A Scarlet Ending

Dancing a duet with my shadow by integrating dance and digital media in an elaborate and entertaining performance

Optical Rogue Waves in Vortex Turbulence

We present a spatio-temporal mechanism for producing 2D optical rogue waves in the presence of a turbulent state with creation, interaction and annihilation of optical vortices. Spatially periodic structures with bound phase lose stability to phase unbound turbulent states in complex Ginzburg- Landau and Swift-Hohenberg models with external driving. When the pumping is high and the external driving is low, synchronized oscillations are unstable and lead to spatio-temporal turbulence with high excursions in amplitude. Nonlinear amplification leads to rogue waves close to turbulent optical vortices, where the amplitude tends to zero, and to probability distribution functions with long tails typical of extreme optical events.Comment: 5 pages, 7 figure

Unlocking the potential of anti-CD33 therapy in adult and childhood acute myeloid leukaemia

Acute Myeloid Leukaemia (AML) develops when there is a block in differentiation and uncontrolled proliferation of myeloid precursors, resulting in bone marrow failure. AML is a heterogeneous disease clinically, morphologically, and genetically, and biological differences between adult and childhood AML have been identified. AML comprises 15-20% of all children less than fifteen years diagnosed with acute leukaemia. Relapse occurs in up to 40% of children with AML and is the commonest cause of death.1,2 Relapse arises from leukaemic stem cells (LSCs) that persist after conventional chemotherapy. The treatment of AML is challenging and new strategies to target LSCs are required. The cell surface marker CD33 has been identified as a therapeutic target, and novel anti-CD33 immunotherapies are promising new agents in the treatment of AML. This review will summarise recent developments emphasising the genetic differences in adult and childhood AML, while highlighting the rationale for CD33 as a target for therapy, in all age groups

Measuring storage and loss moduli using optical tweezers: broadband microrheology

We present an experimental procedure to perform broadband microrheological measurements with optical tweezers. A generalised Langevin equation is adopted to relate the time-dependent trajectory of a particle in an imposed flow to the frequency-dependent moduli of the complex fluid. This procedure allows us to measure the material linear viscoelastic properties across the widest frequency range achievable with optical tweezers.Comment: 5 pages, 3 figure

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

Control of polarization rotation in nonlinear propagation of fully-structured light

Knowing, and controlling, the spatial polarization distribution of a beam is of importance in applications such as optical tweezing, imaging, material processing and communications. Here we show how the polarization distribution is affected by both linear and nonlinear (self-focussing) propagation. We derive an analytical ex- pression for the polarization rotation of fully-structured light (FSL) beams during linear propagation and show that the observed rotation is due entirely to the difference in Gouy phase between the two eigenmodes comprising the FSL beams, in excellent agreement with numerical simulations. We also explore the effect of cross-phase modulation due to self-focusing (Kerr) nonlinearity and show that polarization rotation can be controlled by changing the eigenmodes of the superposition, and physical parameters such as the beam size, the amount of Kerr nonlinearity and the input power. Finally, we show that by biasing cylindrical vector (CV) beams to have elliptical polarization, we can vary the polarization state from radial through spiral to azimuthal using nonlinear propagation

Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201

Label-driven weakly-supervised learning for multimodal deformable image registration

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201

On Visible Homelessness and the Micro-Aesthetics of Public Space

In this article, we investigate the circumstances that have produced the current municipal regulatory approach to homelessness in the City of Melbourne, Victoria, and the ways in which visibly homeless people are policed through a micro-aesthetics of their presence in public space, which involves the monitoring of their bodily demeanour and their physical possessions. Our study contributes to and draws from a range of debates, including studies of the governmental conjunction of poverty and crime, analysis of the co-implication of law and spatiality, research on the criminalisation of homelessness and homeless people, and the burgeoning criminological interest in the significance of the visual field for our understandings of crime and criminality. This article recounts how homelessness, public space and questions of aesthetics have recently coalesced in debates about the regulation of homelessness in the public space of Melbourne’s city centre. It approaches the issues through comparative consideration of genres of municipal management frameworks in other jurisdictions, detailed textual consideration of the Protocol on Homelessness in the City of Melbourne and an empirical study of visible homelessness in the public places of central Melbourne