dataesr/scientific-paragraphs-categorization huggingface dataset:
We believe that these results are a relevant step towards localising the sites that could hold potential for diagnostic refinement of ASD and SZ for the temporal irregularities of their neural functioning. However, given the diagnostic heterogeneity of both ASD and SZ, it is necessary to further ascertain the replicability of these findings before more decisive conclusions can be formulated. The present study has the distinct advantage that rsfMRI data from ASD and SZ and controls were recorded in the same setting, thus eliminating potential confounds related to variation in data collection. However, some limitations remain. On the one hand, some sensory areas, like the postcentral gyrus, have been shown to display decreased sensory sensitivity with age  (Diaz & Yalcinbas, 2021). Although we included age as covariate in our group analysis, age-balanced samples would have been optimal.
Acknowledgments This work was financed by the  IRICE IJES  project  RA0014963  ( Installed Jet Effect Simulator ,  FEDER-FSE Rhône-Alpes ). It was granted access to the  HPC resources of PMCS2I (Pôle de Modélisation et de Calcul en Sciences de l'Ingénieur et de l'Information) of Ecole Centrale de Lyon, PSMN (Pôle Scientifique de Modélisation Numérique)  of  ENS de Lyon , members of  FLMSN (Fédération Lyonnaise de Modélisation et Sciences Numériques ), partner of EQUIPEX EQUIP@MESO, and to the resources of  IDRIS (Institut du Développement et des Ressources en Informatique Scientifique)  under the allocation 2021-2a0204 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). It was performed within the framework of the  LABEX CeLyA  ( ANR-10-LABX-0060 ) of  Université de Lyon , within the  program Investissements d'Avenir  ( ANR-16-IDEX-0005 ) operated by the  French National Research Agency (ANR).
scRNA-seq Data Analysis Raw sequencing data were de-multiplexed and converted to gene-barcode matrices using the Cell Ranger (version 2. 2. 0) mkfastq and count functions, respectively (10x Genomics). The mouse reference genome mm10 was used for alignment. Data were further analyzed in R (version 3. 4. 0) using Seurat (version 3)  [13, 14]. The number of genes detected per cell, the number of UMIs (unique molecular identifiers), and the percent of mitochondrial genes were plotted, and outliers (i. e. , cells that expressed less than 200 or more than 2500 genes) were removed to filter out doublets (two single cells) and dead cells. Differences in the number of UMIs and percentage of mitochondrial reads were regressed out. Raw UMI counts were normalized and log transformed. The single cell data were aligned and projected in a two-dimensional space through t-distributed stochastic neighbor embedding or uniform manifold approximation and projection to allow identification of different cell populations using the Seurat program.
Methods This study was part of a research project on ecosystem services in the Mariño watershed, which had previously built relationships with key individuals and institutions involved in natural resource management  (Vallet et al. , 2019). Fieldwork took place from May to August 2017. Interviews and surveys were conducted in Spanish or Quechua depending on the participants. After introducing our research and informing that all collected data would be anonymized, we asked participants to confirm their consent. Our research protocol was approved by CIFOR Research Ethics Review Committee (reference FTR044).
We summarized the correlations as a network of variables. Using an algorithm for detecting dense subgraphs called cluster_fast_greedy in the R-package igraph  (Csardi, 2018) , we identified groups of VBA variables with strong positive correlations among them. We also applied multivariate analysis with a hierarchical clustering applied to the 14 variables. The elbow method identified the best numbers of clusters between three to five and the number of three clusters led to the most meaningful interpretation. The clusters of VBA were called "hydrosocial perspectives", i. e. sets of beliefs and values of respondents, associated with attitudes toward water management and managers. All statistical analyses were done with R (R Core Team, 2019). Table  1. List of statements that respondents rated on a 7-level Likert scale (strongly disagree, disagree, moderately disagree, neither disagree or agree, moderately agree, agree, strongly agree).
