The SOS screen in Arabidopsis: a search for functions involved in DNA metabolism

The SOS screen, as originally described by Perkins et al. (1999), was setup with the aim of identifying Arabidopsis functions that might potentially be involved in the DNA metabolism. Such functions, when expressed in bacteria, are prone to disturb replication and thus trigger the SOS response. Consistently, expression of AtRAD51 and AtDMC1 induced the SOS response in bacteria, even affecting E. coli viability. 100 SOS-inducing cDNAs were isolated from a cDNA library constructed from an Arabidopsis cell suspension that was found to highly express meiotic genes. A large proportion of these SOS+ candidates are clearly related to the DNA metabolism, others could be involved in the RNA metabolism, while the remaining cDNAs encode either totally unknown proteins or proteins that were considered as irrelevant. Seven SOS+ candidate genes are induced following gamma irradiation. The in planta function of several of the SOS-inducing clones was investigated using T-DNA insertional mutants or RNA interference. Only one SOS+ candidate, among those examined, exhibited a defined phenotype: silenced plants for DUT1 were sensitive to 5-fluoro-uracil (5FU), as is the case of the leaky dut-1 mutant in E. coli that are affected in dUTPase activity. dUTPase is essential to prevent uracil incorporation in the course of DNA replication.

Weaknesses in the initialisation process of the Common Scrambling Algorithm Stream Cipher

The Common Scrambling Algorithm Stream Cipher (CSASC) is a shift register based stream cipher designed to encrypt digital video broadcast. CSA-SC produces a pseudo-random binary sequence that is used to mask the contents of the transmission. In this paper, we analyse the initialisation process of the CSA-SC keystream generator and demonstrate weaknesses which lead to state convergence, slid pairs and shifted keystreams. As a result, the cipher may be vulnerable to distinguishing attacks, time-memory-data trade-off attacks or slide attacks.

Perturbing event logs to identify cost reduction opportunities: a genetic algorithm-based approach

Organisations are constantly seeking new ways to improve operational efficiencies. This research study investigates a novel way to identify potential efficiency gains in business operations by observing how they are carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how they can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A genetic algorithm-based approach is proposed to explore and assess alternative process execution scenarios, where the objective function is represented by a comprehensive cost structure that captures different process dimensions. Experiments conducted with different variants of the genetic algorithm evaluate the approach's feasibility. The findings demonstrate that a genetic algorithm-based approach is able to make use of cost reduction as a way to identify improved execution scenarios in terms of reduced case durations and increased resource utilisation. The ultimate aim is to utilise cost-related insights gained from such improved scenarios to put forward recommendations for reducing process-related cost within organisations.

In search of the missing link in CSR : an analysis of the government influence

Research on corporate social responsibility (CSR) has not differentiated the varying degree of government influence in its multiple roles on different types of CSR. However, different il1fluences resulting from the different roles he govemment plays in the CSR arena an shape different CSR behavior. This paper examines the efficacy of the govemment influence on four types of corporate social responsibilities: legal, economic, philanthropic and ethical. We argue that the govemment influence on firms' CSR disposition varies in intensizv and salience depending on the level of interdependency between the government and the firm and the deployable strategies available to the govemment. We have identified the strongest link between the government as mandator and legal CSR and weakest link between the govemment as endorser and ethical CSR. We provide implications for government policy makers and future studies in this area.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Women IN Sport or Women OR Sport - The search for work/sport/life balance.

The current ‘holy grail’ for our health and well-being centres around the search for, and establishment of, a work/life balance. For many individuals, this appears to be an ever-elusive goal – forever slipping from our grasp as we juggle the day-to-day battle for our attention and time from an array of sources. When we add the word ‘Women’ to this mix, often the number of sources related to these demands multiplies in alignment with the number of roles we fill. To take this to even another level, consider the addition of the words ‘Sport’ or ‘Elite Athlete’ to ‘Women’ and ‘Work/Life Balance’, and the search for the ‘holy grail’ becomes more literal! Many sportswomen at the elite level face significant challenges in balancing working to support themselves and/or their families, studying to lay the foundations of a post-sport career, (often) spending the equivalent of full-time hours training towards their sporting goals, and additionally investing in the things that are important for them outside of these two areas – the ‘Life’ component. Getting the work/life balance ‘balanced’ has been suggested to be a key component of investing in our health and well-being. The same is applicable to sportswomen, with the added suggestion that if the balance between work/sport/life is achieved, this can positively impact upon sporting performance itself. These ideas and observations will be explored via experience within the Australian elite sporting environment from a psychologist’s perspective, with questions and invitations for further discussion.

Phonetic spoken term search using topic information

The aim of spoken term detection (STD) is to find all occurrences of a specified query term in a large audio database. This process is usually divided into two steps: indexing and search. In a previous study, it was shown that knowing the topic of an audio document would help to improve the accuracy of indexing step which results in a better performance for STD system. In this paper, we propose the use of topic information not only in the indexing step, but also in the search step. Results of our experiments show that topic information could also be used in search step to improve the STD accuracy.

