dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, for the root symbiont type by vegetation interaction, we grouped symbiont types at the site level based on each woody plant species (see Table  1 , Table  S1 ), and thus we only estimate the effect of root symbionts for woody plants. We fit Bayesian models using the brms package in R  (Bürkner, 2017). All data were standard normalized prior to modeling to improve model convergence and we logged the bacterial response variable (16S OTU richness) for normality. All models contained a site level random intercept and hierarchical structure as described below and in Table  S1. The Bayesian framework was convenient here due to the somewhat uneven design and multilevel structure of the data (Table  S1 ), and was useful for predicting relationships with reasonable estimates of uncertainties.
We used the posterior distributions of each parameter to calculate the probabilities that it was different from zero, and three probability levels are reported (85, 90 and 95% probabilities, respectively, that the parameter estimate is different from zero). We also used these parameter distributions to calculate pairwise post-hoc comparisons between root symbiont types. To assess the impacts of woody plant encroachment on bacterial and fungal community composition, we used non-metric multidimensional scaling (NMDS) of the Bray-Curtis (fungi) and weighted Unifrac (bacteria) dissimilarity metrics and permutational multivariate analysis of variance (perMANOVA) with the 'adonis' function in the Vegan package in R  (Oksanen, Blanchet, Kindt, Legendre, & O'Hara, 2016)  (999 permutations). We ran three perMANOVA models, first with vegetation type (woody versus herbaceous) as a predictor and site as a strata variable to restrict permutations within sites; next we used root symbiont type, climate, and soil abiotic parameters as predictors with vegetation type as a strata; third we ran a leaf trait model for woody soils only using leaf trait PCA axes 1 and 2 as predictors and no strata variable.
All perMANOVA models had either bacterial or fungal community composition as the response variable. General Model: To assess differences in the relative read abundance of microbial taxa between woody and nonwoody vegetation, we used linear mixed effects models (for normally distributed data) or generalized linear models with a Gamma distribution in the 'lmer' and 'glmer' functions in the lme4 package in R  (Bates, Mächler, Bolker, & Walker, 2014). Read abundances (logged, zeroes removed) of microbial phyla were the response variable, vegetation type (woody/herbaceous) was a fixed effect and site was included as a random effect. General Model: Beta 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 = 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝑡𝑦𝑝𝑒 (1|)+     We also used indicator species analysis to determine which taxa characterized soils from different vegetation types (woody versus herbaceous) using the function 'multipatt' in the indicspecies package in R(De Cáceres, Legendre, Wiser, & Brotons, 2012).
1701979) awarded to  C. Collins  and  J. Diez. C. Collins  was also supported by a  UC President's Dissertation Year Fellowship  and a  UCR Graduate Dean 's  Dissertation Research Grant. M. Spasojevic was supported by the  Niwot Ridge LTER (NSF   DEB-1637686 ). A. Stokes and F. Reverchon were supported by the  French and Mexican governments  ( ECOPICS  project,  ANR-16-CE03-0009  and  CONACYT-2 73659 ). J. Mullerová was supported by a long-term research development project  RVO 67985939  (The  Czech Academy of Sciences ) and Fulbright Grant. C. Alados was supported by the  Ministerio de Economía y Competitividad-MINECO  Project  Nº :  CGL2016-80783-R. Oriol Grau  was supported by the  ERC   Synergy  project, SyG-2013-610028  IMBALANCE-P  and an  INTERACT  grant agreement No:  730938 EU-H2020.
(C) Inhibiting CCR2 increased T cell cytotoxicity against Ccr2 +/+ cancer cells. Cytotoxicity of CD8 + OT-1 T cells toward MMTV-PyMT-chOVA;Ccr2 +/+ or MMTV-PyMT-chOVA;Ccr2 -/-cancer cells, with or without CCR2 inhibitor, measured by chromium (Cr 51 ) release (each dot is the average of triplicate from an independent experiment; n = 4 and 3 for MMTV-PyMT-chOVA;Ccr2 +/+ and MMTV-PyMT-chOVA;Ccr2 -/-cancer cells, respectively). (D) Model of clonality measured by TCR sequencing. (E) Productive clonality in tumors from Ccr2 +/+ and Ccr2 -/-cancer cells (n = 4). (F) Productive clonality in spleens was unchanged between Ccr2 +/+ and Ccr2 -/-tumors (n = 4). Statistical analysis of TCR sequencing data is described in the Materials and methods section. Means ± SEM are indicated. P values were determined for tumor volume analysis at the end time point using t test with Welch's correction (A) or Student's t test (B, C, E, and F).
