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Figure 1-8 shows that the loan department has up to two years worth of data, passbook processing has up to one year of data, DDA applications have up to 30 days of data, and CD processing has up to 18 months of data. The applications were built to service the needs of current balance processing. They were never designed to hold the historical data needed for DSS analysis. It is no wonder, then, that going to existing systems for DSS analysis is a poor choice. But where else is there to go The systems found in the naturally evolving architecture are simply inadequate for supporting information needs. They lack integration and there is a discrepancy between the time horizon (or parameter of time) needed for analytical processing and the available time horizon that exists in the applications.

Note that although you can use asort() and arsort() on indexed arrays, they re commonly used with associative arrays.

The most noble components are in the heart and herein is the cognac spirit to be matured The seconds are recycled The nature of the wood employed for ageing of cognac has great significance The neness of the grain impacts the extent to which phenolics and other tannins are extracted, as does the shape and size of the barrel made from that wood and the extent to which the wood is charred in the shaping process The wood is generally dried in the open air for over 3 years New spirit is introduced to this new wood for a period of 8 12 months before transferring.

Follow these steps to add color to your table s border: 1. Select the table by clicking its top-left corner when the cursor becomes an Object Selection icon. 2. Switch to Outline mode by clicking the Outline tab at the top of the page window. Because the table was selected in Step 1, it is delineated by a black rectangle and should be visible without scrolling. 3. Click the small gray triangle to the right of the word table, and then choose borderColor from the pop-up menu that appears, as shown in Figure 13-19.

can regard as transition states. The local energy minima of folding intermediates will be of intermediate G values between the U and N conformations of the protein. The small amount of protein not in the N or U states at any given time during protein folding is present mainly as folding intermediates along the folding pathway. Based on a range of experimental studies, several models have been proposed to explain protein folding. Usually these studies have focused on small proteins such as lysozyme, ribonuclease and bovine pancreatic trypsin inhibitor. Three classical mechanisms for folding have been proposed (Figure 6.5). The framework model postulates that secondary structure forms independently of tertiary structure and that diffusion and coalescence of regions of secondary structure then go on to form tertiary structure (this is also sometimes called the diffusion-collision model). The hydrophobic collapse model suggests rapid collapse of the protein around hydrophobic side-chains and that secondary and tertiary structures form in the consequently restricted conformational space. Both of these models are consistent with the formation of measurable intermediates in the folding pathway. The nucleation model postulates that localized regions of native secondary structure act as nuclei from which correct tertiary structure is propagated. Like the framework model, this proposes formation of tertiary structure as a

FI GURE 9-55

In eucaryotes, most DNA sequences are noncoding, that is they do not code for protein. Examples include satellite DNA, intervening sequences within genes (introns) and regulatory sequences. The latter control gene expression in eucaryotes and, for this reason, is widely studied. Good examples of these are promotor, enhancer and operator sequences which are targets for highly sequence-speci c DNA-binding proteins (frequently called factors). Binding of protein at these sites is responsible for a regulatory effect involving turning expression of a speci c gene on or off.

learned that lowering the risk of a portfolio is as important as increasing the return on the portfolio. Risk-averse investors ocked to a new breed of hedge fund marketed as either low-risk or low-risk relative to the return achieved. Today, investors measure risk in many ways. These investors reward managers than control risk and pressure managers to control risk. Most of the growth in hedge fund assets has been in strategies have had fairly high returns yet have managed to keep risk exposure to modest levels. Trading counterparties and regulators have also pressured hedge funds to assume lower risks in their portfolios. Leverage is lower. Managers may be more inclined to look for hedges that can reduce risks without signi cantly lowering the return to investors.