Mission path planning of aerial survey tasks using hybrid game evolutionary algorithm

The work presented in this report is aimed to implement a cost-effective offline mission path planner for aerial inspection tasks of large linear infrastructures. Like most real-world optimisation problems, mission path planning involves a number of objectives which ideally should be minimised simultaneously. Understandably, the objectives of a practical optimisation problem are conflicting each other and the minimisation of one of them necessarily implies the impossibility to minimise the other ones. This leads to the need to find a set of optimal solutions for the problem; once such a set of available options is produced, the mission planning problem is reduced to a decision making problem for the mission specialists, who will choose the solution which best fit the requirements of the mission. The goal of this work is then to develop a Multi-Objective optimisation tool able to provide the mission specialists a set of optimal solutions for the inspection task amongst which the final trajectory will be chosen, given the environment data, the mission requirements and the definition of the objectives to minimise. All the possible optimal solutions of a Multi-Objective optimisation problem are said to form the Pareto-optimal front of the problem. For any of the Pareto-optimal solutions, it is impossible to improve one objective without worsening at least another one. Amongst a set of Pareto-optimal solutions, no solution is absolutely better than another and the final choice must be a trade-off of the objectives of the problem. Multi-Objective Evolutionary Algorithms (MOEAs) are recognised to be a convenient method for exploring the Pareto-optimal front of Multi-Objective optimization problems. Their efficiency is due to their parallelism architecture which allows to find several optimal solutions at each time

Users’ perception of using web search engines and their impact on the perceived intention to reuse the systems

In today’s world of information-driven society, many studies are exploring usefulness and ease of use of the technology. The research into personalizing next-generation user interface is also ever increasing. A better understanding of factors that influence users’ perception of web search engine performance would contribute in achieving this. This study measures and examines how users’ perceived level of prior knowledge and experience influence their perceived level of satisfaction of using the web search engines, and how their perceived level of satisfaction affects their perceived intention to reuse the system. 50 participants from an Australian university participated in the current study, where they performed three search tasks and completed survey questionnaires. A research model was constructed to test the proposed hypotheses. Correlation and regression analyses results indicated a significant correlation between (1) users’ prior level of experience and their perceived level of satisfaction in using the web search engines, and (2) their perceived level of satisfaction in using the systems and their perceived intention to reuse the systems. A theoretical model is proposed to illustrate the causal relationships. The implications and limitations of the study are also discussed.

Chaperone-usher fimbriae of Escherichia coli

Chaperone-usher (CU) fimbriae are adhesive surface organelles common to many Gram-negative bacteria. Escherichia coli genomes contain a large variety of characterised and putative CU fimbrial operons, however, the classification and annotation of individual loci remains problematic. Here we describe a classification model based on usher phylogeny and genomic locus position to categorise the CU fimbrial types of E. coli. Using the BLASTp algorithm, an iterative usher protein search was performed to identify CU fimbrial operons from 35 E. coli (and one Escherichia fergusonnii) genomes representing different pathogenic and phylogenic lineages, as well as 132 Escherichia spp. plasmids. A total of 458 CU fimbrial operons were identified, which represent 38 distinct fimbrial types based on genomic locus position and usher phylogeny. The majority of fimbrial operon types occupied a specific locus position on the E. coli chromosome; exceptions were associated with mobile genetic elements. A group of core-associated E. coli CU fimbriae were defined and include the Type 1, Yad, Yeh, Yfc, Mat, F9 and Ybg fimbriae. These genes were present as intact or disrupted operons at the same genetic locus in almost all genomes examined. Evaluation of the distribution and prevalence of CU fimbrial types among different pathogenic and phylogenic groups provides an overview of group specific fimbrial profiles and insight into the ancestry and evolution of CU fimbriae in E. coli.

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

Selection procedures for module discovery : exploring evolutionary algorithms for cognitive science

Evolutionary algorithms are playing an increasingly important role as search methods in cognitive science domains. In this study, methodological issues in the use of evolutionary algorithms were investigated via simulations in which procedures were systematically varied to modify the selection pressures on populations of evolving agents. Traditional roulette wheel, tournament, and variations of these selection algorithms were compared on the “needle-in-a-haystack” problem developed by Hinton and Nowlan in their 1987 study of the Baldwin effect. The task is an important one for cognitive science, as it demonstrates the power of learning as a local search technique in smoothing a fitness landscape that lacks gradient information. One aspect that has continued to foster interest in the problem is the observation of residual learning ability in simulated populations even after long periods of time. Effective evolutionary algorithms balance their search effort between broad exploration of the search space and in-depth exploitation of promising solutions already found. Issues discussed include the differential effects of rank and proportional selection, the tradeoff between migration of populations towards good solutions and maintenance of diversity, and the development of measures that illustrate how each selection algorithm affects the search process over generations. We show that both roulette wheel and tournament algorithms can be modified to appropriately balance search between exploration and exploitation, and effectively eliminate residual learning in this problem.

Decision support for the efficient annotation of bioacoustic events

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories. We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.