The lungs remained in 4% PFA at 4°C for 48 h, followed by incubation in 20% sucrose in PBS at 4°C for 48 h. Lungs were embedded in Tissue-Tek optimal cutting temperature compound embedding solution (Sakura). The blocks were then placed in a custom-made cutting chamber with razor blade inserts every 2 mm, and the block was cut into 2-mm sections. These sectioned lung pieces were then reembedded in fresh optimal cutting temperature compound to allow for crosssectional cuts from the entire lung tissue. These cross sections of the lungs were stained with H&E and scanned by an Aperio ScanScope CS System (Leica Biosystems). The metastatic burden was calculated as the percentage of metastasis/lung area and the number of foci/lung area, determined using Aperio eSlide Capture Devices software (Leica Biosystems).
Cytokine array Tumors (8-10 mm in diameter) were isolated from MMTV-PyMT;Ccr2 +/+ and MMTV-PyMT;Ccr2 -/-mice and immediately snap-frozen in liquid nitrogen. They were then homogenized in PBS with the addition of protease inhibitors (Promega). Proteome Profiler Mouse Cytokine Array Kit, Panel A (R&D Systems) was used according to the manufacturer's instructions. Films were scanned, and spots were analyzed using ImageJ software. (3-[4,5-dimethylthiazol-2-yl] -5-[3-carboxymethoxyphenyl]-2-[4-sulfophenyl]-2H-tetrazolium) assay Proliferation of cancer cells isolated from MMTV-PyMT;Ccr2 +/+ and MMTV-PyMT;Ccr2 -/-mice was measured using the Cell-Titer 96 AQueous One Solution Cell Proliferation Assay (MTS; Promega) according to the manufacturer's instructions. In this assay, the MTS tetrazolium compound (Owen's reagent) was bioreduced by metabolically active cells into a colored formazan product.
Images were collected at 40× magnification using a Leica SP8 confocal or an AX10 microscope and an AxioCam HRc camera (Zeiss) and were analyzed using Volocity software (Version 6. 3. 0; PerkinElmer). Immunohistochemistry and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining Sections were deparaffinized and rehydrated. For phospho-STAT3 and Ki67 staining, antigen retrieval was performed by boiling slides in 10 mM sodium citrate buffer (pH 6. 0) for 6 min in a pressure cooker, while for phospho-STAT1 staining, antigen retrieval was done in Tris EDTA buffer (10 mM Tris base and 1 mM EDTA, pH 9. 0). The slides were blocked with 3% hydrogen peroxide, 1× blocking buffer (PBS containing 2. 5% BSA and 5% goat serum), Fc receptor blocker, and finally avidin/biotin blocking buffer (SP-2001; Vector Laboratories).
ImmunoSEQ Tumors from Ccr2 +/+ and Ccr2 -/-transplants were isolated and immediately placed in liquid nitrogen for shipment. Amplification and sequencing were performed on the immunoSEQ platform (Adaptive Biotechnologies) using a multiplex PCR-based assay that exclusively targets rearranged TCR genes. Sequencing data were analyzed using immunoSEQ Analyzer software. Shannon's Entropy (H), a measure of the richness and uniformity of the frequency of the TCR repertoire distribution, was defined as follows: H - X N i 1 P i log 2 P i , where N equals the number of unique clones and P i the frequency of clones. To account for variation in sequencing depth, entropy was normalized by its maximum value (H max ): H N H / H max. Clonality is defined as C = 1 -H N  (Sherwood et al. , 2013). Sequencing files are available from the immunoSEQ website through accession DOI 10.
, 2014)  with a false discovery rate cutoff of 5%. Hierarchical clustering was performed by sample and gene using normalized and log 2 -transformed gene expression values of differentially expressed genes. All of the analyses described above were performed on the Bioinformatics Shared Resource Galaxy server at CSHL. Gene set enrichment analysis  (Subramanian et al. , 2005)  was performed on a preranked gene list sorted by log 2 FoldChange value against v5. 1 Hallmark gene sets using default parameters. GO term enrichment analysis was performed using GOrilla PANTHER  (Eden et al. , 2009). The RNA-seq data have been deposited in the Sequence Read Archive with accession no. PRJNA605445 (https:// www. ncbi. nlm. nih. gov/Traces/study/?acc=PRJNA605445). BMDC isolation, culture, and activation BMDCs were isolated and cultured as previously described  (Inaba et al.
