Retrospective screening of microarray data to identify candidate IFN-inducible genes in a HTLV-1 transformed model.

HuT-102 cells are considered one of the most representable human T-lymphotropic virus 1 (HTLV-1)-infected cell lines for studying adult T-cell lymphoma (ATL). In our previous studies, genome-wide screening was performed using the GeneChip system with Human Genome Array U133 Plus 2.0 for transforming growth factor-ß-activated kinase 1 (TAK1)-, interferon regulatory factor 3 (IRF3)- and IRF4-regulated genes to demonstrate the effects of interferon-inducible genes in HuT-102 cells. Our previous findings demonstrated that TAK1 induced interferon inducible genes via an IRF3-dependent pathway and that IRF4 has a counteracting effect. As our previous data was performed by manual selection of common interferon-related genes mentioned in the literature, there has been some obscure genes that have not been considered. In an attempt to maximize the outcome of those microarrays, the present study reanalyzed the data collected in previous studies through a set of computational rules implemented using 'R' software, to identify important candidate genes that have been missed in the previous two studies. The final list obtained consisted of ten genes that are highly recommend as potential candidate for therapies targeting the HTLV-1 infected cancer cells. Those genes are ATM, CFTR, MUC4, PARP14, QK1, UBR2, CLEC7A (Dectin-1), L3MBTL, SEC24D and TMEM140. Notably, PARP14 has gained increased attention as a promising target in cancer cells.

Parallel generation of digitally reconstructed radiographs on heterogeneous multi-GPU workstations.

The growing importance of three-dimensional radiotherapy treatment has been associated with the active presence of advanced computational workflows that can simulate conventional x-ray films from computed tomography (CT) volumetric data to create digitally reconstructed radiographs (DRR). These simulated x-ray images are used to continuously verify the patient alignment in image-guided therapies with 2D-3D image registration. The present DRR rendering pipelines are quite limited to handle huge imaging stacks generated by recent state-of-the-art CT imaging modalities. We present a high performance x-ray rendering pipeline that is capable of generating high quality DRRs from large scale CT volumes. The pipeline is designed to harness the immense computing power of all the heterogeneous computing platforms that are connected to the system relying on OpenCL. Load-balancing optimization is also addressed to equalize the rendering load across the entire system. The performance benchmarks demonstrate the capability of our pipeline to generate high quality DRRs from relatively large CT volumes at interactive frame rates using cost-effective multi-GPU workstations. A 5122 DRR frame can be rendered from 1024 × 2048 × 2048 CT volumes at 85 frames per second.

Accelerating DRR generation using Fourier slice theorem on the GPU.

Digitally Reconstructed Radiographs (DRRs) play a vital role in medical imaging procedures and radiotherapy applications. They allow the continuous monitoring of patient positioning during image guided therapies using multi-dimensional image registration. Conventional generation of DRRs using spatial domain algorithms such as ray casting is associated with computational complexity of O(N(3)). Fourier slice theorem is an alternative approach for generating the DRRs in the k-space with reduced time complexity. In this work, we present a high performance, scalable, and optimized DRR generation pipeline on the Graphics Processing Unit (GPU). The strong scaling performance of the presented pipeline is investigated and demonstrated using two contemporary GPUs. Our pipeline is capable of generating DRRs for 512(3) volumes in less than a milli-second.

GPU acceleration for digitally reconstructed radiographs using bindless texture objects and CUDA/OpenGL interoperability.

This paper features an advanced implementation of the X-ray rendering algorithm that harnesses the giant computing power of the current commodity graphics processors to accelerate the generation of high resolution digitally reconstructed radiographs (DRRs). The presented pipeline exploits the latest features of NVIDIA Graphics Processing Unit (GPU) architectures, mainly bindless texture objects and dynamic parallelism. The rendering throughput is substantially improved by exploiting the interoperability mechanisms between CUDA and OpenGL. The benchmarks of our optimized rendering pipeline reflect its capability of generating DRRs with resolutions of 2048(2) and 4096(2) at interactive and semi interactive frame-rates using an NVIDIA GeForce 970 GTX device.

Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification.

We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.