A bloom of Lyngbya majuscula in Shoalwater Bay, Queensland, Australia: An important feeding ground for the green turtle (Chelonia mydas)

Lyngbya majuscula, a toxic cyanobacterium, was observed blooming during June-July (winter) 2002 in Shoalwater Bay, Queensland, Australia, an important feeding area for a large population of green turtles (Chelonia mydas). The bloom was mapped and extensive mats of L majuscula were observed overgrowing seagrass beds along at least 18 km of coast, and covering a surface area of more than I I km(2). Higher than average rainfall preceded the bloom and high water temperatures in the preceding summer may have contributed to the bloom. In bloom samples, lyngbyatoxin A (LA) was found to be present in low concentration (26 mu g kg(-1) (dry weight)), but debromoaplysiatoxin (DAT) was not detected. The diet of 46 green turtles was assessed during the bloom and L. majuscula was found in 51% of the samples, however, overall it contributed only 2% of the animals' diets. L. majuscula contribution to turtle diet was found to increase as the availability of the cyanobacterium increased. The bloom appeared to have no immediate impact on turtle body condition, however, the presence of a greater proportion of damaged seagrass leaves in diet in conjunction with decreases in plasma concentrations of sodium and glucose could suggest that the turtles may have been exposed to a Substandard diet as a result of the bloom. This is the first confirmed report of L. majuscula blooming in winter in Shoalwater Bay, Queensland, Australia and demonstrates that turtles consume the toxic cyanobacterium in the wild, and that they are potentially exposed to tumour promoting compounds produced by this organism. (c) 2005 Elsevier B.V. All rights reserved.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

The toxins of Lyngbya majuscula and their human and ecological health effects

Lyngbya majuscula is a benthic filamentous marine cyanobacterium, which in recent years appears to have been increasing in frequency and size of blooms in Moreton Bay, Queensland. It has a worldwide distribution throughout the tropics and subtropics in water to 30m. It has been found to contain a variety of chemicals that exert a range of biological effects, including skin, eye and respiratory irritation. The toxins lyngbyatoxin A and debromoaplysiatoxin appear to give the most widely witnessed biological effects in relation to humans, and experiments involving these two toxins show the formation of acute dermal lesions. Studies into the epidemiology of the dermatitic, respiratory and eye effects of the toxins of this organism are reviewed and show that Lyngbya induced dermatitis has occurred in a number of locations. The effects of aerosolised Lyngbya in relation to health outcomes were also reported. Differential effects of bathing behaviour after Lyngbya exposure were examined in relation to the severity of health outcomes. The potential for Lyngbya to exhibit differential toxicologies due to the presence of varying proportions of a range of toxins is also examined. This paper reviews the present state of knowledge on the effects of Lyngbya majuscula on human health, ecosystems and human populations during a toxic cyanobacterial bloom. The potential exists for toxins from Lyngbya majuscula affecting ecological health and in particular marine reptiles. (C) 2001 Elsevier Science Ltd. All rights reserved.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Effects of phosphorus on the growth and nitrogen fixation rates of Lyngbya majuscula: implications for management in Moreton Bay, Queensland

Significant acetylene reduction and therefore N-2 fixation was observed for Lyngbya majuscula only during dark periods, which suggests that oxygenic photosynthesis and N-2 fixation are incompatible processes for this species. Results from a series of batch and continuous-flow-culture reactor studies showed that the specific growth rate and N-2 fixation rate of L, majuscula increased with phosphate (P-PO4) concentration up to a maximum value and thereafter remained constant. The P-PO4 concentrations corresponding to the maximum N-2 fixation and maximum growth rates were -0.27 and -0.18 muM respectively and these values are denoted as the saturation values for N-2 fixation and growth respectively. Regular monitoring studies in Moreton Bay, Queensland, show that concentrations Of P-PO4 generally exceed these saturation values over a large portion of the Bay and therefore, the growth of the bloom-forming L, majuscula is potentially maximised throughout much of the Bay by the elevated P-PO4 concentrations. Results from other studies suggest that the elevated P-PO4 concentrations in the Bay can be largely attributed to discharges from waste-water treatment plants (WWTPs), and thus it is proposed that the control of the growth of L. majuscula in Moreton Bay will require a significant reduction in the P load from the WWTP discharges. If the current strategy of N load reduction for these discharges is maintained in the absence of substantial P load reduction, it is hypothesised that the growth of L, majuscula and other diazotrophs in Moreton Bay will increase in the future.

Ecophysiology of the marine cyanobacterium, Lyngbya majuscula (Oscillatoriaceae) in Moreton Bay, Australia

Large blooms of the marine cyanobacterium Lyngbya majuscula in Moreton Bay, Australia (27 degrees 05'S, 153 degrees 08'E) have been re-occurring for several years. A bloom was studied in Deception Bay (Northern Moreton Bay) in detail over the period January-March 2000. In situ data loggers and field sampling characterised various environmental parameters before and during the L. majuscula bloom. Various ecophysiological experiments were conducted on L. majuscula collected in the field and transported to the laboratory, including short-term (2h) C-14 incorporation rates and long-term (7 days) pulse amplitude modulated (PAM) fluorometry assessments of photosynthetic capacity. The effects of L. majuscula on various seagrasses in the bloom region were also assessed with repeated biomass sampling. The bloom commenced in January 2000 following usual December rainfall events, water temperatures in excess of 24 degrees C and high light conditions. This bloom expanded rapidly from 0 to a maximum extent of 8 km(2) over 55 days with an average biomass of 210 g(dw)(-1) m(-2) in late February, followed by a rapid decline in early April. Seagrass biomass, especially Syringodium isoetifolium, was found to decline in areas of dense L. majuscula accumulation. Dissolved and total nutrient concentrations did not differ significantly (P > 0.05) preceding or during the bloom. However, water samples from creeks discharging into the study region indicated elevated concentrations of total iron (2.7-80.6 mu M) and dissolved organic carbon (2.5-24.7 mg L-1), associated with low pH values (3.8-6.7). C-14 incorporation rates by L. majuscula were significantly (P < 0.05) elevated by additions of iron (5 mu M Fe), an organic chelator, ethylenediaminetetra-acetic acid (5 mu M EDTA) and phosphorus (5 mu M PO4-3). Photosynthetic capacity measured with PAM fluorometry was also stimulated by various nutrient additions, but not significantly (P > 0.05). These results suggest that the L. majuscula bloom may have been stimulated by bioavailable iron, perhaps complexed by dissolved organic carbon. The rapid bloom expansion observed may then have been sustained by additional inputs of nutrients (N and P) and iron through sediment efflux, stimulated by redox changes due to decomposing L. majuscula mats. (c) 2004 Elsevier B.V. All rights reserved.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method.

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to 'explain' an observed set of alleles. The other procedure is probabilistic using Bayes' theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.