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Background Lactic acid for polymer applications must be optically pure to allow the final plastic properties to be controlled. We have isolated a strain of Lactobacillus plantarum capable of utilising a range of carbohydrates and tolerant to high sugar and organic acid concentrations. However, like most Lactobacillus plantarum, this strain produces D- and L-lactic acid. To improve the commercial application of this strain gene editing has been used to ensure only one isoform is produced. Methods Using two-step homologous recombination, ldhL was replaced with a truncated version interrupted by a chloramphenicol acetyltransferase resistance marker, then with an unmarked truncated version. The optical purity shift was quantified biochemically by enzymatic assay and HPLC and fermentation performance compared against the unmodified strain using enzymatically hydrolysed municipal solid waste pulp (MSW sugars) as a feedstock. Results The ldhL gene was successfully deleted, leading to a dramatic shift in the optical purity of lactate produced in both synthetic media and MSW sugars. The unmodified strain produced D-lactic acid with an optical purity of 40%, while the mutant achieved 94%. In MSW sugars, the modified strain performed as well as the unmodified control, consuming all available glucose in less than 48 hours with a volumetric productivity of 1.02 g/L/h. Conclusions Deletion of ldhL from our strain of Lactobacillus plantarum allowed exclusive production of D-lactic acid from MSW sugars. This provides a laboratory scale demonstration of the production of optically pure lactic acid from renewable sugars for use in the production of biodegradable plastics.

Computational inference of dated evolutionary histories relies upon various hypotheses about RNA, DNA, and protein sequence mutation rates. Using mutation rates to infer these dated histories is referred to as molecular clock assumption. Coalescent theory is a popular class of evolutionary models that implements the molecular clock hypothesis to facilitate computational inference of dated phylogenies. Cancer and virus evolution are two areas where these methods are particularly important. Methodologically, phylogenetic inference methods require a tree space over which the inference is performed, and geometry of this space plays an important role in statistical and computational aspects of tree inference algorithms. It has recently been shown that molecular clock, and hence coalescent, trees possess a unique geometry, different from that of classical phylogenetic tree spaces which do not model mutation rates. Here we introduce and study a space of discrete coalescent trees, that is, we assume that time is discrete, which is inevitable in many computational formalisations. We establish several geometrical properties of the space and show how these properties impact various algorithms used in phylogenetic analyses. Our tree space is a discretisation of a known time tree space, called t-space, and hence our results can be used to approximate solutions to various open problems in t-space. Our tree space is also a generalisation of another known trees space, called the ranked nearest neighbour interchange space, hence our advances in this paper imply new and generalise existing results about ranked trees.

The general theory developed by Ben Yaacov for metric structures provides Fra��ss�� limits which are approximately ultrahomogeneous. We show here that this result can be strengthened in the case of relational metric structures. We give an extra condition that guarantees exact ultrahomogenous limits. The condition is quite general. We apply it to stochastic processes, the class of diversities, and its subclass of $L_1$ diversities., 22 pages

Smoothing splines can be thought of as the posterior mean of a Gaussian process regression in a certain limit. By constructing a reproducing kernel Hilbert space with an appropriate inner product, the Bayesian form of the V-spline is derived when the penalty term is a fixed constant instead of a function. An extension to the usual generalized cross-validation formula is utilized to find the optimal V-spline parameters., Comment: a draft. not peer-reviewed yet

Diversities are a generalization of metric spaces in which a non-negative value is assigned to all finite subsets of a set, rather than just to pairs of points. Here we provide an analogue of the theory of negative type metrics for diversities. We introduce negative type diversities, and show that, as in the metric space case, they are a generalization of $L_1$-embeddable diversities. We provide a number of characterizations of negative type diversities, including a geometric characterisation. Much of the recent interest in negative type metrics stems from the connections between metric embeddings and approximation algorithms. We extend some of this work into the diversity setting, showing that lower bounds for embeddings of negative type metrics into $L_1$ can be extended to diversities by using recently established extremal results on hypergraphs.

