By Carrie Bengston, James Davidson and Olivier Salvado
Mmm . . . lovely! A hot Indian curry is simmering away on the stove on a wintry night. The smell of spices fills the kitchen. One of the spices is turmeric, from the ginger family. Its vibrant yellow colour comes from the compound curcumin which is finding a use in clinical tests for Alzheimers disease (AD).
Who knew? Soon everyone will! We’re presenting our research this week at a major conference in Copenhagen, AAIC2014.
A clinical trial of the spice-infused eye test is being led by our own Dr Shaun Frost and team, with WA’s Edith Cowan University, US company NeuroVision Imaging, and the McCusker Alzheimer’s Research Foundation in Perth. Several hundred volunteers have taken part. They include healthy people, mildly cognitively impaired people and patients with AD. It’s all part of the Australian Imaging Biomarkers and Lifestyle study of Aging (AIBL)
The trial asks volunteers to come along to two visits for retinal fluorescence imaging, ie an eye scan. This is quick and painless. Patients sit in front of a specialised camera and a photo is taken of the retina at the back of their eye.
Between visits, volunteers eat some curcumin which binds to beta-amyloid plaques, the sticky proteins that indicate Alzheimers, and fluoresces. The plaques (if there are any) show up in the eye scans as bright spots which can be counted and measured. The data is then used to calculate a special number for each patient, a retinal amyloid index (RAI), and compared between healthy, mildly cognitively impaired and AD patients.
Encouragingly, as we announced this week, early results show the amount of plaque in the retina closely mirrors the amount in the brain. If confirmed, retinal imaging may be the beginnings of an easy, non-invasive test for early detection of AD. Combined with results of cognitive tests and other markers it could help doctors diagnose AD more confidently.
Eye scans like this also find plaques when they’re smaller than the ones in brain scans, potentially finding signs of AD earlier – maybe up to 20 years before cognitive symptoms appear. If diagnosed, AD patients could start treatment sooner and have regular eye scans to see which treatments work best for them.
Brain imaging on the cloud
From curry to the cloud. More research presented this week is about more accurately interpreting brain images sometimes used to diagnose AD.
To get a brain scan, a patient lies on a bed in a large machine like a Magnetic Resonance Imaging (MRI) or Positron Emission tomography (PET) scanner. These machines record a series of images through the brain, which are then visually checked by a radiologist who compiles a report for the patient’s doctor.
This visual inspection can be subjective, tedious and time consuming. But recent advances in scientific computing and machine learning allows systems to accurately measure features of the 3D scan, such as brain size or concentration of a tracer molecule, that support a diagnosis.
Using these techniques, a new trend is emerging for improving radiologists’ productivity. Scanners and specialised medical software can report quantitative values and compare them to the values expected for normal, healthy patients – just like blood test results from a pathology lab do.
Our researchers, led by health imaging specialist Associate Prof Olivier Salvado, have just released a new cloud computing application, MILXCloud, that automatically delivers standardised radiology reports.
Users will be able to upload a PET scan and within 15 minutes be emailed a one page quantitative report showing a diagram of the brain with colour coded values compared with what’s normal. This data will help support diagnosis by the radiologist and enhance delivery of eHealth services.
Whether it’s curry or the Cloud, the future of Alzheimer’s detection sure looks bright.
Media: Andreas Kahl | 0407 751 330 | firstname.lastname@example.org
Whether it’s sourdough, seeded rye, gluten-free or plain old white, there’s nothing like tucking into a fresh slice of bread. And it’s little wonder this age-old staple tastes so good – experts have been perfecting the art of bread making for thousands of years.
If we had to name who’s involved in bread making, most of us would probably identify the baker, the farmer who grows the wheat and maybe even the miller who grinds the wheat into flour. But how many people would think of the humble statistician? Dr Emma Huang would – and she’s eager to prove their worth in the process.
Emma is a statistical geneticist working with our Computational Informatics and Food Futures teams. She spends her days searching through thousands of genes for the few that affect yield and disease resistance in wheat.
