential of the intercept is the incidence rate. The variance was adjusted by a scale factor equal to the deviance divided by the residual degrees of freedom to accounting for under/overdispersion in the underlying data  46, 47.
Incidence rate ratios (IRR) and their 95% CIs comparing rates in: a) vaccinated vs unvaccinated and b) individuals with high-risk conditions vs those with no high-risk conditions, were similarly estimated with a Poisson model. A range of identified incidence rates were used to estimate the power of an rVE study by exact method, specifically coded in SAS, based on binomial distribution of cases in investigational groups among overall number of cases, a type I error of 2. 5%, 1:1 allocation ratio and a maximum of 200,000 participants (100k per group). We assumed rVE ranging from 5-20% and expressed the result as a series of heatmaps. Analyses were conducted separately within the Danish and UK databases; subsequent manipulations were performed using Stata v 15. 1 and SAS.
ACKNOWLEDGEMENTS The authors would like to thank  Camille Salamand  and  Celine Monfredo of Sanofi Pasteur for statistical advice and development of Fig. 4. Eleanor Axson ,  Daniel Dedman ,  Arlene Gallagher ,  Tarita Murray-Thomas ,  Stephen Welburn  and  Achim Wolf of CPRD  are thanked for contributions to data access and management, quality assurance and interpretation of findings. This study is based in part on data from the Clinical Practice Research Datalink obtained under license from the  UK Medicines and Healthcare products Regulatory Agency. The data is provided by patients and collected by the  NHS  as part of their care and support. The interpretation and conclusions contained in this study are those of the author/s alone. The study was funded by  Sanofi Pasteur. Sponsor staff were involved in all aspects of data collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
DATA AVAILABILITY These data were obtained from national EHR sources which are subject to local laws and regulations. UK data were provided by Clinical Practice Research Datalink (CPRD) under a licence from the UK Medicines and Healthcare products Regulatory Agency. CPRD data can be obtained by researchers following a successful application to CPRD. All Danish data are governed by the Danish Data Protection Agency and can only be made available to any additional researchers if a formal request is filed with the Danish Authorities.
Bread wheat is one of humanity's most important staple crops, characterized by a large and complex genome with a high level of gene presence/absence variation between cultivars, hampering genomic approaches for crop improvement. With the growing global population and the increasing impact of climate change on crop yield, there is an urgent need to apply genomic approaches to accelerate wheat breeding. With recent advances in DNA sequencing technology, a growing number of highquality reference genomes are becoming available, reflecting the genetic content of a diverse range of cultivars. However, information on the presence or absence of genomic regions has been hard to visualize and interrogate due to the size of these genomes and the lack of suitable bioinformatics tools. To address this limitation, we have produced a wheat pangenome graph maintained within an online database to facilitate interrogation and comparison of wheat cultivar genomes.
Panache is a recent pangenome visualization tool that can process linearized assembly graphs and display shared regions as a web-based dynamic heatmap  (30). Panache has so far only been applied to visualize presence/absence variation in the banana pangenome (  14 ), but has the potential to be expanded to other species, even for crop genomes as large as wheat. Here, we present a graph pangenome representing 16 bread wheat cultivars hosted within a public Wheat Panache database, with a new web-based browser for visualizing genomic regions across the wheat pangenome, along with the graph formatted for minimap2  (31)  and Giraffe  (32). This tool offers researchers and breeders the ability to assess genome variation between these varieties, mining the diversity present in this large and complex genome.
37. 0's Giraffe  (32)  is available at https://doi. org/10. 5281/zenodo. 6085239 (37)  allowing for downstream analyses of the population graph. In summary, we present the first wheat graph pangenome assembly, based on 16 cultivars with an online visual representation of the graph within the Panache visualization tool. The graph assembly will be a valuable tool for wheat genomics researchers looking for a more accurate reference assembly. The web platform Panache allows users to interrogate this graph and search for structural variants around regions of interest.
