dataesr/scientific-paragraphs-categorization huggingface dataset:
Meta-analysis statistics All statistics were run in R  54. We calculated log response ratios on soil C concentrations in the high-frequency plots divided by the concentrations in the unburned plots for each site (RR site ). https://doi. org/10. 1038/s41558-023-01800-7 RR site = ln ( μ burn μ unb ) To determine variable weights (var), we used the inverse of the variance within a site. We calculated the variance within each site by combining the standard deviation (σ), sample size (n) and sample means (μ) for each treatment within each site. var site = σ 2 burn n burn μ 2 burn + σ 2 unb n unb μ 2 unb We then calculated the overall effects, 95% confidence intervals and their significance using multivariate meta-analysis models (rma. mv) in the metaphor package  55. To evaluate the important predictors of fire effects on soil C, we performed model selection on linear meta-analysis models using the glmulti package  55  with the log response ratio as the predicted variable and environmental variables as the predictor variables.
All fitting was done using the mgcv package in R  58. We tested the overall effect of fire on δ  13  C values across sites using a mixed-effects model with site as a random effect. Within each site, we tested for significant effects using linear models (Supplementary Table  3 ). For this analysis we calculate the relative contributions of C3 trees vs C4 grasses in terms of total stocks of SOC. To calculate this, we use bulk density values reported for the site, standardized it to a depth of 0-20 cm and multiplied total soil mass per area by the soil C concentration.
For our statistical analyses, we merged the WorldClim dataset with the model output for direct comparability between the spatial trends in our empirical data. Because we are interested in the factors that determine the impact of fire on soil C, we filtered the data to only include grid cells that had non-zero burned area. All the DGVMs we used in this study explicitly simulate litter burning. The models do not simulate SOC burning except for CLM-Li. CLM-Li simulates SOC burning only over peatlands. However, for consistency with the other models, we did not consider its impact on SOC in the FireMIP simulations.
Upscaling calculations We determined the distribution of savannah-grasslands using the World Wildlife Fund (WWF) ecoregions but excluded the Montane Grasslands and Shrublands category because this included steppe (https:// www. worldwildlife. org/biomes/montane-grasslands-and-shrublands). Maps were downloaded on 1 January 2023. We included the main environmental variables determined in our main meta-regression: percent silt, precipitation seasonality, mean annual temperature, mean annual precipitation and aridity. Climate data were acquired from the same sources as we described above. Percent silt data were taken from the Harmonized World Soils Database v. 1. 2 (accessed 20 May 2020). We used a 17-year duration of fire treatments based on the 1998-2015 GFED4 records. We then applied our statistical model to each gridcell across savannah-grasslands.
We determined the potential significance of cells using the 95% confidence intervals of the model fit. We present a comparison between global data extrapolated over and data used to fit the statistical model in Supplementary Fig. 1. For our calculations, we restricted our analysis to include only environmental conditions covered by our dataset used to build the statistical model. Areas that were experiencing either gains or declines in burned area were determined using remote sensing data on fire occurrences from 1998 to 2015 from a previously published dataset  12. For the declines in burned area, we re-calculated the response ratios in terms of fire exclusion/high frequency to illustrate the potential gain in soil C and transformed them to percent differences; this was repeated for the cases where fire frequency increased.
These were then multiplied to the total SOC stocks in each grid cell calculated from the Harmonized World Soils Database v. 1. 2  63  to transport a percentage to a stock change. As a comparison between the extrapolation-based SOC flux estimates and empirical measurements, we used the SOC fluxes from the six sites from our field campaign where we measured bulk densities to a standardized depth of 20 cm. Field measurement fluxes were 0. 21 ± 0. 10 MgC ha -1 yr -1 (mean ± standard deviation) while model-based estimates were 0. 37 ± 0. 26; the median was 0. 31 MgC ha -1 yr -1 (model-based estimates follow a log normal distribution).
Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Extended Data Fig. 1  | Distribution of semi-arid regions and savannahgrasslands. Distribution of semi-arid regions and savannah-grasslands in yellow with points identifying the locations of our sites in the meta-analysis. We determined the distribution of savannah-grasslands using the WWF ecoregions but excluded the Montane Grasslands & Shrublands category because this included steppe (https://www. worldwildlife. org/biomes/montane-grasslandsand-shrublands). Maps were downloaded on 1/1/2023. We also added the MODIS map of semidry shrub and savannah  64. References for the specific experimental sites are as follows. https://doi. org/10. 1038/s41558-023-01800-7 Extended Data Fig.
Acknowledgements Funding was provided by the  US Department of Agriculture National Institute of Food and Agriculture  grant  2018-67012-28077  and  United Kingdom Research  and Innovation  G123484  (A. F. A. P. ),  Gordon and Betty Moore Foundation  (R. B. J. ) and  Lawrence Fellowship LLNL-LDRD Program  under project number  21-ERD-045  under the auspices of  Department of Energy  Contract  DE-AC52-07NA27344  (K. G. ). The  Cedar Creek Long   Term Ecological Research programme  was funded by  National Science Foundation  grants  DEB-0620652 ,  DEB-1234162 ,  DEB-1831944  and  DBI-2021898  (P. B. R. and S. E. H. ). Sampling in other sites were funded by the  National Park Service and Sequoia Parks Conservancy and South African National Parks.
This comparison is meant to demonstrate the agreement in the direction of change in fire effects on soil C across the climate gradient, not direct comparisons of their quantitative values because of the fundamental differences in how model simulations are conducted compared to field experiments. Points are coloured based on their densities (yellow indicates higher density). Dashed grey line is a generalized additive model fit to the model output and the solid black line is the relationship derived from empirical data with shaded grey illustrating +/-the standard error. Precipitation seasonality is the coefficient of variation of monthly precipitation within a year multiplied by 100. Extended Data Fig. 8 | Density graphs of the distribution of variable values in our dataset used to train the model and the global values across which we extrapolated our model.
Density graphs of the distribution of variable values in our dataset used to train the model and the global values that we extrapolated our model across. MAP = mean annual precipitation in mm yr -1 , MAT = mean annual temperature in degrees Celsius. Aridity index is calculated as the ratio between precipitation and potential evapotranspiration. Precip. seasonality = Precipitation seasonality, which is the coefficient of variation of monthly precipitation within a year multiplied by 100. Sources for these values are explained in detail in the SI, but briefly the climate data come from WorldClim and soils data from the Harmonized World Soils Database v. 1. 2 (accessed 5/20/2020).
We used the RRA of dietary items detected in faecal samples to investigate intrapopulation behavioural plasticity, possibly learnt and transmitted through social learning. According to the 'exclusion principle' variation in a behaviour that is not induced by genetic or environmental differences is likely to result from social learning (van de  Waal et al. , 2015). Therefore, we assessed (a) whether IGV in the diet was greater than intragroup variation; (b) whether IGV was greater when considering all individuals in the group or only adult females, the philopatric sex; and (c) we investigated the relation between the availability of single food items per home range and their consumption. We use these data to illustrate the potential of the method, in particular the use of an eDNA approach as a promising tool to go beyond classical observational analyses of diet composition.
| Studied vervet monkey groups and sampling Our data were collected from four IVP long-term studied groups with overlapping territories (Figure  S1 ): Ankhase (AK), Baie Dankie (BD), Kubu (KB) and Noha (NH). All individuals were identified using specific bodily and facial features (e. g. scars, colours, shape) and each sample was assigned by experienced field assistants to a specific vervet monkey and consequently linked to available life history data. We defined adulthood for males and females separately, as their life cycles follow different patterns: 3 years for females, and 4 years for males if they dispersed, otherwise 5 years. To avoid redundancy, the social factors sex and age were combined into one category with three levels (female adults, male adults, juveniles) during analyses. Infants were not sampled because they are born at the start of the summer and only feed by nursing for the first 3 months.
