dataesr/scientific-paragraphs-categorization huggingface dataset:
LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde's ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017-2018 period over the contiguous United States (CONUS) at a 0.
Atmospheric forecasts are used to force the ISBA LSM to assess the forecast of LSV conditions. In situations when it is possible to predict atmospheric variables with reasonable accuracy days up to two weeks in advance, those same atmospheric predictions can be used to predict the vegetation and soil moisture variables used to characterize drought. The rationale underlying this study is that an LSM that can successfully monitor vegetation conditions forced by accurate atmospheric reanalyses would logically be able to forecast land surface conditions provided that accurate atmospheric forecasts are available. Running the LSV forecasts with atmospheric forecasts also underlines the importance of accurate initial conditions. Improving the accuracy of LSV initial conditions using data assimilation is a key issue.
The novel assessment in this study uses the LDAS-Monde data assimilation system with atmospheric forcing variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) 15-day forecasts for the two-year 2017-2018 period over the contiguous United States (CONUS). In both monitoring and forecasting modes, LDAS-Monde results are compared to several independent datasets for analyzing the accuracy of the LSV modeling and the impact of initial conditions (i. e. , the impact of the data assimilation). To assess root zone and surface soil moisture simulations, an independent dataset of soil moisture measurements from the U. S. Climate Reference Network (USCRN)  [38]  across CONUS is used. The simulations of ET are compared to the satellite driven estimates of the Atmosphere-Land Exchange Inverse (ALEXI)  [39]  surface energy balance model.
The LAI and SSM LDAS-Monde outputs are also compared to the matching satellite-derived products as a way to assess the correct behavior of the data assimilation impact. Section 2 describes the materials and methods used in this study, including assimilated and verification datasets. Section 3 describes the results of the monitoring and forecast experiments by the comparison to verification datasets. Section 4 interprets and discusses these results in the context of known geographic and biophysical patterns. Finally, Section 5 summarizes and concludes this article.
The unmodified atmospheric forcings were ingested into the ISBA LSM. The different ECMWF atmospheric forcing datasets and temporal resolutions can impact the shape and amplitude of the diurnal cycle of shortwave solar radiation. These differences in solar radiation can have a large effect on ET, with stomatal changes responding directly to shortwave radiation in the ISBA LSM. To counteract these problems, the experiments were run using a downward shortwave interpolation scheme, which is intended to better capture the peak hours of solar radiation by using a calculation of the solar zenith angle at each latitude and longitude based on the time of year (http://www. umr-cnrm. fr/surfex/spip. php?article428, last accessed 12 May 2020).
The linear rescaling is performed on a seasonal basis (with a three-month moving window) as suggested by  [16] [17] [18]. The rescaling parameters were derived monthly and screened for the presence of ice and urban surfaces. Average observed SWI over CONUS for 2017-2018 is displayed in Figure  1. The SSM observational dataset is filtered to exclude pixels whose average altitude is above 1500 m above sea level and pixels whose land cover fraction exceeds 15% of urban surfaces. Over the considered period, 2017-2018, the assimilated LAI observations are derived from the PROBA-V (2014-present) mission. This study uses the GEOV2 dataset, which applies the observations to a 1 × 1 km spatial resolution with data steps every 10 days  [47]. These observations are then also interpolated to the 0. 20° × 0.
Independent Evaluation Datasets USCRN is a National Oceanic and Atmospheric Administration (NOAA) network of climatemonitoring stations. USCRN contains 114 sites with hourly soil moisture and temperature measurements at 5, 10, 20, 50, and 100 cm depths, relatively well spread over CONUS. In order to effectively evaluate LDAS-Monde's ability to predict surface variables, the SSM simulations are compared to observed data at all sites with continuous quality-controlled data for all of 2018 in the USCRN over CONUS. The number of these sites differs based on the individual layer of soil moisture. This study uses two layers of measurement from USCRN, the 5 and 20 cm observations. The 5 cm layer is compared to ISBA soil layer 3 (WG3) representing 4-10 cm depth, while the 20 cm observation The SSM product that is assimilated must be rescaled to match the model climatology in units of m 3 m -3 in order to address a possible misclassification of land surface parameters such as soil porosity, wilting point, and field capacity  [44, 45].
