Following a press-release today (23 March 2016), Dr. Laura Phipps from Alzheimer’s Research UK wrote a blog post about my research into Alzheimer’s disease and Parkinson’s disease. Check it out:
The official project description can be found here.
For more about my research, drop me a line.
Our POND team at UCL have modelled the changes in Alzheimer’s disease, confirming our current understanding of this disease, and providing a tool for diagnosis and prognosis. You can read about it for free in the journal Brain here (open access). Title and abstract below.
A data-driven model of biomarker changes in sporadic Alzheimer’s disease
Alexandra Young, Neil Oxtoby, Pankaj Daga, David Cash, ADNI, Nick Fox, Sebastien Ourselin, Jonathan Schott, and Daniel Alexander
We demonstrate the use of a probabilistic generative model to explore the biomarker changes occurring as Alzheimer’s disease develops and progresses. We enhanced the recently introduced event-based model for use with a multi-modal sporadic disease data set. This allows us to determine the sequence in which Alzheimer’s disease biomarkers become abnormal without reliance on a priori clinical diagnostic information or explicit biomarker cut points. The model also characterises uncertainty in the ordering and provides a natural patient staging system. Two hundred and eighty-five subjects (92 cognitively normal, 129 mild cognitive impairment, 64 Alzheimer’s disease) were selected from the Alzheimer’s Disease Neuroimaging Initiative with measurements of 14 Alzheimer’s disease-related biomarkers including cerebrospinal fluid proteins, regional magnetic resonance imaging brain volume and rates of atrophy measures, and cognitive test scores. We used the event-based model to determine the sequence of biomarker abnormality and its uncertainty in various population subgroups. We used patient stages assigned by the event-based model to discriminate cognitively normal subjects from those with Alzheimer’s disease, and predict conversion from mild cognitive impairment to Alzheimer’s disease and cognitively normal to mild cognitive impairment. The model predicts that cerebrospinal fluid levels become abnormal first, followed by rates of atrophy, then cognitive test scores, and finally regional brain volumes. In amyloid-positive (cerebrospinal fluid amyloid-β1–42 < 192 pg/ml) or APOE-positive (one or more APOE4 alleles) subjects, the model predicts with high confidence that the cerebrospinal fluid biomarkers become abnormal in a distinct sequence: amyloid-β1–42, phosphorylated tau, total tau. However, in the broader population total tau and phosphorylated tau are found to be earlier cerebrospinal fluid markers than amyloid-β1–42, albeit with more uncertainty. The model’s staging system strongly separates cognitively normal and Alzheimer’s disease subjects (maximum classification accuracy of 99%), and predicts conversion from mild cognitive impairment to Alzheimer’s disease (maximum balanced accuracy of 77% over 3 years), and from cognitively normal to mild cognitive impairment (maximum balanced accuracy of 76% over 5 years). By fitting Cox proportional hazards models, we find that baseline model stage is a significant risk factor for conversion from both mild cognitive impairment to Alzheimer’s disease (P = 2.06 × 10−7) and cognitively normal to mild cognitive impairment (P = 0.033). The data-driven model we describe supports hypothetical models of biomarker ordering in amyloid-positive and APOE-positive subjects, but suggests that biomarker ordering in the wider population may diverge from this sequence. The model provides useful disease staging information across the full spectrum of disease progression, from cognitively normal to mild cognitive impairment to Alzheimer’s disease. This approach has broad application across neurodegenerative disease, providing insights into disease biology, as well as staging and prognostication.
Working with folk from my old stomping ground at the University of Liverpool, we discovered in the laboratory that a two-dimensional dusty plasma can be adequately described using the ideal gas law – even when shock waves are excited to melt the dust crystal.
The title and abstract are below, but you can read all about it in Physical Review Letters here (or for free on the arXiv here).
Ideal Gas Behavior of a Strongly Coupled Complex (Dusty) Plasma
N.P. Oxtoby, E.J. Griffith, C. Durniak, J.F. Ralph, and D. Samsonov
In a laboratory, a two-dimensional complex (dusty) plasma consists of a low-density ionized gas containing a confined suspension of Yukawa-coupled plastic microspheres. For an initial crystal-like form, we report ideal gas behaviour in this strongly coupled system during shock-wave experiments. This evidence supports the use of the ideal gas law as the equation of state for soft crystals such as those formed by dusty plasmas.
I have started my new role at University College London (UCL). I am working on computational models of neurodegenerative disease progression. Read more here.
