Tocotrienol-rich fraction from palm oil affects gene expression in tumors resulting from MCF-7 cell inoculation in athymic mice

It has recently been shown that tocotrienols are the components of vitamin E responsible for inhibiting the growth of human breast cancer cells in vitro, through an estrogen-independent mechanism. Although tocotrienols act on cell proliferation in a dose-dependent manner and can induce programmed cell death, no specific gene regulation has yet been identified. To investigate the molecular basis of the effect of tocotrienols, we injected MCF-7 breast cancer cells into athymic nude mice. Mice were fed orally with 1 mg/d of tocotrienol-rich fraction (TRF) for 20 wk. At end of the 20 wk, there was a significant delay in the onset, incidence, and size of the tumors in nude mice supplemented with TRF compared with the controls. At autopsy, the tumor tissue was excised and analyzed for gene expression by means of a cDNA array technique. Thirty out of 1176 genes were significantly affected. Ten genes were down-regulated and 20 genes up-regulated with respect to untreated animals, and some genes in particular were involved in regulating the immune system and its function. The expression of the interferon-inducible transmembrane protein-1 gene was significantly up-regulated in tumors excised from TRF-treated animals compared with control mice. Within the group of genes related to the immune system, we also found that the CD59 glycoprotein precursor gene was up-regulated. Among the functional class of intracellular transducers/effectors/modulators, the c-myc gene was significantly down-regulated in tumors by TRF treatment. Our observations indicate that TRF supplementation significantly and specifically affects MCF-7 cell response after tumor formation in vivo and therefore the host immune function. The observed effect on gene expression is possibly exerted independently from the antioxidant activity typical of this family of molecules.     

How to Better Use Expert Advice

This work is concerned with online learning from expert advice. Extensive work on this problem generated numerous expert advice algorithms whose total loss is provably bounded above in terms of the loss incurred by the best expert in hindsight. Such algorithms were devised for various problem variants corresponding to various loss functions. For some loss functions, such as the square, Hellinger and entropy losses, optimal algorithms are known. However, for two of the most widely used loss functions, namely the 0/1 and absolute loss, there are still gaps between the known lower and upper bounds.In this paper we present two new expert advice algorithms and prove for them the best known 0/1 and absolute loss bounds. Given an expert advice algorithm ALG, the goal is to form an upper bound on the regret L _ALG – L* of ALG, where L _ALG is the loss of ALG and L* is the loss of the best expert in hindsight. Typically, regret bounds of a canonical form C · are sought where N is the number of experts and C is a constant. So far, the best known constant for the absolute loss function is C = 2.83, which is achieved by the recent IAWM algorithm of Auer et al. (2002). For the 0/1 loss function no bounds of this canonical form are known and the best known regret bound is , where C ₁ = e – 2 and C ₂ = 2 . This bound is achieved by a P-norm algorithm of Gentile and Littlestone (1999). Our first algorithm is a randomized extension of the guess and double algorithm of Cesa-Bianchi et al. (1997). While the guess and double algorithm achieves a canonical regret bound with C = 3.32, the expected regret of our randomized algorithm is canonically bounded with C = 2.49 for the absolute loss function. The algorithm utilizes one random choice at the start of the game. Like the deterministic guess and double algorithm, a deficiency of our algorithm is that it occasionally restarts itself and therefore forgets what it learned. Our second algorithm does not forget and enjoys the best known asymptotic performance guarantees for both the absolute and 0/1 loss functions. Specifically, in the case of the absolute loss, our algorithm is canonically bounded with C approaching and in the case of the 0/1 loss, with C approaching 3/ . In the 0/1 loss case the algorithm is randomized and the bound is on the expected regret.     

Gambling, health and age: Data from the National Epidemiologic Survey on Alcohol and Related Conditions.

The health effects of recreational gambling are presently unclear, particularly across age groups. Theories of healthy aging suggest that social activities, including gambling, may be beneficial to the health of older adults. Using cross-sectional data from the National Epidemiologic Survey on Alcohol and Related Conditions (N=43,093), the authors examined associations between gambling (categorized as nongambling, recreational gambling, or problem/pathological gambling) and health and functioning measures stratified by age (40-64 years and ≥65). Problem/pathological gambling was uniformly associated with poorer health measures among both younger and older adults. Among younger respondents, poorer health measures were also found among recreational gamblers. However, among older respondents, recreational gambling was associated not only with some negative measures (e.g., obesity) but also with some positive measures (e.g., better physical and mental functioning). Longitudinal studies are needed to clarify the relationship between gambling and health in older adults in the context of healthy aging. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Non‐Volatile ReRAM Devices Based on Self‐Assembled Multilayers of Modified Graphene Oxide 2D Nanosheets

