dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data were analysed using the mixed model procedure of the SAS statistical package (V9. 4, SAS Institute Inc. , Cary, NC, USA). The growth performance, plasma amino acid levels, and blood biochemical data were analysed using a two-way factorial arrangement with a completely randomised design. Carcass characteristics, meat quality, composition of muscle and liver, and gene expression in the muscle were analysed using a twoway factorial arrangement with a randomised complete block design. Correlation analysis was performed using Pearson's correlation coefficient. Since slaughter was divided into two days, the slaughter time was considered a block, and sex (barrows and gilts) and diet (CON, DL-met, and OH-Met) were considered the main effects, as well as the interaction between sex and diet.
Single and multi-species habitat suitability maps We used 100 m-resolution habitat suitability maps resulting from a previous study of the same nine species in Pays-de-la-Loire  (Matutini, Baudry, Fortin, et al. , 2021). This used heterogeneous data from citizen sciences completed by professional surveys to perform and evaluate (with independent dataset) presence-only habitat suitability modelling with functional friction-based and multi-scale predictors (see  Matutini, Baudry, Fortin, et al. , 2021 and Matutini, Baudry, Pain, et al. , 2021  for full details and methods). We produced multi-species suitability maps by compiling individual species maps (Stack-SDM, e. g. Ferrier & Guisan, 2006 ) using a simple probabilistic stacking method (pSSDM, see  Zurell et al. , 2019). We summed continuous individual maps without binary "presence-absence" conversion  (Calabrese et al.
Network patterns To map potential connections between each highly suitable patch (i. e. , patch with a suitability index up to 80%) at regional scale, we used circuit theory with Circuitscape software  (McRae et al. , 2016). Circuit theory considered each cell as an electric node connected to neighbouring cells by resistors and defined by a landscape resistance (or conductance) value  (McRae et al. , 2008). We used highly suitable core habitats as source patches, and a friction map as a measure of conductance (i. e. , conductance of each raster 100 m cell for movement). This method provides an exploration of potential links between nodes according to landscape permeability combining habitat suitability and connectivity modelling  (Ahmadi et al. , 2020; Koen et al. , 2014). Habitat suitability could however be a poor proxy of permeability as species may move in unsuitable habitats  (Keeley et al.
2014). A more rigorous approach would be to integrate independent genetic data to assess our final maps (in addition to the external presence-absence dataset use to evaluate our models), particularly in the two bottleneck sectors. However, these data were not available. This might be worthwhile pursuing for post-validation and further investigations. Our results must also be interpreted keeping in mind the spatial and temporal scales involved. Amphibians have poor movement capacities, rarely exceeding a few kilometres during dispersal movements  (Sinsch, 2014). In this work, continuities were identified from habitat distributions at regional scale and species level reflecting long-term ecological processes and population movements across decades. This study takes a first step towards assessing potential barriers to species distribution shifts in response to global change, but does not directly include landscape dynamics and climate change scenarios.
Fig. 2. Distribution of pixels along the regional habitat suitability gradient covered or not by protected area. Suitability index (from the habitat suitability map) is calculated for each species and varies from 0 to 1. Suitability index of the three groups (ALL, FOR and GEN) is the sum of the individual species index related to each group (pSSDM). The frequency corresponds to a number of pixels covered by protected areas from the Group 1, 2, 3 in relation to the whole region area. Fig. 3. Large scale permeability between high suitability areas and associated continuities patterns confronted with regional green infrastructure. A and B show potential conflict area and issues for continuity conservation at regional scale with a mismatch with the green infrastructure network. Table  1. Description of the nine studied species: conservation status and number of presence-only data available for analysis.
RL FR: France red list; RL Region: regional red list of Pays-de-la-Loire; Priority: regional priority level for species conservation from the regional red list defined by  Marchadour et al. (2009) ; Hab. Dir. N2000: habitats directive classification (Natura 2000); Pop trends: regional population trends from  Marchadour et al. (2009) ; Nb presence data: Number of presence-only data were calculated after a 100 mresolution rasterization and correspond to the number of pixels with at least one observation of the species. 3. Accuracy of stacked species distribution models for three species groups according to the nine studied amphibian species. Results obtained with external data with 100 permutations. Community AUC were obtained using species diversity results for each studied species group and for all species inventoried in the external dataset (n=17).
