dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analyses. To assess shared pollinator taxa between multiple crops within a single growing region and year, we focused on three co-flowering crops in the Bundaberg region: macadamia, avocado and mango. A canonical correspondence analysis (CCA), with crop type and temperature as explanatory variables, was initially undertaken so that differences in pollinator communities between crops could be visualised. We then constructed two bipartite networks with crop type linked to pollinator taxa as nodes: one network (i. e. pollinator visitation network; Fig. 4a ) was weighted with proportional visitation rates (visits per minute) and the other network (i. e. the pollinator effectiveness network; Fig. 4b ) was weighted with proportional pollinator effectiveness (which we define as visitation per minute multiplied by mean SVD values for individual taxa) values.
For our analysis, a crop with low d' would mostly interact with pollinator taxa that are common across the region, while crops with high d' would mostly interact with taxa not found in other crop types. Species strength identifies key influential nodes in the network, where higher values indicate pollinator taxa that are important for crops in the region. To identify if pollinator taxa differed in their network roles between visitation and pollination effectiveness networks, a pairwise comparison of species strength scores was conducted utilising the 'emmeans' package in R  70. To evaluate which taxonomic families visited the same crop grown in different regions, we focused on two crops that were surveyed in two different regions in the same year: avocado (cv. Hass) in Bundaberg and Sunraysia (2015); and mango in Bundaberg (cv.
Null models were constructed for each of the networks; we then compared observed species strength measures to determine whether individual insect visitor taxa were more important than that expected given random interactions. Null models were built using the "nullmodel" and "vaznull" functions from the Bipartite package in R  68, 71. The "vaznull" function randomises the total number of individual interactions observed in the original matrix and produces null model networks where the main constraint is that connectance (number of links in the matrix) will be the same as in the observed network  71. Z-scores [observedmean (null)/standard deviation (null)] were then calculated and used to determine the significance of observed species strength metrics. To assess how landscape composition surrounding orchard sites influenced crop-pollinator networks, we constructed separate bipartite networks with orchard sites and pollinator groups as nodes for each of the seven unique crop, region and year combinations used in this study.
Null models were first used to understand how observed orchard site nodes within a pollinator network varied in their specialisation (d') level from that expected given random interactions between species within crops and regions  72. The construction of null models followed the same method as outlined for question 2, with the use of the 'vaznull' and 'nullmodel' functions from the Bipartite package in R  65, 66. Z-scores were then calculated and used to determine the significance of observed node specialisation (d'). To understand if observed orchard site d' was influenced by surrounding land use types we used generalised linear models (GLMs) with a quasi-binomial error distribution to account for under-dispersion. For each of the three perimeters (500 m, 1 km, 2 km) at which land use type was quantified, separate models were built for each network.
Each model was simplified to include only explanatory variables that contributed at least marginally (p ≤ 0. 10) to model fit. Optimal models were selected using model comparisons conducted using the function anova in R. Spatial autocorrelation was assessed for both raw data and model residuals using the Moran I function in the R package "ape", version 5. 1  73. No evidence of significant spatial autocorrelation was found between any of the sites, and thus sites were considered to be spatially independent in further analyses. All statistical analyses were conducted using R Statistical Software  74.
Acknowledgements We gratefully acknowledge  Dan Bickel ,  Ken Walker ,  Graham Hall ,  Christine Lambkin ,  Chris Burwell  and  Susan Wright  for their assistance with insect taxonomic identification. We also gratefully acknowledge  Samantha Read ,  Oliver Robson ,  Sushil Pandey ,  Murielle Cuenin ,  Thomas Besnier ,  Philomene Brunelliere ,  Thomas Schmitt ,  Liam Kendall ,  William Gibson ,  Clotilde Bois-Marchand ,  Eowyn Lacey ,  Aurelia Webster-Hawes  and  Greg Bible  for their assistance with field work. B. K. W. was supported by a PhD scholarship from the  University of New England. B. K. W. and A. J. R. were supported by a  Federal Government 'Rural Research and Development for Profit'  grant for the project " Multi-scale monitoring tools for managing Australian Tree Crops: Industry meets innovation " ( RnD4Profit-14-01-008 ).
