Purpose: Mutations in the gene that encodes the enzyme acid sphingomyelinase (ASMase) are associated with Niemann-Pick disease, a lysosomal storage disorder. Mice that lack ASMase (ASMase-/-) exhibit age-related retinal degeneration and large increases in accumulation of lipofuscin in the retinal pigment epithelium (RPE). We examined which lipid species accumulate in the retina and the RPE of ASMase-/- mice and whether the retinal degeneration is associated with impaired photoreceptor metabolism and retinyl chromophore processing. Methods: NADPH availability and all-trans retinol formation after rhodopsin bleaching were measured in isolated single rod photoreceptors with fluorescence imaging; sphingolipid levels in retinas and RPEs were measured with LC/MS; relative abundances of different lipid species in different retinal layers were measured with MALDI imaging mass spectrometry. Results: There was no detectable difference in the kinetics of all-trans retinol formation or the NADPH-generating capacity between ASMase-/- and wild-type mice. Sphingomyelin levels were much higher in the retinas and RPEs of ASMase-/- animals compared to wild type, but there were no significant differences for ceramides. There was a large increase in the abundance of bis(monoacylglycero)phosphates (BMPs) in ASMase-/- mice, indicative of lysosomal dysfunction, but no substantial changes were detected for the bis-retinoid A2E. Conclusions: Lysosomal dysfunction and retinal degeneration in ASMase-/- mice are not associated with defects in rod photoreceptor metabolism that affect all-trans retinol formation and availability of NADPH. Lysosomal dysfunction in ASMase-/- mice is not associated with bis-retinoid A2E accumulation.

Autonomous driving technology and platooning driving technology are important directions for the development of intelligent and connected vehicles. Aiming at the motion control problem of autonomous vehicle platoon, this article proposes a multilayer predictive control framework (MPCF) based on heuristic learning agent and improved distributed model. First, the leading autonomous vehicle and following heterogeneous vehicles are modeled, respectively, and the motion control problem of autonomous platoon is described. Then, the multilayer motion control framework is designed, which contains highly automated tracking control optimization for the leading vehicle (LV) and high-precision formation keeping optimization for the following vehicles (FVs). In the upper layer, the heuristic Dyna algorithm-based predictive control (HDY-PC) method is proposed to improve the path tracking performance of the LV. In the lower layer, the improved distributed model-based predictive control (IDM-PC) method is developed to guarantee the motion effectiveness and stability of the vehicle platoon. Besides, the multilayer control framework can handle various communication topologies and dynamic cut-in/cut-out maneuvers. The virtual environment simulation shows that the proposed motion control framework for heterogeneous autonomous vehicle platoon achieves better performance in path tracking and platoon keeping. The adaptability of the framework is also verified using another real-world scene.