Arsenic trioxide downregulates cancer procoagulant activity in MCF-7 and WM-115 cell lines in vitro

© 2015 Termedia Sp. z o. o. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.THE AIM OF THE STUDY: To analyze human breast cancer cell line MCF-7 and human malignant melanoma cell line WM-115 in order to characterize the cellular expression of CP and to evaluate whether ATO may affect this activity, as well as the viability of the cells.MATERIAL AND METHODS: The inhibitory effect of arsenic trioxide on the proliferation of MCF-7 and WM-115 cells were measured with MTT test. The activity of cancer procoagulant after ATO exposure was determined by a specific three-stage chromogenic assay.RESULTS: ATO decreased the CP activity in a dose- and time-dependent manner in MCF-7 cells with no effect on cell proliferation at the same time. However, it affected the CP activity of WM-115 cells in a different way. Reduction in CP activity was followed by an increase after 48 h incubation. The cells viability results showed dose-and time-correlated response within high arsenic concentrations.CONCLUSIONS: Arsenic trioxide downregulates the CP expression in human breast cancer and melanoma cells.Peer reviewedFinal Published versio

The Psychoanalysis of Incest

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Energy-based temporal neural networks for imputing missing values

Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset

Underlying Dynamics of Typical Fluctuations of an Emerging Market Price Index: The Heston Model from Minutes to Months

We investigate the Heston model with stochastic volatility and exponential tails as a model for the typical price fluctuations of the Brazilian S\~ao Paulo Stock Exchange Index (IBOVESPA). Raw prices are first corrected for inflation and a period spanning 15 years characterized by memoryless returns is chosen for the analysis. Model parameters are estimated by observing volatility scaling and correlation properties. We show that the Heston model with at least two time scales for the volatility mean reverting dynamics satisfactorily describes price fluctuations ranging from time scales larger than 20 minutes to 160 days. At time scales shorter than 20 minutes we observe autocorrelated returns and power law tails incompatible with the Heston model. Despite major regulatory changes, hyperinflation and currency crises experienced by the Brazilian market in the period studied, the general success of the description provided may be regarded as an evidence for a general underlying dynamics of price fluctuations at intermediate mesoeconomic time scales well approximated by the Heston model. We also notice that the connection between the Heston model and Ehrenfest urn models could be exploited for bringing new insights into the microeconomic market mechanics.Comment: 20 pages, 9 figures, to appear in Physica