Controlled variation of monomer sequence-distribution in the synthesis of aromatic poly(ether ketone)s

The effects of varying the alkali metal cation in the high-temperature nucleophilic synthesis of a semi-crystalline, aromatic poly(ether ketone) have been systematically investigated, and striking variations in the sequence-distributions and thermal characteristics of the resulting polymers were found. Polycondensation of 4,4'-dihydroxybenzophenone with 1,3-bis(4-fluorobenzoyl)benzene in diphenylsulfone as solvent, in the presence of an alkali metal carbonate M2CO3 (M= Li, Na, K, or Rb) as base, affords a range of different polymers that vary in the distribution pattern of 2-ring and 3-ring monomer units along the chain. Lithium carbonate gives an essentially alternating and highly crystalline polymer, but the degree of sequence-randomisation increases progressively as the alkali metal series is descended, with rubidium carbonate giving a fully random and non-thermally-crystallisable polymer. Randomisation during polycondensation is shown to result from reversible cleavage of the ether linkages in the polymer by fluoride ions, and an isolated sample of alternating-sequence polymer is thus converted to a fully randomised material on heating with rubidium fluoride.

Draft genome sequence of Pseudomonas corrugata, a phytopathogenic bacterium with potential industrial applications

Pseudomonas corrugata was first described as the causal agent of a tomato disease called ‘pith necrosis’ yet it is considered as a biological resource in various fields such as biocontrol of plant diseases and production of industrially promising microbial biopolymers (mcl-PHA). Here we report the first draft genome sequence of this species.

Navigating the labyrinth: a guide to sequence-based, community ecology of arbuscular mycorrhizal fungi

Data generated from next generation sequencing (NGS) will soon comprise the majority of information about arbuscular mycorrhizal fungal (AMF) communities. Although these approaches give deeper insight, analysing NGS data involves decisions that can significantly affect results and conclusions. This is particularly true for AMF community studies, because much remains to be known about their basic biology and genetics. During a workshop in 2013, representatives from seven research groups using NGS for AMF community ecology gathered to discuss common challenges and directions for future research. Our goal was to improve the quality and accessibility of NGS data for the AMF research community. Discussions spanned sampling design, sample preservation, sequencing, bioinformatics and data archiving. With concrete examples we demonstrated how different approaches can significantly alter analysis outcomes. Failure to consider the consequences of these decisions may compound bias introduced at each step along the workflow. The products of these discussions have been summarized in this paper in order to serve as a guide for any researcher undertaking NGS sequencing of AMF communities.

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.