Project description:RVX 208 (RVX-208; RVX000222) is a first-in-class novel small molecule in development by Resverlogix Corporation for acute coronary syndromes, atherosclerosis and Alzheimer disease. It increases the levels of apolipoprotein A1 and high-density lipoprotein cholesterol, thereby potentially reducing the risk for cardiovascular disease. This review discusses the key development milestones and therapeutic trials of this drug. This summary has been extracted from Wolters Kluwer's R&D Insight drug pipeline database. R&D Insight tracks and evaluates drug development worldwide through the entire development process, from discovery, through pre-clinical and clinical studies to market launch. This is an open access article published under the terms of the Creative Commons License "Attribution-NonCommercial-NoDerivative 3.0" (http://creativecommons.org/licenses/by-nc-nd/3.0/) which permits non-commercial use, distribution, and reproduction, provided the original work is properly cited and not altered.
Project description:Peripheral vision is fundamentally limited by the spacing between objects. When asked to report a target's identity, observers make erroneous reports that sometimes match the identity of a nearby distractor and sometimes match a combination of target and distractor features. The classification of these errors has previously been used to support competing 'substitution' [1] or 'averaging' [2] models of the phenomenon known as 'visual crowding'. We recently proposed a single model in which both classes of error occur because observers make their reports by sampling from a biologically-plausible population of weighted responses within a region of space around the target [3]. It is critical to note that there is no probabilistic substitution or averaging process in our model; instead, we argue that neither substitution nor averaging occur, but that these are misclassifications of the distribution of reports that emerge when a population response distribution is sampled. This is a fundamentally different way of thinking about crowding, and on this basis we claim to have provided a mechanism unifying categorically distinct perceptual errors. Our goal was not to model all crowding phenomena, such as the release from crowding when target and flanks differ in color or depth [4]. Pachai et al.[5] have suggested that our model is not unifying because it inaccurately predicts perceptual performance for a particular stimulus. Although we agree that our model does not predict their data, this specific demonstration overlooks the critical aspect of the model: perceptual reports are drawn from a weighted population code. We show that Pachai et al.'s [5] own data actually provide evidence for the population code we have described [3], and we suggest a biologically-plausible analysis of their stimuli that provides a computational basis for their 'grouping' account of crowding.