Further characterization of rat dihydroceramide desaturase: Tissue distribution, subcellular localization, and substrate specificity

The introduction of the double bond in the sphingoid backbone of sphingolipids occurs at the level of dihydroceramide via an NADPH-dependent desaturase, as discovered in permeabilized rat hepatocytes. In the rat, the enzyme activity, which has now been further characterized, appeared to be mostly enriched in liver and Harderian gland. By means of subcellular fractionation of rat liver homogenates and density gradient separation of microsomal fractions, the desaturase was localized to the endoplasmic reticulum. Various detergents were inhibitory to the enzyme, and maximal activities were obtained in the presence of NADPH and when the substrate was complexed to albumin. In the presence of albumin, the chain length of the fatty acid of the truncated dihydroceramides hardly affected the activity. Finally, in view of a likely evolutionary relationship between desaturases and hydroxylases, the formation of hydroxylated intermediates was analyzed. No evidence for their presence was found under our assay conditions. In part presented at the Conférence Jacques Monod “Cell lipids: Topology, transport and signalling functions”, Aussois, May 1997 (France); Van Veldhoven, P.P. “Ceramide biosynthesis: Characterisation of the conversion of dihydroceramide to ceramide”. Contributed equally to this study.     

Free radical polymerization in multilaminated microreactors: 2D and 3D multiphysics CFD modeling

This paper investigates the modeling of styrene free radical polymerization in two different types of microreactor. A multiphysics model which simultaneously takes into account the hydrodynamics, thermal and mass transfer (convection, diffusion and chemical reaction) is proposed. The set of partial differential equations resulting from the model is solved with the help of the finite elements method either in a 2D or a 3D approach. The different modeled microreactors are on one hand an interdigital multilamination microreactor with a large focusing section, and on the other hand a simple T-junction followed by a straight tube with three different radii. The results are expressed in terms of reactor temperature, polydispersity index, number-average degree of polymerization and monomer conversion for different values of the chemical species diffusion coefficient. It was found that the 2D approach gives the same results as the 3D approach but allows to dramatically reduce the computing time. Despite the heat released by the polymerization reaction, it was found that the thermal transfer in such microfluidic devices is high enough to ensure isothermal conditions. Concerning the polydispersity index, the range of diffusion coefficients over which the polydispersity index can be maintained close to the theoretical value for ideal conditions increases as the tube reactor radius decreases. The interdigital multilamination microreactor was found to act as a tubular reactor of 0.78 mm ID but with a shorter length. This underlines that the use of microfluidic devices can lead to a better control of polymerization reactions.     

The impact of print exposure quality and inference construction on syllogistic reasoning.

This study extended the work of S. Siddiqui, R. F. West, and K. E. Stanovich (1998), who studied the link between general print exposure and syllogistic reasoning. It was hypothesized that exposure to certain text structures that contain well-delineated logical forms, such as popularized scientific texts, would be a better predictor of deductive reasoning skill than general print exposure, which is not sensitive to the quality of an individual's reading activity. Furthermore, it was predicted that the ability to generate explanatory bridging inferences while reading would also be predictive of syllogistic reasoning. Undergraduate students (N = 112) were tested for vocabulary, nonverbal cognitive ability, exposure to general print, exposure to popularized scientific literature, and the ability to comprehend texts distinguished by the number of inferences that must be generated to support comprehension. Hierarchical multiple regression analyses showed that a combined measure of exposure to general and scientific literature was a significant predictor of syllogistic reasoning ability. Additionally, the ability to comprehend high-inference-load texts was related to solving syllogisms that were inconsistent with world knowledge, indicating an overlap in deductive reasoning skill and text comprehension processes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

防止放大器输出出现过压的电路

对汽车电子的普遍要求是任何直接连接到线束的设备都必须能承受电池电压的短路.尽管这种要求比较严酷,但它对于汽车的可靠性和安全性是十分必要的.     

