Search results

ß-thalassemia is a hereditary disorder due to mutation in the ß-globin gene on chromosome 11. Out of 200 known ß-globin gene chain mutations recognized, it is better to identify the most common mutation in specific regions and ethnicity for cost-effective molecular diagnosis of this disorder. Therefore, this study aims to practice multiplex-amplification refractory mutation system (ARMS) PCR on patients with thalassemia in Khyber Pakhtunkhwa (KP) to investigate the most common mutations in the ß-globin chain gene.

Twenty-two individuals (patients, their parents and non-affected siblings) with signed consent were studied from six consanguineous families of ß-thalassemia. Blood samples were collected for DNA isolation. For the detection of mutations in the ß-globin gene, ARMS-PCR was used. The amplicon was visualized through 2% Agarose Gel.

The most common mutations among different ethnic groups in the study area residents were Fr 8-9 (+G) and IVS 1-5 (G> C). The prominent enhancing factors for ß-thalassemia are inter-family marriages and lack of awareness.

Multiplex ARMS_PCR is the most valuable technique for assessing multiple mutations in a single reaction tube.

Due to extensively found ethnic and regional variations and a high rate of consanguinity, the Pashtun population has a great risk of mutations in their genome. Therefore, ARMS-PCR is a cost-effective mutational diagnostic strategy that can help to control disease burden.

Limited studies using ARMS-PCR for mutational analysis in the ß-globin gene are conducted. This study is unique as it targeted consanguineous families of KP Pakistan.

For delivering high-quality software applications, proper testing is required. A software test will function successfully if it can find more software faults. The traditional method of assessing the quality and effectiveness of a test suite is mutation testing. One of the main drawbacks of mutation testing is its computational cost. The research problem of this study is the high computational cost of the mutation test. Reducing the time and cost of the mutation test is the main goal of this study.

With regard to the 80–20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. The proposed method statically analyzes the source code of the program to identify the fault-prone locations of the program. Identifying the fault-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm is used for identifying the most fault-prone paths of a program; then, the mutation operators are injected only on the identified fault-prone instructions.

The source codes of five traditional benchmark programs were used for evaluating the effectiveness of the proposed method to reduce the mutant number. The proposed method was implemented in Matlab. The mutation injection operations were carried out by MuJava, and the output was investigated. The results confirm that the proposed method considerably reduces the number of mutants, and consequently, the cost of software mutation-test.

The proposed method avoids the mutation of nonfault-prone (simple) codes of the program, and consequently, the number of mutants considerably is reduced. In a program with n branch instructions (if instruction), there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Identifying the error-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm as a heuristic algorithm is used for identifying the most error-prone paths of a program; then, the mutation operators (faults) are injected only on the identified fault-prone instructions.

The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.

In a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.

In order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.

The main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

Diagnosing genetic neuromuscular disorder such as muscular dystrophy is complicated when the imperfection occurs while splicing. This paper aims in predicting the type of muscular dystrophy from the gene sequences by extracting the well-defined descriptors related to splicing mutations. An automatic model is built to classify the disease through pattern recognition techniques coded in python using scikit-learn framework.

In this paper, the cloned gene sequences are synthesized based on the mutation position and its location on the chromosome by using the positional cloning approach. For instance, in the human gene mutational database (HGMD), the mutational information for splicing mutation is specified as IVS1-5 T > G indicates (IVS - intervening sequence or introns), first intron and five nucleotides before the consensus intron site AG, where the variant occurs in nucleotide G altered to T. IVS (+ve) denotes forward strand 3′– positive numbers from G of donor site invariant and IVS (−ve) denotes backward strand 5′ – negative numbers starting from G of acceptor site. The key idea in this paper is to spot out discriminative descriptors from diseased gene sequences based on splicing variants and to provide an effective machine learning solution for predicting the type of muscular dystrophy disease with the splicing mutations. Multi-class classification is worked out through data modeling of gene sequences. The synthetic mutational gene sequences are created, as the diseased gene sequences are not readily obtainable for this intricate disease. Positional cloning approach supports in generating disease gene sequences based on mutational information acquired from HGMD. SNP-, gene- and exon-based discriminative features are identified and used to train the model. An eminent muscular dystrophy disease prediction model is built using supervised learning techniques in scikit-learn environment. The data frame is built with the extracted features as numpy array. The data are normalized by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations. Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. This paper also deliberates the results of statistical learning carried out with the same set of gene sequences with synonymous and non-synonymous mutational descriptors.

The data frame is built with the Numpy array. Normalizing the data by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn. While learning the SVM model, the cost, gamma and kernel parameters are tuned to attain good results. Scoring parameters of the classifiers are evaluated using tenfold cross-validation using metric functions of scikit-learn library. Results of the disease identification model based on non-synonymous, synonymous and splicing mutations were analyzed.

Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. The performance of the classifiers are increased by using different estimators from the scikit-learn library. Several types of mutations such as missense, non-sense and silent mutations are also considered to build models through statistical learning technique and their results are analyzed.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations.

This study aims to investigate the influence of various categories of institutions on international joint ventures' (IJV) strategic mutation behavior from an institutional perspective.

The authors test their hypotheses using a sample of 494 Chinese small and medium IJVs over a three‐year period (2006‐2008). They conducted empirical result with Cox hazard models.

