In the research reported in this article, the personal computer (PC) is used as a separate, front-end interface for a customized machine control unit, which controls a drilling machine through actuation devices. Both the customized machine control unit and the drilling machine were built in-house. The PC-based computer numerical control drilling machine comprises several integrated technologies, ranging from a neuro-TSP (travelling salesman problem)-based optimizer for ®nding the best sequence of points to be drilled, a customized machine control unit and enhanced parallel port for communication to the drilling machine. Work is continuing to carry out experimental tests on the part produced by the machine and to compare the quality of such parts with those produced by similar machines.
Background: Glycation is a one of the post-translational modifications (PTM) where sugar molecules and residues in protein sequences are covalently bonded. It has become one of the clinically important PTM in recent times attributed to many chronic and age related complications. Being a non-enzymatic reaction, it is a great challenge when it comes to its prediction due to the lack of significant bias in the sequence motifs. Results: We developed a classifier, GlyStruct based on support vector machine, to predict glycated and non-glycated lysine residues using structural properties of amino acid residues. The features used were secondary structure, accessible surface area and the local backbone torsion angles. For this work, a benchmark dataset was extracted containing 235 glycated and 303 non-glycated lysine residues. GlyStruct demonstrated improved performance of approximately 10% in comparison to benchmark method of Gly-PseAAC. The performance for GlyStruct on the metrics, sensitivity, specificity, accuracy and Mathew's correlation coefficient were 0.7013, 0.7989, 0.7562, and 0.5065, respectively for 10-fold crossvalidation.Conclusion: Glycation has emerged to be one of the clinically important PTM of proteins in recent times. Therefore, the development of computational tools become necessary to predict glycation, which could help medical professionals administer drugs and manage patients more effectively. The proposed predictor manages to classify glycated and nonglycated lysine residues with promising results consistently on various cross-validation schemes and outperforms other state of the art methods.
Background: Post-translational modifications are viewed as an important mechanism for controlling protein function and are believed to be involved in multiple important diseases. However, their profiling using laboratorybased techniques remain challenging. Therefore, making the development of accurate computational methods to predict post-translational modifications is particularly important for making progress in this area of research. Results: This work explores the use of four half-sphere exposure-based features for computational prediction of sumoylation sites. Unlike most of the previously proposed approaches, which focused on patterns of amino acid co-occurrence, we were able to demonstrate that protein structural based features could be sufficiently informative to achieve good predictive performance. The evaluation of our method has demonstrated high sensitivity (0.9), accuracy (0.89) and Matthew's correlation coefficient (0.78-0.79). We have compared these results to the recently released pSumo-CD method and were able to demonstrate better performance of our method on the same evaluation dataset. Conclusions: The proposed predictor HseSUMO uses half-sphere exposures of amino acids to predict sumoylation sites. It has shown promising results on a benchmark dataset when compared with the state-of-the-art method. The extracted data of this study can be accessed at https://github.com/YosvanyLopez/HseSUMO.
This article presents the obvious constraints of using an end-controller to produce autonomous navigation and obstacle avoidance behaviour. This system depends solely on the accuracy and precision of the hardware peripherals such as the infrared sensor used in this project to eliminate the noise problem of ultrasonic sensors in most conventional robots. A new approach has been taken to coordinate the infrared sensors in a similar manner to laser sensors. The circular platform design reflects its type of application such as an automated hall cleaning system.
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