submitted to PLOS

analysis/paper/_materials.qmd

@@ -27,6 +27,8 @@ The sample consists of `r nrow(filter(demography, sex == "m"))` males,
 We selected males due to a higher
 occurrence of pipe notches and dental calculus deposits than females (unpublished observation).
 
+*Fig 1 (fig-sample-demography): Overview of sample demography. Left plot is the first batch and right plot is the replication batch with 29 of the individuals from the first batch. eya = early young adult (18-24 years); lya = late young adult (25-34 years); ma = middle adult (35-49 years); old = old adult (50+ years). Male? = probable male; Female? = probable female.*
+
 <!-- use plot to show original and replicated sample demographics? -->
 ```{r}
 #| label: fig-sample-demography

analysis/paper/_quarto.yml

@@ -7,10 +7,11 @@ title: "Multiproxy analysis exploring patterns of diet and disease in dental cal
 author:
   - name: Bjørn Peare Bartholdy
     corresponding: true
-    email: b.p.bartholdy@arch.leidenuniv.nl
+    email: b.p.bartholdy@tudelft.nl
     #orcid: 0000-0003-1689-0557
     affiliations:
       - ref: leiden
+      - ref: tud
   - name: Jørgen B. Hasselstrøm
     affiliations:
       - ref: aarhus
@@ -32,6 +33,8 @@ author:
 affiliations:
   - id: leiden
     name: Department of Archaeological Sciences, Leiden University
+  - id: tud
+    name: Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology
     #address: Einsteinweg 2, 2333 CC, Leiden, Netherlands
   - id: aarhus
     name: Department of Forensic Medicine, Aarhus University
@@ -46,13 +49,15 @@ format:
     output-file: "plos-submission_v1.pdf"
     documentclass: article
     number-sections: true
+    fig-pos: false
     geometry:
-      - top=10mm
-      - bottom=10mm
+      - top=30mm
+      - bottom=30mm
       - left=30mm
       - heightrounded
     include-in-header:
-      file: ../templates/_preface.tex
+    #   file: ../templates/_preface.tex
+      text: \usepackage{lineno}
     template-partials:
       - ../templates/_authors.tex
       - ../templates/_affiliations.tex
@@ -65,8 +70,10 @@ knitr:
   opts_chunk:
     message: false
     comment: "#>"
-    fig-path: "../figures/"
+    fig-path: "../figures/plos-"
     fig-dpi: 600
+language:
+  crossref-fig-title: "Fig"
 bibliography: references.bib
 csl: "apa.csl" # Insert path for the bib-style
 abstract: |
@@ -77,32 +84,29 @@ abstract: |
   population.
   We conducted a study on 41 individuals from Middenbeemster, a 19th century rural
   Dutch archaeological site. Skeletal and dental analysis was performed to explore
-  potential relationships between pathological conditions/lesions and the presence
-  of alkaloids. We also explored other factors potentially affecting the detection
-  of alkaloids, including sample weight and skeletal preservation. Dental calculus
-  was sampled and analysed using ultra-high-performance liquid chromatography-tandem
+  potential relationships between pathological lesions and presence of alkaloids.
+  Dental calculus
+  was analysed using ultra-high-performance liquid chromatography-tandem
   mass spectrometry (UHPLC-ESI-MS/MS).
   We were able to detect nicotine, cotinine, caffeine, theophylline, and salicylic
-  acid.
-  By detecting these compounds we are able to show the consumption of tea and
+  acid, suggesting the consumption of tea and
   coffee and smoking of tobacco on an individual scale, which is also confirmed
   by historic documentation and identification of pipe notches in the dentition.
   Nicotine and/or cotinine was present in 56% of individuals with at least one
   visible pipe notch.
-  We find some influence of skeletal preservation on the detection
-  of alkaloids and salicylic acid, with higher quantities of compounds extracted
-  from well-preserved individuals, and also observe a relationship between weight
-  of the calculus sample and raw quantity of the detected compounds, and we were
-  able to detect alkaloids in samples as small as 2 mg. We found correlations
+  There is some influence of skeletal preservation on the detection
+  of alkaloids, with higher quantities of compounds extracted
+  from well-preserved individuals, and we observe a positive relationship between
+  weight of the calculus sample and quantity of detected compounds, as well as
   between chronic maxillary sinusitis and the presence of multiple alkaloids.
-  We show that there are many limitations that will need to be addressed going
-  forward with this type of analysis, and stress the need for more systematic
+  There are many limitations that will need to be addressed going
+  forward with this type of analysis; we stress the need for more systematic
   research on the consumption of alkaloid-containing items and their subsequent
   concentration and preservation in dental calculus, in addition to how mode of
-  consumption may affect concentrations on different parts of the dentition.
-  Despite the limitations, this preliminary study illustrates the many benefits
-  of using calculus to target a variety of compounds that could have been ingested
-  as medicine or diet, or consumed in a different manner. This method allows us
+  consumption may affect concentrations in the dentition.
+  Despite the limitations, this preliminary study illustrates many benefits
+  of using calculus to target a variety of compounds that could have been consumed
+  as medicine or diet. This method allows us
   to directly address specific individuals, which can be especially useful in
   individuals that are not always well-documented in historic documentation,
   such as rural populations, and especially children and women.

analysis/paper/_results.qmd

@@ -77,11 +77,13 @@ Nicotine and cotinine have the same relative quantities in the samples, i.e., th
 sample with the highest extracted quantity of nicotine also had the highest extracted
 quantity of cotinine (@fig-auth-plot-batch2).
 
+*Fig 2 (fig-auth-plot-batch2): (A) Number of samples in which each compound was detected in the first and second batch. (B) Quantity (ng) of each compound extracted from each sample in batch 2. The plot displays the extracted quantity across the three washes and final calculus extraction (calc). Each coloured line represents a different calculus sample. CBD = cannabidiol; CBN = cannabinol; THC = tetrahydrocannabinol; THCA-A = tetrahydrocannabinolic acid A; THCVA = tetrahydrocannabivarin acid.*
+
 ```{r}
 #| label: fig-auth-plot-batch2
 #| fig-cap: "(A) Number of samples in which each compound was detected in the first and second batch. (B) Quantity (ng) of each compound extracted from each sample in batch 2. The plot displays the extracted quantity across the three washes and final calculus extraction (calc). Each coloured line represents a different calculus sample. CBD = cannabidiol; CBN = cannabinol; THC = tetrahydrocannabinol; THCA-A = tetrahydrocannabinolic acid A; THCVA = tetrahydrocannabivarin acid."
-#| fig-width: 8
-#| fig-height: 6
+#| fig-width: 7
+#| fig-asp: 0.75
 
