dataesr/scientific-paragraphs-categorization huggingface dataset:
The restingstate fMRI data were collected with 1. 4 × 1. 4 × 1. 4 mm resolution, TR = 1. 6 s, 6. 67 min (250 volumes) under anesthesia. No contrast-agent was used during the scans. Structural data (T1w and T2w) were acquired with 0. 3 × 0. 6 × 0. 6 mm resolution, with interpolation on to generate 0. 3 mm isotropic resolution (T1w: TR = 2500 ms, TE = 3. 65 ms, TI = 1100 ms, flip angle = 7 degrees, FOV = 154 mm; T2w: TR = 3000 ms, TE = 307 ms). To evaluate robustness of the macaque findings, we evaluated two additional macaque samples with rsFC data openly available in PRIME-DE (https:// fcon_1000. projects. nitrc. org/indi/indiPRIME. html)  38  , previously used in ref. 41. The samples varied in age-range, sex, and acquisition parameters. The Newcastle dataset (awake  108, 109  ) consisted of 10 rhesus macaques (8 males, age mean ± SD = 8.
28 ± 2. 33, weight = 11. 76 ± 3. 38) scanned on a Vertical Bruker 4. 7 T primate scanner. The fMRI session was acquired with 1. 2 × 1. 2 × 1. 2 mm resolution, TR = 2000 ms, 8. 33-min per scan (250 volumes × 2 scan) per animal. No contrast-agent was used during the scans. In the case of the Oxford dataset (anesthetized), we included nineteen rhesus macaques with preprocessing and surface reconstruction as in previous work  41  (all males, age = 4. 01 ± 0. 98 years, weight = 6. 61 ± 2. 04 kg). The macaques were scanned on a 3T with a 4-channel coil  110. Resting-state fMRI (rs-fMRI) data were collected with 2 mm isotropic resolution, TR = 2000 ms, 53. 3 min (1600 volumes). No contrast-agent was used during the scans. MRI data processing. The structural T1w and T2w images were preprocessed using the customized HCP-like macaque pipeline (doi:10.
5281/record/zenodo. 3888969). In short, the preprocessing includes (1) spatial denoising by a non-local mean filtering operation 111 , (2) brain extraction using ANTs registration with a reference brain mask followed by manually editing to fix the incorrect volume (ITK-SNAP, https://www. itksnap. org) 112 ; (3) tissue segmentation using ANTs joint label fusion algorithm and surface reconstruction (FreeSurfer)  113  ; (4) T1w and T2w alignment (linear) followed by the linear and non-linear registration to the high-resolution template space (0. 3 mm); (5) the native white matter and pial surfaces were registered to the Yerkes19 macaque surface template  114, 115. MPC was computed similarly to the human approach, however due to the thinner cortex in macaques relative to humans we decided to only focus on 9 equidistant surfaces.
Comparisons between gradients and modalities. To assess correlations between spatial maps, we used spin-tests to control for spatial autocorrelation when possible  118. Phylogenetic maps of cortical reorganization and archi-paleocortex distance. To perform phylogenetic decoding, we used cortical reorganization between macaque monkeys and humans  41  (https://github. com/tingsterx/alignment_macaquehuman), as well as a model of the dual origin, similar to previous work  106. Here, we combined the distance to paleo-and archicortex in one map, assigning each parcel with the distance closest to either origin. Transcriptomic association analysis. Given the association between phylogeny and ontogeny  21  , we correlated both maps with post-mortem gene expression data from the Allen Human Brain Atlas (AHBA)  64  and evaluated the spatiotemporal time windows in which these genes are most frequently expressed using developmental gene set enrichment analysis  119.
We assessed spatial correlations of structure-function coupling and the difference between large-scale principal gradients of MPC and rsFC and gene expression patterns. First, we correlated the t-statistics map of the two axes with the post-mortem gene expression maps provided by Allen Institute for Brain Sciences (AIBS) using Neurovault gene decoding  64, 120. Neurovault implements mixed-effect analysis to estimate associations between the input map and the genes of AIBS donor brains yielding the gene symbols associated with the input map. Gene symbols that passed a significance level of FDR-corrected p < 0. 05 were further tested whether they are consistently expressed across the donors using abagen (https://github. com/rmarkello/abagen), which implements prior recommendations for imaging-transcriptomics studies  93.