The ASCAT SWI product is transformed into the model-equivalent SSM with the linear rescaling method as described in  [46] , where the observations' mean and variance are matched to the modeled surface soil moisture mean and variance. The second layer of soil in the ISBA model (WG2, 1-4 cm depth) is used to represent the topsoil properties. This rescaling gives, in practice, very similar results to cumulative distribution function (CDF) matching. The linear rescaling is performed on a seasonal basis (with a three-month moving window) as suggested by  [16] [17] [18]. The rescaling parameters were derived monthly and screened for the presence of ice and urban surfaces. Average observed SWI over CONUS for 2017-2018 is displayed in Figure  1. The SSM observational dataset is filtered to exclude pixels whose average altitude is above 1500 m above sea level and pixels whose land cover fraction exceeds 15% of urban surfaces.
Over the considered period, 2017-2018, the assimilated LAI observations are derived from the PROBA-V (2014-present) mission. This study uses the GEOV2 dataset, which applies the observations to a 1 × 1 km spatial resolution with data steps every 10 days  [47]. These observations are then also interpolated to the 0. 20 • ×0. 2 • model grid. Averaged LAI observations for 2017-2018 are displayed in Figure  1.
Independent Evaluation Datasets USCRN is a National Oceanic and Atmospheric Administration (NOAA) network of climate-monitoring stations. USCRN contains 114 sites with hourly soil moisture and temperature measurements at 5, 10, 20, 50, and 100 cm depths, relatively well spread over CONUS. In order to effectively evaluate LDAS-Monde's ability to predict surface variables, the SSM simulations are compared to observed data at all sites with continuous quality-controlled data for all of 2018 in the USCRN over CONUS. The number of these sites differs based on the individual layer of soil moisture. This study uses two layers of measurement from USCRN, the 5 and 20 cm observations. The 5 cm layer is compared to ISBA soil layer 3 (WG3) representing 4-10 cm depth, while the 20 cm observation is compared to an average of ISBA layers 4 and 5 (WG4 and WG5) representing 10-20 and 20-40 cm, respectively, weighted by each layer's respective thickness.
ALEXI  [39, [48] [49] [50]  is a surface energy balance model calculating ET based on a two-source land surface representation of the energy budget. The model treats the land surface as a combination of soil and vegetation with unique fluxes, temperatures, and coupling with the atmosphere. This single model formulation can be applied to wide ranging moisture and vegetation conditions, including partially vegetated surfaces. ALEXI is produced at both the global scale and over CONUS at 0. 05 • × 0. 05 • and 0. 04 • × 0. 04 • spatial resolution, respectively. This independent dataset is used to evaluate LDAS-Monde's parameterization of ET processes.
LDAS-Monde Within the SURFEX (Surface Externalisée Version 8. 1) modeling system  [51]  developed by CNRM (http://www. umr-cnrm. fr/surfex), a package allows the assimilation of satellite-derived products into the ISBA land surface model using the LDAS-Monde data assimilation system. LDAS-Monde is a global scale LDAS, with options to couple to hydrological models. LDAS-Monde also provides statistics that can be used to monitor the impact of the assimilation of satellite observations. The specific components of the system are detailed below. The LSM used in this study is the CO 2 -responsive  [30] [31] [32] , multi-layer soil  [52, 53]  version of the ISBA model. This version of the model allows the representation of vegetation biomass and LAI, in addition to exchanges in CO 2 , energy, and water fluxes between the surface and the atmosphere.
