dataesr/scientific-paragraphs-categorization huggingface dataset:
0536, in HPC p = 0. 0003 and in DCx p < 0. 00 Figure  6B ). Moreover, it also revealed an increase in the number of fluorescent spots SoCx and PirCx (Mann-Whitney tests, 1 g vs. 2 g p <0. 0001 and p = 0. 0024, respective Figure  6B ). The quantitative analyses of images from Nanozoomer showed an increase in fluorescence level in HPC and DCx due to hypergravity exposure (Mann-Whitney tests, 1 g vs. 2 g in THAL p = 0. 369, in HPC p = 0. 033, in DCx p = 0. 016 and in LCx p = 0. 265; Figure  5B ). The analysis with QuPath software was used to segregate fluorescent areas from the background in several brain regions (Figure  6A ) using the same filtering parameters in both 1 g and 2 g conditions. The analyses confirmed that the exposure to hypergravity increased the number of fluorescent spots in HPC and DCx, but not in THAL (Mann-Whitney tests, 1 g vs.
In conclusion, our results showed an increase in the transfer of fluorescent molecules from blood to tissues, suggesting a global modification in effluxes due to hypergravity. To assess the alterations in the BBB in centrifuged mice, we focused the molecular investigation on gene expression using a set of primers targeting consensual genes involved in BBB efficacy. As reviewed recently  [85, 86] , all of the proteins encoded by the genes studied here are involved in the scaffoldings required to maintain endothelial cell interactions to create the BBB, as well as in the initiation of angiogenesis and/or vascular repair. The database queries concerning the expression level in non-neuronal cells of the brain indicated that the proteins encoded by studied genes are also expressed in endothelial cells, but not exclusively (Supplementary Table  S2 ).
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/cells12050734/s1. Comparisons between the RTqPCR data and queries of databases are organized in Supplementary Tables S1, S2 and S3. Table  S1 : List of genes modulated by 2 g, Table  S2 : Cell types expressing genes modified by 2 g exposure and Table  S3 : Comparison of gene expressions with database from  [29].
Acknowledgments: We thank  Laurence Vico  for the access to SAINBIOSE U1059 facilities during centrifugation campaigns;  Laurence Vico  and  Mathieu Beraneck  for helpful discussions;  Anne Prévot  for reading the MS. The Nanozoomer imaging was conducted in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM-Bordeaux University , member of the national infrastructure  France BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). We thank  Ghislaine Roux  (PLEXAN) for technical assistance and breeding management. The results here have been presented in the  Federation of European Neuroscience Societies congress, Paris 2022 , and the  German Neuroscience Society Meeting ,  Göttingen 2023.
Noell Lake Between March 20 and 23, 2019, in situ data were collected on Noell Lake to develop an understanding of the ice and snow cover properties (Fig. 2A ). Thirty-four measurements of ice thickness were made across the lake, including eight ice core locations, with an average ice thickness of 87 cm. Ice cores were used to develop a better understanding of the ice structure at Noell Lake and help define ice stratigraphy for the lake. Ice structure properties showed an average snow ice thickness of 16 cm and clear ice thickness of 79 cm, snow ice thickness ranged from 10 to 24 cm and clear ice from 55 to 85 cm. Snow ice was observed at all measurement sites with bubble size ranging from <1 mm to over 10 mm (Fig. 2B ). Only two ice core locations taken at the southern end of the lake showed tubular bubble development; the thickness of these layers was 17 and 12 cm.
Malcolm Ramsay Lake Field measurements for Malcolm Ramsay Lake were recorded between November  15, 2009, and April 4, 2010. Full details about this dataset can be found in  Gunn et al. (2018) ; data used in this study focused on the measurements recorded to provide context for RADARSAT-2 overpasses (Fig. 3A ). Snow depth and ice thickness measurements were taken along a 100 m transect adjacent to the sites highlighted in Fig. 1. Snow depth measurements were recorded every 0. 5 m using a SnowHydro Magnaprobe  (Sturm and Holmgren, 1999). Ice thickness measurements were recorded every 25 m. In addition to ice thickness, ice cores were extracted from the sites during intensive observations of the lake (Fig. 3B ). These cores provide further information about the ice stratigraphy and are used to estimate the thickness of tubular bubble layers at the different sites (Fig.
