Leonid Hrebien

An In Vivo Study of Cardiac Pacemaker Optimization by Pulse Shape Modification

This paper is an experimental extension of theoretical work previously published by one of the authors. Results of pacing a canine heart using four different increasing exponential and ramp-like waveforms are presented. It is shown that these waveforms reduce the cardiac threshold energy by as much as 14 percent when compared with a pure rectangular pulse shape and by about 21 percent when compared with a standard cardiac pacer pulse. It is also found that above threshold energy is reduced by 50-60 percent. These results offer promise of increasing the life of certain new pacer power sources currently being tested. In addition, it may now be possible to pace at lower energy levels, thereby reducing tissue damage near the stimulating electrode, without running as much of a risk of exit block as with a comparable rectangular pulse pacer.

A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow

Cellular binding of annexin V and membrane permeability to 7‐aminoactinomycin D (7AAD) are important tools for studying apoptosis and cell death by flow cytometry. Combining viability markers with cell surface marker expression is routinely used to study various cell lineages. Current classification methods using strict thresholds, or “gates,” on the fluorescent intensity of these markers are subjective in nature and may not fully describe the phenotypes of interest. We have developed objective criteria for phenotypic boundary recognition through the application of statistical pattern recognition. This task was achieved using artificial neural networks (ANNs) that were trained to recognize subsets of cells with known phenotypes, and then used to determine decision boundaries based on statistical measures of similarity. This approach was then used to test the hypothesis that erythropoietin (EPO) inhibits apoptosis and cell death in erythroid precursor cells in murine bone marrow.

New criteria for selecting differentially expressed genes.

One of the major concerns in detecting changes in higher moments is these changes may be due to outliers or process errors that are not biologically significant. For example, a larger variance observed in the expression levels may simply due to the larger variation in the data collecting process. Several outliers, which exhibit some extreme expression levels than the rest of the samples, may also increase the variance or skewness of the expression levels significantly. So it is very important to reduce the effect of outliers and process errors by proper experimental designs [27], such as technical replicates and biological replicates, before high sensitivity criterion, such as ADS, can be applied. We have presented and demonstrated the operation of two new criteria, ADS and the MDS, for identifying differentially expressed genes. These two criteria were compared with several commonly used criteria, namely WTS, WRS, FCS, and ICE. Experiments with simulated data show ADS to be more powerful than the WTS. When high-sensitivity screening is required, ADS appears to be preferable to WTS. When an FPR similar to WTS is desired, MDS should be used. The popular Wilcoxon rank sum is a more conservative approach that should be employed when the lowest FPR is desired, even at the expense of lower TPRs. ICE is a less desirable criterion because it does not perform well for data generated by the normal model. FCS gave results similar to those of WTS. Evaluation of these algorithms using real biological datasets showed that ADS and MDS flagged several biologically significant genes that were missed by WTS, besides selecting most of the genes that are also selected by WTS.

Classification of SELDI-ToF mass spectra of ovarian cancer serum samples using a proteomic pattern recognizer

High-throughput mass spectrometry technologies, such as surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-ToF-MS), generate large sets of complex data. The high dimensionality of these datasets poses analytical and computational challenges to the task of spectrum classification. In this paper, we describe a fast pattern recognition system for SELDI-ToF mass spectra, which hones in on spectrum subsets with high discriminatory power. The system incorporates a new filter for removal of common characteristics and noise. Our method is demonstrated on a set of 215 SELDI-ToF mass spectra of serum samples from ovarian cancer patients. We show that our system can extract the discriminatory subsets, and that the use of the new filter improves classification accuracy and computational speed.

Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow.

Analysis of multicolor flow cytometric data is traditionally based on the judgment of an expert, generally time consuming, sometimes incomplete and often subjective in nature. In this article, we investigate another statistical method using a Sequential Univariate Gating (SUG) algorithm to identify regions of interest between two groups of multivariate flow cytometric data. The metric used to differentiate between the groups of univariate distributions in SUG is the Kolmogorov‐Smirnov distance (D) statistic. The performance of the algorithm is evaluated by applying it to a known three‐color data set looking at activation of CD4+ and CD8+ lymphocytes with anti‐CD3 antibody treatment and comparing the results to the expert analysis. The algorithm is then applied to a four‐color data set used to study the effects of recombinant human erythropoietin (rHuEPO) on several murine bone marrow populations. SUG was used to identify regions of interest in the data and results compared to expert analysis and the current state‐of‐the‐art statistical method, Frequency Difference Gating (FDG). Cluster analysis was then performed to identify subpopulations responding differently to rHuEPO. Expert analysis, SUG and FDG identified regions in the data that showed activation of CD4+ and CD8+ lymphocytes with anti‐CD3 treatment. In the rHuEPO treated data sets, the expert and SUG identified a dose responsive expansion of only the erythroid precursor population. In contrast, FDG resulted in identification of regions of interest both in the erythroid precursors as well as in other bone marrow populations. Clustering within the regions of interest defined by SUG resulted in identification of four subpopulations of erythroid precursors that are morphologically distinct and show a differential response to rHuEPO treatment. Greatest expansion is seen in the basophilic and poly/orthochromic erythroblast populations with treatment. Identification of populations of interest can be performed using SUG in less subjective, time efficient, biologically interpretable manner that corroborates with the expert analysis. The results suggest that basophilic erythroblasts cells or their immediate precursors are an important target for the effects of rHuEPO in murine bone marrow. The MATLAB implementation of the method described in the article, both experimental data and other supplemental materials are freely available at http://web.mac.com/acidrap18.

