Disulphide Bridge Prediction using Fuzzy Support Vector Machines

One of the major contributors to the native form of protien is cystines forming covalent bonds in oxidized state. The Prediction of such bridges from the sequence is a very challenging task given that the number of bridges will rise exponentially as the number of cystines increases. We propose a novel technique for disulphide bridge prediction based on Fuzzy Support Vector Machines. We call the system DIzzy. In our investigation, we look at disulphide bond connectivity given two Cystines with and without a priori knowledge of the bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features such as the probability of occurrence of each amino acid in different secondary structure states along with psiblast profiles. The performance is compared with normal support vector machines. We evaluate our method and compare it with the existing method using SPX dataset

Performance Enhancement of a Novel Interleaved Boost Converter by using a Soft-Switching Technique

ABSTRACT In this paper a novel Interleaved Boost Converter (IBC) with soft-switching techniques is Auxiliary circuit acts as support circuit to both main switches(two conditions) and reduce the total losses and improve efficiency& power factor for large loads. The operational principle, theoretical analysis, and design method of the proposed converter are presented. The entire proposed system will be tested using MATLAB/SIMULINK and the simulation results are also presented

A schematic representation of CONOTOXIN(PDB (protein databank) ID 1AS5) showing disulphide bonds

<p><b>Copyright information:</b></p><p>Taken from "Prediction of cystine connectivity using SVM "</p><p></p><p>Bioinformation 2005;1(2):69-74.</p><p>Published online 7 Dec 2005</p><p>PMCID:PMC1891633.</p><p></p

City dashboards and the achilles\u2019 heel of smart cities: Putting governance in action and in space

City dashboards have developed in the last few years as ways for aggregating and disseminating data related to urban areas to a wide range of citizens and city users. Such data come from official sources, in line with a growing policy of opening (public) data, as well as taken from the \u2018big data\u2019 realm. Dashboards were developed with different aims and design, in many cases still representing academic experiments and tests of city \u2013 to citizens interface and communication channels, rather than being considered as a consistent part of urban spatial planning policies. In many cases \u2013 as is the case of many studies and reports on urban areas \u2013 dashboards are used to produce indicators and indexes related to the performances of cities. However, such indexes are seldom used in planning as benchmarks for policies. In such sense, the use \u2013 or non-use - of such indicators in planning and governing urban territories remain one of the \u2018Achilles\u2019 heels\u2019 of (Smart) cities. The idea of this paper, in line with other research carried on by different research groups, is of changing the model of urban dashboards from a linear one (that follows the logic of: data input \u2013 processing \u2013 visualization \u2013 information output) to a circular one (data input \u2013 processing \u2013 visualization \u2013 information output \u2013 indicators \u2013 use of indicators in planning \u2013 new data production \u2013 new data input). Here we propose a model for inserting data (results) of policies announced at urban level into such a framework, in order to allow users to understand the level of application of the different policies in time and the policy makers to evaluate the effects of such policies in different moments, so to calibrate their application in the future