The pharmacological regulation of cellular mitophagy

Small molecules are pharmacological tools of considerable value for dissecting complex biological processes and identifying potential therapeutic interventions. Recently, the cellular quality-control process of mitophagy has attracted considerable research interest; however, the limited availability of suitable chemical probes has restricted our understanding of the molecular mechanisms involved. Current approaches to initiate mitophagy include acute dissipation of the mitochondrial membrane potential (ΔΨm) by mitochondrial uncouplers (for example, FCCP/CCCP) and the use of antimycin A and oligomycin to impair respiration. Both approaches impair mitochondrial homeostasis and therefore limit the scope for dissection of subtle, bioenergy-related regulatory phenomena. Recently, novel mitophagy activators acting independently of the respiration collapse have been reported, offering new opportunities to understand the process and potential for therapeutic exploitation. We have summarized the current status of mitophagy modulators and analyzed the available chemical tools, commenting on their advantages, limitations and current applications

Expansion of Indecency Regulation: Presented by the Federalist Society\u27s Telecommunications Practice Group

This is a transcript of the November 10, 2005, panel discussion at the National Lawyer\u27s Convention presented by the Federalist Society\u27s Telecommunications Practice Group. The panelists debate and discuss the Federal Communications Commission\u27s ( FCC ) regulation of indecent content

Convergence acceleration for multiobjective sparse reconstruction via knowledge transfer

© Springer Nature Switzerland AG 2019. Multiobjective sparse reconstruction (MOSR) methods can potentially obtain superior reconstruction performance. However, they suffer from high computational cost, especially in high-dimensional reconstruction. Furthermore, they are generally implemented independently without reusing prior knowledge from past experiences, leading to unnecessary computational consumption due to the re-exploration of similar search spaces. To address these problems, we propose a sparse-constraint knowledge transfer operator to accelerate the convergence of MOSR solvers by reusing the knowledge from past problem-solving experiences. Firstly, we introduce the deep nonlinear feature coding method to extract the feature mapping between the search of the current problem and a previously solved MOSR problem. Through this mapping, we learn a set of knowledge-induced solutions which contain the search experience of the past problem. Thereafter, we develop and apply a sparse-constraint strategy to refine these learned solutions to guarantee their sparse characteristics. Finally, we inject the refined solutions into the iteration of the current problem to facilitate the convergence. To validate the efficiency of the proposed operator, comprehensive studies on extensive simulated signal reconstruction are conducted