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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.* + ```{r} #| label: fig-sample-demography diff --git a/analysis/paper/_quarto.yml b/analysis/paper/_quarto.yml index 6cb2e82..cb6177c 100644 --- a/analysis/paper/_quarto.yml +++ b/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. diff --git a/analysis/paper/_results.qmd b/analysis/paper/_results.qmd index 0d21dbd..622b9b1 100644 --- a/analysis/paper/_results.qmd +++ b/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$rho %>% ggcorrplot::ggcorrplot( method = "circle", type = "lower", diff --git a/analysis/paper/paper.qmd b/analysis/paper/paper.qmd index 8dca10a..43a92d9 100644 --- a/analysis/paper/paper.qmd +++ b/analysis/paper/paper.qmd @@ -10,6 +10,13 @@ library(knitr) source(here("analysis/scripts/setup-qmd.R")) options(ggplot.discrete.colour = function() scale_fill_viridis_d()) +knitr::opts_chunk$set( + fig.show = "hide", + fig.path = "../figures/plos-", + dev = "tiff", + dev.args = list("tiff" = list(compression = "lzw")) + #fig.keep = "none" +) ``` diff --git a/analysis/paper/paper.tex b/analysis/paper/paper.tex index 08105af..e4c7449 100644 --- a/analysis/paper/paper.tex +++ b/analysis/paper/paper.tex @@ -38,7 +38,7 @@ \KOMAoptions{parskip=half}} \makeatother \usepackage{xcolor} -\usepackage[top=10mm,bottom=10mm,left=30mm,heightrounded]{geometry} +\usepackage[top=30mm,bottom=30mm,left=30mm,heightrounded]{geometry} \setlength{\emergencystretch}{3em} % prevent overfull lines \setcounter{secnumdepth}{5} % Make \paragraph and \subparagraph free-standing @@ -114,26 +114,7 @@ \usepackage[normalem]{ulem} \usepackage{makecell} \usepackage{xcolor} -\usepackage{fancyhdr, graphicx} -\usepackage[hidelinks]{hyperref} - -\fancyhead{\vspace{70pt}} -\renewcommand{\headrulewidth}{1.8pt} - -\makeatletter -\def\@maketitle{% - \newpage - \hfill\href{https://doi.org/10.24072/pci.archaeo.100389}{\includegraphics[width=3.5cm]{badge_PCI_Archaeology.png}}\hfill - \null - \begin{center} -{\LARGE -\textbf\newline{\@title} -} -%\newline - -\end{center} - \par} -\makeatother +\usepackage{lineno} \makeatletter \makeatother \makeatletter @@ -157,9 +138,9 @@ \newcommand\listtablename{List of Tables} \fi \ifdefined\figurename - \renewcommand*\figurename{Figure} + \renewcommand*\figurename{Fig} \else - \newcommand\figurename{Figure} + \newcommand\figurename{Fig} \fi \ifdefined\tablename \renewcommand*\tablename{Table} @@ -216,39 +197,47 @@ \begin{document} %% modified from: https://github.com/quarto-journals/plos/blob/main/_extensions/plos/partials/before-body.tex -\maketitle +%\maketitle \begin{center} -\thispagestyle{fancy} +%\thispagestyle{fancy} + +\LARGE{Multiproxy analysis exploring patterns of diet and disease in +dental calculus and skeletal remains from a 19th century Dutch +population} % Insert author names, affiliations and corresponding author email \normalsize{Bjørn Peare -Bartholdy}\textsuperscript{1*}, \normalsize{Jørgen B. -Hasselstrøm}\textsuperscript{2}, \normalsize{Lambert K. -Sørensen}\textsuperscript{2}, \normalsize{Maia +Bartholdy}\textsuperscript{1,2*}, \normalsize{Jørgen B. +Hasselstrøm}\textsuperscript{3}, \normalsize{Lambert K. +Sørensen}\textsuperscript{3}, \normalsize{Maia Casna}\textsuperscript{1}, \normalsize{Menno Hoogland}\textsuperscript{1}, \normalsize{Historisch Genootschap -Beemster}\textsuperscript{3}, \normalsize{Amanda G. +Beemster}\textsuperscript{4}, \normalsize{Amanda G. Henry}\textsuperscript{1}\vspace{12pt} -\rule{\textwidth}{1.8pt}\vspace{8pt} +%\rule{\textwidth}{1.8pt}\vspace{8pt} \end{center} \textsuperscript{\footnotesize{1}}\textit{\small{Department of Archaeological Sciences, Leiden -University}}\\ \textsuperscript{\footnotesize{2}}\textit{\small{Department +University}}\\ \textsuperscript{\footnotesize{2}}\textit{\small{Faculty +of Mechanical, Maritime and Materials Engineering, Delft University of +Technology}}\\ \textsuperscript{\footnotesize{3}}\textit{\small{Department of Forensic Medicine, Aarhus -University}}\\ \textsuperscript{\footnotesize{3}}\textit{\small{Historisch +University}}\\ \textsuperscript{\footnotesize{4}}\textit{\small{Historisch Genootschap Beemster}} % Use the asterisk to denote corresponding authorship and provide email address in note below. -* \small{b.p.bartholdy@arch.leidenuniv.nl} +* \small{b.p.bartholdy@tudelft.nl} \vspace{12pt} +\linenumbers + \begin{abstract} Dental calculus is an excellent source of information on the dietary patterns of past populations, including consumption of plant-based @@ -257,45 +246,40 @@ consumption by individuals within a 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 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 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 +explore 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, 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. There is some +influence of skeletal preservation on the detection of alkaloids, 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 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 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 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. +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. 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 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. \end{abstract} \small \textbf{Keywords:} dental calculus; LC-MS/MS; alkaloids; dental pathology; sinusitis; caffeine; tobacco \\ -\vfill +%\vfill -\newgeometry{top=30mm,left=30mm}\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[borderline west={3pt}{0pt}{shadecolor}, frame hidden, boxrule=0pt, interior hidden, sharp corners, enhanced, breakable]}{\end{tcolorbox}}\fi +%\newgeometry{top=30mm,left=30mm}\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[frame hidden, sharp corners, interior hidden, boxrule=0pt, enhanced, borderline west={3pt}{0pt}{shadecolor}, breakable]}{\end{tcolorbox}}\fi \hypertarget{introduction}{% \section{Introduction}\label{introduction}} @@ -393,24 +377,16 @@ \section{Materials}\label{materials}} biological differences in dental calculus formation and drug metabolism that are related to age and sex (Huang et al., 2023; Uno et al., 2017; White, 1997). The sample consists of 27 males, 11 probable males, 2 -probable females, and 1 female (Figure~\ref{fig-sample-demography}). We +probable females, and 1 female (\textbf{?@fig-sample-demography}). We selected males due to a higher occurrence of pipe notches and dental calculus deposits than females (unpublished observation). -\begin{figure} - -{\centering \includegraphics{paper_files/figure-pdf/fig-sample-demography-1.pdf} - -} - -\caption{\label{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.} - -\end{figure} +\emph{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.} \hypertarget{methods}{% \section{Methods}\label{methods}} @@ -540,9 +516,9 @@ \subsection{Statistical analysis}\label{statistical-analysis}} by using quartiles. Polychoric correlation was applied to the paired dichotomous variables and dichotomous-ordinal variables. -All statistical analysis was conducted in R version 4.3.1 (2023-06-16), -Beagle Scouts, (R Core Team, 2020). Data wrangling was conducted with -the \textbf{tidyverse} (Wickham et al., 2019) and visualisations were +All statistical analysis was conducted in R version 4.3.2 (2023-10-31), +Eye Holes, (R Core Team, 2020). Data wrangling was conducted with the +\textbf{tidyverse} (Wickham et al., 2019) and visualisations were created using \textbf{ggplot2} (Wickham, 2016). Polychoric correlations were calculated with the \textbf{psych} package (Revelle, 2022). @@ -601,15 +577,9 @@ \section{Results}\label{results}} and 2. Nicotine and cotinine have the same relative quantities in the samples, i.e., the sample with the highest extracted quantity of nicotine also had the highest extracted quantity of cotinine -(Figure~\ref{fig-auth-plot-batch2}). - -\begin{figure} +(\textbf{?@fig-auth-plot-batch2}). -{\centering \includegraphics{paper_files/figure-pdf/fig-auth-plot-batch2-1.pdf} - -} - -\caption{\label{fig-auth-plot-batch2}(A) Number of samples in which each +\emph{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 @@ -618,33 +588,23 @@ \section{Results}\label{results}} tetrahydrocannabinol; THCA-A = tetrahydrocannabinolic acid A; THCVA = tetrahydrocannabivarin acid.} -\end{figure} - To see if preservation of the skeletal remains had any effect on the detection of compounds, we compare extracted quantities of compounds to the various levels of skeletal preservation. Our results from batch 2 suggest that detection of a compound may be linked to the preservation of the skeleton, with better preservation leading to increased -extraction quantity (Figure~\ref{fig-detection-preservation}A). We also +extraction quantity (\textbf{?