The Minimal Ancestral Deviation (MAD) method is a recently introduced procedure for estimating the root of a phylogenetic tree, based only on the shape and branch lengths of the tree. The method is loosely derived from the midpoint rooting method, but, unlike its predecessor, makes use of all pairs of OTUs when positioning the root. In this note we establish properties of this method and then describe a fast and memory efficient algorithm. As a proof of principle, we use our algorithm to determine the MAD roots for simulated phylogenies with up to 100,000 OTUs. The calculations take a few minutes on a standard laptop.

Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline -- which we name the V-spline -- that incorporates position and velocity information and a penalty term that controls acceleration. We introduce a particular adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is given and we detail the performance of the V-spline on four particularly challenging test datasets. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle., Comment: 25 pages, submitted

In the case of a linear state space model, we implement an MCMC sampler with two phases. In the learning phase, a self-tuning sampler is used to learn the parameter mean and covariance structure. In the estimation phase, the parameter mean and covariance structure informs the proposed mechanism and is also used in a delayed-acceptance algorithm. Information on the resulting state of the system is given by a Gaussian mixture. In on-line mode, the algorithm is adaptive and uses a sliding window approach to accelerate sampling speed and to maintain appropriate acceptance rates. We apply the algorithm to joined state and parameter estimation in the case of irregularly sampled GPS time series data., Comment: adaptive MCMC method application. need further work

We define a set inner product to be a function on pairs of convex bodies which is symmetric, Minkowski linear in each dimension, positive definite, and satisfies the natural analogue of the Cauchy-Schwartz inequality (which is not implied by the other conditions). We show that any set inner product can be embedded into an inner product space on the associated support functions, thereby extending fundamental results of Hormander and Radstrom. The set inner product provides a geometry on the space of convex bodies. We explore some of the properties of that geometry, and discuss an application of these ideas to the reconstruction of ancestral ecological niches in evolutionary biology.

Pathogens can alter epithelial polarity by recruiting polarity proteins to the apical membrane, but how a change in protein localization is linked to polarity disruption is not clear. In this study, we used chemically induced dimerization to rapidly relocalize proteins from the cytosol to the apical surface. We demonstrate that forced apical localization of Par3, which is normally restricted to tight junctions, is sufficient to alter apical membrane identity through its interactions with phosphatidylinositol 3-kinase (PI3K) and the Rac1 guanine nucleotide exchange factor Tiam1. We further show that PI3K activity is required upstream of Rac1, and that simultaneously targeting PI3K and Tiam1 to the apical membrane has a synergistic effect on membrane remodeling. Thus, Par3 coordinates the action of PI3K and Tiam1 to define membrane identity, revealing a signaling mechanism that can be exploited by human mucosal pathogens.

The embedding of finite metrics in $\ell_1$ has become a fundamental tool for both combinatorial optimization and large-scale data analysis. One important application is to network flow problems in which there is close relation between max-flow min-cut theorems and the minimal distortion embeddings of metrics into $\ell_1$. Here we show that this theory can be generalized considerably to encompass Steiner tree packing problems in both graphs and hypergraphs. Instead of the theory of $\ell_1$ metrics and minimal distortion embeddings, the parallel is the theory of diversities recently introduced by Bryant and Tupper, and the corresponding theory of $\ell_1$ diversities and embeddings which we develop here., Comment: 19 pages, no figures. This version: further small corrections

Simulations often involve the use of model parameters which are unknown or uncertain. For this reason, simulation experiments are often repeated for multiple combinations of parameter values, often iterating through parameter values lying on a fixed grid. However, the use of a discrete grid places limits on the dimension of the parameter space and creates the potential to miss important parameter combinations which fall in the gaps between grid points. Here we draw parallels with strategies for numerical integration and describe a Markov chain Monte-Carlo strategy for exploring parameter values. We illustrate the approach using examples from phylogenetics, archaeology, and epidemiology., Comment: 29 pages incl. supplementary information. 4 figures, 2 supp. figures, 2 supp. tables