Infants of the previous year were already 1 year old, and thus in the juvenile class. Approximately 0. 5 cm 3 from inside a scat were collected with gloves and a disposable plastic spoon into 20 mL HDPE scintillation vials (Carl Roth GmbH, Karlsruhe, Germany) and covered with 10 mL absolute ethanol, immediately after an individual was observed defecating. After 24-36 h, the ethanol was replaced by silica gel beads and samples stored until DNA extraction. For this study we use the sequence data of 372 faecal samples collected in summer, from mid-November 2018 to mid-March 2019 (Table  S1 ). Fifty and 95% core areas of each group's home range were calculated based on GPS positions (639 scat sampling and 4669 locations of observations, Table  S2 ) using the Brownian bridge movement model  (Horne et al.
, 2007). A full year's data was included, as the use of more data provided better estimates of the model parameters for the different home ranges and their respective core areas. Furthermore, these remain stable throughout the year in this species  (Cheney, 1981; Struhsaker, 1967). Subsequently, to account for variable plant distributions across groups' home ranges, ten square vegetation plots (each 1600 m 2 ) were randomly allocated per 50% core area of each group using QGIS software, and the vegetation cover of 52 presumed forage plants was recorded to estimate local abundance (Figure  S1 ). The final dataset comprised coverage data of 27 plants that the monkeys consumed, and which were also detected in faecal samples. Species accumulation curves (SACs) made with the vegan package  (Oksanen et al.
Bioinformatic processing of raw sequences was conducted with the OBITOOls package  (Boyer et al. , 2016)  and in R (version 4. 0. 2). All details of experimental conditions and sequence alignment, filtering, clustering, data cleaning based on controls and taxonomic assignments are described in  (Brun, Schneider, Mas Carrió, Dongre, van de Waal, et al. , 2022).
| Data analyses In order to test the assumption that intergroup variation was greater than intragroup variation, we used the weighted means of Bray-Curtis dissimilarities ranging from 0 (complete overlap) to 1 (complete nonoverlap). For all analyses, data was aggregated as the mean of RRA per plant item and per monkey to account for pseudoreplication. Dietary patterns of the four groups were visualised with non-metrical dissimilarity scaling (NMDS). The assumption of homogeneity of group dispersions was tested with the betadisper function in vegan package  (Oksanen et al. , 2014). If these were homogenous, we performed PERMANOVA with 9999 permutations and Bonferroni correction on Bray-Curtis dissimilarity matrices to test the effect of the group and sex/age variables on diet composition. If dispersions were heterogenous, we used beta regression models taking into account these dispersions to assess differences in proportions of dietary plant species per group with the package BeTareg (Cribari-Neto & Zeileis, 2010) and the joint-tests function in the emmeans package  (Lenth, 2019)  to assess the main effects of the models.
For certain plant species (n = 9), we investigated the extent to which consumption was related to environmental factors, that is, plant coverage in the groups' home ranges. In a first step, a feature selection analysis based on a random forest algorithm with 2000 permutations was conducted in the R BOruTa package  (Kursa & Rudnicki, 2010)  to determine, which species were significantly impacting on IGV in diet. Of the 61 plant species consumed, the random forest algorithm identified 16 species that differed significantly between groups and for nine of these distribution data was available (Figure  S4 , Table  S3 ). Subsequently, for these nine species RRA data was corrected for the coverage in a group's territory to account for environmental differences (RRA*(1-(percentage of coverage in the group's territory/100))).
Background. Data on comorbidities in children on kidney replacement therapy (KRT) are scarce. Considering their high relevance for prognosis and treatment, this study aims to analyse the prevalence and implications of comorbidities in European children on KRT. Methods. We included data from patients <20 years of age when commencing KRT from 2007 to 2017 from 22 European countries within the European Society of Paediatric Nephrology/European Renal Association Registry. Differences between patients with and without comorbidities in access to kidney transplantation (KT) and patient and graft survival were estimated using Cox regression. Results. Comorbidities were present in 33% of the 4127 children commencing KRT and the prevalence has steadily increased by 5% annually since 2007. Comorbidities were most frequent in high-income countries (43% versus 24% in low-income countries and 33% in middle-income countries).
LAY SUMMARY Kidney transplantation (KT) is considered the optimal treatment for children who suffer from permanent kidney failure, because it leads to a lower mortality and higher quality of life compared with dialysis. Children on dialysis frequently suffer from diseases of other organs (comorbidities) that can directly lower their life expectancy and could potentially represent a barrier for transplantation, posing an additional disease burden for these children. In this study we looked at data from a large multinational registry for children with kidney failure who require kidney replacement. Using these data, we studied whether these children suffered from comorbidities and whether these impact their life expectancy or their access to KT. We found that more and more children with kidney failure suffer from comorbidities when starting kidney replacement therapy.