2D nanomaterials have been actively utilized in non‐volatile resistive switching random access memory (ReRAM) devices due to their high flexibility, 3D‐stacking capability, simple structure, transparency, easy fabrication, and low cost. Herein, it demonstrates re‐writable, bistable, transparent, and flexible solution‐processed crossbar ReRAM devices utilizing graphene oxide (GO) based multilayers as active dielectric layers. The devices employ single‐ or multi‐component‐based multilayers composed of positively charged GO (N‐GO(+) or NS‐GO(+)) with/without negatively charged GO(‐) using layer‐by‐layer assembly method, sandwiched between Al bottom and Au top electrodes. The device based on the multi‐component active layer Au/[N‐GO(+)/GO(‐)]_n/Al/PES shows higher ON/OFF ratio of ≈10⁵ with switching voltage of ?1.9 V and higher retention stability (≈10⁴ s), whereas the device based on single component (Au/[N‐GO(+)]_n/Al/PES) shows ≈10³ ON/OFF ratio at ±3.5 V switching voltage. The superior ReRAM properties of the multi‐component‐based device are attributed to a higher coating surface roughness. The Au/[N‐GO(+)/GO(–)]_n/Al/PES device prepared from lower GO concentration (0.01%) exhibits higher ON/OFF ratio (≈10⁹) at switching voltage of ±2.0 V. However, better stability is achieved by increasing the concentration from 0.01% to 0.05% of all GO‐based solutions. It is found that the devices containing MnO₂ in the dielectric layer do not improve the ReRAM performance.     

An efficient two phase approach for solving reliability–redundancy allocation problem using artificial bee colony technique

The main goal of the present paper is to present a two phase approach for solving the reliability–redundancy allocation problems (RRAP) with nonlinear resource constraints. In the first phase of the proposed approach, an algorithm based on artificial bee colony (ABC) is developed to solve the allocation problem while in the second phase an improvement of the solution as obtained by this algorithm is made. Four benchmark problems in the reliability–redundancy allocation and two reliability optimization problems have been taken to demonstrate the approach and it is shown by comparison that the solutions by the new proposed approach are better than the solutions available in the literature.     

DF-LDA tree: a nonlinear multilevel classifier for pattern recognition

This article presents a new nonlinear classifier by arranging linear classifiers in a tree structure. The proposed classifier, called the direct fractional-step linear discriminant (DF-LDA) tree, adopts a tree structure containing a DF-LDA at each node. The structure of the tree classifier evolves as the training proceeds, so there is no need to decide any parameters as a priori. Due to the many DF-LDAs arranged in the tree structure, classification performance of the proposed classifier is improved over single-shot DF-LDA. The proposed DF-LDA tree is tested on various synthetic and real datasets. Experimental results show that the proposed classifier leads to very satisfactory results in terms of classification accuracy.     

Studies on the Cell Structure and Mechanical Properties of NR/Latex Reclaim/HSR Based Micrqcellular Sheets

In natural rubber/high styrene resin microcellular sheets, part of natural rubber was replaced by latex reclaim prepared from waste latex products. The mechanical properties and cell structure of the products were evaluated. It was found that latex reclaim can replace about 30% of natural rubber without affecting the technical properties of the microcellular sheets.     

Heat transfer in micropolar boundary layer flow near a stagnation point

Using the micropolar theory, a set of boundary layer equations are obtained for the two dimensional flow of an incompressible micropolar fluid near a stagnation point. The boundary conditions of isothermal and insulated wall are taken with VISCOUS DISSIPATION effects. Numerical results for the temperature distribution has been shown graphically for different values of the material parameters and Prandtl number.     

Dielectric behavior of natural rubber composites in microwave fields

In this article, we report the preparation of conducting natural rubber (NR) with polyaniline (Pani). NR was made into a conductive material by the compounding of NR with Pani in powder form. NR latex was made into a conductive material by the in situ polymerization of aniline in the presence of NR latex. Different compositions of Pani–NR semi‐interpenetrating networks were prepared, and the dielectric properties of all of the samples were determined in microwave frequencies. The cavity perturbation technique was used for this study. A HP8510 vector network analyzer with a rectangular cavity resonator was used for this study. S bands 2–4 GHz in frequency were used. Thermal studies were also carried out with thermogravimetric analysis and differential scanning calorimetry. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2682–2686, 2007