A FAST ITERATIVE METHOD FOR EIKONAL EQUATIONS

In this paper we propose a novel computational technique to solve the Eikonal equation efficiently on parallel architectures. The proposed method manages the list of active nodes and iteratively updates the solutions on those nodes until they converge. Nodes are added to or removed from the list based on a convergence measure, but the management of this list does not entail an extra burden of expensive ordered data structures or special updating sequences. The proposed method has suboptimal worst-case performance but, in practice, on real and synthetic datasets, runs faster than guaranteed-optimal alternatives. Furthermore, the proposed method uses only local, synchronous updates and therefore has better cache coherency, is simple to implement, and scales efficiently on parallel architectures. This paper describes the method, proves its consistency, gives a performance analysis that compares the proposed method against the state-of-the-art Eikonal solvers, and describes the implementation on a single instruction multiple datastream (SIMD) parallel architecture.open393

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network

With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble Model), which is tested on 7,002 samples representing over 31 different cancer types collected from The Cancer Genome Atlas (TCGA) database. We first systematically examined the impact of the input features. Features known to be associated with specific cancers had relatively high importance in our initial prediction model. We further investigated various machine learning classifiers and feature selection methods to derive the ensemble-based cancer type prediction model achieving up to 84% classification accuracy in the nested 10-fold cross-validation. Finally, we narrowed down the target cancers to the six most common types and achieved up to 94% accuracy

Interpreting positive signs of the supraspinatus test in screening for torn rotator cuff.

The purpose of this study was to investigate the validity of the supraspinatus test as a screening test for detecting torn rotator cuff and to determine what its valuable positive signs were. Both the empty-can test and full-can test were performed on 200 shoulders diagnosed by magnetic resonance imaging (MRI)-and in some cases, surgical findings-to have full-thickness or partial-thickness torn rotator cuff s, or no tear in the rotator cuff . During the maneuver, the presence of pain or weakness or both pain and weakness were recorded as positive signs, and the distribution of these signs were analyzed according to the degree of tear. The predictive values were calculated in 2 ways by considering (1) only full-thickness tears as tears and (2) both full- and partial-thickness tears as tears. The 2 tests and the 2 ways of considering partial-thickness tears were compared. Pain and weakness were severity-dependent, and the empty-can test had a higher incidence of pain. The sensitivities of the 2 supraspinatus tests in all positive signs were higher when including partial-thickness tears in the tear group ; however, their specificities were higher when excluding partial-thickness tears. Both pain and weakness were interpretive for the supraspinatus test, and both tests were sensitive to full- and partial- thickness tears and specific for full-thickness tears

A FAST ITERATIVE METHOD FOR SOLVING THE EIKONAL EQUATION ON TRIANGULATED SURFACES

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton-Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512-2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.open4

Catalytic pyrolysis of Laminaria japonica over nanoporous catalysts using Py-GC/MS

The catalytic pyrolysis of Laminaria japonica was carried out over a hierarchical meso-MFI zeolite (Meso-MFI) and nanoporous Al-MCM-48 using pyrolysis gas chromatography/mass spectrometry (Py-GC/MS). The effect of the catalyst type on the product distribution and chemical composition of the bio-oil was examined using Py-GC/MS. The Meso-MFI exhibited a higher activity in deoxygenation and aromatization during the catalytic pyrolysis of L. japonica. Meanwhile, the catalytic activity of Al-MCM-48 was lower than that of Meso-MFI due to its weak acidity