dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Selection of the urban areas. We have chosen the cities of our database following two criteria. First, we selected medium to large cities (with a population of over 300 000) in order to get a large share of the global urban population. Second, we selected cities of different cultural and historical backgrounds and tried to get a wide geographical coverage. In this way, we defined a first sample of 281 cities. However, data collection was possible in 192 cities of this sample ( Fig. 1  ), mainly due to the availability of real estate data, as detailed in the next sections. Figure  4 , in the supplementary material, presents the original 281 cities. Grid analysis. For each city, we designed a georeferenced grid of a 1 km 2 resolution, encompassing the whole urban area ( Fig. 2  ). We used these grids to aggregate land cover, population density, real estate, and transportation data at the same resolution.
Land cover. We used the European Space Agency land cover data, available worldwide at a 300 m spatial resolution on an annual basis from 1992 to 2015  [2]. These data allow to identify urbanized areas, as well as to distinguish constrained areas, i. e. locations where housing construction is impossible or difficult (water bodies,…) from unconstrained areas (see table Esac-ciReclassification. xlsx in the Zenodo repository). Population density. We used the GHS-POP layers of the open and free GHSL (Global Human Settlement Layer) data of the European Commission, providing population counts per grid cell at a 250 m resolution worldwide and available for 1975, 1990, 20 0 0 and 2015  [ 1 , 4 ]. Real estate. We have collected real estate data on rents, sale prices, and dwelling sizes by web scrapping real estate websites from 2017 to 2020.
We selected these websites following four criteria: • the website must have a nationwide coverage to ensure consistent results in each country, • it must geolocalize the dwellings, • it must have values for both rent or sale prices and dwelling sizes, and • it has to be written in the local language and to propose prices in local currency to limit real estate ads targeting expatriates. Real estate websites and scrapping dates can be found in table DataSources. xlsx , available in the Zenodo repository. Then, we aggregated the data and displayed them at the pixel level: • we aggregated dwelling sizes by computing the mean and the median of data per pixel. • we compute rents or sale prices per sqm by dividing total rents or sale prices by dwelling sizes, and then display the mean and the median per pixel.
Alternatively, we regress total rents or sale prices by dwelling sizes for each pixel to find an estimate of rents or sale prices per sqm for each pixel. • as a robustness check, we also display these data applying three outliers' exclusion methods on prices per sqm before the aggregation: boxplot (with a 1. 5 coefficient), percentiles (excluding the top and bottom 2. 5% values), and hampel. Transportation. We collected transport distances and durations to the city centers using Google Maps and Baidu Maps APIs (Application Programming Interfaces). Different methods have been used in the urban economics literature to identify city centers. Most rely on job density data  [5] [6] [7] [8] [9]  , which are unfortunately not available on a consistent basis in our sample of cities. Therefore, we defined city centers by a compromise between five qualitative criteria: the geographical center of the data, the historical center of the cities, the location of public transports hubs, the official central business district, and the city hall location.
We collected transport data from the centers defined above to each grid cell at typical afternoon rush hours 1. We collected, when available, both driving and public transport data. Trans- port data sources and rush hours can be found in table DataSources. xlsx , available in the Zenodo repository. It was not possible to collect transport data from each grid cell, so we collected data from 10% of all cells 2  , and then interpolated them using the INTERPP function from R package AKIMA. 3 ode availability. Detailed code generating the database can be accessed from the source code hosted via Github at https://github. com/CIRED/gridded _ dataset _ 192 _ cities.
Technical Validation Land cover. For land cover, we rely on the validation performed in the ESA CCI technical report  [2]. Validating ESA CCI land cover data against GlobCover 2009 data, the authors found an overall accuracy of 71. 45%, with the highest accuracies for rainfed cropland, irrigated cropland, broadleaved evergreen forest, and urban areas. The report mentions that other validation tests are undergoing. Population density. For population density, we rely on the existing validations of the GHSL data. Validation tests of the GHSL built-up data, from which the GHSL population data are directly derived, are presented in the JRC technical report  [11]. In this report, the GHSL built-up data are validated using two sources: a systematic field survey managed by EUROSTAT and a set of digital cartographic products with building footprints at a scale of 1:10,0 0 0 or better gathered from data portals of official websites of public governmental institutions.
