Influence of Tuning Element Relief Patches on Pain as Analyzed by the Resonant Recognition Model

Integrating Resonant Recognition Model and Stockwell Transform for Localization of Hotspots in Tubulin

Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper presents a signal processing technique combining resonant recognition model (RRM) and Stockwell Transform (ST) for the identification of hotspots corresponding to a particular functionality. The characteristic frequency (CF) representing a specific biological function is determined using the RRM. Then the spectrum of the protein sequence is computed using ST. The CF is filtered from the ST spectrum using a time-frequency mask. The energy peaks in the filtered sequence represent the hotspots. The hotspots predicted by the proposed method are compared with the experimentally detected binding residues of Tubulin stabilizing drug Taxol and destabilizing drug Colchicine present in the Tubulin protein. Out of the 53 experimentally identified hotspots, 60% are predicted by the proposed method whereas around 20% are predicted by existing machine learning based methods. Additionally, the proposed method predicts some new hot spots, which may be investigated.

Explanation of Osteoblastic Differentiation of Stem Cells by Photo Biomodulation Using the Resonant Recognition Model

Differentiation of stem cells into different tissues is a promising approach to treat a large number of diseases, as well as for tissue transplantation and repair. It has been shown that parathyroid hormone, similarly to stromal self-derived factor, and the radiation of specific electromagnetic frequencies of blue and green light, can encourage stem cell differentiation into osteoblasts. Here, we analysed parathyroid hormone, its receptor and stromal self-derived factor using the Resonant Recognition Model, which proposes that protein function is based on specific frequencies of electromagnetic radiation within ultra-violet, visible, infra-red and far infra-red light. The purpose of this research is to predict the characteristic frequencies related to parathyroid hormone activities, particularly differentiation of stem cells into osteoblasts. We have found that the most effective wavelength for stem cell differentiation would be 502 nm, which is between 420 nm and 540 nm, already experimentally proven to be effective in stimulating osteoblast differentiation. Thus, we propose that wavelength radiation of 502 nm will be even more efficient for differentiation of stem cells into osteoblasts.