Wigs.

Abstract

Wigs were originally popularised in Britain by Queen Elizabeth I, who is said to have owned no fewer than 80, and Mary Queen of Scots is reputed to have worn one at her execution. Louis XIII of France went prematurely bald in 1624 and by disguising his baldness with a wig, started a fashion that lasted over 150 years. In the seventeenth century the grandest wigs were worn by the wealthy and important, hence the expression "big wigs." The demand for wigs was so great that children were forbidden to go out alone in case thieves cut off their hair. Today five types of wigs are worn: necessity wigs to cover baldness, fashion or fun wigs, legal wigs, wigs worn for religious reasons, and theatrical wigs. Necessity and fun wigs merely differ in their indications for use. A variety offibres are used in their construction, but the commonest material is acrylic, which makes high quality and fairly cheap wigs.

Guidelines for the management of alopecia areata

“…For many female patients with extensive alopecia areata a wig or hairpiece is the most effective solution 59 . Some men also request a wig although male wigs rarely appear as natural.…”

A Survey on the Microscopical and Chemical Analysis of Synthetic Wig Fibers

“…setwd("C:/Users/Theresa/Documents/Thesis/FTIR") getwd() # sets the working directory library(scatterplot3d) # loads package needed for 3d plots raw.FTIR = read.csv("data_no2_MA fingerprint_first.csv", header=TRUE, sep=",", row.names=1) head(raw.FTIR) # reads in the file and confirms only the first 6 rows of each column rather than consuming the # workspace with the whole file sc.FTIR = apply(raw.FTIR,2,function(x) (x-mean(x))/sd(x)) # mean centers the data and divides it by the standard deviation to get unit variance (This step is # needed for the correlation matrix.) (2,2)) barplot(FTIR.relvar [1:10], main = "PC Variances", names.arg = paste("PC", 1:10)) # creates a bar graph for amount of variance in first 10 PCs loadings = eigen.FTIR$vectors scores = sc.FTIR %*% eigen.FTIR$vectors # computes the loadings and scores for PCA plot(scores[,1:2], type = "n", main = "PC1 v PC2", xlab = paste("PC 1(", PC.var [1], "%)", sep = ""), ylab = paste("PC 2(", PC.var [2], "%)", sep = "")) # points(scores[,1:2]) # creates a biplot for PC1 vs PC2 showing scores. The points function can be used instead of labeling the # spectra with text samples = read.csv("sample names_no2_MA spectrum_first.csv", header=TRUE, sep=",", colClasses=c("character")) text(scores[,1],scores[,2], rownames(samples), col="blue", cex=0.7) # plots the scores as samples using text.…”

“…The points function can be used instead of labeling the # spectra with text samples = read.csv("sample names_no2_MA spectrum_first.csv", header=TRUE, sep=",", colClasses=c("character")) text(scores[,1],scores[,2], rownames(samples), col="blue", cex=0.7) # plots the scores as samples using text. Each number represents a different spectrum scatterplot3d(scores[,1:3], main = "PCA plot for First Derivative Modacrylic Spectra (1800-650cm-1)", xlab = paste("PC 1(", PC.var [1], "%)", sep = ""), ylab = paste("PC 2(", PC.var [2], "%)", sep = ""), zlab = paste("PC 3(", PC.var [3], "%)", sep = ""),pch=16, highlight.3d=TRUE, type="h") # creates a 3d plot for PC1 vs PC2 vs PC3 samples = read.csv("sample names_no2_MA spectrum_first.csv", header=TRUE, sep=",", colClasses=c("character")) text(scores[,1],scores[,2],scores [,3], rownames(samples)) # plots the scores as samples using text for 3d plot dev.off() # saves biplot for PC1 v PC2 as jpeg file 4. R-script for performing HCA on both the fingerprint region and alkane region of the FTIR data setwd("C:/Users/Theresa/Documents/Thesis/FTIR") getwd() # sets the working directory ##HCA on the fingerprint region FTIR = read.csv("data_no2_MA fingerprint_first.csv", header=TRUE, sep=",", row.names=1) head(FTIR) # reads in the file and confirms only the first 6 rows of each column rather than consuming the # workspace with the whole file dist.FTIR = dist(FTIR, method = "euclidean") # computes the distance matrix using Euclidean distance distsq.FTIR = dist.FTIR^2 # squares the distance matrix as needed for Ward's method hca.FTIR = hclust(distsq.FTIR, method = "ward") # performs HCA on distance matrix using Ward's method plot(hca.FTIR, labels = row.names(FTIR), main = "Cluster Dendrogram for First Derivative Modacrylic Spectra (1800-650cm-1)", lwd = 0.5, cex = 0.5) rect.hclust(hca.FTIR, h=10, border="red") # plots the HCA dendrogram with red borders around the clusters plot(1:448, hca.FTIR$height, xlab = "Wig Samples", ylab = "Linkage Distance", main = "Linkage Distances for First Derivative Modacrylic Spectra (1800-650cm-1) ", type = "p") # plots linkage distances to determine at what distance to cut the tree hca.…”

“…Synthetic wigs tend to be more popular as they are more affordable, more weather resistant, and easier to maintain [1]. The synthetic fibers are manufactured to mimic the appearance of natural hair for either cosmetic or costume use.…”

“…Repeatedly drawing the tow through a large metal toothcomb, which is termed "hackling," blends the different colored fibers [2]. The blended fibers are sewn together in wefts and ultimately sewn to the wig cap [1]. If the manufacturer wishes to add curls to the wig, the wefts of synthetic fibers are rolled onto various sized aluminum pipes and placed in large ovens to set the curl [2].…”