dataesr/scientific-paragraphs-categorization huggingface dataset:
Laboratory procedures Completing the neutralizing assays on neutralizing antibodies against EV-A71 for all study participants is a resource-intensive task. For Children cohort, using multistage proportional stratified random sampling, we selected the specimens of 50% of enrolled participants aged 1-5 years for neutralizing assays. Considering the relatively smaller study population size in the 6-to 9-year-old age group, laboratory assays were conducted for all specimens in this age group. For Neonates cohort, the specimens of all participants were tested for neutralizing antibodies. The EV-A71 strain (FY573, GenBank accession number: HM064456. 1) used in this study was isolated from a child with HFMD from Fuyang city of Anhui province in 2008. Serum samples were inactivated at 56 °C for 30 min and then serially diluted 4-fold from 1:8 to 1:2048 with duplicate wells of each dilution.
Enhanced surveillance of HFMD National enhanced HFMD surveillance has been conducted across mainland China since May 2008 2. The specimens were collected from all severe HFMD cases and the first 5 reported mild cases in each county every month and tested for enteroviruses at local Centres for Disease Control and Prevention using PCR. Test results are characterized as (1) negative for enterovirus or positive for (2) EV-A71, (3) CVA16, or (4) other enteroviruses. Anhua County is part of Hunan Province, southern China. We obtained the surveillance data for Hunan province from the national enhanced HFMD surveillance for 2013-2016.
We further described the age patterns for the proportion of susceptible individuals (i. e. , individuals whose titres were less than 16), new infections and geometric mean titre (GMT) from 0-12 years old. Moreover, we estimated the probability of returning to being susceptible to EV-A71 after natural infection between 0-12 years old. Using data from enhanced HFMD surveillance in Hunan province, we calculated the incidence rate of EV-A71-associated HFMD.
Statistical analysis All analyses were performed in R version 3. 5. 0 (R Foundation for Statistical Computing, Vienna, Austria, https://www. r-project. org/) and SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). For Children cohort, we calculated that a sample size of 700 participants per age group for the 1-year and 2-year age groups and of 650 per age group for the other age groups would allow a 10% annual risk of EV-A71 infection to be estimated with a statistical significance level of 5%, a 2. 5% marginal error, and a dropout rate of 21% for the 1-year and 2-year age groups and 15% for the other age groups. Similarly, for Neonates cohort, a sample size of 900 infants would allow a 10% annual risk of EV-A71 infection  23. No study participants in Children cohort were administered EV-A71 vaccines during the study period.
Seven study participants in Neonates cohort were administered EV-A71 vaccines after 6 months of age during the study period; thus, their antibody titres after vaccination were excluded from this analysis  23. Seroprevalence was calculated for each visit and the probability of EV-A71 infection for each season. The calculation of incidence was restricted to those for whom paired serological samples (before and after HFMD epidemics 23 ) were available. We used a binomial distribution to derive the 95% confidence intervals (using R package binom). The seroprevalence and incidence were then standardized to the age structure (1-9 years population) of Anhua County according to the 2013 National Bureau of Statistics Dataset. We calculated GMT by age group for all participants in Children and Neonates cohort, and used t distribution to drive 95% confidence interval.
To explore the dynamics of neutralizing antibody titres after natural infection, we further excluded the maternal antibody titres in Neonates cohort, and then calculated GMT for those with positive titres by age group. We applied generalized linear mixed models (PROC glimmix in SAS) using B-splines to fit the dynamics of the proportion of susceptible individuals and GMT. The model selection including B-spline's degree and knots, and model parameters were based on Akaike Information Criterion (details shown in the Supplementary Notes). Moreover, using survival analysis (survfit function in R package survival), we estimated the probability of returning to being susceptible to EV-A71 after natural infection. This analysis was further stratified by initial antibody titres in order to explore the difference of immunity duration.
Acknowledgements H. Y. acknowledges financial support from the  Key Program of the National Natural Science Foundation of China  (No. 82130093 ) and the  Li Ka Shing Oxford Global Health Programme  (No. LG33 ). G. Z. acknowledges financial support from the  National Natural Science Foundation of China  (grant number  31670159 ). L. G. acknowledges financial support from the  Chinese Preventive Medicine Association  (No. 20101801 ); the  Hunan Provincial Natural Science Foundation  ( 2019JJ80115 ); and the  Scientific Research Project of Hunan Provincial Health Commission  ( B2019039 ). J. P. , B. N. and S. C. acknowledge financial support from the  AXA Research Fund , the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases programme  (Grant  ANR-10-LABX-62-IBEID ), the  Models of Infectious Disease Agent Study of the National Institute of General Medical Sciences , and the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ).
