Citrus by Pierre Laszlo: a book review

Citrus: A History by Pierre Laszlo

This fun little book from 2007 contains everything you ever wanted to know about the history, economics, and properties of oranges, lemons, limes, grapefruit, and other citrus fruit.

Somewhat unusually for a book of this kind, it even includes a number of recipes – for fried valencia orange slices, sea bass with tangerine juice, marmalade, tarte au citron, citrus sabayon, orange mousse, and a few other things. For example:


12 limes, halved
1 medium onion, peeled and quartered
4 hot green chilli peppers
1 inch ginger root
4 oz seedless raisins
7 green cardamom pods
1 tablespoon black peppercorns
1 tablespoon coriander seeds
1 tablespoon mustard seeds
4 dried red chilli peppers
1 1/2 cups cider vinegar
3 tablespoon coarse salt
1 lb light brown sugar

  • Juice the limes. Discard 6 lime halves.
  • In a food processor, combine remaining 18 lime halves, green chilli peppers, onion, ginger and raisins. Chop finely. Place mixture in a non-metal bowl.
  • Open cardamom pods. In a heavy skillet, toast peppercorns, cardamom seeds, mustard seeds, coriander seeds, and the dried red chillies for about 3 minutes, stirring constantly. Let the spices cool on a dry plate, then grind finely.
  • Add spices, lime juice, sugar, and vinegar to the chopped fruit mixture. Stir thoroughly, cover, and let steep at room temperature for 2 days.
  • On the third day, pour mixture into an enamelled pot (no stainless steel), add salt, and bring to a boil slowly. Simmer, uncovered, for 30 minutes.
  • Place in prepared clean jars. Close jars with a tight-fitting lid. Store in a cool place.

Still-Life, Jan Davidsz. de Heem, 1642

I found the chapter on art a little frustrating, though. There are many oranges in art and lemons in art, but authors who discuss such works should provide a few more illustrations. And equating oranges with “golden apples” in Greek mythology was rather dubious, I thought. But overall, I enjoyed reading this book.

Citrus: A History by Pierre Laszlo: 3½ stars

Synthetic Populations

Last year I wrote a Virtual Synthetic Population tool for estimating anthropometric measurements. There are many applications for such measurements, but most nations in the world have not had detailed anthropometric surveys done. Estimation is therefore necessary in many cases, and I’ve used the tool in my consulting work.

The Virtual Synthetic Population tool (written in R, of course!) starts with a detailed sample of anthropometric measurements for a group of several thousand Americans. This sample is then transformed (using simulated annealing) to match available data for various nations (e.g. data collected by the WHO on height and BMI). Other anthropometric variables are then transformed using correlations within the dataset.

For example, bideltoid breadth (the maximum horizontal distance between the lateral margins of the upper arms on the deltoid muscles – see diagram below) can be predicted from the combination of height and BMI (specifically, linear regression on height and BMI predicts 72% of the variance in bideltoid breadth).

Given Australia’s geographical location, I’m particularly interested in anthropometric estimates for the countries of the Pacific. The island nation of Nauru makes a good case study:

The population of Nauru is only about 9500. Among other things, the citizens of Nauru are keen players of Australian rules football:

A 2007 WHO report indicates that Nauruan males aged 15+ had a mean height of 168.1 cm and a mean BMI of 31.7  kg/m2. Of the roughly 3200 males in this age group, approximately 82.1% had a BMI of 25 or more, and 55.7% had a BMI of 30 or more. Using these statistics and the Virtual Synthetic Population tool, we can estimate the anthropometrics of these 3200 males. In particular, the diagram below shows the estimated height and bideltoid breadth for this population. Taller individuals can be found on the right of the chart, and heavier individuals at the top (each figure is to scale – click to zoom):

Given this synthetic population, we can summarise the distributions of the variables (for example, the 5% and 95% quantiles for bideltoid breadth are 44.8 cm and 61.3 cm respectively). Alternatively, the eight people below (chosen from the synthetic population) define an ellipse in height/BMI space that encloses 95% of the population – that is, 95% of the males aged 15+ fall within the extremes defined by these eight people. We can also use the entire synthetic population (or some random sample of it) within a computer simulation.

Mars in the Night Sky

Mars is growing brighter in the sky, appearing close to the star Antares and the planet Saturn (see NASA image above). Later this month, Mars will reach the point of closest approach, and hence largest apparent size.