For each gene, we estimated the whole-brain expression map and correlated it between all pair of different donors. Only genes showing consistent whole-brain expression pattern across donors (r > 0. 5) were retained. In a second stage, gene lists that were significant were fed into enrichment analysis, which involved comparison against developmental expression profiles from the BrainSpan dataset (http://www. brainspan. org) using the cell-typespecific expression analysis (CSEA) developmental expression tool (http://genetics. wustl. edu/jdlab/csea-tool-2)  119. As the AIBS repository is composed of adult postmortem datasets, it should be noted that the associated gene symbols represent indirect associations with the developmental data. and September 2020  60. Each participant underwent a single testing session.
One 7 min rs-fMRI scan was acquired using multiband accelerated 2D-BOLD echo-planar imaging MRI data preprocessing. Raw DICOMS were sorted by sequence into distinct directories using custom scripts. Sorted files were converted to NIfTI format using dcm2niix (v1. 0. 20200427; https://github. com/rordenlab/dcm2niix)  123 , renamed, and assigned to their respective subject-specific directories according to BIDS standards  124. Agreement between the resulting data structure and BIDS standards was ascertained using the BIDS-validator (v1. 5. 10; DOI: 10. 5281/zenodo. 3762221). All further processing was performed via micapipe, an openly accessible processing pipeline for multimodal MRI data (https://micapipe. readthedocs. io/). As previously described  60  , rs-fMRI data were preprocessed using a combination of AFNI  125  and FSL 126.
eNKI dataset. To evaluate whether differences in rs-fMRI preprocessing between humans and macaques can explain differences observed in coupling of MPC and rsFC between both species, we evaluated structure-function coupling in an additional human sample (eNKI subsample. N = 100, age-range 18-40 years) preprocessed analogously to the macaque datasets, including temporal compression, motion correction, 4D global scaling, nuisance regression (Friston's 24 model, cerebrospinal fluid and white matter), linear and quadratic detrends, bandpass filtering (0. 01-0. 1 Hz), and surface registration (for details, see ref. 130  ). The NKI dataset was obtained from the publicly shared enhanced Nathan Kline Institute-Rockland Sample data repository. Test-retest reliability. To evaluate test-retest reliability of our measures we used the test-retest sample of HCP  7.
Acknowledgements We would like to thank the various contributors to the open-access databases that our data was downloaded from. HCP data were provided by the  Human Connectome Project, Washington University , the  University of Minnesota , and  Oxford University Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ; 1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. For enhanced NKI, we would like to thank the principal support for the enhanced NKI- RS  project is provided by the  NIMH   BRAINS R01MH094639-01  ( PI Milham ). Funding for key personnel was also provided in part by the  New York State Office of Mental Health and Research Foundation for Mental Hygiene.
Funding for the decompression and augmentation of administrative and phenotypic protocols provided by a grant from the  Child Mind Institute  ( 1FDN2012-1 ). Additional personnel support provided by the  Center  for the Developing Brain at the  Child Mind Institute , as well as  NIMH   R01MH081218 ,  R01MH083246 , and  R21MH084126. Project support also provided by the  NKI Center for Advanced Brain Imaging (CABI) , the  Brain Research Foundation  (Chicago, IL), and the  Stavros Niarchos Foundation. This study was supported by the  Deutsche Forschungsgemeinschaft (DFG ,  EI 816/21-1 ), the  National Institute of Mental Health  ( R01-MH074457 ), the  Helmholtz Portfolio Theme "Supercomputing and Modeling for the Human Brain " and the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant Agreement No.
785907  ( HBP SGA2 ). S. L. V. was supported by  Max Planck Gesellschaft  ( Otto Hahn  award). B. P. was funded by the  National Research Foundation of Korea  ( NRF2021R1F1A1052303 ),  Institute for Information and Communications Technology Planning and Evaluation (IITP)  funded by the  Korea Government (MSIT)  ( 2020-0-01389 ,  Artificial Intelligence Convergence Research Center, Inha University ;  2021-0-02068 ,  Artificial Intelligence Innovation Hub ), and  Institute for Basic Science  ( IBS-R015-D1 ). B. T. T. Y. is supported by the  Singapore National Research Foundation (NRF)  Fellowship (Class of 2017), the  NUS Yong Loo Lin School of Medicine  ( NUHSRO/2020/124/TMR/ LOA ) and the  Singapore National Medical Research Council (NMRC) LCG  ( OFLCG19-May-0035 ). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect views of the Singapore NRF or the Singapore NMRC.
