Nanoparticle-induced negative differential resistance and memory effect in polymer bistable light-emitting device

Recently, electrical bistability was demonstrated in polymer thin films incorporated with metal nanoparticles [J. Ouyang, C. W. Chu, C. R. Szmanda, L. P. Ma, and Y. Yang, Nat. Mater. 3, 918 (2004)]. In this letter, we show the evidence that electrons are the dominant charge carriers in these bistable devices. Direct integration of bistable polymer layer with a light-emitting polymer layer shows a unique light-emitting property modulated by the electrical bistability. A unique negative differential resistance induced by the charged gold nanoparticles is observed due to the charge trapping effect from the nanoparticles when interfaced with the light-emitting layer

Myocardial Defect Detection Using PET-CT: Phantom Studies

It is expected that both noise and activity distribution can have impact on the detectability of a myocardial defect in a cardiac PET study. In this work, we performed phantom studies to investigate the detectability of a defect in the myocardium for different noise levels and activity distributions. We evaluated the performance of three reconstruction schemes: Filtered Back-Projection (FBP), Ordinary Poisson Ordered Subset Expectation Maximization (OP–OSEM), and Point Spread Function corrected OSEM (PSF–OSEM). We used the Channelized Hotelling Observer (CHO) for the task of myocardial defect detection. We found that the detectability of a myocardial defect is almost entirely dependent on the noise level and the contrast between the defect and its surroundings

Posterior Estimation for Dynamic PET imaging using Conditional Variational Inference

This work aims efficiently estimating the posterior distribution of kinetic parameters for dynamic positron emission tomography (PET) imaging given a measurement of time of activity curve. Considering the inherent information loss from parametric imaging to measurement space with the forward kinetic model, the inverse mapping is ambiguous. The conventional (but expensive) solution can be the Markov Chain Monte Carlo (MCMC) sampling, which is known to produce unbiased asymptotical estimation. We propose a deep-learning-based framework for efficient posterior estimation. Specifically, we counteract the information loss in the forward process by introducing latent variables. Then, we use a conditional variational autoencoder (CVAE) and optimize its evidence lower bound. The well-trained decoder is able to infer the posterior with a given measurement and the sampled latent variables following a simple multivariate Gaussian distribution. We validate our CVAE-based method using unbiased MCMC as the reference for low-dimensional data (a single brain region) with the simplified reference tissue model.Comment: Published on IEEE NSS&MI

MR-based motion correction for cardiac PET parametric imaging: a simulation study

Background: Both cardiac and respiratory motions bias the kinetic parameters measured by dynamic PET. The aim of this study was to perform a realistic positron emission tomography-magnetic resonance (PET-MR) simulation study using 4D XCAT to evaluate the impact of MR-based motion correction on the estimation of PET myocardial kinetic parameters using PET-MR. Dynamic activity distributions were obtained based on a one-tissue compartment model with realistic kinetic parameters and an arterial input function. Realistic proton density/T1/T2 values were also defined for the MRI simulation. Two types of motion patterns, cardiac motion only (CM) and both cardiac and respiratory motions (CRM), were generated. PET sinograms were obtained by the projection of the activity distributions. PET image for each time frame was obtained using static (ST), gated (GA), non-motion-corrected (NMC), and motion-corrected (MC) methods. Voxel-wise unweighted least squares fitting of the dynamic PET data was then performed to obtain K1 values for each study. For each study, the mean and standard deviation of K1 values were computed for four regions of interest in the myocardium across 25 noise realizations. Results: Both cardiac and respiratory motions introduce blurring in the PET parametric images if the motion is not corrected. Conventional cardiac gating is limited by high noise level on parametric images. Dual cardiac and respiratory gating further increases the noise level. In contrast to GA, the MR-based MC method reduces motion blurring in parametric images without increasing noise level. It also improves the myocardial defect delineation as compared to NMC method. Finally, the MR-based MC method yields lower bias and variance in K1 values than NMC and GA, respectively. The reductions of K1 bias by MR-based MC are 7.7, 5.1, 15.7, and 29.9% in four selected 0.18-mL myocardial regions of interest, respectively, as compared to NMC for CRM. MR-based MC yields 85.9, 75.3, 71.8, and 95.2% less K1 standard deviation in the four regions, respectively, as compared to GA for CRM. Conclusions: This simulation study suggests that the MR-based motion-correction method using PET-MR greatly reduces motion blurring on parametric images and yields less K1 bias without increasing noise level

Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the ‘normativeness’ of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.publishedVersio

Libra: A Library for Reliable Distributed Applications

This paper describes libra, a library to support efficient reliable distributed applications. libra is designed to meet two objectives: to simplify the development of reliable distributed applications, and to achieve fault-tolerance at low run-time cost. The first objective is met by the provision of fault-tolerance transparency anda simple, easy to use high-level message passing interface. Fault-tolerance is provided to applications transparently by libra and is based on distributed consistent checkpointing and rollback-recovery integrated with a user-level network communication protocol. The second objective is met by the use of protocols which minimise communication overhead for taking a consistent distributed checkpoint and catching messages in transit, and impose low overhead in terms of running times. The paper presents measurements backing up these claims

Checkpointing and Recovery for Distributed Shared Memory Applications

This paper proposes an approach for adding fault tolerance, based on consistent checkpointing, to distributed shared memory applications. Two different mechanisms are presented to efficiently address the issue of message losses due to either site failures or unreliable non-FIFO channels. Both guarantee a correct and efficient recovery from a consistent distributed system state following a failure. A variant of the two-phase commit protocol is employed such that the communication overhead required to take a consistent checkpoint is the same as that of systems using a one-phase commit protocol, while our protocol utilises stable storage more efficiently. A consistent checkpoint is committed when the first phase of the protocol finishes