dataesr/scientific-paragraphs-categorization huggingface dataset:
Registered death records at local registries are forwarded to the national civil registry office. Town and District Officers are required to submit monthly reports of deaths reported within their area to the Ministry of Internal Affairs for electoral roll purposes  [19]. The routine death reporting systems collectively cover the whole country; however, some deaths may not be recorded (or captured) in any system, while others may be recorded across multiple systems. Reconciling data from multiple sources improves the validity of the derived mortality estimates, and capture-recapture assessments can be used to estimate the reporting completeness of the reconciled data. Previous manual reconciliation of available medical certification data, Civil Registry data, and community nursing reports by Hufanga et al.
[9]  and Carter et al. [5] , estimated the reconciled data for 2005-2009 as 58-88% complete. Within the past decade, the MoH introduced policies and procedures to improve death reporting, certification and registration in Tonga. These include enhancing the role of medical records staff to ensure the completion of death certificates by clinicians  [20] , and requiring the completion of death certificates for hospital deaths and notification of community deaths within 24 h of death  [5, 17]. MoH policy requires all reported deaths to be medically certified. The Ministry of Justice has also improved the management of Civil Registry records  [21]. Routine validation of the MoH birth and mortality datasets against the Civil Registry data  [5, 21]  has been implemented. Work by the authors (CF, CL, LF) has also contributed to improving the completeness and accuracy of records in the MoH MCCD database and the RHS collection.
Given the increased availability of these data, it is now possible to produce nationally representative mortality estimates based on updated and reconciled deaths and denominators. The present study investigates levels of all-cause infant, under-five and adult mortality, and LE at birth in Tonga by sex over 2010-2018. Empirical mortality data sources were reconciled and the completeness of death reporting in these systems was assessed. Published estimates by national and international agencies were also assessed and compared with the empirical results.
Data collection Mortality data for 2010-2018 were obtained from four sources: the MoH MCCD database, the Vaiola hospital information system ('deceased' discharge records), RHS section (community nursing reports), and the Civil Registry database. Town and District Officer reports from the Ministry of Internal Affairs were not available for analysis. Hospital discharge records of deaths were included to ensure all hospital deaths were captured. Not all deaths that have an issued MCCD (and subsequently recorded by the civil registry) may be recorded in the MoH MCCD database, as manual transfer procedures within the hospital to the MCCD database are not necessarily complete. Overseas deaths and deaths among non-residents and foreign nationals were excluded in order to focus on Tongan deaths, and minimise the possibility of non-residents being included in the analysis.
The deaths of Tongan citizens overseas may be registered at the civil registry by close family members of the deceased. Tongan citizens may have been overseas for a short period to receive medical treatment, or living abroad for an extended period, prior to death; as data on this period is unavailable, all overseas deaths have been excluded. As denominators for the calculation of infant mortality rate (IMR) and under-five mortality rate (U5MR), live births in 2010-2018 were obtained from the MoH live birth database (data from certificates of live births), and the Civil Registry database, which are considered > 95% complete when compared against direct and indirect estimates of births from the census  [22] , and estimates from the Pacific Community calculated from crude birth rates and population estimates  [4, 23].
Deduplication and reconciliation of births and deaths Duplicate death records were removed from the individual death data sources: 35 records in the civil registry, 107 in the medical certificate database, 118 in RHS reports, but none from the hospital information system. The death datasets were then combined, and duplicate records between each source (within the combined dataset) were linked and deduplicated. The birth datasets were similarly reconciled. Deterministic and probabilistic record linkage was performed, employing a SAS application, The Link King v9. 0  [24]. Deterministic linkage uses specific criteria with varying levels of agreement among several individual identifying variables, including names, age, date of death and address between records and between data sources. Multiple criteria are used to link records at different certainty levels.
