Bioenergetic studies on the quinone electron acceptors of photosystem II

Photosystem II (PSII) is a membrane-bound protein complex found in plants, algae and cyanobacteria that converts light into chemical energy. Despite extensive research, many energetic and mechanistic questions of PSII remain unresolved. Here the energetics and kinetics of the electron-acceptor side of PSII from Thermosynechococcus elongatus were investigated using biophysical approaches. Based on data from electron paramagnetic resonance and thermoluminescence measurements, the two midpoint potentials of the terminal electron acceptor, QB, were measured (Em(QB/QB•−) = 92 mV; Em(QB•−/QBH2) = 43 mV). It was found that i) QB•− is significantly stabilized, contradicting the recent literature, ii) the energy-gap between QA and QB is larger than previously assumed (235 mV instead of ≈ 80 mV), contradicting the older literature, and iii) the release of QBH2 into the pool is thermodynamically favourable, ( ≈ 50 meV). No significant shift of the QB midpoint potentials in response to the loss of the Mn4O5Ca cluster was found. These findings allow for a better understanding of charge separation and the energetics of PSII. Isolated PSII from T. elongatus is used in many structural and functional studies but the electron acceptor side kinetics of this organism are poorly defined. Using absorption spectroscopy, the kinetics which were previously treated as a single “fast phase”, were resolved as follows: QA•−→ Fe 3+ (t1/2 = 50 µs); QA•−→QB(t1/2 = 350 µs); QA•−→ QB•− (t1/2 = 1.3 ms). Furthermore, the kinetic data analysis developed in this work allowed the proportions of these reactions to be determined under a range of conditions. It was found that in long dark-adapted samples up to 50% of the non-heme iron was oxidized and this oxidation was inhibited when bicarbonate was present. These data will be useful for future research on PSII and help understanding the mechanism of electron transfer on the acceptor side.Open Acces

Automated mass spectrum generation for new physics

We describe an extension of the FeynRules package dedicated to the automatic generation of the mass spectrum associated with any Lagrangian-based quantum field theory. After introducing a simplified way to implement particle mixings, we present a new class of FeynRules functions allowing both for the analytical computation of all the model mass matrices and for the generation of a C++ package, dubbed ASperGe. This program can then be further employed for a numerical evaluation of the rotation matrices necessary to diagonalize the field basis. We illustrate these features in the context of the Two-Higgs-Doublet Model, the Minimal Left-Right Symmetric Standard Model and the Minimal Supersymmetric Standard Model.Comment: 11 pages, 1 table; version accepted by EPJ

Evolving test instances of the Hamiltonian completion problem

Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is usually no standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are usually not diverse enough to draw sound conclusions. That is why recent work proposes a new methodology to generate a diverse set of instances by using an evolutionary algorithm. We can then analyze the resulting graphs and get key insights into which attributes are most related to algorithm performance. We can also fill observed gaps in the instance space in order to generate graphs with previously unseen combinations of features. This methodology is applied to the instance space of the Hamiltonian completion problem using two different solvers, namely the Concorde TSP Solver and a multi-start local search algorithm.Comment: 12 pages, 12 figures, minor revisions in section

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. They are demonstrated for two specific combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. Computational results show that the algorithm achieves promising results for both problems and eight new best solutions for a benchmark set of instances are found for the former problem. These results indicate that the algorithm is competitive with the state-of-the-art. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices made by constructive heuristics