The migration of the PCR products was carried out on an ABI 3130 xl Genetic Analyzer using the LIZ500 size standard (Applied Biosystems); alleles were scored using GeneMapper 5 software (Applied Biosystems). We then calculated all basic genetic parameters including allele frequencies, number of alleles (N all ), polymorphic information content (PIC) for each locus and the observed (H o ) and expected (H e ) heterozygosity using CERVUS v. 3. 0. 7  (Kalinowski et al. , 2007; Marshall et al. , 1998). We tested for the null independence between loci from statistical genotypic disequilibrium analysis using Genepop V4. 4  (Rousset, 2008). The significance of a deviation from the Hardy-Weinberg equilibrium including a Bonferroni correction and null allele frequencies were estimated using CERVUS v.
3. 0. 7. The admixture model of the STRUCTURE software  (Pritchard et al. , 2000)  was used to identify potential hybrids in the field population by modelling cluster assignments for K = 1-5 clusters. We made 10 independent runs for each K to confirm consistency across runs. In all simulations, we performed a burn-in period of 10 000 iterations and 1 000 000 Markov chain Monte Carlo iterations. To determine the most likely value of K, we used the ΔK method of  Evanno et al. (2005)  implemented in STRUCTURE HARVESTER  (Earl & Vonholdt, 2012). We combined the results from the 10 replicate runs into one output using CLUMPP software  (Jakobsson & Rosenberg, 2007).
| Mitochondrial genetic variation We obtained a total of 154 COI sequences from the 157 juveniles and adults sampled in the same habitat. Amongst them, we identified 20 haplotypes (Genbank accessions MZ930411-30) amongst which 9, 5, 4 and 2 haplotypes belonged, respectively, to the L1, L2, L3 and L4 mitochondrial lineages (Figure  1 ). No haplotype was identified as belonging to the L5 lineage. In the field population, the majority of the individuals belonged to the L2 (50. 65%) and the L3 (35. 71%) lineages, whereas 10. 39% belonged to the L1 lineage and only 3. 25% to the L4 lineage (Figure  2 ).
DATA AVA I L A B I L I T Y S TAT E M E N T The COI sequences that support the findings of this study are openly available in Genbank accessions MZ930411-30. The microsatellite genotypes that support the findings of this study are available in Dryad, Dataset, https://doi. org/10. 5061/dryad. xksn0 2vj4. The paternity assignment data that supports the findings of this study are available in the supplementary material of this article.
Further site specific explanations for grouping of faults are reported in the results section. The principal kinematic axes (incremental strain axes) and P-and T-dihedra for each fault plane were calculated using FaultKin 8. 1. 2 software  (Allmendinger et al. , 2011; Marrett & Allmendinger, 1990). We then used FaultKin to determine the principal kinematic axes for each population of faults and associated fault plane solutions using a linked Bingham analysis of the P-and T-axes  (Betka et al. , 2016; Marrett & Allmendinger, 1991). For each data set, we used the Max Compatible Faults algorithm to remove faults with P-dihedra that did not encompass the bulk shortening axis of the sample set (e. g. ,  Allmendinger et al. , 1989). We interpret these remaining data, which constitute ∼22% of the total data set, to represent faults that may have formed during a different deformation event, or were measured from fault slickenlines with ambiguous kinematic indicators (e.
Acknowledgments Funding for this project is provided by  National Science Foundation Earth Science (NSF EAR)  Grant  1756943  to  K. Morell , and  NSF EAR  Grant  1756834  to  C. Regalla. Additionally ,  N. Harrichhausen  was supported by a  National Science and Engineering Research Council (NSERC)   Post Graduate Scholarship  and  Geological Society of America (GSA)   Graduate Student Research Grant. The authors would like to thank the  Pacheedaht First Nation  for generously granting access to their land and for their interest in our research;  Parks Canada  for providing access, allowing for sample collection, and providing logistical support for work in Pacific Rim National Park; and TimberWest Forest Corporation and  Island Timberlands  for providing land access and logistical support for work on their respective properties.
Data Availability Statement Additional figures are provided in the Supplementary Material. Tabulated structural data, and Sr isotope data  (Harrichhausen et al. , 2022)  are stored in a data repository that can be accessed at: https://datadryad. org/stash/ dataset/doi:10. 25349/D9CD03. Topographical data used to create geology maps are from the Canadian Digital Elevation Model Database available at: https://open. canada. ca/data/en/dataset/957782bf-847c-4644-a757-e383c0057995. Stereonet 10. 1. 6 and FaultKin 8. 1. 2, used for structural analyses and kinematic inversions, are both available at https://www. rickallmendinger. net/.
