The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria

BACKGROUND: We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P

Core-based Methodology: An Automated Approach for Implementing a Complete System From Algorithms to a Heterogeneous Network Including FPGAs

In this paper, we present a methodology for implementing a complete Digital Signal Processing (DSP) system onto a heterogeneous network including Field Programmable Gate Arrays (FPGAs) automatically. The methodology aims to allow design refinement and real time verification at the system level. The DSP application is constructed in the form of a Data Flow Graph (DFG) which provides an entry point to the methodology. The netlist for parts that are mapped onto the FPGA(s) together with the corresponding software and hardware Application Protocol Interface (API) are also generated. Using a set of case studies, we demonstrate that the design and development time can be significantly reduced using the methodology developed.

Semiring induced valuation algebras: Exact and approximate local computation algorithms

Local computation in join trees or acyclic hypertrees has been shown to be linked to a particular algebraic structure, called valuation algebra.There are many models of this algebraic structure ranging from probability theory to numerical analysis, relational databases and various classical and non-classical logics. It turns out that many interesting models of valuation algebras may be derived from semiring valued mappings. In this paper we study how valuation algebras are induced by semirings and how the structure of the valuation algebra is related to the algebraic structure of the semiring. In particular, c-semirings with idempotent multiplication induce idempotent valuation algebras and therefore permit particularly efficient architectures for local computation. Also important are semirings whose multiplicative semigroup is embedded in a union of groups. They induce valuation algebras with a partially defined division. For these valuation algebras, the well-known architectures for Bayesian networks apply. We also extend the general computational framework to allow derivation of bounds and approximations, for when exact computation is not feasible.

Algorithms and architectures for high performance recursive filtering

Recently, a number of most significant digit (msd) first bit parallel multipliers for recursive filtering have been reported. However, the design approach which has been used has, in general, been heuristic and consequently, optimality has not always been assured. In this paper, msd first multiply accumulate algorithms are described and important relationships governing the dependencies between latency, number representations, etc are derived. A more systematic approach to designing recursive filters is illustrated by applying the algorithms and associated relationships to the design of cascadable modules for high sample rate IIR filtering and wave digital filtering.

Fast VLSI algorithms for division and square root

Real time digital signal processing demands high performance implementations of division and square root. This can only be achieved by the design of fast and efficient arithmetic algorithms which address practical VLSI architectural design issues. In this paper, new algorithms for division and square root are described. The new schemes are based on pre-scaling the operands and modifying the classical SRT method such that the result digits and the remainders are computed concurrently and the computations in adjacent rows are overlapped. Consequently, their performance exceeds that of the SRT methods. The hardware cost for higher radices is considerably more than that of the SRT methods but for many applications, this is not prohibitive. A system of equations is presented which enables both an analysis of the method for any radix and the parameters of implementations to be easily determined. This is illustrated for the case of radix 2 and radix 4. In addition, a highly regular array architecture combining the division and square root method is described. © 1994 Kluwer Academic Publishers.

Rapid silicon design of discrete orthonormal wavelet transforms based algorithms

A methodology has been developed which allows a non-specialist to rapidly design silicon wavelet transform cores for a variety of specifications. The cores include both forward and inverse orthonormal wavelet transforms. This methodology is based on efficient, modular and scaleable architectures utilising time-interleaved coefficients for the wavelet transform filters. The cores are parameterized in terms of wavelet type and data and coefficient word lengths. The designs have been captured in VHDL and are hence portable across a range of silicon foundries as well as FPGA and PLD implementations.