Manipulation of lipids in animal-derived foods: can it contribute to public health nutrition?

Foods derived from animals are an important source of nutrients for humans. Concerns have been raised that due to their SFA content, dairy foods may increase the risk of cardiometabolic disease. Prospective studies do not indicate an association between milk consumption and increased disease risk although there are less data for other dairy foods. SFA in dairy products can be partially replaced by cis-MUFA through nutrition of the dairy cow although there are too few human studies to conclude that such modification leads to reduced chronic disease risk. Intakes of LCn-3 FA are sub-optimal in many countries and while foods such as poultry meat can be enriched by inclusion of fish oil in the diet of the birds, fish oil is expensive and has an associated risk that the meat will be oxidatively unstable. Novel sources of LCn-3 FA such as kirll oil, algae, and genetically modified plants may prove to be better candidates for meat enrichment. The value of FA-modified foods cannot be judged by their FA composition alone and there needs to be detailed human intervention studies carried out before judgements concerning improved health value can be made. Practical applications: The amount and FA composition of dietary lipids are known to contribute to the risk of chronic disease in humans which is increasing and becoming very costly to treat. The use of animal nutrition to improve the FA composition of staple foods such as dairy products and poultry meat has considerable potential to reduce chronic risk at population level although judgements must not be based simply on FA composition of the foods.

Conditioning of the weak-constraint variational data assimilation problem for numerical weather prediction

4-Dimensional Variational Data Assimilation (4DVAR) assimilates observations through the minimisation of a least-squares objective function, which is constrained by the model flow. We refer to 4DVAR as strong-constraint 4DVAR (sc4DVAR) in this thesis as it assumes the model is perfect. Relaxing this assumption gives rise to weak-constraint 4DVAR (wc4DVAR), leading to a different minimisation problem with more degrees of freedom. We consider two wc4DVAR formulations in this thesis, the model error formulation and state estimation formulation. The 4DVAR objective function is traditionally solved using gradient-based iterative methods. The principle method used in Numerical Weather Prediction today is the Gauss-Newton approach. This method introduces a linearised `inner-loop' objective function, which upon convergence, updates the solution of the non-linear `outer-loop' objective function. This requires many evaluations of the objective function and its gradient, which emphasises the importance of the Hessian. The eigenvalues and eigenvectors of the Hessian provide insight into the degree of convexity of the objective function, while also indicating the difficulty one may encounter while iterative solving 4DVAR. The condition number of the Hessian is an appropriate measure for the sensitivity of the problem to input data. The condition number can also indicate the rate of convergence and solution accuracy of the minimisation algorithm. This thesis investigates the sensitivity of the solution process minimising both wc4DVAR objective functions to the internal assimilation parameters composing the problem. We gain insight into these sensitivities by bounding the condition number of the Hessians of both objective functions. We also precondition the model error objective function and show improved convergence. We show that both formulations' sensitivities are related to error variance balance, assimilation window length and correlation length-scales using the bounds. We further demonstrate this through numerical experiments on the condition number and data assimilation experiments using linear and non-linear chaotic toy models.

Conditioning and preconditioning of the optimal state estimation problem

Optimal state estimation is a method that requires minimising a weighted, nonlinear, least-squares objective function in order to obtain the best estimate of the current state of a dynamical system. Often the minimisation is non-trivial due to the large scale of the problem, the relative sparsity of the observations and the nonlinearity of the objective function. To simplify the problem the solution is often found via a sequence of linearised objective functions. The condition number of the Hessian of the linearised problem is an important indicator of the convergence rate of the minimisation and the expected accuracy of the solution. In the standard formulation the convergence is slow, indicating an ill-conditioned objective function. A transformation to different variables is often used to ameliorate the conditioning of the Hessian by changing, or preconditioning, the Hessian. There is only sparse information in the literature for describing the causes of ill-conditioning of the optimal state estimation problem and explaining the effect of preconditioning on the condition number. This paper derives descriptive theoretical bounds on the condition number of both the unpreconditioned and preconditioned system in order to better understand the conditioning of the problem. We use these bounds to explain why the standard objective function is often ill-conditioned and why a standard preconditioning reduces the condition number. We also use the bounds on the preconditioned Hessian to understand the main factors that affect the conditioning of the system. We illustrate the results with simple numerical experiments.

The recuperated materiality of disability and animal studies

This chapter considers a perhaps unexpected connection between disability and animal studies, given disability studies' understandable reluctance to be associated with animal liberation/rights struggles. It finds that both fields remain rooted in ideas of experience and feeling, or the notion of an essential embodied experience, even while they offer up critiques of the way essentialism operates more broadly to disenfranchise or disadvantage the groups they represent. The chapter goes on to analyse what the implications are of this notion of 'embodiment' and the materialism that accompanies it, it foregrounds the contradictions that ensue, and then discusses what this means for the way political action is (and can be) conceived of.