薄膜晶体硅太阳能电池的潜力

如果效率和成本目标能够实现，薄膜晶体硅太阳能电池有潜力替代目前在光伏市场上占主导地位的多晶硅太阳能电池。     

Relation between shear banding and penetration characteristics of conventional tungsten alloys

The dynamic compression failure and ballistic penetration characteristics of conventional tungsten alloys similar in strength were investigated. Dynamic compression failure properties were generated with a symmetric Taylor test technique and penetration characteristics were obtained with 44 mm kinetic penetrators against an 300 HB hardness steel target at 1400 m/s. From shear crack length data generated with Taylor specimens impacted at different impact speeds a critical speed characterizing shear band initiation was deduced. The critical equivalent plastic strain at shear band initiation sites, obtained from the numerical simulation of the Taylor test at the critical impact speed, was found to decrease with the increase of the penetration performance. These results reinforce the argument that shear band formation is a failure mechanism associated with the erosion process for conventional tungsten alloys.     

AN ANALYSIS OF NEURAL NETWORKS AS SIMULATORS AND EMULATORS

Recent advances in artificial neural networks (ANNs) have led to the design and construction of neuroarchitectures as simulator and emulators of a variety of problems in science and engineering. Such problems include pattern recognition, prediction, optimization, associative memory, and control of dynamic systems. This paper offers an analytical overview of the most successful design, implementation, and application of neuroarchitectures as neurosimulators and neuroemulators. It also outlines historical notes on the formulation of basic biological neuron, artificial computational models, network architectures, and learning processes of the most common ANN; describes and analyzes neurosimulation on parallel architecture both in software and hardware (neurohardware); presents the simulation of ANNs on parallel architectures; gives a brief introduction of ANNs in vector microprocessor systems; and presents ANNs in terms of the "new technologies". Specifically, it discusses cellular computing, cellular neural networks (CNNs), a new proposition for unsupervised neural networks (UNNs), and pulse coupled neural networks (PCNNs).     

Two-stage Robust Network Design with Exponential Scenarios

We study two-stage robust variants of combinatorial optimization problems on undirected graphs, like Steiner tree, Steiner forest, and uncapacitated facility location. Robust optimization problems, previously studied by Dhamdhere et al. (Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005), Golovin et al. (Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006), and Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007), are two-stage planning problems in which the requirements are revealed after some decisions are taken in Stage 1. One has to then complete the solution, at a higher cost, to meet the given requirements. In the robust k-Steiner tree problem, for example, one buys some edges in Stage 1. Then k terminals are revealed in Stage 2 and one has to buy more edges, at a higher cost, to complete the Stage 1 solution to build a Steiner tree on these terminals. The objective is to minimize the total cost under the worst-case scenario. In this paper, we focus on the case of exponentially many scenarios given implicitly. A scenario consists of any subset of k terminals (for k-Steiner tree), or any subset of k terminal-pairs (for k-Steiner forest), or any subset of k clients (for facility location). Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007) give an LP-based general framework for approximation algorithms for a class of two stage robust problems. Their framework cannot be used for network design problems like k-Steiner tree (see later elaboration). Their framework can be used for the robust facility location problem, but gives only a logarithmic approximation. We present the first constant-factor approximation algorithms for the robust k-Steiner tree (with exponential number of scenarios) and robust uncapacitated facility location problems. Our algorithms are combinatorial and are based on guessing the optimum cost and clustering to aggregate nearby vertices. For the robust k-Steiner forest problem on trees and with uniform multiplicative increase factor for Stage 2 (also known as inflation), we present a constant approximation. We show APX-hardness of the robust min-cut problem (even with singleton-set scenarios), resolving an open question of (Dhamdhere et al. in Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005) and (Golovin et al. in Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006).     

TOWARD LEARNING GRID INFRASTRUCTURES: AN OVERVIEW OF RESEARCH ON GRID LEARNING SERVICES

The Learning Grid refers to the promise of projects that pool together instructional materials on distant computers. The Grid provides a wide range of available and potential learning services and resources and does not simply refer to taking advantage of the multiplying effects of connectivity. It supports the personalized use of the collective intelligence provided by networked computers and supports the exchange, negotiation, and dialogue within and among virtual, evolutionary, and pervasive learning communities. This article provides an overview of papers from the first workshop on Grid Learning Services, which brought together researchers discussing their views of infrastructure, services, and resources. It also addresses several research questions, including: What are the relevant resources and services and how can they be identified or built? How do they rely on the basic open Grid service architecture? How can intelligent tutoring systems be built on the Grid? How do the performance, efficiency, usability, and the global ability of those services meet individual and collective users' expectations?