Optimization of Low-Dose Tomography via Binary Sensing Matrices

X-ray computed tomography (CT) is one of the most widely used imaging modalities for diagnostic tasks in the clinical application. As X-ray dosage given to the patient has potential to induce undesirable clinical consequences, there is a need for reduction in dosage while maintaining good quality in reconstruction. The present work attempts to address low-dose tomography via an optimization method. In particular, we formulate the reconstruction problem in the form of a matrix system involving a binary matrix. We then recover the image deploying the ideas from the emerging field of compressed sensing (CS). Further, we study empirically the radial and angular sampling parameters that result in a binary matrix possessing sparse recovery parameters. The experimental results show that the performance of the proposed binary matrix with reconstruction using TV minimization by Augmented Lagrangian and ALternating direction ALgorithms (TVAL3) gives comparably better results than Wavelet based Orthogonal Matching Pursuit (WOMP) and the Least Squares solution

Reconstruction of sparse-view tomography via preconditioned Radon sensing matrix

Computed Tomography (CT) is one of the significant research areas in the field of medical image analysis. As X-rays used in CT image reconstruction are harmful to the human body, it is necessary to reduce the X-ray dosage while also maintaining good quality of CT images. Since medical images have a natural sparsity, one can directly employ compressive sensing (CS) techniques to reconstruct the CT images. In CS, sensing matrices having low coherence (a measure providing correlation among columns) provide better image reconstruction. However, the sensing matrix constructed through the incomplete angular set of Radon projections typically possesses large coherence. In this paper, we attempt to reduce the coherence of the sensing matrix via a square and invertible preconditioner possessing a small condition number, which is obtained through a convex optimization technique. The stated properties of our preconditioner imply that it can be used effectively even in noisy cases. We demonstrate empirically that the preconditioned sensing matrix yields better signal recovery than the original sensing matrix

On-chip label-free protein analysis with downstream electrodes for direct removal of electrolysis products.

The ability to apply highly controlled electric fields within microfluidic devices is valuable as a basis for preparative and analytical processes. A challenge encountered in the context of such approaches in conductive media, including aqueous buffers, is the generation of electrolysis products at the electrode/liquid interface which can lead to contamination, perturb fluid flows and generally interfere with the measurement process. Here, we address this challenge by designing a single layer microfluidic device architecture where the electric potential is applied outside and downstream of the microfluidic device while the field is propagated back to the chip via the use of a co-flowing highly conductive electrolyte solution that forms a stable interface at the separation region of the device. The co-flowing electrolyte ensures that all the generated electrolysis products, including Joule heat and gaseous products, are flowed away from the chip without coming into contact with the analytes while the single layer fabrication process where all the structures are defined lithographically allows producing the devices in a simple yet highly reproducible manner. We demonstrate that by allowing stable and effective application of electric fields in excess of 100 V cm-1, the described platform provides the basis for rapid separation of heterogeneous mixtures of proteins and protein complexes directly in their native buffers as well as for the simultaneous quantification of their charge states. We illustrate this by probing the interactions in a mixture of an amyloid forming protein, amyloid-β, and a molecular chaperone, Brichos, known to inhibit the process of amyloid formation. The availability of a platform for applying stable electric fields and its compatibility with single-layer soft-lithography processes opens up the possibility of separating and analysing a wide range of molecules on chip, including those with similar electrophoretic mobilities

Cause of Death and Predictors of All-Cause Mortality in Anticoagulated Patients With Nonvalvular Atrial Fibrillation : Data From ROCKET AF

