Variability of SCC mec in the Zurich area

A periodic survey of methicillin-resistant Staphylococcus aureus (MRSA) in Zurich in 2004 and 2006 revealed a consistently low prevalence of MRSA. SCCmec and ccr typing showed fluctuations in the proportions of SCCmec types and in the carriage of mobile virulence determinants. Together with the presence of variant SCCmecs these findings suggest a high clonal diversity and level of SCCmec recombination. The prevalence of a local "drug clone", associated with low-level methicillin resistance and rapid growth, significantly decreased. This clone had spread among intraveneous drug users, steadily increasing from 1994 to 2001 and was dominant in 2001. Apparently, changes in the management of the Zurich drug scene have restricted the spread of this clon

Automated generation of constructive ordering heuristics for educational timetabling

Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For examination timetabling and university course timetabling problems essentially graph colouring heuristics have been used for this purpose. The process of deriving heuristics manually for educational timetabling is a time consuming task. Furthermore, according to the no free lunch theorem different heuristics will perform well for different problems and problem instances. Hence, automating the induction of construction heuristics will reduce the man hours involved in creating such heuristics, allow for the derivation of problem specific heuristics and possibly result in the derivation of heuristics that humans have not thought of. This paper presents generation construction hyper-heuristics for educational timetabling. The study investigates the automatic induction of two types of construction heuristics, namely, arithmetic heuristics and hierarchical heuristics. Genetic programming is used to evolve arithmetic heuristics. Genetic programming, genetic algorithms and the generation of random heuristic combinations is examined for the generation of hierarchical heuristics. The hyper-heuristics generating both types of heuristics are applied to the examination timetabling and the curriculum based university course timetabling problems. The evolved heuristics were found to perform much better than the existing graph colouring heuristics used for this domain. Furthermore, it was found that the while the arithmetic heuristics were more effective for the examination timetabling problem, the hierarchical heuristics produced better results than the arithmetic heuristics for the curriculum based course timetabling problem. Genetic algorithms proved to be the most effective at inducing hierarchical heuristics

Targeted knock-down of miR21 primary transcripts using snoMEN vectors induces apoptosis in human cancer cell lines

We have previously reported an antisense technology, 'snoMEN vectors', for targeted knock-down of protein coding mRNAs using human snoRNAs manipulated to contain short regions of sequence complementarity with the mRNA target. Here we characterise the use of snoMEN vectors to target the knock-down of micro RNA primary transcripts. We document the specific knock-down of miR21 in HeLa cells using plasmid vectors expressing miR21-targeted snoMEN RNAs and show this induces apoptosis. Knock-down is dependent on the presence of complementary sequences in the snoMEN vector and the induction of apoptosis can be suppressed by over-expression of miR21. Furthermore, we have also developed lentiviral vectors for delivery of snoMEN RNAs and show this increases the efficiency of vector transduction in many human cell lines that are difficult to transfect with plasmid vectors. Transduction of lentiviral vectors expressing snoMEN targeted to pri-miR21 induces apoptosis in human lung adenocarcinoma cells, which express high levels of miR21, but not in human primary cells. We show that snoMEN-mediated suppression of miRNA expression is prevented by siRNA knock-down of Ago2, but not by knock-down of Ago1 or Upf1. snoMEN RNAs colocalise with Ago2 in cell nuclei and nucleoli and can be co-immunoprecipitated from nuclear extracts by antibodies specific for Ago2

Higgs Boson Masses in the Complex NMSSM at One-Loop Level

The Next-to-Minimal Supersymmetric Extension of the Standard Model (NMSSM) with a Higgs sector containing five neutral and two charged Higgs bosons allows for a rich phenomenology. In addition, the plethora of parameters provides many sources of CP violation. In contrast to the Minimal Supersymmetric Extension, CP violation in the Higgs sector is already possible at tree-level. For a reliable understanding and interpretation of the experimental results of the Higgs boson search, and for a proper distinction of Higgs sectors provided by the Standard Model or possible extensions, the Higgs boson masses have to be known as precisely as possible including higher-order corrections. In this paper we calculate the one-loop corrections to the neutral Higgs boson masses in the complex NMSSM in a Feynman diagrammatic approach adopting a mixed renormalization scheme based on on-shell and $\bar{DR}$ conditions. We study various scenarios where we allow for tree-level CP-violating phases in the Higgs sector and where we also study radiatively induced CP violation due to a non-vanishing phase of the trilinear coupling $A_t$ in the stop sector. The effects on the Higgs boson phenomenology are found to be significant. We furthermore estimate the theoretical error due to unknown higher-order corrections by both varying the renormalization scheme of the top and bottom quark masses and by adopting different renormalization scales. The residual theoretical error can be estimated to about 10%

A stochastic local search algorithm with adaptive acceptance for high-school timetabling

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective 'heuristic to choose heuristics' to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism. © 2014 Springer Science+Business Media New York

A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain. © 2016 Massachusetts Institute of Technolog

Nitrosylation of Myoglobin and Nitrosation of Cysteine by Nitrite in a Model System Simulating Meat Curing

Demand is growing for meat products cured without the addition of sodium nitrite. Instead of the direct addition of nitrite to meat in formulation, nitrite is supplied by bacterial reduction of natural nitrate often added as vegetable juice/powder. However, the rate of nitrite formation in this process is relatively slow, and the total ingoing nitrite is typically less than in conventional curing processes. The objective of this study was to determine the impact of the rate of addition of nitrite and the amount of nitrite added on nitrosylation/nitrosation reactions in a model meat curing system. Myoglobin was preferentially nitrosylated as no decrease in sulfhydryl groups was found until maximum nitrosylmyoglobin color was achieved. The cysteine–myoglobin model retained more sulfhydryl groups than the cysteine-only model (p \u3c 0.05). The rate of nitrite addition did not alter nitrosylation/nitrosation reactions (p \u3e 0.05). These data suggest that the amount of nitrite but not the rate of addition impacts the nitrosylation/nitrosation reactions this syste