A Review on Management of Amblyopia

Amblyopia is a visual cortex neurodevelopmental condition cause am vision abnormalities during childhood. It is one of the most typical causes of vision loss at an early age. It occurs due to abnormal development of the visual cortex. The part receiving signals from the diseased eye does not receive it correctly and thus develops abnormally. This abnormal development during the critical period of growth of child results in brain damage. Depending on its aetiology the types of amblyopia are Strabismic amblyopia, Visual deprivation amblyopia, Anisometric, Ametropic, Meridional, Toxic amblyopia. Clinical features are visual acuity is reduced, the effect of neutral density filter, the Crowding phenomenon is present. Complications of amblyopia include a Lazy eye becoming weak permanently, the eye may move out from the visual axis (squints). When treating amblyopia, our goal is that the eyes will work together in unison at an equal level; this will create a clear vision in the lazy eye. Amblyopia is treated in various ways depending on the seriousness of the disease and the patient's age. Patching of the non-amblyopic eye, as well as treatment with drugs like atropine, are common treatments. Vision therapy and some modifications to spectacles and contact lenses have been discovered to be effective in treating amblyopia in recent years. Modern Treatment- Falling Blocks, Occlu-pad. With current breakthroughs in amblyopia therapy, the success rate of a multimodal strategy is also improving. The purpose of this review article is to present information on the management of amblyopia. Literature on AMBLYOPIA MANAGEMENT has been taken from PubMed, Scopus, Science Direct, and other internet resources.

A Novel Flow Cytometric Approach for the Quantification and Quality Control of Chlamydia trachomatis Preparations

Chlamydia trachomatis is an obligate intracellular pathogenic bacterium with a biphasic developmental cycle manifesting two distinct morphological forms: infectious elementary bodies (EBs) and replicative intracellular reticulate bodies (RBs). Current standard protocols for quantification of the isolates assess infectious particles by titering inclusion-forming units, using permissive cell lines, and analyzing via immunofluorescence. Enumeration of total particle counts is achieved by counting labeled EBs/RBs using a fluorescence microscope. Both methods are time-consuming with a high risk of observer bias. For a better assessment of C. trachomatis preparations, we developed a simple and time-saving flow cytometry-based workflow for quantifying small particles, such as EBs with a size of 300 nm. This included optimization of gain and threshold settings with the addition of a neutral density filter for small-particle discrimination. The nucleic acid dye SYBR® Green I (SGI) was used together with propidium iodide and 5(6)-carboxyfluorescein diacetate to enumerate and discriminate between live and dead bacteria. We found no significant differences between the direct particle count of SGI-stained C. trachomatis preparations measured by microscopy or flow cytometry (p > 0.05). Furthermore, we completed our results by introducing a cell culture-independent viability assay. Our measurements showed very good reproducibility and comparability to the existing state-of-the-art methods, indicating that the evaluation of C. trachomatis preparations by flow cytometry is a fast and reliable method. Thus, our method facilitates an improved assessment of the quality of C. trachomatis preparations for downstream applications.

State Estimation and Smoothing for the Probability Hypothesis Density Filter

<p>Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from data. The Probability Hypothesis Density (PHD) filter is an effective approach for this problem. The method uses a first-order moment approximation to develop a recursive algorithm for the optimal Bayesian filter. The PHD recursion can implemented in closed form in some restricted cases, and more generally using Sequential Monte Carlo (SMC) methods. The assumptions made in the PHD filter are appealing for computational reasons in real-time tracking implementations. These are only justifiable when the signal to noise ratio (SNR) of a single target is high enough that remediates the loss of information from the approximation. Although the original derivation of the PHD filter is based on functional expansions of belief-mass functions, it can also be developed by exploiting elementary constructions of Poisson processes. This thesis presents novel strategies for improving the Sequential Monte Carlo implementation of PHD filter using the point process approach. Firstly, we propose a post-processing state estimation step for the PHD filter, using Markov Chain Monte Carlo methods for mixture models. Secondly, we develop recursive Bayesian smoothing algorithms using the approximations of the filter backwards in time. The purpose of both strategies is to overcome the problems arising from the PHD filter assumptions. As a motivating example, we analyze the performance of the methods for the difficult problem of person tracking in crowded environments</p>

State Estimation and Smoothing for the Probability Hypothesis Density Filter

<p>Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from data. The Probability Hypothesis Density (PHD) filter is an effective approach for this problem. The method uses a first-order moment approximation to develop a recursive algorithm for the optimal Bayesian filter. The PHD recursion can implemented in closed form in some restricted cases, and more generally using Sequential Monte Carlo (SMC) methods. The assumptions made in the PHD filter are appealing for computational reasons in real-time tracking implementations. These are only justifiable when the signal to noise ratio (SNR) of a single target is high enough that remediates the loss of information from the approximation. Although the original derivation of the PHD filter is based on functional expansions of belief-mass functions, it can also be developed by exploiting elementary constructions of Poisson processes. This thesis presents novel strategies for improving the Sequential Monte Carlo implementation of PHD filter using the point process approach. Firstly, we propose a post-processing state estimation step for the PHD filter, using Markov Chain Monte Carlo methods for mixture models. Secondly, we develop recursive Bayesian smoothing algorithms using the approximations of the filter backwards in time. The purpose of both strategies is to overcome the problems arising from the PHD filter assumptions. As a motivating example, we analyze the performance of the methods for the difficult problem of person tracking in crowded environments</p>

Double Adaptive Intensity-Threshold Method for Uneven Lidar Data to Extract Road Markings

Due to the large volume and high redundancy of point clouds, there are many dilemmas in road-marking extraction algorithms, especially from uneven lidar point clouds. To extract road markings efficiently, this study presents a novel method for handling the uneven density distribution of point clouds and the high reflection intensity of road markings. The method first segments the point-cloud data into blocks perpendicular to the vehicle trajectory. Then it applies the double adaptive intensity-threshold method to extract road markings from road surfaces. Finally, it performs an adaptive spatial density filter based on the density distribution of point-cloud data to remove false road-marking points. The average completeness, correctness, and F measure of road-marking extraction are 0.827, 0.887, and 0.854, respectively, indicating that the proposed method is efficient and robust.