Data availability This study followed institutional review board guidelines of corresponding institutions. Human data analyzed in our main results were obtained from the open-access HCP S1200 young adult sample (HCP; http://www. humanconnectome. org/). MICs replication data is openly available at https://portal. conp. ca/dataset?id=projects/mica-mics. Macaque data was obtained from PRIME-DE (https://fcon_1000. projects. nitrc. org/indi/ indiPRIME. html; University of California, Davis). Heritability analyses were performed using Solar Eclipse 8. 4. 0 (https://www. solar-eclipse-genetics. org), and data on the pedigree analysis is available here: https://www. nitrc. org/projects/se_linux/  104, 131. Gradient mapping analyses was based on BrainSpace (https://brainspace. readthedocs. io/ en/latest/).
Transcriptomic association analyses were conducted using NeuroVault (https://neurovault. org), abagen tools (https://github. com/rmarkello/abagen)  93 , and celltype-specific expression analysis (CSEA) (http://genetics. wustl. edu/jdlab/csea-tool-2)  119 upplementary analysis were performed using the MICS dataset (https://portal. conp. ca/ dataset?id=projects/mica-mics) and eNKI dataset (https://fcon_1000. projects. nitrc. org/ indi/enhanced/). Source data are provided with this paper and code for visualization of parcel results on surface linked in our study's Github repository (https://github. com/ CNG-LAB/structure_function). Source data are provided with this paper.
Code availability Code for generation of MPC is available at (https://github. com/MICA-MNI/MPC, now https://github. com/MICA-MNI/micapipe). Heritability analyses were performed using Solar Eclipse 8. 4. 0 (http://www. solar-eclipse-genetics. org), and data on the pedigree analysis is available here: https://www. nitrc. org/projects/se_linux/. The code for connectome gradient generation are available at https://github. com/MICA-MNI/ BrainSpace. Transcriptomic association analyses were conducted using NeuroVault (https://neurovault. org), cell-type-specific expression analysis (CSEA) (http://genetics. wustl. edu/jdlab/csea-tool-2), and abagen tools (https://github. com/rmarkello/abagen). Further code to visualize the data is available at (https://github. com/CNG-LAB/ structure_function) and Zenodo: (https://doi.
This work was supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ) through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes, and AlloSpace ,  ANR-21-CE44-0034-01 ). IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ).
Bioluminescence Imaging In vitro bioluminescence was performed by seeding a range of cell densities (from 625 to 2 × 10 4 cells/well) into an optical bottom 96-well plate with black walls (#165,305, ThermoFisher, Altrincham, UK). The cells were allowed to attach for 3 h prior to the addition of 5. 12 mM D-Luciferin. Imaging was performed immediately after substrate addition without an emission filter, a 13. 3 cm field of view (FOV), an f-stop of 1 and a binning of 8. For in vivo bioluminescence imaging, mice received a subcutaneous (SC) injection of D-Luciferin (10 µL/g (body weight) of a 47 mM stock solution) after cell injection. After 20 min, the animals were imaged with an IVIS Spectrum instrument (Perkin Elmer, Beaconsfield, UK). Data are displayed in radiance (photons/second/centimeter2/steradian), where the signal intensity scale is normalised to the acquisition conditions.
Imaging Cells in culture were imaged by light microscopy with a Leica DM IL microscope coupled to a DFC420C camera. Confocal images were acquired on a Leica DMi8 with an Andor Dragonfly spinning disk, coupled to an EMCCD camera using a 10×/0. 45 air objective. Z-stacks were captured using the 488, 561 and 637 nm laser lines. The emission filters used were 525/50, 600/50 and 700/75. Maximum-intensity projections, three-dimensional reconstructions and image analyses were completed using the IMARIS version 9. 9. 0 (Bitplane, Schlieren, Switzerland) software packages, processed with ImageJ 3D viewer version 1. 53c  [32]. Three-dimensional surfaces were obtained via un-stacking the. ims image in Image J and reconstructed via variable threshold intensity (3D slicer). Three-dimensional elaboration was performed using NVIDIA ® Quadro 6000 GPUs and exported as.
