194 resultados para sequence components
Resumo:
Webb et al. (2009) described a late Pleistocenecoral sample wherein the diagenetic stabilization of original coral aragonite to meteoric calcite was halted more or less mid-way through the process, allowing direct comparison of pre-diagenetic and post-diagenetic microstructure and trace element distributions. Those authors found that the rare earth elements (REEs) were relatively stable during meteoric diagenesis, unlike divalent cations such as Sr,and it was thus concluded that original, in this case marine, REE distributions potentially could be preserved through the meteoric carbonate stabilization process that must have affected many, if not most, ancient limestones. Although this was not the case in the analysed sample, they noted that where such diagenesis took place in laterally transported groundwater, trace elements derived from that groundwater could be incorporated into diagenetic calcite, thus altering the initial REE distribution (Banner et al., 1988). Hence, the paper was concerned with the diagenetic behaviour of REEs in a groundwater-dominated karst system. The comment offered by Johannesson (2011) does not question those research results, but rather, seeks to clarify an interpretation made by Webb et al. (2009) of an earlier paper, Johannesson et al. (2006).
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Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.
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We present a formalism for the analysis of sensitivity of nuclear magnetic resonance pulse sequences to variations of pulse sequence parameters, such as radiofrequency pulses, gradient pulses or evolution delays. The formalism enables the calculation of compact, analytic expressions for the derivatives of the density matrix and the observed signal with respect to the parameters varied. The analysis is based on two constructs computed in the course of modified density-matrix simulations: the error interrogation operators and error commutators. The approach presented is consequently named the Error Commutator Formalism (ECF). It is used to evaluate the sensitivity of the density matrix to parameter variation based on the simulations carried out for the ideal parameters, obviating the need for finite-difference calculations of signal errors. The ECF analysis therefore carries a computational cost comparable to a single density-matrix or product-operator simulation. Its application is illustrated using a number of examples from basic NMR spectroscopy. We show that the strength of the ECF is its ability to provide analytic insights into the propagation of errors through pulse sequences and the behaviour of signal errors under phase cycling. Furthermore, the approach is algorithmic and easily amenable to implementation in the form of a programming code. It is envisaged that it could be incorporated into standard NMR product-operator simulation packages.
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This paper considers VECMs for variables exhibiting cointegration and common features in the transitory components. While the presence of cointegration between the permanent components of series reduces the rank of the long-run multiplier matrix, a common feature among the transitory components leads to a rank reduction in the matrix summarizing short-run dynamics. The common feature also implies that there exists linear combinations of the first-differenced variables in a cointegrated VAR that are white noise and traditional tests focus on testing for this characteristic. An alternative, however, is to test the rank of the short-run dynamics matrix directly. Consequently, we use the literature on testing the rank of a matrix to produce some alternative test statistics. We also show that these are identical to one of the traditional tests. The performance of the different methods is illustrated in a Monte Carlo analysis which is then used to re-examine an existing empirical study. Finally, this approach is applied to provide a check for the presence of common dynamics in DSGE models.
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The major limitation of current typing methods for Streptococcus pyogenes, such as emm sequence typing and T typing, is that these are based on regions subject to considerable selective pressure. Multilocus sequence typing (MLST) is a better indicator of the genetic backbone of a strain but is not widely used due to high costs. The objective of this study was to develop a robust and cost-effective alternative to S. pyogenes MLST. A 10-member single nucleotide polymorphism (SNP) set that provides a Simpson’s Index of Diversity (D) of 0.99 with respect to the S. pyogenes MLST database was derived. A typing format involving high-resolution melting (HRM) analysis of small fragments nucleated by each of the resolution-optimized SNPs was developed. The fragments were 59–119 bp in size and, based on differences in G+C content, were predicted to generate three to six resolvable HRM curves. The combination of curves across each of the 10 fragments can be used to generate a melt type (MelT) for each sequence type (ST). The 525 STs currently in the S. pyogenes MLST database are predicted to resolve into 298 distinct MelTs and the method is calculated to provide a D of 0.996 against the MLST database. The MelTs are concordant with the S. pyogenes population structure. To validate the method we examined clinical isolates of S. pyogenes of 70 STs. Curves were generated as predicted by G+C content discriminating the 70 STs into 65 distinct MelTs.
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The 12 to 13 July 2003 andesite lava dome collapse at the Soufrière Hills volcano, Montserrat, provides the first opportunity to document comprehensively both the sub-aerial and submarine sequence of events for an eruption. Numerous pyroclastic flows entered the ocean during the collapse, depositing approximately 90% of the total material into the submarine environment. During peak collapse conditions, as the main flow penetrated the air–ocean interface, phreatic explosions were observed and a surge cloud decoupled from the main flow body to travel 2 to 3 km over the ocean surface before settling. The bulk of the flow was submerged and rapidly mixed with sea water forming a water-saturated mass flow. Efficient sorting and physical differentiation occurred within the flow before initial deposition at 500 m water depth. The coarsest components (∼60% of the total volume) were deposited proximally from a dense granular flow, while the finer components (∼40%) were efficiently elutriated into the overlying part of the flow, which evolved into a far-reaching turbidity current.
