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Abstract Background Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult. Results We developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO) annotations and chromatin immunoprecipitation (ChIP-chip) data. Conclusion Our approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at http://www.benoslab.pitt.edu/astro/.

In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as package in the TETRAD source code (written in Java). Simulations can be done varying the number of runs, sample sizes, and data modalities. Performance on this simulated data can then be compared for a number of algorithms, with parameters varied and with performance statistics as selected, producing a publishable report. The package presented here may also be used to compare structure learning methods across platforms and programming languages, i.e., to compare algorithms implemented in TETRAD with those implemented in MATLAB, Python, or R. [ABSTRACT FROM AUTHOR]

Robot-based neurorehabilitation strategies often ignore cognitive performance during treatment, but this is a need in populations dealing with a wide variety of cognitive and motor impairments, such as the stroke and HIV populations, for which an association between the two have been established. In this study, we concurrently measure cognitive and motor performance on a robotic cognitive-motor task and quantify cognitive-motor interference. We apply this method to a pilot group of healthy, stroke, and HIV-stroke subjects, and we demonstrate the potential of smoothness and correct response rate as metrics to capture motor and cognitive-related dual-task effects.

There is an increasing population of people living with both HIV and stroke around the world with no effective neurorehabilitation strategies to deal with the combination of physical, cognitive, and social impairment that result from both diseases. This gap is caused by a lack of tools that are able to assess the various impairments across the HIV-stroke spectrum. Rehabilitation robotics provide a potential approach to address this problem. In this study, we implement a motor and cognitive task on the Haptic TheraDrive, a single degree-of-freedom upper limb rehabilitation robot. We collect data on healthy and HIV-stroke subjects from both upper limbs. Our preliminary data show that mean performance error on a trajectory tracking task and total score and reaction time on the n-back task are metrics that show differences between HIV-stroke patients and a healthy population.

In this study, we aim to explore ways to objectively assess cognitive deficits in the stroke and HIV/stroke populations, where cognitive and motor impairments can be hard to separate. Using an upper limb rehabilitation robot called the Haptic TheraDrive, we collect performance error scores and motor learning data on the impaired and unimpaired limb during a trajectory tracking task. We compare these data to clinical cognitive scores. The preliminary results suggest a possible relationship between unimpaired upper limb performance error and visuospatial/executive function cognitive domains, but more work needs to be done to further investigate this. The potential of using robot-assisted technologies to measure unimpaired limb kinematics as a tool to assess cognitive deficits would be useful to inform more effective rehabilitation strategies for HIV, stroke, and HIV/stroke populations.