, 2018). Details on the model parameters used in this study can be found in Section 2. 6. Roughness of interfaces and substrate components within the model are set using the Fresnel equations  (Picard et al. , 2018) , the integral equation model (IEM)  (Fung et al. , 1992) , or Geometrical Optics  (Tsang and Kong, 2001 ) that determine the reflectivity and transmittivity coefficients. The user can define a customized passive or active sensor with specific frequency, incidence angle, and polarization or choose from a list of pre-built sensors. For this study, only co-polarizations are evaluated as cross-polarized data are underestimated with the current implementation of IEM in SMRT. When the necessary parameters are defined, the discrete ordinate and eigenvalue Fig. 2. A) snow cover on Noell Lake during the field campaign between March 20-23, 2019, B) sample of snow ice from an ice core obtained on Noell Lake, C) black and white image of the bottom of the ice recorded using a GoPro Hero7 camera showing a smooth ice bottom with no extruding tubular bubbles.
CLIMo simulations Field observations collected from both lakes provide data on ice thickness, snow depth, ice layers, and bubble characteristics. However, these measurements do not provide information on the layer temperature and do not offer data continuous in time. Therefore, to better parameterize SMRT, the Canadian Lake Ice Model (CLIMo) was used to address data gaps and realistically represent ice layer temperatures  (Murfitt et al. , 2022). CLIMo has been used in a number of studies in parallel or to support observations made from both spaceborne and ground-based systems  (Gunn et al. , 2015b; Kang et al. , 2012; Pour et al. , 2012; Surdu et al. , 2015)  and to investigate long term changes in ice cover under different climate scenarios  (Brown and Duguay, 2011a, 2012). A full description of CLIMo and the associated equations can be found in  Duguay et al.
(2003). CLIMo was forced using daily meteorological data (2 m air temperature, relative humidity, 10 m wind speed, cloud cover, and snow accumulation) from the ERA5 reanalysis dataset  (Hersbach et al. , 2020). To ensure that CLIMo inputs provide a good representation of local meteorological conditions, Environment and Climate Change Canada stations were used to validate ERA5 data. For Noell Lake the Inuvik Climate station (-133. 52 • , 68. 32 • ; 23. 2 km south of Noell Lake, Environment and Climate Change Canada, 2021c) was used and the Churchill Climate station was used for Malcolm Ramsay Lake (-94. 07 • , 58. 73 • ; 16 km west of Malcolm Ramsay Lake, Environment and Climate Change Canada, 2021d). Consistent temperature and snow data are available for the Inuvik station from 2000 to 2020 and for Churchill from 2005 to 2020, days with no data were excluded from validation.
The ERA5 data were found to be a fair representation of the local station data with root mean square errors (RMSE) of <2. 24 • C and < 0. 11 m for air temperature and snow depth respectively. Strong correlation values also exist between the two datasets with Kendall's Tau-b ≥0. 78 (p < 0. 01). Both field data and past studies of CLIMo were used to parameterize the model, references for these values are listed in Table  1. These same settings were used in  Murfitt et al. (2022). Regarding precipitation, the percentage scenario parameter in CLIMo helps characterize the difference between snow accumulation on-land and on-ice. Snow on-ice tends to be shallower compared to on-land due to snow redistribution  (Duguay et al. , 2003; Sturm and Liston, 2003). Various values of this percentage were tested for both lakes.
SMRT parameterization CLIMo was run using a 5-layer simulation (one layer of snow and four layers of ice) that was translated for input into SMRT. Due to the lack of field data throughout the ice season for Noell Lake, the output from CLIMo was used to parameterize snow depth, ice thickness, and layer temperature within SMRT for each date of image acquisition. The proportion of snow ice in the ice column was generalized throughout the ice season to match field observations of 17%. The amount of snow ice for each day was subtracted from the total ice thickness determined from CLIMo and the remaining ice was divided into three equal layers of clear ice. Visual representation of the configuration of CLIMo data to SMRT input for Noell Lake can be found in Appendix B. Tubular bubbles were excluded from Noell Lake to best generalize the ice cover.