Changes in law environment will increase the likelihood of IJVs' strategic mutation. Changes in governmental policy will increase the likelihood of IJVs' strategic mutation. The positive correlativity between the variance of law environment and the propensity of IJVs' strategic mutation will be positively moderated by the distance of normative institutional pillar. The positive correlativity between the variance of governmental policy and the propensity of IJVs' strategic mutation will be negatively moderated by IJV performance.

Variation of regulatory institutional pillar increases the likelihood of IJVs' strategic mutation. Meanwhile, the effects of law environment and governmental policy, which are two types of regulatory institutional pillar, are moderated by normative institutional pillar and firms' performance, respectively.

The purpose of this paper is to improve the existing differential evolution (DE) mutation operator so as to accelerate its convergence.

A new general donor form for mutation operation in DE is presented, which defines a donor as a convex combination of the triplet of individuals selected for a mutation. Three new donor schemes from that form are deduced.

The three donor schemes were empirically compared with the original DE version and three existing variants of DE by using a suite of nine well‐known test functions, and were also demonstrated by a practical application case – training a neural network to approximate aerodynamic data. The obtained numerical simulation results suggested that these modifications to the mutation operator could improve the DE's convergence performance in both the convergence rate and the convergence reliability.

Further research is still needed for adequately explaining why it was possible to simultaneously improve both the convergence rate and the convergence reliability of DE to that extent despite the well‐known “No Free Lunch” theorem. Also further research is considered necessary for outlining more distinctively the particular class of problems, where the current observations can be generalized.

More complicated engineering problems could be solved sub‐optimally, whereas their real optimal solution may never be reached subject to the current computer capability.

Though DE has demonstrated a considerably better convergence performance than the other evolutionary algorithms (EAs), its convergence rate is still far from what is hoped for by scientists. On the one hand, a higher convergence rate is always expected for any optimization method used in seeking the global optimum of a non‐linear objective function. On the other hand, since all EAs, including DE, work with a population of solutions rather than a single solution, many evaluations of candidate solutions are required in the optimization process. If evaluation of candidate solutions is too time‐consuming, the overall optimization cost may become too expensive. One often has to limit the algorithm to operate within an acceptable time, which maybe is not enough to find the global optimum (optima), but enough to obtain a sub‐optimal solution. Therefore, it is continuously necessary to investigate the new strategies to improve the current DE algorithm.

The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems (AISs).

The proposed operators are applied in substitution to the suppression and mutation operators used in AISs. The proposed mechanisms were tested in the opt-aiNet, a continuous optimization algorithm inspired in the theories of immunology. The traditional opt-aiNet uses a suppression operator based on the immune network principles to remove similar cells and add random ones to control the diversity of the population. This procedure is computationally expensive, as the Euclidean distances between every possible pair of candidate solutions must be computed. This work proposes a self-organizing suppression mechanism inspired by the self-organizing criticality (SOC) phenomenon, which is less dependent on parameter selection. This work also proposes the use of the q-Gaussian mutation, which allows controlling the form of the mutation distribution during the optimization process. The algorithms were tested in a well-known benchmark for continuous optimization and in a bioinformatics problem: the rigid docking of proteins.

The proposed suppression operator presented some limitations in unimodal functions, but some interesting results were found in some highly multimodal functions. The proposed q-Gaussian mutation presented good performance in most of the test cases of the benchmark, and also in the docking problem.

First, the self-organizing suppression operator was able to reduce the complexity of the suppression stage in the opt-aiNet. Second, the use of q-Gaussian mutation in AISs presented better compromise between exploitation and exploration of the search space and, as a consequence, a better performance when compared to the traditional Gaussian mutation.

Ovarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.

In the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.

The XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.

The final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

A significant subset of patients (12 per cent) with triple negative breast cancer (TNBC) is BRCA mutation carriers, which can be identified through genetic testing. The purpose of this paper is to evaluate the referral practice for TNBC patients with reference to New South Wales (NSW) referral guidelines at the time of diagnosis and to assess the effectiveness of such guidelines in identifying BRCA mutations. Robust health governance requires monitoring of adherence to evidence-based guidelines such as those that underpin referral for cancer genetic testing in this clinical scenario.

The authors conducted a retrospective clinical audit of identified TNBC patients at St Vincent’s Hospital (SVH) between 2006 and 2016 in NSW, comparing referral practice to guidelines extant at the time of diagnosis. Family history was considered for age guideline-inappropriate referrals to SVH while the results of BRCA gene testing were assessed for all referred.

Overall, of the 17 patients eligible for referral based on the age criterion, 10 (58.5 per cent) were referred appropriately; however, there were substantial improvements from 2012 with 100 per cent referred. Of note, 12 (33.4 per cent) of 36 patients referred to SVH were referred outside of guidelines, pointing to other reasons for referral, such as patient age (OR 0.945; 95% CI 0.914–0.978) and calendar year (OR: 1.332; 95% CI: 1.127–1.575) at TNBC diagnosis. Referral guidelines captured 66.67 per cent of identified deleterious BRCA mutations in those tested.

Substantial under-referral of guideline-eligible patients was identified, with evidence-based guidelines effective in identifying high-risk individuals for BRCA mutation testing. There was, however, a substantial proportion of guideline-inappropriate referrals.