Statistical analysis All statistical analyses were performed using GraphPad Prism Version 6 or newer software. Data were analyzed using Kaplan-Meier survival analysis, Student's t tests, ANOVA, χ 2 test, and log-rank (Mantel-Cox) test, as indicated in the figure legends, with an α of 0. 05. For tumor volume analysis, oneway ANOVA followed by Sidak's multiple comparisons test; Welch's ANOVA followed by Dunnett's T3 multiple comparisons test; or Student's t tests were performed as indicated in the legends. The number of sampled units, n, is indicated in the figure legends. Online supplemental material Fig. S1  (related to Fig. 1 ) shows CCR2 expression in spontaneous MMTV-PyMT tumors and lungs. Fig. S2  shows the flow cytometry gating strategy for immune cells.
Acknowledgments We thank  Laura Maiorino  for drawing the included illustrations. This work was supported by funds from the  CSHL Cancer Center  ( P30-CA045508 ) and the  Department of Defense  ( W81XWH-14-1-0078 ) to M. Egeblad; from the  Charles and Marie Robertson Foundation  to X. -Y. He; from the  Lustgarten Foundation , the  National Cancer Institute , and the  Cedar Hill Foundation  to  D. T. Fearon ; from the  Rita Allen Foundation  and the  V Foundation for Cancer Research  to C. O. dos Santos; and a  Starr Centennial Scholarship  from the  Watson School of Biological Sciences  to  E. Bružas. R. Yan  is a George A. and  Marjorie H. Anderson Fellow. M. Egeblad ,  C. O. dos Santos , and L. Van Aelst were supported by a grant from the  Simons Foundation  to CSHL. Support was also provided by the  State of New York  (contract no.
Climate models are in need of improved constraints for water vapor transport in the atmosphere, and tritium can serve as a powerful tracer in the hydrological cycle. Although the general principles of tritium distribution and transfer processes within and between the various hydrological compartments are known, variation on short timescales and aspects of altitude dependence are still under debate. To address questions regarding tritium sources, sinks, and transfer processes, the sampling of individual precipitation events in Corte on the island of Corsica in the Mediterranean Sea was performed between April 2017 and April 2018. Tritium concentrations of 46 event samples were compared to their moisture origin and corresponding air mass history. Air mass back-trajectories were generated from the novel high-resolution ERA5 dataset from the ECMWF (European Centre for Medium-Range Weather Forecasts).
To better understand short-term tritium variations in precipitation, air mass backtrajectory modeling was applied in this study to identify the sources of water vapor for individual precipitation events and in order to compare the resulting tritium concentrations with their expected origin. In particular, we aimed to compare spatial tritium distribution patterns from the Global Network of Isotopes in Precipitation (GNIP; IAEA/WMO, 2019) with moisture source regions of discreetly sampled events. This comparison also aimed to assess the influence of sampling on different timescales. Additionally, we examined correlations between tritium concentrations and the altitude history of air masses to outline the contributions of upper atmosphere tritium to single precipitation events. The improved resolution of available meteorological grid datasets in the form of the new ERA5 dataset (Copernicus Climate Change Service (C3S), 2017) from the European Centre for Medium-Range Weather Forecasts (ECMWF) was an important prerequisite for our high-resolution study.
HYSPLIT trajectory model The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) of the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory  (Rolph et al. , 2017; Stein et al. , 2015)  was used to identify the origin of air masses at the precipitation sampling site during sampled rain events. With the model's meteorological information, it is possible to identify locations where air masses gained and lost moisture along their travel paths to the study site. The HYSPLIT model requires input in the form of meteorological grid data. We used the newly available ERA5 grids (Copernicus Climate Change Service (C3S), 2017) from the European Centre for Medium-Range Weather Forecasts. This dataset originally has a horizontal resolution of 31 km on a global scale, a vertical resolution of 37 interpolated pressure levels, and a temporal resolution of 1 h.