The matching variables used to link the death datasets were: name (first, middle, last, mother's name); sex; age; date of death; and island group of death. Date of birth and place of residence are collected in some, but not all death datasets, and thus were used alternatively in place of the age variable in multiple rounds of deduplication. The matching variables for the birth datasets were: names (first, middle, last), mother's name; father's name; sex; date of birth; place of birth, and island group of birth. Datasets were pre-processed to standardise the format of the matching variables. Any records with unknown sex (n = 28) or age (n = 100) were proportionately redistributed among the remaining dataset, first by sex, then by age. All putative linked records where a variable differed, as well as all putative non-linked records, were manually reviewed by Tongan health staff familiar with the reporting systems and naming conventions in Tonga.
Capture-recapture analysis The completeness of the reconciled dataset in each three-year period was assessed by capture-recapture analyses, which compares proportions of records reported in common across different sources to estimate the number of unreported deaths  [27]. The capturerecapture method has been used previously for Tongan mortality data  [9] , and is less sensitive than indirect methods (e. g. Brass growth balance) to significant population movement. The annual emigration rate in Tonga was 1. 9% over 2006-2011  [28]. The age groups < 5 years, 5-59 years, and > 60 years were analysed separately, to account for age variation in death rates. Capturerecapture analyses require data sources with the same coverage of the population to be investigated. Hospital discharge records are from Tonga's main hospital only, whereas the MCCD, community nursing reports and civil registry each covers the entire population.
Consequently, to meet the population coverage assumption of the sources, the capture-recapture analysis included three-source categories: (1) MCCDs and hospital discharge records combined, (2) civil registry, and (3) RHS community nursing reports. Two-source analysis relies on the assumption that the sources are independent, whereas three-source analysis can account for dependencies between sources (where reporting a death to one source influences the probability that the death will be reported to another source). The three-source model included dependence between the MCCD (combined with hospital discharge) and RHS, and between the MCCD (combined with hospital discharge) and the civil registry, as previously performed by Hufanga et al. [9]  although without the hospital discharge records. There is a positive dependence between the MCCD database and RHS nursing reports because the MoH manages both sources, and the same staff complete the RHS nursing reports and the notice of death forms that doctors use to complete the MCCD for deaths in the community setting.
There is a positive dependence between the MCCD database and the civil registry because the civil registry generally requests a MCCD, if available, for death registration. The selected three-source model was also based on the statistical significance of associations between sources (maximum likelihood) and goodness-of-fit criteria (Bayesian Information Criterion and the Akaike Information Criterion). The dependencies were incorporated using log-linear modelling that included three parameters, one for each source, plus two interaction-terms for the pair-wise dependencies. The GENMOD procedure in SAS 9. 4 was used for this analysis.
Mortality statistical analysis The reconciled reported deaths for 2010-2018 were aggregated into triennia (2010-2012, 2013-2015, and 2016-2018) to reduce stochastic fluctuations in annual mortality rates due to small numbers. The mortality data were tabulated by sex, and by age group: < 12 months, 1-4 years, five-year age intervals from 5 years up to 74 years, and > 75 years. Age-specific mortality rates were calculated using total live births as the denominator for age groups < 1 year and < 5 years, and Census population estimates within ages 1-4 years, and for each age group 5 years and over. Denominator populations by age and sex for the triennial mortality rates were calculated by applying linear interpolation or projection to the 2006, 2011 and 2016 Census populations [15,  29, 30]. Confidence intervals (95%CI) for mortality rates are based on the Poisson method for < 100 deaths  [31] , or normal approximation of the binomial for ≥ 100 deaths  [32].
All life tables and mortality calculations were performed using the Life Table and Probability of Dying Calculation Tool developed by the authors (RT, SM)  [35]. Statistical significance of secular changes over the triennia by sex were assessed by Poisson regression of age-specific counts of deaths (offset by the log of the denominator population) against each triennia (p < 0. 05) using GENMOD procedure in SAS 9. 4, and by examining 95%CIs.
