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Neurobiology is a vast and fascinating subject in its own right and in this chapter we have really only begun to scratch the surface. Nerve Cells and Animal Behavior, by Peter Simmons and David Young (1999, Cambridge University Press, Cambridge) provides a useful overview of the breadth of the topic and is an effective entry point to the wider literature. Behavioral Neurobiology by Thomas Carew (2000, Sinauer Associates, Sunderland, MA) is an excellent introduction to both the breadth and depth of the subject; it concentrates on a few case studies but thoroughly investigates every aspect of them, and will excite any student of behavior.

For evaluation, critical ESD metrics can be established by the ratio of the various parameters (e.g., the ratio of ESD robustness to capacitance load). Richier et al. [19] provided a study of comparison of the various ESD options for RF CMOS applications where there were two fundamental criteria. The loading capacitance was required to be below a given critical capacitance value. An ESD metric was established as the ratio of ESD robustness per unit capacitance (e.g., ESD HBM robustness [kV]/capacitance [fF]). From this metric, ESD design curves can be constructed that evaluates the relative quality of different ESD devices. Establishing an ESD design curve, where the y-axis is the loading capacitance of the ESD network, and the x-axis is the ESD HBM failure level, the different devices can be compared for the aforementioned metrics. The slope of the experimental results can be expressed at any size device, as CESD VESD The inverse of the slope can also be expressed as a FOMESD FOMESD VESD CESD

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Livestock breed can affect the quality characteristics of the meat produced, either because the breed has naturally adapted to stressful environmental conditions or because two or more breeds have been purposefully crossbred to increase prevalence of desirable qualities. Often these modi cations improve one set of attributes at the expense of another. For example, Brahman cattle are used extensively in the southwestern United States because of their tolerance to adverse environmental conditions; however, Brahman carcasses have tenderness issues. Toughness of meat from Brahman cattle has been associated with high levels of calpastatin in the muscle (Ibrahim et al. 2008). The Japanese Wagyu breed produces highly marbled, tender meat. Cross breeding Brahman with Wagyu cattle to produce Waguli cattle, which have a high degree of marbling and low calpastatin activity in the tissue, results in more tender meat immediately after slaughter. Tenderness of meat from Brahman cattle does catch up with suf cient aging (14 d). Schone et al. (2006) reported initial tenderness differences in beef from Holstein and Simmental cattle, in addition to different responses to aging. Some breed differences (Nelore, Simmental, Simbrasil) in initial postmortem beef tenderness are lost after 7 days of aging (Bianchini et al. 2007). According to Hocquette et al. (2006), cattle

where the F(g) is a binary lter-matrix for each group that de nes the pattern of complete (1) and incomplete (0) data entries (for further details, see McArdle & Anderson, 1990; McArdle & Hamagami, 1992) This multiple-group incomplete patterning approach is identical to the statistical models in which we t structural models to the raw score information for each person on each variable at each time The available information for any subject on any data point (ie, data at any occasion) is used to build up a likelihood function, and the numerical routine is used to optimize the model parameters with respect to the available data (Neale et al, 1999; Hamagami & McArdle, 2001) This method assumes the invariance of all growth parameters across different patterns of data is a rigid form of longitudinal convergence (after Bell, 1954; see McArdle & Bell, 2000).

p(x n )K(x n , x n ),

Although invariance is a reasonable goal in many studies, it is not necessarily a hypothesis that can be tested with all incomplete patterns (McArdle & Anderson, 1990; Miyazaki & Raudenbush, 2000; Willet & Sayer, 1995) One key assumption in our use of these MLE-based techniques is that the incomplete data are missing at random (MAR; Little & Rubin, 1987) This assumption does not require the data to be missing completely at random (MCAR), but MAR does assume there is some observed information that allows us to account for and remove the bias in the model estimates created by the lack of complete data (eg, Hedecker & Gibbons, 1997; McArdle, 1994; McArdle & Hamagami, 1992) In many cases, this MAR assumption is a convenient starting point, and allows us to use all the available information in one analysis.

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