I will present some results on dusty plasmas at this year’s American Physical Society March Meeting in Boston, USA. My talk is scheduled for the High Pressure: Experiment session on Thursday March 1. I’ll be talking about combining Rankine-Hugoniot shock relations and target tracking to derive an equation of state for a dusty plasma. There is a brief video here.
Our group’s latest paper has been
accepted for publication published in Physics of Plasmas:
“Tracking shocked dust: state estimation for a complex plasma during a shock wave”
Neil P. Oxtoby, Jason F. Ralph, Céline Durniak and Dmitry Samsonov
(read the abstract and download from Physics of Plasmas or the arXiv.)
The motion of “dust” particles in a complex plasma are obtained by computer-processing frames of a high-speed video. This gives us particle positions as a function of time. An individual particle’s velocity is usually obtained from consecutive positions – a technique known as particle tracking velocimetry (PTV). This yields an estimate of average velocity between frames with precision limited by the precision of the particle’s positions. In particular, pixel locking will propagate into velocity estimated using PTV.
We include a Bayesian inference step in the tracking procedure – using an extended Kalman filter to predict the particle position, velocity and acceleration. The prediction is based on a priori knowledge of the dust dynamics. We show that Prediction + Measurement (in a weighted sum) = significantly higher precision than PTV.
We also go further to use an interacting multiple model (IMM) filter that handles the shock wave excitation nicely – see the paper for details (quite technical).
The bottom line for physics:
Target tracking (state estimation) can significantly improve the precision of velocity estimates for the dust. This is of major importance for calculating condensed-matter-like quantities such as pressure/stress, kinetic temperature, and dynamic viscosity – to name a few. We calculated a pressure-volume diagram from our results, showing excellent qualitative agreement between experiment and simulation.
Our paper has now been published in Physical Review Letters here.
Our paper has been accepted for publication in Physical Review Letters.
Today we submitted a paper on quantum filtering using a digitized weak measurement record:
“Quantum filtering one bit at a time”
Jason F. Ralph and Neil P. Oxtoby
(read the abstract and download from arXiv:1108.0823)
The classical analogue signal originating from diffusive weak measurement of a qubit (or two) is digitized into a single bit based on its sign (+/-). We show that such a one-bit record (OBR) can be used to purify a single- or double-qubit, both with and without feedback control. Quantum discord and classical correlations between a pair of qubits are also very well reproduced by the OBR.
The bottom line:
we have made a significant reduction in the data required to accurately reconstruct a quantum state with only minor reduction in the filter performance.
I recently visited Garmisch-Partenkirchen to participate in the 6th annual International Conference on the Physics of Dusty Plasmas. The conference was excellent. Full of very interesting talks and a decent social program, too. See photos below.
I presented a poster at the conference, entitled “Tracking Compressed Dust”. This extended my work on accurate determination/estimation of the physics of dusty plasma crystals and liquids.
Contact me for the PDF of my poster, or for more details on my work on state estimation and tracking in dusty plasmas.
Our group recently published a paper entitled “Molecular Dynamics Simulations of Dynamic Phenomena in Complex Plasmas” in IEEE Transactions on Plasma Science.
You can find out more by reading the abstract here.
I’ll be presenting a talk in Orlando, Florida (U.S.A.) at the SPIE conference “Signal and Data Processing of Small Targets 2010” on Tuesday, 6 April (the final talk of the day).
It’s in the Sensor Data and Information Exploitation program track of the “Defense, Security and Sensing” symposium.*
If you can make it there, come and say “g’day”. No doubt there’ll be time for some beer and physics afterwards.
Contact me for the slides from my talk. The title and abstract are below:
Tracking interacting dust: comparison of tracking and state estimation techniques for dusty plasmas
Neil Oxtoby, Jason F. Ralph, Dmitry Samsonov, Céline Durniak
Complex (dusty) plasmas are a convenient mesoscopic test-bed for exploring kinematics of microscopic systems in different phases (fluid-like, crystal-like). Micron-sized ‘dust’ grains within an ion-electron plasma interact via a screened Coulomb interaction. These dust-dust interactions are the principal effect in the observed particle dynamics. This work investigates the accuracy of measurement and tracking techniques in the presence of complex nearest-neighbour interactions and how modern state estimation methods can be used to monitor this complex system. The principal requirement is to simplify the tracking algorithms to reduce the computational costs without reducing the accuracy of the particle tracks.
Update: pics of the shuttle launch:
Video is available on You Tube: here