Results Results of the laboratory analyses of event water samples can be found in Table  1  and are also archived in the World Data Center PANGAEA 1. Tritium values range between 1. 62 and 10. 07 TU with a mean value of 4. 52 TU. A seasonal distribution with a spring to early summer maximum can be observed. The relative contribution of moisture to the 46 precipitation samples that originated from the five tritium source re-  gions was calculated following Eq. (  1 ) and is displayed in Fig. 3. The average moisture contribution of each region is shown in Table  2. Most of the moisture at the sampling site (around 60 %) has its origin in the Mediterranean Sea. Western Europe and the Atlantic Ocean are the other two major contributors with around a 10 %-15 % moisture contribution, respectively.
Continental Europe and Africa show only minor contributions with values from 10 % (mean) to 0 % (median). Relative contributions for some of these regions are not normally distributed, which results in the deviation of the mean from median values. This is especially the case for the African and continental European regions, where the dataset is skewed because they do not contribute any moisture for most events. The mean and median moisture contribution of the constructed tritium source regions does not vary considerably with the chosen trajectory backward runtimes (Table  2 ) of 3, 5, 7, and 10 d. The results of the tritium model in source regions (Table  2 ) calculated using Eq. (  1 ) show the highest tritium concentrations in continental Europe with around 8. 8 TU followed by western Europe with 7.
Basic trends in tritium concentrations The measured tritium values in precipitation at Corte are well within the range of observed tritium variances measured by European GNIP stations. A Shapiro-Wilk test reveals that the tritium concentrations of precipitation events are normally distributed (p = 0. 0053), and a Q-Q plot shows a higher occurrence of high-concentration outliers. These four events with the highest tritium concentration will be discussed later. The mean tritium value of 4. 52 TU measured at Corte over the sampling period compares well to the annual averages of GNIP stations in western Europe and on the shores of the Mediterranean Sea (Fig. 2 ), such as the nearby stations in Girona (4. 55 TU), Palma de Mallorca (4. 62 TU), Monaco (3. 76 TU), and Ninfa (4. 69 TU). These observations indicate that measurements of tritium in precipitation over the course of 1 year produce a distribution of concentration data that statistically represent averaged values for spatial comparison.
Data availability. Sample data can be accessed on the PANGAEA online data repository (https://doi. org/10. 1594/PANGAEA. 911474;Juhlke et al. , 2020). Third party data used in this study include data from the  IAEA/WMO GNIP program  and the  Copernicus Climate Change Service  ( C3S ). See the Reference section of this paper for the full citation. Acknowledgements. The authors would like to thank  Martin Häusser ,  Isabel Knerr , and the whole CorsicArchive team for sampling and field support. We also wish to thank the  laboratory team of the Noble Gas Laboratory  from the  IUP Bremen. Additionally , we thank the two anonymous reviewers for their helpful comments. Financial support. This research has been supported by the  Deutsche Forschungsgemeinschaft  (grant nos. GE 2338/1-1 ,  TR1201/2-1 , and  BE1780/45-1 ).
Here, we focus on the study of East Antarctic Plateau ice cores, as these low-accumulation ice cores are particularly affected by both layering and smoothing. We use high-resolution methane data to test a simple trapping model reproducing the layered gas trapping artifacts for different accumulation conditions typical of the East Antarctic Plateau. We also use the high-resolution methane measurements to estimate the gas age distributions of the enclosed air in the five newly measured ice core sections. It appears that for accumulations below 2 cm ice equivalent yr -1 the gas records experience nearly the same degree of smoothing. We therefore propose to use a single gas age distribution to represent the firn smoothing observed in the glacial ice cores of the East Antarctic Plateau. Finally, we used the layered gas trapping model and the estimation of glacial firn smoothing to quantify their potential impacts on a hypothetical 1.
Gas age distributions can be calculated for the purpose of estimating firn smoothing. In the case of modern ice cores this may be accomplished by using gas trapping models parameterized by firn air and pore closure data  (Buizert et al. , 2012; Witrant et al. , 2012). However, to estimate the gas age distribution in bubbles it is necessary to use a depth profile of the progressive closure of pores in the firn, quantifying the transformation of open pores into closed bubbles. Due to the reopening of closed pores at the surface of the firn samples used for porosity measurements, such profiles of pore closure are associated with large measurement uncertainties  (Schaller et al. , 2017; Fourteau et al. , 2019a). Moreover, a specific problem arises in the case of glacial ice cores from East Antarctica.