M. E. S. is supported by a  University of New England  Postdoctoral Fellowship. B. G. H. and R. R. were supported through the  programme Bee Minus to Bee Plus and Beyond: Higher Yields from Smarter , Growth-focused Pollination Systems C11X1309. Data from selected orchards collected as part of  Horticulture Innovation Australia Limited  (Optimising pollination of macadamia & avocado in Australia MT 13060) was later synthesised for analysis by L. E. and B. C. with support from  PH15000  ' Strengthening and enabling effective pollination for Australia ' , funded by  Hort Frontiers Pollination Fund , part of the Hort Frontiers strategic partnership initiative developed by  Hort Innovation. R. R. was supported by the  Ian Potter Foundation  (ref: 20160225 ), a  Rural Industries Research and Development Corporation  grant for the project " Secure Pollination for More Productive Agriculture  ( RnD4Profit-15-02-035 )" and an  Australian Research Council   Discovery Early Career Researcher Award   DE170101349.
Moreover, Brazilian strains are also novel MST types, since its spacer sequences are diverse from the ones described among the 65 different MST types in the database (http://ifr48. timone. univ-mrs. fr/mst/coxiella_burnetii/groups. html). A phylogenic analysis performed with the six cox spacers of sample CbG_SVB22 (Figure  2 ) placed the Brazilian ruminant strain close to MST 20 and MST 61, which belongs to the monophyletic group 2, according to Glazunova et al. [10]. Accordingly, in the previous phylogenetic tree constructed with all loci, the strain At12 continued alone in a monophyletic branch of the tree drawn with only the six spacers common to CbG_SVB22 and the 65 MST types. A phylogenic analysis performed with the six cox spacers of sample CbG_SVB22 (Figure  2 ) placed the Brazilian ruminant strain close to MST 20 and MST 61, which belongs to the monophyletic group 2, according to Glazunova et al.
Multilocus Variable Number Tandem Repeats Analysis (MLVA) As in the MST typing, the full MLVA typing scheme was only possible for the strain At12 (Table  3 ). A minimum spanning tree (Figure  3 ) of the MLVA analysis using the six loci common to all strains tested showed that the ruminant strains could be clustered in three different variants with separation within possible host-adapted subtypes. Strain At12, from Argentina, was represented far from samples from Brazil, which reinforces the separated evolution. We obtained a similar result with a minimum spanning tree built with all loci (Figure  4 ). All South American MLVA types were novel genotypes, once they were different from the 88 genotypes deposited in the MLVA database.
Discussion This paper describes the genetic diversity of C. burnetii from Brazil and Argentina using MST and MLVA typing schemes. We demonstrate that C. burnetii strains circulating in some parts of those countries are novel genotypes, diverse from the genotypes already described in literature and present in public databases. Our results suggest independent evolution of the Argentine strain At12 isolated by Pacheco et al. [11]  and a common ancestor between the Brazilian strains and both MST 20 and MST 61, possibly inserted in the territory due to the trade of animals and animal products. MST was performed for the Argentinian strain At12 isolated from a tick  [11]  and for the Brazilian strains detected in fetuses from cattle (CbB_F2), and vaginal swabs were taken from a goat (CbG_SVB22) and a ewe (CbO_sn2).
The combination of alleles of all strains have not been observed among 65 MST genotypes currently recognized in the Coxiella MST database, representing new genotypes of C. burnetii circulating in Brazil and Argentina. Furthermore, loci Cox20, Cox37, Cox51, Cox56, and Cox57 of strain At12 revealed several polymorphisms, with Cox 56 being highly polymorphic with changes in nucleobases in positions 89, 93, 95, 132, 183, and 245. Those alleles were assigned as new Cox spacers for each locus. The polymorphism observed for the referred loci is in agreement with the observation of Glazunova et al. , which reported that those spacers are highly variable  [10]. The Brazilian strains from goats (CbG_SVB22), cattle (CbB_F2), and ovine (CbO_sn2) have the same MST loci combination for Cox 2, Cox 18, Cox 37, and Cox 61 and seem to be the same MST clone of C.
Because the lack of data for some loci can lead to impairment of kinship between strains, a minimum spanning tree was drawn with the six genotyped loci common to all strains in the MLVA analysis (ms21, ms22, ms23, ms28, ms33, and ms34) (Figure  3 ), decreasing the discriminatory power. This approach allowed the observation of possible host-adapted lineage and the proximity between cattle and goat strains from Brazil and its similarity with the French strain, CbB1, while the ovine strains are linked in proximity to Cb175_Guiana (MST 17), Nine Mile (MST 16), and #755 (MST 16) from French Guiana, USA, and Poland, respectively. Comparing the six loci (ms21, ms22, ms23, ms28, ms33, and ms34) in the MLVA database resulted in novel MLVA genotypes. Cattle and goat samples from Brazil presented fingerprints more related to MLVA6 Nijmegen type 10 (CbB1 and Cbg1506), which were isolated from cattle and goats in France  [9] , while Brazilian ovine samples were closer to MLVA6Nijmegen types 1 (Nine Mile) and 33 (Cb175_Guyana), isolated from ticks and humans  [9, 20].
