OSCR

Epigenetic Changes Associated With a Phenotypic Transition in Namibian Cheetahs.

Code ↔ Paper

12 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 12 matches
  1. [1] § Material and Methods › Gene Ontology Term Assignment ↔ 03_GO_term_assignment.R, lines 1–62 · score 0.83 · pathway enrichment, Fisher combined, biological process terms, GO terms, Profiler, background
  2. [2] § Material and Methods › Differential Methylation Analysis Within a Multi‐Method Cross‐Validation Framework ↔ 04_Biomarker_analysis.R, lines 1–51 · score 0.72 · biomarker selection, selected biomarkers, Random Forest, outer CV folds, ElasticNet, predictive
  3. [3] § Material and Methods › Differential Methylation Analysis Within a Multi‐Method Cross‐Validation Framework ↔ 01_DMR_analysis.R, lines 47–90 · score 0.72 · loIDo, pDMRs, inner CV, outer CV, cDMRs, model
  4. [4] § Material and Methods › Bioinformatics Processing ↔ 01_DMR_analysis.R, lines 242–291 · score 0.70 · vmu ajub asm, raw, gcf, v1, promoter, position
  5. [5] § Material and Methods › Study Animals and Sample Collection ↔ Supplementary_Figure_5.R, lines 153–197 · score 0.67 · 2–3.5, 3.5–7, body mass, Age class, SD, male
  6. [6] § Material and Methods › Differential Methylation Analysis Within a Multi‐Method Cross‐Validation Framework ↔ 01_DMR_analysis.R, lines 47–90 · score 0.66 · pDMRs, inner CV, outer CV, cDMRs, covariate, filter
  7. [7] § Material and Methods › Gene Ontology Term Assignment ↔ Figure_3.R, lines 1–54 · score 0.66 · associated GO terms, Ranked terms, biological process, Jaccard, pathway, TOP80
  8. [8] § Material and Methods › Biomarker Selection and Predictive Power Estimation ↔ 04_Biomarker_analysis.R, lines 1–51 · score 0.66 · random forest, outer CV fold, power, ElasticNet, biomarkers, Selection
  9. [9] § Results › Body Mass Differences Between Floaters That Remained Floaters and Floaters That Became Territorial Males ↔ Supplementary_Figure_5.R, lines 153–197 · score 0.61 · 2–3.5, 3.5–7, age class, proxy, Welch, SD
  10. [10] § Results › Promoter‐Overlapping cDMRs Associated With the Spatial Tactic‐Shift ↔ Figure_4.R, lines 1–56 · score 0.53 · promoter overlapping, tactic shift, cDMRs, age, gene
  11. [11] § Material and Methods › Differential Methylation Analysis Within a Multi‐Method Cross‐Validation Framework ↔ Helper_functions.R, lines 143–185 · score 0.53 · inner folds, edgeR, subset, windows, fitted, outer
  12. [12] § Results › Promoter‐Overlapping cDMRs Associated With the Spatial Tactic‐Shift ↔ Figure_4.R, lines 1–56 · score 0.52 · Promoter overlapping, tactic shift, cDMRs, Figure 4, GO, age

