Auditory system plasticity in children after long periods of complete deafness

Separation and identification of proteins by two-dimensional (2-D) electrophoresis can be used for protein-based gene expression analysis. In this report single protein spots, from polyvinylidene difluoride blots of micropreparative E. coli 2-D gels, were rapidly and economically identified by matching their amino acid composition, estimated pI and molecular weight against all E. coli entries in the SWISS-PROT database. Thirty proteins from an E. coli 2-D map were analyzed and identities assigned. Three of the proteins were unknown. By protein sequencing analysis, 20 of the 27 proteins were correctly identified. Importantly, correct identifications showed unambiguous "correct" score patterns. While incorrect protein identifications also showed distinctive score patterns, indicating that protein must be identified by other means. These techniques allow large-scale screening of the protein complement of simple organisms, or tissues in normal and disease states. The computer program described here is accessible via the World Wide Web at URL address (http:@expasy.hcuge.ch/).     

Hidden Markov models for the stimulus-response relationships of multistate neural systems

Given recent experimental results suggesting that neural circuits may evolve through multiple firing states, we develop a framework for estimating state-dependent neural response properties from spike train data. We modify the traditional hidden Markov model (HMM) framework to incorporate stimulus-driven, non-Poisson point-process observations. For maximal flexibility, we allow external, time-varying stimuli and the neurons' own spike histories to drive both the spiking behavior in each state and the transitioning behavior between states. We employ an appropriately modified expectation-maximization algorithm to estimate the model parameters. The expectation step is solved by the standard forward-backward algorithm for HMMs. The maximization step reduces to a set of separable concave optimization problems if the model is restricted slightly. We first test our algorithm on simulated data and are able to fully recover the parameters used to generate the data and accurately recapitulate the sequence of hidden states. We then apply our algorithm to a recently published data set in which the observed neuronal ensembles displayed multistate behavior and show that inclusion of spike history information significantly improves the fit of the model. Additionally, we show that a simple reformulation of the state space of the underlying Markov chain allows us to implement a hybrid half-multistate, half-histogram model that may be more appropriate for capturing the complexity of certain data sets than either a simple HMM or a simple peristimulus time histogram model alone.     

From software extensions to product lines of dataflow programs

Dataflow programs are widely used. Each program is a directed graph where nodes are computations and edges indicate the flow of data. In prior work, we reverse-engineered legacy dataflow programs by deriving their optimized implementations from a simple specification graph using graph transformations called refinements and optimizations. In MDE speak, our derivations were PIM-to-PSM mappings. In this paper, we show how extensions complement refinements, optimizations, and PIM-to-PSM derivations to make the process of reverse engineering complex legacy dataflow programs tractable. We explain how optional functionality in transformations can be encoded, thereby enabling us to encode product lines of transformations as well as product lines of dataflow programs. We describe the implementation of extensions in the \(\mathtt{ReFlO}\) tool and present two non-trivial case studies as evidence of our work’s generality.     

A noise attribute thresholding method for document image binarization

A new thresholding method, called the noise attribute thresholding method (NAT), for document image binarization is presented in this paper. This method utilizes the noise attribute features extracted from the images to make the selection of threshold values for image thresholding. These features are based on the properties of noise in the images and are independent of the strength of the signals (objects and background) in the image. A simple noise model is given to explain these noise properties. The NAT method has been applied to the problem of removing text and figures printed on the back of the paper. Conventional global thresholding methods cannot solve this kind of problem satisfactorily. Experimental results show that the NAT method is very effective. Received July 05, 1999 / Revised July 07, 2000     

Kinematics of heelstrike during walking and carrying: implications for slip resistance testing

Slip resistance measurements of shoes and floors are used to evaluate the potential for slip and fall injuries. These measurements are believed to have increased validity when they more closely reflect actual heelstrike biomechanics during locomotion. The purpose of this study was to describe heelstrike kinematics during load carrying to provide data towards improved slip resistance testing. Foot kinematics during load carrying (unladen and carrying from 0 to 13.5 kg) at various cadences (70, 90, 100 steps/min) was recorded. Measures before, during and after heelstrike were analysed. Cadence was an important predictor for all variables measured, associated with changes from 13% to over 100%. The load carried was an important predictor for only the heel slip distance after heelstrike, but this effect needs to be investigated further. These results can be used to improve the fidelity of slip resistance measurements, which is critical to reduce slip and fall injuries in the workplace or during activities of daily living.