Given very limited existing studies and associated large uncertainty, the response of the northern high-latitude permafrost under solar geoengineering deserves further investigation. Additionally, given the climate model differences in dealing with 105 stratospheric aerosols and their chemistry, using a multi-model approach helps quantify the uncertainties associated with the response. In this study, we investigate the response of the northern high-latitude permafrost ecosystem under solar geoengineering experiments using multiple models. Our study is organized as follows: In section 2, the observation based dataset, model simulations and methods are presented. In section 3, we present the responses of northern high-latitude permafrost and 110 terrestrial carbon to solar geoengineering. Section 4 is discussion and conclusions.
, 1997)  and Northern Circumpolar Soil Carbon Database version 2 (NCSCDv2)  (Hugelius et al. , 2013 (Hugelius et al. , , 2014) 120 ) 120  datasets. Changes in permafrost extent and area, active layer thickness (ALT), carbon fluxes and carbon stocks over the permafrost region under the four scenarios at the end period of this century (2080-2099) relative to the baseline period are analyzed. The multi-model response is reported as the ensemble mean and one standard error.
Observation based datasets The NCSCDv2 provides estimated storage of SOC in the northern circumpolar permafrost region identified by the IPA 125 permafrost map. The dataset aggregates pedons from regional soil maps homogenized to the U. S. Soil Taxonomy, allowing https://doi. org/10. 5194/esd-2022-34 Preprint. Discussion started: 22 July 2022 c Author(s) 2022. CC BY 4. 0 License. SOC storage to be calculated. In pedons where the dataset is incomplete, gap-filling was used to complete calculations over the whole IPA permafrost map. The soil organic carbon content (SOCC) (units of kg C m -2 ) over the northern circumpolar permafrost region from NCSCDv2 is available at four soil layers: 0-30 cm, 0-100 cm, 100-200 cm and 200-300 cm depth, and at a horizontal resolution of 0. 5° × 0. 5° is used in this study.
Model simulations The five CMIP6 ESMs used in this study are: CESM2-WACCM, UKESM1-0-LL, CNRM-ESM2-1, IPSL-CM6A-LR and MPI-ESM1-2-LR (Table  1 ). We downloaded monthly data of surface air temperature (tas), precipitation rate (pr), surface downwelling shortwave radiation (rsds), surface upwelling shortwave radiation (rsus), soil temperature (tsl), terrestrial carbon pool (cLand), soil organic carbon pool (cSoil), vegetation carbon pool (cVeg), litter carbon pool (cLitter, not available for 135 UKESM1-0-LL), coarse wood debris pool (cCwd, only available for CESM2-WACCM), gross primary production (gpp), net primary production (npp), heterotrophic respiration (rh) and net ecosystem production (nep, not available for UKESM1-0-LL) from G6solar and G6sulfur simulations along with their historical, ssp245 and ssp585 simulations.
The slower accumulation in SOC under ssp585 is mainly due to enhanced soil carbon decomposition under much warmer temperatures compared with the other three scenarios. There is a large spread in the baseline permafrost SOC amount among 335 the five earth system models, which affects the SOC available to participate in microbial decomposition under warmer conditions  (Chen et al. , 2020). In the five models, only CESM2-WACCM simulates a baseline permafrost SOC of 847. 3 Pg C that is comparable to the observational estimate of 1091. 3 Pg C according to the NCSCDv2 dataset. However, CESM2-WACCM projects net loss in SOC by 1. 3, 0. 8, 2. 8 and 11. 7 Pg C during this century for G6solar, G6sulfur, ssp245 and ssp585, respectively, as a result of its large baseline SOC stock and rapid soil carbon decomposition under warming.
Changes in thawed permafrost soil carbon 410 As ALT deepens under warmer climates, more previously frozen SOC becomes vulnerable to decomposition within thawed soil volume, especially during summer when both annual ALT and SOC decomposition rates reach their maximum. The CESM2-WCCM simulation of historical permafrost extent based on annual maximum ALT, and permafrost SOC are both close to the observational estimates. CESM2-WACCM also has a sensitivity of thawed permafrost volume to global MAAT close to the median of CMIP6 models  (Burke et al. , 2020). Hence, CESM2-WACCM simulated ALT changes can be useful in 415 representing SOC thaw and exposure to accelerated decomposition during summer under the four scenarios. As layered soil carbon mass is not available, we use the total SOC simulated by CESM2-WACCM and the vertical SOC density profile derived from the NCSCDv2 dataset to distribute simulated SOC with soil depth and neglect changes in vertical SOC density profile under warming.
