Fast Predictive Simple Geodesic Regression

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.Comment: 19 pages, 10 figures, 13 table

Comparison of bulk and microfluidic methods to monitor the phase behaviour of nanoparticles during digestion of lipid-based drug formulations using in situ X-ray scattering.

The performance of orally administered lipid-based drug formulations is crucially dependent on digestion, and understanding the colloidal structures formed during digestion is necessary for rational formulation design. Previous studies using the established bulk pH-stat approach (Hong et al. 2015), coupled to synchrotron small angle X-ray scattering (SAXS), have begun to shed light on this subject. Such studies of digestion using in situ SAXS measurements are complex and have limitations regarding the resolution of intermediate structures. Using a microfluidic device, the digestion of lipid systems may be monitored with far better control over the mixing of the components and the application of enzyme, thereby elucidating a finer understanding of the structural progression of these lipid systems. This work compares a simple T-junction microcapillary device and a custom-built microfluidic chip featuring hydrodynamic flow focusing, with an equivalent experiment with the full scale pH-stat approach. Both microfluidic devices were found to be suitable for in situ SAXS measurements in tracking the kinetics with improved time and signal sensitivity compared to other microfluidic devices studying similar lipid-based systems, and producing more consistent and controllable structural transformations. Particle sizing of the nanoparticles produced in the microfluidic devices were more consistent than the pH-stat approach

Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms

We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters

Physical parameters affecting sonoluminescence: A self-consistent hydrodynamic study

We studied the dependence of thermodynamic variables in a sonoluminescing ~SL! bubble on various physical factors, which include viscosity, thermal conductivity, surface tension, the equation of state of the gas inside the bubble, as well as the compressibility of the surrounding liquid. The numerical solutions show that the existence of shock waves in the SL parameter regime is very sensitive to these factors. Furthermore, we show that even without shock waves, the reflection of continuous compressional waves at the bubble center can produce the high temperature and picosecond time scale light pulse of the SL bubble, which implies that SL may not necessarily be due to shock waves

Potassium channel gene mutations rarely cause atrial fibrillation

BACKGROUND: Mutations in several potassium channel subunits have been associated with rare forms of atrial fibrillation. In order to explore the role of potassium channels in inherited typical forms of the arrhythmia, we have screened a cohort of patients from a referral clinic for mutations in the channel subunit genes implicated in the arrhythmia. We sought to determine if mutations in KCNJ2 and KCNE1-5 are a common cause of atrial fibrillation. METHODS: Serial patients with lone atrial fibrillation or atrial fibrillation with hypertension were enrolled between June 1, 2001 and January 6, 2005. Each patient underwent a standardized interview and physical examination. An electrocardiogram, echocardiogram and blood sample for genetic analysis were also obtained. Patients with a family history of AF were screened for mutations in KCNJ2 and KCNE1-5 using automated sequencing. RESULTS: 96 patients with familial atrial fibrillation were enrolled. Eighty-three patients had lone atrial fibrillation and 13 had atrial fibrillation and hypertension. Patients had a mean age of 56 years at enrollment and 46 years at onset of atrial fibrillation. Eighty-one percent of patients had paroxysmal atrial fibrillation at enrollment. Unlike patients with an activating mutation in KCNQ1, the patients had a normal QT(c )interval with a mean of 412 ± 42 ms. Echocardiography revealed a normal mean ejection fraction of 62.0 ± 7.2 % and mean left atrial dimension of 39.9 ± 7.0 mm. A number of common polymorphisms in KCNJ2 and KCNE1-5 were identified, but no mutations were detected. CONCLUSION: Mutations in KCNJ2 and KCNE1-5 rarely cause typical atrial fibrillation in a referral clinic population

Attitudes and expectations of patients with advanced cancer towards community palliative care service in Hong Kong

Conference Theme: Happy Staff - Healthy People (開心員工 - 共建民康)published_or_final_versionThe 2010 Hospital Authority Convention, Hong Kong, 10-11 May 2010

A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore

10.1371/journal.pone.0017468PLoS ONE63

A statistical framework for integrating two microarray data sets in differential expression analysis

<p>Abstract</p> <p>Background</p> <p>Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis.</p> <p>Results</p> <p>Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models.</p> <p>Conclusion</p> <p>The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.</p