BackgroundCaspases are a family of cysteinyl proteases that regulate apoptosis and other biological processes. Caspase-3 is considered the central executioner member of this family with a wide range of substrates. Identification of caspase-3 cellular targets is crucial to gain further insights into the cellular mechanisms that have been implicated in various diseases including: cancer, neurodegenerative, and immunodeficiency diseases. To date, over 200 caspase-3 substrates have been identified experimentally. However, many are still awaiting discovery.ResultsHere, we describe a powerful bioinformatics tool that can predict the presence of caspase-3 cleavage sites in a given protein sequence using a Position-Specific Scoring Matrix (PSSM) approach. The present tool, which we call CAT3, was built using 227 confirmed caspase-3 substrates that were carefully extracted from the literature. Assessing prediction accuracy using 10 fold cross validation, our method shows AUC (area under the ROC curve) of 0.94, sensitivity of 88.83%, and specificity of 89.50%. The ability of CAT3 in predicting the precise cleavage site was demonstrated in comparison to existing state-of-the-art tools. In contrast to other tools which were trained on cleavage sites of various caspases as well as other similar proteases, CAT3 showed a significant decrease in the false positive rate. This cost effective and powerful feature makes CAT3 an ideal tool for high-throughput screening to identify novel caspase-3 substrates.The developed tool, CAT3, was used to screen 13,066 human proteins with assigned gene ontology terms. The analyses revealed the presence of many potential caspase-3 substrates that are not yet described. The majority of these proteins are involved in signal transduction, regulation of cell adhesion, cytoskeleton organization, integrity of the nucleus, and development of nerve cells.ConclusionsCAT3 is a powerful tool that is a clear improvement over existing similar tools, especially in reducing the false positive rate. Human proteome screening, using CAT3, indicate the presence of a large number of possible caspase-3 substrates that exceed the anticipated figure. In addition to their involvement in various expected functions such as cytoskeleton organization, nuclear integrity and adhesion, a large number of the predicted substrates are remarkably associated with the development of nerve tissues.
The aim of this article is to present the potential of Kernel Principal Component Analysis (Kernel PCA) in the field of vision based robot localization. Using Kernel PCA we can extract features from the visual scene of a mobile robot The analysis Is applied only to loel fealum so as to guarantee better computational performance as well as translation invariance. Compared with the classical Prinripal Component Analysis (PCA), Kernel PCA results show superiority in localization and robustness in presence of noisy scenes. The key success of the kernel PCA is the use of fractional power polynomial kernels. I. INTRODUCTlUNThe problem of robot localization can be classified as either global or local localization [IO]. In global localization, the robot tries to discover its position without previous knowledge about its location. In local localization, the robot must update its position using its current data from its sensors as well as the previous information that it has already accumulated. The lack of any historical information about its surroundings makes the global localization more challenging [7]. The idea of feature based robot localization involves representing the robot environment as a topological map by means of a large set of features. The main properties of these features are: (1) They should be a compressed form of the original Scenes so as to speed up the computation of the comparisons, still, they should maintain distinguishing representations of the scenes. (2) They should exhibit invariance against different transformations on the scenes such as translation and scale. (3) They should also exhibit robusmess against noise or illumination changes, which the robot encounters during its navigation. PCA has been applied in the field of robot localization. In [SI active vision is combined with robot localization using PCA. In [I] and [I31 the study of the problem of batch learning and the use of incremental PCA is presented.Their idea is lo deal with on-line leaning of the robot landmarks without recomputing the PCA for the whole samples each time. The work done in [41 presents the effect of illumination on PCA. They present illumination invariant feaNres by filtering the eigenimages rather than filtering the original samples. In [12] a comparison among different vision-based mbot localization approaches is made. Their results show that PCA is more robust and accurate than other methods such as edge density based, hut also show that PCA requires more computation power. Robot localization using PCA can be classified as either local and global based on the feature extraction applied. In the global based approach [8], the whole image is considered as a sample and applied to the PCA as a vector. An example of global features is the work done in [31, where PCA is globally applied to panoramic images, hey introduce robust PCA using an expectation maximization approach where outliers can be resolved. On the other band, in the local based approach, a set of landmarks (small patches) are first selected from the image...
Abstract-Vision-based robot localization in outdoor environments is difficult because of changing illumination conditions. Another problem is the rough and cluttered environment which makes it hard to use visual features that are not rotation invariant. A popular method that is rotation invariant and relatively robust to changing illumination is the Scale Invariant Feature Transform (SIFT). However, due to the computationally intensive feature extraction and image matching, localization using SIFT is slow. On the other hand, techniques which use global image features are in general less robust and exact than SIFT, but are often much faster due to fast image matching. In this paper, we present a hybrid localization approach that switches between local and global image features. For most images, the hybrid approach uses fast global features. Only in difficult situations, e.g. containing strong illumination changes, the hybrid approach switches to local features. To decide which features to use for an image, we analyze the particle cloud of the particle filter that we use for position estimation. Experiments on outdoor images taken under varying illumination conditions show that the position estimates of the hybrid approach are about as exact as the estimates of SIFT alone. However, the average localization time using the hybrid approach is more than 3.5 times faster than using SIFT.
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