dataesr/scientific-paragraphs-categorization huggingface dataset:
All spatial and statistical analyses were done in R version 3. 6. 3  (68). Global-, regional-, and biome-level anthrome area changes over time (anthrome trajectories) were computed and charted based on DGG land areas. For protected areas, KBAs, and other variables that occupied only part of DGG cells, anthrome sums were weighted by the relative areas of each respective variable in each DGG cell. HYDE 3. 2 data were also used to compute total populations at the global, regional, and biome levels over time, and these were overlaid on anthrome trajectory charts after scaling them to their maximum value (typically the population in 2017 CE). To examine relationships between anthrome maps and global maps of biodiversity and other ancillary variables, we fitted a series of generalized linear models to each biodiversity pattern using the anthrome maps from a succession of years as categorical predictors.
Lower AIC values indicate better performance or a closer association between the anthrome maps of a given time step and the global patterns of a given biodiversity variable. The AIC is only comparable for models fitted to the same dataset, so we rescaled the AIC values to a common range of [0, 1] according to the minimum and maximum AIC for each dataset. This rescaling allowed us to visualize the anthrome time steps that best predicted each biodiversity variable. We repeated this analysis for all ice-free land areas (global lands), Indigenous lands, potential and likely natural land areas (NMH classes 3 and 4), protected areas, and for each world region separately.
ACKNOWLEDGMENTS. We thank the editor and three anonymous reviewers for helpful advice that greatly improved this manuscript. N. B. was supported by the  Max Planck Society. S. D. has been partly supported by the  Newton Fund (Natural Environmental Research Council-UK and CONICET-Argentina ), and the  Inter-American Institute for Climate Change Research Small   Grant Program   090. J. G. was supported by  NSF   CAREER  grant  EAR-1753186. J. C. S. was supported by  VILLUM FONDEN Investigator  grant  16549. David Tryse  and  Tanya Birch  of  Google Earth Outreach  provided invaluable assistance with online mapping. The research reported in this paper contributes to the Global Land Programme (GLP. earth). Ellis et al. PNAS | 7 of 8 People have shaped most of terrestrial nature for at least 12,000 years https://doi.
Data Availability. Source code to reproduce these analyses and visualizations is available as an R research compendium on the Harvard Dataverse [(77), https://doi. org/10. 7910/DVN/6FWPZ9]. Although the entire analysis can be made from the source code using publicly available data, intermediate data products such as the full Level 12 DGG anthrome classification, maps, and summary statistics are available on the Harvard Dataverse [(78), https://doi. org/10. 7910/DVN/E3H3AK].
as a set of 1,434,024 equal area hexagons of ∼96 km2 ["DGG cells"; median area = 96. 19 km 2 ; Level 12 Icosahedral Snyder Equal Area (ISEA) Aperture 3 Projection (67) ]. Raster data in 5 arc minutes and other formats were converted to the DGG system using R [v. 3. 6. 3 (68)] with the packages ddgridR [v. 2. 0. 4 (69)], exactextractr [v. 0. 4. 0 (70)], sf [v. 0. 9-5 (71) ], and tidyverse [v. 1. 3. 0 (72)]. Anthromes and HYDE 3. 2 Data Inputs. Anthrome maps were produced by classifying global maps of human populations and land use from the HYDE 3. 2 dataset (48) after conversion to the DGG system. HYDE 3. 2 data are provided for 73 time steps between 10,000 BCE to 2017 CE (1,000 y increments from 10,000 BCE to 1 CE, 100 y increments from 100 CE to 1700 CE, 10 y increments from 1710 to 1990 CE, and 1 y increments from 2000 CE to 2017 CE).
Annual data for 2000 to 2009 CE and for 2011 to 2016 CE were removed prior to analysis, leaving anthrome, population, and land use datasets at 60 time increments, with decadal resolution from 1700 to 2010 CE and finishing with 2017 CE, the most recent HYDE 3. 2 data available. Land areas for each grid cell were also computed from raster data provided in the HYDE 3. 2 dataset. Ancillary Variables: Regions, Biomes, Biodiversity, and Conservation. Ramankutty and Foley's "potential natural vegetation" dataset (73) , which combines remotely sensed and modeled inputs, was acquired with the HYDE 3. 2 dataset, converted to DGG format, and aggregated from 15 to 8 biome classes. Global maps of NMH land (53) were acquired and converted to DGG format. Additional global variables were acquired in native DGG format from Locke et al.
(49), including "Olson Biomes" [recently updated as the Resolve 2017 biomes (74)], vertebrate species richness (55), the number of vertebrate species threatened with extinction (55), KBAs (54), terrestrial protected areas [World Database of Protected Areas (52) supplemented with data for Chinese protected areas as described in Locke et al. (49)], The Human Footprint [2013 CE; (3)], Indigenous lands (51), the world regions of Ellis et al. (1), and maps of the 3GC (49).
Export to PNG Format Export to PNG was made with 3 different softwares: (i) Anaconda 3. 7 1  and the pyPNG package 2 for Figures  1, 2A, 3A, B;  (ii) Labview 2017 and Vision 2017 (National Instruments) for Figures  2B, 5C ; and (iii) the FIJI distribution of ImageJ software  (Schindelin et al. , 2012; Rueden et al. , 2017)  for Figure  6B. Signals were normalized to 256 values from 0 to 255 simply by subtracting the minimal value of the signal, then dividing by the maximal value and multiplying by 255. It was then saved as PNG format in 8-bits range (256 gray values). For Figure  4C , as the signal was binarized we saved it with a 1-bit range (2 gray values). This script and others are available in Python and Labview format in a GitHub depository: https: //github. com/Sylvain-Deposit/PNG-Entropy.
A minimal file of PNG format is composed of a header and several parts of data, named critical chunks 3. To these minimum requirements it is possible to add ancillary chunks 4  containing various information such as Software name, ICC profile, pixels dimensions, etc. If useful, this is hindering the estimation of entropy as it represents an overhead to the final size of the file. To estimate this overhead for each of our software we saved an image of 100 × 100 values of zeros, which corresponds to black in 8-bits gray levels and has an entropy of 0. With pyPNG, Fiji and Labview we obtained three PNG files of size 90, 90, and 870 Bytes, respectively. When repeating the experiment of Figure  1 , we obtained similar linear fits of slopes 1. 21 (R 2 = 0. 99), 1. 18 (R 2 = 0. 99), and 1. 21 (R 2 = 0.
Neuronal Modeling A single compartment model was simulated with NEURON 7. 7 5. All simulations were run with 100-µs time steps. The nominal temperature was 37 • C. The voltage dependence of activation and inactivation of Hodgkin-Huxley-based conductance models were taken from  Hu et al. (2009)  for g Nav and g KDR. The equilibrium potentials for Na + , K + , and passive channels were set to +60, -90, and -77 mV, respectively. The conductances densities were set to 0. 04 S/cm 2 , 0. 01 S/cm 2 , and 3. 33 × 10 -5 S/cm 2 for g Nav and g KDR and passive channels, respectively. The model was stimulated using various numbers of excitatory synapses using the AlphaSynapse PointProcess of the NEURON software. The time constant and reversal potential were the same for every synapses and were set to 0.
Place Fields Simulation To simulate place fields recordings, we programmed a simple random-walk in a 20 × 20 cells area with a place field at its center. A mock animal was placed randomly in this area and allowed to "run" for a finite set of step (typically, 10,000 steps). For each step of the simulation, the animal could choose uniformly between one of the 8 neighboring cell or stay in the same cell. For each step of the simulation, the spike frequency of a recorded place field was generated by calculating the distance x of the animal from the place field center and indexing this distance to a sigmoid curve F max 1+e (x-x 0 )×Slope with x 0 = 7, Slope = 2 and F max = 10 to obtain a place field of roughly 3 cells of radius in the center of the simulated area (Figure  5 ). This script and others are available in Labview format in a GitHub depository: https://github.
The obtained curve has similar characteristics to the previously calculated MI (Figure  5C , right). From this third test, we conclude that we can apply the same algorithm to more complex data like Place Fields recordings. Even if the data are not homogenous, a simple pairing transform can map all 2D coordinates to unique 1D index, progressively building a 2D image (Figure  5B ). This image can then be saved as PNG to estimate its Entropy Rate and Mutual Information.
Advantages of the PNG Method The main advantage of this method is that it relies on previously developed compression algorithms that were already shown as optimal  (Huffman, 1952; Ziv and Lempel, 1977). Moreover, it does not need any specialized software or any knowledge in programming language, as the PNG format is ubiquitous in informatics. For example, the ImageJ software is widely used in neuroscience and can export data as PNG. A second advantage is the speed of execution. As an example, the information transfer rates calculation (Figure  4 ) took a bit more than 2 h for the quadratic extrapolations method. Saving the same signals in PNG to calculate the PNG Rates took less than 30 s on the same laptop computer. As so, this method is extremely easy, quick, and does not need any knowledge in mathematics for correcting the sampling bias.
ACKNOWLEDGMENTS We thank  Q. Bernard ,  C. Leterrier ,  V. Marra ,  T. Jensen ,  K. Zheng ,  S. Martiniani , and  R. Beck  for helpful discussions. A first version of this manuscript has been released on bioRxiv (Zbili and Rama, 2020), https://www. biorxiv. org/content/10. 1101/2020. 08. 04. 236174v2.
Note: The final version of this article is published on Nature: https://www. nature. com/articles/s41586-021-03780-0. We analyze Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) near-UV spectra of 25 bright Type O and B stars in the Galaxy. When available, we include archival Very Large Telescope (VLT) Ultraviolet and Visual Echelle Spectrograph (UVES) high-resolution optical spectra of these targets. The selection of the 25 stars in our sample, as well as the data collection and handling, is described in the Methods and the sample properties are listed in Extended Data Table  1. The locations of our targets on the Galactic plane are visualized in Fig. 1. We identify and analyze the absorption lines from Mg i, Al ii, Si ii, Cr ii, Fe ii, Co ii, Ni ii, Zn ii, and Ti ii.
Each method uses a specific parameter to estimate the overall amount of dust depletion in a system, either [Zn/Fe] fit or F * , as explained in the Methods. The individual dust-corrected abundances derived with the relative method are shown in Fig. 2 , for individual metals. Figure  3  shows the total metallicities towards the 25 lines of sight in our sample, which are listed in Extended Data Table  3  and derived with the relative and F * methods from the fit to the data in Extended Data Figures  2  and 3 , respectively. We measure total metallicities [M/H] tot ranging between -0. 76 dex and +0. 26 dex, where about two thirds of our sample show sub-Solar metallicities. The average metallicity in our sample is -0. 26 ± 0. 06 dex (i. e. 55 ± 7% Solar) with a standard deviation of 0. 28 dex.
Sample selection and data collection We select 25 stars and observe them as part of the cycle 25 HST program ID 15335 (PI De Cia). The sample was selected to include hot stars (spectral Type O or B) which have line-of-sight measurements of H i, H 2 , and Ti ii in the literature  [30, 31, 32] , high-enough rotational velocities ( 50 km s -1 ) to allow us to disentangle between stellar and ISM feature in the spectra, and fairly diverse values of dust reddening and Galactic longitude. The distances r are taken from the Gaia DR2 archive  [33, 34]. The values of H i and H 2 are taken from  [31]  and  [30] , respectively, as also collected by  [32]  for most of our targets, and assuming their average uncertainties of 0. 1 dex. For HD 62542 we adopt the column densities of atomic and molecular hydrogen from  [35].
Nevertheless, we tested the robustness of the column density determinations with the AOD method with an independent method, the Voigt-profile fit, which decomposes and models the line profiles in their individual velocity components and fits all transitions simultaneously. The Voigtprofile fit can use narrow b-values to account for saturation. These tests are aimed at further assessing potential saturation of Zn ii absorption lines. Saturation for other ions considered here is less probable because of the availability of weaker absorption lines to measure. Using the VoigtFit software  [42] , we model lines of Zn ii and Cr ii towards the eight targets that have the strongest Zn ii absorption and show potential for saturation (Extended Data Figure  1 ), and with a column density of Zn ii constrained with the AOD method ( Extended Data Table  2 ), namely: θ 1 Ori C, HD 73882, HR 4908, θ Mus, HD 149404, HD 154368, HD 199579, and HD 206267.
Acknowledgements A. D. C. thanks  Cesare Chiosi  and the "Galaxies and the  Universe" group at the University of Geneva  for discussions, and  Barry Holl  for help with navigating the Gaia archive. A. D. C. , T. R. H. , C. K. , and  J. K. K. acknowledge support by the  Swiss National Science Foundation  under grant  185692. Based on observations with the  NASA/ESA Hubble Space Telescope  obtained at the  Space Telescope Science Institute (STScI) , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. E. B. J. was supported by grant number  HST-GO-15335. 002-Afrom STScI  to Princeton University. Based on data obtained from the  ESO Science Archive Facility. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.
Data Availability The observational data used in this work are publicly available in the Mikulski Archive for Space Telescope (HST/STIS data, Program ID 15335, PI De Cia, DOI: 10. 17909/t9-r14v-tp03) and the ESO Science Archive Facility (VLT/UVES data http://archive. eso. org/wdb/wdb/adp/phase3_spectral/form?). The data used in Figures and Tables are available as electronically readable Source Data files, except for the publicly available observational data (Extended Data Figure  1 ).
Source Data Analysis Summary data were extracted by 1 analyst and 100% qualitychecked by a second analyst. Two independent reviewers assessed the risk of bias (with discrepancies adjudicated by a third advisor) based on guidance from the Center for Reviews and Dissemination  [8]. Eligible studies included at least one outcome of interest: OS, PFS, objective response rate (ORR), duration of response, or adverse events (AEs). ORR was only reported as "confirmed" in 2 publications (CheckMate 459 and IMbrave150)  [5, 7]. If a publication did not specify whether objective response by Response Evaluation Criteria in Solid Tumors (RECIST) version 1. 1 was measured by independent review facility or investigator assessment, independent assessment was assumed. Safety outcomes included patients reporting any AEs, serious, severe, or treatment-related AEs leading to dose interruption, and AEs leading to treatment discontinuation.
Results The database search identified 8,783 records, of which 101 underwent full-text review (Fig. 1 ). Nine studies (IM-brave150  [7] , CheckMate 459  [5] , REFLECT  [4] , Asia-Pacific  [12] , SHARP  [2] , SIRveNIB  [13] , SARAH  [14] , SIRTACE  [15] , and Pitton et al. [16] ), with 3,897 participants constituted the all-trials evidence network (online suppl. Fig. 1 ). Study and patient characteristics are provided in online suppl. Table  3. Some differences in study designs, populations, interventions, and outcomes were observed. Median follow-up time was shorter in the IMbrave150 treatment arms (8. 9 and 8. 1 months) than in REFLECT (27. 7 months), Check-Mate 459 (15. 2 and 13. 4 months), SIRTACE (10. 7 months), or SARAH (27. 9 and 28. 1 months; online suppl. Table  3 ). Some study outcomes were not comparable in the alltrials evidence network (online suppl.
Relative efficacy results in the subgroups were generally consistent with the primary findings, though limited by the availability of reported subgroup data (online suppl. Table  6 ). Similar to the PFS findings, 2 sensitivity analyses of ORR using restricted evidence networks showed the same point estimates with greater uncertainty (online suppl. Table  7 ). Duration of response was not reported in comparators' trials and could not be evaluated. The comparative assessment of AEs was performed qualitatively. The available safety information from CheckMate 459 was not adequate for comparison (only grade 3-4 treatment-related AEs and AEs leading to treatment discontinuation were reported in the abstract). Occurrence of any AEs was similar with atezolizumabbevacizumab, lenvatinib, and sorafenib (Table  2 ).
Image Acquisition and Processing We performed MRIs at 38-44 weeks on a 3T Tim Trio scanner (Siemens) on unsedated infants, as previously described. 24  We acquired single-voxel 1 H-MR spectra using point-resolved spectroscopy (TR ¼ 2000 ms, TE ¼ 30 and 270 ms, 128 signal averages). Data were acquired using an isotropic voxel (15 mm)  3  in the left BG and right frontal WM (Online Supplemental Data) for TE ¼ 30 ms and in the right frontal WM for TE ¼ 270 ms. A single voxel (14. 37 mm)  3  was extracted from the left BG region of a multivoxel point-resolved spectroscopy chemical shift imaging scan (TR ¼ 1700 ms, TE ¼ 270 ms, 16 Â 16 grid, and 1 signal average). All voxels were placed (or extracted) in regions that had no apparent lesions. The LCModel  25  (http://www. lcmodel. com/) quantified metabolite ratios to creatine using a simulated metabolite basis set  26  generated with the VeSPA software package  27  (https://github.
Statistical Analysis Multiple linear regression (SAS 3. 8; SAS Institute) was performed with STEP at term or 3 months as the response variable and metabolite ratios from TE ¼ 30 or 270 ms or DKI-derived metrics as predictor variables. Gestational age at birth and MR imaging were included in all models to account for degree of prematurity. For the DKI metrics, STEP was modeled in two ways: 1. For a single ROI, each diffusion parameter was averaged separately over all voxels and included as an independent covariate (eg, STEP term ¼ FA sCC 1 KFA sCC ). This approach yields a parsimonious model in which a single WM tract could be sampled with several different diffusion parameters to model prognosis in a clinical setting. 2. To assess whether global brain dysmaturity or injury is more important to functional outcome prediction, we used a single diffusion metric averaged within each ROI as the covariates in the models (eg, STEP term ¼ FA sCC 1 FA PTR 1 FA PLIC ).
For more advanced diffusion imaging, DKI is easily performed within 4-6-minutes by adding the b-value 2000s/mm 2 to standard DTI protocols with 30 directions per nonzero b-value. Numer- ous open-source platforms (eg, Diffusional Kurtosis Estimator,  33  https://github. com/m-ama/DKE, DESIGNER,  28  /pyDesigner, and https://pydesigner. readthedocs. io/en/latest/) are available that provide semiautomated off-line DKI analysis. Limitations of our study include our sample size, which precluded assessing whether brain dysmaturity or specific sites of injury were more important in neuroimaging models and any contributions of sex. Also, 12 months is a typical time for general developmental delays to manifest but is not optimal for diagnosing cerebral palsy. Another limitation is the combination of single-voxel and CSI data for the TE = 270 ms MR spectroscopy data modeling, which had slightly different TR values as well as differing spectral and shim quality.
The recombinant ZgHAD was shown to dehalogenate bromo-and iodoacetic acids, and gene knockout in Z. galactanivorans revealed a direct role of ZgHAD in tolerance against both haloacetic acids. Analyses of metagenomic and metatranscriptomic datasets confirmed that L-2-HADs from group A were well-represented in terrestrial and marine bacteria, whereas ZgHAD homologs (group B L-2-HADs) were mainly present in marine bacteria, and particularly in host-associated species. Our results suggest that ZgHAD homologs could be key enzymes for marine Bacteroidetes, by conferring selective advantage for the recycling of toxic halogen compounds produced in particular marine habitats, and especially during interactions with macroalgae.
This bacterium has been developed as a laboratory model for marine Bacteroidetes, because of its capacities to degrade algal polysaccharides. Its genome also features several genes related to halogen metabolism  (Fournier et al. , 2014; Barbeyron et al. , 2016). Thanks to a recent protocol development  (Zhu et al. , 2017) , a genetic knockout approach was conducted, demonstrating the potential in vivo protective role of this L-2-HAD when Z. galactanivorans was exposed to toxic halogenated substrates. In addition, the distribution and expression of phylogenetically distinct L-2-HADs were analyzed using metagenomic and metatranscriptomic datasets, highlighting an underexplored marine biodiversity. Finally, the laboratory functional characterization of Z. galactanivorans L-2-HAD and environmental survey results on homologous proteins are discussed in light of the biological and ecological roles of these enzymes in marine Bacteroidetes in their natural coastal habitats.
Phylogenetic Analysis of Microbial L-2-HADs The phylogenetic analysis of microbial L-2-HAD proteins was based on a subset of 352 protein sequences, including the biochemically and structurally characterized enzymes (Supplementary Table  1 ) and their closest counterparts, in addition to the closest relatives of ZgHAD, identified by BlastP search on protein databases (Supplementary Table  2 ). The L-2-HAD proteins mainly clustered into two well-supported groups (Figure  1A ), with an additional small clade of proteobacterial sequences related to the L-2-DhlB of Ancylobacter aquaticus UV5. The first group (group A) mostly comprises proteobacterial L-2-HAD sequences (122/182 sequences) that are divided into four clades, respectively containing the biochemically characterized L-2-HADs L-DEX, DhlB, DehIVa and DehPsi.
Distribution of the Two L-2-HAD Major Phylogenetic Groups in the Environment Our phylogenetic analysis suggested that the two different phylogenetic groups A and B, showing different conserved amino-acid signatures, could be differently distributed in the terrestrial and marine environments (Figure  1 ). To test this hypothesis, we investigated (i) the overall metagenomic abundance distribution of L-2-HAD homologs in contrasted ecosystems using the IMG/MER database (IMG/MER) and (ii) the specific marine distribution of the L-2-HADs through their gene number and gene expression using the Ocean Gene Atlas database (OGA). Among the 25 analyzed ecosystems corresponding to 2,500 metagenomes of IMG/MER (Supplementary Table  3 ), a total of 4,265 hits were found. The group A L-2-HAD sequences, represented by the DehIVa homologs, were ten times more frequent (3,800 hits) than the group B L-2-HAD sequences (365 hits), represented by the ZgHAD homologs.