Early microvascular damage in diabetes (e.g. capillary nonperfusion and ischemia) can now be assessed and quantified with optical coherence tomography-angiography (OCT-A). The morphology of vascular tissue is indeed affected by different factors; however, there is a paucity of data examining whether OCT-A metrics are influenced by ocular, systemic and demographic variables in subjects with diabetes. We conducted an observational cross-sectional study and included 434 eyes from 286 patients with diabetes. Foveal avascular zone (FAZ) area, FAZ circularity, total and parafoveal vessel density (VD), fractal dimension (FD), and vessel diameter index (VDI) from the superficial capillary plexus OCT-angiogram were measured by a customized automated image analysis program. We found that diabetic retinopathy (DR) severity was associated with increased FAZ area, decreased FAZ circularity, lower VD, lower FD, and increased VDI. Enlarged FAZ area was correlated with shorter axial length and thinner central subfield macular thickness. Decreased FAZ circularity was correlated with a reduction in visual function. Decreased VD was correlated with thinner macular ganglion-cell inner plexiform layer. Increased VDI was correlated with higher fasting glucose level. We concluded that the effects of ocular and systemic factors in diabetics should be taken into consideration when assessing microvascular alterations via OCT-A.

Intravitreal bevacizumab appeared to result in stabilisation of vision and reduction of exudative retinal detachment in PCV patients. However, intravitreal bevacizumab monotherapy had limited effectiveness in causing regression of the polypoidal lesions in ICGA, and additional PDT appeared to be useful for treating these lesions.

This paper concerns the systolic array computation of the generalized singular value decomposition. Numerical algorithms for both one-and two -dimensional systolic architectures are discussed.

No abstract

No abstract

Abstract.Identifying the patterns of large data sets is a key requirement in data mining. A powerful technique for this purpose is the principal component analysis (PCA). PCA-based clustering algorithms are effective when the data sets are found in the same location. In applications where the large data sets are physically far apart, moving huge amounts of data to a single location can become an impractical, or even impossible, task. A way around this problem was proposed in [10], where truncated singular value decompositions (SVDs) are computed locally and used to reduce the communication costs. Unfortunately, truncated SVDs introduce local approximation errors that could add up and would adversely affect the accuracy of the final PCA. In this paper, we introduce a new method to compute the PCA without incurring local approximation errors. In addition, we consider the situation of updating the PCA when new data arrive at the various locations.

No abstract

This study aimed to examine the rate of re-operation in women with endometriosis over a 10-year period. This was a retrospective study set in a university hospital in the UK. Notes of all women diagnosed with endometriosis were reviewed and data entered on a standard proforma. A total of 486 out of 988 procedures were for treatment of endometriosis. Some 240 (49%) had pelvic pain and 246 (51%) had subfertility. The mean age of those women who had a re-operation was lower than those who did not have any further operations. Using logistic regression, three factors were found to be the most important factors influencing the likelihood of women having re-operation - in decreasing order of importance, these factors were: (1) age, (2) pregnancy achievement and (3) improvement of symptoms. Re-operation occurred in 51% of our study population, the information may be useful for guidance of our patients.