Data source and study population On an annual basis, the European Society for Paediatric Nephrology/European Renal Association (ESPN/ERA) Registry collects data from 37 European countries on all children receiving KRT in Europe  [15]. We included data from patients <20 years of age when commencing KRT between 1 January 2007 and 31 December 2017 from 22 European countries. We grouped countries into low, middle and high income according to their gross domestic product per capita (GDP) tertiles: Albania, Belarus, Bulgaria, Georgia, Republic of North Macedonia, Serbia and Turkey (low income); Croatia, Estonia, Hungary, Latvia, Lithuania, Poland, Russia and Slovakia (middle income); and the Czech Republic, Greece, Ireland, Portugal, Slovenia, Spain and Switzerland (high income). GDP data were obtained from the World Bank database  [16].
Statistical analyses Data were analysed using R version 3. 6. 3 (R Foundation for Statistical Computing, Vienna, Austria) and SAS statistical software package 9. 4 (SAS Institute, Cary, NC, USA). Descriptive statistics were used to compare baseline characteristics between patient groups. We used the chi-squared test for categorical variables and the Kruskal-Wallis test for numerical variables. Trends were studied using the JoinPoint regression program, as described previously  [18]. Only countries contributing data for the entire time interval between 2007 and 2017 were included in the trend analysis. Univariate patient and graft survival at 5 years was explored using Kaplan-Meier survival estimates. A cumulative incidence competing risks (CICR) model was used for access to KT, with death during dialysis as a competing event.
ACKNOWLEDGEMENTS We would like to thank the patients, their parents and the staff of all the dialysis and transplant units who have contributed data via their national registries and contact persons. We also would like to thank  R. Topaloglu ,  J. Oh ,  Z. Massy ,  T. Jahnukainen  and  M. D. Sinha  for being members of the  ESPN/ERA Registry Committee ;  D. Shtiza ,  F. Engler ,  J. Kerschbaum ,  G. Mayer ,  R. Kramar ,  D. Pokrajac ,  K. van Hoeck  and the centre contributors to the Belgian Registry Committee;  D. Roussinov ,  S. Baiko ,  O. Raikevich-Liachovskaya ,  A. Duderavich ,  I. Sheuchuk ,  E. Maurer ,  G. F. Laube ,  C. E. Kuehni ,  P. Paprvex ,  S. Tschumi ,  L. Mader  and the  Swiss Paediatric Renal Registry ;  A. Elia ,  T. Seeman ,  K. Vondrak ,  K. Rascher ,  E. Nüsken ,  L.
2 ). The frequency shift is due to anharmonic damping of atomic oscillators of the crystal structure, which contracts at low temperature  (Scott 1968 , Salje et al. 2000). We evaluated and mapped the intensities of the RBs at each local time using the dataset available (Extended Data Fig. 2 ) highlighting that the RB intensities decrease from the cold hours of the night to the hot hours of the day. To search for differences and trends between RB intensities across EQs, we determined (Fig. 3 , the error maps are reported in Supplementary Materials Extended Data Fig. 4 ) the ratios of RB intensities measured at EQ6 (8:40 pm), EQ1 (3:00 pm), EQ3 (12:30 pm), and EQ4 (10:00 am) to those measured at EQ2 (3:20 am), which correspond to the coldest temperatures on the overall surface (Figure  S1  in Rozitis et al 2020).
Continuous linear scans were performed for 4. 3 hours (a full Bennu rotation) during each EQ. Distance between the spacecraft and asteroid surface was approximately 5 km; thus, the OTES footprint on the surface covered a circle of about 40 m, with some variation in shape and dimension with latitude. Five of the seven EQ stations were used in our analysis; the two stations EQ5 and EQ7 that were perpendicular to the terminator were excluded because of the large differences in temperature caused by the high ratio of shadowed to illuminated regions. Data were selected in terms of quality to avoid large gaps in space and phase inversion. Spectra were also selected to have a mean emission angle (MEA) < 50°. For each observational station, about 2000 spectra were selected for the nal analysis (2180 in EQ1, 2213 in EQ2, 2280 in EQ3, 1972 in EQ4, and 2406 in EQ6).
