Intravitreal AAV2.COMP-Ang1 prevents neurovascular degeneration in a murine model of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population in the United States. The vision-threatening processes of neuroglial and vascular dysfunction in DR occur in concert, driven by hyperglycemia and propelled by a pathway of inflammation, ischemia, vasodegeneration, and breakdown of the blood retinal barrier. Currently, no therapies exist for normalizing the vasculature in DR. Here we show that a single intravitreal dose of adeno-associated virus serotype 2 encoding a more stable, soluble, and potent form of angiopoietin 1 (AAV2.COMP-Ang1) can ameliorate the structural and functional hallmarks of DR in Ins2Akita mice, with sustained effects observed through six months. In early DR, AAV2.COMP-Ang1 restored leukocyte-endothelial interaction, retinal oxygenation, vascular density, vascular marker expression, vessel permeability, retinal thickness, inner retinal cellularity, and retinal neurophysiological response to levels comparable to non-diabetic controls. In late DR, AAV2.COMP-Ang1 enhanced the therapeutic benefit of intravitreally-delivered endothelial colony-forming cells by promoting their integration into the vasculature and thereby stemming further visual decline. AAV2.COMP-Ang1 single-dose gene therapy can prevent neurovascular pathology, support vascular regeneration, and stabilize vision in DR.

Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

Validation of a 3D damage model for predicting the response of composite structures under crushing loads

A 3D intralaminar continuum damage mechanics based material model, combining damage mode interaction and material nonlinearity, was developed to predict the damage response of composite structures undergoing crush loading. This model captures the structural response without the need for calibration of experimentally determined material parameters. When used in the design of energy absorbing composite structures, it can reduce the dependence on physical testing. This paper validates this model against experimental data obtained from the literature and in-house testing. Results show that the model can predict the force response of the crushed composite structures with good accuracy. The simulated energy absorption in each test case was within 12% of the experimental value. Post-crush deformation and the damage morphologies, such as ply splitting, splaying and breakage, were also accurately reproduced. This study establishes the capability of this damage model for predicting the responses of composite structures under crushing loads.