Acknowledgements This paper is a contribution from the  Transformative Adaptation Research Alliance  (TARA, https//research. csiro. au/tara/),an international network of researchers and practitioners dedicated to the development and implementation of novel approaches to transformative adaptation to global change. The funding partners that have supported this research include the  International Climate Initiative  ( IKI , project  15_III_075 ) of the  German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) , the  Norwegian Agency for Development Cooperation  (agreement  QZA-016/0110 ), the  French funding agency for research  (project  TRASSE   ANR-CONACYT-17-CE32-0012 ), the  European Union 's  H2020 research and innovation program  ( SINCERE  Project), the  French Ministry for the Ecological and Inclusive Transition , and the  CGIAR Research Program on Forests, Trees and Agroforestry (CRP FTA)  with financial support from the  CGIAR Fund.
In the past 20 years, the area of vegetation improvement accounted for 88. 09% and the area of degradation took up 11. 91% in this region. Specifically, compared with the period of 2000-2009, the area of vegetation improvement decreased slightly, and the area of degradation increased during 2010-2019  [29]. We considered three vegetation indices based on vegetation cover, canopy structure, and vegetation productivity, combined with climate factors and urbanization rate data to comprehensively analyze the forest, shrub, grassland, and cropland dynamics separately in the study region. The results will help to understand the changes and dynamics of various vegetation types in the Beijing-Tianjin-Hebei region.
Study Area The Beijing-Tianjin-Hebei region includes Beijing, Tianjin, and Hebei Province. It is located in North China at 113 • 27 -119 • 50 E, 36 • 05 -42 • 40 N, with a total area of 218,000 km 2 (Figure  1 ). The terrain is high in the northwest and low in the southeast, showing vertical climatic zones. The regional climate is temperate to warm-temperate and semi-humid to semi-arid continental monsoon climates  [30]. Vegetation types mainly include deciduous broadleaf forests, mixed forests, closed shrublands, open shrublands, grassland, and cropland. period of 2000-2009, the area of vegetation improvement decreased slightly, and the area of degradation increased during 2010-2019  [29]. We considered three vegetation indices based on vegetation cover, canopy structure, and vegetation productivity, combined with climate factors and urbanization rate data to comprehensively analyze the forest, shrub, grassland, and cropland dynamics separately in the study region.
Datasets The moderate-resolution imaging spectroradiometer (MODIS) is a sensor mounted on the Terra and Aqua satellites. It has 36 medium-resolution spectral bands, including 250 m (band 1-2), 500 m (band 3-7), and 1000 m (band 8-36). It is mainly used to obtain observational data of global biological and physical processes with a revisit cycle of 1-2 days. All the vegetation indicators data are from the United States Geological Survey (USGS, Reston, VA, USA, https://www. usgs. gov, accessed on 24 February 2022). The LAI.
Datasets The moderate-resolution imaging spectroradiometer (MODIS) is a sensor mounted on the Terra and Aqua satellites. It has 36 medium-resolution spectral bands, including 250 m (band 1-2), 500 m (band 3-7), and 1000 m (band 8-36). It is mainly used to obtain observational data of global biological and physical processes with a revisit cycle of 1-2 days. All the vegetation indicators data are from the United States Geological Survey (USGS, Reston, VA, USA, https://www. usgs. gov, accessed on 24 February 2022). The LAI data come from the MOD15A2H dataset. The algorithm of this dataset is to select the best pixels from the Terra satellite sensors within 8 days to obtain a composite dataset with a period of 8 days and a resolution of 500 m. The NDVI data come from the MOD13Q1 dataset. The algorithm synthesis uses the images with less cloud, low viewing angle, and highest pixel value with a spatial resolution of 250 m.
The NPP data comes from the MOD17A3HGF dataset, which is the improved MOD17. The annual NPP value is derived from the sum of all 8-day net photosynthetic products in a given year and will be generated at the end of each year with a spatial resolution of 500 m. The landcover classification criteria are based on the MODIS land cover type yearly global 500 m dataset and the annual land cover product of China (CLCD)  [31]. For the MODIS dataset, band "LC_Type1" was selected as classification criteria, which is based on IGBP classification and divides the land use/land cover into 17 categories. The CLCD is Landsat-derived 30 m dataset. The land use is divided into cropland, forest, shrub, grassland, water, snow/ice, barren, impervious, and wetland. The annual average temperature, precipitation, and urbanization rates are from the official websites of statistics or metrology of each province/city.