For the current distance to Mars, see Wolfram’s calculator or the live diagram of the solar system at Fourmilab, which includes images (green lines show orbits below the plane of the ecliptic):

Live Solar System image

Cheerleaders in the news

The Internet appears to have melted down again over the above infographic from the University of Washington cheerleading team. Now I must admit to being bemused by the whole US cheerleading phenomenon, but nothing here seems particularly surprising. Cheerleading is a form of public entertainment combining dance and gymnastics. It relies partly on communicating facial expression to distant spectators (hence the lipstick and false eyelashes). America being what it is, there is often a big emphasis on being “family friendly,” rather than “sexy” (hence the restrictions on makeup and tattoos). Avoiding injury is also important (hence the restrictions on body piercing and jewellery).

Simple cheerleading stunt with “flyer” (top), “bases” (sides), and “back spot” (rear) – photo: “unoguy”

The more disturbing feature of the infographic, at least at first sight, is the requirement for bare midriffs in auditions. This is apparently a technique for circumventing laws against having weight restrictions, by allowing fitness to be judged visually. Different team roles (see above) have different body types, with “flyers” needing to be lighter, while “bases” and “back spots” need to be stronger (see below). A healthy BMI is required in each case. Fitness restrictions are therefore not unreasonable.

I cannot help but think that the US has larger problems right now than the kind of eyeshadow that cheerleaders in Washington state wear. Cheerleaders themselves have far more serious problems than being asked to wear false eyelashes (injury rates, for example, and underpayment). And US universities have far more serious problems than whether their cheerleaders sport fake tans. So I’m still kind of confused as to why this infographic is big news.

For aficionados, the R code for the BMI diagram is:

bmif <- function (kg, m) { kg/m^2 }

invf <- function (b) {
	Vectorize (function (h) { b*h^2 })

regionf <- function (b, clr) {
	f <- invf(b)
	hh <- seq(1.4,2.1,0.001)
	xx <- f(hh)
	hh <- c(hh, 2.1, 1.4, 1.4)
	xx <- c(xx, 200, 200, xx[1])
	polygon(xx, hh, col = clr)

linef <- function (b) {
	f <- invf(b)
	hh <- seq(1.4,2.1,0.001)
	xx <- f(hh)
	lines(xx, hh, lwd=2, col="grey40")

inchf <- Vectorize(function (h) {
	ft <- h %/% 12
	h <- h %% 12
	if (h == 0) paste (ft, "'", sep="")
	else paste (ft, "' ", h, '"', sep="")

postscript(file="BMI_Cheer.eps", onefile=FALSE, horizontal=FALSE, width = 10, height = 6)
par(mar=c(4.3, 4.3, 2, 3)) # c(bottom, left, top, right)
plot(c(40,140), c(1.5,2), type="n", xlab="Weight (kg)", ylab="Height (m)", las=1, cex.axis=1.2, cex.lab=1.5)

inches <- 5*12 + seq(0,21,3)
axis(side=4, at=inches*0.0254, labels=inchf(inches), las=1, cex.axis=1.2)

pounds <- seq(100,300,50)
axis(side=3, at=pounds*0.45359237, labels=paste(pounds, " lb", sep=""), cex.axis=1.2)

regionf(18.5, "limegreen")
regionf(25, "darkorange")
regionf(30, "tomato")
for (i in seq(1.5,2,0.1)) { lines(c(0,300),c(i,i), col="grey80") }
for (i in seq(40,160,10)) { lines(c(i,i), c(0,3), col="grey80") }
labs <- c("Underweight\n(BMI < 18.5)", "Normal\n(BMI 18.5–25)", "Overweight\n(BMI 25–30)",  "Obese\n(BMI > 30)")
text(c(50, 82, 104.5, 125), rep(1.95, 4), labs, font=2, cex=0.8)

names <- c("Cheerleaders\noverall", "Bases", "Flyers", "Back spots")
heights <- c(160.2, 161.2, 155.8, 170.4)/100
weights <- c(57.2, 62.3, 50.4, 63.5)
points(weights, heights, pch=19, cex=1.2, lwd=3)
text(weights, heights, pos=c(2,4,1,3), cex=1.2, labels=names, offset=0.5)


May the Fourth

May the Fourth, and it seems that Trump has clinched the Republican nomination.

Adding in the so-called superdelegates, Clinton needs 164 out of 1,114 remaining to get the Democrat nomination, which is pretty well guaranteed.

Trump needs 189 out of 445 delegates, which, now that Cruz has dropped out, also seems pretty likely, barring something surprising happening in California.

More Wellcome winners

The Wellcome Image Awards have announced more winners. Alfred Anwander (MPI-CBS) produced this view of nerve fibre wiring in the brain of a young healthy adult:

I also liked this Swallowtail butterfly head (Macroscopic Solutions):

All images used under Creative Commons license (CC BY-NC-ND). Click images to zoom and/or read more about these lovely pictures, and check out the other 2016 winners.