dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This study was supported by funding from  Alexander von Humboldt Foundation  to M. C. D. ; the  Agencia Nacional de Promoción Científica y Tecnológica  ( PID-2011-0059 ,  PIBT/09-2013 ,  PICT-2016-4647  and  PICT2019-0656  to L. M. ) and from  EU-LAC Health-Neurodegeneration   JOINT CALL 2016  ( EULACH16  to L. M. ) of  Argentina. The Funding of the  ALEXANDROS  study was provided by the  Chilean National Fund for Science and Technology (FONDECYT)  grant  1130947. The genotyping for the Chilean series was funded by  Genome Research @ Ace Alzheimer Center Barcelona  project (GR@ACE), supported by  Grifols SA, Fundación bancaria 'La Caixa' ,  Ace Alzheimer Center Barcelona  and  CIBERNED. The genotyping of Argentinian samples was funded by  JPND EADB  grant ( German Federal Ministry of Education and Research ,  BMBF :  01ED1619A.
PM and other aerosols are associated with respiratory illnesses and premature deaths as well as heart problems and debilitating asthma  89. We select a just (NSH) boundary of 15 μg per m 3 mean annual exposure to PM 2. 5 to avoid a high likelihood of significant harm from aerosols (Table  1  and Supporting Information) based on World Health Organization We also emphasize that exposure of a location does not necessarily imply responsibility for causing or addressing these environmental impacts. We invite the reader to investigate the consequences of different boundary values using the code in the code availability information. 2021  88  guidelines (Table  1 ) and European Union and US Environmental Protection Agency air quality standards  90, 91. Such local and regional guidance is needed because PM 2.
Novel entities and other pollutants We acknowledge the risks to Earth system stability and human wellbeing from other air and water pollutants, for which there are already well-accepted guidelines  88  , and the emerging threats from novel entities, new forms of existing substances and modified life forms that are geologically or evolutionarily novel and could have large-scale unwanted geophysical or biological impacts on the Earth system  27, 97. Evidence on the diverse risk potentials of novel entities, such as microplastics, 'forever chemicals', antibiotics, radioactive waste, heavy metals or other emerging contaminants, for Earth system function and human health and food security is increasing, but knowledge gaps on the scale and scope of potential impacts remain 98. Persson et al. 97  reported that humanity has crossed the PB for novel entities, although data limitations and quantification are challenging even for the known novel entities.
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-023-06083-8. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Safe ESBs We used two main groups of approaches to setting safe ESBs: a 'multiple elements' approach and a 'spatial aggregation' approach. We describe these methods here in general terms, with technical details available in Supplementary Methods. These boundaries are aimed at protecting Earth system stability and life-support systems for as many species as possible, but they may not protect all species or all humans today, as further elaborated in our justice analysis. For climate and biosphere, we assessed critical thresholds for a range of 'elements' relevant to each Earth system domain through literature review and modelling. • For climate, we based our data on those found in a recent assessment of climate tipping elements  16  combined with evidence on biosphere and cryosphere function and palaeoclimate variability (Supplementary Methods).
For water and nutrients, we identified subglobal boundaries relevant to these systems and then converted them into global boundaries using models or simple aggregation. (1) For surface water flows, we used an emerging consensus in the literature to set boundaries on the alterations (increase or decrease) to local-scale surface water flows that protect freshwater ecosystems and fisheries (Supplementary Methods) and applied this to the global land surface area. While the safe alterations can be summed to a global alteration budget, to ensure aquatic ecosystem protection, the safe ESB is best implemented and interpreted according to the subglobal boundary. To derive the safe levels of monthly flow alteration volumes for all land area globally, we analysed water balance model (WBM) runs coupled with the TerraClimate dataset of monthly climate forcings (Supplementary Methods has further information).
(2) For groundwater, our approach is based on preventing declines in local aquifer levels by setting the maximum safe average annual drawdown equal to the average annual recharge (Supplementary Methods). We estimated the annual groundwater recharge and drawdown for all land surface areas using Gravity Recovery and Climate Experiment satellite data covering the period from 2003 to 2016 coupled with data from the Global Land Data Assimilation National Oceanic and Atmospheric Administration Land Surface Model L4 v. 2. 1 (Supplementary Methods has more detailed information). (  3 ) For nitrogen, we used three regional environmental boundaries: significant disruption to freshwater ecosystems (from total N runoff), groundwater potability (from nitrate leaching) and terrestrial ecosystems (from atmospheric N deposition due to ammonia and nitrogen oxide emissions) across wide areas based on critical concentration limits for each.