Data Processing This study, based on the GEE platform, set Beijing, Tianjin, and Hebei Province as the research area, selecting LAI, NDVI, and NPP from MODIS products between 2001 and 2020 to achieve long-term, large-scale batch processing. The workflow is shown in Figure  2. Taking Tianjin's 2001-2020 average LAI of the growing season as a case, the steps are as follows: Import the MOD15A2H dataset, MCD12Q1. 006 products, and CLCD datasets into the GEE platform. After selecting bands, filter region, filter time period, quality control, and other pre-processing, we set the MODIS land cover classification value 1-5 as forest, 6-7 as shrubland, 8-10 as grassland, and 12 and 14 as cropland. Similarly, 1-4 represents cropland, forest, shrubland, and grassland, respectively, in CLCD. Firstly, calculate the average value of the growing season LAI during 2001-2020.
Secondly, mask them with MODIS landcover and CLCD, respectively. Obtain the patch images of forest, shrubland, grassland, and cropland in Tianjin. Finally, average the value in each category to obtain an average value of the growing season LAI of four landcover types. When the batch processing is completed on GEE, the average values of LAI in each area during the study period are calculated and analyzed. We add the layers in the form of visual images and export them to ArcGIS and other data processing software for further analysis  [41]. Import the MOD15A2H dataset, MCD12Q1. 006 products, and CLCD datasets into the GEE platform. After selecting bands, filter region, filter time period, quality control, and other pre-processing, we set the MODIS land cover classification value 1-5 as forest, 6-7 as shrubland, 8-10 as grassland, and 12 and 14 as cropland.
Mann-Kendall Test Firstly, calculate the average value of the growing season LAI during 2001-2020. Secondly, mask them with MODIS landcover and CLCD, respectively. Obtain the patch images of forest, shrubland, grassland, and cropland in Tianjin. Finally, average the value in each category to obtain an average value of the growing season LAI of four landcover types. When the batch processing is completed on GEE, the average values of LAI in each area during the study period are calculated and analyzed. We add the layers in the form of visual images and export them to ArcGIS and other data processing software for further analysis  [41].
Results We compared vegetation indicators value from MODIS IGBP landcover and CLCD to examine whether land use/landcover (LULC) maps with higher accuracy and resolution would perform better in this study. Take the growing season average LAI value in the Beijing-Tianjin-Hebei region for example, three-quarters of the vegetation types showed obviously different results under the two classification criteria of MODIS LULC and CLCD (Figure  3 ). Specifically, LAI values of forest and shrubland extracted by CLCD show an abnormal lower value than those from MODIS LULC and other researchers' results  [44]. LAI value of grassland from MODIS is slightly higher than that from CLCD, while cropland has closer data under the two criteria. Generally, the LAI value quality defined by MODIS LULC is more suitable in this study.
The average LAI value of the whole area defined by both criteria is the same, indicating that the difference in the LAI value of each vegetation type comes only from different classification criteria. It shows although CLCD has a higher spatial resolution, the MODIS LULC is more consistent with other vegetation indices data resources, we will use the data defined by MODIS IGBP landcover for the following analysis. In terms of LAI and NDVI values, Beijing outperforms Hebei, followed by Tianjin. For NPP, the results show that the value in Beijing and Hebei are close, with Tianjin remaining the lowest, relatively. The MODIS IGBP landcover change in the study region has shown in Figure  4. Overall, deciduous broadleaved forests and mixed forests are the dominant forest types in Beijing. Tianjin was dominated by deciduous broadleaf forests before 2015, and then sizable mixed forests grew or were planted.
Hebei's forest species are more abundant, including evergreen needleleaf forests, deciduous needleleaf, deciduous broadleaf forests, and mixed forests. The shrubs in Beijing, Tianjin, and early Hebei are major closed shrublands. However, since 2016, sizable open shrublands appeared in Hebei. All the grass types (woody savannas, savannas, and grassland) can be seen in the Beijing-Tianjin-Hebei region. The functions of cropland in Beijing and Tianjin are relatively simple, while in Hebei, there are large areas where cropland and natural vegetation are mixed. LULC is more consistent with other vegetation indices data resources, we will use the data defined by MODIS IGBP landcover for the following analysis. The temporal change of four vegetation types in Beijing, Tianjin, and Hebei are consistent with the overall trend (Figure  5b ).