By understanding the complex genetics of cultivated plants like wheat, Emma is helping farmers select the best crop varieties needed to produce the perfect loaf of bread.
“The impact of statistics in bread making starts well before preheating the oven. Statisticians are crucial in implementing efficient experimental design to compare different varieties of wheat for desirable characteristics,” says Emma.
After completing a Bachelor of Science in Mathematics at Caltech and a Doctor of Philosophy in Biostatistics at the University of North Carolina, Emma left the States to join our team in Brisbane.
Here she is using her mathematical expertise to detect regions of the wheat plants genome – or its inheritable traits – that are directly related to enhanced crop performance. This allows breeders to selectively breed specific genes, reducing the amount of time it takes to improve our food supply.
Her goal is to eventually be able to model the entire process of bread making, incorporating the effects of environment and genetics all the way from growing plants in the field, to milling the flour and baking the bread.
When she’s not crunching numbers in the name of food, Emma does her own private research into the best cuisine the world has to offer, indulging at world class restaurants like Spain’s El Celler de Can Roca. But fitness freaks don’t fret, she works off the extra calories playing water polo and going for ocean swims.
“Sometimes I think I was destined to be a statistical geneticist. Both my mother and aunt are qualified statisticians, my siblings all studied mathematics at university, and even my fiancé is a statistician!”
Who better to investigate the impact of genetics on our everyday life?
For more information on careers at CSIRO, follow us on LinkedIn.
By Carrie Bengston
What would happen if zombies invaded the planet? World War Z tells the story with Brad Pitt and a much bigger film budget than we have.
But it will hearten you to know that a team in Canada has actually crunched the numbers for a zombie apocalypse. They created a mathematical model for zombie infection, suggesting that only quick, aggressive attacks can stave off the doomsday scenario.
The take home message from the maths? Hit ‘em hard and hit ‘em often.
Maths can help us explore all kinds of real and hypothetical scenarios. You might not think it, but maths is vital in understanding the complex and dynamic planet we call home.
This was made clear last week at Mathematics of Planet Earth Year: The Conference, where over 200 people gathered to hear what maths is telling us about our precious planet.
For our young graduate fellows who are test driving a maths research career, the conference was a chance to see the limitless applications of their chosen field. These include an amazing variety of natural and human-organised aspects of planet Earth discussed during the conference.
From understanding climate and weather patterns to identifying pests that threaten our biosecurity using 3D insect imaging, it was evident how important maths is in understanding the many challenges facing Earth today.
Mathematical modelling has even been used to protect people in earthquake-prone areas, promote sustainable dairy farming and watch a virus spread within a plant.
Maths adds a lot of value to our own research too. For instance, our mathematical scientists have contributed to important discoveries about Alzheimer’s disease. Take a look:
2013 is the International year of Mathematics of Planet Earth. Learn more about how maths and stats are helping us understand the challenges of our world.
By Arwen Cross
Love it or hate it, Canberra is our capital city. But it isn’t the only thing named Canberra. Hamish Boland-Rudder has found five other things with the same name, which he described in an article in the Canberra Times last week. It looks like most of them post-date our 100 year old capital, and were named after it.
Canberra is an indigenous placename, but sadly we don’t know what it means or exactly what location the name originally referred to. Some popular suggestions about the meaning of Canberra are ‘meeting place’ or, more racily, ‘the space between a woman’s breasts’, referring to the shapes of Mt Ainslie and Black Mountain. Linguist Harold Koch’s research shows that neither of these explanations is very believable (see page 153, Chapter 5 of Naming and re-naming the Australian landscape). But it’s a rare honour for an Australian capital city to have an indigenous name.
The Canberra distance
One of the things that’s named after the city of Canberra is the Canberra distance, a mathematical function used to sort things according to their similarity.
The Canberra distance was invented by CSIRO scientists Bill Williams and Godfrey Lance in the 1960s. They developed it based on the Manhattan distance, which may have inspired them to name it after their own city, Canberra.
The Canberra distance isn’t a distance in the everyday sense like how far away ANU is from the nearest pub. Mathematicians recognise lots of different types of distances, for example:
- Euclidian distance – the straight line distance between two points. This is similar to our everyday idea of distance.