Wavelet decomposition method on EEG analysis of G-LOC phenomena

Acceleration (+Gz) induced loss of consciousness (G-LOC) during high +Gz flight maneuvers continues to be a hazard for pilots of high performance aircraft. In the centrifuge studies, G-LOC detection of pilots flying under high +Gz forces is usually made by an observer outside of the gondola and therefore depends upon the reaction time and the alertness of the individual who does the monitoring. The authors propose to use the discrete wavelet transform together with the application of 1/f power distribution theory, to analyze the EEG signals of the pilot during high +Gz simulations. Analyzed by these algorithms, the EEG signal of pilot during G-LOC undergoes significant changes compared to the normal condition, and the authors further propose to classify the conditions of pilot under high +Gz into a set of states. This type of monitoring system may give the observer a cleat indication on the condition of the pilot without human error and in minimum reaction time.

Synchronized External Pulsation for Improved Tolerance to Acceleration Stress: Model Studies and Preliminary Experiments

Synchronized external pulsation is proposed as a method to improve tolerance to acceleration stress. This technique uses a modified anti-G suit which is pressurized and depressurized synchronously with the heart cycle. The feasibility of the procedure has been studied using a computer model of the cardiovascular system which includes the effects of Gz stress, and contains simulations of baroreceptor control of heart rate and venous tone. Model predictions indicate that for unprotected subjects, carotid pressure at eye level (ophthalmic artery pressure) decreases to 20 mmHg (beginning of central light loss) at approximately +3.6 Gz. Applying standard anti-G suit pressure to the model increases this level to 5.3 Gz. When synchronized external pulsation of 2 psi is superimposed on the standard anti-G suit pressure, the tolerance to acceleration stress is further augmented by at least 0.9 G above the protection afforded by the standard anti-G suit alone. A set of preliminary experiments on human subjects to test the feasibility of using the technique in the high-G environment has also been carried out. The results under various protection modes compare favorably to the model predictions. Our results suggest that the computer model presented here is a useful tool for studying cardiovascular responses under +GZ stress. It also indicates that using synchronized external pulsation pressure superimposed on the standard anti-G suit pressure may offer extra protection to acceleration stress.

Analysis of cytokine release assay data using machine learning approaches

The possible onset of Cytokine Release Syndrome (CRS) is an important consideration in the development of monoclonal antibody (mAb) therapeutics. In this study, several machine learning approaches are used to analyze CRS data. The analyzed data come from a human blood in vitro assay which was used to assess the potential of mAb-based therapeutics to produce cytokine release similar to that induced by Anti-CD28 superagonistic (Anti-CD28 SA) mAbs. The data contain 7 mAbs and two negative controls, a total of 423 samples coming from 44 donors. Three (3) machine learning approaches were applied in combination to observations obtained from that assay, namely (i) Hierarchical Cluster Analysis (HCA); (ii) Principal Component Analysis (PCA) followed by K-means clustering; and (iii) Decision Tree Classification (DTC). All three approaches were able to identify the treatment that caused the most severe cytokine response. HCA was able to provide information about the expected number of clusters in the data. PCA coupled with K-means clustering allowed classification of treatments sample by sample, and visualizing clusters of treatments. DTC models showed the relative importance of various cytokines such as IFN-γ, TNF-α and IL-10 to CRS. The use of these approaches in tandem provides better selection of parameters for one method based on outcomes from another, and an overall improved analysis of the data through complementary approaches. Moreover, the DTC analysis showed in addition that IL-17 may be correlated with CRS reactions, although this correlation has not yet been corroborated in the literature.

Two compartment fusion system designed for physiological state monitoring

A two-compartment fusion system designed to reduce high rates of false alarm (FAR) in single channel monitoring systems was tested with physiological data from pilots exposed to high +Gz forces on a human centrifuge. The first compartment expands input signals into the time-frequency domain, where transient changes are captured by wavelet coefficients in frequency ranges of interest. The second compartment optimally combines local decisions of various statistics using a unifying operation rule regardless of individual subject physiology and channel features. Three channels were used to measure respiration, blood pressure, and electroencephalogram under various high performance aircraft maneuver profiles: rapid onset run (ROR) to a fixed plateau, gradual onset run (GOR) at 0.1 Gz per second onset, and simulated aerial combat (SACM) profiles. Pilots sometimes perform anti-G straining maneuvers (AGSM) against the blood pressure drop at head level for greater tolerance. Signals were simultaneously processed to decide the presence of such AGSM. Significant reductions of FAR when detecting AGSM by signal fusion were achieved in our experiment (10/spl sim/38% during ROR/GOR, 25/spl sim/35% during SACM, and 21/spl sim/36% overall), when compared to single channel monitoring. This implies that our approach is very promising and system performance can be enhanced even with poor quality signals.

Common characteristics and noise filtering and its application in a proteomic pattern recognition system for cancer detection

High-throughput mass spectrometry technologies, such as surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-ToF-MS), generate large sets of complex data. The high dimensionality of these datasets poses analytical and computational challenges to the task of spectrum classification. In this paper, we describe a common characteristics and noise filter, which hones in on spectrum subsets with high discriminatory power. The filter is incorporated in a proteomic pattern recognition system. Our method is demonstrated on a set of 322 SELDI-ToF mass spectra of serum samples from prostate cancer patients and a control group. We show that our system can extract the discriminatory subsets from these spectra, and improve classification accuracy and computational speed compared to existing techniques.