For Malcolm Ramsay Lake, field data were used to parameterize layer thickness in SMRT and CLIMo was used for layer temperature. Snow/grey ice, clear ice, and tubular bubble layers were observed for Malcolm Ramsay Lake. Therefore, CLIMo data were translated into both a two-layer (snow/grey ice and clear ice) and a three-layer (snow/grey ice, clear ice, and tubular bubble layer) set-up for SMRT. A visual representation can be found in Appendix B. Bulk snow density values acquired in the field for the two lakes were used to parameterize optical radius (R o ) for snow grains that are needed for SHS in SMRT. Optical radius is calculated using Eq. (  1 ), R o = 3 ρ ice *SSA (1) where ρ ice is the density of ice (916. 7 kg m -3 ) and SSA is the specific surface area (m 2 kg -1 ). SSA values were not collected in the field, therefore a recent relation identified by  Vargel et al.
To address this issue the values were incremented by 0. 00001 for ice and 0. 0001 for snow. These values were reset to the baseline (0. 4 and 0. 1) for each model run. A summary of constant parameters for SMRT model runs can be found in Appendix B. RMSH and correlation length could not be estimated in the field. From previous studies, RMSH is the key parameter impacting backscatter from lake ice  (Atwood et al. , 2015; Murfitt et al. , 2022; Tian et al. , 2015) , therefore we focus on selecting the optimal value for this parameter. For Noell Lake, the optimal value was only determined for RADARSAT-2 data as it provided concurrent HH and VV data. Sentinel-1 data for Noell Lake was reserved for validation of these results. For Malcolm Ramsay Lake, the optimal value was determined using L, C, and X-band SAR imagery and SMRT outputs.
For Churchill, mean backscatter was extracted using a 50 m buffer around each of the field sites. For Noell Lake, RMSE is calculated using all RADARSAT-2 incidence angles. For Malcolm Ramsay Lake, all frequencies were used at each site. The modelling results and selected optimal values were validated using the closest available image. Validation was done using correlation statistics (Spearman's ρ), and RMSE calculated in R via the hydroGOF package (Zambrano-Bigiarini, 2017). Images acquired after May 8 for Noell Lake and after April 16 for Malcolm Ramsay Lake were not included in validation due to changes in ice surface conditions due to melt onset after these dates; this is addressed in the discussion.
Gunn et al. (2015a)  modelled backscatter for Malcolm Ramsay Lake during the 2010-2011 ice season. Modelled Xband VV backscatter compared to ground-based scatterometer data showed underestimation by up to 5 dB, likely due to the use of a fixed roughness at the ice-water interface. On the other hand,  Tian et al. (2015)  modelled C-band VV backscatter from lake ice on the Qinghai-Tibet Plateau (QTP) for ice seasons between 2003 and 2010. Roughness height was estimated using an empirical model relating roughness height to ice thickness. Results of modelling for the QTP also show underestimation of backscatter. While the results of the modelling from the current paper are comparable to these past examples, the modelling presented differs by assuming roughness increased throughout the ice season and was independent of other properties (i.
Acknowledgements The authors would like to thank the reviewers for their constructive comments that helped improve the communication and presentation of material contained in the paper. The authors would also like to thank  Aaron Thompson  and the staff at the  Aurora Research Institute, Inuvik, NT, Canada , for their support during the field campaign in 2019. The authors would also like to acknowledge  Natural Resources Canada  in providing RADARSAT-2 images and the  German Aerospace Centre  for providing TerraSAR-X images.
Funding This work was supported by the  Vanier Canadian Graduate Scholarship  awarded to  Justin Murfitt  and the  Natural Sciences and Engineering Research Council of Canada (NSERC ,  RGPIN-2017-05049 ) to  Claude Duguay. SMRT development was funded by  ESA  Project  4000112698/14/NL/LvH  " Microstructural Origin of Electromagnetic Signatures in Microwave Remote Sensing of Snow ". Work on the.