Sequencing Analysis All DNA sequences generated during the experiment were deposited at GenBank. This included strain At12 (accession number MK502121-MK502130), CbB_F2 (accession number MK502135-MK502139), CbG_SVB22 (accession number MK168659-MK168664), and CbO_sn2 (accession number MK502131-MK502134).
According to Svraka et al. [28] , and as recommended by the Université de Paris-Sud (http://mlva. u-psud. fr/MLVAnet/spip. php?rubrique50), for each marker, the fragment length of the sample was compared to the fragment length of the reference strain for determining the number of repeats. According to the same authors and the website, the genotypes were considered distinct from each other when the number of repeats of at least one of the markers differed. The MLVA pattern of the isolates was compared in the database (http://mlva. u-psud. fr/mlvav4/genotyping/) to check similarities with other isolates.
Minimum Spanning Tree Analysis The software BioNumerics version 7. 5 (Applied Maths NV, Sint-Martens-Latem, Bélgica) (Available from http://www. applied-maths. com) was used to create a minimum spanning tree with the following strains from goats, ewes, cattle, ticks, and humans: Namibia, Priscilla Q177, Z3055, #801, CbuG_Q212, Cb175, RSA331, #755, CS-Florian, CbuK_Q154, Scurry Q217, CS-Dayer, Nine Mile, and Dugway 5J108-111. All MLVA patterns for these strains can be found at the public database MLVA bank (http://microbesgenotyping. i2bc. paris-saclay. fr/databases/view/966).
Phylogeny Tree Construction Phylogenetic relationships were assessed using the website (phylogeny. fr) [29]. Briefly, a multiple sequence comparison by log-expectation (MUSCLE) alignment of the sequences (version 3. 8. 31_2) [29,30], followed by G blocks curation (version 0. 91. 1) [29,31] for cleaning aligned sequences was performed. Subsequentially, to construct the phylogenic tree, phylogeny and the tree rendering were performed using phylogeny software based on the maximum-likelihood (PhyML, version 3. 1_1)  [29, 32] , dynamic graphics, and annotations for phylogenetic analyses of trees  (TreeDyn, version 198. 3) [29, 33, 34]. The robustness of the generated tree was evaluated through a comparison with the phylogenic tree available at the MST database for C. burnetii.
91. CC-BY-NC 4. 0 International license a and 2. 77, reaching no statistically significant difference. In addition, two independent flow cytometry experiment measuring the total excitation on 10 000 cells did not show differences in the geometric mean fluorescence intensity between STAT and VBNC. The βoxidation pathway described in the KEGG database was used to visualize the 63 cellular proteins differentially produced in VBNC and the 8 proteins for fatty acid degradation metabolism (Figure  8C ). The fatty acid degradation pathway (β oxidation) is wellcharacterized in Saccharomyces cerevisiae with several genes found also in C. neoformans genome (Figure  8D ). Interestingly, the corresponding proteins in C. neoformans were known to be under produced in VBNC. The 9 deletion mutants of proteins identified from the KEGG database and by the orthology with Saccharomyces genome available in the Madhani collection were tested in the model for their ability to generate VBNC.
If these results are in favor of the indispensability of acetyl-CoA for the maintenance of C. neoformans infection, the relationship between acetyl-CoA, PA -a precursor of acetyl-CoA -and dormant cells should be further analyzed. The fact that PA was able to resuscitate VBNC cells, confirms the potential relevance of the link made by the proteomic study between VBNC and metabolic pathways of acetyl-CoA. Qualitative and quantitative changes in fatty composition occurs in C. neoformans depending on the growth phase, with an increase during growth progression  [83]. In addition, KEGG pathway mapping of the 63 proteins regulated in hypoxia showed an involvement of 8 proteins involved in fatty acid degradation. The 8 proteins involved were downregulated in hypoxia compared to normoxia. Another reconstruction of the fatty acid degradation pathway using Saccharomyces genome database involved the 4 proteins also downregulated in hypoxia FAA2 (CNAG_03019), POX1 (CNAG_07747), FOX2 (CNAG_05721), POT1 (CNAG_00490).
