Molecular SIMS at surfaces: Thiophene on silver

Molecular SIMS, an important development in mass spectrometry of complex organic molecules, has application to the analysis of reactive adiayers on surfaces. We report here the discovery by SIMS of thiophene self-hydrogenation on silver foil.     

Hydrolysis of Cutin by PET-Hydrolases

Functionalisation of synthetic polymers by using enzymes has been recently demonstrated. The major advantage of enzymes over chemical processes lies in their surface specific and endo-wise mode of action. Surface hydrophilisation of PET with lipases and cutinases leads to a dramatic increase of the surfacial acid and hydroxyl group content while conventional chemical treatment does not cause any change. However, this PET-hydrolysing activity by enzymes from distinct classes has not yet been correlated to activity on natural polyesters. Here, we show that lipases, cutinases and a PHA-depolymerase are all capable of hydrolysing PET, while only lipases and cutinases also hydrolysed cutin to various degrees. Lipases showed a higher specificity for terminal fatty acids while the cutinases preferred hydroxy fatty acids during cutin hydrolysis.     

Limit on nonlocality in any world in which communication complexity is not trivial

Bell proved that quantum entanglement enables two spacelike separated parties to exhibit classically impossible correlations. Even though these correlations are stronger than anything classically achievable, they cannot be harnessed to make instantaneous (faster than light) communication possible. Yet, Popescu and Rohrlich have shown that even stronger correlations can be defined, under which instantaneous communication remains impossible. This raises the question: Why are the correlations achievable by quantum mechanics not maximal among those that preserve causality? We give a partial answer to this question by showing that slightly stronger correlations would result in a world in which communication complexity becomes trivial.     

An Optimal Algorithm for the <Emphasis Type="Italic">k</Emphasis>-Fixed-Endpoint Path Cover on Proper Interval Graphs

In this paper we consider the k-fixed-endpoint path cover problem on proper interval graphs, which is a generalization of the path cover problem. Given a graph G and a set T of k vertices, a k-fixed-endpoint path cover of G with respect to T is a set of vertex-disjoint simple paths that covers the vertices of G, such that the vertices of T are all endpoints of these paths. The goal is to compute a k-fixed-endpoint path cover of G with minimum cardinality. We propose an optimal algorithm for this problem with runtime O(n), where n is the number of intervals in G. This algorithm is based on the Stair Normal Interval Representation (SNIR) matrix that characterizes proper interval graphs. In this characterization, every maximal clique of the graph is represented by one matrix element; the proposed algorithm uses this structural property, in order to determine directly the paths in an optimal solution.     

Functional Additive Mixed Models

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, for example, spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well, and also scales to larger datasets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach.