dataesr/scientific-paragraphs-categorization huggingface dataset:
More recent studies, based on compilations and regression analysis of permeability estimates from a range of in situ and laboratory hydraulic testing techniques, support the idea of lower permeability values in stable crystalline rock  14  and batholiths down to depths of 1. 3 km (Fig. 2b ). Evaluating whether those relationships hold at greater depths is difficult with only one hydraulic test beyond a depth of 1. 3 km available in those databases; a value of 10 -19 m 2 measured over a depth interval of 4. 3-9. 0 km in the Kola Superdeep Borehole in Russia  27. These previous attempts to estimate permeability are inadequate to understand the past behaviour of fluids in Precambrian rocks. Numerical models have used a wide range of permeability values for these settings, from as low as 10 -21 for Atikokan, Canada 28 to 10 -16 m 2 for Fennoscandia  29.
In order for the permeabilities found here to match those found from hydraulic testing, flow system lengths would need to increase by an order of magnitude, which is inconsistent with detailed studies of the regional scale flow systems in cratons  28, 45, [47] [48] [49] [50] [51]. These results could also be achieved if porosity was increased to ~10%, which is inconsistent with measurements for cratons  58  , or if hydraulic gradients were reduced by an order of magnitude. However, it should also be noted that the median log k for depths >1 km in the Achtziger-Zupancic et al. 14  database is -19. 0, which is similar to those found in the analysis presented here. Data at those depths were not considered in the regression analysis for cratons and shields in that study. It is instructive to compare the results obtained here with those for batholiths (large masses of relatively homogeneous intrusive igneous rock)  16  (Table  1 ; Fig.
Acknowledgements The authors are grateful for comments from three reviewers. Peter Higgins  and  Jeffrey McDonnell  provided insightful feedback on earlier versions of this manuscript. This research was funded by a  Natural Sciences and Engineering Research Council of Canada Discovery  Grant to Ferguson, a  Global Water Futures  grant and a  US National Science Foundation -Frontier Research in Earth Sciences  (# 2120733 ) to McIntosh and Ferguson. Research funding for B. S. L. and O. W. came from the  Nuclear Waste Management Organisation (NWMO) , with additional funding provided by the  Natural Sciences and Engineering Research Council of Canada Discovery  and  NFREF  grants awarded to B. S. L. B. S. L. is a Director of the  CIFAR Earth 4D Subsurface Science and Exploration program  and J.
Fluorophore selection depends on the optical system that will be used for analysis and must be chosen such as the optical system used can detect signal in this wavelength. Note: In this FISH-flow assay, we used a combination of 4 different probe sets (40-48 probes each) covering 2 regions of the RdRp gene, and the S and N genes (Wuhan strain, reference genome: GenBank : NC_045512) as described in the table below. In contrast, targeting only the most variable S gene failed to detect SARS-CoV-2 infected cells obtained from samples from infected patients (data not shown). The detection of SARS-CoV-2 variants may require the design of variant-specific probes, although the combination of probes described here detected alpha, beta, delta, and omicron variants (see below). It should be noted that using only probe set 1 that targets the RdRp gene did not detect SARS-CoV-2 alpha infection.
Proceed for flow cytometry acquisition, in this protocol Guava 12-HT (Millipore) was used. Further analyze the flow cytometry data with dedicated software such as GuavaSoft 3. 4 (Millipore) as used in the present study, or FlowJo (BD) as in Zhou et al. 1  Outline of the gating strategy is presented in Figure  1A. As shown in Figure  1B , both viral RNA and Spike are detected in an MOI-dependent fashion. In the present context of acute infection, detection of viral RNA is usually more sensitive than that of Spike.
ACKNOWLEDGMENTS This work is supported by joint funding from the  Agence Nationale de la Recherche (France)  and  Fondation pour la Recherche Medicale (France) :  Flash COVID ANR -FRM :  ANR-20-COVI-0024. Funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript. A. Z. and F. H. were supported by the  China Scholarship Council. The authors are grateful to  Pr. J. Dubuisson  and  Dr. Sandrine Belouzard  ( Centre d'infection et d'Immunitede Lille, CNRS UMR 9017, INSERM U1019, Lille, France ) and  Dr. Arielle Rosenberg  ( Hopital Cochin, Paris, France ) for kindly providing viruses. The authors acknowledge the  IMAG'IC facility  for instruction on the IXplore Spin Confocal Imaging Microscope System (Olympus) during the COVID-19 confinement period.
Main experimental design The data comes from 7 sessions in 5 patients with pharmacologically intractable epilepsy (all right-handed, 4 females, 24-42 years old), who were implanted with depth electrodes for about a week to determine the seizure onset zone for possible subsequent surgical resection  54  , at the epilepsy unit of the University Hospital of Nancy, France. All patients gave written informed consent and the study, including number of patients, selection criteria, and electrode implantation locations (REUNIE, trial No. 2015-A01951-48, ClinicalTrials. gov identifier NCT02877576) was approved by the ethical committee CPP Est III, No 16. 02. 01. The electrode locations were based exclusively on clinical criteria and were verified by CT fused with preoperative MRI. Here we report data from sites in the right (2 patients) and left (3 patients) faceselective midfusiform region.
place; familiar vs. unknown face; familiar vs. unknown place) and discrimination of face/place identify, respectively. The colored dashed lines below the chance levels show the time periods where each decoder is significantly larger than chance (permutation test, p < 0. 05). We observe different time profiles of the decoders, with the decoding of faces vs. places having an earlier inflection point and higher slope compared to the other ones (right panels; two-sided t-test; N = 24). The inflection point was also earlier for the familiar vs. unknown face decoder compared to the face and place picture identification. Source data are provided as a Source Data file. blanks in between, with a software written in Matlab (R2018a) that used the Psychophysics Toolbox. This sequence of picture presentations was triggered with a keypress by the patient, it lasted 30 s and was repeated 15 times.
The four coefficients were estimated with an iterative least squares estimation using the MATLAB function nlinfit. To compare the different cumulative decoding curves, we created 30 realizations with a random selection of half the number of units each, discarding 6 realizations in which the root mean square error of the fit of at least one of the cumulative curves was >0. 1. The slope and inflection points of the curves were compared using an ANOVA test and t-test post-hoc comparisons. The statistical significance of decoding performance was established using a permutation test (comparing with the values of the 1000 surrogates shuffling labels, using a rank test).
The acquisition time for the anatomical scan was about five min. Functional images were collected with a T2*-weighted simultaneous multi-slice echo planar imaging (SMS EPI) sequence (TR = 1500 ms, TE = 34 ms, FA = 72°, FOV = 240 × 240 mm 2 , voxel size = 2. 5 mm isotropic, matrix size = 96 × 96, interleaved), which acquired 44 oblique-axial slices covering the entire temporal and occipital lobes. fMRI Analysis. The functional runs were motion-corrected with reference to the average image of the first functional run using a 6-degree rigid body translation and rotation using a motion correction software (MCFLIRT; www. fmrib. ox. ac. uk/fsl), and spatially smoothed with a Gaussian kernel of 3 mm (FWHM; i. e. full width at half maximum). Linear trends from the preprocessed time series of each voxel were removed.
However, this random term was not selected in the final models as it increased rather than reduced the AIC of the model, compared to the model with only the Stream as a random term. Goodness of fit for the three comparisons was given as R 2 following the method by  Nakagawa and Schielzeth (2013)  for linear mixed-effects models, in which marginal R 2 (variance explained by fixed factor) and conditional R 2 (variance explained by both fixed and random factors) are computed (package "MuMIn",  Barton, 2023). We observed that linearity of the size spectra varied a lot among the four site types. To test for the presence of biases from linearity (i. e. secondary structures as in  Kerr and Dickie, 2001) , standard linear models were built for each site type, modelling log 10 abundance values as a function of log 10 body mass (log M), stream and the interaction between them.
R 2 values for each of the 4 models were used to report linearity of size spectra of each site type. We were interested to know if the changes in the size spectra were based on a change of the structure of the assemblage, i. e. a change of the relative abundance of species of different size, or on intraspecific changes of body size, with larger or smaller individuals within taxa depending on the resource quality and/or quantity. To do this, first the composition of the assemblage was compared across the 20 sites with a non-metric multidimensional scaling (NMDS) using the average per site values for all the taxa and based on the Bray-Curtis dissimilarity matrix (package "vegan",  Oksanen et al. , 2022). A PERMANOVA test was performed to assess differences among site types (10 6 permutations).
Statistical analyses We standardised the flowering records per species and individual by using the following formula: where FT Ind is the day of the year a plant flowered and FT First is the day of the year the first plant of this species flowered. We analysed our data in two different ways: multiple single-species models and a single multi-species model. For the single-species analyses of flowering time, we used linear models which included temporal origin (past vs. current) as an explanatory variable and the initial size, depending on the year of flowering, as a covariate. For the multi-species analysis of flowering time, we applied linear mixed-effects models using random slopes of species within the temporal origins as a random factor, temporal origin, flowering year (flowered in the first year vs.
To test whether the differences in aridity between the two temporal origins are related to the differences in flowering onset we calculated the IDM difference (IDM Diff = IDM Current -IDM Past ) and the FT difference (FT Diff = FT Current -FT Past ) for each species. We ran linear models with the FT Diff as a response variable and the IDM Diff , the flowering year, the region and the interaction of the last two as explanatory variables. For all models, we visually checked the residuals for normality and heteroscedasticity. All analyses were done in R (Version 4. 0. 2) using the packages plyr for data management  (Wickham 2011 ) and lme4  (Bates et al. 2015)  and lmerTest  (Kuznetsova et al. 2017)  for running our models using the lmer() function and Type III anovas for significance testing.
Genetic marker analysis To assess whether non-adaptive processes contributed to changes in genetic variation between the past and the current populations, we quantified genetic relatedness, genetic diversity within, and genetic differentiation between the two temporal origins. We used leaf samples of 4-10 (average 9. 7) common garden plants per population. We produced genomic single nucleotide polymorphism (SNP) marker data following a ddRAD protocol  (Peterson et al. 2012) , performed SNP genotyping with dDocent 2. 6. 0  (Puritz et al. 2014 ) and conducted SNP filtering following O'  Leary et al. (2018). The final data set consisted of 230 samples, between 11 and 20 (average 17. 7) per species and between 3 and 10 (average 8. 9) per temporal origin, genotyped at between 391 and 2677 (average 1223) bi-allelic SNP loci.
Acknowledgements We thank  Nicolai Friesen  and  Jaroslav Kloster  ( University of Osnabrück ) for their help with the seed collections in the Osnabrück region,  Thomas Duerbye  and  Elke Zippel  ( Berlin Botanic Garden and Botanical Museum ) for their help with seed collections in Berlin, authorities in France, Belgium and Germany for issuing the permits for seed sampling,  Lisa Ehmann ,  Lisa Henres ,  Christiane Karasch-Wittmann ,  Denise Klein ,  Erik Lemke  and  Eva Schloter  for supporting the work in the greenhouse,  Madalin Parepa  ( University of Tübingen ) for discussions on data analyses, and  Stephan Schreiber  and  Matthias Bernt  ( UFZ Halle ) for their help with sequencing and bioinformatic analyses.
Data availability The experimental data are available from the digital library of Thüringen (DLT): https:// www. db-thuer ingen. de/ recei ve/ dbt_ mods_ 00050 983. Raw sequence data have been deposited in the European Nucleotide Archive (ENA) at EMBL under accession number PRJEB47887 (https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 47887) with individual accession numbers ERS7667629 to ERS7668109.
Surface mining is inherently linked to climate change, but more precise monitoring of carbon dioxide (CO 2 ) emissions is necessary. Here we combined the geolocation of mine sites and carbon stock datasets to show that if all legal active mining sites in Brazil are exploited over the next decades, 2. 55 Gt of CO 2 equivalent (CO 2 eq) will be emitted due to the loss of vegetation (0. 87 Gt CO 2 eq) and soil (1. 68 Gt CO 2 eq). To offset these emissions, we propose constructing soils (Technosols) from mine and other wastes for mine reclamation. We show that this strategy could potentially offset up to 60% (1. 00 Gt CO 2 eq) of soil-related CO 2 emissions. When constructed with suitable parent materials, Technosols can also restore important soil-related ecosystem services while improving waste management.
These mineral phases are highly reactive towards SOM and can protect it from decomposers through the formation of mineral-organic associations  24, 25. Additionally, the presence of polyvalent cations, such as Ca 2+ and Mg 2+ can favor these associations through a process known as cation-bridging  26. In this process, the adsorption of polyvalent cations onto mineral surfaces reverses their negative charges, favoring interactions with negatively charged organic functional groups  27. Thus, under high chemical weathering rates such as those found in tropical or subtropical climates, Technosols constructed with significant quantities of easily weatherable minerals have the potential to function as efficient carbon sinks. Importantly, our data included several Technosol types, many of which were not intentionally constructed for reclamation or CO 2 sequestration purposes.
Methods To calculate the carbon stocks in Brazilian mining areas, we first determined the geolocation and areas of all legal mining sites in the country. We downloaded a dataset that includes the geolocation of the mining sites from the National Mining Agency's SIGMINE platform (see Data Availability). The SIGMINE is an online platform that is developed and maintained by the National Mining Agency. It shows the location and information of all active legal mining sites in Brazil. The information in this system is georeferenced and presented as satellite images and digital maps in vector and raster formats, using SIRGAS 2000 data as a reference. We extracted all available data and preprocessed it into groups of similar mineral substances (see the list in the Supplementary Information file). We selected mining processes with the following stages: mining concession ("Concessão de lavra"), authorization and permits ("Licenciamento"), extraction register ("Registro de TOC stock = 2.
00 Gt CO 2 eq. Importantly, mine reclamation with Technosols would lead to the recovery of soil-related ecosystem services. extração"), and artisanal and small-scale mining ("Lavra garimpeira"), which are representative of active mining sites. Duplicate entries were filtered according to process ID and process number, and offshore mining processes were excluded. Finally, from ~205,000 raw entries, representing ~1. 8 million km 2 of mine sites, we used ~33,000 entries (54 thousand km 2 ) in this study (Supplementary Table  S1 ). These mining areas are referred as (LAMS) and were used to estimate the carbon stocks lost due to mining activities. We used the aboveground carbon map of Brazil 48 to estimate the above-ground biomass carbon (AGBC) and the Soil Grids v2 dataset  49  to estimate the (SOC) stocks (Supplementary Fig.
S1 ) within LAMS. We uploaded the polygon dataset from SIGMINE to Google Earth Engine to sample the raster values at the original spatial resolution for both layers within LAMS polygons. We exported the results as text files for exploratory data analysis and visualization using the R programming language. Carbon stocks in each pixel were obtained by multiplying the carbon stock values (in kg m -2 ) by the pixel area in m 2. The final carbon stocks in mass unit were estimated as the sum of all carbon stocks in each pixel within the LAMS polygons for the tropical, subtropical (i. e. , temperate), and dry climatic regions. Since the dataset for SOC stock only contained information for the 0-30 cm layer, we estimated the stocks at 0-100 cm depth by applying a conversion factor of 1. 887 (i. e.
, 1/0. 53)  50. We applied this factor considering that the SOC found in the 0-30 cm layer represented 53% of the total stock found in the 0-100 cm profile  50. The SOC stocks were then converted from C to CO 2 by multiplying by 3. 67. Detailed data on SOC stocks in natural soils within LAMS are available in Supplementary Table  S2. To estimate the SOC stocks in Technosols, we utilized an updated dataset for Technosols carbon stocks  21  (see Data Avaialbility). The dataset includes several parameters for each observation, such as climate type, SOC, soil organic matter content (SOM), bulk density (BD), and layer depth. For each climate type (tropical, subtropical, and dry), we extracted observations from soil samples that were identified as "Technosols" or "constructed soil" and contained information on SOC or SOM, BD, and soil sampling to a depth of 30 cm.
However, exploratory analyses showed that the data did not follow a normal distribution (Supplementary Fig. S2 ), which is necessary to construct confidence intervals. Therefore, we performed Box-Cox transformation using Statistica software (version 13. 5. 0. 17) with the maximum likelihood method to define the best λ to bring the data close to normality. After transformation, we confirmed the data for normality using the Shapiro-Wilk test, a powerful and widely available test for normality  51. We present the results (means and 95% confidence intervals) as backtransformed data. Finally, the SOC stocks in t C ha -1 were multiplied by the values of the areas of each climatic region to obtain the SOC stocks in mass units. As for natural soils, we expressed the SOC stocks in Technosols in CO 2 eq by multiplying the C content by the 3.
Acknowledgements The authors thank  Vanessa Erler Sontag  ( Illus Scientia ) for the designing of figures. F. R. thanks the  São Paulo Research Foundation (FAPESP)  for the financial support ( 2019/18324-3 ). M. R. C and T. O. F. thank the  Brazilian National Council for Scientific and Technological Development (CNPq)  for their research productivity fellowship ( 311787/2021-5  and  305013/2022-0 ) and X. L. O. is thankful for the  Consellería de Educación, Universidade e Formación Profesional-Xunta de Galicia (Axudas á consolidación e   estruturación de unidades de investigación competitivas do SUG del Plan Galego IDT ,  Ambiosol  Group ref. 2018-PG036 ).
Data availability The data used to estimate the mining areas in Brazil are available at https://geo. anm. gov. br/portal/apps/webappviewer/index. html?id=6a8f5ccc4b6a4c2bba79759aa952d908. The data used to calculate the soil carbon stocks are available at https://files. isric. org/soilgrids/ latest/. The used to calculate AGBC stocks are available at https://snd. gu. se/sv/ catalogue/study/ecds0244/1#dataset. The data used to estimate the Technosol SOC stocks are available at https://doi. org/10. 5281/zenodo. 7879530.
Here we present a comprehensive database of hightemporal resolution time series of DO concentration and temperature across 14 mountain lakes located in the French Alps, joined with a set of attributes for the lakes and their catchment. First, we quantified the duration of hypoxia spatially, i. e. , among lakes, and temporally, i. e. , among years within lakes. We then identified which of ice phenology, DO concentrations at the ice onset or DO decay rate was the main driver of hypoxia duration. As DO decay rate stood out as the dominant predictor of hypoxia duration, we evaluated, in a second step, the relative contribution of the biogeochemical and physical controls to DO decay rates. In the final step, we proposed a simple mechanistic model as a proof-of-concept of the mechanisms derived from the data analysis.
5 C) 2 m below the surface, along with a temperature-DO sensor (PME Minidot, accuracy AE 0. 1 C and AE 0. 3 mg O 2 L À1 ) placed 1 m above the sediment and anchored to a submerged buoy at the maximum lake depth. Both water temperature and DO concentration were recorded at 1-h intervals. Twenty-one DO and water temperature data series were available.
Reaction-diffusion model To assess the mechanistic processes linking predictors to DO decay in the field dataset, we used a simplified model of coupled DO and water temperature under the ice, set on a hypothetical alpine lake with a parabolic morphology of 20 m max depth (average max depth was 17 m in our dataset) and 5 ha surface area (average surface area was 4. 8 m in our dataset). The modeling framework considers diffusionreaction equations for temperature and oxygen. The model solves the one-dimensional (vertical), diffusion reaction equation for horizontally averaged scalar field, C(z,t) being either the DO or the T field A z ð Þ ∂C z,t ð Þ ∂t ¼ ∂ ∂z A z ð ÞK z, t ð Þ ∂C z,t ð Þ dz þ ∂A z ð Þ ∂z F with, • A(z) the bathymetry; • K(z,t) the vertical turbulent diffusivity defined as a function of the buoyancy frequency (N 2 ): K z ¼ aN À2b  (Jassby and Powell 1975; Heinz et al.
2021 ). Boundary conditions for water temperature were set at 0 C at the surface and 3 C at the lake bottom. ii. Initial sediment heat release set at 0, 1, 2, and 4 W m À2. Values for the initial sediment heat flux were determined based on simulations on a well-established bulk model FLake  (Golosov and Kirillin 2010)  for a lake of similar latitude and longitude as Lake Anterne with a light extinction coefficient ranging between 0. 4 and 4 m À1 (i. e. , Secchi depth within 0. 5-5 m, comparable to the range found in our dataset). Such values for the initial sediment heat release are in line with those reported in previous studies  (Kirillin et al. 2012; Zdorovennova et al. 2021 ).
The duration of hypoxia was neither correlated to ice cover duration (Fig. 3a ), nor to DO saturation at the ice onset (DO init , Fig. 3b ). However, the duration of hypoxia among lakes and years was strongly dependent on the DO decay rate K exp (Fig. 3c ). Because the coefficient K exp was, by far, the best predictor of hypoxia duration in our dataset, the subsequent analyses focused on uncovering the processes governing the DO decay rate in mountain lakes. The DO decay coefficient K exp ranged from 0. 0016 to 0. 0626 d À1 , i. e. , e-folding decay timescales between 598 and 16 d, respectively. K exp showed substantial variability among lakes, and among years for a single lake (Fig. 4a ). Some lakes showed temporary breaks in the DO decay trends (for instance, Merlet supérieur, Muzelle in 2014-2015 and Bresses inférieur, Fig.