Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Genetic algorithms to schedule container transfers at multimodal terminals

Optimising the container transfer schedule at the multimodal terminals is known to be NP-hard, which implies that the best solution becomes computationally infeasible as problem sizes increase. Genetic Algorithm (GA) techniques are used to reduce container handling/transfer times and ships' time at the port by speeding up handling operations. The GA is chosen due to the relatively good results that have been reported even with the simplest GA implementations to obtain near-optimal solutions in reasonable time. Also discussed, is the application of the model to assess the consequences of increased scheduled throughput time as well as different strategies such as the alternative plant layouts, storage policies and number of yard machines. A real data set used for the solution and subsequent sensitivity analysis is applied to the alternative plant layouts, storage policies and number of yard machines.

A penalty-based grouping genetic algorithm for multiple composite SaaS components clustering in Cloud

Software as a Service (SaaS) in Cloud is getting more and more significant among software users and providers recently. A SaaS that is delivered as composite application has many benefits including reduced delivery costs, flexible offers of the SaaS functions and decreased subscription cost for users. However, this approach has introduced a new problem in managing the resources allocated to the composite SaaS. The resource allocation that has been done at the initial stage may be overloaded or wasted due to the dynamic environment of a Cloud. A typical data center resource management usually triggers a placement reconfiguration for the SaaS in order to maintain its performance as well as to minimize the resource used. Existing approaches for this problem often ignore the underlying dependencies between SaaS components. In addition, the reconfiguration also has to comply with SaaS constraints in terms of its resource requirements, placement requirement as well as its SLA. To tackle the problem, this paper proposes a penalty-based Grouping Genetic Algorithm for multiple composite SaaS components clustering in Cloud. The main objective is to minimize the resource used by the SaaS by clustering its component without violating any constraint. Experimental results demonstrate the feasibility and the scalability of the proposed algorithm.

Energy-efficient virtual machine placement in data centers by genetic algorithm

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases.

Australian lungfish (Neoceratodus forsteri: Dipnoi) have low genetic variation at allozyme and mitochondrial DNA loci : a conservation alert?

Genetic variation at allozyme and mitochondrial DNA loci was investigated in the Australian lungfish, Neoceratodus forsteri Krefft 1870. Tissue samples for genetic analysis were taken non-lethally from 278 individuals representing two spatially distinct endemic populations (Mary and Burnett rivers), as well as one population thought to be derived from an anthropogenic translocation in the 1890's (Brisbane river). Two of 24 allozyme loci resolved from muscle tissue were polymorphic. Mitochondrial DNA nucleotide sequence diversity estimated across 2,235 base pairs in each of 40 individuals ranged between 0.000423 and 0.001470 per river. Low genetic variation at allozyme and mitochondrial loci could be attributed to population bottlenecks, possibly induced by Pleistocene aridity. Limited genetic differentiation was detected among rivers using nuclear and mitochondrial markers suggesting that admixture may have occurred between the endemic Mary and Burnett populations during periods of low sea level when the drainages may have converged before reaching the ocean. Genetic data was consistent with the explanation that lungfish were introduced to the Brisbane river from the Mary river. Further research using more variable genetic loci is needed before the conservation status of populations can be determined, particularly as anthropogenic demands on lungfish habitat are increasing. In the interim we recommend a management strategy aimed at conserving existing genetic variation within and between rivers.

Kallikrein 14 is down-regulated by androgen receptor signalling and harbours genetic variation that is associated with prostate tumour aggressiveness

Kallikrein 14 (KLK14) has been proposed as a useful prognostic marker in prostate cancer, with expression reported to be associated with tumour characteristics such as higher stage and Gleason score. KLK14 tumour expression has also shown the potential to predict prostate cancer patients at risk of disease recurrence after radical prostatectomy. The KLKs are a remarkably hormone-responsive family of genes, although detailed studies of androgen regulation of KLK14 in prostate cancer have not been undertaken to date. Using in vitro studies, we have demonstrated that unlike many other prostatic KLK genes that are strictly androgen responsive, KLK14 is more broadly expressed and inversely androgen regulated in prostate cancer cells. Given these results and evidence that KLK14 may play a role in prostate cancer prognosis, we also investigated whether common genetic variants in the KLK14 locus are associated with risk and/or aggressiveness of prostate cancer in approximately 1200 prostate cancer cases and 1300 male controls. Of 41 single nucleotide polymorphisms assessed, three were associated with higher Gleason score (≥7): rs17728459 and rs4802765, both located upstream of KLK14, and rs35287116, which encodes a p.Gln33Arg substitution in the KLK14 signal peptide region. Our findings provide further support for KLK14 as a marker of prognosis in prostate cancer.

