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The escalating global demand for protein and the imperative to meet sustainable development goals have driven the emergence of biotransformation platforms, with methanotrophs showing significant potential in this field. In this paper, the metabolism, nutritional requirements, cultivation strategies, and bioreactors of methanotrophs are reviewed. Integrating upstream and downstream technologies is also advocated to advance the development of methanotroph biotransformation platforms toward a circular economy model. The advancements in utilizing biogas as a viable carbon source and wastewater as a nitrogen source are discussed, emphasizing the need for detailed quality control and safety assessments to ensure the suitability of single-cell protein as animal feed. In general, by integrating advanced nutrient recovery technologies to define new process routes, methanotroph biotransformation platforms can bring better environmental benefits by reducing carbon emissions and saving resources. Shifting to renewable energy is crucial for achieving environmental sustainability. By using renewable energy to power microbial fermentation, biomass dehydration, and waste recycling, the platform can offset high energy consumption and attain significant market competitiveness with traditional protein sources.

We consider a regularization problem whose objective function consists of a convex fidelity term and a regularization term determined by the $\ell_1$ norm composed with a linear transform. Empirical results show that the regularization with the $\ell_1$ norm can promote sparsity of a regularized solution. It is the goal of this paper to understand theoretically the effect of the regularization parameter on the sparsity of the regularized solutions. We establish a characterization of the sparsity under the transform matrix of the solution. When the fidelity term has a special structure and the transform matrix coincides with a identity matrix, the resulting characterization can be taken as a regularization parameter choice strategy with which the regularization problem has a solution having a sparsity of a certain level. We study choices of the regularization parameter so that the regularization term alleviates the ill-posedness and promote sparsity of the resulting regularized solution. Numerical experiments demonstrate that choices of the regularization parameters can balance the sparsity of the solutions of the regularization problem and its approximation to the minimizer of the fidelity function.