- Levenshtein distance – the distance between two words measured by how many single-character edits are needed to change one into the other.
- Canberra distance – a measure of similarity and dissimilarity between groups.
So what can you use the Canberra distance for?
It’s often used to sort plants and animals into groups that are more closely or distantly related to each other. Although it can be used outside biology too.
Let’s say you want to separate the sheep from the goats in your large herd. You might need to consider several criteria to make your decision:
- Binary data – has a beard/doesn’t have a beard
- Ordered categorical data – hair very woolly/ hair moderately woolly/ hair not woolly
- Quantitative data – a measurement like weight in kilograms or height in centimetres
The Canberra distance is a way to use all these criteria together to separate individuals according to how similar or dissimilar they are. In our case, we’ll separate the herd according to how sheepy or goaty they are.
If you’ve got a large herd, you’d start by measuring all the criteria for each animal. Then you’d need some statistics, including the Canberra distance, to cluster the data into groups of animals that were similar across several characteristics.
You might find that your herd was made up of three groups of animals: a large cluster of animals with sheepy characteristics, a small cluster with goaty characteristics, and another cluster of animals that is part sheep and part goat.
Going nuts with peanut research
An actual example of the use of the Canberra distance in biological research comes from peanut breeding. Our chief mathematical scientist Bronwyn Harch worked on this project for her PhD. Instead of separating sheep from goats, she was organising peanut plants into groups.
Peanut plants are an important agricultural crop, and plant breeders are always working on new varieties that will grow better in particular climates, be more resistant to disease, or produce a better quality peanut.
To do this they keep collections of seeds from many different peanut varieties. Some wild peanuts might be very resistant to disease but have a poor yield of nuts, while commercial varieties might be disease sensitive but high yielding. Peanut breeders cross varieties to generate new ones and select the offspring with desirable characteristics.
Bronwyn looked at the characteristics of peanuts in the Australian peanut collection and in the world peanut collection in India. Her analysis helped her advise Australian peanut breeders about what characteristics were missing in their collection that they could find in the world collection. Her work also informed scientists going into the Amazon rainforest to collect new peanut plants from the wild about what characteristics to look out for.
The Canberra distance is a useful mathematical tool. But now I’m going to put my feet up and enjoy some of Hamish Boland-Rudder’s other Canberra discoveries – perhaps a glass of Canberra ale.
I would like to thank David Nash and Bob Anderssen for their advice on this article.
By Arwen Cross
Today is world maths day, and students around the world are playing in the World Education Games. If you can solve fifty maths games in an hour, imagine what you could achieve if you applied your maths to finding cancer genes, or timetabling trains. Three vacation scholars in our Mathematics, Informatics and Statistics division have been working on these problems, although they did have 12 weeks instead of an hour to do it.
Shila has been using bioinformatics to look at the genes that are expressed (turned on) in bowel cancer cells compared to normal cells from the same patient. Her statistics should help lab scientists decide which of two methods they should use to measure gene expression in the tissue samples.
Scientists measure RNA levels to see what genes are turned on in the cancer samples, but they have two ways of measuring it. They can use the poly-A method to measure just the genes that make proteins, or they can measure whole RNA. The advantage of whole RNA is that you can find out about regulatory RNA as well as the RNA that gets made into protein. The disadvantage is that the method is newer, so the scientists can’t be sure if it’s suited to this application.
Shila’s statistical analysis showed that the two methods pick up mostly the same differences between cancer and normal tissue. That’s a good thing – it shows both methods are working for genes that make proteins. But her analysis also showed that there are differences between what genes the two methods find. The differences could represent regulatory RNA genes that are involved in the cancer process. Scientists will have to go back to the lab to confirm this.
Shila loves the exploratory aspect of working with large amounts of data, and uncovering patterns in it that you can’t pick out without statistics to help you. She’s off to do a PhD next year.
Josh and Joe have been using maths for solving problems related to trains, including timetables and infrastructure planning.