 auth_plot <- uhplc_data_long %>%
   filter(
@@ -144,6 +146,8 @@ to increased extraction quantity ([@fig-detection-preservation]A). We also
 find a weak positive correlation between the weight of the calculus sample and the
 quantity of compound extracted from the calculus ([@fig-detection-preservation]B).
 
+*Fig 3 (fig-detection-preservation): (A) Violin plot with overlaid box plots depicting the distribution of extracted quantities of each compound from batch 2 separated by state of preservation of the skeleton. (B) Extracted quantity (ng) of compound plotted against weights of the calculus samples from batch 2. r = Pearson correlation coefficient.*
+
 ```{r}
 #| label: fig-detection-preservation
 #| fig-cap: "(A) Violin plot with overlaid box plots depicting the distribution of extracted quantities of each compound from batch 2 separated by state of preservation of the skeleton. (B) Extracted quantity (ng) of compound plotted against weights of the calculus samples from batch 2. r = Pearson correlation coefficient."
@@ -358,11 +362,13 @@ Remaining correlations were weak or absent (@fig-polycorr).
 Correlations with age will be depressed because age was largely controlled for
 in the sample selection.
 
+*Fig 4 (fig-polycorr): Plot of the polychoric correlations (*rho*). Larger circles and increased opacity indicates a stronger correlation coefficient. OA = osteoarthritis; VOP = vertebral osteophytosis; SN = Schmorl’s nodes; DDD = degenerative disc disease; CO = cribra orbitalia; CMS = chronic maxillary sinusitis; SA = salicylic acid.*
+
 ```{r}
 #| label: fig-polycorr
 #| fig-cap: "Plot of the polychoric correlations (*rho*). Larger circles and increased opacity indicates a stronger correlation coefficient. OA = osteoarthritis; VOP = vertebral osteophytosis; SN = Schmorl’s nodes; DDD = degenerative disc disease; CO = cribra orbitalia; CMS = chronic maxillary sinusitis; SA = salicylic acid."
 #| fig-width: 5
-#| fig-height: 4
+#| fig-asp: 0.8
 polycorr$rho %>%
   ggcorrplot::ggcorrplot(
     method = "circle", type = "lower", 

analysis/paper/paper.qmd

@@ -10,6 +10,13 @@ library(knitr)
 source(here("analysis/scripts/setup-qmd.R"))
 
 options(ggplot.discrete.colour = function() scale_fill_viridis_d())
+knitr::opts_chunk$set(
+  fig.show = "hide",
+  fig.path = "../figures/plos-",
+  dev = "tiff",
+  dev.args = list("tiff" = list(compression = "lzw"))
+  #fig.keep = "none"
+)
 ```
 
 <!-- Keywords: {{< meta keywords >}} -->