(2) Wet bulb temperature (T W ) exposure was calculated for the historical time period of 1979-2014 and the Shared Socio-Economic Pathway 2-4. 5 future scenario for 2015-2100. Wet bulb temperature was calculated following the Davies-Jones 121 method. Global gridded temperature and relative humidity data with a grid spacing of 1. 25° × 1. 25° at 6-h intervals were downloaded from a bias-corrected global dataset  122  based on 18 models from the Coupled Model Intercomparison Project Phase 6 and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 dataset. We aggregated the data to create a maximum daily T W dataset and then interpolated this to match the 1° × 1° grid spacing of the spatially explicit data for the 2020 population distribution (most recent available, global total 7.
(3) We calculate the number of people displaced from the human climate niche 8 at different levels of warming, following the method of Lenton et al. 41. The number of people exposed to mean annual temperatures greater than 29 °C was calculated for different global mean temperature increases under four different Shared Socio-Economic Pathways. We used the downscaled spatially explicit output from the Coupled Model Intercomparison Project phase 6 available from the WorldClim v. 2. 0 database at 0. 0833° (approximately 10-km) resolution (available at https://worldclim. org). The exposed population is based on a 2010 population of 6. 9 billion with spatial distribution as given by the History Database of the Global Environment 3. 2 database  126. The mean annual temperature threshold of 29 °C was chosen as it is beyond what humans have historically been exposed to 8.
Acknowledgements This work is part of the  Earth Commission , which is hosted by  Future Earth  and is the science component of the Global Commons Alliance. The Global Commons Alliance is a sponsored project of  Rockefeller Philanthropy Advisors , with support from the  Oak Foundation, MAVA, Porticus , the  Gordon and Betty Moore Foundation , the  Tiina and Antti Herlin Foundation ,  William and Flora Hewlett Foundation  and the  Global Environment Facility. The Earth Commission is also supported by the  Global Challenges Foundation  and the  Frontiers Research Foundation. Individual researchers were supported by the  European Research Council  (Grant on Climate Change and Fossil Fuel  101020082  to J. G. and  Advanced Grant  grant  ERC-2016-ADG 743080  to  J. Rockström ), the  Open Society Foundations  (J.
Data availability The data supporting Figs. 2  and  3 are available at https://doi. org/10. 6084/m9. figshare. 22047263. v2 and https://doi. org/10. 6084/ m9. figshare. 20079200. v2, respectively. We rely on other published datasets for the climate boundary  16  , N boundary  72  (model files are at https://doi. org/10. 5281/zenodo. 6395016), phosphorus  73, 74  (scenario breakdowns are at https://ora. ox. ac. uk/objects/uuid:d9676f6b-abba-48fd-8d94-cc8c0dc546a2, and a summary of agricultural sustainability indicators is at https://doi. org/10. 5281/zenodo. 5234594), current N surpluses  129, 130  (the repository at https://dataportaal. pbl. nl/downloads/IMAGE/GNM) with the critical N surplus limit  72  subtracted, and estimated subglobal P concentration in runoff based on estimated P load to freshwater  131  and local runoff data  132, 133.
Code availability The code used to produce Figs. 2  and  3 are available at https://doi. org/10. 6084/m9. figshare. 22047263. v2 and https://doi. org/10. 6084/ m9. figshare. 20079200. v2, respectively. The code used to make the nutrient Earth system boundary layers in Fig. 3  is available at https://doi. org/10. 5281/zenodo. 7636716. The code used to make the surface water layer in Fig. 3  and derive the subglobal Earth system boundaries for surface water is available at https://doi. org/10. 5281/zenodo. 7674802. The code to estimate current functional integrity is available at https:// figshare. com/articles/software/integrity_analysis/22232749/2. The code to derive the groundwater layer in Fig. 3  and derive the total annual groundwater recharge is available at https://doi. org/10. 5281/ zenodo.