From comparing GHSL data with these two sources, total accuracies are 0. 9628 and 0. 8996 respectively. Other validation tests have been conducted by independent studies; for instance, comparing the GHSL built-up data with Baidu data in 20 Chinese cities, Liu et al. (2020)  [12]  find an R2 of 0. 76. The GHSL population data are more difficult to validate due to the lack of independent and comparable data. However, benchmarking using the GEOSTAT 2011 resident population data on 18 European countries, Freire et al. (  2016 )  [4]  found a correlation of 0. 83. Real estate. The quality of real estate prices, rents and dwelling size data differs from one city to another. A first source of error comes from the quantity of data that we have been able to collect. To assess the quality of the real estate data, we computed two variables at the city level: the market data cover, as the total population of the city divided by the number of ads, and the spatial data cover, as the number of pixels for which we have real estate data divided by the number of inhabited pixels.
For market data cover, we found that in 95 cities out of 192, we have scrapped more than 1 rent ad per 10 0 0 inhabitants, and that in 174 cities out of 192, we have scrapped more than 1 rent ad per 10,0 0 0 inhabitants. For spatial data cover, we found that in 109 cities out of 192, the spatial data cover of rent ads is above 10%, and that in 153 cities out of 192, the spatial data cover is above 5%. We report in our data the number of ads per pixel. A second potential source of mistake is the systemic bias coming from our data sources. The websites that we scrapped present ads, which may not necessarily reflect the actual rents or prices, if margins of negotiations exist, for instance. They may also be biased and present only dwellings that are not representative of the actual dwelling stock.
This may especially be the case if, in a city, online ad websites are not the main way of buying or renting a dwelling. To mitigate these risks, we tried to make sure that the websites we scrapped are actually used by locals. To assess these two risks, we checked the validity of our rents and real estate prices against 4 external databases providing averages per city. We used two crowd-sourced websites aiming at describing the cost of living across the world for expatriates, Numbeo  [13]  and Expatistan  [14]  , to estimate rents and property prices around the world. We also used two databases built for real estate investors: UBS "Prices and Earnings" database  [15]  , and data from CBRE, an expert in real estate and services based in London, whose data were obtained from various local sources  [16].
When averaged over the cities, or over the inner core and the outer core of the cities, our data broadly agree with these data ( Fig. 3  ). Transportation. Overall, Google maps and Baidu maps data have several advantages compared with other transport data. Google maps data have a wide coverage compared with other data sources such as OpenStreetMap  [19]  and are regularly updated. Furthermore, they allow for measuring travel times and distances with and without congestion  [20]. For these reasons, they have been used in recent research papers to compute travel times and distances  [21]. However, the quality of Baidu and Google maps data is difficult to assess, as these websites use closed algorithms based on users' travel data  [ 17 , 18 ]. It should be expected that the quality of these data is a function of the number of users in each city, and should therefore be higher in developed country cities and in large population cities  [22] [23] [24].
45 µm. This step was repeated six times on the same aliquot to recover 95% of soluble ions and then 25 µL were injected. Soluble anions concentrations were determined by chemically suppressed ion chromatography (carbonate 1mM/bicarbonate 3. 5 mM eluents and AG14A/AS14A columns, 1. 2 mL/min) using a Dionex DX 100 ion chromatograph. Soluble cations concentrations were determined by chemically suppressed ion chromatography (methane sulfonic acid 20 mM eluents and CG12A/CS12A columns, 1 mL/min) using a Dionex ICS 900 ion chromatograph. The calibration ranges of soluble anions and soluble cations were from 0. 5 to 100 ppm. Chromatographic data were analyzed by Chromeleon ® software (Thermo Fisher Scientific, Waltham, MA, USA).
