Rice Tungro Bacilliform

Many well-known specialists have contributed to this book which presents for the first time an in-depth look at the viruses, their satellites and the retrotransposons infecting (or occuring in) one plant family: the Poaceae (Gramineae). After molecular and biological descriptions of the viruses to species level, virus diseases are presented by crop: barley, maize, rice, rye, sorghum, sugarcane, triticales, wheats, forage, ornamental and lawn. A detailed index of the viruses and taxonomic lists will help readers in the search for information.

Improving detection probabilities for pests in stored grains

BACKGROUND: The presence of insects in stored grains is a significant problem for grain farmers, bulk grain handlers and distributors worldwide. Inspections of bulk grain commodities is essential to detect pests and therefore to reduce the risk of their presence in exported goods. It has been well documented that insect pests cluster in response to factors such as microclimatic conditions within bulk grain. Statistical sampling methodologies for grains, however, have typically considered pests and pathogens to be homogeneously distributed throughout grain commodities. In this paper we demonstrate a sampling methodology that accounts for the heterogeneous distribution of insects in bulk grains. RESULTS: We show that failure to account for the heterogeneous distribution of pests may lead to overestimates of the capacity for a sampling program to detect insects in bulk grains. Our results indicate the importance of the proportion of grain that is infested in addition to the density of pests within the infested grain. We also demonstrate that the probability of detecting pests in bulk grains increases as the number of sub-samples increases, even when the total volume or mass of grain sampled remains constant. CONCLUSION: This study demonstrates the importance of considering an appropriate biological model when developing sampling methodologies for insect pests. Accounting for a heterogeneous distribution of pests leads to a considerable improvement in the detection of pests over traditional sampling models.

Clone Detection in Repositories of Business Process Models

Business process model repositories capture precious knowledge about an organization or a business domain. In many cases, these repositories contain hundreds or even thousands of models and they represent several man-years of effort. Over time, process model repositories tend to accumulate duplicate fragments, as new process models are created by copying and merging fragments from other models. This calls for methods to detect duplicate fragments in process models that can be refactored as separate subprocesses in order to increase readability and maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

Fast and accurate phonetic spoken term detection

For the first time in human history, large volumes of spoken audio are being broadcast, made available on the internet, archived, and monitored for surveillance every day. New technologies are urgently required to unlock these vast and powerful stores of information. Spoken Term Detection (STD) systems provide access to speech collections by detecting individual occurrences of specified search terms. The aim of this work is to develop improved STD solutions based on phonetic indexing. In particular, this work aims to develop phonetic STD systems for applications that require open-vocabulary search, fast indexing and search speeds, and accurate term detection. Within this scope, novel contributions are made within two research themes, that is, accommodating phone recognition errors and, secondly, modelling uncertainty with probabilistic scores. A state-of-the-art Dynamic Match Lattice Spotting (DMLS) system is used to address the problem of accommodating phone recognition errors with approximate phone sequence matching. Extensive experimentation on the use of DMLS is carried out and a number of novel enhancements are developed that provide for faster indexing, faster search, and improved accuracy. Firstly, a novel comparison of methods for deriving a phone error cost model is presented to improve STD accuracy, resulting in up to a 33% improvement in the Figure of Merit. A method is also presented for drastically increasing the speed of DMLS search by at least an order of magnitude with no loss in search accuracy. An investigation is then presented of the effects of increasing indexing speed for DMLS, by using simpler modelling during phone decoding, with results highlighting the trade-off between indexing speed, search speed and search accuracy. The Figure of Merit is further improved by up to 25% using a novel proposal to utilise word-level language modelling during DMLS indexing. Analysis shows that this use of language modelling can, however, be unhelpful or even disadvantageous for terms with a very low language model probability. The DMLS approach to STD involves generating an index of phone sequences using phone recognition. An alternative approach to phonetic STD is also investigated that instead indexes probabilistic acoustic scores in the form of a posterior-feature matrix. A state-of-the-art system is described and its use for STD is explored through several experiments on spontaneous conversational telephone speech. A novel technique and framework is proposed for discriminatively training such a system to directly maximise the Figure of Merit. This results in a 13% improvement in the Figure of Merit on held-out data. The framework is also found to be particularly useful for index compression in conjunction with the proposed optimisation technique, providing for a substantial index compression factor in addition to an overall gain in the Figure of Merit. These contributions significantly advance the state-of-the-art in phonetic STD, by improving the utility of such systems in a wide range of applications.