Fourteau et al. (2017)  proposed an empirical model to reproduce the artifacts observed in the Vostok ice core during the Dansgaard-Oeschger event 17. However, this model has only been applied to a single event and might not be directly applicable for different periods and conditions. For this work, we analyzed methane concentrations in five ice core sections from the East Antarctic Plateau. These sections cover accumulation and temperature conditions representative of East Antarctica for both the glacial and interglacial periods. The new data are used to test the layered gas trapping model proposed by  Fourteau et al. (2017) , with the goal of rendering the model applicable to a range of accumulation rates characteristic of glacial and interglacial conditions on the East Antarctic Plateau.
2 ka, DO6-9, and DO21 events. We use these discrete methane data to correct our CFA data for the dissolution, in the Holocene and DO6-9 Dome C ice core sections, and in the DO21 event section in Vostok. The comparison between the already-calibrated methane dataset of  Loulergue et al. (2008)  and our corrected CFA measurements is given for the DO6-9 period in Fig. 2. Note that in the case where our CFA data and the independent absolute measurements do not originate from the same ice core, the match between the datasets is performed on sections with low methane variability to reduce the influence of firn smoothing. The signals after correction for the dissolution of methane are displayed in light blue in the upper panels of each part of Figs. 3  and 4. The gas age chronologies indicated in the figures are described in Sect.
This value is in line with the work of  Dällenbach et al. (2000)  and has been corroborated using the EPICA Dronning Maud Land (EDML) methane record (EPICA Community  Members et al. , 2006 ). Yet, it remains possible that the interhemispheric gradient was not constant during this period of global methane rise. The use of a high-resolution and weakly smoothed Antarctic record as the atmospheric reference would resolve this interhemispheric gradient problem, but such a record is not available. The original data of  Chappellaz et al. (2013)  present various gaps that were filled by computing a splined version of the original NEEM CFA record. The spline was chosen to not induce smoothing in the NEEM record and to overlap with the original dataset in parts where data already existed. It is shown together with the original  Chappellaz et al.
, 2016). A comparison of the calcium and methane datasets is shown in Fig. 6. The depth difference between the EDC96 ice core, in which the methane measurements were performed, and the EDC99 ice core, in which the calcium measurements were performed, was taken into account using 18 individual volcanic markers  (Parrenin et al. , 2012). The calcium data clearly show a transition between two parts; ice below the depth of 718 m is characterized by relatively high calcium values and a high variability, while ice above 718 m is characterized by low calcium values and a low variability. The transition between these two parts corresponds to the high methane plateau of the DO7 event. It also matches with the transition described above between the two regions of large and small numbers of layering artifacts.
Removing layered trapping artifacts Since layered gas trapping artifacts are not due to chronologically ordered atmospheric variations, a proper interpretation of gas records requires them to be removed from the datasets. As pointed out by  Fourteau et al. (2017) , in some sections of the records these artifacts only exhibit negative or positive methane anomalies. They cannot therefore be removed by applying a running average without introducing bias in the signal. As the layering artifacts can be visually distinguished, it would be possible to remove them manually from the data. However, such a procedure would be cumbersome due to the large number of CFA data points. Hence, to clean the three new methane signals presenting layering artifacts (EDC96, EDC99 and Vostok) we use a recursive cleaning procedure similar to the algorithm described by  Fourteau et al.
In some sections of the ice cores, the signal displays high numbers of artifacts with methane anomalies of 50 ppbv or more, as seen for instance in the onset of the DO8 in Fig. 4b. Under these conditions, the running median is influenced by the presence of the artifacts and yields a biased signal. To circumvent this problem, we manually provided a set of values that correspond to the artifact-free part of the signal. As it is easy to visually distinguish the layering artifacts from the rest of the signal, the manual selection of these points is fairly unambiguous and straightforward. The rest of the algorithm then proceeds as normal, which allows us to use the automated determination of analytical noise and cleaning of the data. This also ensures a better consistency of cleaning between the different CFA datasets.
Code availability. The programs used for data processing and modeling were developed using Python 3. 0 and available packages. They will be provided upon request to the corresponding authors. Data availability. The high-resolution Modern Dome C, Holocene Dome C, DO6-9 Dome C, and DO21 Vostok methane datasets are available on the World Data Center for Paleoclimatology: https://www. ncdc. noaa. gov/paleo-search/study/28831 (last access: 2 March 2020). The high-resolution Modern Lock-In dataset is available on the PANGAEA database: https://doi. org/10. 1594/PANGAEA. 909627 (Fourteau et al. ,  2019b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-16-503-2020-supplement.