Paper

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The authors' code

R · 291 lines · 10 KB · no license · 3 matches

  1. ################################################################################
  2. ################################################################################
  5. ## ANALYSIS: Main tactic-associated DMR analysis used throughout the manuscript.
  6. ##
  7. ## Required external data files (supplied separately):
  8. ## - data/cov.rda [object: cov]
  9. ## - data/full_table.rda [object: full_table]
  10. ## - data/GCF_027475565.1_VMU_Ajub_asm_v1.0_genomic_addPromoter.gtf
  11. ##
  12. ## Cross-script result files created here:
  13. ## - DMRs_sets/full_outer_fits.rda
  14. ## - DMRs_sets/fold_specific_cDMRs.rda
  15. ## - DMRs_sets/cDMRs_m.rda
  16. ## - DMRs_sets/my_genes.rda
  17. ## - DMRs_sets/biomarker_inputs.rda
  18. ##
  19. ## To run properly, users need to download the package meth01 available here:
  20. ## https://github.com/vullioud/meth01 and the raw data stored here: XXXX
  22. ## This script represent the first part of the analysis focusing on extracting DMRs
  23. ## within a nested leave-one-ID-out framework using 3 different methods.
  26. ################################################################################
  27. ################################################################################
  29. ## Load necessary packages and functions
  30. rm(list = ls())
  33. ## Project-specific functions & packages
  34. source("Helper_functions.R")
  36. ## Lab-specific package to deal with MBD-seq data and needed dependencies
  37. library(meth01)
  39. ## basic data-processing packages
  40. library(future)
  41. library(furrr)
  42. library(ggplot2)
  43. library(dplyr)
  44. library(tidymodels)
  45. library(ggvenn)
  47. ################################################################################
  49. #### 1. load the data
  50. #### Create the ML table (wide format)
  51. #### Create a 2 CV Leave one-ID out (loIDo) sets
  53. ################################################################################
  56. load("data/cov.rda") ## preprocessed input supplied in the separate data archive
  57. load("data/full_table.rda") ## preprocessed input supplied in the separate data archive
  59. ## create the
  61. my_ml_table <- create_ml_table(count_data = full_table, covariate_dat =cov)
  63. ## create split table
  64. set.seed(1)
  65. my_splitted_table_cv <- create_split_table(data = cov, id_col = "ID",
  66. tactic_col = "tactic",
  67. method = "cv",
  68. outer_v = 10,
  69. inner_v = 9) |>
  70. mutate(ID_out = map_chr(splits, ~ assessment(.x)$ID[1])) ## can take only the first [1] as it is the same for the 2 samples
  74. ################################################################################
  76. #### 2. fit full edgeR (for finaly inference on full set)
  77. #### fit models on outer CV
  78. #### - filter for pDMRs[m/f]
  79. #### fit models on inner CV
  80. #### - filter for cDMRs[m/f]
  82. ################################################################################
  85. ### edgeR on full set (for final inference)
  86. pred <- meth01::find_coef(cov, "~ tactic + ID")[2] # extract coef of interest
  88. full_set_edgeR <- run_edgeR_analysis(full_table, cov, "~ tactic + ID",
  89. pred, "QLF", pos_filter = 1,
  90. bind_with = "/")
  95. ### fit models/test on outer CV-folds
  97. future::plan(future::multisession, workers = 10)
  99. CV_out_PCa <- find_DMRs_outer_cv(cov = cov,
  100. my_table = full_table,
  101. my_splitted_table = my_splitted_table_cv,
  102. method = "edgeR",
  103. formula = "~ tactic + ID")
  105. CV_out_PCo <- find_DMRs_outer_cv(cov = cov,
  106. my_table = full_table,
  107. my_splitted_table = my_splitted_table_cv,
  108. method ="edgeR",
  109. formula = " ~ body_weight + ID")
  111. CV_out_nP <- find_DMRs_outer_cv(cov = cov,
  112. my_table = full_table,
  113. my_splitted_table = my_splitted_table_cv,
  114. method = "wilco",
  115. formula = "~ tactic + ID") ## need tactic to get the good coef
  117. future::plan(future::sequential)
  119. ## Filter for pDMRs[m/f] p < 0,01
  121. pDMRs_PCa <- map(CV_out_PCa, ~ .x$pos_ID[.x$p <= 0.01])
  122. pDMRs_PCo <- map(CV_out_PCo, ~ .x$pos_ID[.x$p <= 0.01])
  123. pDMRs_nP <- map(CV_out_nP, ~ .x$pos_ID[.x$p <= 0.01])
  126. ## get a quick look at the dim of the results
  127. list_of_full_outer_fits <- list(PCa = CV_out_PCa,
  128. Pco = CV_out_PCo,
  129. nP = CV_out_nP)
  131. list_of_outer_pDMRs <- list(PCa = unique(unlist(pDMRs_PCa)),
  132. PCo = unique(unlist(pDMRs_PCo)),
  133. nP = unique(unlist(pDMRs_nP)))
  135. ggvenn(list_of_outer_pDMRs)
  136. map(list_of_outer_pDMRs, length)
  138. ## save full sets (Can be needed in other scripts)
  139. save(list_of_full_outer_fits, file = "DMRs_sets/full_outer_fits.rda")
  142. ###############################################################################
  144. #### Fit models/test on the inner CV-folds on the pDMRs position.
