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Biomedical subjects

I N Bankman

Publications and source records attributed to I N Bankman.

7 recordsLinked to original sources

Solid-state DNA sizing by atomic force microscopy.

Atomic force microscopy (AFM) allows rapid, accurate, and reproducible visualization of DNA adsorbed onto solid supports. The images reflect the lengths of the DNA molecules in the sample. Here we propose a solid-state DNA sizing (SSDS) method based on AFM as an analytical method for high-throughput applications such as finger-printing, restriction mapping, +/- screening, and genotyping. For this process, the sample is first deposited onto a solid support by adsorption from solution. It is then dried and imaged under ambient conditions by AFM. The resulting images are subjected to automated determination of the lengths of the DNA molecules on the surface. The result is a histogram of sizes that is similar to densitometric scans of DNA samples separated on gels. A direct comparison of SSDS with agarose gel electrophoresis for +/- screening shows that it produces equivalent results. Advantages of SSDS include reduced sample size (i.e., lower reagent costs), rapid analysis of single samples, and potential for full automation using available technology. The high sensitivity of the method also allows the number of polymerase chain reaction cycles to be reduced to 15 or less. Because the high signal-to-noise ratio of the AFM allows for direct visualization of DNA-binding proteins, different DNA conformations, restriction enzymes, and other DNA modifications, there is potential for dramatically improving the information content in this type of analysis.

Adsorption

Optimal detection, classification, and superposition resolution in neural waveform recordings.

The effects of noise autocorrelation on neural waveform recognition (detection, classification, and superposition resolution) are investigated in this study using microelectrode recordings from the cortex of a monkey. Optimal waveform recognition is accomplished by passing the data through a whitening filter before matched filtering for detection or template matching for classification and superposition resolution. Template matching without whitening requires about 40% higher signal-to-noise ratio than template matching with whitening for comparable classification and superposition resolution. The comparable difference for detection is 15%.

Action Potentials

Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks.

The main difficulties in reliable automated detection of the K-complex wave in EEG are its close similarity to other waves and the lack of specific characterization criteria. We present a feature-based detection approach using neural networks that provides good agreement with visual K-complex recognition: a sensitivity of 90% is obtained with about 8% false positives. The respective contribution of the features and that of the neural network is demonstrated by comparing the results to those obtained with i) raw EEG data presented to neural networks, and ii) features presented to Fisher's linear discriminant.

Electroencephalography

Noise reduction in biological step signals: application to saccadic EOG.

A weighted filter for noise reduction in nonrecurrent step signals where adaptive filtering cannot be applied is described. An optimal correction of a conventional finite impulse response (FIR) filter is achieved by using a priori knowledge of noise variance and a continuous estimation of the error signal's power. The weighted filter provides an optimal compromise between noise filtering and distortionless tracking. The prior knowledge required is that of the noise power and the lowest frequency in the noise spectrum. Application of the weighted filter to the saccadic electro-oculogram (EOG) results in better estimations of saccade duration and velocity.

Electricity

Identification of dynamic mechanical parameters of the human chest during manual cardiopulmonary resuscitation.

Survival from cardiac arrest is dependent on timely cardiopulmonary resuscitation (CPR). Since CPR is often unsuccessful, the outcome may be improved by a better understanding of the relationship between force applied to the sternum and the resulting hemodynamic effects. The first step in this complex chain of interactions is the mechanical response of the chest wall to cyclical compression. We formulated a dynamic mechanical model of the chest response and developed a method of identification of the model parameters based on force, displacement, and acceleration data acquired during cyclical compressions. The elasticity, damping, and equivalent mass of the human chest were estimated with a constrained nonlinear least-mean-square identification technique. The method was validated on data acquired from a test apparatus built for this purpose. The model fit was measured with the normalized chi-square statistic on residuals obtained between recorded force and force predicted by the model. In the analysis of one human chest, the elasticity was found to be nonlinear and statistically different during compression and release. A considerable amount of damping was found, with no significant difference between compression and release. The equivalent mass was too small to be determined accurately. This method can be used to obtain the dynamic mechanical parameters of the human chest and may lead to a better understanding of CPR.

Biomechanical Phenomena