Stochastic Reliability Analysis of Two Identical Cold Standby Units with Geometric Failure & Repair Rates

The present paper is based on the analysis of an two identical unit system wherein initially one unit is in operative state and other is in cold standby and total failure of the unit is via partial failure mode. Single repairman is always available with the system for any repair of failed unit (partially/ completely). Failure/repair time is considered to be a Geometric distribution. Measures of system effectiveness had also been calculated for the system

Kernel-based machine learning protocol for predicting DNA-binding proteins

DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition, overall charge and positive potential patches on the protein surface. In total 121 DNA-BPs and 238 non-binding proteins are used to build and evaluate the protocol. In self-consistency, accuracy value of 100% has been achieved. For cross-validation (CV) optimization over entire dataset, we report an accuracy of 90%. Using leave 1-pair holdout evaluation, the accuracy of 86.3% has been achieved. When we restrict the dataset to less than 20% sequence identity amongst the proteins, the holdout accuracy is achieved at 85.8%. Furthermore, seven DNA-BPs with unbounded structures are all correctly predicted. The current performances are better than results published previously. The higher accuracy value achieved here originates from two factors: the ability of the SVM to handle features that demonstrate a wide range of discriminatory power and, a different definition of the positive patch. Since our protocol does not lean on sequence or structural homology, it can be used to identify or predict proteins with DNA-binding function(s) regardless of their homology to the known ones

Analysis and Measurement Technique for 1- dB Compression Point of Single Balanced RF Mixer

The present paper discusses the basic working and operation of RF Mixers being used broadly for communication purposes. In the present paper we have discussed various important parameters of Mixer and elaborately described the instrument setup and measurement techniques for measuring the most vital parameter for analyzing performance of mixer i.e., 1-dB Compression Point (P1dB). 1- dB compression point is very significant parameter as it indicates the power level that causes the gain to drop by 1 dB from its small signal value. In this paper we have described test set up for measuring this important parameter with various measuring instruments and has obtained good agreement between the predictions and experimental data. Index Terms- Single diode mixers, 1 dB Compression Point (P1dB), Down Converter, VM-3 Receiver, Power meter, Attenuato

Development of candidate vaccine strategies against Rift Valley fever virus

Rift Valley Fever virus (RVFV) is an arthropod-borne bunyavirus that causes a zoonotic disease associated with abortion storms, neonatal mortality in livestock and hemorrhagic fever with a high case/fatality ratio in humans. To date, vaccine developments against RVF have been based on inactivated or attenuated strains but their widespread use has been hampered due to deleterious effects or incomplete protection, justifying further studies to improve the existing vaccines or to develop others. To address this, DNA plasmid and alphavirus replicon vector (VEEV) expressing RVFV Gn glycoprotein were constructed and evaluated for their ability to induce protective immune responses in mice against RVFV. An experimental live-attenuated vaccine (MP12) and its inactivated counterpart (WIV MP12) were developed to serve as benchmarks for comparison. Test vaccine candidates efficiently expressed the RVFV glycoprotein in vitro and elicited anti-RVFV antibody responses in immunized mice, as determined by RVFV specific ELISA, IgG isotype ELISA, and virus neutralization. Interestingly, these vaccine strategies elicited cellular immune responses as determined by Gn specific ELISPOT. More importantly these vaccines not only protected immunized mice from virulent RVFV when challenged via intraperitoneal route, but also conferred protection when challenged via aerosol route. This work is of public health significance as it describes the development of safe and effective vaccine candidates that have the ability to protect both livestock and humans against possible routes of exposure to this zoonotic threat

Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique

<p>Abstract</p> <p>Background</p> <p>In <it>supervised learning</it>, traditional approaches to building a classifier use two sets of examples with pre-defined classes along with a learning algorithm. The main limitation of this approach is that examples from both classes are required which might be infeasible in certain cases, especially those dealing with biological data. Such is the case for membrane-binding peripheral domains that play important roles in many biological processes, including cell signaling and membrane trafficking by reversibly binding to membranes. For these domains, a well-defined <it>positive </it>set is available with domains known to bind membrane along with a large <it>unlabeled </it>set of domains whose membrane binding affinities have not been measured. The aforementioned limitation can be addressed by a special class of <it>semi-supervised </it>machine learning called <it>positive-unlabeled (PU) </it>learning that uses a positive set with a large unlabeled set.</p> <p>Methods</p> <p>In this study, we implement the first application of <it>PU-learning </it>to a protein function prediction problem: identification of peripheral domains. <it>PU-learning </it>starts by identifying reliable negative (<it>RN</it>) examples iteratively from the unlabeled set until convergence and builds a classifier using the positive and the final <it>RN </it>set. A data set of 232 positive cases and ~3750 unlabeled ones were used to construct and validate the protocol.</p> <p>Results</p> <p>Holdout evaluation of the protocol on a left-out positive set showed that the accuracy of prediction reached up to 95% during two independent implementations.</p> <p>Conclusion</p> <p>These results suggest that our protocol can be used for predicting membrane-binding properties of a wide variety of modular domains. Protocols like the one presented here become particularly useful in the case of availability of information from one class only.</p

Improvement of alignment accuracy utilizing sequentially conserved motifs

Background: Multiple sequence alignment algorithms are very important tools in molecular biology today. Accurate alignment of proteins is central to several areas such as homology modelling, docking studies, understanding evolutionary trends and study of structure-function relationships. In recent times, improvement of existing progressing programs and implementation of new iterative algorithms have made a significant change in this field. Results: We report an alignment algorithm that combines progressive dynamic algorithm, local substructure alignment and iterative refinement to achieve an improved, user-interactive tool. Large-scale benchmarking studies show that this FMALIGN server produces alignments that, aside from preservation of functional and structural conservation, have accuracy comparable to other popular multiple alignment programs. Conclusions: The FMALIGN server allows the user to fix conserved regions in equivalent position in the alignment thereby reducing the chance of global misalignment to a great extent. FMALIGN is available at http://caps.ncbs.res.in/FMALIGN/Home.html

Computational Study Reliability of Diesel Engine through Electrical Assembly

Consistency of a system is the probability that, under defined operational and environmental conditions, the device can perform a specific function at the end over a specified time [Dinkar et al. 2018]. Reliability must be defined by definitive norm based on many variables, maximum of that may random [Bhardwaj et al. (2018)]. Reliability is arduous to scale, because no such tool is there to do this for specific machine. The reliability of the diverse units of complex machinery depends on their output process, the standard of the composites used in their design, operating conditions, etc. Despite these criteria the reliability of the system is very much related to diverse forms of uncertainty. The numerical assessment of ambiguities is therefore the starting point for a numerical assessment of reliability [Bhardwaj et al. (2019)]. The theory of probability is a concept concerned with the analysis of risk [Li, Y. - F et al. 2006]. Maintenance regulations can address issues pertaining to general maintenance, fix or dump laws, rules on emergency reporting, inventory control, supply of spare parts, etc. Such maintenance policies may be defined in advance and implementation decisions may be taken accordingly. The design process involves unit length assessments, research protocols, degree of automation as well as integrated redundancies, test time, specific test facilities and measures for safety; and so forth [Allan, T.M., 2012]. Performance is an attribute of reliability to reparable systems which accounts for the quality and service property of a component or device. ABC appraisal seeks to distinguish the item from each other and decide the importance of the element and the degree as required by company

SCANMOT: searching for similar sequences using a simultaneous scan of multiple sequence motifs

Establishment of similarities between proteins is very important for the study of the relationship between sequence, structure and function and for the analysis of evolutionary relationships. Motif-based search methods play a crucial role in establishing the connections between proteins that are particularly useful for distant relationships. This paper reports SCANMOT, a web-based server that searches for similarities between proteins by simultaneous matching of multiple motifs. SCANMOT searches for similar sequences in entire sequence databases using multiple conserved regions and utilizes inter-motif spacing as restraints. The SCANMOT server is available via