A comparison of the role of two blue-green algae in THM and HAA formation

The contribution of two blue-green algae species, Anabaeria flos-aquae and Microcystis aeruginosa, to the formation of trihalomethanes (THMs) and haloacetic acids (HAAs) was investigated. The experiments examined the formation potential of these disinfection by-products (DBPs) from both algae cells and extracellular organic matter (EOM) during four algal growth phases. Algal cells and EOM of Anabaena and Microcystis exhibited a high potential for DBP formation. Yields of total THMs (TTHM) and total HAAs (THAA) were closely related to the growth phase. Reactivity of EOM from Anabaena was slightly higher than corresponding cells, while the opposite result was found for Microcystis. Specific DBP yields (yield/unit C) of Anabaena were in the range of 2-11 mu mol/mmol C for TTHM and 217 mu mol/mmol C for THAA, while those of Microcystis were slightly higher. With regard to the distributions of individual THM and HAA compounds, differences were observed between the algae species and also between cells and EOM. The presence of bromide shifted the dominant compounds from HAAs to THMs. (C) 2009 Elsevier Ltd. All rights reserved.

Effects of forage source and soybean and marine algal oil supplementation on milk fatty acid composition in ewes

The objective of the present studies was to determine effects of basal dietary forage source on the response of milk fatty acid composition to an oil supplement based (2:1, respectively, w/w) on soybean oil and marine algae biomass oil high in cis-9, cis-12 C18:2n − 3 and C22:6n − 3, respectively. In Study 1, Hampshire × Dorset ewes (48) were randomly assigned to one of four treatments and 12 pens in a completely randomized design blocked on the basis of lambing date and number of lambs suckled. Control rations (60:40 forage:concentrate, dry matter (DM) basis) based on alfalfa pellets (AP) or corn silage (CS) were fed from lambing. Beginning at 22 days postpartum, three pens of ewes fed AP and three pens of ewes fed CS were supplemented with oil (30 g/kg of ration DM) in place of corn meal. Average ewe DM intake (DMI) and average daily gain (ADG) were measured weekly. Milk yield and composition were measured at 42 days postpartum. DMI was lower (P<0.02) for CS and for oil, but milk yield was not affected by forage source or oil supplementation. Milk fat content was higher for oil (P<0.10) and milk protein content was higher for AP (P<0.04). Total CLA concentration (g/100 g fatty acids) increased (P<0.01) with CS and oil, and the response to oil was greater for AP (P<0.04). Similarly, total trans-C18:1 and C22:6ω−3 concentrations were higher for CS and oil, but the response to oil was greater for CS (P<0.06 and P<0.01, respectively). In Study 2, the experiment was repeated using alfalfa haylage (AH) instead of AP. The DMI decreased (P<0.05) with oil feeding, but was not affected by forage source. Milk yield was decreased by feeding oil with AH, but not by feeding oil with CS (P<0.03). Milk fat content tended to be increased by feeding oil with AH, but tended to be decreased by feeding oil with CS (P<0.08). Total CLA concentration was increased (P<0.01) for AH versus CS and by oil, and the response to oil supplementation was greater for AH (P<0.01). In contrast, total trans-C18:1 concentration was higher for CS versus AH, with a greater response to oil for CS (P<0.05). Feeding marine oil increased the C22:6ω−3 (P<0.01) concentration, and the response was greater for AH (P<0.04). To further characterize the response of milk fat composition to dietary oil in ewes, a third study used six pens of three ewes each assigned to either the control CS diet used for Study 2 or the same diet supplemented with 45 g/kg (DM basis) of the oil mixture. Feeding oil had no effect on DMI, milk yield or milk fat concentration, but again increased (P<0.001) total trans-C18:1 and C22:6ω−3 concentrations and numerically increased (114%) total CLA concentration. Milk fatty acid composition responses to supplemental vegetable and marine oils were affected by forage source. Milk trans-C18:1 concentration was higher when CS was fed in Studies 1 and 2, but the effect of forage species on CLA concentration differed between studies, which may reflect differences in diet PUFA content and consumption, as well as amounts of dietary starch and fiber consumed. Despite large increases in trans-C18:1 concentration, milk fat content was not decreased by feeding unsaturated oils to ewes, even at diet levels of 45 g/kg of ration DM.

Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research

The effectiveness of development assistance has come under renewed scrutiny in recent years. In an era of growing economic liberalisation, research organisations are increasingly being asked to account for the use of public funds by demonstrating achievements. However, in the natural resources (NR) research field, conventional economic assessment techniques have focused on quantifying the impact achieved rather understanding the process that delivered it. As a result, they provide limited guidance for planners and researchers charged with selecting and implementing future research. In response, “pathways” or logic models have attracted increased interest in recent years as a remedy to this shortcoming. However, as commonly applied these suffer from two key limitations in their ability to incorporate risk and assess variance from plan. The paper reports the results of a case study that used a Bayesian belief network approach to address these limitations and outlines its potential value as a tool to assist the planning, monitoring and evaluation of development-orientated research.

Modelling fixed plant and algal dynamics in rivers: an application to the River Frome

The development of eutrophication in river systems is poorly understood given the complex relationship between fixed plants, algae, hydrodynamics, water chemistry and solar radiation. However there is a pressing need to understand the relationship between the ecological status of rivers and the controlling environmental factors to help the reasoned implementation of the Water Framework Directive and Catchment Sensitive Farming in the UK. This research aims to create a dynamic, process-based, mathematical in-stream model to simulate the growth and competition of different vegetation types (macrophytes, phytoplankton and benthic algae) in rivers. The model, applied to the River Frome (Dorset, UK), captured well the seasonality of simulated vegetation types (suspended algae, macrophytes, epiphytes, sediment biofilm). Macrophyte results showed that local knowledge is important for explaining unusual changes in biomass. Fixed algae simulations indicated the need for the more detailed representation of various herbivorous grazer groups, however this would increase the model complexity, the number of model parameters and the required observation data to better define the model. The model results also highlighted that simulating only phytoplankton is insufficient in river systems, because the majority of the suspended algae have benthic origin in short retention time rivers. Therefore, there is a need for modelling tools that link the benthic and free-floating habitats.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

Optimized hash for network path encoding with minimized false positives

The Bloom filter is a space efficient randomized data structure for representing a set and supporting membership queries. Bloom filters intrinsically allow false positives. However, the space savings they offer outweigh the disadvantage if the false positive rates are kept sufficiently low. Inspired by the recent application of the Bloom filter in a novel multicast forwarding fabric, this paper proposes a variant of the Bloom filter, the optihash. The optihash introduces an optimization for the false positive rate at the stage of Bloom filter formation using the same amount of space at the cost of slightly more processing than the classic Bloom filter. Often Bloom filters are used in situations where a fixed amount of space is a primary constraint. We present the optihash as a good alternative to Bloom filters since the amount of space is the same and the improvements in false positives can justify the additional processing. Specifically, we show via simulations and numerical analysis that using the optihash the false positives occurrences can be reduced and controlled at a cost of small additional processing. The simulations are carried out for in-packet forwarding. In this framework, the Bloom filter is used as a compact link/route identifier and it is placed in the packet header to encode the route. At each node, the Bloom filter is queried for membership in order to make forwarding decisions. A false positive in the forwarding decision is translated into packets forwarded along an unintended outgoing link. By using the optihash, false positives can be reduced. The optimization processing is carried out in an entity termed the Topology Manger which is part of the control plane of the multicast forwarding fabric. This processing is only carried out on a per-session basis, not for every packet. The aim of this paper is to present the optihash and evaluate its false positive performances via simulations in order to measure the influence of different parameters on the false positive rate. The false positive rate for the optihash is then compared with the false positive probability of the classic Bloom filter.

Bayesian model comparison with un-normalised likelihoods

Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to the intractability of their likelihood functions. Several methods that permit exact, or close to exact, simulation from the posterior distribution have recently been developed. However, estimating the evidence and Bayes’ factors for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates. An initial investigation into the theoretical and empirical properties of this class of methods is presented. Some support for the use of biased estimates is presented, but we advocate caution in the use of such estimates.