Acknowledgments: In vivo imaging data in this article were obtained in the  Centre for Preclinical Imaging (CPI) of the University of Liverpool. The CPI has been funded by a  Medical Research Council (MRC)  grant ( MR/L012707/1 ). All microscopy data were acquired at the  Centre for Cell Imaging (CCI) of the University of Liverpool. The confocal system used in this work was funded by  BBSRC  grant number  BB/R01390X/1. The histology work was performed at the histology facility of the  University of Liverpool. Three-dimensional surface models were performed in  DiMo lab, University of Liverpool , using NVIDIA GPU hardware. The authors gratefully acknowledge these facilities for their support and assistance in this work. We thank  Marie Held  ( CCI ) for the expert advice on image analysis and  Jennifer Adcott  for training and help with the use of the Dragonfly.
To do so, we apply the newly developed CFs to all non-empty market data sets from the ecoinvent database version 3. 7. 1  (Moreno Ruiz et al. 2020; Wernet et al. 2016 ) and compare the LCIA results with those for the abiotic depletion potential (ADP) ultimate reserves method  (van Oers et al. 2019 (van Oers et al. , 2002)) , the surplus ore potential (SOP) method  (Vieira et al. 2016)  and the surplus cost potential (SCP) method  (Vieira et al. 2016). The latter two methods are included in ReCiPe 2016  (Huijbregts et al. 2017). Materials and methods are detailed in "Sect. 2," results are presented and analyzed in "Sect. 3," and conclusions are drawn in "Sect. 4. ".
Therefore, despite the potential limitations associated with the study of single cycles (e. g. , mass-balance discrepancies between different metals used in the same applications), we here focus on the individual cycles of 61 metals as studied by Charpentier  Poncelet et al. (2022b). In the latter article, the losses of metals are evaluated over time and their average lifetimes in the economy are estimated based on the most recent data possible (most typically between 2010 and 2020). The latter correspond to the average duration over which metals remain in use in the economy after extraction. Midpoint CFs for 61 metals are derived from the results of that article. The Python code and compiled datasets underlying that study are accessible online  (Helbig and Charpentier Poncelet 2022). The overview of developments proposed in this article based on our previous work is presented in Fig.
Endpoint characterization factors based on metal prices As the final step of the impact pathway, we evaluate the potential socio-economic impacts due to the dissipation of different mineral resources (cf. Fig. 1 ). The Joint Research Centre (JRC) of the European Commission suggested that the average prices of resources over a given period can be used as a proxy to reflect the complex utility that resources have for humans and are practical to do so since the data is easily available  (Beylot et al. 2020a ). The underlying assumption of using the prices of metals as an indication of their socio-economic value is that prices reflect at least to some extent the value of metals for society, albeit not perfectly (see discussion in, e. g. ,  Beylot et al. 2020a; Ecorys 2012; Henckens et al. 2016; Huppertz et al.
Prices for individual platinum group metals (PGMs) except osmium are compiled from  Johnson Matthey (2021). The price of niobium is calculated from data provided by  Metalary (2021)  considering a niobium content of 69. 9% in niobium oxides (Nb 2 O 5 ). Prices for ten of the rare earth elements (REEs) are compiled from data underlying a report of the French geological survey (BRGM)  (Bru et al. 2015, p. 151-158). These are yttrium (Y), lanthanum (La), cerium (Ce), praseodymium (Pr), neodymium (Nd), samarium (Sm), europium (Eu), gadolinium (Gd), dysprosium (Dy), and terbium (Tb). Different periods are considered to estimate the price of other metals due to a lack of data. Given that iron ores are directly refined into steel, the price of iron is derived from that of steel. The 10-year period for the latter ranges from 2001 to 2010 because no data are available for subsequent years  (Kelly and Matos 2014).
Osmium (one of the PGMs), for which no price data is available, is estimated to remain at a constant price of $US400 per ounce  (Labbé and Dupuy 2014). The price of scandium is estimated from the price of scandium oxides between 2012 and 2018, which ranged between $US4600 and $US5400 per kilogram (European Commission 2020b). For the latter, we consider an average yearly price of $US5000 per kg from 2012 to 2018 and a scandium content of 65. 2% in scandium oxides (Sc 2 O 3 ). The price of thulium (Tm) is calculated based on the price of its oxides in the third semester of 2015  (Bru et al. 2015)  and considering a stoichiometric content of 87. 4%. Similarly, the prices of the remaining REEs, namely erbium (Er), ytterbium (Yb), holmium (Ho), and lutetium (Lu), are calculated from the average yearly prices of their oxides between 2009 and 2014 as reported by  Stormcrow (2014)  and considering their respective stoichiometric contents in oxides (approximately 87.
