Fast solution methods

International audienceThe standard boundary element method applied to the time harmonic Helmholtz equation yields a numerical method with $O(N^3)$ complexity when using a direct solution of the fully populated system of linear equations. Strategies to reduce this complexity are discussed in this paper. The $O(N^3)$ complexity issuing from the direct solution is first reduced to $O(N^2)$ by using iterative solvers. Krylov subspace methods as well as strategies of preconditioning are reviewed. Based on numerical examples the influence of different parameters on the convergence behavior of the iterative solvers is investigated. It is shown that preconditioned Krylov subspace methods yields a boundary element method of $O(N^2)$ complexity. A further advantage of these iterative solvers is that they do not require the dense matrix to be set up. Only matrix–vector products need to be evaluated which can be done efficiently using a multilevel fast multipole method. Based on real life problems it is shown that the computational complexity of the boundary element method can be reduced to $O(N \log_2 N)$ for a problem with $N$ unknowns

Effects of Lipid Peroxidation-Derived Products on the Growth of Human Colorectal Cancer Cell Line HT-29

Epidemiologic investigations indicate a close relationship between colorectal cancer and fat intake. However, to date the effects of lipid peroxidation-derived products that are formed from fat (especially free or esterified unsaturated fatty acids) on the initiation or progression of colorectal cancer have not been investigated extensively. Therefore, in the present study, we examined the effects of fatty acids, fatty acid hydroperoxides and aldehydes on the growth of human colorectal cancer cell line HT-29. At concentrations of 1 and 10 µM, linoleic, arachidonic and eicosapentaenoic acids, and 13-hydroperoxyoctadecadienoic and 15-hydroperoxyeicosapentaenoic acids had no significant effects on the growth of HT-29 cells. 4-Hydroxynonenal and 4-hydroxyhexenal had no significant effects on the growth of HT-29 cells up to 10 µM, whereas 4-oxononenal potently inhibited HT-29 cell growth (1–10 µM, 16–85% inhibition). Further experiments concerning DNA fragmentation, expression levels of Bax and Bcl-2 mRNA, expression levels of pro-caspase-3 and caspase-3 proteins, and activity of caspase-3 suggested that 4-oxononenal may increase the sensitivity of HT-29 cells to apoptosis through a decreased expression level of Bcl-2 and then increased formation of caspase-3 from pro-caspase-3

Non-transgenic genome modifications in a hemimetabolous insect using zinc-finger and TAL effector nucleases

Hemimetabolous, or incompletely metamorphosing, insects are phylogenetically relatively basal and comprise many pests. However, the absence of a sophisticated genetic model system, or targeted gene-manipulation system, has limited research on hemimetabolous species. Here we use zinc-finger nuclease and transcription activator-like effector nuclease technologies to produce genetic knockouts in the hemimetabolous insect Gryllus bimaculatus. Following the microinjection of mRNAs encoding zinc-finger nucleases or transcription activator-like effector nucleases into cricket embryos, targeting of a transgene or endogenous gene results in sequence-specific mutations. Up to 48% of founder animals transmit disrupted gene alleles after zinc-finger nucleases microinjection compared with 17% after microinjection of transcription activator-like effector nucleases. Heterozygous offspring is selected using mutation detection assays that use a Surveyor (Cel-I) nuclease, and subsequent sibling crosses create homozygous knockout crickets. This approach is independent from a mutant phenotype or the genetic tractability of the organism of interest and can potentially be applied to manage insect pests using a non-transgenic strategy. © 2012 Macmillan Publishers Limited. All rights reserved

Alleviating Surgeons’ Stress through Listening to Natural Sounds in a Half-Encapsulated Rest Space after an Operation: A Pilot, Longitudinal Field Study

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Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content

Rare-earth elements like neodymium, terbium and dysprosium are crucial to the performance of permanent magnets used in various green-energy technologies like hybrid or electric cars. To address the supply risk of those elements, we applied machine-learning techniques to design magnetic materials with reduced neodymium content and without terbium and dysprosium. However, the performance of the magnet intended to be used in electric motors should be preserved. We developed machine-learning methods that assist materials design by integrating physical models to bridge the gap between length scales, from atomistic to the micrometer-sized granular microstructure of neodymium-iron-boron permanent magnets. Through data assimilation, we combined data from experiments and simulations to build machine-learning models which we used to optimize the chemical composition and the microstructure of the magnet. We applied techniques that help to understand and interpret the results of machine learning predictions. The variables importance shows how the main design variables influence the magnetic properties. High-throughput measurements on compositionally graded sputtered films are a systematic way to generate data for machine data analysis. Using the machine learning models we show how high-performance, Nd-lean magnets can be realized