537 resultados para domoic acid, biotoxins, detection


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The present invention relates to recombinant cells, particularly recombinant plant cells, which are capable of producing dihydrosterculic acid and/or derivatives thereof. The present invention also relates to methods of producing oil comprising dihydrosterculic acid and/or derivatives thereof.

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The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

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The ubiquitin-proteasome system targets many cellular proteins for degradation and thereby controls most cellular processes. Although it is well established that proteasome inhibition is lethal, the underlying mechanism is unknown. Here, we show that proteasome inhibition results in a lethal amino acid shortage. In yeast, mammalian cells, and flies, the deleterious consequences of proteasome inhibition are rescued by amino acid supplementation. In all three systems, this rescuing effect occurs without noticeable changes in the levels of proteasome substrates. In mammalian cells, the amino acid scarcity resulting from proteasome inhibition is the signal that causes induction of both the integrated stress response and autophagy, in an unsuccessful attempt to replenish the pool of intracellular amino acids. These results reveal that cells can tolerate protein waste, but not the amino acid scarcity resulting from proteasome inhibition.

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Generally wireless sensor networks rely of many-to-one communication approach for data gathering. This approach is extremely susceptible to sinkhole attack, where an intruder attracts surrounding nodes with unfaithful routing information, and subsequently presents selective forwarding or change the data that carry through it. A sinkhole attack causes an important threat to sensor networks and it should be considered that the sensor nodes are mostly spread out in open areas and of weak computation and battery power. In order to detect the intruder in a sinkhole attack this paper suggests an algorithm which firstly finds a group of suspected nodes by analyzing the consistency of data. Then, the intruder is recognized efficiently in the group by checking the network flow information. The proposed algorithm's performance has been evaluated by using numerical analysis and simulations. Therefore, accuracy and efficiency of algorithm would be verified.

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Telomerase is an extremely important enzyme required for the immortalisation of tumour cells. Because the gene is activated in the vast majority of tumour tissues and remains unused in most somatic cells, it represents a marker with huge diagnostic, prognostic and treatment implications in cancer. This article summarises the basic structure and functions of telomerase and considers its clinical implications in colorectal and other cancers.

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In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

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Traffic incidents are key contributors to non-recurrent congestion, potentially generating significant delay. Factors that influence the duration of incidents are important to understand so that effective mitigation strategies can be implemented. To identify and quantify the effects of influential factors, a methodology for studying total incident duration based on historical data from an ‘integrated database’ is proposed. Incident duration models are developed using a selected freeway segment in the Southeast Queensland, Australia network. The models include incident detection and recovery time as components of incident duration. A hazard-based duration modelling approach is applied to model incident duration as a function of a variety of factors that influence traffic incident duration. Parametric accelerated failure time survival models are developed to capture heterogeneity as a function of explanatory variables, with both fixed and random parameters specifications. The analysis reveals that factors affecting incident duration include incident characteristics (severity, type, injury, medical requirements, etc.), infrastructure characteristics (roadway shoulder availability), time of day, and traffic characteristics. The results indicate that event type durations are uniquely different, thus requiring different responses to effectively clear them. Furthermore, the results highlight the presence of unobserved incident duration heterogeneity as captured by the random parameter models, suggesting that additional factors need to be considered in future modelling efforts.

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Ubiquitination involves the attachment of ubiquitin to lysine residues on substrate proteins or itself, which can result in protein monoubiquitination or polyubiquitination. Ubiquitin attachment to different lysine residues can generate diverse substrate-ubiquitin structures, targeting proteins to different fates. The mechanisms of lysine selection are not well understood. Ubiquitination by the largest group of E3 ligases, the RING-family E3 s, is catalyzed through co-operation between the non-catalytic ubiquitin-ligase (E3) and the ubiquitin-conjugating enzyme (E2), where the RING E3 binds the substrate and the E2 catalyzes ubiquitin transfer. Previous studies suggest that ubiquitination sites are selected by E3-mediated positioning of the lysine toward the E2 active site. Ultimately, at a catalytic level, ubiquitination of lysine residues within the substrate or ubiquitin occurs by nucleophilic attack of the lysine residue on the thioester bond linking the E2 catalytic cysteine to ubiquitin. One of the best studied RING E3/ E2 complexes is the Skp1/Cul1/F box protein complex, SCFCdc4, and its cognate E2, Cdc34, which target the CDK inhibitor Sic1 for K48-linked polyubiquitination, leading to its proteasomal degradation. Our recent studies of this model system demonstrated that residues surrounding Sic1 lysines or lysine 48 in ubiquitin are critical for ubiquitination. This sequence-dependence is linked to evolutionarily conserved key residues in the catalytic region of Cdc34 and can determine if Sic1 is mono- or poly-ubiquitinated. Our studies indicate that amino acid determinants in the Cdc34 catalytic region and their compatibility to those surrounding acceptor lysine residues play important roles in lysine selection. This may represent a general mechanism in directing the mode of ubiquitination in E2 s.