Comparison of 2010-2018 empirical Tonga mortality data with available published estimates Available published mortality estimates for Tonga were obtained from Tongan Government websites (MoH and Department of Statistics), and relevant international agencies. National sources include the MoH annual report [36], Tonga Census reports  [22, 28] , Tonga 2019 Multiple Indicator Cluster Survey (MICS)  [37] , and Tonga 2012 Demographic and Health Survey (DHS)  [38]. Modelled mortality estimates by international agencies include: the United Nations Inter-agency Group for Child Mortality Estimation (UN-IGME)  [39] ; WHO Global Health Estimates  [40] ; the Global Burden of Disease (GBD) Study by the Institute of Health Metrics and Evaluation  [41] ; the World Bank  [42] ; and the United Nations Statistical Yearbook  [43].
The estimates from the external sources were examined for their plausibility and consistency with the empirical estimates. Confidence intervals were not available for estimates from the Tongan MoH report, Tongan 2011 and 2016 Censuses, Tongan 2019 MICS, Tongan 2012 DHS by sex, WHO Global Health Estimates, World Bank, and United Nations Statistical Yearbook. The UN-IGME and GBD report Bayesian uncertainty intervals (UI) with their estimates to account for errors. Biases associated with data inputs, uncertainties in the model parameters, and methodological differences make UI incomparable with frequentist confidence intervals (CI). The UN-IGME provides 90%UIs for the child mortality estimation, which is not comparable with the more conventional wider 95%UI. Direct comparison between 90%UI and 95%UI, and frequentist 95%CI of estimates are therefore not valid.
Previous empirical assessment of Tongan mortality for 2001-2004 and 2005-2009 was based on less than complete datasets, and limited data from the outer islands  [9]. Datasets for 2001-2004 were more limited than for 2005-2009. Based on reconciled deaths, the previous completeness of the civil registry was estimated at 55%, medical certificates 67%, and community nursing datasets 35%  [9] , which is much lower than in the present study. Capture-recapture analysis of the reconciled data for 2005-2009 estimated completeness as 58-88%  [9] , also considerably lower than the > 95% estimated completeness of the reconciled data for 2010-2018 used in the present study, which included hospital discharge data as well. Although the mortality estimates calculated by Hufanga et al. [9]  were adjusted for underreporting, these were based on the estimated completeness of data for the main island (Tongatapu) only.
2 3 ). The MoH estimates are calculated from datasets which were not reconciled and consequently not as complete as those used in the present study. The numerators for the MoH-reported IMR and U5MR were based on RHS community nursing reports only. However, the reported annual LE estimates were consistent with the present study estimates, and correspond with estimates derived from unadjusted reconciled data for 2005-2009  [9]. Census estimates of early child mortality are calculated indirectly using data from Census questions on children ever-born and children surviving (CEBCS) by age group of mother. The large difference in U5MR between the 2011 Census, and the 2016 Census, and the markedly low 2016 Census estimates for U5MR, indicate possible biases from retrospective reporting by mothers of births and surviving children.
The DHS and MICS estimates were derived from retrospective full birth history by interviews from samples of Tongan women aged 15-49 years: the DHS included 3068 women (97% response rate) and 1749 under-five children, of which < 95% had their births registered or had a birth certificate  [38] ; the MICS included 2903 women (92% response rate) and 1347 under-five children which had 98% birth registration  [37]. The reported estimates are subject to recall inaccuracy, underreporting of early infant deaths, and misreporting of age at death  [38]. This may explain the sex-specific differences between the DHS (2012) and the empirical estimates. Mortality indices modelled and published by international agencies are based on limited empirical mortality data from Tonga. The WHO  [40] , GBD  [41]  and UN-IGME mortality databases  [39]  published in 2018-2020 include death registration data for years prior to 2006 only, and Census estimates up to 2006; the 2012 DHS was also included in the GBD and UN-IGME databases, and the 2019 MICS preliminary results included by UN-IGME.
