From 2deb65d3953da529ee3dec52341ac1c19f88736f Mon Sep 17 00:00:00 2001
From: Willy Kuo <willy.kuo@physiol.uzh.ch>
Date: Mon, 30 Sep 2024 18:47:02 +0200
Subject: [PATCH] Temporarily remove publication.qmd for testing purposes
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----
-title: 'In vivo Imaging of Central Nervous System Fluid Spaces using Synchrotron Radiation Micro-Computed Tomography'
-date: '2024-09-01'
-First author: 'Marta Girona Alarcón'
-Last author: 'Vartan Kurtcuoglu'
-author:
- - name: 'Marta Girona Alarcón (first author)'
- orcid: 0009-0006-2111-2716
- affiliations:
- - name: The Interface Group, Institute of Physiology, University of Zurich
-
- - name: 'Vartan Kurtcuoglu (last author)'
- orcid: 0000-0003-2665-0995
- affiliations:
- - name: The Interface Group, Institute of Physiology, University of Zurich
-format:
- html:
- grid:
- body-width: 1500px
----
-
-In this hub, we provide additional materials, methods and results for the publication.
-
-## Supplemental Results
-
-In this [DOI](https://doi.org/10.5281/zenodo.13773081), we provide full resolution datasets for download from the publication data and supplemental results:
-
-### Pilot post mortem experiments
-
-In a previous beamtime (ESRF, 2020, December), *ex vivo* imaging was performed. The goal of the pilot experiments was to chose a contrast agent for the presented *in vivo* experiments. The list of used contrast agents is available under the Zenodo download entry "tables_exVivo.7z". We considered the Barium-based contrast agent (CA) to be the most promissing one and therefore decided to employ it for the *in vivo* experiments (Fig. 1).
-
-
-
-```{r librLoad}
-library(dplyr)
-library(kableExtra)
-# SET options for table rendering in this page
-options(DT.options = list(fixedHeader = TRUE,
- scrollX = TRUE,
- scrollY = "800px",
- paging = FALSE,
- scrollCollapse = TRUE,
- autoWidth = TRUE))
-```
-
-```{r}
-tbl <- read.csv(file = 'exVivo_Contrast_agent_per_mouse_list.csv')
-kable(tbl, format = "markdown")
-```
-
-Table 1. List of *ex vivo* mouse number, including the injected site and contrast agent.
-
-### CSF spaces segmentation
-
-In the publication, we have shown the need of infusing contrast agent to achieve a semi-automatic segmentation. Here, we show a rendering of one segmentation. In the downloads, we have provided a segmentation of a timeseries imaging in the brain ventricles, which can be found under "videos_Reconstructions.7z".
-
-{width="900"}
-
-### Timeseries movies
-
-Movement of contrast agent over time for the presented timeseries can be observed in projections and reconstructions as movies under the folders "videos_Reconstructions.7z" and "videos_Projections.7z" in [Zenodo](https://doi.org/10.5281/zenodo.13773081).
-
-## Additional materials
-
-We have additively manufactured the mouse holder, optimized for vertical imaging while ensuring fixation of the mouse skull (Fig. 3).
-
-::: {layout-ncol="2"}
-{width="650"}
-
-{width="300"}
-:::
-
-Additionally, we assambled a customized mouse stage for two surgical procedures: tracheotomy and cisterna magna infusion (Fig. 5).
-
-{width="700"}
-
-## Equipment and consumables
-
-The following table provides details of consumables, hardware and software that have been employed for this experiments.