Acknowledgements This work would not have been possible without the support of the  Pays de la Loire Herpetological Group , the  CPIE Regional Union  and the  French BirdLife partner (LPO). We are especially grateful to  Morgane Sineau  and  Benoit Marchadour  who coordinate regional naturalists' databases. We also acknowledge the many naturalists involved, for access to data and for additional fieldwork, especially  Dorian Angot ,  Baptiste Gaboriau  and  Martin Bonhomme. We thank  Jean Secondi  and  Aurélien Besnard  for providing helpful discussion. Our work was supported by funding from  Ecole Supérieure d'Agricultures d'Angers ,  Angers Loire Metropole , The  French Society for Ecology and Evolution  ( SFE 2  ) and " Humanité et Biodiversité ".
We investigate the most severe rainfall event ever experienced in Ho Chi Minh City (HCMC), Vietnam. It occurred on November 25th, 2018 when typhoon (TY) Usagi directly hit HCMC. During this event, there was more than 300 mm in rainfall over 24h which led to flooding and considerable material damages. We propose an in-depth study of the hydrological response of this urban estuary region at a short time scale by focusing on the days before and after typhoon Usagi. For the first time in a data scarce region, satellite and in-situ measurements were gathered and jointly analyzed during an unprecedented extreme event that affected millions of people in the HCMC megalopolis. We use a set of data analysis and signal processing tools to characterize and quantify both coastal and continental effects on the hydrosystem.
Methods and Data The impact of a typhoon (TY) on an hydrological system can be separated into two main drivers: continental and coastal (Fig. 3 ). The first in the form of rainfall-runoff, infiltration and exfiltration influenced by topography and land use. The second in the form of astronomical tide and storm surge. In order to characterize the hydrological impact of TY Usagi on the tidal river system, the following general steps are applied (Fig. 3 ) and explained in detail in the following sections. We start by selecting a collection of data representative of the factors influencing the impact of a typhoon. Different gridded precipitation products are evaluated against in-situ measurements to enable a high temporal and spatial resolution study of the precipitation pattern during Usagi. We study topography and land use maps together with the obtained precipitation time series.
Performance evaluation of gridded datasets of precipitation over HCMC The accuracy of 5 gridded satellite precipitation datasets was evaluated against in-situ measurements. In order to match the location of observations and dataset pixels, the precipitation values of the datasets were spatially interpolated to the location of ground observations using the bi-linear interpolation method  (Gaborit et al. , 2013; Ang et al. , 2022). Additionally, we match the temporal resolution of the datasets and the observations such that a evaluation can be made. We chose the cumulative precipitation over 3 days as the adequate time resolution for the period of available observed precipitation data  (2016) (2017) (2018). The evaluation was performed over 9 rain gauge locations where the data quality and availability of ground observations were deemed sufficient.
The evaluation was carried out using three statistical indicators: mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R). These indicators have been widely used in literature for this purpose  (Xiang et al. , 2021; Ang et al. , 2022). MBE is a statistical assessment of the mean difference between the interpolated and observed rainfall, and RMSE represents the standard deviation between the dataset and the observations. The lower absolute values of MBE and RMSE indicate the better performance of the gridded dataset. R measures the degree of linear correlation between the gridded and observed datasets. Higher values of R indicate higher accuracy of the product in estimating precipitation. The equation, range, and optimal value of each index are presented in Table  1.
Ground-based and satellite precipitation datasets Table  2  summarizes the precipitation datasets and observations used in this study. Gridded precipitation products can be classified into three categories based on differences in the data sources and retrieval models including gauge-interpolated, satellitebased, and reanalysis precipitation datasets  (Jiang et al. , 2021). Satellite-based datasets use polar-orbiting passive microwave   (Larson and Peck, 1974; Kidd and Huffman, 2011). Often this datasets offset their limited abilities by blending rain gauge data with their measurements  (Mizukami and Smith, 2012). Reanalysis-related datasets generate several meteorological variables with a consistent spatial and temporal resolution by assimilating observations such as weather stations, satellites, ships, and buoys based on different climate models.
