Abstract: A technique is described for quantitating biological indicators, such as viral load, using interferometric interactions such as quantum resonance interferometry. A biological sample is applied to an array information structure that has a plurality of elements that emit data indicative of viral load. A digitized output pattern of the arrayed information structure is interferometrically enhanced by generating interference between the output pattern and a reference wave. The interferometrically enhanced output pattern is then analyzed to identify emitted data indicative of viral load which in turn is used to determine viral load.

Abstract: A technique is disclosed that is useful for determining the presence of specific hybridization expression within an output pattern generated from a digitized image of a biological sample applied to an arrayed platform. The output pattern includes signals associated with noise, and signals associated with the biological sample, some of which are degraded or obscured by noise. Signal processing, such as interferometry, or more specifically, resonance interferometry, and even more specifically quantum resonance interferometry or stochastic resonance interferometry, is used to amplify signals associated with the biological sample having an intensity lower than the intensity of signals associated with noise so that they may be clearly distinguished from background noise. The improved detection technique allows rapid, reliable, and inexpensive measurements of arrayed platform output patterns.

Abstract: The current invention discloses a novel spectral transformation technique for characterizing digitized intensity output patterns from microarrays. This method yields improved sensitivity with reduced false positives and false negatives. Current microarray methods are overly sensitive to the detection of a visible distinction between pixels associated with probes and pixels associated with background. In one embodiment, a technique is disclosed that comprises the steps of: extracting pixels associated with an object of interest and transforming such pixels from an intensity representation to a spectral representation. In some embodiments, the extraction is based on a tessellated logarithmic spiral extraction that may yield a pixel core with a sampling of both foreground and background pixels. This core may then be computationally rescaled by 10X-10,000X to enhance spatial resolution.

Abstract: A technique is described for determining the effectiveness of medical therapy and dosage formulations by analyzing dot spectrograms representative of quantized hybridization activity in biological samples, such as DNA, RNA or other protein biomolecular array samples, taken at different sampling times from a patient undergoing the treatment. The technique directly lends itself to disease progression analysis based on markers such as viral load. In accordance with the technique, a viral diffusion curve associated with a therapy of interest is generated and each dot spectrogram is then mapped to the viral diffusion curve using fractal filtering to yield a filtered viral diffusion curve for each sample. A degree of convergence between the filtered viral diffusion curves is determined.

Abstract: A technique is described for determining the effectiveness of medical therapy and dosage formulations by analyzing dot spectrograms representative of quantized hybridization activity in biological samples, such as DNA, RNA, or other protein biomolecular array samples, taken at different times from a patient undergoing the medical therapy. This technique enables disease progression analysis based on surrogate markers such as viral load. In accordance with the technique, a viral diffusion curve associated with a therapy of interest is generated and each dot spectrogram is then mapped to a viral diffusion curve using fractal filtering. Next, degree of convergence towards the peak of VDC, between the sample points on a filtered viral diffusion curve is determined. The technique allows for point-of-care viral load detection biosensors to accurately and reliably predict the likelihood of disease progression.

Abstract: A technique is described for identifying mutations, if any, present in a biological sample, from a pre-selected set of known mutations. The method can be applied to DNA, RNA and peptide nucleic acid (PNA) microarrays. The method analyzes a dot spectrogram representative of quantized hybridization activity of oligonucleotides in the sample to identify the mutations. In accordance with the method, a resonance pattern is generated which is representative of nonlinear resonances between a stimulus pattern associated with the set of known mutations and the dot spectrogram. The resonance pattern is interpreted to a yield a set of confirmed mutations by comparing resonances found therein with predetermined resonances expected for the selected set of mutations.

Abstract: A technique is described for identifying mutations, if any, present in a biological sample, from a pre-selected set of known mutations. The method can be applied to DNA, RNA and peptide nucleic acid (PNA) microarrays. The method analyzes a dot spectrogram representative of quantized hybridization activity of oligonucleotides in the sample to identify the mutations. In accordance with the method, a resonance pattern is generated which is representative of nonlinear resonances between a stimulus pattern associated with the set of known mutations and the dot spectrogram. The resonance pattern is interpreted to a yield a set of confirmed mutations by comparing resonances found therein with predetermined resonances expected for the selected set of mutations.

Abstract: Nonlinear control method is provided for a closed-loop trajectory in a control system of the form x=.function.(x)+u through a sliding surface, s, chosen such that s=e+.lambda.e=, where e is the trajectory, and .lambda. is a positive constant using a control law of the form ##EQU1## which exploits terminal attractors of the form ##EQU2## where .alpha. is a constant greater than zero, .beta..sub.n, .beta..sub.d =(2i+1), where i belongs to the set of positive integers chosen for .beta..sub.n and .beta..sub.d, and .beta..sub.d >.beta..sub.n for convergence in finite time. For a system in which an initial S.sub.i is zero, a control law is used of the form ##EQU3## which yields a control equation ##EQU4## where .delta..sub.n, .delta..sub.d =(2i+1), wherein i belongs to the set of positive integers chosen for .delta..sub.n and .delta..sub.d, and .delta..sub.d >.delta..sub.n for retaining convergence in finite time.