Arachidonic acid inhibits lipogenic gene expression in 3T3-L1 adipocytes through a prostanoid pathway

This report examines the effect of polyunsaturated fatty acids (PUFA) on lipogenic gene expression in cultured 3T3-L1 adipocytes. Arachidonic acid (20:4, n-6) and eicosapentaenoic acid (20:5, n-3) suppressed mRNAs encoding fatty acid synthase (FAS) and S14, but had no effect on beta-actin. Using a clonal adipocyte cell line containing a stably integrated S14CAT fusion gene, oleic acid (18:1, n-9), arachidonic acid (20:4, n-6) and eicosapentaenoic acid (20:5, n-3) inhibited chloramphenicol acetyltransferase (CAT) activity with an ED50 of 800, 50, and 400 microM, respectively. Given the high potency of 20:4, n-6, its effect on adipocyte gene expression was characterized. Arachidonic acid suppressed basal CAT activity, but did not affect glucocorticoid-mediated induction of S14CAT expression. The effect of 20:4, n-6 on S14CAT expression was blocked by an inhibitor of cyclooxygenase implicating involvement of prostanoids. Prostaglandins (PGE2 and PGF2alpha at 10 microM) inhibited CAT activity through a pertussis toxin-sensitive Gi/Go-coupled signalling cascade. Our results suggest that 20:4, n-6 inhibits lipogenic gene expression in 3T3-L1 adipocytes through a prostanoid pathway. This mechanism of control differs from the polyunsaturated fatty acid-mediated suppression of hepatic lipogenic gene expression.     

Packet Switching Interconnection Networks for Modular Systems

Adding buffers to a packet switching network can increase throughput in certain system architectures. A word of warning—don't make them too large.     

Detecting memory leaks in managed languages with Cork

A memory leak in a managed language occurs when the program inadvertently maintains references to objects that it no longer needs. Memory leaks cause systematic heap growth that degrade performance and can result in program crashes after perhaps days or weeks of execution. Prior approaches for detecting memory leaks rely on heap differencing or detailed object statistics which store state proportional to the number of objects in the heap. These overheads preclude their use on the same processor for deployed long‐running applications. This paper introduces Cork as a tool that accurately identifies heap growth caused by leaks. It is space efficient (adding less than 1% to the heap) and time efficient (adding 2.3% on average to total execution time). We implement this approach of examining and summarizing the class of live objects during garbage collection in a class points‐from graph (CPFG). Each node in the CPFG represents a class and edges between nodes represent references between objects of the specific classes. Cork annotates nodes and edges with the corresponding volume of live objects. Cork identifies growing data structures across multiple collections and computes a class slice to identify leaks for the user. We experiment with two functions for identifying growth and show that Cork is accurate: it identifies systematic heap growth with no false positives in 4 of 15 benchmarks we tested. Cork's slice report enabled us to quickly identify and eliminate growing data structures in large and unfamiliar programs, something their developers had not previously done. Copyright © 2009 John Wiley & Sons, Ltd.     

Development of the home care client satisfaction instrument

The method proposed by Sheiner and Beal plays a cornerstone role in the evaluation of the predictive performance. Recently, the Sheiner and Beal's method has been intensively used in pharmacokinetic studies, however, this method has been used in a somewhat confusing way by several authors. Therefore, if one wants to use this method, one might find different examples to follow in the pharmacokinetic literature. Meanwhile, several detailed points were not given by Sheiner and Beal in their original paper. Clearly, the principles suggested by Sheiner and Beal are simple, but the practical application is something of an art. In this work we addressed these detailed points and indicated the confusion in using the Sheiner and Beal's method.     

Performance enhancement in buffered delta networks using crossbar switches and multiple links

It is shown that the throughput of buffered delta networks can be increased substantially by modifying the structure and operation of their crossbar switches, by using multiple links to connect the switches in the network, or by combining multiple delta networks in parallel. The variations tried in the structure of the crossbar switches are the size of the switch and the number of buffers in each switch, their capacity, and their position in the switch. Among the alternatives mentioned above, the use of two parallel links for each switch connection results in the highest throughput improvement.