Clone detection in repositories of business process models

As organizations reach to higher levels of business process management maturity, they often find themselves maintaining repositories of hundreds or even thousands of process models, representing valuable knowledge about their operations. Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

Discriminative optimisation of the figure of merit for phonetic spoken term detection

This work proposes to improve spoken term detection (STD) accuracy by optimising the Figure of Merit (FOM). In this article, the index takes the form of phonetic posterior-feature matrix. Accuracy is improved by formulating STD as a discriminative training problem and directly optimising the FOM, through its use as an objective function to train a transformation of the index. The outcome of indexing is then a matrix of enhanced posterior-features that are directly tailored for the STD task. The technique is shown to improve the FOM by up to 13% on held-out data. Additional analysis explores the effect of the technique on phone recognition accuracy, examines the actual values of the learned transform, and demonstrates that using an extended training data set results in further improvement in the FOM.

Analysis of detection performance in spectrum sensing optimisation for long sensing periods

Spectrum sensing optimisation techniques maximise the efficiency of spectrum sensing while satisfying a number of constraints. Many optimisation models consider the possibility of the primary user changing activity state during the secondary user's transmission period. However, most ignore the possibility of activity change during the sensing period. The observed primary user signal during sensing can exhibit a duty cycle which has been shown to severely degrade detection performance. This paper shows that (a) the probability of state change during sensing cannot be neglected and (b) the true detection performance obtained when incorporating the duty cycle of the primary user signal can deviate significantly from the results expected with the assumption of no such duty cycle.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Development of a high-level gene expression system for the production of bioplastics in sugarcane

Despite various approaches, the production of biodegradable plastics such as polyhydroxybutyrate (PHB) in transgenic plants has met with limited success due largely to low expression levels. Even in the few instances where high levels of protein expression have been reported, the transgenic plants have been stunted indicating PHB is phytotoxic (Poirier 2002). This PhD describes the application of a novel virus-based gene expression technology, termed InPAct („In Plant Activation.), for the production of PHB in tobacco and sugarcane. InPAct is based on the rolling circle replication mechanism by which circular ssDNA viruses replicate and provides a system for controlled, high-level gene expression. Based on these features, InPAct was thought to represent an ideal system to enable the controlled, high-level expression of the three phb genes (phbA, phbB and phbC) required for PHB production in sugarcane at a preferred stage of plant growth. A Tobacco yellow dwarf virus (TbYDV)-based InPAct-phbA vector, as well as linear vectors constitutively expressing phbB and phbC were constructed and different combinations were used to transform tobacco leaf discs. A total of four, eight, three and three phenotypically normal tobacco lines were generated from discs transformed with InPAct-phbA, InPAct-phbA + p1300-TaBV P-phbB/phbC- 35S T, p1300-35S P-phbA-NOS T + p1300-TaBV P-phbB/phbC-35S T and InPAct-GUS, respectively. To determine whether the InPAct cassette could be activated in the presence of the TbYDV Rep, leaf samples from the eight InPActphbA + p1300-TaBV P-phbB/phbC-35S T plants were agroinfiltrated with p1300- TbYDV-Rep/RepA. Three days later, successful activation was indicated by the detection of episomes using both PCR and Southern analysis. Leaf discs from the eight InPAct-phbA + p1300-TaBV P-phbB/phbC-35S T transgenic plant lines were agroinfiltrated with p1300-TbYDV-Rep/RepA and leaf tissue was collected ten days post-infiltration and examined for the presence of PHB granules. Confocal microscopy and TEM revealed the presence of typical PHB granules in five of the eight lines, thus demonstrating the functionality of InPActbased PHB production in tobacco. However, analysis of leaf extracts by HPLC failed to detect the presence of PHB suggesting only very low level expression levels. Subsequent molecular analysis of three lines revealed low levels of correctly processed mRNA from the catalase intron contained within the InPAct cassette and also the presence of cryptic splice sites within the intron. In an attempt to increase expression levels, new InPAct-phb cassettes were generated in which the castorbean catalase intron was replaced with a synthetic intron (syntron). Further, in an attempt to both increase and better control Rep/RepA-mediated activation of InPAct cassettes, Rep/RepA expression was placed under the control of a stably integrated alc switch. Leaf discs from a transgenic tobacco line (Alc ML) containing 35S P-AlcR-AlcA P-Rep/RepA were supertransformed with InPAct-phbAsyn or InPAct-GUSsyn using Agrobacterium and three plants (lines) were regenerated for each construct. Analysis of the RNA processing of the InPAct-phbAsyn cassette revealed highly efficient and correct splicing of the syntron, thus supporting its inclusion within the InPAct system. To determine the efficiency of the alc switch to activate InPAct, leaf material from the three Alc ML + InPAct-phbAsyn lines was either agroinfiltrated with 35S P-Rep/RepA or treated with ethanol. Unexpectedly, episomes were detected not only in the infiltrated and ethanol treated samples, but also in non-treated samples. Subsequent analysis of transgenic Alc ML + InPAct-GUS lines, confirmed that the alc switch was leaky in tissue culture. Although this was shown to be reversible once plants were removed from the tissue culture environment, it made the regeneration of Alc ML + InPAct-phbsyn plant lines extremely difficult, due to unintentional Rep expression and therefore high levels of phb expression and phytotoxic PHB production. Two Alc ML + InPAct-phbAsyn + p1300-TaBV P-phbB/phbC-35S T transgenic lines were able to be regenerated, and these were acclimatised, alcohol-treated and analysed. Although episome formation was detected as late as 21 days post activation, no PHB was detected in the leaves of any plants using either microscopy or HPLC, suggesting the presence of a corrupt InPAct-phbA cassette in both lines. The final component of this thesis involved the application of both the alc switch and the InPAct systems to sugarcane in an attempt to produce PHB. Initial experiments using transgenic Alc ML + InPAct-GUS lines indicated that the alc system was not functional in sugarcane under the conditions tested. The functionality of the InPAct system, independent of the alc gene switch, was subsequently examined by bombarding the 35S Rep/RepA cassette into leaf and immature leaf whorl cells derived from InPAct-GUS transgenic sugarcane plants. No GUS expression was observed in leaf tissue, whereas weak and irregular GUS expression was observed in immature leaf whorl tissue derived from two InPAct- GUS lines and two InPAct-GUS + 35S P-AlcR-AlcA P-GUS lines. The most plausible reason to explain the inconsistent and low levels of GUS expression in leaf whorls is a combination of low numbers of sugarcane cells in the DNA replication-conducive S-phase and the irregular and random nature of sugarcane cells bombarded with Rep/RepA. This study details the first report to develop a TbYDV-based InPAct system under control of the alc switch to produce PHB in tobacco and sugarcane. Despite the inability to detect quantifiable levels of PHB levels in either tobacco or sugarcane, the findings of this study should nevertheless assist in the further development of both the InPAct system and the alc system, particularly for sugarcane and ultimately lead to an ethanol-inducible InPAct gene expression system for the production of bioplastics and other proteins of commercial value in plants.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. ex- act clones, can be identified and factored out as shared subprocesses, the repository’s maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.

