Carbon Chain Length Modulates MDA-MB-231 Breast Cancer Cell Killing Mechanisms by Mitochondrially Targeted Aryl−Urea Fatty Acids

Targeting the tumor cell mitochondrion could produce novel anticancer agents. We designed an aryl−urea fatty acid ( 1 g ; 16({[4-chloro-3-(trifluoromethyl)phenyl]carbamoyl}amino)hexadecanoic acid) that disrupted the mitochondrion and decreased MDA-MB-231 breast cancer cell viability. To optimize the aryl−ureas the present study evaluated mitochondrial targeting by 1 g analogues containing alkyl chains between 10–17 carbons. Using the dye JC-1, the C12−C17 analogues efficiently disrupted the mitochondrial membrane potential (IC₅₀s 3.5±1.2 to 7.6±1.1 μM) and impaired ATP production; shorter analogues were less active. 7-Aminoactinomycin D/annexin V staining and flow cytometry showed that these agents activated the killing mechanisms of necrosis and apoptosis to varying extents (7-aminoactinomycin D/annexin V staining ratios 4.3–6.0). Indeed, 1 g and its C17 analogue preferentially activated necrosis and apoptosis, respectively (ratios 2.1 and 16). Taken together, alkyl chain length is a determinant of mitochondrial targeting by aryl−ureas and can be varied to develop analogues that activate apoptosis or necrosis in a regulated fashion.     

Dissociating uncertainty responses and reinforcement signals in the comparative study of uncertainty monitoring.

Although researchers are exploring animals' capacity for monitoring their states of uncertainty, the use of some paradigms allows the criticism that animals map avoidance responses to error-causing stimuli not because of uncertainty monitored but because of feedback signals and stimulus aversion. The authors addressed this criticism with an uncertainty-monitoring task in which participants completed blocks of trials with feedback deferred so that they could not associate reinforcement signals to particular stimuli or stimulus-response pairs. Humans and 1 of 2 monkeys were able to make cognitive, decisional uncertainty responses that were independent of feedback or reinforcement history within a task. This finding unifies the comparative literature on uncertainty monitoring. The dissociation of performance from reinforcement has theoretical implications, and the deferred-feedback technique has many applications. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

ASIC原型构建：是做还是买？

随着数字集成电路(IC)的设计变得更加复杂,验证其功能的工作也越来越复杂了。在能被设计的门电路数量和能在合理时间内被验证的门电路数量之间一直存在差距,而这些年来,EDA厂商们在缩小这种差距方面几乎无所作为。     

获得新生的维氏电桥振荡器

1940年,William Hewlett和DavidPackard两人将他们在车库中制作的一种产品投放市场,这种产品就是维氏电桥(Wien-Bridge)振荡器。它由一个单极点高通滤波器与一个单极点低通滤波串联而成。为了保持增益恒定不变,该振荡器电路采用一只白炽灯作指示灯来提供AGC(自动增益控制)。正如所有的白炽灯那样,这种指示灯具有非线性的电阻值。当你给这一振荡器电路通电时,冷白炽灯的电阻很小,从而产生很高的增益。随着增益的增大,温度不断升高的白炽灯的电阻也随之增大。因此,白炽灯具有AGC功能。这种振荡器电路一直使用了60多年,现在仍然在使用中。维氏电桥振荡器存在的唯一问题就是,增益小于1时,它不工作。我在一家电话公司供职时,     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

NMDA Receptor Modulation of Incidental Learning in Pavlovian Context Conditioning.

Rats exposed to a footshock show conditional fear when reexposed to the shock context. Immediate presentation of shock after placement in the context significantly reduces this fear. Preexposure to the context in the absence of shock, coupled with a minimum preshock interval during training, overcomes this immediate shock deficit. Because rats learn about the context during preexposure and express that learning after being reinforced, the context preexposure effect is an aversive analogue of latent learning. The authors examined the effect of the N-methyl-D-aspartate (NMDA) receptor antagonist D,L-2-amino-5-phosphovalerate (APV) on the facilitatory effect of context preexposure. Rats were preexposed to a chamber after APV administration. The next day they were placed in the same chamber without drug and received shock 35 s later. APV blocked the facilitatory effect of preexposure. Therefore NMDA receptors are important for contextual latent learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Hydrogen annealing effect on DC and low-frequency noise characteristics in CMOS FinFETs

The hydrogen annealing process has been used to improve surface roughness of the Si-fin in CMOS FinFETs for the first time. Hydrogen annealing was performed after Si-fin etch and before gate oxidation. As a result, increased saturation current with a lowered threshold voltage and a decreased low-frequency noise level over the entire range of drain current have been attained. The low-frequency noise characteristics indicate that the oxide trap density is reduced by a factor of 3 due to annealing. These results suggest that hydrogen annealing is very effective for improving device performance and for attaining a high-quality surface of the etched Si-fin.     

Discrete Wavelet Transform: Architectures, Design and Performance Issues

Due to the demand for real time wavelet processors in applications such as video compression [1], Internet communications compression [2], object recognition [3], and numerical analysis, many architectures for the Discrete Wavelet Transform (DWT) systems have been proposed. This paper surveys the different approaches to designing DWT architectures. The types of architectures depend on whether the application is 1-D, 2-D, or 3-D, as well as the style of architecture: systolic, semi-systolic, folded, digit-serial, etc. This paper presents an overview and evaluation of the architectures based on the criteria of latency, control, area, memory, and number of multipliers and adders. This paper will give the reader an indication of the advantages and disadvantages of each design.     

Value of information in remanufacturing complex products

Remanufacturing facilities usually face a trade-off between limited information about remanufacturing yields and potentially long supplier lead times. To improve production performance, these firms may attempt to acquire more timely and accurate information about remanufacturing yields or alternatively, to reduce the lead times of purchased parts. We develop four decision-making models to evaluate the impact of yield information and supplier lead time on manufacturing costs. We identify the operating conditions under which these capabilities are valuable, along with their relative impact on facility performance. Each model is formulated as an infinite horizon, stochastic dynamic program (Markov decision process). Our results indicate that the yield information is generally quite valuable, while investments in supplier responsiveness provide trivial returns to products with few parts. However, as product complexity increases with large number of target parts, the value of short lead times increases.