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.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.