Experimental Setup In this study, the LDAS-Monde offline system is used in a forecast mode, forced by ECMWF atmospheric forecasts over the CONUS domain. It produces one-through 14-day LSV forecasts for each day in the 2017-2018 period. The maximum 14-day lead time corresponds to potential changes in LSVs' conditions impacting agricultural decisions such as the timing of irrigation, planting, and harvesting. The LSV forecasts are initialized by either the open-loop (OL) and by the analysis resulting from the DA experiment based on the sequential use of the SEKF to assimilate SSM and LAI. The OL corresponds to model-only simulations, without data assimilation. The OL-and SEKF-based forecast runs differ by changing their initialized conditions, with SEKF having what should be a more accurate initial state due to the assimilation of observations.
The bootstrapping method can be summarized by the repeated random removal of points and recalculation of the desired variable. In this analysis, this removal process is repeated 10,000 times in order to generate a sufficiently large number of samples in order to find 99% confidence intervals. Bootstrapping was applied to the correlation values of all evaluation datasets (CGLS SSM, CGLS LAI, ALEXI ET, and USCRN in situ soil moisture) versus matching OL and SEKF variables.
Evaluation Using Satellite-Derived Products LDAS-Monde has the ability to ingest atmospheric forecasts in order to output forecasts of land surface conditions. Presented here are the results of comparing LDAS-Monde-forecasted SSM, LAI, and ET to the satellite-derived evaluation datasets of the same variables. This analysis is not only focused on how the forecasts perform in a general sense (i. e. , how accurate are these predictions 14 days in advance), but also focuses on the impact of the initialization, that is, if the forecast is initialized by the OL or SEKF. The correlations and RSMD values for SSM, LAI, and ET are summarized in Table  2. The bootstrapped 99% confidence intervals of correlation (not shown) range from ± 0. 001 to ± 0. 003 for SSM (either OL or SEKF), ± 0. 003 to ± 0. 004 for LAI (either OL or SEKF), and ± 0.
Do LSV Initial Conditions Influence Their Forecasts? The results of this experiment demonstrate that the influence of the initial condition on the LSV forecast depends on exactly what the LSV in question is, as well as the evaluation dataset. For example, the evaluation of SSM using USCRN in situ observations resulted in no discernible difference between the OL and SEKF initial conditions at 5cm, and a small, but not significant difference at 20cm. At 5 cm depth, averaged LDAS-Monde OL-and SEKF-initialized correlations to the in situ observations did not significantly differ at 5 cm depth for any forecast period. Still, the correlations at FC1 generally agree with other similar comparisons such as  [34] , with the main difference being that this study only uses a two-year time period. This could be caused by a few different factors.
Can Data Assimilation Improve the Accuracy of Initial Conditions of LSV Forecasts? Previous experiments using LDAS-Monde  [33, 34, 62]  showed that assimilated LAI and SSM satellite-derived products increase their correlations and decrease RMSD with LDAS-Monde LAI and SSM after data assimilation, averaged over the domain. This is a good check to be sure that the data assimilation is working as intended. The response of ET to the data assimilation is of special importance due to its independence from the assimilated datasets. Furthermore, the analysis of how these statistics evolve as a function of forecast lead time is a question of interest (i. e. , how long do these variables provide useful, usable information). Several previous studies have looked at the impact of initial conditions on LSVs.
As with LAI and the first several forecast days of SSM, ET forecasts initialized by the SEKF state are significantly better than their OL-initialized counterparts, once again showing the utility of the data assimilation to provide more accurate initial conditions. ET correlations and RMSD are very good metrics that demonstrate the potential value in this new predictive capacity due to the completely independent ALEXI evaluation dataset. Additionally, ET shows stronger persistence from the more accurate SEKF initial conditions than SSM, which is likely due to the impact on ET of the relatively slow LAI and root zone soil moisture dynamics. ET continues to have very good correlations even with two-weeks lead time. This independent variable can give stakeholders an advanced look at how much potential evaporation stress their crops may be exposed to in the near future and can provide necessary information on the timing of irrigation.
