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Glaucoma is the second leading cause of blindness worldwide. Often the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus, digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In this work, we present a new framework for detecting glaucomatous changes in the ONH of an eye using the method of proper orthogonal decomposition (POD). A baseline topograph subspace was constructed for each eye to describe the structure of the ONH of the eye at a reference/ baseline condition using POD. Any glaucomatous changes in the ONH of the eye present during a follow-up exam were estimated by comparing the follow-up ONH topography with its baseline topograph subspace representation. Image correspondence measures of L 1 and L 2 norms, correlation, and image Euclidean distance (IMED) were used to quantify the ONH changes. An ONH topographic library built from the Louisiana State University Experimental Glaucoma study was used to evaluate the performance of the proposed method. The area under the receiver operating characteristic curves (AUC) were used to compare the diagnostic performance of the POD induced parameters with the parameters of Topographic Change Analysis (TCA) method. The IMED and L 2 norm parameters in the POD framework provided the highest AUC of 0.94 at 10° field of imaging and 0.91 at 15° field of imaging compared to the TCA parameters with an AUC of 0.86 and 0.88 respectively. The proposed POD framework captures the instrument measurement variability and inherent structure variability and shows promise for improving our ability to detect glaucomatous change over time in glaucoma management.

A limited number of nitric oxide (NO)-generating drugs are available for clinical use for acute and chronic conditions. Most of these agents are organic nitrates, which do not directly release NO; tolerance to the drugs develops, in part, as a consequence of their conversion to NO. We synthesized nitrosyl-cobinamide (NO-Cbi) from cobinamide, a structural analog of cobalamin (vitamin B12). NO-Cbi is a direct NO-releasing agent that we found was stable in water, but under physiologic conditions, it released NO with a half-life of 30 mins to 1 h. We show in five different biological systems that NO-Cbi is an effective NO-releasing drug. First, in cultured rat vascular smooth muscle cells, NO-Cbi induced phosphorylation of vasodilator-stimulated phosphoprotein, a downstream target of cGMP and cGMP-dependent protein kinase. Second, in isolated Drosophila melanogaster Malpighian tubules, NO-Cbi-stimulated fluid secretion was similar to that stimulated by Deta-NONOate and a cGMP analog. Third, in isolated mouse hearts, NO-Cbi increased coronary flow much more potently than nitroglycerin. Fourth, in contracted mouse aortic rings, NO-Cbi induced relaxation, albeit to a lesser extent than sodium nitroprusside. Fifth, in intact mice, a single NO-Cbi injection rapidly reduced blood pressure, and blood pressure returned to normal after 45 mins; repeated NO-Cbi injections induced the expected fall in blood pressure. These studies indicate that NO-Cbi is a useful NO donor that can be used experimentally in the laboratory; moreover, it could be developed into a vasodilating drug for treating hypertension and potentially other diseases such as angina and congestive heart failure.

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This paper presents the results of a study on shortterm electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting.

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Purpose To evaluate the suitability of including both Heidelberg Retina Tomograph-I (HRT-I) and HRT-II examinations in the same longitudinal series for HRT topographic change analysis (TCA) and to evaluate parabolic error correction (PEC) to improve the agreement between HRT-I and HRT-II examinations. Methods A total of 66 eyes from the University of California San Diego Diagnostic Innovations in Glaucoma Study with baseline HRT-I and HRT-II examinations obtained on the same day and Z3 HRT-II follow-up examinations were included. Two TCA analyses, HRT-I examination at baseline (HRT-I-mixed series) and HRT-II examination at baseline (HRT-II-only series) were compared. Agreement between the HRT-Imixed and HRT-II-only series were estimated using Bland-Altman plots. Agreement was assessed: (1) using the current HRT software settings (PEC applied only to HRT-II-only series), and (2) modified HRT settings (PEC also applied to HRT-I-mixed series). Results With current HRT software settings, the HRT-I-mixed series significantly overestimated change locations (ie, red pixels) compared with the HRT-II-only series as indicated by statistically significant proportional biases in the Bland-Altman analysis. By applying PEC to HRT-I-mixed series there were no statistically significant biases in the TCA parameter estimates compared with the HRT-II-only series. Conclusion In some eyes, HRT-I and HRT-II baseline examinations are not interchangeable in TCA analysis without parabolic error correction. HRT-I-mixed series detected more changes characteristic of glaucoma when there were only minimal changes in the HRT-II-only series. Our results suggest that in the majority of cases, with PEC, HRT-I examinations may be included in a longitudinal series containing HRT-II examinations.

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