This knowledge would be valuable to disentangle the physiological and metabolic mechanisms of RYGBP, but also to conceive less invasive alternative treatments for morbid obesity and its comorbidities. To our knowledge, studies combining and integrating data from the microbiota, gut and brain with the aim to document the effects of RYGBP at a systemic level are very scarce or inexistent. A validated analytical procedure such as weighted correlation network analysis (WGCNA)  [12]  is a powerful tool to integrate different large datasets and elaborate hypotheses linking different factors (e. g. , microbiota and plasma metabolites) associated with surgery type and functional responses. The present study aimed at investigating in obese minipigs, a pertinent preclinical model for nutrition and neurosciences research, the consequences of weight loss, induced either by RYGBP or SHAM surgery combined with a pair-feeding procedure, on brain functions, microbiota composition and metabolomic profiles.
2. 3. Design of the study (Fig. 1 ) In order to study the specific effect of the RYGBP in obese Yucatan minipigs without any food intake bias, we compared the RYGBP procedure with a SHAM surgery combined with a pairfeeding strategy. This procedure guaranteed similar energy intake and nutrient supply in both groups, with the hypothesis/aim to trigger similar weight loss but different effects and adaptation mechanisms of the microbiotaegutebrain axis. At the central level, BOLD fMRI brain responses to oral sucrose perception were mapped in anaesthetized animals and differential mRNA expression in brain ROIs was investigated post-mortem. At the peripheral level, cecum microbiota and plasma metabolomic analyses were performed. Multi-omic analysis strategy was used to integrate these data from various sources.
Data processing and statistical image analysis Data analysis was performed with SPM12 (version 6906, Wellcome Department of Cognitive Neurology, London, UK) as previously described  [21, 22]. Voxel-based statistic: first-level (withinindividual contrast) and second-level (within-group contrast) statistics were assessed with a threshold set at P < 0. 05 to produce the brain maps of activation. Small Volume Correction (SVC)-based statistics: twelve anatomical ROIs corresponding to six bilateral brain structures previously studied in Gauthier et al. [18]  were used: nucleus accumbens (Ac), caudate nucleus (Cd), putamen (Put), orbitofrontal cortex (OFC), and anterior and dorsolateral prefrontal cortex (A-PFC and DL-PFC). They were studied with a P-value corrected with a Bonferroni correction at a threshold of 0.
Cecum microbiota analysis The total cecum DNA was extracted and then sequenced as described by Anathar et al. [27]. Amplification of bacteria DNA was realized using 5 0 ACGGRAGGCAGCAG et 3' AGGATTAGA-TACCCTGGTA primers. Sequencing was performed at the Genotoul GeT-PlaGe, Toulouse, France using Illumina MiSeq technology. The 16 S rRNA sequences were managed with FROGs (Find Rapidly OUT with Galaxy Solution), a bioinformatic pipeline. Quality control depletions conserved amplicon between 380 and 500 bp, sequences with ambiguous bases and sequences that did not contain good primers. Swarm was used to perform clustering with an aggregation maximal distance of 1 base  [28]. Affiliation was performed using the Silva 16 S database and NCBI Basic Local Alignment Search Tool (BLAST). Manual annotation completed the initial affiliation in case of bad annotation or multi affiliation.
Phyloseq package was used for analysis. Operation Taxonomic Units (OTUs) found in less than 3 samples and whose abundance was less than 5. 10 À5 % were eliminated. Sample depth was normalized using GMPR  [29]. Alpha diversity indexes and beta diversity indexes (Bray Curtis and Weighted UniFrac distance between samples) were calculated. Multivariate analysis of variance (MANOVA) was performed using Adonis function with 999 permutations. To identify differential abundance of OTUs between RYGBP and SHAM surgery, DESeq2 was used  [30]. No material was available for one animal in the RYGBP group because of sequencing failure due to poor sample quality. 2. 9. Plasma liquid chromatography high resolution mass spectrometry (LC-HRMS) based metabolomic analysis 2. 9. 1. Plasma sample preparation Plasma samples (30 mL) have been extracted following the Bligh and Dyer method  [31].
