dataesr/scientific-paragraphs-categorization huggingface dataset:
Averages for the systems with ice data assimilation and no data assimilation are represented by the DA and NA panels, respectively. All maps have the 0 • longitude placed at 06:00, while the bounding latitude is 67 • N. Reanalyses labeled in blue and red highlight whether the datasets were built with or without ice data assimilation, respectively. We make use of wavelet analysis  (Torrence and Compo, 1998)  to evaluate whether the time series displayed in Fig. 7b  and f exhibit a significant band(s) of variability. Figure  7c  and d reveal that the ice volume anomaly from GECCO2 presents two bands of significant variability, as highlighted by the horizontal gray bars in Fig. 7d. The first spans from 4. 4 to 6. 1 years, and it is present in the first half of the time series but does not persist over time (black contours in Fig.
Discussion and conclusions The first aim of this study was to evaluate how the SIT from the reanalyses compares against observational datasets, either draft or SIT. We have used three different metrics to perform this comparison: the correlation coefficient (R), as a measure of the linear correlation between datasets; the mean residual sum of squares (MRSS), as an indicator of whether reanalysis values are good predictors for the observations; and the root mean square error (RMSE), which directly compares how the SIT from the reanalyses approaches the SIT from observations. The results show that some of the reanalyses have a relatively good correspondence either comparing SIT and draft or SIT from both sources of data. This is the case, for instance, for the TOPAZ4 product. A direct comparison between SIT from all reanalyses and observations indicates RMSEs ranging from 0.
7 to 1. 1 m. PIOMAS has the best agreement with the observational datasets. A particular case is GECCO2, which presents a relatively small RMSE and a good correlation with the SIT observational datasets, as well as a linear relationship. However, this same product is weakly correlated with the draft observational datasets as well as having poor predictive skill. One of our main goals in performing such a comparison was to identify whether or not systems built with assimilation of sea ice concentration data are closer to observations, compared to the products built with no sea ice data assimilation. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better. One could speculate that some reanalyses do not reflect the covariances between sea ice concentration and SIT well.
This may explain why in the specific location addressed in Fig. 2  the reanalyses with data assimilation showed relatively longer timescales compared to the reanalyses without data assimilation. The pan-Arctic AWM timescale and AWM length scale from G2V3 are 5 months and 728. 2 km, respectively. Without sea ice data assimilation (G2V1), the AWM timescale and AWM length scale increase to 5. 5 months and 745. 3 km, respectively. Likely, the main reason why the assimilation of sea ice concentration data impacts the timescales and length scales of the SIT field is linked to the fact that when a reanalysis assimilates sea ice information, the system is forced towards the assimilated conditions, different from what occurs with free-running models. Eventually, data assimilation introduces SIT increments that are not necessarily physical and so contributes to an attenuation in the correlation of this variable at a certain grid cell both in time, with their future estimations, and in space, with the neighboring grid points.
The timescale would be more useful if used in combination with the knowledge of its variability. Further studies are required to evaluate the performance of timescales and length scales in providing support for the optimal design of observational programs, though this work already shows some promising results in that direction. Data availability. All datasets used in this work are freely available, as follows: Reanalyses data are from https://icdc. cen. uni-hamburg. de/1/daten/reanalysis-ocean/oraip. html (University of Hamburg, 2019). PIOMAS is from http://psc. apl. uw. edu/research/ projects/arctic-sea-ice-volume-anomaly/  (Zhang and Rothrock, 2003). Observational data are from http://psc. apl. uw. edu/sea_ice_ cdr/  (Schweiger, 2017).
Figure4. Comparison between sea ice thickness from reanalyses and sea ice thickness (green points) or draft (black points) from observational datasets. The lines represent the linear fits having the reanalysis as the predictor and the observations as predicted variables. The mean residual sum of squares (MRSS) from the fit, the correlation coefficient (R), and the root mean square error (RMSE) are also displayed for each comparison. RMSE is calculated only when comparing SIT from both sources (green) but not when comparing SIT and draft (black). Reanalyses labeled in blue and red highlight whether the datasets were built with or without sea ice data assimilation, respectively.