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New substation automation applications, such as sampled value process buses and synchrophasors, require sampling accuracy of 1 µs or better. The Precision Time Protocol (PTP), IEEE Std 1588, achieves this level of performance and integrates well into Ethernet based substation networks. This paper takes a systematic approach to the performance evaluation of commercially available PTP devices (grandmaster, slave, transparent and boundary clocks) from a variety of manufacturers. The ``error budget'' is set by the performance requirements of each application. The ``expenditure'' of this error budget by each component is valuable information for a system designer. The component information is used to design a synchronization system that meets the overall functional requirements. The quantitative performance data presented shows that this testing is effective and informative. Results from testing PTP performance in the presence of sampled value process bus traffic demonstrate the benefit of a ``bottom up'' component testing approach combined with ``top down'' system verification tests. A test method that uses a precision Ethernet capture card, rather than dedicated PTP test sets, to determine the Correction Field Error of transparent clocks is presented. This test is particularly relevant for highly loaded Ethernet networks with stringent timing requirements. The methods presented can be used for development purposes by manufacturers, or by system integrators for acceptance testing. A sampled value process bus was used as the test application for the systematic approach described in this paper. The test approach was applied, components were selected, and the system performance verified to meet the application's requirements. Systematic testing, as presented in this paper, is applicable to a range of industries that use, rather than develop, PTP for time transfer.
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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.
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This report documents the results of a qualitative study of young people experiencing disadvantage who are responsible for feeding themselves. The purpose of the study was to explore the knowledge, skills and behaviours they use in their day to day eating. The results of this study were considered alongside those of an earlier study of Australian food experts in order to develop a definition of food literacy, identify its components and propose a model for its relationship with diet quality and chronic disease. This young people's study also examined how young people's relationship with food developed and its relationship with the social determinants of health. This report will help practitioners working in food literacy better target their practice and investment.
The use of virtual prototyping to rehearse the sequence of construction work involving mobile cranes
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Purpose – Rehearsing practical site operations is without doubt one of the most effective methods for minimising planning mistakes, because of the learning that takes place during the rehearsal activity. However, real rehearsal is not a practical solution for on-site construction activities, as it not only involves a considerable amount of cost but can also have adverse environmental implications. One approach to overcoming this is by the use of virtual rehearsals. The purpose of this paper is to investigate an approach to simulation of the motion of cranes in order to test the feasibility of associated construction sequencing and generate construction schedules for review and visualisation. Design/methodology/approach – The paper describes a system involving two technologies, virtual prototyping (VP) and four-dimensional (4D) simulation, to assist construction planners in testing the sequence of construction activities when mobile cranes are involved. The system consists of five modules, comprising input, database, equipment, process and output, and is capable of detecting potential collisions. A real-world trial is described in which the system was tested and validated. Findings – Feedback from the planners involved in the trial indicated that they found the system to be useful in its present form and that they would welcome its further development into a fully automated platform for validating construction sequencing decisions. Research limitations/implications – The tool has the potential to provide a cost-effective means of improving construction planning. However, it is limited at present to the specific case of crane movement under special consideration. Originality/value – This paper presents a large-scale, real life case of applying VP technology in planning construction processes and activities.
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The native Australian fly Drosophila serrata belongs to the highly speciose montium subgroup of the melanogaster species group. It has recently emerged as an excellent model system with which to address a number of important questions, including the evolution of traits under sexual selection and traits involved in climatic adaptation along latitudinal gradients. Understanding the molecular genetic basis of such traits has been limited by a lack of genomic resources for this species. Here, we present the first expressed sequence tag (EST) collection for D. serrata that will enable the identification of genes underlying sexually-selected phenotypes and physiological responses to environmental change and may help resolve controversial phylogenetic relationships within the montium subgroup.
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Background. A variety of interactions between up to three different movement proteins (MPs), the coat protein (CP) and genomic DNA mediate the inter- and intra-cellular movement of geminiviruses in the genus Begomovirus. Although movement of viruses in the genus Mastrevirus is less well characterized, direct interactions between a single MP and the CP of these viruses is also clearly involved in both intra- and intercellular trafficking of virus genomic DNA. However, it is currently unknown how specific these MP-CP interactions are, nor how disruption of these interactions might impact on virus viability. Results. Using chimaeric genomes of two strains of Maize streak virus (MSV) we adopted a genetic approach to investigate the gross biological effects of interfering with interactions between virus MP and CP homologues derived from genetically distinct MSV isolates. MP and CP genes were reciprocally exchanged, individually and in pairs, between maize (MSV-Kom)- and Setaria sp. (MSV-Set)-adapted isolates sharing 78% genome-wide sequence identity. All chimaeras were infectious in Zea mays c.v. Jubilee and were characterized in terms of symptomatology and infection efficiency. Compared with their parental viruses, all the chimaeras were attenuated in symptom severity, infection efficiency, and the rate at which symptoms appeared. The exchange of individual MP and CP genes resulted in lower infection efficiency and reduced symptom severity in comparison with exchanges of matched MP-CP pairs. Conclusion. Specific interactions between the mastrevirus MP and CP genes themselves and/or their expression products are important determinants of infection efficiency, rate of symptom development and symptom severity. © 2008 van der Walt et al; licensee BioMed Central Ltd.
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This paper evaluates the age-associated changes of resting ventilation of 115 high- and low-altitude Aymara subjects, of whom 61 were from the rural Aymara village of Ventilla situated at an average altitude of 4,200 m and 54 from the rural village of Caranavi situated at an average altitude of 900 m. Comparison of the age patterns of resting ventilation suggests the following conclusions: 1) the resting ventilation (ml/kg/min) of high-altitude natives is markedly higher than that of low-altitude natives; 2) the age decline of ventilation is similar in both lowlanders and highlanders, but the starting point and therefore the age decline are much higher at high altitude; 3) the resting ventilation that characterizes high-altitude Andean natives is developmentally expressed in the same manner as it is at low altitude; and 4) the resting ventilation (ml/kg/min) of Aymara high-altitude natives is between 40–80% lower than that of Tibetans. Am J Phys Anthropol 109:295–301, 1999. © 1999 Wiley-Liss, Inc.
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Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.