Acknowledgments: The  Unité de Chimie Environnementale et Interactions sur le Vivant (UCEIV)  and  Centre Commun de Mesures (CCM) , participate in the  Climibio  project, which is financed by the  Communauté Urbaine de Dunkerque , the  Région Nord-Pas de Calais , the  Ministère de l'Enseignement Supérieur et de la Recherche , the  National Center for Scientific Research in France (CNRS)  and  European Regional Development Fund (ERDF). We would like to thank  Denis Marin  who drew the map of the study area.
With the development of sophisticated water quality models and the advances in computational power, data assimilation (DA) techniques, especially ensemble-based methods (the ensemble Kalman filter and particle filter), are attracting considerable attention in water quality modeling for improving the estimation of state variables and parameters in water quality models. The ensemble Kalman filter (EnKF) has become the most popular DA method while the particle filter (PF), which does not rely on Gaussian or quasi-linearity assumptions, is seldom applied in water quality modeling. Here, we present a comparison between the PF and EnKF for the update of model parameters related to river metabolism. The two filters are implemented in ProSe-PA, a hydro-biogeochemical software, and their performance is assessed on two synthetic case studies.
The aim of this paper is therefore to carry out a comparison of the PF and EnKF for DO data assimilation and for the estimation of model parameters. The performances of the PF and EnKF are evaluated on the basis of synthetic case studies using the hydro-biogeochemical program ProSe-PA  (Wang et al. , 2019). First, the recovery of DO concentrations by reaeration in a river system is built to model a slightly nonlinear system (Section 2. 4. 1). The computational time, the simulated DO concentrations, and the posterior distributions of the reaeration coefficient using different ensemble sizes are assessed (Section 3. 1). Second, the PF and EnKF are applied in a strongly nonlinear system represented by heterotrophic bacterial activities in the river (Section 2. 4. 2). The uncertainties in parameter estimation and the PF set-up are finally discussed (Sections 4.
Particle filter A PF was implemented in ProSe-PA software and its efficiency was demonstrated in a synthetic case study  (Wang et al. , 2019)  and in a real system  (Wang et al. , 2022). The PF is a method based on the Bayes theorem (Bayes, 1763) and the Markov property  (Markov, 1906). It integrates observations y * at each time step into the forward model (ProSe-PA) in order to approximate the posterior pdf  (| * ) by a set of particles each associated with a weight (ω). For each particle, the weight is calculated using the Bayes theorem and Markov property  (Doucet and Johansen, 2011; Wang et al. , 2019) , as stated by Eq. (4): 𝜔 𝑡 ∝ 𝑓 (𝐲 * 𝐭 |𝐱)𝜔 𝑡-1 (4) where  ( ) can be approximated by the ensemble particles  (Doucet et al. , 2001) : 𝑓 (𝐱 𝐭 |𝐲 * 𝟏∶𝐭 ) ≈ 𝑁 ∑ 𝑖=1 ω𝑖 𝑡 𝛿 𝐱 𝑖 𝑡 ( 5 ) where  is the ensemble size and  the Dirac measure.
In such case, the posterior pdf  (  | * 1∶ ) cannot be approximated adequately by the ensemble particles (Eq. (  5 )). To reduce the degeneracy effect, a resampling procedure is used. The resampling procedure roughly duplicates particles with high weights while eliminating particles with near-zero weights. A series of resampling methods were proposed in the literature and reviewed by  Li et al. (2015). The systematic resampling technique  (Kitagawa, 1996; Moradkhani et al. , 2005a; Li et al. , 2015)  was chosen for the ProSe-PA software. It is not necessary to resample the particles at each time step, but only when the particles show some signs of degeneracy. The criterion for performing the resampling is based on the variance of the weights, which indicates the degree of degeneracy. Kong et al.