  145. #### This step will filter for the coherent and sig (as for the Hyena paper)
  151. cDMRs_PCa <- find_coherent_DMRs_inner_cv(cov = cov,
  152. my_pos = pDMRs_PCa,
  153. my_cv_table = my_splitted_table_cv,
  154. my_table = full_table,
  155. method = "edgeR",
  156. keep_all = F,
  157. formula = " ~ tactic + ID")
  159. cDMRs_PCo <- find_coherent_DMRs_inner_cv(cov = cov,
  160. my_pos = pDMRs_PCo,
  161. my_cv_table = my_splitted_table_cv,
  162. my_table = full_table,
  163. method = "edgeR",
  164. keep_all = F,
  165. formula = " ~ body_weight + ID")
  168. cDMRs_nP <- find_coherent_DMRs_inner_cv(cov = cov,
  169. my_pos = pDMRs_nP,
  170. my_cv_table = my_splitted_table_cv,
  171. my_table = full_table,
  172. method = "wilco",
  173. keep_all = F,
  174. formula = " ~ tactic + ID")
  177. ## get a quick look at the dim of the results
  180. list_of_cDMRs <- list(PCa = unique(unlist(cDMRs_PCa)),
  181. PCo = unique(unlist(cDMRs_PCo)),
  182. nP = unique(unlist(cDMRs_nP)))
  186. ###############################################################################
  188. ## Get the cDMRs[m] and the genes associated. i,e the cDMRs_mf in all folds
  190. list_of_cDMRs_mf <- list(PCa = cDMRs_PCa,
  191. PCo = cDMRs_PCo,
  192. nP =cDMRs_nP)
  194. ## Used by Supplementary Figures 1 and 2.
  195. save(list_of_cDMRs_mf, file = "DMRs_sets/fold_specific_cDMRs.rda")
  197. ## Used by the biomarker analysis.
  198. save(my_splitted_table_cv, my_ml_table, cDMRs_PCa, cDMRs_PCo, cDMRs_nP,
  199. file = "DMRs_sets/biomarker_inputs.rda")
  203. filter_coherent_split <- function(my_list_of_cv, name, threshold){
  204. as.data.frame(table(unlist(my_list_of_cv))) |> filter(Freq >=threshold) |>
  205. separate(Var1, into = c("chr", "start", "stop", "gene"), sep = "/", remove = F) |>
  206. mutate(method = name)
  207. }
  209. ### ge coherent sets cDMRs[m]
  211. cDMRs_m <- map(1:3, ~ filter_coherent_split(list_of_cDMRs_mf[[.x]], names(list_of_cDMRs_mf[.x]), 10))
  212. names(cDMRs_m) <- names(list_of_cDMRs_mf)
  214. my_genes <- list(PCa = unique(cDMRs_m$PCa$gene),
  215. PCo = unique(cDMRs_m$PCo$gene),
  216. nP = unique(cDMRs_m$nP$gene)) |> map(~.x[!(.x == "intergenic")])
  218. save(cDMRs_m, file = "DMRs_sets/cDMRs_m.rda")
  219. save(my_genes, file = "DMRs_sets/my_genes.rda")
  224. ################################################################################
  225. ## get some numbers (first § of the results)
  227. all_cDMRs_mf <- as.data.frame(table(unlist(list_of_cDMRs_mf)))
  228. hist(all_cDMRs_mf$Freq)
  229. sum(all_cDMRs_mf$Freq >= 30) ## fully coherent across all 3 method
  230. sum(all_cDMRs_mf$Freq <=1) ## single [m/f]
  231. nrow(all_cDMRs_mf) ## total
  234. ## by methods
  236. full_split_long <- function(my_list_of_cv, name){
  237. as.data.frame(table(unlist(my_list_of_cv))) |>
  238. separate(Var1, into = c("chr", "start", "stop", "gene"), sep = "/", remove = F) |>
  239. mutate(method = name)
  240. }
  242. raw_numbers_cDMRs_m <- map_dfr(1:3, ~ full_split_long(list_of_cDMRs_mf[[.x]], names(list_of_cDMRs_mf[.x]))) |>
  243. group_by(method) |>
  244. summarise(
  245. n_single = sum(Freq ==1),
  246. prop_single = sum(Freq == 1)/n(),
  247. n_full = sum(Freq == 10),
  248. prop_full = sum(Freq == 10) /n(),
  249. .groups = "drop")
  252. ### check overlap at the different levels
  253. # genes
  254. ggvenn(my_genes)
  256. # cDMRs[m]
  257. ggvenn(map(cDMRs_m, ~ .x$Var1))
  258. map(cDMRs_m, ~ length(.x$Var1))
  260. # cDMRs[m/f]
  261. ggvenn(list_of_cDMRs)
  262. map(list_of_cDMRs, length)
  264. ## get annotation and effect size for all cDMRs[m/f]
  267. anot_path <- "data/GCF_027475565.1_VMU_Ajub_asm_v1.0_genomic_addPromoter.gtf"
  268. my_annot_cDMRs_mf <- add_annotation(all_cDMRs_mf |> rename(pos_ID = Var1), path_to_annotation_file = anot_path, var_to_keep = c("type", "gene_id"), bind_with = "/")
  271. effect_size_cDMRs_mf <- map_dfc(CV_out_PCa, ~ .x$effect_size) |>
  272. mutate(pos_ID = CV_out_PCa[[1]]$pos_ID) |> ## add the pos ID the fist set is enought as they all have the same dim
  273. filter(pos_ID %in% my_annot_cDMRs_mf$pos_ID)
  275. my_annot_cDMRs_mf <- my_annot_cDMRs_mf |> left_join(effect_size_cDMRs_mf) |>
  276. mutate(in_nP = pos_ID %in% cDMRs_m$nP$Var1,
  277. in_PCa = pos_ID %in% cDMRs_m$PCa$Var1,
  278. in_PCo = pos_ID %in% cDMRs_m$PCo$Var1)
  280. coherent_set <- my_annot_cDMRs_mf |> filter(Freq == 30)
  281. sum(coherent_set$...1 >= 0) ## get hyper for coherent set
  283. coherent_set <- coherent_set |> unnest_longer(annotation) |> unnest(annotation)
  284. coherent_set |> group_by(type) |> summarise(n = n(), hypo = sum(effect_size < 0), ## get the numbers by type
  285. hyper =sum(effect_size > 0))
  286. length(unique(coherent_set$gene_id)) ## get the total number of gens within the coherent set
  288. sum(my_annot_cDMRs_mf$...1[my_annot_cDMRs_mf$in_nP] > 0)
  289. sum(my_annot_cDMRs_mf$...1[my_annot_cDMRs_mf$in_nP] < 0) # np hypo
  290. sum(my_annot_cDMRs_mf$...1[my_annot_cDMRs_mf$in_PCa] > 0)
  291. sum(my_annot_cDMRs_mf$...1[my_annot_cDMRs_mf$in_PCa] < 0) # Pca hhyp

