Targeting Cell Death Mechanism Specifically in Triple Negative Breast Cancer Cell Lines

Triple negative breast cancer (TNBC) is currently associated with a lack of treatment options. Arsenic derivatives have shown antitumoral activity both in vitro and in vivo; however, their mode of action is not completely understood. In this work we evaluate the response to arsenate of the double positive MCF-7 breast cancer cell line as well as of two different TNBC cell lines, Hs578T and MDA-MB-231. Multimodal experiments were conducted to this end, using functional assays and microarrays. Arsenate was found to induce cytoskeletal alteration, autophagy and apoptosis in TNBC cells, and moderate effects in MCF-7 cells. Gene expression analysis showed that the TNBC cell lines’ response to arsenate was more prominent in the G2M checkpoint, autophagy and apoptosis compared to the Human Mammary Epithelial Cells (HMEC) and MCF-7 cell lines. We confirmed the downregulation of anti-apoptotic genes (MCL1, BCL2, TGFβ1 and CCND1) by qRT-PCR, and on the protein level, for TGFβ2, by ELISA. Insight into the mode of action of arsenate in TNBC cell lines it is provided, and we concluded that TNBC and non-TNBC cell lines reacted differently to arsenate treatment in this particular experimental setup. We suggest the future research of arsenate as a treatment strategy against TNBC.     

Die Beurteilung des Wassergehaltes von Fleisch- und Wurstwaren

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Angaben zur chemischen Zusammensetzung der wichtigeren ungarischen Pflaumensorten

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Core–shell nanowires: From the ensemble to single-wire characterization

Recent investigations of core–shell nanowires using synchrotron radiation techniques deduced the average structural parameters of heterostructure core–shell nanowires. Here, we report on first results and discuss the problems arising when measuring such complex nanostructures by using nanofocusing X-ray techniques. InAs/InAsP core–shell nanowires exhibit a certain bending, the origin of which is described using finite element simulations assuming a displacement of the core, and a gradient in the chemical composition of the wire’s shell.     

Neural networks for modelling and fault detection of the inter-stand strip tension of a cold tandem mill

This paper deals with the multilayered approach of the high-order neural network applied in a robust fault detection scheme. To introduce dynamic properties in these networks, a dynamic high-order neural unit is presented. It is shown that these networks can approximate any function with less parameters than in the case of multi-layer perceptron neural network. Such networks have good modelling properties, which make them useful for designing residuals in fault detection of dynamic processes. A method of computing a variable threshold derived from the confidence interval prediction is applied in order to obtain robustness in the fault detection process. Application of these networks for system identification and robust fault detection of the inter-stand strip tension of a continuous five stands cold mill is presented in the final part.     

Glasses as engineering materials: A review

Glass products have applications in design engineering, and they can solve many special problems. These materials can work in situations in which plastics and metals would fail and need to be part of designer’s repertoire. In some situations, by using these materials, some difficult problems would be solved. This paper contains a number of chapters as follows: a brief about ceramics family, a short history of glass, a brief about physics and the technology of glass fabrication, recently developed glasses with special destinations, testing methods and news about glass parts processing (grinding, waterjet processing, laser cutting, nanoimprint lithography, etc.). The last chapter of this review paper contain some strategic lines of glass usage in industry and estimations about the future of glass development.     

Periodic autoregressive model identification using genetic algorithms

Periodic autoregressive (PAR) models extend the classical autoregressive models by allowing the parameters to vary with seasons. Selecting PAR time‐series models can be computationally expensive, and the results are not always satisfactory. In this article, we propose a new automatic procedure to the model selection problem by using the genetic algorithm. The Bayesian information criterion is used as a tool to identify the order of the PAR model. The success of the proposed procedure is illustrated in a small simulation study, and an application with monthly data is presented.