Pinosylvin Extract Retinari™ Sustains Electrophysiological Function, Prevents Thinning of Retina, and Enhances Cellular Response to Oxidative Stress in NFE2L2 Knockout Mice.

Chronic oxidative stress eventually leads to protein aggregation in combination with impaired autophagy, which has been observed in age-related macular degeneration. We have previously shown an effective age-related macular degeneration disease model in mice with nuclear factor-erythroid 2-related factor-2 (NFE2L2) knockout. We have also shown pinosylvin, a polyphenol abundant in bark waste, to increase human retinal pigment epithelium cell viability in vitro. In this work, the effects of commercial natural pinosylvin extract, Retinari™, were studied on the electroretinogram, optical coherence tomogram, autophagic activity, antioxidant capacity, and inflammation markers. Wild-type and NFE2L2 knockout mice were raised until the age of 14.8 ± 3.8 months. They were fed with either regular or Retinari™ chow (141 ± 17.0 mg/kg/day of pinosylvin) for 10 weeks before the assays. Retinari™ treatment preserved significant retinal function with significantly preserved a- and b-wave amplitudes in the electroretinogram responses. Additionally, the treatment prevented thinning of the retina in the NFE2L2 knockout mice. The NFE2L2 knockout mice showed reduced ubiquitin-tagged protein accumulation in addition to local upregulation of complement factor H and antioxidant enzymes superoxide dismutase 1 and catalase. Therefore, the treatment in the NFE2L2 KO disease model led to reduced chronic oxidative stress and sustained retinal function and morphology. Our results demonstrate that pinosylvin supplementation could potentially lower the risk of age-related macular degeneration onset and slow down its progression.

Sequential Monte Carlo for Bayesian matching of objects with occlusions.

We consider the problem of locating instances of a known object in a novel scene by matching the fiducial features of the object. The appearance of the features and the shape of the object are modeled separately and combined in a Bayesian framework. In this paper, we present a novel matching scheme based on Sequential Monte Carlo, in which the features are matched sequentially, utilizing the information about the locations of previously matched features to constrain the task. The particle representation of hypotheses about the object position allow matching in multimodal and cluttered environments, where batch algorithms may have convergence difficulties. The proposed method requires no initialization or predetermined matching order, as the sequence can be started from any feature. We also utilize a Bayesian model to deal with features that are not detected due to occlusions or abnormal appearance. In our experiments, the proposed matching system shows promising results, with performance equal to batch approaches when the target distribution is unimodal, while surpassing traditional methods under multimodal conditions. Using the occlusion model, the object can be localized from only a few visible features, with the nonvisible parts predicted from the conditional prior model.