Multispectral flow cytometry (ImageStreamX, Amnis Corporation) was used to quantify mitochondrial fluorescence as described before  [23, 88]. For each population of interest, the geometric mean was calculated using the IDEAS software. In specific experiments, yeasts were sorted on a BD FACS aria™ II flow cytometer. The gating strategy included the exclusion of the aggregates and the doublets. Subsequently, the sorting was carried out according to the intensity of CMFDA in 3 populations (CMFDA high , CMFDA medium and CMFDA low ).
Dynamic exclusion was enabled for 45 s. CC-BY-NC 4. 0 International license a Data processing and analysis. All data were searched using Andromeda  [100]  with MaxQuant software  [101, 102]  version 1. 5. 3. 8 against the H99 strain proteins database (6980 entries) and the H99 strain mitochondria proteins database (13 entries) concatenated with usual known mass spectrometry contaminants and reversed sequences of all entries. Andromeda searches were performed choosing trypsin as specific enzyme with a maximum number of two missed cleavages. Possible modifications included carbamidomethylation (Cys, fixed), oxidation (Met, variable) and Nter acetylation (variable). The mass tolerance in MS was set to 20 ppm for the first search then 6 ppm for the main search and 10 ppm for the MS/MS. Maximum peptide charge was set to seven and five amino acids were required as minimum peptide length.
Analysis of enriched processes for secreted proteins "Gene Ontology (GO) enrichment analysis" The fungifun2 v2. 2. 8 BETA  [108]  site (https://elbe. hki-jena. de/fungifun/fungifun. php) was used by selecting for each sample of secreted proteins the corresponding cellular protein background and to study the cellular proteins the background was the whole database. For this analysis we selected only the proteins identified in the 3 replicates per condition. For (http://jvenn. toulouse. inra. fr/app/index. html) with proteins only present in the 3 replicates for each condition (secretome and proteome).
Protein/protein interactions analysis Analysis of protein-protein interactions of intracellular proteins regulated during hypoxia. STRING v10. 5 software (https://string-db. org) was used  [110]. As this software used the strain JEC21 annotation, the list of orthologous proteins was transferred from H99 to JEC21 with a 62/63 protein match. The nodes directly related to the input were indicated with colors while the nodes of a higher iteration/depth were indicated in white. KEGG pathway mapping and fatty acid degradation cycle reconstruction. KEGG pathway database (https://www. genome. jp/kegg-bin) was used to map the pathways. It was rendered with the intracellular proteins significantly and differently produced in hypoxia: 63 proteins were transferred from H99 to JEC21. Nineteen proteins belonged to general metabolic pathway and more precisely 8 to fatty acid degradation ("map" pathways were not colored, Cryptococcus-specific pathways were colored green, entries were colored red).
RNAseq analysis For transcriptome analysis, yeasts incubated in 8D-HYPOx/8D-NORMOx, as well as STAT and for further analysis. STAR version 2. 5. 0a [112] , with default parameters, was used for alignment on the reference genome (PRJNA411 from NCBI Bioproject). Read counts per gene. CC-BY-NC 4. 0 International license a were measured using featureCounts version 1. 4. 6-p3 [113]  from Subreads package (parameters: -t gene -s 0). Results are summarized using multiqc version 0. 7  [114]. Count data were analyzed using R version 3. 3. 1 and the Bioconductor package DESeq2 version 1. 12. 3  [115]. Normalization and dispersion estimations were performed with DESeq2 using the default parameters but statistical tests for differential expression were performed without applying the independent filtering algorithm.
Differential expression analysis To identify the expression differences across conditions, the read counts were filtered and normalized using the R package "DESeq2". Genes with read counts below a threshold of 10 in all samples were filtered out. After normalization of read counts, differentially expressed genes for each condition were identified using the generalized linear model, which perform pairwise comparisons among each of the conditions (STAT, 8D-HYPOx, 8D-NORMOx and LOG). Genes passing the threshold, (FDR <5%), were considered significantly differentially expressed and were then considered to be up or down-regulated according to the direction of the fold change. The k-means clustering analyses were performed using R tools, "kmeans". CC-BY-NC 4. 0 International license a and "heatmap.
2". To discover the underlying pathways of these differentially expressed genes, we also carried out functional annotation analysis by summarizing the data, including known H99 gene annotation, S. cerevisiae gene ortholog, Gene Ontology (GO), KEGG pathway, and pfam domain, from FungiDB database  [116]. GO enrichment and KEGG pathway enrichment for these genes were then assessed by using Fisher's Exact Test with multiple testing correction of FDR threshold at 5%. Furthermore, conserved domains of each C. neoformans gene were assessed by using the amino acid sequence to search the NCBI Conserved Domain Database (CDD)  [117]. The presence of signal peptide sites was predicted by SignalP 4. 1  [118].