Figure5. Square matrix plot displaying the root mean square error (RMSE) (a) and the correlation coefficient (R) maps (b), estimated from the sea ice thickness time series, at every grid point, and between all pairs of reanalyses. The numbers at the top of each panel indicate the respective value calculated with data from all grid points. All maps have the 0 • longitude placed at 06:00, while the bounding latitude is 67 • N. Reanalyses labeled in blue and red highlight whether the datasets were built with or without ice data assimilation, respectively.
Acknowledgements. The work presented in this paper has received funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement no. 727862: APPLI- CATE  project (Advanced prediction in Polar regions and beyond). David Docquier  is funded by the  EU Horizon 2020   PRIMAVERA  project, grant agreement no. 641727. We thank two anonymous reviewers for their constructive suggestions and criticism. We thank  Axel Schweiger  for making the observational dataset available by means of the  Unified Sea Ice Thickness Climate Data Record  (http://psc. apl. uw. edu/sea_ice_cdr; last access:  15 July 2018 ) and also for kindly making some aspects of the data clear. The BGEP data used in Fig. 2 were collected and made available by the Beaufort Gyre Exploration Project based at the  Woods Hole Oceanographic Institution  (http://www.
Source data THAOS was established in 2007 and is the largest ongoing, worldwide, longitudinal, observational survey created to study differences in disease presentation, diagnosis, and natural history in a large, geographically dispersed, heterogeneous patient population, and to generate descriptive safety summaries in treated subjects (ClinicalTrials. gov: NCT00628745). 20 All study sites received ethical or institutional review board approval prior to subject enrolment, and each subject provided written informed consent. The study followed the International Conference on Harmonisation Good Clinical Practice guidelines and the principles of the Declaration of Helsinki. Data were collected during routine clinical practice at the discretion of the investigator and include cardiac and neurologic findings, renal function assessments, quality of life assessments, hospitalizations, medications, and transplant history.
Study population and assessments All symptomatic subjects with confirmed ATTRwt or ATTRm enrolled in the THAOS registry in European countries were included in the analysis. Baseline electrocardiograph data and interventricular septal thickness measurements were collected for the THAOS registry and these data were assessed with respect to subject's geographical location, genotype, and baseline characteristics (all at time of enrolment in THAOS). Subjects were grouped by both genotype and phenotype. Genotype categories were: ATTRwt; mutations associated with cardiac disease (Val122Ile,  21  Leu111Met,  22  Thr60Ala,  23  or Ile68Leu 24 ); Val30Met mutation with earlyonset disease (onset age <50 years); Val30Met mutation with late-onset disease (age > _50 years); and all other mutations. Phenotype categories, based on clinical presentation at the time of inclusion in THAOS were: mainly neurologic [i.
e. TTR-familial amyloid polyneuropathy (TTR-FAP)]; mainly cardiac (i. e. a cardiac phenotype); and mixed (cardiac þ neurologic) (see Supplementary material online for further details). Simple binary and continuous variables are shown using descriptive statistics. Differences in mean outcomes between genotype and phenotype groups were assessed for statistical significance by: analysis of variance, to calculate P-values by comparing means between groups for continuous variables; the Pearson v 2 test to calculate P-values for variables with cell counts >5; the Kruskal-Wallis test to calculate P-values by comparing medians between groups for continuous variables; and the Fisher's exact test to calculate P-values for non-ordinal variables with cell counts < _5. Follow-up data were obtained from the periodical, scheduled visits (with 6-month intervals).
Subject demographics and genotype As of January 2016, 1417 symptomatic subjects in nine European countries were enrolled in THAOS, all of these in continental Western Europe. Of these, there were 1411 symptomatic subjects across the nine countries with subject data (Table  1  and Figure  1  show the distribution across countries   1 ). In subjects with ATTRm in European countries in the THAOS registry, Val30Met was the most frequent mutation (1038 subjects, 73. 6%), with most of these subjects being from Portugal, followed by Ile68Leu (28 subjects, 2. 0%), with most subjects from Italy (Table  1 ). Types of mutations present differed highly between: Portugal and Sweden, in which there were endemic aggregations of Val30Met [779 (99. 1%) and 128 (90. 1%) subjects, respectively]; and the other European countries, which had a greater distribution of genotypes (Table  1 ).