Genetic analysis of maize streak virus isolates from Uganda reveals widespread distribution of a recombinant variant

Maize streak virus (MSV) contributes significantly to the problem of extremely low African maize yields. Whilst a diverse range of MSV and MSV-like viruses are endemic in sub-Saharan Africa and neighbouring islands, only a single group of maize-adapted variants - MSV subtypes A1 -A6 - causes severe enough disease in maize to influence yields substantially. In order to assist in designing effective strategies to control MSV in maize, a large survey covering 155 locations was conducted to assess the diversity, distribution and genetic characteristics of the Ugandan MSV-A population. PCR-restriction fragment-length polymorphism analyses of 391 virus isolates identified 49 genetic variants. Sixty-two full-genome sequences were determined, 52 of which were detectably recombinant. All but two recombinants contained predominantly MSV-A1-like sequences. Of the ten distinct recombination events observed, seven involved inter-MSV-A subtype recombination and three involved intra-MSV-A1 recombination. One of the intra-MSV-A1 recombinants, designated MSV-A1 UgIII, accounted for >60% of all MSV infections sampled throughout Uganda. Although recombination may be an important factor in the emergence of novel geminivirus variants, it is demonstrated that its characteristics in MSV are quite different from those observed in related African cassava-infecting geminivirus species. © 2007 SGM.

Global genetic diversity and geographical and host-species distribution of beak and feather disease virus isolates

Psittacine beak and feather disease (PBFD) has a broad host range and is widespread in wild and captive psittacine populations in Asia, Africa, the Americas, Europe and Australasia. Beak and feather disease circovirus (BFDV) is the causative agent. BFDV has an ~2 kb single stranded circular DNA genome encoding just two proteins (Rep and CP). In this study we provide support for demarcation of BFDV strains by phylogenetic analysis of 65 complete genomes from databases and 22 new BFDV sequences isolated from infected psittacines in South Africa. We propose 94% genome-wide sequence identity as a strain demarcation threshold, with isolates sharing > 94% identity belonging to the same strain, and strain subtypes sharing> 98% identity. Currently, BFDV diversity falls within 14 strains, with five highly divergent isolates from budgerigars probably representing a new species of circovirus with three strains (budgerigar circovirus; BCV-A, -B and -C). The geographical distribution of BFDV and BCV strains is strongly linked to the international trade in exotic birds; strains with more than one host are generally located in the same geographical area. Lastly, we examined BFDV and BCV sequences for evidence of recombination, and determined that recombination had occurred in most BFDV and BCV strains. We established that there were two globally significant recombination hotspots in the viral genome: the first is along the entire intergenic region and the second is in the C-terminal portion of the CP ORF. The implications of our results for the taxonomy and classification of circoviruses are discussed. © 2011 SGM.

Adaptive sizing of populations and number of islands in distributed genetic algorithms

Deciding the appropriate population size and number of is- lands for distributed island-model genetic algorithms is often critical to the algorithm’s success. This paper outlines a method that automatically searches for good combinations of island population sizes and the number of islands. The method is based on a race between competing parameter sets, and collaborative seeding of new parameter sets. This method is applicable to any problem, and makes distributed genetic algorithms easier to use by reducing the number of user-set parameters. The experimental results show that the proposed method robustly and reliably finds population and islands settings that are comparable to those found with traditional trial-and-error approaches.

Dynamic optimization of migration topology in internet-based distributed genetic algorithms

Distributed Genetic Algorithms (DGAs) designed for the Internet have to take its high communication cost into consideration. For island model GAs, the migration topology has a major impact on DGA performance. This paper describes and evaluates an adaptive migration topology optimizer that keeps the communication load low while maintaining high solution quality. Experiments on benchmark problems show that the optimized topology outperforms static or random topologies of the same degree of connectivity. The applicability of the method on real-world problems is demonstrated on a hard optimization problem in VLSI design.

Genetic polymorphisms in the human tissue kallikrein (KLK) locus and their implication in various malignant and non-malignant diseases

The Kallikrein (KLK) gene locus encodes a family of serine proteases and is the largest contiguous cluster of protease-encoding genes attributed an evolutionary age of 330 million years. The KLK locus has been implicated as a high susceptibility risk loci in numerous cancer studies through the last decade. The KLK3 gene already has established clinical relevance as a biomarker in prostate cancer prognosis through its encoded protein, prostate-specific antigen. Data mined through genome-wide association studies (GWAS) and next-generation sequencing point to many important candidate single nucleotide polymorphisms (SNPs) in KLK3 and other KLK genes. SNPs in the KLK locus have been found to be associated with several diseases including cancer, hypertension, cardiovascular disease and atopic dermatitis. Moreover, introducing a model incorporating SNPs to improve the efficiency of prostate-specific antigen in detecting malignant states of prostate cancer has been recently suggested. Establishing the functional relevance of these newly-discovered SNPs, and their interactions with each other, through in silico investigations followed by experimental validation, can accelerate the discovery of diagnostic and prognostic biomarkers. In this review, we discuss the various genetic association studies on the KLK loci identified either through candidate gene association studies or at the GWAS and post-GWAS front to aid researchers in streamlining their search for the most significant, relevant and therapeutically promising candidate KLK gene and/or SNP for future investigations.