That is because the EnKF assumes Gaussian errors for forecast states and parameters, which is not realistic for the functioning of an aquatic system (lake, river)  (Huang et al. , 2013; Wang et al. , 2022). Evensen et al. (2022, p. 95 ) also stated that ''Commonly-used ensemble data-assimilation methods, like the EnKF. , only sample the posterior pdf correctly in the Gausslinear case and typically fail in cases with strong nonlinearity''. The PF overcomes the Gaussian assumption of EnKF. The implementation of PF into a complex hydro-biogeochemical program (ProSe-PA) has been proved to be realistic  (Wang et al. , 2019)  and its performance (calculation cost, DO concentration updates, parameter estimation) has been recently evaluated for the Seine River system, France  (Wang et al. , 2022).
This mechanism is specific to cachectic mice as we did not observe anti-inflammatory effects or hyperglycaemia in CT mice treated with the AHR agonist. Further studies will be needed to clarify how AHR improves hepatic gluconeogenesis in cancer cachexia. A recent review outlined the fact that AHR pathway may have cell type-specific regulatory effects in liver disease. 3 ing an existing dataset,  41  we found that AHR protein is primarily expressed in hepatic stellate cells and also in smaller quantities in liver sinusoidal endothelial cells, Kupffer cells, cholangiocytes and hepatocytes. This differential expression of AHR in liver cell subtypes might be an explanation as to why FICZ treatment improved the hepatic expression of some genes involved in liver inflammation but not others. Our findings corroborate those of Narasimhan et al.
Acknowledgements We thank  Bouazza Es Saadi ,  Isabelle Blave  and  Martin Nicolas  for their skilled technical assistance. We thank  Sébastien Pyr dit Ruys  for his advice on co-immunoprecipitation assays. We are grateful to  Dr. Caroline Bouzin  and the  IREC Imaging Platform (2IP)  for their assistance with histological analyses and to  Prof. Ana Beloqui  for access to a Meso Scale Discovery microplate reader. We also thank  Sabrina En Nouaimy  for her help during mouse experiments. The authors of this manuscript certify that they comply with the ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle. S17.
Funding information This work was supported by the  Fonds De La Recherche Scientifique -FNRS (F. R. S. -FNRS)  under Grant  MIS F. 4512. 20. LBB is a  Collen-Francqui Research Professor  and the recipient of subsidies from the  Fonds Spéciaux de la Recherche (FSR, UCLouvain , including the  Action de Recherche Concertée LIPOCAN  [ 19-24. 096 ]), the  Télévie , the  Walloon Region  in the context of the funding of the strategic axis FRFS-WELBIO (40009849), the  Excellence of Science (EOS)  programme ( 40007505 ) and the  Fondation Louvain. NMD is a recipient of grants from the  F. R. S. -FNRS  ( PINT-MULTI R. 8013. 19  [NEURON-ERANET, call 2019] and  PDR T. 0068. 19 ). MMT was a  Research Fellow  from the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA, F.
R. S. -FNRS ). AB was funded by the  American Cancer Society  ( Research Scholar Grant   132013-RSG-18-010-01-CCG ) and the  National Institute of Arthritis and Musculoskeletal and Skin Diseases  ( R01AR079379 ). JRH was supported by a  T32 Institutional Training Grant  from the  National Institutes of Health (NIH)  ( AR065971 ). The funders had no role in study design, data collection and analysis, interpretation of the results, decision to publish or preparation of the manuscript.
The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. The presented products are fully validated, e. g. , global user's and producer's accuracies for the annual temporary crop product are 88. 5% and 92. 1%, respectively. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and push.
Seasonal active irrigation maps Irrigation can be applied in many ways, some of which are easier to detect from space than others. Additionally, the reason to irrigate might differ per crop and region  (Burt et al. , 2000). For example, in some countries, only the most valuable crops are irrigated and saved from dehydration due to a general water shortage, whilst in other countries irrigation is widely applied to maximize crop yield. The WorldCereal irrigation product was primarily trained using irrigation data from (semi-)arid climate zones. This was done partly by choice (the impact of irrigation on the environment is larger in dry regions), but also by necessity since there is limited irrigation data available for temperate regions. Our definition, therefore, primarily focuses on irrigation that is applied to enable healthy crop growth, instead of maximizing crop yield.