Prognosis of ATTR amyloidosis in continental Western Europe In this analysis, there was a significantly poorer prognosis for subjects with cardiac mutations or ATTRwt. This observation is consistent with the differences in clinical measures and medical history between the genotype and phenotype categories and previous surveys of these patients. For example, subjects with ATTRwt and cardiac mutations had notably higher median levels of BNP, cardiac troponin T, and NTproBNP. A prior survey of cardiac biomarkers in subjects enrolled in THAOS identified higher plasma levels of BNP/ NTproBNP as an independent predictor of survival. 40 Together, these data should guide cardiologists in Europe, where the presence of symmetric LV hypertrophy and mildly depressed LVEF, especially in elderly men, should prompt the suspicion of ATTR amyloidosis if LV hypertrophy is unexplained.
Limitations While this analysis demonstrated lower survival rates in subjects with cardiac disease, it was not designed to assess the relative contribution of specific factors other than phenotype and genotype group (controlled by age and sex). Other variables may also impact survival but are less likely to be as relevant for all the groups in this analysis. For example, cardiac measures may be relevant in patients with cardiac disease but not for patients with neurologic disease, while liver transplant status has been shown to have prognostic value for patients with the Val30Met mutation but less so for other mutations. 41, 42 As THAOS is a large, international registry, there are inevitable variations in the clinical investigations and how complete the data, including data follow-up, are for each subject.
While there are specific guidelines for the collection and reporting of data,  20  the registry relies on assessments conducted in participating centres and there are no core data collection laboratories, so there may be variations in data availability and integrity. There are probably also non-random variations in missing data as, for example, cardiac symptoms in subjects with the neurologic phenotype may not be observed, or assessed, by the neurologist (and vice versa). Assignment of subjects to phenotype may also be imprecise as characterization of the cardiac phenotype was based on ECG and clinical data, which may not have been systematically obtained for subjects being treated by a neurologist. In this regard, selection of subjects included in the THAOS registry itself may be biased, as when it was established the majority of investigators were neurologists.
Introduction After water vapor, carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) are the three main greenhouse gases (GHGs) in terms of radiative forcing. Increases in these GHG concentrations in the atmosphere are driving anthro-pogenic global warming. Understanding the magnitude of GHG fluxes in natural ecosystems has recently become a priority in the study of GHG balances  (Merbold et al. , 2015). Tropical intact forests cover 1392 Mha globally and represent about 70 % of the total tropical forest area  (1949 Mha) , which accounts for the largest area of global forest biomes (∼ 50 %). Very few reliable long-term datasets on full GHG balances are available from tropical ecosystems, despite their known importance for the global cycles of these three GHGs  (Dutaur and Verchot, 2007).
Each chamber was purged for 15 s prior to each measurement and 45 s after each measurement in order to flush the lines and restore background gas levels in the system. The flow rate during the purging and the measurements was ∼ 2. 8 L min -1 between the LI-8150 and the chambers, which ensures sufficient air mixing in the chamber headspace during the measurements  (Görres et al. , 2016). Flow rates in the subsampling lines (LI-8100 and Picarro) were lower and set between 1. 5 and 1. 7 L min -1 as recommended by the manufacturers. The LI-8100 software provided the rate of CO 2 concentration increase in the chamber, which was used to quantify the flux of CO 2 from the soil surface into the atmosphere (taking into account the enclosed soil surface area and the total system volume). A subsampling loop was inserted after the analyzer (LI-8100A) and before the multiplexer (LI-8150) to pull the air sample through the Picarro G2308 CRDS analyzer for the determination of CH 4 and N 2 O concentrations and flux estimations, before going back to the chamber (Fig.