Procedure and assessment MSSA were identified daily on Emergency Room databases. In addition to medical assistance, psychiatric assessment of MSSA is always performed during hospitalization once the medical condition is sufficiently stabilized. Researchers involved in the study evaluated the patient during the first week after the MSSA. It was independent of clinical assistance and provided by expert psychiatrists during hospitalization. Psychosocial, clinical and suicidiological variables related to suicide risk and protective factors  [31]  were recorded using an ad hoc questionnaire that included: • Social-demographics: age, sex, nationality, marital status, educational level, employment situation, and legal problems. • Physical and/or sexual abuse during their childhood or/and adulthood.
Videos are recorded at a 16-bit resolution and at a high frame rate. The typical field of view is a 8 × 8 cm 2 domain, imaged at 120 Hz with a resolution of 100 µm. Images of particle streaks are used to show qualitatively the turbulent flow field in the cavity (see Figure  1e ). This figure has been produced by processing raw images using basic functions of the ImageJ software (version 1. 52a), and the trajectories are followed over 4 wave periods. The quantitative data analysis of the flow is performed using particle imaging velocimetry (PIV) and particle tracking velocimetry (PTV) algorithms as described in  [37].
To achieve these objectives, we analyzed a dataset including the biological and physico-chemical results of 2007 sampling events from a large-scale monitoring survey routinely performed in French wadeable streams. We have identified five (diatoms), eight (macroinvertebrates), and eight (fishes) trait syndromes, either positively or negatively related to an increasing toxicity gradient of different clusters of micropollutant types or modes of action. Our analyses identified several key trait categories and sets of paragons, exhibiting good potential for highlighting exposure by specific micropollutant types and modes of action. Overall, trait syndromes might represent a novel and integrative bioassessment tool, driven by the diversity of trait-based responses to increasing gradients of micropollutant toxic cocktails.
Benthic Diatom, Macroinvertebrate, and Fish Data To investigate the link between micropollutants and trait syndromes, we used data extracted from a national database (http://www. naiades. eaufrance. fr, accessed on 1 June 2020) maintained as part of the monitoring programs implemented in France following the European Water Framework Directive (WFD;  [43] ). This database gathers the abundances of three biological compartments, namely benthic diatoms, benthic macroinvertebrates, and fishes. The three biological compartments were sampled following standardized protocols, namely the normalized Biological Diatom Index sampling protocol  [44] , the multihabitat sampling protocol for macroinvertebrates  [45] , and the standardized electrofishing protocol  [46]. Here, we only kept the sampling events (i.
e. , "site x year" couples), during which the three biological compartments were sampled within a period of three months. Our work dataset was thus composed of three tables compiling the abundances of diatoms (1279 taxa), macroinvertebrates (344 taxa), and fishes (63 taxa) for 2007 sampling events, which exhibited a complete description of the chemical (a maximum of one pressure type without information) and hydromorphological pressures (no missing information). For further details on the pressure descriptions, see  [47]. These sampling events were performed at 947 sites over the period of 2005-2015 (with an average of 2. 1 sampling events per site). The three abundance datasets were combined with three trait datasets, describing various biological attributes and ecological preferences of the taxa identified in each of the three abundance datasets.
e. , by assigning to a given taxon an affinity score ranging from "0" (=no affinity) to "5" (=high affinity) to each category of the trait  [50] ); (ii) a disjunctive approach (i. e. , the category is used (=1) or not used (=0) by the taxon). Before further calculations, all the affinity scores of a given taxon for a given trait were expressed as relative frequencies by dividing each affinity score by the sum of the scores of the taxon over all the categories of this trait. The trait descriptions are available in the Supplementary Materials (Tables  S1-S3 , for diatom, macroinvertebrate, and fish traits, respectively). For each sampling event and biological compartment, the mean relative frequency of each trait category in each assemblage (i. e. , the community-weighted mean; CWM) was calculated using the corresponding abundance and trait datasets and the following formula: CW M m = ∑ i (RA mi × log(A i + 1)) ∑ i log(A i + 1) (1) where CWM m is the community-weighted mean of the trait category m for a given sampling event, RA mi is the relative affinity of taxon i for the trait category m, and A i is the abundance of the taxon i in the sampling event.