Availability of data and materials Empirical datasets analysed during the current study are not publicly available due to ethical restriction (contains personal identifiable information) and are held on secure servers with restricted access. Access was obtained through the approval of the research ethics committee and data custodians. Datasets from international agencies supporting the conclusions of this article are publicly available in the World Health Organization Global Health Observatory data repository, https://apps. who. int/gho/data/node. main. 686 ?lang=en [57], Global Burden of Disease study Global Health Data Exchange http://ghdx. healthdata. org/gbd-2019 [41] , and United Nations Inter-agency Group for Child Mortality Estimation web portal https://childmortality. org/ data  [39].
2c ). Besides classical magnetic enhancement, an additional effect influences the relationship between χ lf and χ leading to prominently elevated values of the χ lf / χ ratio. We relate this to wind vigour effects  (Begét and Hawkins, 1989 ) associated with strong winds delivering coarser material (with elevated χ lf / χ ) from source areas in proximity to the studied LPS in Kashmir (Fig. 2 ). When comparing the Kashmir dataset to last glacial data from localities in Eurasia (using their respective age models; Fig. 2c ), it becomes apparent that magnetic enhancement is similar to datasets from Romania and Iran. In contrast, magnetic enhancement of Tajik loess shows relatively elevated χ and/or reduced χ. The dataset from Kashmir bridges these two data populations/trends and shows features of both.
Acknowledgements. We thank the staff of the  Wanihama Middle School  for providing the access to the Wanihama section and general support. Sumiko Tsukamoto  is thanked for the fantastic teamwork in the framework of the project " Loess in India: recorder of the Indian Monsoon  system". Samples were processed in the LIAG laboratories. We thank especially  Kathrin Worm  and  Sonja Riemenschneider  for their help with sample processing and measurements. We thank  Hao Lu  and  Martin Kehl  for sharing grain size data from  Darai Kalon  and  Toshan ,  Now Deh , respectively. We also thank  Tobias Sprafke  and another reviewer for their constructive feedback through the review process, which helped to improve this article. This is IPGP contribution number  4235. Financial support. This research was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ; grant no.
Therefore, the events we consider are based on temperature, precipitation or wind speed, and which occurred in the four seasons. In the mid-latitudes, those quantities are linked to the large-scale atmospheric circulation  [13]. This justifies a focus on the atmospheric dynamics that is related to the considered extremes. Therefore, we shall examine how the synoptic scales of the atmosphere that occur during regional temperature, precipitation or wind events can be affected by climate change. We will determine the values of dynamical indicators of the atmospheric circulation in a reanalysis dataset, for reference extreme events. Then, we will assess how those dynamical indicators during those reference events will change, as a response to a future climate scenario (here SSP585). This will be performed by sampling the atmospheric variability due to future climate change from a multi-model ensemble (the Coupled Model Intercomparison Project, phase 6: CMIP6  [14] ).
Before performing such a task, a bias adjustment method will be applied to the modelled CMIP6 fields of geopotential height at 500 hPa (Z500), in order to remove their main biases with respect to a reanalysis dataset. To do so, two experiments will be conducted. The first one will directly correct the modelled Z500, while the second one will first detrend the Z500 fields before applying the bias adjustment. The detrending corresponds to removing the spatially uniform shift of Z500, mostly caused by the warming over the region. Thus, by detrending, we will remove the first-order thermodynamic effect of warming on Z500 fields. This will allow the bias correction method to be applied on anomalies to focus on changes essentially due to dynamical changes of the state of the atmosphere. This procedure allows comparing the relative contributions of thermodynamical and dynamical changes of those atmospheric features.
Bias Correction and Trend Removal Given that the models have biased representation of the Z500, we apply a statistical bias correction  [45]  on the Z500 fields allowing to account for climate change  [46]. The statistical bias correction method applied is the Cumulative Distribution Functiontransform (CDF-t) method, developed by Michelangeli et al. [45]. The bias corrections were made through the CDF-t R package (available at https://cran. r-project. org/web/ packages/CDFt accessed on 29 October 2021). More theoretical and technical details, as well as first validations and comparisons can be found in  [9, 46]. This approach links the cumulative distribution function (CDF) of a climate variable (here Z500) from GCM simulations to be corrected, to the CDF of this variable from a reference dataset, here the ERA5 reanalysis dataset  [15].
