Hyperactivity of the renin-angiotensin system (RAS) resulting in elevated Angiotensin II (Ang II) contributes to all stages of inflammatory responses including ocular inflammation. The discovery of angiotensin-converting enzyme 2 (ACE2) has established a protective axis of RAS involving ACE2/Ang-(1-7)/Mas that counteracts the proinflammatory and hypertrophic effects of the deleterious ACE/AngII/AT1R axis. Here we investigated the hypothesis that enhancing the systemic and local activity of the protective axis of the RAS by oral delivery of ACE2 and Ang-(1-7) bioencapsulated in plant cells would confer protection against ocular inflammation. Both ACE2 and Ang-(1-7), fused with the non-toxic cholera toxin subunit B (CTB) were expressed in plant chloroplasts. Increased levels of ACE2 and Ang-(1-7) were observed in circulation and retina after oral administration of CTB-ACE2 and Ang-(1-7) expressing plant cells. Oral feeding of mice with bioencapsulated ACE2/Ang-(1-7) significantly reduced endotoxin-induced uveitis (EIU) in mice. Treatment with bioencapsulated ACE2/Ang-(1-7) also dramatically decreased cellular infiltration, retinal vasculitis, damage and folding in experimental autoimmune uveoretinitis (EAU). Thus, enhancing the protective axis of RAS by oral delivery of ACE2/Ang-(1-7) bioencapsulated in plant cells provide an innovative, highly efficient and cost-effective therapeutic strategy for ocular inflammatory diseases.

This paper addresses the need for surveillance of fugitive methane emissions over broad geographical regions. Most existing techniques suffer from being either extensive (but qualitative) or quantitative (but intensive with poor scalability). A total of two novel advancements are made here. First, a recursive Bayesian method is presented for probabilistically characterizing fugitive point-sources from mobile sensor data. This approach is made possible by a new cross-plume integrated dispersion formulation that overcomes much of the need for time-averaging concentration data. The method is tested here against a limited data set of controlled methane release and shown to perform well. We then present an information-theoretic approach to plan the paths of the sensor-equipped vehicle, where the path is chosen so as to maximize expected reduction in integrated target source rate uncertainty in the region, subject to given starting and ending positions and prevailing meteorological conditions. The information-driven sensor path planning algorithm is tested and shown to provide robust results across a wide range of conditions. An overall system concept is presented for optionally piggybacking of these techniques onto normal industry maintenance operations using sensor-equipped work trucks.

In a recent paper, we developed a novel quantized kernel least mean square algorithm, in which the input space is quantized (partitioned into smaller regions) and the network size is upper bounded by the quantization codebook size (number of the regions). In this paper, we propose the quantized kernel least squares regression, and derive the optimal solution. By incorporating a simple online vector quantization method, we derive a recursive algorithm to update the solution, namely the quantized kernel recursive least squares algorithm. The good performance of the new algorithm is demonstrated by Monte Carlo simulations.

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