Acknowledgements We would like to acknowledge  Christina A. Cuomo ,  Laurent Châtre ,  Guilhem Janbon ,  Jean-Yves Coppée ,  Pierre Rocheteau ,  Pierre Henri Commere ,  Christine Schmitt ,  Olivier Gorgette ,  Jacomine Krijnse-Locker ,  Caroline Proux  for their comments on this work and help at different steps of the study.
Furthermore, we checked the H2Bub1 levels of several OsbZIP46 target genes in the osbzip46 mutant and wild-type lines by ChIP-qPCR. The results showed that the H2Bub1 levels of these genes were decreased in the osbzip46 mutant (Figure  4I ). To establish this point further, global H2Bub1 profile in the osbzip46 mutant was examined using ChIP-seq (Figure  S5 ). In comparison with the wild type, 1287 genes exhibited differential H2Bub1 levels in the osbzip46 mutant under drought stress condition (Supplemental Data Set 5). Notably, all of these genes were down-regulated in the mutant, and 223 of them were directly target genes of OsbZIP46, among which 83 genes were drought-responsive (Figure  4J , Figure  S5 ). Consistent with this, Gene Ontology analysis for the 223 target genes showed significant enrichments in the functional category of "response to stress" (Figure  4K ).
Plant materials and growth conditions The full-length coding regions of OsHUB1 and OsHUB2 were cloned into the pCAMBIA1301U (driven by a maize ubiquitin promoter) vector using the KpnI and BamHI sites, and introduced into Zhonghua 11 (Oryza sativa ssp, japoniaca) by Agrobacterium tumefaciens-mediated transformation. T-DNA insertion mutants oshub2-1 (03Z11AY86-3, in Zhonghua 11 background), oshub2-2 (PFG_2A-00426. R, in Dongjin background), osbzip46 (PFG_1B-09829. L, in Dongjin background) and osotld1 (PFG_1C-11130. R, in Hwayoung background) were obtained from the RMD or POSTECH mutant database  (Jeon et al. , 2000; Jeong et al. , 2006; Zhang et al. , 2006). The plants were grown in the field from April to October under natural conditions at Wuhan, China, or in the greenhouse with a 12-h-light/12-h-dark cycle at 25-30 °C in winter.
ChIP-qPCR and ChIP-Seq The chromatin immunoprecipitation (ChIP) assays were conducted as described previously  (Huang et al. , 2007). Briefly, 2 g of mutant and wild-type seedlings (each with three biological replicates) were harvested and crosslinked in 1% formaldehyde under vacuum, and chromatin was extracted and fragmented by sonication. For the immunoprecipitations, specific antibodies (anti-H2Bub1, Millipore, 05-1312; anti-OsbZIP46, produced by ABclonal company in China), together with Dynabeads TM protein A or G (Invitrogen, 10002D/10004D), were added to the sonicated chromatin followed by incubation overnight. The precipitated and input DNAs were further analyzed using qPCR with specific primers (Supplemental Data Set 6) or sequencing. OsACTIN (Os11g06390) was used as a negative control locus in the ChIP-qPCR assays.
Acknowledgements We are grateful to  Dr. Shouyi Chen  ( Institute of Genetics and Developmental Biology, Chinese Academy of Sciences ) for the gift of vectors for the transcriptional regulation activity assay in protoplasts. We are grateful to  Dr. Yongxiu Liu  ( Institute of Botany, Chinese Academy of Sciences ) for the gift of oshub2-2 mutant. This work was supported by grants from the  National Natural Science Foundation of China  ( 31771359 ,  31821005 ) and the  National Key Research and Development Program of China  ( 2016YFD0100600 ). No conflict of interest declared.
Data availability Sequence data from this article can be found in Rice Genome Annotation Project website (http://rice. plantbiology. msu. edu/) under the following accession numbers: OsHUB1, Os04g46450; OsHUB2, Os10g41590; OsOTLD1, Os04g33780; OsbZIP46, Os06g10880; MODD, Os03g11550; LEA3, Os05g46480; LEA14, Os01g50910; RAB16b, Os11g26780; RAB21, Os11g26790; PP2C51, Os05g49730; OsACTIN, Os11g06390. The raw and processed ChIP-seq and RNA-seq data have been deposited to Gene Expression Omnibus (GEO) database under the series accession number GSE117047.