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.

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.

Comparison of algal and fish sources on the oxidative stability of poultry meat and its enrichment with omega-3 polyunsaturated fatty acids

Human consumption of long-chain n-3 polyunsaturated fatty acids (LC n-3 PUFA) is below recommendations, and enriching chicken meat (by incorporating LC n-3 PUFA into broiler diets) is a viable means of increasing consumption. Fish oil is the most common LC n-3 PUFA supplement used but is unsustainable and reduces the oxidative stability of the meat. The objective of this experiment was to compare fresh fish oil (FFO) with fish oil encapsulated (EFO) in a gelatin matrix (to maintain its oxidative stability) and algal biomass at a low (LAG, 11), medium (MAG, 22), or high (HAG, 33 g/kg of diet) level of inclusion. The C22:6n-3 contents of the FFO, EFO, and MAG diets were equal. A control (CON) diet using blended vegetable oil was also made. As-hatched 1-d-old Ross 308 broilers (144) were reared (21 d) on a common starter diet then allocated to treatment pens (4 pens per treatment, 6 birds per pen) and fed treatment diets for 21 d before being slaughtered. Breast and leg meat was analyzed (per pen) for fatty acids, and cooked samples (2 pens per treatment) were analyzed for volatile aldehydes. Concentrations (mg/100 g of meat) of C20:5n-3, C22:5n-3, and C22:6n-3 were (respectively) CON: 4, 15, 24; FFO: 31, 46, 129; EFO: 18, 27, 122; LAG: 9, 19, 111; MAG: 6, 16, 147; and HAG: 9, 14, 187 (SEM: 2.4, 3.6, 13.1) in breast meat and CON: 4, 12, 9; FFO: 58, 56, 132; EFO: 63, 49, 153; LAG: 13, 14, 101; MAG: 11, 15, 102; HAG: 37, 37, 203 (SEM: 7.8, 6.7, 14.4) in leg meat. Cooked EFO and HAG leg meat was more oxidized (5.2 mg of hexanal/kg of meat) than the other meats (mean 2.2 mg/kg, SEM 0.63). It is concluded that algal biomass is as effective as fish oil at enriching broiler diets with C22:6 LC n-3 PUFA, and at equal C22:6n-3 contents, there is no significant difference between these 2 supplements on the oxidative stability of the meat that is produced.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.