In vivo Mouse Imaging of the Central Nervous System Fluid Spaces with High Spatiotemporal Resolution using Synchrotron Radiation Computed Tomography
IEEE2024
Authors
Affiliation
Marta Girona Alarcón (first author)
The Interface Group, Institute of Physiology, University of Zurich
Vartan Kurtcuoglu (last author)
The Interface Group, Institute of Physiology, University of Zurich
Published
January 1, 2024
Code
# EDIT INPUTS HERE ---------------------------# Define the rows of the input table, each row is c('input1','input2','input',....). Separate the rows by comma except the last rowinputs <-data.frame(rbind(c("synchrotron_microCT","SPring-8_May_2023","Mice_Information_SPring-8_20230508.csv","JP21","Experiment 1"),c("synchrotron_microCT","SPring-8_May_2023","Mice_Information_SPring-8_20230508.csv","JP28","Experiment 2"),c("synchrotron_microCT","SPring-8_May_2023","Mice_Information_SPring-8_20230508.csv","JP23","Experiment 3") ))# Assign column namescolnames(inputs) <-c("EXPERIMENT_TYPE", "FACILITY", "FILE","SUBJECT","ADD_INFO")
Supplemental Results
Note: Supplemental Methods are supposed to go to beamtime pages
Links to protocol pages
Synchrotron µCT Data
Code
# | warning: false # --- Do NOT EDIT --- # This code chunk gathers the rows from tables specified in the inputslibrary(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Code
# Loop thru the input rowsdata_used <-list()for (i in1:nrow(inputs)){ row_filepath <-file.path(here::here(), 'experiments',inputs$EXPERIMENT_TYPE[i], inputs$FACILITY[i], inputs$FILE[i]) data_used[[i]] <-read.csv(row_filepath) %>%filter(SubjectID == inputs$SUBJECT[i]) # read table and filter subject}# Gather list elements in a table data_used <-do.call(rbind,data_used) data_used$experiment <- inputs$ADD_INFO # Add additional input column #Display DT::datatable(data_used, filter ='top')