01_DMR_analysis.R at commit 102a625, no license · at the source

Overview

Authors: Colin Vullioud1,2, Diana Geweiler1,3, Joerg Melzheimer4, Sonja Heinrich4, Jörns Fickel1,3, Bettina Wachter4, Alexandra Weyrich1
ORCID iDs: Colin Vullioud, Joerg Melzheimer, Sonja Heinrich, Jörns Fickel, Bettina Wachter, Alexandra Weyrich
  1. Department of Evolutionary Genetics Leibniz‐Institute for Zoo and Wildlife Research (IZW) Berlin Germany
  2. Chair of Modelling of Social‐Ecological Systems University of Freiburg Freiburg im Breisgau Germany
  3. University of Potsdam Potsdam Germany
  4. Department of Evolutionary Ecology Leibniz‐Institute for Zoo and Wildlife Research (IZW) Berlin Germany
Institutions: University of Freiburg (Germany); Leibniz Institute for Zoo and Wildlife Research (Germany); University of Potsdam (Germany)
Journal: Molecular ecology, volume 35, issue 17, article e70527
Dates: received 27 January 2026; accepted 13 August 2026; published online 7 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/mec.70527 · PMID 42703678 · PMCID PMC13548066 · OpenAlex W7211909533
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
Keywords: conservation biomarker, DNA methylation, life‐history transition, low genetic diversity, spatial tactic, threatened species
MeSH: Acinonyx*, DNA Methylation*, Epigenesis, Genetic*, Animals, Female, Genetic Variation, Male, Namibia, Phenotype, Sequence Analysis, DNA (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Deutsches Zentrum für integrative Biodiversitätsforschung Halle‐Jena‐Leipzig; Leibniz Competition Fund (SAW‐2018‐IZW‐3‐EpiRank); Messerli‐Stiftung; Leibniz‐IZW
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

In populations with low genetic diversity, the capacity to adapt to environmental change is limited. While epigenetic mechanisms can generate phenotypic plasticity, their role in mediating adaptive life‐history transitions in the wild is largely unknown. Here, we show that in adult Namibian cheetahs ( Acinonyx jubatus jubatus ), a species with remarkably low genetic diversity, the transition from a floater to a male holding a territory is associated with widespread DNA methylation changes, with a predominance of hypermethylation in territorial males. Using paired blood samples collected from the same individuals before and after becoming territorial, we identified differentially methylated genes which function in pathways related to anatomical and head development, brain development and neurodevelopment, cAMP biosynthesis and ion channel transport, consistent with the observed phenotypic shifts in morphology, spatial movement and aggressive behaviour. These findings suggest that DNA methylation mediates this life‐history transition at the molecular level. More broadly, they provide rare evidence that epigenetic regulation is relevant in phenotypic diversity within species with low genetic diversity, potentially supporting adaptive potential at the population level.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 12 matches between paragraphs and lines of code.

