Translational Retinal Research and Therapies.

The following review summarizes the state of the art in representative aspects of gene therapy/translational medicine and evolves from a symposium held at the School of Veterinary Medicine, University of Pennsylvania on November 16, 2017 honoring Dr. Gustavo Aguirre, recipient of ARVO's 2017 Proctor Medal. Focusing on the retina, speakers highlighted current work on moving therapies for inherited retinal degenerative diseases from the laboratory bench to the clinic

The Effect of Cone Opsin Mutations on Retinal Structure and the Integrity of the Photoreceptor Mosaic

Purpose. To evaluate retinal structure and photoreceptor mosaic integrity in subjects with OPN1LW and OPN1MW mutations. Methods. Eleven subjects were recruited, eight of whom have been previously described. Cone and rod density was measured using images of the photoreceptor mosaic obtained from an adaptive optics scanning light ophthalmoscope (AOSLO). Total retinal thickness, inner retinal thickness, and outer nuclear layer plus Henle fiber layer (ONL+HFL) thickness were measured using cross-sectional spectral-domain optical coherence tomography (SD-OCT) images. Molecular genetic analyses were performed to characterize the OPN1LW/OPN1MW gene array. Results. While disruptions in retinal lamination and cone mosaic structure were observed in all subjects, genotype-specific differences were also observed. For example, subjects with “L/M interchange” mutations resulting from intermixing of ancestral OPN1LW and OPN1MW genes had significant residual cone structure in the parafovea (∼25% of normal), despite widespread retinal disruption that included a large foveal lesion and thinning of the parafoveal inner retina. These subjects also reported a later-onset, progressive loss of visual function. In contrast, subjects with the C203R missense mutation presented with congenital blue cone monochromacy, with retinal lamination defects being restricted to the ONL+HFL and the degree of residual cone structure (8% of normal) being consistent with that expected for the S-cone submosaic. Conclusions. The photoreceptor phenotype associated with OPN1LW and OPN1MW mutations is highly variable. These findings have implications for the potential restoration of visual function in subjects with opsin mutations. Our study highlights the importance of high-resolution phenotyping to characterize cellular structure in inherited retinal disease; such information will be critical for selecting patients most likely to respond to therapeutic intervention and for establishing a baseline for evaluating treatment efficacy

The Conditional Lucas & Kanade Algorithm

The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense image and object alignment. The approach is efficient as it attempts to model the connection between appearance and geometric displacement through a linear relationship that assumes independence across pixel coordinates. A drawback of the approach, however, is its generative nature. Specifically, its performance is tightly coupled with how well the linear model can synthesize appearance from geometric displacement, even though the alignment task itself is associated with the inverse problem. In this paper, we present a new approach, referred to as the Conditional LK algorithm, which: (i) directly learns linear models that predict geometric displacement as a function of appearance, and (ii) employs a novel strategy for ensuring that the generative pixel independence assumption can still be taken advantage of. We demonstrate that our approach exhibits superior performance to classical generative forms of the LK algorithm. Furthermore, we demonstrate its comparable performance to state-of-the-art methods such as the Supervised Descent Method with substantially less training examples, as well as the unique ability to "swap" geometric warp functions without having to retrain from scratch. Finally, from a theoretical perspective, our approach hints at possible redundancies that exist in current state-of-the-art methods for alignment that could be leveraged in vision systems of the future.Comment: 17 pages, 11 figure

Group Analysis of Self-organizing Maps based on Functional MRI using Restricted Frechet Means

Studies of functional MRI data are increasingly concerned with the estimation of differences in spatio-temporal networks across groups of subjects or experimental conditions. Unsupervised clustering and independent component analysis (ICA) have been used to identify such spatio-temporal networks. While these approaches have been useful for estimating these networks at the subject-level, comparisons over groups or experimental conditions require further methodological development. In this paper, we tackle this problem by showing how self-organizing maps (SOMs) can be compared within a Frechean inferential framework. Here, we summarize the mean SOM in each group as a Frechet mean with respect to a metric on the space of SOMs. We consider the use of different metrics, and introduce two extensions of the classical sum of minimum distance (SMD) between two SOMs, which take into account the spatio-temporal pattern of the fMRI data. The validity of these methods is illustrated on synthetic data. Through these simulations, we show that the three metrics of interest behave as expected, in the sense that the ones capturing temporal, spatial and spatio-temporal aspects of the SOMs are more likely to reach significance under simulated scenarios characterized by temporal, spatial and spatio-temporal differences, respectively. In addition, a re-analysis of a classical experiment on visually-triggered emotions demonstrates the usefulness of this methodology. In this study, the multivariate functional patterns typical of the subjects exposed to pleasant and unpleasant stimuli are found to be more similar than the ones of the subjects exposed to emotionally neutral stimuli. Taken together, these results indicate that our proposed methods can cast new light on existing data by adopting a global analytical perspective on functional MRI paradigms.Comment: 23 pages, 5 figures, 4 tables. Submitted to Neuroimag

'Intentional genetic manipulation' as a conservation threat

Wildlife ranching including the hunting, collection, sales and husbandry of wild animals in captivity, is practised worldwide and is advocated as an approach towards the conservation of wild species. While many authors have explored the biological impacts of intensive wild population management, primarily with respect to disease transmission (especially in ungulates and fish), the evolutionary and demographic effects of wildlife ranching have been examined less intensively. We discuss this issue through the case of intensive wildlife management in southern Africa. The genetic consequences of this global practice, with an emphasis on Africa, were addressed by a motion passed at the 2016 IUCN World Congress- ‘Management and regulation of intensive breeding and genetic manipulation of large mammals for commercial purposes’. Here, we highlight concerns regarding intensive breeding programs used to discover, enhance and propagate unusual physical traits, hereafter referred to as ‘Intentional Genetic Manipulation’. We highlight how ‘Intentional Genetic Manipulation’ potentially threatens the viability of native species and ecosystems, via genetic erosion, inbreeding, hybridisation and unregulated translocation. Finally, we discuss the need for better policies in southern Africa and globally, regarding ‘Intentional Genetic Manipulation’, and the identification of key knowledge gaps

Compressive Sequential Learning for Action Similarity Labeling

Human action recognition in videos has been extensively studied in recent years due to its wide range of applications. Instead of classifying video sequences into a number of action categories, in this paper, we focus on a particular problem of action similarity labeling (ASLAN), which aims at verifying whether a pair of videos contain the same type of action or not. To address this challenge, a novel approach called compressive sequential learning (CSL) is proposed by leveraging the compressive sensing theory and sequential learning. We first project data points to a low-dimensional space by effectively exploring an important property in compressive sensing: the restricted isometry property. In particular, a very sparse measurement matrix is adopted to reduce the dimensionality efficiently. We then learn an ensemble classifier for measuring similarities between pairwise videos by iteratively minimizing its empirical risk with the AdaBoost strategy on the training set. Unlike conventional AdaBoost, the weak learner for each iteration is not explicitly defined and its parameters are learned through greedy optimization. Furthermore, an alternative of CSL named compressive sequential encoding is developed as an encoding technique and followed by a linear classifier to address the similarity-labeling problem. Our method has been systematically evaluated on four action data sets: ASLAN, KTH, HMDB51, and Hollywood2, and the results show the effectiveness and superiority of our method for ASLAN