M. Kaste on työryhmän ROCKET AF Steering Comm jäsen.Background-Atrial fibrillation is associated with higher mortality. Identification of causes of death and contemporary risk factors for all-cause mortality may guide interventions. Methods and Results-In the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) study, patients with nonvalvular atrial fibrillation were randomized to rivaroxaban or dose-adjusted warfarin. Cox proportional hazards regression with backward elimination identified factors at randomization that were independently associated with all-cause mortality in the 14 171 participants in the intention-to-treat population. The median age was 73 years, and the mean CHADS(2) score was 3.5. Over 1.9 years of median follow-up, 1214 (8.6%) patients died. Kaplan-Meier mortality rates were 4.2% at 1 year and 8.9% at 2 years. The majority of classified deaths (1081) were cardiovascular (72%), whereas only 6% were nonhemorrhagic stroke or systemic embolism. No significant difference in all-cause mortality was observed between the rivaroxaban and warfarin arms (P=0.15). Heart failure (hazard ratio 1.51, 95% CI 1.33-1.70, P= 75 years (hazard ratio 1.69, 95% CI 1.51-1.90, P Conclusions-In a large population of patients anticoagulated for nonvalvular atrial fibrillation, approximate to 7 in 10 deaths were cardiovascular, whereasPeer reviewe

Design and analysis of authenticated key agreement scheme in cloud-assisted cyber–physical systems

© 2018 Elsevier B.V. With advancements in engineering and science, the application dimensions of Cyber–Physical System (CPS) are increasing due to their improving efficiency, safety, reliability, usability and autonomy. By providing on-demand access to shared processing resources, cloud computing reduces infrastructure costs. Ensuring quality of service and information privacy and security is important in such environments. In this paper, we design a new authentication scheme related to the cloud-assisted CPS in two directions: (1) authentication between a user and a cloud server, and (2) authentication between a smart meter and a cloud server. In the former situation, any external party (user) can access the information stored in a cloud server provided that the user is legal and has the right to access information. In the later situation, a smart meter and a cloud server authentication is needed for secure communication of data stored in the cloud server. In both cases, both entities first mutually authenticate each other and only after successful authentication with the help of a trusted authority, establish a session key for their future secure communication. The proposed scheme deals with both the cases and provides high security as compared to other related works, which is shown through formal and informal security analysis. In addition, the mutual authentication using the widely-accepted Burrows–Abadi–Needham logic (BAN logic) and also formal security verification using the broadly-used Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool demonstrate further that the scheme is strong in security. Finally, the proposed scheme is shown to be efficient in terms of communication and computation costs as compared to those for other related existing schemes

Novel Light Weight Compressed Data Aggregation using sparse measurements for IoT networks

Optimal data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing data aggregation methods have proven that compressed sensing is promising for data aggregation. However, they compromise either on energy efficiency or recovery fidelity and require complex on-node computations. In this paper, we propose a novel Light Weight Compressed Data Aggregation (LWCDA) algorithm that randomly divides the entire network into non-overlapping clusters for data aggregation. The random non-overlapping clustering offers two important advantages: 1) energy efficiency, as each node has to send its measurement only to its cluster head, 2) highly sparse measurement matrix, which leads to a practically implementable framework with low complexity. We analyze the properties of our measurement matrix using restricted isometry property, the associated coherence and phase transition. Through extensive simulations on practical data, we show that the measurement matrix can reconstruct data with high fidelity. Further, we demonstrate that the LWCDA algorithm reduces transmission cost significantly against baseline approaches, implying thereby the enhancement of the network lifetim

Highly efficient binding of paramagnetic beads bioconjugated with 100 000 or more antibodies to protein-coated surfaces

We report here the first kinetic characterization of 1 mu m diameter superparamagnetic particles (MP) decorated with over 100 000 antibodies binding to protein antigens attached to flat surfaces. Surface plasmon resonance (SPR) was used to show that these antibody-derivatized MPs (MP-Ab(2)) exhibit irreversible binding with 100-fold increased association rates compared to free antibodies. The estimated upper limit for the dissociation constant of MP-Ab2 from the SPR sensor surface is 5 fM, compared to 3-8 nM for the free antibodies. These results are explained by up to 2000 interactions of MP-Ab(2) with protein-decorated surfaces. Findings are consistent with highly efficient capture of protein antigens in solution by the MP-Ab(2) and explain in part the utility of these beads for ultrasensitive protein detection into the fM and aM range. Aggregation of these particles on the SPR chip, probably due to residual magnetic microdomains in the particles, also contributes to ultrasensitive detection and may also help drive the irreversible binding