The Frequency of Signs of Meibomian Gland Dysfunction in Children with Epidermolysis Bullosa.

PURPOSE: To determine the frequency of meibomian gland dysfunction (MGD) in children with epidermolysis bullosa (EB). DESIGN: Hospital-based cross-sectional study. PARTICIPANTS: One hundred five children with different forms of EB. METHODS: Prospective ophthalmic examination of children with EB presenting over seventeen months including meibomian gland assessment using a recognized classification. MAIN OUTCOME MEASURES: Frequency of MGD. RESULTS: One hundred five children were recruited, 8.6% with junctional EB, 34.3% with simplex EB, 34.3% with autosomal recessive dystrophic EB, and 22.9% autosomal dominant dystrophic EB. Mean age was 7.42 years (range, 0.08-17.75 years). Ninety-two children (87.62%) demonstrated 1 or more features of MGD. CONCLUSIONS: Most children with EB exhibit signs of MGD. To the best of our knowledge, this is the first prospective ocular surface evaluation in children with EB to include lid margin evaluation using a recognized classification system. Our findings help explain some of the ocular surface anomalies seen in children with EB.

Refractive and ocular motility findings in children with epidermolysis bullosa.

BACKGROUND AND PURPOSE: Epidermolysis bullosa (EB) is a genetic disorder resulting in blistering of the skin and mucous membranes. While ocular surface complications in EB patients are well documented, there are no reports* on the incidence of refractive errors and orthoptic problems in these patients. PATIENTS AND METHODS: Fifty-five consecutive EB patients underwent examination including visual acuity, cover test, ocular motility and assessment of binocular single vision, cycloplegic refraction, and fundus examination. RESULTS: Twenty-one of the 55 patients (38%) had reduced visual acuity (0.3 logMAR or less) in at least one eye, and 13 / 55 (24%) had bilaterally reduced visual acuity. Twenty-nine percent of patients had refractive errors; 20% hypermetropic, 5% myopic, 16% astigmatism, and 11% anisometropic. Fifteen percent of patients had a constant or intermittent tropia; 9% exotropia and 4% esotropia. Thirteen percent of patients had a significant phoria (> 10(Δ)); all were exophoric. Sixteen percent had convergence insufficiency. This cohort of EB patients demonstrated a high prevalence of reduced visual acuity, strabismus and refractive errors compared with normal pediatric data from the literature. CONCLUSIONS: A large proportion of patients with EB across the subgroups have clinically significant refractive errors and strabismus. In view of the improved survival of EB patients, visual morbidity is of increasing importance. This study highlights the need for careful orthoptic and ophthalmic surveillance of children with EB.

Optimization via intermittency with a self-organizing neural network.

One of the major obstacles in using neural networks to solve combinatorial optimization problems is the convergence toward one of the many local minima instead of the global minima. In this letter, we propose a technique that enables a self-organizing neural network to escape from local minima by virtue of the intermittency phenomenon. It gives rise to novel search dynamics that allow the system to visit multiple global minima as meta-stable states. Numerical experiments performed suggest that the phenomenon is a combined effect of Kohonen-type competitive learning and the iterated softmax function operating near bifurcation. The resultant intermittent search exhibits fractal characteristics when the optimization performance is at its peak in the form of 1/f signals in the time evolution of the cost, as well as power law distributions in the meta-stable solution states. TheN-Queens problem is used as an example to illustrate the meta-stable convergence process that sequentially generates, in a single run, 92 solutions to the 8-Queens problem and 4024 solutions to the 17-Queens problem.

A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization.

The self-organizing neural network (SONN) for solving general "0-1" combinatorial optimization problems (COPs) is studied in this paper, with the aim of overcoming existing limitations in convergence and solution quality. This is achieved by incorporating two main features: an efficient weight normalization process exhibiting bifurcation dynamics, and neurons with additive noise. The SONN is studied both theoretically and experimentally by using the N-queen problem as an example to demonstrate and explain the dependence of optimization performance on annealing schedules and other system parameters. An equilibrium model of the SONN with neuronal weight normalization is derived, which explains observed bands of high feasibility in the normalization parameter space in terms of bifurcation dynamics of the normalization process, and provides insights into the roles of different parameters in the optimization process. Under certain conditions, this dynamical systems view of the SONN reveals cascades of period-doubling bifurcations to chaos occurring in multidimensional space with the annealing temperature as the bifurcation parameter. A strange attractor in the two-dimensional (2-D) case is also presented. Furthermore, by adding random noise to the cost potentials of the network nodes, it is demonstrated that unwanted oscillations between symmetrical and "greedy" nodes can be sufficiently reduced, resulting in higher solution quality and feasibility.