Acknowledgements We acknowledge the  World Climate Research Programme  for coordinating and promoting CMIP and all participants of CMIP6 and GeoMIP. We thank the  Super Computing Center of Beijing Normal University  for providing computing resources. We thank  Dr. Burke  for providing the computer code calculating permafrost probability with the observation-based relationship. This study was supported by  National Nature Science Foundation of China  (No. 41875126 ). Andy Jones was supported by the  555   Met Office Hadley Centre Climate Programme  funded by  BEIS. Ulrike Niemeier  has been supported by the  Deutsche Forschungsgemeinschaft Research Unit VollImpact  ( FOR2820  (grant no. 398006378 )) and used resources of the Deutsches https://doi. org/10. 5194/esd-2022-34Preprint. Discussion started: 22 July 2022 c Author(s)  2022.
sh script in BBTOOLS to remove bacterial contaminants (Paenibacillus sp. , which are often present in fungal samples), relying on k-mer matching (k=25 mm=f mkf=0. 05) (https://sourceforge. net/ projects/bbtools/). The mitochondria were assembled separately from the FALCON pre-assembled reads (preads), using an in-house tool (assemblemito. sh) to filter the preads, and polished with 'Arrow' in SMRTLINK v. 5. 0. 1. 9578 (https://github. com/ PacificBiosciences/GenomicConsensus). A secondary FALCON assembly was generated using the mitochondria-filtered preads, improved with FINISHERSC v. 2. 1  (Lam et al. , 2015) , and polished with 'Arrow'.
Genome annotation and principal component analysis All genomes were annotated using the JGI Annotation Pipeline  (Grigoriev et al. , 2014) , which combines several gene predictions and annotation methods with transcriptomics data and integrates the annotated genomes into MycoCosm (https://mycocosm. jgi. doe. gov), a web-based fungal resource for comparative analysis  (Grigoriev et al. , 2014). Completeness of genome annotation was assessed using BUSCO v. 4. 0. 6  (Simão et al. , 2015) , with the ascomycetes_odb10 database. Functional annotation was done using INTERPROSCAN v. 5. 35-74. 0. Carbohydrate-active enzymes were annotated using the CAZy annotation pipeline  (Lombard et al. , 2014). Gene count heatmaps were created using the 'heatmap. 2' function of R/GPLOTS, with z-score normalization.
Hierarchical clustering with Euclidean distance and average-linkage clustering was carried out on copy-number using the 'hclust' function in R. Phylogenetic principal component analysis (phylo-PCA) on CAZyme and Pfam copy numbers was performed on the phylogenetic size-corrected dataset, followed by PCA using the 'phyl. pca' function from PHYTOOLS  (Revell, 2009). A copy number matrix normalized by proteome size and the species tree were used as input data. Independent contrasts were calculated under the Brownian motion model and the parameter mode 'cov'.
1. 4. rev22 (-gt 0. 3). All the filtered MSAs were concatenated. FASTTREE inferred the species tree (-spr 4 -mlacc 2 -slownni -gamma). We used Computational Analysis of Gene Family Evolution (CAFE) v. 4. 2. 1  (De Bie et al. , 2006)  to study the gene expansions of the orthogroups along with the evolutionary history. CAFE requires two main inputs: the gene counts in the orthogroups and an ultrametric species tree. We directly used the gene counts from ORTHOFINDER. Only the gene families for which P < 0. 01 were considered for further analysis. The Viterbi method was applied to them to compute branch-specific P-values to detect rapid expansions and contractions at each node. The enrichment of Pfam domains and orthologous clusters was tested using a two-tailed Fisher's exact test (FET) and corrected for multiple testing using the Benjamini-Hochberg method in PYTHON v.
Differential expression analysis In addition to the transcriptomics experiments described earlier and used only for genome annotation, we performed differential gene expression analysis using previously published data for two species: P. domesticum and P. omphalodes. The FASTQ files were downloaded from GenBank -Bioproject PRJNA662999. Pyronema domesticum was grown for 4 d with different 1. 5% agar plate treatments: sucrose (20 g l À1 ) minimal medium, water agar, pyrolyzed soil from Illilouette Creek Basin (10 g l À1 ), or 750°Cburned white-pine char (10 g l À1 ). Three independent replicates were sequenced for each sample  (Fischer et al. , 2021). The second dataset described the transcriptional profile of P. omphalodes (syn. Pyronema confluens) during its white-light-induced sexual development  (Traeger et al.