Each sample contains information on either its land cover/use, crop type, irrigation status or a combination of these. A timestamp, derived as accurately as possible, allows to assign a sample to a specific year and growing season(s). Finally, a confidence score indicates the expected quality of a sample and was derived at the original reference dataset source level based on the combined expert assessment of spatial, temporal and thematic accuracy. A large number of samples is located in the EU and the USA, thanks to major contributions of European Land Parcel Identification System (LPIS) datasets and points sampled from the USDA Crop Data Layer. These datasets were therefore subsampled for training to reduce the spatial bias and keep the amount of training input extractions manageable. The detailed description of this reference data module and the harmonization process followed can be found in  Boogaard et al.
(in review). Irrigation training data was especially sparse, consisting of only 36,000 rainfed and 26,000 irrigated samples divided over 19 countries. Since this was too limited to train a global irrigation mapping system, manually collected samples were included. The irrigated samples of this dataset consisted of center pivot irrigation sites which were visually selected using Google Satellite and Bing Aerial base layers. A minimum NDVI peak threshold of 0. 4 was set to prevent including center pivots that were not showing any cropping activity during the training period. Rainfed samples were primarily collected from Europe, Northern Africa, India, Australia, and Argentina. The accuracy of this dataset was verified using the FAO AQUASTAT data on areas equipped for irrigation  (FAO, 2016).
Inputs and pre-processing 175 The satellite-based inputs used to create the WorldCereal products are optical Sentinel-2, radar Sentinel-1 and thermal Landsat 8 time series. Sentinel-2 optical bands were first subjected to cloud and shadow masking by applying a dilated version of the binarized SEN2COR scene classification mask  (Main-Knorn et al. , 2017). For crop type mapping, a cropspecific Growing Degree Days (GDD) normalization step was performed on the original time series using mean daily temperature data from the global AgERA5 reanalysis dataset  (Boogaard et al. , 2020). The aim of this step is to better align 180 the time series of identical crops that are growing under different temperature regimes. The procedure is outlined in detail in   Cintas et al. (2023). Next, depending on the product, either the original or GDD-normalized acquisitions were composited to 10-day regular timestamps using a median operator.
Next, crop specific GDD normalization was done in case of crop type mapping  (Cintas et al. , 2023). Finally, a 12-day compositing was performed using a mean operator and any missing values due to for example temporary unavailability of the satellite were linearly interpolated. The Landsat 8 Collection 2 Level 2 surface temperature band ST_B10 was first masked using an eroded and dilated version of the mask originally delivered with the product, in turn based on the CFMask algorithm. The data were then composited into 16-day time series using a median filter. The remaining missing values in particularly cloudy periods were linearly interpolated. Next to the main satellite inputs, the workflow also makes use of ancillary data sources. We used the Copernicus DEM -Global and European Digital Elevation Model (COP-DEM) at approximately 30 m spatial resolution ("GLO-30").
The original 30 m data were resampled to 20 m spatial resolution to align with the Sentinel-2 tile grid and to be compatible with the classification workflow. Another auxiliary layer is based on biome membership. Based on the 846 ecoregions of the Ecoregions2017 map  (Dinerstein et al. , 2017) , the world was stratified into 13 biomes. Biome membership allows a classification model for implicit grouping of training and inference data based on shared characteristics as described by their biome. These biomes were originally obtained as discrete vector polygons. Using these in the classification can cause the appearance of hard border artefacts in the products. To avoid these artefacts and reflect the natural and gradual transitions between biomes, we derived a set of continuous biome raster datasets.
The original biome polygons were first simplified with a tolerance of 0. 01 °, buffered at 0. 05 ° and rasterized with a resolution of 0. 01 °. The obtained raster datasets were then filtered with a Gaussian kernel of radius 0. 5 °, with maximum amplitude of 1 at the center and zero at the borders. The resulting filtered biomes gradually transition from 0 to 1 in a range of 1 ° around the original discrete biome borders. This means that points close to the biome boundaries will have a certain degree of membership also with other nearby biomes, representing the gradual transition between different biomes.