In this analysis 5 datasets that belong to the three categories were chosen in order to examine their different performances over HCMC. All datasets that were used are freely available online via public repositories. Global Precipitation Climatology Centre (GPCC) dataset. The GPCC provides the largest gauge-based dataset product derived from quality controlled station data sourced from national meteorological and hydrological services, global and regional data collections as well as WMO GTS-data. This GPCC product is recommended to be used when the daily precipitation information is of highest importance, e. g. for analyses of extreme events and related statistics at daily resolution. Despite its low spatial resolution, the GPCC product was chosen as the gauge-based dataset since it is recommended for analyses of extreme events and related statistics at daily resolution  (Jiang et al.
, 2021). Climate Prediction Center MORPHing technique (CMORPH) dataset. This product consists of satellite precipitation estimates that have been bias corrected and reprocessed using the CMORPH to form a global, high resolution precipitation analysis at very high spatial and temporal resolution. We chose this product due to its advection scheme of cloud features and wide range of applications including hydrological studies and extreme event analysis  (Joyce et al. , 2004). (Islam and Cartwright, 2020) , it was chosen since it can capture the spatial distribution of typhoon precipitation centers  (Jiang et al. , 2021).
Classical harmonic analysis has been developed and widely used to analyze and predict tides  (Jin et al. , 2018; Trinh et al. , 2020; Couasnon et al. , 2022). The UTide package developed by  Codiga (2011)  and implemented in Python by  Bowman (2020)  was used for extracting the tidal constituents. This package provides harmonic analysis with up to 146 tidal constituents. We use all available constituents for our tidal prediction. However, we exclude the constituents whose harmonic constants usually include mostly quasi-periodic meteorological effects (Mm, MSf, Mf, Sa, Ssa, S1). Excluding these long-term constituents from the analysis avoids over fitting the tidal prediction with frequencies that capture non-astronomical effects on water level such as wind, temperature and atmospheric pressure  (Parker, 2007).
Water level data Time series data at 10 minute intervals were obtained from a 2-year measurement campaign  (2017) (2018)   for Environmental Information  (Caldwell et al. , 2015). A statistical description of these datasets can be found on Table  B1  in Appendix B. The stations of Phu Cuong, Thao Dien and Vung Tau depicted in orange in Fig. 1  will also be referred to as the upstream, urban and coastal stations, respectively.
Other data Topography. The topography maps were obtained from the NASA Shuttle Radar Topographic Mission (SRTM) 90 m DEM Digital Elevation Database. The SRTM mission has provided digital elevation data (DEMs) for over 80 % of the globe. This data is currently distributed free of charge by USGS and is available for download from the National Map Seamless Data Distribution System, or the USGS ftp site. The vertical error of the DEM's is reported to be less than 16m  (Farr et al. , 2007). Land Use. The land use map of the region around HCMC was obtained from the Large-scale Land Use Land Cover (LULC) website (https://www. eorc. jaxa. jp/ALOS/en/dataset/lulc/lulc_vnm_v2109_e. htm). LULC information was derived using a randomforest-based algorithm and several geospatial data sources, including Landsat and Sentinel-1 and -2 imagery  (Phan et al.
, 2021). The final product contains annual land cover information from 1990 to 2020 in Vietnam at a 30 m resolution. This data is independently validated with field surveys and visual interpretation data. The overall accuracy of the level-1 layer ranges from 86 % to 9 2%. Wind. ERA5 outputs for 10 m u and v wind components were used to map the approach of typhoon Usagi towards the southern coast of Vietnam. This data is provided free of charge at the Copernicus Climate Data Store (https://cds. climate. copernicus. eu) with an hourly resolution and global grid of 0. 25°for the period 1959 to present  (Hersbach et al. , 2020).
Assessment of precipitation products The results of all statistical indices (RMSE, MBE and R) for each station are presented in Fig. 4. The worst performing dataset over this domain is ERA5 with large values of RMSE and MBE (both > 100 mm/3 days) and low linear correlation (R ≤ 0. 4). This indicates that ERA5 is overestimating rainfall over the whole domain. The ERA5 dataset that was evaluated has a 0. 5°g rid which is not capable of capturing the precipitation patterns over HCMC. The other gridded products perform relatively well in stations within the city center (20 ≤ RMSE < 50; -20 ≤ MBE < 50 mm/3 days). This indicates that all datasets, with the exception of ERA5, are able to estimate rainfall patterns over HCMC. The gauge-based dataset GPCC is able to do so with the most coerce grid: a daily temporal window and spatial resolution of 1°.