To evaluate the quality of OSM data, similarities between OSM features and their homologous features represented in a reference database are relevant metrics. However, reference databases do not exist everywhere or are not freely available. Thus, having data quality assessment methods that rely only on intrinsic indicators (i. e. based on data itself without considering external information) would be useful in these cases. This article specifically uses the radial distance as a target quality metric to measure the quality of shapes. Its aim is to build a random-forest based classification method that reconstructs whether this distance is higher or lower than a specified threshold, using only intrinsic indicators as inputs. The classification algorithm is evaluated on a first dataset by computing the ROC (Receiver Operating Characteristic) curve and using the AUC (Area Under Curve) as an evaluation metric.
INTRODUCTION The use of data produced voluntarily or involuntarily by citizens or communities, also known as crowdsourced data, s  (See et al. , 2016)  to update or enrich authoritative databases  (Liu et al. , 2015 , Ivanovic et al. , 2020) , to make decisions  (Westrope et al. , 2014) , becomes a popular topics in the field of Geographic Information Science (GIS). More specifically, Volunteered Geographic Information (VGI) has studied since 2007  (Goodchild, 2007) , and VGI data and projects have taken a prominent position since then, giving birth to research works aiming at assessing the quality of VGI, or their possible uses. Indeed, different challenges exist when dealing with VGI. The last does not always follow strict sets of specifications, and when specifications exist, their enforcement may be looser to encourage contributor involvement.
Being created or edited by contributors of varying expertise, VGI also often has heterogeneous quality. On the other hand, data quality is a crucial issue for both scientist and users of spatial data. For example, incomplete or imprecise data can lead to erroneous findings and inadequate decisions in diverse fields where spatial data are involved, such as urban planning, route optimization, or infrastructure sizing. Thus, VGI quality assessment presents multiple challenges and opportunities becoming a prominent research issue with various data quality assessment research works  (Zielstra and Zipf, 2010 , Goodchild and Li, 2012 , Ivanovic et al. , 2019) , reviews  (Antoniou and Skopeliti, 2015, Senaratne et al. , 2017) , theoretic frameworks  (Barron et al. , 2014)  or taxonomies  (Degrossi et al.
, 2018). OpenStreetMap (OSM) is one of the most popular and wideranging VGI project. The aim of the OSM project is to create a world wide open geographic database, independent of institutional and commercial databases, based on openness and flexibility to encourage wide participation in the project. Since its * Corresponding author creation in 2004, it has rallied a wide community of contributors that constantly edits and improves its database (e. g. adding new features, modifying existing one, importing datasets from open data). OSM has also developed activities of relief mapping after natural catastrophes  (Kogan et al. , 2016 , Poiani et al. , 2016). As a consequence, OSM data quality assessment is also a particularly important research subject, since they are are often used and treated as reliable data in many applications.
In many countries VGI can be evaluated by comparing them with reference data produced by national mapping agencies  (Antoniou and Skopeliti, 2015)  using ISO (International Organization for Standardization)  ISO 19157-2013 standard (e. g. completeness, consistency, spatial accuracy, temporal accuracy, and thematic accuracy). Data quality assessment becomes even more crucial in the absence of reference data, as OSM data can then be used as a guide for public, commercial or humanitarian actions. In such cases, the usefulness of VGI increases and quality-controlled VGI can be used as a substitute for deriving reference data for specific applications. Assessing the quality of VGI with only intrinsic characteristics, is still largely an open challenge  (Barron et al. , 2014 , Ivanovic et al. , 2019 , Truong et al.
, 2019) , and the extent to which quality measurements can be inferred from intrinsic indicators is still an open issue  (Maidaneh Abdi et al. , 2020 , Xu et al. , 2017). The goal of this paper is to propose an approach to assess the shape accuracy of OSM building using only intrinsic indicators. To reach this goal, we propose a machine learning method based on Random Forest approach which uses intrinsic indicators to classify the shape building into two classes: high and low shape accuracy with respect to a threshold. Specifically, we use the radial distance to measure shape accuracy of an OSM building with respect to the shape of its homologous feature issued from authoritative building datasets. Our proposed approach is based on a twofold process: 1. We first train and evaluate our classification method on an initial dataset, and identify the most useful intrinsic indicators.
