A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicate a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in th proposed method is demonstrated.

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We have studied coupled neural populations in an effort to understand basic mechanisms that maintain their normal synchronization level despite changes in the inter-population coupling levels. Towards this goal, we have incorporated coupling and internal feedback structures in a neuron-level population model from the literature. We study two forms of internal feedback--regulation of excitation, and compensation of excessive excitation with inhibition. We show that normal feedback actions quickly regulate/compensate an abnormally high coupling between the neural populations, whereas a pathology in these feedback actions can lead to abnormal synchronization and "seizure"-like high amplitude oscillations. We then develop an external control paradigm, termed feedback decoupling, as a robust synchronization control strategy. The external feedback decoupling controller acts to achieve the operational objective of maintaining normal-level synchronous behavior irrespective of the pathology in the internal feedback mechanisms. Results from such an analysis have an interesting physical interpretation and specific implications for the treatment of diseases such as epilepsy. The proposed remedy is consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology.

In an effort to understand basic functional mechanisms that can produce epileptic seizures, some key features are introduced in coupled lumped-parameter neural population models that produce "seizure"-like events and dynamics similar to the ones during the route of the epileptic brain towards seizures. In these models, modified from existing ones in the literature, internal feedback mechanisms are incorporated to maintain the normal low level of synchronous behavior in the presence of coupling variations. While the internal feedback is developed using basic feedback systems principles, it is also functionally equivalent to actual neurophysiological mechanisms such as homeostasis that act to maintain normal activity in neural systems that are subject to extrinsic and intrinsic perturbations. Here it is hypothesized that a plausible cause of seizures is a pathology in the internal feedback action; normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to "seizure"-like high amplitude oscillations. Several external seizure-control paradigms, that act to achieve the operational objective of maintaining normal levels of synchronous behavior, are also developed and tested in this paper. In particular, closed-loop "modulating" control with predefined stimuli, and closed-loop feedback decoupling control are considered. Among these, feedback decoupling control is the consistently successful and robust seizure-control strategy. The proposed model and remedies are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology. The results from the analysis of these models have two key implications, namely, developing a basic theory for epilepsy and other brain disorders, and the development of a robust seizure-control device through electrical stimulation and/or drug intervention modalities.

Body fluid assessment is important for managing chronic kidney disease (CKD) and heart failure (HF). However, accurate detection of fluid retention remains elusive. The Fluid Removal During Adherent Renal Monitoring (FARM) study is a prospective, nonrandomized trial examining the performance of a wireless, noninvasive, multisensor fluid monitoring system, applied to the chest, to determine its performance and reliability during hemodialysis. Patients undergoing regular hemodialysis (n=25) were monitored continuously for 2 consecutive dialysis sessions and the interdialysis period. Physiologic variables, including tissue impedance, were recorded. The volume of fluid removed and weight change during dialysis were measured. An average of 3.4±1.2 L of fluid was removed during dialysis, which was associated with an increase in bioimpedance of 11.3±7.2 Ω. Change in bioimpedance was highly correlated with the amount of fluid removed but less so with weight loss. Normalized bioimpedance change (21.0%±12.1% increase from baseline, P<001) was larger than the normalized weight change (3.6%±1.1%, P<.01), suggesting a higher sensitivity and dynamic range than weight change for detecting fluid removal. The fluid monitoring system accurately tracked fluid and weight loss in patients during hemodialysis, supporting its use as a tool for the management of patient fluid status in disease states. Congest Heart Fail. 2012;18:32–36. ©2011 Wiley Periodicals, Inc.