Genetic association of the KLK4 locus with risk of prostate cancer

The Kallikrein-related peptidase, KLK4, has been shown to be significantly overexpressed in prostate tumours in numerous studies and is suggested to be a potential biomarker for prostate cancer. KLK4 may also play a role in prostate cancer progression through its involvement in epithelial-mesenchymal transition, a more aggressive phenotype, and metastases to bone. It is well known that genetic variation has the potential to affect gene expression and/or various protein characteristics and hence we sought to investigate the possible role of single nucleotide polymorphisms (SNPs) in the KLK4 gene in prostate cancer. Assessment of 61 SNPs in the KLK4 locus (±10 kb) in approximately 1300 prostate cancer cases and 1300 male controls for associations with prostate cancer risk and/or prostate tumour aggressiveness (Gleason score <7 versus ≥7) revealed 7 SNPs to be associated with a decreased risk of prostate cancer at the Ptrend<0.05 significance level. Three of these SNPs, rs268923, rs56112930 and the HapMap tagSNP rs7248321, are located several kb upstream of KLK4; rs1654551 encodes a non-synonymous serine to alanine substitution at position 22 of the long isoform of the KLK4 protein, and the remaining 3 risk-associated SNPs, rs1701927, rs1090649 and rs806019, are located downstream of KLK4 and are in high linkage disequilibrium with each other (r2≥0.98). Our findings provide suggestive evidence of a role for genetic variation in the KLK4 locus in prostate cancer predisposition.

A web application to perform linkage disequilibrium and linkage analyses on a computational grid

Motivation: Unravelling the genetic architecture of complex traits requires large amounts of data, sophisticated models and large computational resources. The lack of user-friendly software incorporating all these requisites is delaying progress in the analysis of complex traits. Methods: Linkage disequilibrium and linkage analysis (LDLA) is a high-resolution gene mapping approach based on sophisticated mixed linear models, applicable to any population structure. LDLA can use population history information in addition to pedigree and molecular markers to decompose traits into genetic components. Analyses are distributed in parallel over a large public grid of computers in the UK. Results: We have proven the performance of LDLA with analyses of simulated data. There are real gains in statistical power to detect quantitative trait loci when using historical information compared with traditional linkage analysis. Moreover, the use of a grid of computers significantly increases computational speed, hence allowing analyses that would have been prohibitive on a single computer. © The Author 2009. Published by Oxford University Press. All rights reserved.

Genetic variability and antimicrobial resistance of Ureaplasma parvum in response to maternal erythromycin treatment : a study in pregnant sheep

Background: Ureaplasmas are the most frequently isolated microorganisms from the amniotic fluid (AF) of pregnant women and can cause chronic infections that are difficult to eradicate with standard macrolide treatment. We tested the effects of erythromycin treatment on phenotypic and genotypic markers of ureaplasmal antimicrobial resistance in sheep. Method: At 50 days of gestation (d, term=145d) 12 pregnant ewes received intra-amniotic injections of U. parvum serovar 3 (erythromycin-sensitive, 2x104 colony-forming-units). At 100d ewes received: erythromycin treatment (500 mg, q3h for 4 days, IM, n=6) or no treatment (n=6). Fetuses were delivered surgically (125d) and AF and chorioamnion were collected for: culture, minimum inhibitory concentration (MIC) and minimum biofilm inhibitory concentration (MBIC) testing; 23S rRNA sequencing; and detection of macrolide-lincosamide-streptogramin resistance (MLSr) genes. Results: MICs of erythromycin, azithromycin and roxithromycin against AF isolates were low (range = 0.06 mg/L to 1.0 mg/L); however, chorioamnion isolates demonstrated increased resistance to roxithromycin (0.13 – 5.33 mg/L). 62.5% of chorioamnion ureaplasmas formed biofilms in vitro and mutations (125 nucleotides, 29.6%) were found in the 23S rRNA gene (domain V) of chorioamnion (but not AF) ureaplasmas. MLSr genes (ermB, msrC and msrD) were detected in 100% of chorioamnion isolates and only msrD was detected in AF isolates (40%). Conclusions: 23S rRNA mutations and MLSr genes occurred independently of erythromycin treatment, suggesting that the anatomical site of infection and microenvironment may exert selective pressures on ureaplasmas that cause genetic changes and alter antimicrobial sensitivity profiles. These results have serious implications for treatment of in utero infections.