Even though CMORPH presents a sophisticated advection scheme (0. 25°), its performance is similar to the higher spatial resolution IMERG (0. 1°) across all metrics. Additionally, the performance of MSWEP (0. 25°) is slightly better than CMORPH over the city center. However, this better performance can also be attributed to the incorporation of in-situ data in addition to the satellite data used to produce MSWEP. Nonetheless, this shows that a grid size of 0. 25°suffices to estimate rainfall over HCMC using hybrid measurements sourced from satellites and in-situ data. The degree of linear correlation between all datasets and rainfall gauges is always below 0. 7 for almost all stations. MSWEP shows higher values of R (0. 4 ≤ R < 0. 7) than all other datasets. In fact, all other datasets show very low values of R (R < 0.
5) indicating that these datasets have a weak linear relationship with the observed daily data. The main factor contributing to the difficulty in accurately portraying the precipitation over Ho Chi Minh City is the convective pattern of precipitation events leading to the non-uniform distribution of rain at the scale of the city  (Vachaud et al. , 2019). It can happen that it rains heavily in one district whereas in another it does not rain at all. The spatial resolution of all datasets (always at least ≥0. 1 • ) remains too coarse to accurately capture the spatial variability during rainfall events. Additionally, this evaluation is undertaken at the relatively high timescale of the day against scarce insitu measurements (both in time and space) which impacts the performance of the datasets.
Conclusions In this paper, we investigate the most severe rainfall event ever experienced in Ho Chi Minh City (HCMC), Vietnam. It occurred on November 25th, 2018 when typhoon Usagi directly hit HCMC. During this event, there was more than 300 mm of rainfall over 24h which led to flooding and considerable material damages. In this work, we put forward an evaluation of its impact on the hydrological system of the region by using a set of tools to characterize and quantify both coastal and continental effects. For the first time in a data scarce region, all hydrological information was gathered and analyzed during an unprecedented extreme event that affected millions of people in the HCMC megalopolis. We go from an hydrological approach to a data analysis and signal processing approach in order to analyze coastal and river water levels, rainfall and flood.
This approached not only allowed a thorough investigation of the typhoon's impacts but also allowed new insights on the hydrological behaviour of this region. From the evaluation of five research-quality precipitation datasets against in-situ measurements, we find that the MSWEP dataset shows the best performance in estimating rainfall over the region of interest and can capture the precipitation behaviour during TY Usagi (R=0. 6; MBE=-4. 3 mm and RMSE=7. 6 mm). Hence, it is used to analyze TY Usagi's rainfall over the watershed. A statistical overview of the dataset shows that the rainfall brought by Usagi constituted the maximum daily rainfall in the MSWEP data series. We observe that the impact of increased wet season rainfall on river water levels is dwarfed by the seasonal coastal storm surge caused by the wind pattern of the East Asian summer monsoon, that typically lasts from November to April.
directed by the Centre Asiatique de Recherche sur l'Eau (CARE). Measurements were performed using CTD Diver sensors at two locations: Phu Cuong and Thao Dien as shown in Figure 1. Phu Cuong and Thao Dien are located at about 137 and 60 km from the coast (along river), respectively. In Camenen et al. (2021) the quality of these measurements has been validated against data of the Center of Environmental Monitoring (CEM) of Vietnam. This data is complemented by the record at the Vung Tau tide gauge from the research quality dataset available through the Joint Archive for Sea Level of the NOAA National Centers https://doi. org/10. 5194/egusphere-2023-396 Preprint. Discussion started: 10 March 2023 c Author(s) 2023. CC BY 4. 0 License.
The EdU-positive cells were visualized using a confocal laser scanning microscope (LSM 700; Carl Zeiss MicroImaging GmbH, Oberkochen, Germany) with a Plan Apochromat 40×/1. 3 numerical aperture oil-immersion objective. At least five photos per condition were taken, and positive cells were counted using ImageJ software (v1. 54d). Trypan blue exclusion assays were used to determine the viability of PANC-1 and Mia PaCa-2 cell models after exposition to pH e 6. 6 for different time points (2 h, 24 h, 48 h, 72 h, and 96 h, 15 days, and 30 days). PANC-1 and Mia PaCa-2 cells were seeded in 6-well plates at a density of 2 × 10 5 cells/well and allowed to adhere overnight. Cells were then treated with the acidic medium for the different time points. Cells were trypsinized at specific time points and stained with trypan blue to assess the live/dead cell counts following counting on a hemocytometer.