-
-```{r}
-tbl <- read.csv(file = 'Equipment_table.csv')
-DT::datatable(tbl, extensions = c('FixedHeader'), filter = 'top', rownames = FALSE)
-```
-
-<!-- Do not edit below this line !! -->
-
-## Links to protocol pages
-
-```{r getrepo}
-library(dplyr)
-library(here)
-
-# Read repository URL information from _quarto.yml file
-vars <- yaml::read_yaml(file.path(here::here(),'_quarto.yml'),)
-repo_url <- vars$website$`repo-url`
-repo_url_basepath <- file.path(repo_url,'-', 'tree','master','webpage_contents')
-```
-
-All protocols can be found this website's repository: [`r repo_url`](repo_url)
-
-```{r protocols}
-library(dplyr)
-library(here)
-
-# Read repository URL information from _quarto.yml file
-vars <- yaml::read_yaml(file.path(here::here(),'_quarto.yml'),)
-
-# Retrieve which are data types and facilities relevant to this publication
-inputs <- read.csv(file = 'input_mice.csv')
-
-# Find protocols and files
-folders <- unique(cbind(inputs$Data_type,inputs$Facility_name_YYYY_month)) # find table's unique variations of
-paths <- dir(path = file.path('..','..','experiments',inputs$Data_type, inputs$Facility_name_YYYY_month, "protocols"),
- pattern='*.pdf',full.names = TRUE)
-
-# Add URL of the repo with additional info about the branch
-paths <- as.data.frame(file.path(repo_url_basepath,'experiments', sapply(strsplit(paths,'experiments'),'[[',2)))
-
-colnames(paths) <- 'Protocols'
-
-# Add markdown formatting so that it becomes a link
-paths$Protocols <- paste0('[', sapply(strsplit(paths$Protocols,'experiments'),'[[',2),'](', paths$Protocols,')')
-
-
-# Render table
-kableExtra::kable(paths,format = 'markdown')
-
-
-```
-
-## Sample information
-
-`r getwd()`
-
-```{r}
-library(dplyr)
-library(kableExtra)
-
-# Take the relevant rows from the tables specified in inputs
-mice_used <- list()
-scans_used <- list()
-
-# Loop through mice as defined in the mice_input table
-inputs <- read.csv(file = 'input_mice.csv')
-for (i in 1:nrow(inputs)){
-
- # Find experiment table for this mouse
- row_filepath <- file.path(here::here(), 'experiments',inputs$Data_type[i], inputs$Facility_name_YYYY_month[i], "metadata_tables",inputs$Metadata_file_mouse[i])
- # Read mice info file
- mice_used[[i]] <- read.csv(row_filepath) %>% filter(Subject_ID == inputs$Subject_ID[i]) # read table and filter subject
- mice_used[[i]]$Subject_pubID <- inputs$Subject_pubID[i] # Add additional input column
- mice_used[[i]]$Correction_factor <- inputs$Correction_factor[i] # Add additional input column
-
- # Find and read scan lists for that subject
- row_filepath_scans <- file.path(here::here(), 'experiments',inputs$Data_type[i], inputs$Facility_name_YYYY_month[i], "metadata_tables",inputs$Metadata_file_scan[i])
- tmp_tbl_scans <- read.csv(row_filepath_scans) %>% filter(Subject_ID == inputs$Subject_ID[i])
-
- # Providing the preview images
- # ------------------------------------------------
- # Build URL based on the REpo URL (retrieved from _quarto.yml file)
- image_url <- file.path(repo_url_basepath,'experiments',inputs$Data_type[i], inputs$Facility_name_YYYY_month[i],'preview_images',tmp_tbl_scans$Preview_Image_LowQuality)
-
- # Find relative path to image and COPY it into the publication folder
- source_images_relativepath <- gsub(repo_url_basepath,file.path('..','..'),image_url)
- #copy_image_dir <- file.path(getwd(),'preview_images')
- #file.copy(source_images_relativepath,copy_image_dir)
-
-
-"C:/Users/gorka/Gitlab_crs/fdcns/webpage_contents/publications/20240901_Marta_GironaAlarcon/images"
-# Add HTML to display images in table and make them clickable link
- tmp_tbl_scans$Preview_Image_LowQuality <- paste0('<a href=\'', image_url,'\' target=\'_blank\'>','<img src=\'', source_images_relativepath, '\' height=\'70\'></a>')
-
- # ------------------------------------------------
-
-
- # join tables ---------------------------------------------
- scans_used[[i]] <- full_join(x=mice_used[[i]],
- y = tmp_tbl_scans,
- by=join_by("Subject_ID"),
- suffix = c('.mice','.scans'),
- keep=FALSE)
-
-}
-
-
-# Gather list elements in a table
-mice_used <- do.call(rbind,mice_used)
-scans_used <- do.call(rbind,scans_used)
-
-
-#Change position of some columns
-mice_used <- relocate(mice_used, Correction_factor, .before = 7)
-mice_used <- relocate(mice_used, Subject_pubID, .before = 1)
-scans_used <- relocate(scans_used, Correction_factor, .before = 7)
-scans_used <- relocate(scans_used, Subject_pubID, .before = 1)
-scans_used <- relocate(scans_used, Preview_Image, .before = 1)
-
-## Gather code assumming there is only one data type
-mouse_codebook <- read.csv(file = dir(path = file.path(here::here(), 'experiments',inputs$Data_type[1]), pattern = 'Mouse.*codebook.csv',full.names = TRUE))
-scan_codebook <- read.csv(file = dir(path = file.path(here::here(), 'experiments',inputs$Data_type[1]), pattern = 'Scan.*codebook.csv',full.names = TRUE))
-joint_codebook <- bind_rows(mouse_codebook, scan_codebook) %>% distinct()
-
-
-```
-
-::: panel-tabset
-## Mice
-
-```{r}
-DT::datatable(mice_used, extensions = c('FixedHeader'), filter = 'top', rownames = FALSE)
-
-```
-
-#### Codebook
-
-```{r}
-kable(joint_codebook, format = "markdown")
-```
-
-## Mice and scans info
-
-```{r}
-DT::datatable(scans_used, escape = FALSE, extensions = c('FixedHeader'), filter = 'top', rownames = FALSE)
-```
-
-
-
-#### Codebook
-
-```{r}
-kable(mouse_codebook, format = "markdown")
-```
-:::
--
GitLab