Materials and Methods We used publicly available genome assemblies, including fifteen high-quality Triticum aestivum genome assemblies  (8)  and the IWGSC v1 T. aestivum cv. Chinese Spring assembly  (7) , to assemble a graph using minigraph v0. 14  (23). To optimize assembly, we used k-mers that appear fewer than 100 times (-f. 1) for the graph assembly and assembled the graph genome by genome, starting with IWGSC v1 followed by alphabetical order of cultivar names, ending with the T. aestivum ssp. spelta PI190962 assembly. All assemblies were aligned with the final graph using minimap2 v2. 18  (31)  and alignments were converted to BED format. The main graph was linearized using gfatools gfa2bed v0. 4 with default parameters (https://github. com/lh3/gfatools/releases) and merged with all minimap2 alignments using bedtools v2.
Extraction and processing of fossil proxies Sediment samples were extracted from the top 3. 0 m of the composite Bulusan sequence as a continuous series of 4 cm long slices of 1 cm 3 volume, with each slice corresponding to a ~20-year interval. Standard protocols were employed for the extraction and counting of fossil pollen  (Bennett and Willis 2001) , and a reference collection of pollen types from the region was generated using herbarium samples, and combined with printed and online sources (e. g. , Australasian Pollen and Spore Atlas Online Database (APSA Members 2007), Pollen flora of the Philippines (Jagudilla-Bulalacao 1997), Pollen Flora of Taiwan  (Huang 1972) ). Five hundred terrestrial pollen grains were counted and identified in each sample. PARs were calculated as the number of pollen grains deposited in 1 cm 2 of sediment per year according to the standard procedures  (Stockmarr 1971 ) and used to infer changes in the abundance of plant taxa over time.
Generalised additive modelling The temporal trends in mean and variance of the generated PARs records were estimated using Generalised Additive Models (GAMs) following  (Simpson 2018). GAMs are statistical models that can be used to estimate trends in mean and variance as smooth functions of time using automatic smoothness selection methods that objectively determine the complexity of the fitted trend  (Hastie & Tibshirani 1986; Hastie & Tibshirani 1990; Wood 2017). GAMs are particularly powerful for analysing palaeoenvironmental time series because they allow to estimate complex, non-linear trends and to identify time intervals of significant change. Two GAM models were applied in R version 3. 6. 3 (R Core Team 2020) using the mgcv 1. 8-31 package  (Wood 2018 ): gam, that accounts for heteroscedasticity, and gamm+car(1), that accounts for the car(1) correlation.
In both GAM models we used tweedie distribution to limit predicted values to positive numbers. We checked if the size of the basis expansion is sufficient by fitting a different number of basis functions (k) using gam. check function. For the gam models, we tested and, where appropriate, accounted for heteroscedasticity that may have arisen due to variation in sedimentation rates over time, and thereby, in the span of time that each sample represents. The fitted GAM smoother was considered significant at p-value <0. 05. Both gamm+car(1) and gam models for individual PAR datasets were plotted with confidence intervals that were generated using the critical value from the t-distribution. We also used GAMs to assess the effect of abiotic factors (δDwax, macrocharcoal influx rates, sedimentary δ 15 N and Pconc) on the plant taxa abundance (PARs) and C3/ C4 vegetation composition (δ 13 Cwax).
Macrocharcoal influx rates were transformed using square root function to reduce effect of a small number of disproportionately large values (log function not used due to many zero values). Two GAMs were applied as described above. In both GAMs for PARs we used a Gamma distribution to limit predicted PAR values to positive numbers (tweedie family was also attempted but the models did not converge over similar ρ values). Since the Gamma family does not support zero values, these were removed from PAR datasets prior to the analysis. In the macrocharcoal influx rates and Pconc datasets, two points identified by preliminary analysis as strong outliers were removed prior to modelling. For the δ 13 Cwax record, we used a Gaussian distribution. The two models (gam and gamm+car(1)) both included all four abiotic factors as explanatory variables, and were compared according to their Akaike Information Criterion (AIC) coefficients.