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1.6. FREE FATTY ACIDS Most methods for the quantitation of free fatty acids involve chemical derivatization followed by gas chromatography mass spectrometry. However, a colorimetric method is available for the quantitation of long-chain (C > 10) free fatty acids in plasma, and is based on the color developed by cobalt soaps of free fatty acids dissolved in chloroform.31 The assay involves four solutions: (1) chloroform:heptane:methanol (4:3:2 by volume); (2) 0.035 M HCl; (3) the salt reagent, consisting of 8 mL of triethanolamine added to 100 mL of an aqueous salt solutions containing 20 g Na2SO4, 10 g Li2SO4, and 4 g Co(NO3)2.6H2O; and (4) the indicator solution consisting of 20-mg 1-nitroso-2-naphthol in 100 mL of 95% ethanol. The procedure uses 100-mL samples (heptane blank, standard, or plasma sample) combined with 4.0 mL of chloroform/heptane/methanol and 1.0 mL 0.035 M HCl in sealed vials. After mixing and centrifugation steps, the upper aqueous methanol phase is discarded, and 2.0 mL of the salt reagent is added and mixed. A second centrifugation step is followed by combining 2.0 mL of the upper phase with 1.0 mL of the indicator solution. After 20-min absorbance is measured at 435 nm. Linear calibration curves were obtained for palmitic acid over the 0.10 1.2-mM concentration range, with few interferences. Protein (2% BSA) and many major metabolites such as lactic acid or b-hydroxybutyrate do not interfere, but some color development is observed for high concentrations of phospholipids such as lecithin.

On the surface, adding redundancy and reliability to a network should be no more difficult than solving the split-brain problem that we discuss in detail in 15, Local Clustering and Failover However, all of a host s resources are contained within or controlled by the system In the network, resources are highly distributed, have multiple points of control, and are subject to outside forces Network reliability poses several challenges to the system management team: Network failures come and go A period of very high traffic, followed by a lull in activity, followed by another peak, may appear to be a prolonged failure In this case, network latency and network failure produce the same client-side symptoms Measurement and monitoring tools must be able to distinguish between the two cases, and network segregation techniques provide isolation and protection against some forms of traffic-driven meltdowns.

It s hard to define where the network is A typical path from a PC to a server located in a machine room on the same campus will touch a switch or LAN concentrator in a local office wiring closet, a router or two in the same building, another router entering the data center building, and one or more machine room high-speed networking switches Add in network interface cards on PC, server, routers, and switches, and you have a dozen or more points of failure What if the backplane on a switch fails Or one outbound interface card on a router Each one of these pieces of equipment may be controlled by a different operations organization, adding a people angle to the complexity Latency contributes to perceived network failures A router that is overloaded or dropping packets in the middle of a long network path will greatly impact end-to-end reliability.

1. H.-W. Hsu, Separation by Centrifugal Phenomena , Techniques of Chemistry, Vol. XVI, John Wiley & Sons, Inc., New York, 1981, p. 111. 2. D. Rickwood and G. D. Birnie, in Centrifugal Separations in Molecular and Cell Biology. G. D. Birnie and D. Rickwood, Eds., Butterworths, London, 1978, pp. 1 3. 3. P. Sheeler, Centrifugation in Biology and Medical Science, John Wiley & Sons, Inc., New York, 1981, pp. 17 20. 4. V. Sitaramam and M. K. J. Sarma, Proc. Natl. Acad. Sci. U.S.A. 78, 1981, 3441 3445. 5. D. Rickwood, Ed., Centrifugation-A Practical Approach, IRL Press, Oxford, UK, 1984, pp. 295 304.

Network design and traffic management help to reduce network latency, or at least control it, but it remains an external factor in service reliability measurement Denial-of-service attacks are real Most simply, a denial-of-service attack means that a user cannot get access to a host or network service because all of the input capacity is tied up dealing with bogus requests Here s a simple example using the ever-popular bank teller model: Five people simultaneously cash their paychecks, take the amount in pennies and unwrap them, then ask to deposit the pennies by counting them All other bank customers queued for a teller are stuck while the bogus requests are handled All real work is held pending while the annoyance requests are serviced..