Supplementary Materials: The following are available online at http://www. mdpi. com/2072-4292/12/12/2020/s1, Supplement -Microwave Vegetation Optical Depth. Figure  S1 : A) Correlation between LDAS-Monde SEKF LAI at FC1 vs. X-Band VOD from VODCA B) NICR illustrating the improvement or degradation in correlation between the VOD dataset and the SEKF and OL LAI C) Average monthly correlation scores between the VOD dataset and the SEKF and OL LAI.
SARS-CoV-2 and ACE2 The ACE2 protein is enriched in the enterocytes in the small intestine and the renal tubules, as well as in the lung alveolar epithelial cells, the heart cells, the arterial smooth muscle cells, and the gastrointestinal system  [133, 134]. This protein is best known for cleaving several peptides within the reninangiotensin system and other substrates  [135]. ACE2 protein is rare in circulation, but widely expressed in organs, such as kidneys, the gastrointestinal tract, and at relatively lower levels, the lungs  [136]. Recently, based on scRNA-seq datasets, Zou et al. [105]  constructed a 2019-nCoV infection-related risk map of different organs including nasal mucosa, respiratory tract, bronchus, and lung. They found that pulmonary AT2 cells and respiratory epithelial cells exhibited high ACE2 expression.
Human and non-human primates share the identity sequences in the regions and residues, implying that ACE2 from non-human primates may recognize 2019-nCoV and mediate its infection  [42]. As a result, non-human primates may be susceptible to 2019-nCoV and serve as animal models for antiviral research or intermediate hosts for cross-species transmission. Analysis of coding-region variants using the GTEx database in ACE2 and of the expression quantitative trait loci (eQTL) variants, which may affect the expression of ACE2, support the existence of ACE2 mutants resistant to S-protein binding of coronavirus in different populations  [150]. East Asian populations have much higher AFs in eQTLvariants associated with higher ACE2 expression in tissues, suggesting different susceptibility or response to SARS-CoV-2 compared to other populations under similar conditions  [150].
Based on the high-homology between the Spike protein epitopes of taxonomically-related coronaviruses, Tilocca et al. hypothesized that past contact with infected dogs shield humans against the circulating SARS-CoV-2  [155]. By using the Immune Epitope Database and Analysis Resource (IEDB), Grifoni et al. found that SARS-CoV has high sequence similarity to SARS-CoV-2, and is the best characterized coronavirus in terms of epitope responses. Multiple specific regions with high homology to the SARS-CoV in addition to a priori potential B and T cell epitopes for SARS-CoV-2 have been  [156]. Authors suggest these regions are promising targets for immune recognition of SARS-CoV-2 and can facilitate effective vaccine design against this virus  [156]. Compared with other CoV species, bovine CoV is the genetically closest counterpart to human coronaviruses  [157, 158].
Two thirds of PBCRs who collected Indigenous status data (67%), from nine of 25 countries responded that cancer statistics for Indigenous people were reported using registry data. Key barriers to the collection of Indigenous status information included the lack of data collection at the point of care (79%), lack of transfer of Indigenous status to the cancer registry (46%), inadequate information systems (43%), and legislative limitations (32%). Important variations existed among world regions, although the lack of Indigenous status data collection at the point of care was commonly reported across all regions. CONCLUSION High-quality data collection is lacking for Indigenous peoples in many countries. To ensure the design and implementation of cancer control activities required to reduce disparities for Indigenous populations, health information systems, including cancer registries, need to be strengthened, and this must be done in dialogue with Indigenous leaders.