Data analysis A spectral index was de ned to quantify RB intensity in OTES spectra. In each spectrum the index was evaluated as the difference of mean value in the range 900-1000 cm -1 (channels 900. 7137-1004. 6422 cm -1 ) and nearby continuum calculated in the two ranges 500-600 cm -1 (channels 502. 3211-606. 2495 cm -1 ) and 1100-1200 cm -1 (channels 1099. 9099-1203. 8385 cm -1 ) (Extended Data Fig. 1 ). The error was evaluated as standard error of single channel values in the selected range. Global maps of RB intensity were evaluated for each of the ve studied EQ observations (Extended Data Fig. 2 ). Global OTES observations were projected onto a global shape model of Bennu (v20 produced from SPC;  Barnouin et al, 2019)  with Spacecraft Ephemeris Planets Instruments C-Matrix Events (SPICE), with which we computed observation geometries  (Acton et al 1996).
3 panel A ) and with the best surface coverage among all the EQs studied. Spectral analysis was performed by averaging multiple spectra of superimposed OTES footprints of each spot on Bennu' surface for each EQ observation. The average spectra of each EQ at every spot are reported in Extended Data Fig. 5. RB intensities were evaluated with the same index used on a single spectrum in the global maps analysis, and the errors were evaluated as standard deviation. Spacecraft clock, and other information for each single spectrum used in the analysis of six spots is reported in Supplementary Table  1. Method references Figure  1  Global average spectra at EQs 2, 6, 1, 3, and 4. Spectra were selected with mean emission angle <50° and a fully-lled eld of view. We averaged about 2000 spectra at each station, shaded areas represent the mean errors.
Ruminants are able to produce food for human consumption from plants, thanks to rumen bacteria. Bacteria are able to transform feed to microbial proteins and to biohydrogenate unsaturated fatty acids, contributing directly to fine milk composition. The database consists of daily records of milk yield, somatic cell score and 17 milk components such as fatty acids and proteins from 795 Lacaune dairy ewes. Ruminal samples were extracted from ewes using a gastric tube and sequenced to determine the bacterial composition by metabarcoding 16S rRNA gene on a next-generation sequencing platform. From bioinformatics analysis, 9,536,442 sequences were retained and re-grouped into 2,059 affiliated OTUs, represented by 751 to 168,617 sequences. Overall, 2,059 OTUs from 795 samples were attributed to 11 phyla.
Background & Summary Ruminants have the particular capacity to transform plant fiber into usable energy for the production of human food. This is achieved through ruminal symbiosis with a diverse microbial community, mainly composed of bacteria that transform dietary carbohydrates, proteins and complex lipids into volatile fatty acids, lactate, microbial proteins and vitamins 1. To study rumen bacterial communities, one of the most commonly used techniques is targeting sequencing of the 16S rRNA gene of prokaryotes. 16S rRNA sequencing is a powerful and robust way to study large cohorts of animals  2  , which combined with zootechnical traits allows the study of associations between rumen microbiota and the livestock production. A large dataset was generated through an experiment involving 795 mid-lactation adult dairy ewes from which we collected rumen and milk samples for five years under the same housing conditions.
Rumen samples were obtained directly from the rumen with a gastric tube and then sequenced by the aforementioned targeting sequencing of the 16S rRNA gene using a next-generation sequencing platform. The bioinformatics process of the resulting millions of DNA sequences generated 2,059 operational taxonomic units (OTUs) as final data, whose taxonomic affiliation was retrieved from the SILVA database  3. Milk samples were obtained from each ewe along with the rumen sample. From daily milk samples, the milk yield and somatic cell counts were recorded, and mid-infrared (MIR) spectra were retrieved to predict fat and protein contents and fatty acid and protein profiles. The dataset presented was partially analyzed by Martinez Boggio et al. 4  , but here we describe in detail the experimental conditions.