Robotic pilot study for analysing spasticity: clinical data versus healthy controls.

BACKGROUND: Spasticity is a motor disorder that causes significant disability and impairs function. There are no definitive parameters that assess spasticity and there is no universally accepted definition. Spasticity evaluation is important in determining stages of recovery. It can determine treatment effectiveness as well as how treatment should proceed. This paper presents a novel cross sectional robotic pilot study for the primary purpose of assessment. The system collects force and position data to quantify spasticity through similar motions of the Modified Ashworth Scale (MAS) assessment in the Sagittal plane. Validity of the system is determined based on its ability to measure velocity dependent resistance. METHODS: Forty individuals with Acquired Brain Injury (ABI) and 45 healthy individuals participated in a robotic pilot study. A linear regression model was applied to determine the effect an ABI has on force data obtained through the robotic system in an effort to validate it. Parameters from the model were compared for both groups. Two techniques were performed in an attempt to classify between healthy and patients. Dynamic Time Warping (DTW) with k-nearest neighbour (KNN) classification is compared to a time-series algorithm using position and force data in a linear discriminant analysis (LDA). RESULTS: The system is capable of detecting a velocity dependent resistance (p<0.05). Differences were found between healthy individuals and those with MAS 0 who are considered to be healthy. DTW with KNN is shown to improve classification between healthy and patients by approximately 20 % compared to that of an LDA. CONCLUSIONS: Quantitative methods of spasticity evaluation demonstrate that differences can be observed between healthy individuals and those with MAS of 0 who are often clinically considered to be healthy. Exploiting the time-series nature of the collected data demonstrates that position and force together are an accurate predictor of patient health.

An order estimation based approach to identify response genes for microarray time course data.

Gene expression profiles from microarray time course experiments provide a unique opportunity to examine genome-wide signal processing and gene responses. A fundamental issue in microarray experiments is that the treatment condition can only be controlled at the cell level rather than at the gene level. The treatment condition does not affect all genes equally. Some genes depend on other genes to detect external changes. The dependency between genes is not fully deterministic and may vary with treatment condition. Thus the expression of each gene is potentially affected by two confounding effects: the treatment effect and the gene context effect arising from the regulatory interactions among genes. This gene context effect is hard to isolate. Neither can it be simply ignored. Instead, this gene context information which may be different under different treatment conditions is of primary biological interest. We introduce an approach which deals with the confounding effects and takes into account the uncontrollable gene context effect. Our method is based on the estimation of the number of hidden states, which, in our development, corresponds to the order of a hidden Markov model (HMM). For each gene, its observed expression is modeled by a gamma distribution determined by the corresponding hidden state at each time point. Those genes showing evidence for more than one hidden state can be categorized as the signalling genes, or in a wider sense, as the response genes which are coordinated by a cell system in reaction to a specific external condition. These response genes can be used in the comparison of different treatment conditions, to investigate the gene context effect under different treatments. Microarray time course data are also analyzed to demonstrate our method.

A time series analysis of the relationship of ambient temperature and common bacterial enteric infections in two Canadian provinces.

The incidence of enteric infections in the Canadian population varies seasonally, and may be expected to be change in response to global climate changes. To better understand any potential impact of warmer temperature on enteric infections in Canada, we investigated the relationship between ambient temperature and weekly reports of confirmed cases of three pathogens in Canada: Salmonella, pathogenic Escherichia coli and Campylobacter, between 1992 and 2000 in two Canadian provinces. We used generalized linear models (GLMs) and generalized additive models (GAMs) to estimate the effect of seasonal adjustments on the estimated models. We found a strong non-linear association between ambient temperature and the occurrence of all three enteric pathogens in Alberta, Canada, and of Campylobacter in Newfoundland-Labrador. Threshold models were used to quantify the relationship of disease and temperature with thresholds chosen from 0 to -10 degrees C depending on the pathogen modeled. For Alberta, the log relative risk of Salmonella weekly case counts increased by 1.2%, Campylobacter weekly case counts increased by 2.2%, and E. coli weekly case counts increased by 6.0% for every degree increase in weekly mean temperature. For Newfoundland-Labrador the log relative risk increased by 4.5% for Campylobacter for every degree increase in weekly mean temperature.