Liquid chromatography settings Compound separation was performed using a Hypersil GOLD-C18 column (1. 9 mm, 100 mm Â 2. A same QC sample (mix of all samples) was injected regularly throughout the run after every five samples to monitor the stability of system. A random injection order was used to avoid confounding effects in case of signal drift during MS acquisition. 2. 10. MS and MS/MS settings 2. 10. 1. Full MS mode Q Exactive Orbitrap MS was operated in switching positive and negative electrospray ionization modes (ESIþ and ESI-respectively). Data were acquired in full scan mode over the mass-tocharge ratio (m/z) range 65e975 at a resolving power of 70 000 full width half maximum (FWHM) at m/z 200. Automatic gain control (AGC Target) was set at 1. 10 6 and maximum injection time (IT) at 100 ms.
Data-dependent analysis (DDA) mode For annotation purpose, iterative data dependent MS/MS experiments were run on QC pool samples (iterations ¼ 3). Iterative exclusion lists were created with IE-Omics R script. In brief, for DDA measurements, a duty cycle consisted of a full scan acquisition, followed by a TopN MS/MS (MS2) data dependent fragmentation event, taking the 3 most abundant ion species not on the dynamic exclusion list. Blank samples injections were used to create the initial positive mode and the negative mode exclusions lists. For the 3 iterative injections, a new Top 3 DDA the exclusion list was updated with IE-Omics script. For every iterative DDA measurement, two injections of samples were done to cover both polarities. The full MS1 events were acquired over the mass-to-charge ratio (m/z) range 65e975 at a resolving power of 70 000 FWHM at m/z 200.
Data processing Preprocessing of the data (peak detection, integration, peak filtration, peak identification, peak grouping and smoothing, retention time correction, integration, annotation), QC (metabolites correlation analysis) and statistical analysis (univariate testing and multivariate modeling) were conducted on the online and freely available Workflow4Metabolomics (W4M) platform (https:// workflow4metabololomics. org), which provided a highperformance and user-friendly environment for computational analysis  [32]. Detailed steps and parameters that were used for the different steps are publicly available on the W4M repository (https://workflow4metabolomics. usegalaxy. fr/u/sblat/w/hosmtaboo-p3-neg-imported-from-uploaded-file and https:// workflow4metabolomics. usegalaxy. fr/u/sblat/w/workflowconstructed-from-history-hos-p3-rp-pos-imported-from-uploaded-file for the negative and positive ionization modes respectively).
Principal component analysis (PCA) was used for multivariate exploration of clusters and trends among the observations. Orthogonal supervised partial least-squares-discriminant analysis (OPLS-DA) were also built (the significance of the Q2Y prediction performance metric was assessed by comparison with 1000 models built after random permutation of the response values). The variable that were significant for the classification performances between treatments (OPLS-DA) were selected with Biosigner wrapper algorithm  [33]. A non-supervised analysis was performed using WGCNA (see below) to identify key features. 2. 10. 3. 1. Weighted correlation network analysis (WGCNA). WGCNA  [12]  was performed using WGCNA R package to identify networks based on correlation analysis, and specifically to identify variables of cecal microbiota, plasma metabolome and brain mRNA expression that were associated with traits of interest (type of surgery and BOLD fMRI responses).
WGCNA was performed separately on three datasets (brain mRNA expression, plasma metabolomic profiles and cecal microbiota composition). As final integrative analysis required the same number of animals in each dataset, metabolomic and brain gene analyses for the animal that did permit the microbiota analysis were excluded. Consequently, WGCNA analysis was performed on 7 minipigs in the RYGBP vs. 8 in the SHAM group. For each of these datasets, the correlation matrix identified interconnected features (e. g. , genes, metabolite features or OTUs) and assigned them to coexpression modules. One module (the grey one) was reserved for features that did not show enough coexpression metrics. In plasma metabolome and microbiota WGCNA analyses, highly correlated modules were then merged using the Euclidean distance (cut height) depending on visual interpretation of the clustering dendrogram of OTUs or features metabolites.
The assigned modules, differentiated by colors, were summarized by eigenvector for each animal (sample). Associations between animals' traits (i. e. , type of surgery, BOLD fMRI responses, and gender) and modules were identified with Spearman correlation test (Sup. Figures 1e3 corresponding to WGCNA analysis steps in each sublayer). Modules significantly associated with surgery type or BOLD fMRI responses were used for further analysis. OPLS regressions were performed in selected modules to compute the Variable Importance Projection (VIP) score and the adjusted P-value from Spearman correlation test of each feature with the trait of interest. Features with a VIP score >1 and an adjusted P-value <0. 05 were considered as drivers of those modules. 2. 10. 3. 2. Multi-WGCNA analysis strategy. Association between WGCNA modules generated from the 3 datasets were explored using modules eigenvectors correlation.