Flux calculations All flux estimations were carried out using commercially available SoilFluxPro software (LI-COR Biosciences). An R script (Supplement) was created to merge all the Picarro files from a given week in order to import them into the SoilFlux-Pro software. The Picarro creates one file per hour and when Picarro files are not merged, SoilFluxPro software is not able to deal with measurements overlapping between two distinct Picarro files (e. g. , when a single measurement is performed from 09:50 to 10:15 UTC-3), leading to incorrect estimation of CH 4 and N 2 O fluxes. To avoid underestimation of fluxes (Fig. S1  in the Supplement), CO 2 , CH 4 , and N 2 O fluxes were measured as exponential fit of gas concentration with time using SoilFluxPro software and include a 60 s dead band to account for soil surface pressure disturbances due to the closing of the chamber.
Results and discussions A cleaning frequency of once a week was necessary and sufficient to remove falling leaves and branches from the automatic chamber system, prevent leaks, and generate a continuous dataset of soil GHG fluxes from this tropical forest. Temperature variations are typically small below the canopy due to the shadowing by dense canopy crown and microclimatic conditions. During the study period, temperature at 2 m in height varied from 22 • C in the night to 28 • C during the day. The presence of water condensation inside the tubing lines was carefully checked every week and never occurred during the study period. The automatic chamber system worked well most of the time, but some data gaps did exist. Over the 17 592 individual flux estimations, 343 (1. 9 %) had to be discarded because of (1) problems in the connection between the chamber and the multiplexer (154 measurements, 0.
4 ). This dataset is therefore not long enough to detect the seasonal variation in soil respiration that was highlighted in previous study  (Rowland et al. , 2014; Rubio and Detto, 2017). We did found that soil respiration tended to decrease in very humid soils (Fig. S2  in the Supplement) as highlighted previously at the same site  (Rowland et al. , 2014)  but more data are needed to precisely disentangle the importance of seasonal and diurnal variability from the responses to environmental triggers of soil respiration. Nonetheless, even during this relatively short period, our data clearly demonstrated a strong spatial variability in soil respiration, even at a low spatial scale (Fig. 5 , Table  2 ), with some local spots clearly displaying stronger values of soil respiration during the study period.
Conclusions We demonstrated here that the combination of a commercial soil GHG chamber system -the LI-8100A Automated Soil CO 2 Flux System -running in parallel with a Picarro G2308, enables the continuous long-term measurement of CO 2 , CH 4 , and N 2 O simultaneously under tropical conditions. Similar configurations have been recently implemented in temperate climate  (Petrakis et al. , 2017a, b) , but to our knowledge, this is the first time that this experimental setup is fully described and tested under tropical field conditions for the measurement of the three soil GHG fluxes simultaneously. Additionally, our study determined the optimal chamber closure time for each GHG. The sampling system of SHORT and LONG closure times with a weekly rotation presented here has three major advantages, which ultimately can provide high confidence in the annual estimation of the full GHG budgets of tropical soils: (1) the LONG closure time allows a reliable estimation of the low N 2 O fluxes in this ecosystem, which was clearly not achieved using a shorter closure time; (2) the number of data points per day are sufficiently high (9 to 10 measurements per day) to capture potential diurnal variation  (Nicolini et al.
Data availability. Data used in this study can be freely accessed through the Zenodo repository: https://doi. org/10. 5281/zenodo. 2555299  (Courtois and Stahl, 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-16-785-2019-supplement. Author contributions. JVB and NA designed the experiment and EAC, CS, BB, and DB carried it out. EAC and CS prepared the paper with contributions from all co-authors. EAC and CS contributed equally to the paper. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. This research was supported by the  European Research Council Synergy  grant  ERC-2013-SyG 610028-IMBALANCE-P. We thank  Jan Segers  for help in the initial setting of the system and  Renato Winkler from Picarro  and  Rod Madsen  and  Jason Hupp  from  LI-COR  for their help in combining the systems. We thank the staff of Paracou station, managed by  UMR Ecofog  ( CIRAD, INRA; Kourou ), which received support from " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ,  ANAEE-France :  ANR-11-INBS-0001 ). This study was conducted in collaboration with the Guyaflux program belonging to SOERE F-ORE-T, which is supported annually by  Ecofor, Allenvi , and the  French national research infrastructure, ANAEE-F. This program  also received support from an  "investissement d'avenir" grant  from the  Agence Nationale de la Recherche  ( CEBA , ref  ANR-10-LABX-25-01 ).