Joe worked on the problem of how to timetable trains most efficiently when mining trains have to travel on sections of single track. If there are only a few places to overtake, the number of trips is limited because only one train can travel in one direction at a time. That makes optimal timetabling really important. Since the timetabling problem is too complex for solver software products, Joe worked with other methods to get the best solution.
Joe travelled from Brisbane to Melbourne for his vacation scholarship because he was so keen to do a project in operations research. He chose this area because it combines all the best things he likes about maths, programming and problem solving. Joe will be doing an honours year in his maths and computational science course this year.
Josh worked on a tablet interface for our IFAP software which helps planners design infrastructure. It provides information about questions like whether it would be more useful to build a new train line to a mine, or to upgrade another line that services mines which are increasing their output. Governments use this type of tool so they can plan infrastructure funding to get the best outcome for the money spent. Josh worked on developing an Android application so that IFAP can be used on mobile devices.
Josh enjoyed applying his uni knowledge to a new area, and found it useful to link his experience of different technologies and find out how they work together. He’ll be continuing his undergraduate degree this year.
From cancer genes to train timetables, maths can help solve a lot of problems. Students worldwide are participating in World Maths Day today. Maybe some of them will be vacation scholars too in a couple of years.
By Bruce Tabor
On 15 February, the sky over Russia was lit up by a great ball of fire – the Chelyabinsk meteor. NASA’s infrasound data can tell us a lot about it. But amazingly, so can amateur sleuths using YouTube, Google Earth, and some trigonometry.
The Chelyabinsk meteor entered the atmosphere, and exploded at high altitude near the Russian city of Chelyabinsk at 9:20 am local time Friday 15 February. Normally we rely on national space-science agencies to reconstruct these events but with some data from the web and some high school physics, you too could try your hand.
Stefan Geens of Ogle Earth was inspired to use maths to find out about the meteor using footage from car dash-board and building security cameras in Russia, which have proliferated as a way of fighting crime.
He made some assumptions about straight lines and constant speeds, and got some videos of the meteor over Revolution Square, Chelyabinsk. Then he used the distance between light posts to do some trigonometry.
To use this method you need some information. The explosion occurred at an elevation of 40 degrees almost due south of Chelyabinsk’s Revolution Square. The meteor was travelling a little south of due west.
You can get more information about distances using the gap between the flash of light and the sound of the explosion in the security camera footage. Time-stamped surveillance suggests a delay about 2.5 minutes until the shock wave reached the city. Assuming an average speed of sound of say 300 metres per second, you can calculate a distance.
Using two sides and one angle, you’re ready to do some trigonometry. The meteor exploded 45 km away at a height of about 35,000 metres. That’s three times higher than commercial airlines fly.
Most meteors start out their lives as asteroids, but when these rocks enter the atmosphere at high speed they change their name to meteor. Asteroids move through space on their own paths, but if they pass very close to us they can be effected by Earth’s gravity. Some of them enter our atmosphere and become meteors.
It’s friction with the atmosphere that makes them burn up as their kinetic energy gets converted to other forms like heat, light and sound (great balls of fire!).
So how much kinetic energy did the Chelyabinsk meteor have? NASA used infrasound data to find out. They estimate that the meteor had a diameter of 17 metres, a mass of 10 000 tonnes and entered the Earth’s atmosphere at nearly 18 kilometres per second.
Kinetic energy increases with the square of speed, so the astronomical velocity of the meteor meant that it had a lot of energy. And within a fraction of a second this energy of about 2 petajoules – that is 2 with 15 zeros – was converted into heat, light, and a blast wave.
There was 50 times more energy released by the Chelyabinsk meteor than would be released by an explosion of the same mass of TNT. That’s 30 times the Hiroshima blast and the largest energy release from a meteor since 1908 (when the Tunguska event released the equivalent of 10-15 megatons of TNT). Fortunately this blast occurred high in the atmosphere, which is why the damage on the ground was mostly limited to shattered windows.
All I can say is – goodness, gracious, great balls of fire!
This article celebrates 2013, the year of Maths of Planet Earth. The article was written by Bruce Tabor and edited by Arwen Cross. Thanks to John Sarkissian for proofreading for us.