@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 -(Figure~\ref{fig-detection-preservation}B). - -\begin{figure} - -{\centering \includegraphics{paper_files/figure-pdf/fig-detection-preservation-1.pdf} +(\textbf{?@fig-detection-preservation}B). -} - -\caption{\label{fig-detection-preservation}(A) Violin plot with overlaid +\emph{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.} -\end{figure} - The presence of pipe notch(es) in an individual and concurrent detection of nicotine and/or cotinine is used as a crude indicator of the accuracy of the method. Only males were used in accuracy calculations, as pipe @@ -765,24 +725,16 @@ \subsection{Correlations between detected alkaloids and and DDD (-0.42), age-at-death and theophylline (-0.45), theophylline and age-at-death (-0.45), caffeine and periodontitis (0.49), cotinine and CMS (0.43). Remaining correlations were weak or absent -(Figure~\ref{fig-polycorr}). Correlations with age will be depressed +(\textbf{?@fig-polycorr}). Correlations with age will be depressed because age was largely controlled for in the sample selection. -\begin{figure} - -{\centering \includegraphics{paper_files/figure-pdf/fig-polycorr-1.pdf} - -} - -\caption{\label{fig-polycorr}Plot of the polychoric correlations -(\emph{rho}). Larger circles and increased opacity indicates a stronger +\emph{Fig 4 (fig-polycorr): Plot of the polychoric correlations +(}rho\emph{). 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.} -\end{figure} - \hypertarget{discussion}{% \section{Discussion}\label{discussion}} @@ -805,7 +757,7 @@ \section{Discussion}\label{discussion}} Nicotine and its principal/main metabolite, cotinine, were strongly positively correlated, both in concentration and presence/absence in -individuals (Table~\ref{tbl-pearson} and Figure~\ref{fig-polycorr}). The +individuals (Table~\ref{tbl-pearson} and \textbf{?@fig-polycorr}). The detection of nicotine and cotinine is not surprising, as pipe-smoking in the Beemsterpolder is well-documented in the literature (Aten et al., 2012; Bouman, 2017), and visible on the skeletal remains as pipe notches @@ -823,7 +775,7 @@ \section{Discussion}\label{discussion}} Theophylline and caffeine were positively correlated in our samples, though to a lesser extent than nicotine and cotinine, so we are unable to determine if they originated from the same source -(Table~\ref{tbl-pearson} and Figure~\ref{fig-polycorr}). Caffeine and +(Table~\ref{tbl-pearson} and \textbf{?@fig-polycorr}). Caffeine and theophylline have very similar chemical structures, so we expect they would experience similar rates of incorporation and degradation, allowing us to interpret the ratio and correlations between the @@ -863,7 +815,7 @@ \section{Discussion}\label{discussion}} our samples decreased over the three washes, followed by a sharp increase in the final calculus extraction, which is what we would expect to see if the salicylic acid was incorporated during life -(Figure~\ref{fig-auth-plot-batch2}). It is important to note that, +(\textbf{?@fig-auth-plot-batch2}). It is important to note that, especially with salicylic acid, there is a possibility for the compound to enter the calculus through contact with the surrounding soil. Salicylic acid is a very mobile organic acid (Badri \& Vivanco, 2009; diff --git a/analysis/templates/before-body.tex b/analysis/templates/before-body.tex index 6b95a7f..65b2d6b 100644 --- a/analysis/templates/before-body.tex +++ b/analysis/templates/before-body.tex @@ -1,16 +1,18 @@ %% modified from: https://github.com/quarto-journals/plos/blob/main/_extensions/plos/partials/before-body.tex -\maketitle +%\maketitle \begin{center} -\thispagestyle{fancy} +%\thispagestyle{fancy} + +\LARGE{$title$} % Insert author names, affiliations and corresponding author email $by-author:_authors.tex()[, ]$\vspace{12pt} -\rule{\textwidth}{1.8pt}\vspace{8pt} +%\rule{\textwidth}{1.8pt}\vspace{8pt} \end{center} @@ -25,6 +27,8 @@ \vspace{12pt} +\linenumbers + $if(abstract)$ \begin{abstract} $abstract$ @@ -34,6 +38,6 @@ $if(keywords)$ \small \textbf{Keywords:} $for(keywords)$ $keywords$ $endfor$ $endif$ \\ -\vfill +%\vfill -\newgeometry{top=30mm,left=30mm} +%\newgeometry{top=30mm,left=30mm}