STATE OF THE ART AND METHOD CHOICES To evaluate the quality of VGI , and specifically OSM data, a traditional approach is to compare VGI with data from a reference database, in areas where such a reference database exists. It consists in first matching VGI and reference datasets to define homologous features (i. e. features belonging to different databases but representing the same entities from the real world) and then to compute extrinsic indicators between homologous features  (Girres and Touya, 2010 , Haklay, 2010 , Zielstra and Zipf, 2010 , Van Damme and Olteanu-Raimond, 2022). Among the many research works on OSM data quality, several have focused on positional accuracy  (Haklay, 2010 , Kounadi, 2009 , Fan et al. , 2014 , Van Damme and Olteanu-Raimond, 2022) , but some of them also tackled other components of spatial data quality, like semantic accuracy  (Girres and Touya, 2010, Van Damme and Olteanu-Raimond, 2022) , completeness  (Zielstra and Zipf, 2010 , Fan et al.
The first is the impossibility to assess the quality of VGI, if reference data are not available. The second is that the metrics are highly dependent on the models of the geographic databases being compared and assumes that the reference database is the perfect representation of the reality. Other research proposed data quality assessment methods relying on intrinsic characteristics of the data  (Barron et al. , 2014). These intrinsic characteristics can be of several types (e. g. history, topology, internal consistency), and can be about the data themselves, or about the process of how these data were created. In  (Hashemi and Ali Abbaspour, 2015) , the authors use the editing history to identify topological inconsistencies. Among approaches using only the data themselves, some use spatial context to analyze the spatial consistency of data (see  (Touya and Brando-Escobar, 2013) , where the author specifically identify level of detail inconsistencies), while others rely on the geometries of individual objects  (Maidaneh Abdi et al.
, 2020). Some studies concentrate on the history of edition of an object to evaluate its quality  (Barron et al. , 2014). Several studies analyze quality through the contributors who created and edited the data, and evaluate characteristics of these contributors such as experience, local knowledge, credibility  (Flanagin and Metzger, 2008 , Van Exel et al. , 2010 , Bégin et al. , 2018). In  (Truong et al. , 2019) , the authors analyze contributor interactions and identify different contributor profiles to refine this knowledge about contributors. Finally, another category of data quality assessment approaches concerns the mixed approaches combining extrinsic and intrinsic metrics. It is about inferring an extrinsic indicator by using intrinsic indicators based on the research hypothesis that the discrepancies between VGI and reference data can be reconstructed from intrinsic indicators.
Machine learning methods are used to establish the relationships between intrinsic features and the target extrinsic indicator  (Mohammadi and Malek, 2015 , Xu et al. , 2017 , Maidaneh Abdi et al. , 2020). The last showed that individual intrinsic indicators gave relevant information about shape accuracy, but that they did not capture absolute accuracy very well. Random forests  (Breiman, 2001 ) is a machine learning method that is both flexible and relatively resistant to over-fitting. It can be used in different context both for classification and regression  (Criminisi et al. , 2012). Thus, we presume that quality assessment of OSM data can be conducted with either a regression or a classification method. Since, one of the uses of quality assessment in an operational context could be to identify data that one can consider as reliable, and data that are too uncertain and warrant additional control, it makes sense to consider the quality assessment such a classification issue by imposing a threshold on the target metric.
Moreover, classification also has the merit to be more robust to outliers than regression  (Ivanovic et al. , 2019). In this paper, the goal is to study whether or not machine learning classification approaches are able to assess the shape quality of VGI. We focus here on OSM building footprints. Thus, we aim to use a random forest classifier to reconstruct an extrinsic metric capturing the spatial shape accuracy of objects, using intrinsic indicators of the object as inputs. As the extrinsic metric, we choose the radial distance which is computed between an OSM building and its homologous object in a reference database. We make the hypothesis that the radial distance is a satisfactory proxy for the shape accuracy of an OSM building footprint. There is no unique way to define a distance between two polygonal shapes ; several metrics can be used to characterize shape  (Basaraner and Cetinkaya, 2017, Zhang and Lu, 2004) , and many of these metrics are not very discriminatory.