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Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

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Amplification of the Plasmodium falciparum multidrug resistance 1 gene (pfmdr1) has been implicated in multidrug resistance, including in vitro resistance to artelinic acid (AL). The stability and fitness of having multiple copies of pfmdr1 are important factors due to their potential effects on the resistance phenotype of parasites. These factors were investigated by using an AL-resistant line of P. falciparum (W2AL80) and clones originating from W2AL80. A rapid reduction in pfmdr1 copy number (CN) was observed in the uncloned W2AL80 line; 63% of this population reverted to a CN of <3 without exposure to the drug. Deamplification of the pfmdr1 amplicon was then determined in three clones, each initially containing three copies of pfmdr1. Interestingly, two outcomes were observed during 3 months without drug pressure. In one clone, parasites with fewer than 3 copies of pfmdr1 emerged rapidly. In two other clones, the reversion was significantly delayed. In all subclones, the reduction in pfmdr1 CN involved the deamplification of the entire amplicon (19 genes). Importantly, deamplification of the pfmdr1 amplicon resulted in partial reversal of resistance to AL and increased susceptibility to mefloquine. These results demonstrate that multiple copies of the pfmdr1-containing amplicon in AL-resistant parasites are unstable when drug pressure is withdrawn and have practical implications for the maintenance and spread of parasites resistant to artemisinin derivatives.

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This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.

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Thraustochytrids have become of considerable industrial and scientific interest in the past decade due to their health benefits. They have been proven to be the principle source in marine and estuarine fish diets with high percentage of long chain (LC) or polyunsaturated fatty acids (PUFA). Therefore, the oil extracted from fish for human document.forms[0].elements[13].select();consumption is rich in PUFA with high omega-3 fatty acid content. Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) of all of the omega-3 fatty acids, are considered beneficial essential oils for humans with a wide range of health benefits. These include brain and neural development in infants, general wellbeing of adults and drug delivery through precursor molecules. They have become one of the most extensively studied organisms for industrial oil preparations as PUFA extraction from fish becomes less profitable. Many forms of these Thraustochytrid oils are being trialled for human consumption all over the world. In Australia, there has been little research performed on these organisms in the past ten years. A few Australian studies have been conducted in the form of comparative studies related to PUFA production within the related genera, but not focussed on their identification or cellular and genomic characterisation. Therefore, the main aim of this study was to investigate the morphological and genetic characteristics of Australian Thraustochytrids in order to aid in their identification and characterisation, as well as to better understand the effect of environmental conditions in the regulation of PUFA production. It was also noted that there was a knowledge gap in the preservation and total genomic DNA extraction of these organisms for the purposes of scientific research. The cryopreservation of these organisms for studies around the world follows existing generic methods. However, it is well understood that many of these generic methods attract not only high costs for chemicals, but also uses considerable storage space and other resources, all of which can be improved with new or modified approaches. In this context, a simple and inexpensive bead preservation method is described, without compromising the storage shelf life. We also describe, for the first time, the effects of culture age on the successful cryopreservation of Thraustochytrids. It was evident in the literature that DNA and RNA extractions for molecular and genetic studies of Thraustochytrids follow the classical phenol-chloroform extraction methods. It was also observed that modern protocols failed to avoid the use of phenol-chloroform rather than improving preparation and cell disruption. In order to provide a high quantity and quality DNA extraction, a modified protocol has been introduced that employs the use of modern commercial extraction kits and standard laboratory equipment. Thraustochytrids have been shown to be highly conserved in their 18S rDNA gene sequences, which is used as the current standard for identification. It was demonstrated that the 18S rDNA gene sequence limits the recognition of closely related genera or within the genera from each member. Therefore, it was proposed that another profile, such as a randomly amplified polymorphic DNA (RAPD) based profiling system, be tested for use in the characterisation of Thraustochytrids. The RAPD profiles were shown to provide a unique DNA fingerprint for each isolate and small variations in their genome were able to be detected. This method involved the use of a minimum number of standard arbitrary primers and with an increase in the number of different primers used, a very high discrimination between organisms could be achieved. However, the method was not suitable for taxonomic purposes because the results did not correlate with other taxonomic features such as morphology. Another knowledge gap was found with respect to Australian Thraustochytrid growth characteristics, in that these had not been recorded and published. In order to rectify this, a record of colony and microscopic features of 12 selected isolates was performed. The results of preliminary studies indicated that further microbiological and biochemical studies are needed for full characterisation of these organisms. This information is of great importance to bio-prospecting of new Thraustochytrids from Australian ecosystems and would allow for their accurate identification, and so permit the prediction of their PUFA capability by comparison with related genera/species. It was well recognized that environmental stress plays a role in the PUFA production and is mainly due to the reactive oxygen species as abiotic stress (Chiou et al., 2001; Okuyama et al., 2008; Shabala et al., 2009; Shabala et al., 2001). In this aspect, this study makes the first attempt towards better understanding of this phenomenon by way of the use of real-time PCR for the detection of environmental effects on the regulation of PUFA production. Three main environmental conditions including temperature, pH and oxygen availability were monitored as stress inducers. In summary, this study provides novel approaches for the preservation and handling of Thraustochytrids, their molecular biological features, taxonomy, characterisation and responses to environmental factors with respect to their oil production enzymes. The information produced from this study will prove to be vital for both industrial and scientific investigations in the future.

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Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.

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Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.