Application of characterization factors to 6000 life cycle inventory data sets This objective of this application study is to investigate the general trends that could arise from using the ADR and LPST methods to evaluate the potential impacts due to the dissipation of mineral resources, and to compare these impact assessment results with those of widely used LCIA methods characterizing flows of metal resources in order to determine how they might differ. The assessment is realized by applying LCIA methods to multiple data sets rather than compare their CFs together. In order to do so, CFs from the selected LCIA methods are applied to the metal resource flows for all of the 5999 non-empty market LCI data sets (6) CF LPV i = LPST i × price i (7) TPVLR = ∑ n i=1 m i × CF PVLRi (8) TLPV = ∑ n i=1 m i × CF LPVi of the ecoinvent database version 3.
7. 1  (Moreno Ruiz et al. 2020; Wernet et al. 2016) , using allocation at point of substitution (APOS). Market data sets represent the average consumption mixes for a given region and product  (Wernet et al. 2016). We consider flows of metal resources included in the ecoinvent 3. 7. 1 database that are covered by the ADR and LPST methods. These include 45 flows, categorized as "metals, in ground" (e. g. , aluminum, in ground) as identified in Table  S4  of the SI. In order to support the analysis of the contribution of different sections of economic activity to the inventory totals, the LCI data sets are subdivided by the International Standard Industrial Classification of All Economic Activities (ISIC) classification (United Nations 2008). Sections of economic activity regroup multiple sectors (e.
(2014)  and the critical review of the life cycle initiative's task force on mineral resources  (Berger et al. 2020; Sonderegger et al. 2020). Rørbech et al. (2014)  applied the CFs from eleven LCIA methods to mineral resource flows (including energy minerals, minerals, and metals) for all market data sets included in the ecoinvent version 3. 0 database. The life cycle initiative's taskforce on mineral resources critically reviewed 27 LCIA methods assessing the impacts of mineral resource use  (Sonderegger et al. 2020).
Coverage of mineral resource flows by selected LCIA methods The ReCiPe 2016 method includes CFs for 75 resource flows of mineral resources, 26 of which are mineral compounds or ores. Parts of the latter 26 flows are no longer included in the ecoinvent database because many mineral compounds were converted to pure flows of metal content since version 3. 6 (Moreno  Ruiz et al. 2019). The latest update for the ADP ultimate reserves method for elements (van Oers et al. 2019) covers 76 elements. These include the 61 metallic elements that are also covered by the ADR and LPST methods, in addition to calcium, cesium, potassium, sodium, and a few non-metals (e. g. , halogens). Table  S3  in the SI presents the 65 metallic elements included in the latest ADP method for elements  (van Oers et al. 2019 ) and indicates whether they are included in the ReCiPe 2016 and the ADR/LPST methods or not.
Consequently, some by-product metals like gallium and scandium have relatively high midpoint CFs (production losses are here accounted for as dissipation, leading to highly dissipative profiles for these metals) and even greater endpoint CFs (due to their relatively high prices). Thirdly, unquantified uncertainty may arise from using price data from different sources, for metals of possibly different qualities or purities, and in a few instances, over different time series. Fourthly, some metals that are often used in a compound form, such as the magnesium content of magnesia or the lanthanum content of lanthanum oxides, may not have the same potential value as their refined metal form. Nevertheless, most metals are almost exclusively used as pure metals or alloys  (Graedel et al. 2022; UNEP 2011).
It should be noted that, given the overall great recyclability of iron (or steel) in most of its applications  (Pauliuk et al. 2017) , its relative impacts would likely be much lower for LCI that account for the recycling in the end-of-life modeling beyond what is already accounted for in LCI databases (the same could be expected for other wellrecycled metals). In this case, the extraction of metal would be allocated between different product systems (or between different life cycles) and therefore would be lower than a product system using only primary metal. As for other metals, iron's relative share of impacts is increasingly similar to its share of the inventory flows when considering longer time horizons for the assessment using the LPST method, because the midpoint CF LPST25 are more differentiated than the CF LPST100 and the CF LPST500 (i.