, 2013)  -Bioproject PRJNA177769: the samples sex-dev (sexual development -GSM1020388 and GSM1020389) and veg (growth in darkness, no sexual development -GSM1020390 and GSM1020391). In both experiments, the fungi were grown for 4 d on agar plates, RNAs were isolated from surface mycelium/fruiting bodies, and two independent biological replicates were sequenced. Quality trimming was performed using TRIMMOMATIC v. 0. 36  (Bolger et al. , 2014)  (ILLUMINACLIP:TruSeq3-PE. fa:2:30:10:2:keepBoth Reads LEADING:15 TRAILING:15 SLIDINGWINDOW:4:15 MINLEN:50). Filtered reads from each library were aligned to the reference genome using HISAT2 v. 2. 1. 0 (Kim et al. , 2019) , and 'featureCounts' was used to generate the raw gene counts. DESEQ2 v. 1. 18. 1  (Love et al. , 2014)  was subsequently used to determine differentially expressed genes in the charcoal, soil, and water conditions in comparison to sucrose as a control, and sexdev to veg as a control.
Phylogeny and functional annotation of pyrophilous fungi The genomes of six pyrophilous species were sequenced using long-read PacBio technology, assembled into 41-701 scaffolds with lengths ranging from 38-117 Mbp, and annotated with predicted 10 413-13 093 gene models. BUSCO  (Simão et al. , 2015)  analysis showed 96. 3-98. 2% completeness using the Ascomycetes dataset (Table  1 ). We reconstructed the phylogeny from 2057 single-copy orthologs (1011 944 amino acid positions) using 18 representative Pezizales taxa and Trichoderma reesei (Hypocreales) as the outgroup (Fig. 1a ). With high bootstrap support (all nodes 100%), the tree shows a topology consistent with the Pezizales order. In our study, five of the seven pyrophilous fungi belong to the Pyronemataceae family, and the other two, P.
echinospora and M. snyderi, to the Pezizaceae and Morchellaceae families, respectively. Regarding genomic metrics among the Pyronemataceae fungi, only W. mikolae showed a larger genome size (117. 29 Mb) with 55. 8% of repetitive content, comparable with other ectomycorrhizal truffle genomes (Fig. 1b ). Tricharina praecox showed the smallest genome within this dataset (38. 36 Mb), 6. 9% of which consisted of repetitive elements. Other than these two cases, pyrophilous assemblies showed similar metrics of size and numbers of repeats compared to their close relatives. In the pangenome distribution (Fig. 1c ), the homologous gene set that was found in all 19 species (blue in the bar plot in Fig. 1c ) showed a low variance (mean AE SD = 4145 AE 131) among all genomes. The number of unique genes positively correlates with proteome size (R 2 = 0.
New Phytologist (2022) 236: 1154-1167 www. newphytologist. com P. echinospora; Geocar1, G. carbonaria; Morsny1, M. snyderi), which appear after the transition shown in Fig. 2. An exception is the conifer ectomycorrhizal W. mikolae (Fig. 2 ), which is present in the early stages of post-fire colonization  (Bruns et al. , 2020; Miyauchi et al. , 2020)  but disappears rapidly due to the death of its plant host. In order to find species-specific genomic features, we performed an enrichment analysis of Pfam domains of unique genes (shown in grey in the barplot in Fig. 1c ) on each pyrophilous species individually (P < 0. 05, Fisher's exact test, Benjamini-Hochberg adjusted P-values, abbreviated as FET). This analysis showed only one enriched domain, the collagen triple helix repeat (PF01391) in P.
omphalodes (Table  S1 ). All species-wide enrichment tests identified ankyrin-related domains (PF12796, PF13606, PF13637, and PF13857). To infer enriched functional gene families in pyrophilous fungi, we analyzed all Pfam domain counts and homologous protein groups generated with Markov Clustering Algorithm (MCL) across 19 Pezizomycetes genomes. Among the Pfam domains in early and late colonizers, 11 and 3 were overrepresented, respectively, and 9 were underrepresented in both cases (FET, P < 0. 05) (Table  S2 ). In the early colonizers, the most overrepresented domains were CBM14 (PF01607), arthropod defensin (PF01097), collagen triple helix repeat (PF01391), STAND  proteins (PF17111), and SUN b-glucosidase (PF03856). In the late colonizers, the cellulose-binding module 1 -CBM1 (PF00734) and AA9formerly GH61 (PF03443) were overrepresented.