In total, 371 registries were invited to participate (Fig  1 ). The Indigenous World yearbooks list 69 countries with Indigenous populations; we identified registries in 61 of these, and eligible registries were invited from across 54 countries. Topics of interest were determined by the research team, which is experienced in both Indigenous health and cancer registry data collection. Questions were formulated, structured using branch logic and trialed by the research team members for face validity and readability. Topics were kept to a minimum to reduce the burden for registry staff. Up to 19 questions were asked dependent on respondent's previous answers. The survey took approximately 10 to 20 minutes to complete. The questions focused on collection of Indigenous status, reasons for noncollection, methods and sources for collection, the assessment of the quality and/or completeness of Indigenous status information, and whether Indigenous cancer statistics had been reported.
To avoid confusion over the two concepts, we referred to collection as entailing both collection (capture of data) and recording (documenting of data) of Indigenous status information. Openended questions sought to gain additional insight into the barriers to data collection and reporting. The survey was administered online using the data collection software program REDCap  25  and in two waves (February to March and May to June 2019) because the updated 2019 Indigenous World yearbook became available during the first wave. For both waves, an e-mail invitation to complete the survey was sent to all registries. Where e-mails were undeliverable, all attempts were made to identify updated contact information and resend the e-mail. Two reminders were automatically sent to registries that had not yet responded, approximately 1 week apart.
Linkage to specific tribal registers was used mostly in India and the United States. Less than half of PBCRs that captured Indigenous status were aware of completeness (38%) or accuracy (47%) assessments of these data. Such assessments were more commonly reported among PBCRs in the Pacific and Asia (Table1). In response to open-ended questions, data linkage commonly arose as a means of verifying the completeness and/or accuracy of the Indigenous status variable in the cancer registry. One PBCR described the need for best practice business rules to guide the assessment of data completeness and accuracy, especially when multiple sources of Indigenous status were available. Another noted that accurate ascertainment of Indigenous status using linked data is challenging but that continued improvement is ongoing through close collaboration with Indigenous groups.
Integrated registry systems, which use ongoing linkages, may provide an efficient method of cancer surveillance for Indigenous peoples and other minority populations. However, Indigenous people have the right to choose when and to whom they identify, and data linkage practices should be devised by or in collaboration with Indigenous leaders and communities. Data linkage also was suggested as a means to assess the quality of the Indigenous status variable used by the cancer registry, as has been done before. 37, 38 Even though the right to self-identify in the health care system is an important element of self-determination and adopted as best practice in increasingly more countries, it can create a challenge to the reporting of cancer statistics for Indigenous people. 20, 28 he propensity to identify as Indigenous may vary over time and across settings, which can affect the reliability of the Indigenous status measure.
29, 30 In Australia, where Indigenous status information is largely drawn from hospital separations data, best-practice algorithms have been derived to established reliable cancer data for Indigenous Australians. 39 Data linkage also may overcome the discordance in how numerator and denominator data are collected for Indigenous people. 15, 22, 30 is study provides evidence on the routine collection and reporting of Indigenous status information by a global sample of cancer registries. The findings may inform the development or strengthening of guidelines for these activities. Regional variations in the barriers to collection and reporting highlight the need for tailored guidelines and mechanisms to achieve the aim of improved Indigenous identification in cancer data. We acknowledge that respondents from the PBCRs offered their personal views, which may not reflect the official view or practices of the cancer registry.
06 • W, 46. 17 • N; Fig. 1 ). Our principal aim is to document its morphometry and provide a comparative dataset for future studies aiming to test whether sinuous ridges elsewhere on Mars are eskers. Our raw measurements (Sections 4. 1 and 4. 2) highlight complexity in the morphology of the NW Tempe Terra esker, and suggest that the ridge is in fact a composite esker formation comprising two 'stacked' ridge members (Section 4. 3). We apply a novel morphometric approach to our raw measurements to explore this hypothesis (Section 4. 4), and present the morphometries of the constituent ridge members that we identify (Section 4. 5). Based on insights from eskers on Earth (Section 5. 1; e. g. ,  Banerjee and McDonald, 1975; Brennand, 1994; Burke et al. , 2008 Burke et al. , , 2010 Burke et al. , , 2012 Burke et al.