Objective We aimed to study the epidemiology of the prodromal and mild stages of Alzheimer's disease (AD) patients who are eligible for clinical trials with diseasemodifying therapies. settings We analysed two large complementary databases to study the incidence and characteristics of this population on a nationwide scope in France from 2014 to 2018. The National Alzheimer Database contains data from 357 memory centres and 90 private neurologists. Data from 2014 to 2018 have been analysed. Participants Patients, 50-85 years old, diagnosed with AD who had an Mini-Mental State Exam (MMSE) score of ≥20 were included. We excluded patients with mixed and non-AD neurocognitive disorders. Primary outcome measure Descriptive statistics of the population of interest was the primary measure. results In the National Alzheimer Database, 550 198 patients were assessed.
Among them, 72 174 (13. 1%) were diagnosed with AD and had an MMSE ≥20. Using corrections for specificity of clinical diagnosis of AD, we estimated that about 50 000 (9. 1%) had a prodromal or mild AD. In the combined electronic clinical records database of 11 French expert memory centres, a diagnosis of prodromal or mild AD, certified by the use of cerebrospinal fluid AD biomarkers, could be established in 195 (1. 3%) out of 14 596 patients. Conclusions AD was not frequently diagnosed at a prodromal or mild dementia stage in France in 2014 to 2018. Diagnosis rarely relied on a pathophysiological marker even in expert memory centres. National databases will be valuable to monitor early stage AD diagnosis efficacy in memory centres when a diseasemodifying treatment becomes available.
Also, since 2009, a national medical database has been running, the 'Banque Nationale Alzheimer (BNA)' (The National Alzheimer's Bank), which was set up as part strengths and limitations of this study ► Use of two large, multicentric, national databases representative of the French healthcare network on Alzheimer's disease. ► Absence of data to explain the low rate of early Alzheimer's disease diagnosis in the French national Alzheimer database (BNA). ► A retrospective design for the electronic record file database that does not allow for definitive epidemiological conclusions. Open access of the national Alzheimer's Plan (2008-2012) and has already been thoroughly described. 11 ur aim was to evaluate the incidence of early AD patients who would be eligible for a disease-modifying drug trial in a French multicentric retrospective study in France between 2014 and 2018.
First database: electronic clinical records database A retrospective multicentric study based on a database from 11 'Centres de mémoire, de recherche et de ressources' (Memory, Research and Resource Centres), CMRR. In alphabetical order: Dijon, Lille, Nantes, Nancy, Nice, Paris-Lariboisière, Paris-Pitié-Salpêtrière, Poitiers, Rouen, Strasbourg, Tours) volunteered to participate in this study. Each of these centres kept their patient files in local electronic clinical records (ECR). The criteria of selected patients who would be eligible for a disease-modifying drug trial were derived from that of the Expedition III clinical trial ( Clinicaltrials. org: NCT02760602) and are in line with the International Working Group (IWG) guidelines. 4 5 The selected criteria, demographic (age, gender, education level, distance to CMRR, caregiver, referral) and medical data (conventional anti-AD treatment, participation in a clinical trial) were recorded in this population.
Second database: National Alzheimer database (BNA) In order to broaden the analysis to a national level and extend it over time, the BNA was used to assess the total number of clinically diagnosed AD and MCI (divided into amnestic (aMCI) and non-amnestic MCI (naMCI) based on clinical assessment) individuals in 2014 and 2015 who had an MMSE score ≥20. From 2016 onwards, the BNA structure changed to integrate the Diagnostic and Statistical Manual fifth edition nomenclature  12  and to require stating whether CSF biomarkers or genetic features were used to ascertain AD diagnosis or not. These results were stratified by the type of clinic (primary memory clinic, CMRR or private neuro specialist) in which the patients had been evaluated. Among the data systematically collected, we assessed the Mini Mental State Evaluation (MMSE) score  13  and the diagnosis according to the International Classification of Diseases, 10th Revision.