Biophysical Reviews and Letters

Production of energy is a foundation of life. The metabolic rate of organisms (amount of energy produced per unit time) generally increases slower than organisms’ mass, which has important implications for life organization. This phenomenon, when considered across different taxa, is called interspecific allometric scaling. Its origin has puzzled scientists for many decades, and still is considered unknown. In this paper, we posit that natural selection, as determined by evolutionary pressures, leads to distribution of resources, and accordingly energy, within a food chain, which is optimal from the perspective of stability of the food chain, when each species has sufficient amount of resources for continuous reproduction, but not too much to jeopardize existence of other species. Metabolic allometric scaling (MAS) is then a quantitative representation of this optimal distribution. Taking locomotion and the primary mechanism for distribution of energy, we developed a biomechanical model to find energy expenditures, considering limb length, skeleton mass and speed. Using the interspecific allometric exponents for these three measures and substituting them into the locomotion-derived model for energy expenditure, we calculated allometric exponents for mammals, reptiles, fish, and birds, and compared these values with allometric exponents derived from experimental observations. The calculated allometric exponents were nearly identical to experimentally observed exponents for mammals, and very close for fish, reptiles and the basal metabolic rate (BMR) of birds. The main result of the study is that the MAS is a function of a mechanism of optimal energy distribution between the species of a food chain. This optimized sharing of common resources provides stability of a food chain for a given habitat and is guided by evolutionary pressures and natural selection.

This paper presents computational methods for separating oncogenic mutations from neutral mutations of the p53 protein using mathematical processing of the following calculated values for each sample: $W_d,T\mathrm{\Delta }S,\mathrm{\Delta }H,lg[K_d]$, machine learning methods were used to determine the main physical quantities for predicting experimental values [Urea]50%, Kd, $\mathrm{\Delta }\mathrm{\Delta }G$, which determine the stability of mutant monomers of the p53 protein in various denatured alcohol concentrations and when bound to the p53-Bcl-Xl biocomplex, as well as RNA expression; machine learning methods are presented to determine key calculated quantities and dependencies, which will thus help predict experimental results. The description of the software package that implements the presented algorithm is given on the website https://binomlabs.com/.

The nonlinear response of the dynamics of the stochastic neuronal activity based on the leaky integrate-and-fire model was reformulated using the Loewner theory. We observed that the signal-to-noise ratio (SNR), which is an indicator of stochastic resonance (SR), and fluctuation-dissipation relation (FDR) for the neuronal dynamics were newly obtained in the theoretical framework of the Loewner evolution. Particularly, we focus on the role of the Loewner entropy $S_{\mathrm{\text{Loew}}}$, defined as the entropy of the Loewner driving force, while showing the efficacy of Loewner time conversion to theorize the nonlinear characteristic of the neuronal dynamics. The present results indicate a possible approach to the novel formulation of biophysical models.

This study focuses on producing Copper sulphide (CuS) nanoparticles via the sol–gel technique. Characterization methods such as X-ray diffraction (XRD), FTIR, UV spectroscopy, and PL spectroscopy were used to analyze the samples. Various Nanocomposites were created through sol–gel and electro-polishing techniques, yielding core-shell nanostructures via oxidation or sulfidation treatment. These CuS nanocomposites displayed distinct optical absorption peaks reflecting their structural characteristics. Notably, sol–gel methodologies successfully generated copper sulfide (CuS) nanoparticles sized between 30nm and 40nm. This result is verified by XRD data and field emission scanning electron microscope (FESEM) images in this study.