The choice of names and symbols for quantities in chemistry

The importance of maintaining a clear distinction between the names and symbols for quantities and the names and symbols for units.

Choice of designs and doses for early phase trials

This paper reviews state-of-art statistical designs for dose-escalation procedures in first-into-man studies. The main focus will be on studies in oncology, as most statistical procedures for phase I trials have been proposed in this context. Extensions to situations such as the observation of bivariate outcomes and healthy volunteer studies are also discussed. The number of dose levels and cohort sizes used in early phase trials are considered. Finally, this paper raises some practical issues for dose-escalation procedures.

Deciphering the complexity of sainfoin (Onobrychis viciifolia) proanthocyanidins by MALDI-TOF mass spectrometry with a judicious choice of isotope patterns and matrixes

Use of superdihydroxybenzoic acid as the matrix enabled the analysis of highly complex mixtures of proanthocyanidins from sainfoin (Onobrychis viciifolia) by MALDI-TOF mass spectrometry. Proanthocyanidins contained predominantly B-type homopolymers and heteropolymers up to 12- mers (3400 Da). Use of another matrix, 2,6-dihydroxyacetophenone, revealed the presence of A-type glycosylated dimers. In addition, we report here how a comparison of the isotopic adduct patterns, which resulted from Li and Na salts as MALDI matrix additives, could be used to confirm the presence of A-type linkages in complex proanthocyanidin mixtures. Preliminary evidence suggested the presence of A-type dimers in glycosylated prodelphinidins and in tetrameric procyanidins and prodelphinidins.

In search of the aim of the free movement of persons provisions: has the Court of Justice missed the point?

The Court of Justice has, over the years, often been vilified for exceeding the limits of its jurisdiction by interpreting the provisions of Community legislation in a way not seem originally envisaged by its drafters. A recent example of this approach was a cluster of cases in the context of the free movement of workers and the freedom of establishment (Ritter-Coulais and its progeny), where the Court included within the scope of those provisions situations which, arguably, did not present a sufficient link with their (economic) aim. In particular, in that case law the Court accepted that the mere exercise of free movement for the purpose of taking up residence in the territory of another Member State whilst continuing to exercise an economic activity in the State of origin, suffices for bringing a Member State national within the scope of Articles 39 and 43 EC. It is argued that the most plausible explanation for this approach is that the Court now wishes to re-read the economic fundamental freedoms in such a way as to include within their scope all economically active Union citizens, irrespective of whether their situation presents a sufficient link with the exercise of an economic activity in a cross-border context. It is suggested that this approach is problematic for a number of reasons. It is, therefore, concluded that the Court should revert to its orthodox approach, according to which only situations that involve Union citizens who have moved between Member States for the purpose of taking up an economic activity should be included within the scope of the market freedoms.

Likelihood-free estimation of model evidence

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of “likelihood-free” methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few years, following their integration with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) in order to better explore the parameter space. They have been applied primarily to estimating the parameters of a given model, but can also be used to compare models. Here we present novel likelihood-free approaches to model comparison, based upon the independent estimation of the evidence of each model under study. Key advantages of these approaches over previous techniques are that they allow the exploitation of MCMC or SMC algorithms for exploring the parameter space, and that they do not require a sampler able to mix between models. We validate the proposed methods using a simple exponential family problem before providing a realistic problem from human population genetics: the comparison of different demographic models based upon genetic data from the Y chromosome.