Project description:Over the past decade, several large registries of patients with idiopathic pulmonary fibrosis (IPF) have been established. These registries are collecting a wealth of longitudinal data on thousands of patients with this rare disease. The data collected in these registries will be complementary to data collected in clinical trials because the patient populations studied in registries have a broader spectrum of disease severity and comorbidities and can be followed for a longer period of time. Maintaining the quality and completeness of registry databases presents administrative and resourcing challenges, but it is important to ensuring the robustness of the analyses. Data from patient registries have already helped improve understanding of the clinical characteristics of patients with IPF, the impact that the disease has on their quality of life and survival, and current practices in diagnosis and management. In the future, analyses of biospecimens linked to detailed patient profiles will provide the opportunity to identify biomarkers linked to disease progression, facilitating the development of precision medicine approaches for prognosis and therapy in patients with IPF.
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.