An improved method to detect damage using modal strain energy based damage index

This paper presents two novel concepts to enhance the accuracy of damage detection using the Modal Strain Energy based Damage Index (MSEDI) with the presence of noise in the mode shape data. Firstly, the paper presents a sequential curve fitting technique that reduces the effect of noise on the calculation process of the MSEDI, more effectively than the two commonly used curve fitting techniques; namely, polynomial and Fourier’s series. Secondly, a probability based Generalized Damage Localization Index (GDLI) is proposed as a viable improvement to the damage detection process. The study uses a validated ABAQUS finite-element model of a reinforced concrete beam to obtain mode shape data in the undamaged and damaged states. Noise is simulated by adding three levels of random noise (1%, 3%, and 5%) to the mode shape data. Results show that damage detection is enhanced with increased number of modes and samples used with the GDLI.

Detection of tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 2(PAI-2) in gingival crevicular fluid from healthy, gingivitis and periodontitis patients

Background: The regulation of plasminogen activation is a key element in controlling proteolytic events in the extracellular matrix. Our previous studies had demonstrated that in inflamed gingival tissues, tissue-type plasminogen activator (t-PA) is significantly increased in the extracellular matrix of the connective tissue and that interleukin 1β (IL-1β) can up regulate the level of t-PA and plasminogen activator inhibitor-2 (PAI-2) synthesis by human gingival fibroblasts. Method: In the present study, the levels of t-PA and PAI-2 in gingival crevicular fluid (GCF) were measured from healthy, gingivitis and periodontitis sites and compared before and after periodontal treatment. Crevicular fluid from106 periodontal sites in 33 patients were collected. 24 sites from 11 periodontitis patients received periodontal treatment after the first sample collection and post-treatment samples were collected 14 days after treatment. All samples were analyzed by enzyme-linked immunosorbent assay (ELISA) for t-PA and PAI-2. Results: The results showed that significantly high levels of t-PA and PAI-2 in GCF were found in the gingivitis and periodontitis sites. Periodontal treatment led to significant decreases of PAI-2, but not t-PA, after 14 days. A significant positive linear correlation was found between t-PA and PAI-2 in GCF (r=0.80, p<0.01). In the healthy group, different sites from within the same subject showed little variation of t-PA and PAI-2 in GCF. However, the gingivitis and periodontitis sites showed large variation. These results suggest a good correlation between t-PA and PAI-2 with the severity of periodontal conditions. Conclusion: This study indicates that t-PA and PAI-2 may play a significant rôle in the periodontal tissue destruction and tissue remodeling and that t-PA and PAI-2 in GCF may be used as clinical markers to evaluate the periodontal diseases and assess treatment.