vullioud/Vullioud_et_al_2026_Molecular_Ecology_scripts

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 102a62569432a9b2d16ca90153ed689ed3661863, 10 August 2026
Languages: R (19)
Size: 22 files, 19 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (16 files), ggplot2 (11 files), circlize (3 files), ComplexHeatmap (3 files), glmnet (2 files), patchwork (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
20 files
At the source: github.com/vullioud/Vullioud_et_al_2026_Molecular_Ecology_scripts

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 19 scripts, each with its path and the digest of its content;
  • 12 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

Sequencing data will be made available on NCBI (https://www.ncbi.nlm.nih.gov/sra/PRJNA1499706) and DMGs and GO terms lists are available on Dryad (DOI: 10.5061/dryad.8gtht774k (https://doi.org/10.5061/dryad.8gtht774k)). The code will be open on github for transparency and reproducibility: https://github.com/vullioud/Vullioud_et_al_2026_Molecular_Ecology_scripts.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 10 MeSH terms, 4 funders, 49 references.

Cite

This paper

Vullioud, C., Geweiler, D., Melzheimer, J., Heinrich, S., Fickel, J., Wachter, B., & Weyrich, A. (2026). Epigenetic Changes Associated With a Phenotypic Transition in Namibian Cheetahs. Molecular ecology, 35(17), e70527. https://doi.org/10.1111/mec.70527

BibTeX

@article{vullioud2026epigenetic,
author = {Vullioud, Colin and Geweiler, Diana and Melzheimer, Joerg and Heinrich, Sonja and Fickel, Jörns and Wachter, Bettina and Weyrich, Alexandra},
title = {{Epigenetic Changes Associated With a Phenotypic Transition in Namibian Cheetahs}},
journal = {Molecular ecology},
year = {2026},
month = sep,
volume = {35},
number = {17},
pages = {e70527},
publisher = {Wiley},
issn = {0962-1083},
doi = {10.1111/mec.70527},
url = {https://doi.org/10.1111/mec.70527},
pmid = {42703678},
pmcid = {PMC13548066}
}

RIS

TY - JOUR
AU - Vullioud, Colin
AU - Geweiler, Diana
AU - Melzheimer, Joerg
AU - Heinrich, Sonja
AU - Fickel, Jörns
AU - Wachter, Bettina
AU - Weyrich, Alexandra
TI - Epigenetic Changes Associated With a Phenotypic Transition in Namibian Cheetahs
T2 - Molecular ecology
J2 - Mol Ecol
PY - 2026
DA - 2026/09/01
VL - 35
IS - 17
SP - e70527
SN - 0962-1083
PB - Wiley
DO - 10.1111/mec.70527
UR - https://doi.org/10.1111/mec.70527
LA - en
ER -

CSL-JSON

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"id": "10.1111/mec.70527",
"type": "article-journal",
"title": "Epigenetic Changes Associated With a Phenotypic Transition in Namibian Cheetahs",
"container-title": "Molecular ecology",
"author": [
{
"family": "Vullioud",
"given": "Colin"
},
{
"family": "Geweiler",
"given": "Diana"
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{
"family": "Melzheimer",
"given": "Joerg"
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{
"family": "Heinrich",
"given": "Sonja"
},
{
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"given": "Jörns"
},
{
"family": "Wachter",
"given": "Bettina"
},
{
"family": "Weyrich",
"given": "Alexandra"
}
],
"container-title-short": "Mol Ecol",
"volume": "35",
"issue": "17",
"page": "e70527",
"DOI": "10.1111/mec.70527",
"PMID": "42703678",
"PMCID": "PMC13548066",
"ISSN": "0962-1083",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/mec.70527",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

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[6] doi:10.1016/j.isci.2026.116439 [code]
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Journal: iScience
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[7] doi:10.1016/j.isci.2026.115982 [code]
Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis.
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In common: glmnet, circlize, ComplexHeatmap, 3 other tools, genetics / omics
[8] doi:10.3390/ijms27093997 [code]
Coordinated Multicellular Immune Programs and Drug Targets Revealed by Single-Cell Analysis in Driver-Mutated NSCLC.
Journal: International journal of molecular sciences
In common: glmnet, circlize, ComplexHeatmap, 3 other tools, genetics / omics
[9] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: glmnet, circlize, ComplexHeatmap, 3 other tools, cellular / molecular
[10] doi:10.1038/s41586-026-10512-9 [code]
Astrocyte glucocorticoid receptor signalling restricts neuronal plasticity.
Journal: Nature
In common: circlize, ComplexHeatmap, ggplot2, 1 other tool, cellular / molecular, 3 references

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