Dynamical Indicators and Atmospheric Circulation The local dimension and persistence characterise the recurrences of a system around a state in phase space. In our case, the state is the Z500 map for a given extreme event. Values of d and θ are obtained for every day in the dataset of interest. As d provides information on the number of pathways the system can take to reach and leave a state of Z500  [59, 60] , it acts as a proxy for the system's active number of degrees of freedom around the state of interest. 0 < θ ≤ 1 is a metric of persistence  [61]  of an atmospheric circulation state in time, i. e. , how long the system typically stays around the state of interest. A very persistent state (i. e. , with θ -1 close to zero) is highly stable (and therefore also highly predictable), while a very unstable state yields θ = 1 and therefore low persistence.
Discussion and Conclusions It has been argued that a major challenge for a complete understanding of climate extremes is the assessment of the atmospheric circulation leading to those events  [7, 8]. We have illustrated a statistical methodology to assess how the atmospheric circulation properties can change for emblematic recent events that hit France as the beginning of the 21st century, with a multi-model ensemble of climate simulations. This study provides an update of the analyses of Yiou et al. [62]  and Faranda et al. [9]  with CMIP6  [14]  and ERA5  [15] , who essentially used the CMIP5 database  [63]  and NCEP reanalysis  [64]  for attribution. The results were obtained from the physical hypothesis that extreme values of temperature, precipitation or wind speed in France are linked to synoptic atmospheric circulation.
We find that the changes are almost always amplified with the amplitude of climate change (from 2021-2060 to 2061-2100) when keeping the thermodynamic trend. This enhancement suggests the robustness of the results (which are not obtained at random). However, we cannot exclude that interdecadal variability affects the results when removing the trend. Our results should be interpreted as changes in the likelihood of synoptic circulation (quality of analogues) and predictability of events (d-θ) as a response to climate change, conditional to an emission scenario (SSP585) and a set of climate simulations (from CMIP6). The contrasting signals between the "raw" and "detrended" Z500 in CMIP6 illustrate the relative contributions of thermodynamical and dynamical effects in EEA. The orders of magnitudes are similar to what was found for a specific event (winter 2013/2014)  [7, 71, 72] , albeit with other datasets.
Funding: This research was funded by  French ANR  project  No. ANR-20-CE01-0008  (SAMPRACE), the  French Convention de Service Climatique (COSC) , and the  ERA4CS  project  EUPHEME. DF has been supported by the  ANR  Project  No. ANR-19-ERC7-0003  (BOREAS) and by a  LEFE-MANU-INSU-CNRS grant "DINCLIC". MV has been  supported by the  CoCliServ  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate  and cofunded by the  European Union  (Grant No. 690462 ). MV has also been supported by project  C3S 428J  (" HR-CDFt ").
Data Availability Statement: ERA5 reanalysis data are available from the Copernicus platform (https://climate. copernicus. eu/climate-reanalysis) (last access on 29 October 2021). CMIP6 data (with bias correction) are available on the esgf platform at IPSL (https://esgf-node. ipsl. upmc. fr/) (accessed on 29 October 2021).
The Myc-driven aggressive mouse lymphoma model provided conclusive data on the interpretation of lymphoma pathology in a microenvironmental context. For validation in human specimens, the lymphoma entities DLBCL and Burkitt's lymphoma were chosen, which share an aggressive growth kinetics with the Em-Myc mouse model. Although HEV dedifferentiation was identified as a major cause of insufficient T lymphocyte immigration and, thus, loss of immunosurveillance in lymphoma, the study could not fully solve a repair mechanism suitable for a putative translational approach in immunotherapy. Potentially, immature endothelial precursors could contribute to the loss of HEV structures. samples were authenticated by the commercial providers. Flow cytometry was used to verify human origin and B-or T cell lineage derivation, according to the phenotypes provided by the repository.