Cells were washed two times with PBS, and cell nuclei were stained with a solution of 1:1000 DAPI in PBS for 15 min at room temperature. Cells were washed twice with PBS and air dried, and the coverslips were mounted on clean microscope glass slides using Glycergel mounting medium (Dako; Cat# C0563) and stored in the dark at 4 • C. Glass slides were then examined using a confocal laser scanning microscope (LSM 700; Carl Zeiss MicroImaging GmbH) with a Plan Apochromat 40×/1. 3 numerical aperture oil-immersion objective. The images were analyzed in Zeiss LSM Image Browser software.
Analysis of Cell Morphology: Cell Area and Cell Circularity Cells were plated in the appropriate pH e -target medium and grown to approximately 80% confluency. Four random fields of each cell condition were imaged using a brightfield microscope (Nikon Eclipse TS100). ImageJ software quantified the cell area and circularity by manually segmenting the brightfield images. Over 500 cells for each condition were considered for the cell area and circularity analysis.
Membranes were then washed for 3 × 5 min and 1 × 10 min in 1X TNT. Peroxidase activity was revealed using SuperSignal West Dura or SuperSignal West Femto chemiluminescent substrate (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer's instructions. Chemiluminescent signals were captured on Amersham Imager 600 (GE Healthcare Life Sciences, Chicago, IL, USA) and quantified using the densitometric analysis option in ImageJ/Fiji version 1. 53 software. All band density values were normalized to β-actin or Calnexin, used as loading controls, and then compared to the control condition.
Intracellular pH Measurements The intracellular pH of PANC-1 cells was measured using fluorescent live-cell imaging (Axiovert TV100, Zeiss, Oberkochen, Germany) as described previously  [45]. Cells were loaded with the fluorescent pH indicator 2 7 -bis(carboxyethyl)-5-carboxyfluorescein (BCECF-AM) (3 µM) for up to 2 min. The excitation wavelength alternated between 490 nm and 440 nm. The emitted fluorescence was detected at 510 nm. The mean fluorescence of each cell was measured in 10 s intervals. Data acquisition and the polychromator (Visitron Systems, Puchheim, Germany) were controlled by the program VisiView (Visitron Systems). The cells were superfused with prewarmed (37 • C) CO 2 /HCO 3 --buffered Ringer's solution (116 mM NaCl; 24 mM NaHCO 3 ; 5. 4 mM KCl; 0. 8 mM MgCl 2 ; 1. 2 mM CaCl 2 ; 5.
5 mM Glucose) at pH 7. 4. NaHCO 3 was lowered to 4. 7 mM for pH 6. 6. pH measurements were calibrated with a two-point calibration (130 mM KCl; 1. 2 mM CaCl 2 ; 0. 8 mM MgCl 2 ; 10 mM Hepes; 5. 5 mM Glucose; pH 7. 5 and pH 6. 5; supplemented with 10 µM Nigericin) (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany). For data analysis, the mean fluorescence intensity of the cell area was measured and corrected for background fluorescence. Afterward, the 490 nm/440 nm ratio was determined, and the pH i was calculated with a linear regression of pH 6. 5 and 7. 5.
RNA-Sequencing and Analysis Total RNA was isolated from cultured PANC-1 cells using the NucleoSpin RNA Plus kit (Macherey Nagel Bioanalysis™), according to the manufacturer's instructions. Libraries for the RNA-Seq analysis were prepared with 200 ng of RNA using the Illumina Stranded mRNA Prep Kit (Illumina, Inc. , San Diego, CA, USA), according to the manufacturer's protocol. All the libraries were sequenced on an Illumina NovaSeq 6000 system using paired-end NovaSeq 6000 S2 Reagent Kit (200 cycles). Raw sequencing reads were first trimmed using BBDuk (from BBTools suite v35. 85) to remove possible adapter contamination and then aligned to the reference genome (Ensembl Release 106) using STAR aligner v2. 5  [47]. RSEM v1. 3. 0  [48]  was used to quantify transcript abundances, while differential expression analysis was carried out using the package DESeq2 v1.