Statistical analysis All analyses were performed using the open-source R statistical software (R: A Language and Environment for Statistical Computing; R Core Team, R Foundation for Statistical Computing, Vienna, Austria; https://www. r-project. org/). Following packages were used: WGCNA, DeSeq2, Phyloseq, ropls, ggplot2. For simple comparisons, we ran nonparametric ManneWhitney U tests. For repeated comparisons, we ran Type-III ANOVAs to test the Surgery type * Time interaction with post hoc Tukey tests for repeated measures.
4. 3. Long-term plasma metabolome analysis between SHAM and RYGBP animals (Fig. 6 ) Data processing resulted in the detection of 2331 and 2659 ion features in the positive and negative ionization modes, respectively. Multivariate analysis by PCA was used first to visualize groups, trends, and outliers among observations. The first 4 components captured 55% of the total variation in both ionization modes. Clusters were detected and the surgery effect (RYGBP vs. SHAM) and the sex (Male vs. Female) were well discriminated. As surgery and sex clusters were orthogonal regarding the two main axes of the PCA, supervised multivariate analysis were also performed using orthogonal partial least-square analysis (OPLS-DA, Fig. 6 ). Using OPLS-DA, RYGBP and SHAM clusters were very well discriminated, Q2Y prediction performance metric P-value was significant (P ¼ 0.
Data integration results All data integration was detailed in Table  1  concerning association between WGCNA modules and animal traits of interest. Figure  1 ) WGCNA clustered the 258 target genes (67 genes in 4 different brain areas, 10 excluded) into 3 modules (Soft power 10, signed type) containing respectively 54, 44 and 74 genes for the 'blue', 'brown', and 'turquoise' modules, respectively. None of them were correlated to RYGBP but 2 modules (the 'blue' and 'brown') were correlated with a fMRI response to sucrose stimulation. Among the 54 genes clustered in the 'blue' module, 5-Hydroxytryptamine (Serotonin) receptor 2 B (5HT2B), Neurotrophin 3 (NTF3), Zonulin 1 (ZO1), Toll-like receptor 4 TLR4 and HTR1F expression in the hippocampus and Interleukin 1 Beta receptor (IL1bR) gene expression in the striatum were identified as drivers (P adjusted<0.
05 and VIP score >1) for the correlation with BOLD fMRI responses in the hypothalamus to Sucrose 5% stimulation. The 'brown' module containing 44 genes (mostly from gene expression in the hippocampus, n ¼ 43) was correlated to the Cd and Put BOLD fMRI responses to the oral sucrose 5% stimulation (r ¼ 0. 66, P ¼ 0. 008) but no gene passed our cut off selection to be defined as driver. 4. 4. 2. WGCNA of microbiota analysis (detailed in the sup. Figure  2 ) WGCNA clustered the 848 OTUs into 27 modules (Soft power 14, signed type). The 27 modules correlated were merged into 14 modules using a cut-height of 0. 40. Among them, the 'lightcyan' and the 'salmon' modules, containing 52 and 19 OTUs respectively, were highly positively correlated with RYGBP surgery (r ¼ 0. 84 P < 0. 001 and r ¼ 0. 87 P < 0.
3. FMRI brain responses to oral sucrose stimulation after RYGBP compared to diet restriction induced weight loss following sham surgery (A) Brain activation maps to sucrose 5%, sucrose 15% and Effect of a 3-fold increase in sucrose concentration (sucrose-15% vs. sucrose 5%) on brain activations (B) Corrected SVC-based statistic in fronto striatal regions implicated in food reward and the inhibitory cognitive control each stimulation. * significant Family Wise Error (FWE) correction at P < 0. 05. BP, Roux-en-Y gastric bypass; Suc, Sucrose; Cont, control. response correlation. However, Lachnospiraceae was identified as a driver for the correlation with the surgery in this module. 4. 4. 3. WGCNA of metabolomic analysis (detailed in the sup. Figure  3 ) WGCNA clustered the 4990 metabolites features into 47 modules (Soft power 14, type signed).