Studies on Industrially Significant Haloalkaline Protease from Bacillus sp. JSGT Isolated from Decaying Skin of Tannery

Eight bacterial strains were isolated from collagen layer of decaying skin sample. Three isolates exhibited the prominent zones of clearance on skim milk agar medium at pH 9.5. These isolates were then characterized and identified. One of the haloalkalophilic isolates belonged to the genus Bacillus. Maximum enzyme activity (228.29 ± 1.89 PU/ ml) was found at pH 9 and temperature 37°C in the strain which is designated as Bacillus sp. JSGT. Basic properties such as effects of different temperature, pH, metal ions and inhibitors on protease activity were also studied. Maximum activity was obtained at pH 9 at 55°C. Ca+2 and Mg+2 ions were found to enhance the relative enzyme activity up to 158 and 136% respectively. However, the activity of protease was completely inhibited by phenyl methyl sulfonyl fluoride (PMSF) that showed its serine nature. The results indicated that enzyme produced by Bacillus sp. JSGT is active within broad ranges of temperature and pH. These characteristics render its potential use in leather and detergent industries

Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers

Purpose: The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. Methods: FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. Results: FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G(1) and G(2) combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G(1) and G(2) the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. Conclusions: VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.open0

Controllable real-time locomotion using mobility maps

Graph-based approaches for sequencing motion capture data have produced some of the most realistic and controllable character motion to date. Most previous graph-based approaches have employed a run-time global search to find paths through the motion graph that meet user-defined constraints such as a desired locomotion path. Such searches do not scale well to large numbers of characters. In this paper, we describe a locomotion approach that benefits from the realism of graph-based approaches while maintaining basic user control and scaling well to large numbers of characters. Our approach is based on precomputing multiple least cost sequences from every state in a state-action graph. We store these precomputed sequences in a data structure called a mobility map and perform a local search of this map at run-time to generate motion sequences in real time that achieve user constraints in a natural manner. We demonstrate the quality of the motion through various example locomotion tasks including target tracking and collision avoidance. We demonstrate scalability by animating crowds of up to 150 rendered articulated walking characters at real-time rates

Agreement between the Heidelberg Retina Tomograph (HRT) stereometric parameters estimated using HRT-I and HRT-II

Purpose. To assess agreement between Heidelberg Retina Tomograph (HRT)-I and HRT-II stereometric parameters and to determine whether parabolic error correction (PEC) to the topographies improves agreement. Methods. University of California San Diego Diagnostic Innovations in Glaucoma Study participants with two HRT-II examinations (n = 380) or one HRT-I and one HRT-II examinations (n = 344) acquired on the same day were included. From the group of 380 eyes, 200 eyes were randomly selected to estimate the repeatability coefficients of HRT-II rim area and volume, cup area and volume, and mean retinal nerve fiber layer (RNFL) thickness parameters (HRT-II control group), and the remaining 180 eyes were used to assess agreement between two HRT-II examinations (HRT-II study group). Agreement between stereometric parameters of HRT-I and HRT-II examinations (HRT-I vs. HRT-II study group) were assessed with (1) no PEC, (2) HRT PEC, and (3) a modified PEC. Bland-Altman plots were used to assess agreement using estimates of bias and clinical limits of agreement (CLA) based on repeatability coefficients. Results. In the HRT-II study group, agreement between stereometric parameters was good, with no statistically significant biases. For all parameters, differences were within the CLA in 94% of participants. In the HRT-I vs. HRT-II study group, there was a small statistically significant bias between the stereometric parameters, but all differences were within CLA for ≥95% of participants. In both study groups, PEC did not improve agreement. Conclusions. Agreement between HRT-I and HRT-II stereometric parameters was good, and PEC did not improve agreement. These results suggest that HRT-I and HRT-II examinations can be used interchangeably to detect changes in stereometric parameters over time. Copyright © 2011 American Academy of Optometry

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images.

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection. © 2014 Elsevier Ltd

A unified framework for glaucoma progression detection using Heidelberg Retina Tomograph images.

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropathy characterized by distinctive changes in the optic nerve head (ONH) and visual field. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. In this context, ocular imaging equipment is increasingly sophisticated, providing quantitative tools to measure structural changes in ONH topography, an essential element in determining whether the disease is getting worse. In particular, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatous progression using HRT images. In contrast to previous works that do not integrate a priori knowledge available in the images, particularly the spatial pixel dependency in the change detection map, the Markov Random Field is proposed to handle such dependency. To the best of our knowledge, this is the first application of the Variational Expectation Maximization (VEM) algorithm for inferring topographic ONH changes in the glaucoma progression detection framework. Diagnostic performance of the proposed framework is compared to recently proposed methods of progression detection

Glaucoma progression detection using variational expectation maximization algorithm

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropthy characterized by distinctive changes in the optic nerve head (ONH) and visual field. In this context, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatour progression using the HRT images. In contrast to previous works that do not integrate a priori knowledge available on the images and particularly the spatial pixel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To our knowledge, the task of inferring the glaucomatous changes with a Variational Expectation Maximization VEM algorithm will be used for the first time in the glaucoma diagnosis framework. We then compared the diagnostic performance of the proposed framework to existing methods of progression detection. © 2013 IEEE