Calculation of Micropollutant Toxicity in Water Environmental data were associated with the 2007 sampling events feeding the abundance datasets. If information on 174 micropollutants was available in our raw dataset, only 120 compounds or compound families were kept for the calculation of the joint toxicity of mixtures in the water of each sampling event (Table  S4 ). Only the micropollutants with available (i) water concentration in the sampled sites (measured during routine stream monitoring in France; http://www. naiades. eaufrance. fr, accessed on 1 March 2021), (ii) toxicity value (expressed as the median effective concentration, EC 50 ) for the model species of the three biological compartments, and (iii) known mode of action (for pesticides) were selected for the calculation of their joint toxicity in water.
These micropollutants belonged to five types-"mineral micropollutants", "pesticides", "polycyclic aromatic hydrocarbons (PAHs)", "polychlorinated biphenyls (PCBs)", and "(other) organic micropollutants". The toxicity values were retrieved from three open databases-the Pesticide Properties DataBase (PPDB; http://sitem. herts. ac. uk/aeru/ppdb/, accessed on 1 March 2021),  Malaj et al. (2014, [51] ), and the INERIS database (https://substances. ineris. fr/fr/search, accessed on 1 March 2021). The modes of action were also retrieved for the 120 micropollutants using the R4P database  [52]. Each pesticide has been allocated to a single mode of action among thirteen different MoAs (Table  1 ). It was indeed not possible to allocate a precise mode of action for the four remaining micropollutant types.
As a result, the MoA label provided in Table  1  corresponds to the function targeted by the compound according to the R4P database for pesticides, or simply the name of the micropollutant type for the other types. Afterwards, and for readability, all the MoAs (for pesticides) and the five other types of micropollutants have all been labeled as MoAs. From this dataset compiling environmental concentrations and EC 50 values for each micropollutant, the toxicity of each MoA was expressed for each sampling event and each biological compartment as a "sum of toxic units" with the following formula: TU MoA = ∑ i [MP] i /EC 50i (2) where TU MoA is the sum of the toxic units of all the micropollutants exhibiting the same MoA, [MP] i is the water column concentration of a micropollutant i exhibiting this MoA for a given sampling event, and EC 50i is the 50% effective concentration of the micropollutant i for a given biological compartment (diatoms, macroinvertebrates, or fishes).
Statistical Analyses In order to identify trait syndromes responding in a similar way to specific MoAs or groups of MoAs, we calculated the non-parametric correlation coefficients (Spearman's rho;  [53] ) between each pair of trait category (from the CWM m calculation) × MoA (expressed as the sum of toxic units). The calculations were done independently for each biological compartment from the database of the 2007 sampling events (Figure  1 , step 1). The sums of the toxic units (x) were log(x + 1)-transformed before calculations. The strength and direction of the relationships between the trait categories and MoAs are illustrated by a heatmap drawn for each biological compartment. Only trait categories that correlated with at least three of the twelve MoAs selected for the analyses (Spearman's test; p < 0.
We applied the commonly used "Phenon line" method  [55] , which requires the drawing of a line across the dendrogram at a certain similarity level. The selection of the partitioning level depends mainly on the specific shape of the dendrogram. We have marginally adapted this partitioning level to maximize the evenness of the trait categories among the trait syndromes and avoid having some trait syndromes with a too low or high number of trait categories. All statistical analyses were done using R software version 4. 0. 3 [57]. The correlation coefficients (Spearman's rho) between each pair of trait category × MoA were calculated by using the cor. test() function. The heatmaps were drawn using the function heatmap2() from the package gplots  [58]. The hierarchical cluster analyses allowed us to identify which trait syndromes were performed by using the hclust() function, with ward.