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5 These capabilities are provided for illustration purposes. Actual MCDs are de ned in UAProf speci cation.

Ladrat et al. 2006). The proteasome, rst isolated in 1980 (Wilk and Orlowski 1980), is a barrel-shaped polypeptide structure with active sites in its interior core (Mykles and Harie 1995; Attaix et al. 1998, 2001; Glickman and Cienchanover 2002). A 20S latent form is part of the 26S proteasome (Attaix et al. 1998, 2001) and can be activated by mild denaturing treatments (Yamamoto et al. 2009) such as heat, chemicals, or high pressure. At least ve multipeptidase activities (Mykles and Harie 1995) have been described: trypsin-like, chymotrypsin-like, branched-chain amino acid-preferring, small neutral amino acid-preferring, and peptidylglutamyl peptide hydrolase (PGPH). The MCP proteasome enzymes have optimal pH activity at pH 7.0 8.0, and the proteasome is found in the sarcoplasm (Foucrier et al. 2001) of skeletal muscle. Control of indiscriminate proteolysis appears to be regulated by two methods. The MCP preferentially degrades polypeptides that have been ubiquitinated and secondly by physical size limitation; that is, only polypeptides that can pass through the narrow 10 13 opening to the central core of the barrel are easily degraded into 6 12 amino acid fragments in a single pass (Attaix et al. 1998, 2001; Glickman and Cienchanover 2002). It is likely that another protease, calpains for example, acts in concert or synergy to release proteins from the myo brillar assembly in order to make large proteins available for degradation into amino acids by the MCP (Hasselgren 1999; Allen and Goll 2003). The MCP plays a major role in degrading sarcoplasmic proteins and myo brillar fragments; however, there is insuf cient evidence that MCP breaks down the same proteins in postmortem muscle as in in vitro tests (Huang et al. 2007). Proteasomes remained relatively stable throughout 7 days of aging in beef and rabbit muscle (Yamamoto et al. 2009), supporting their potential role in meat tenderization.

Then the Bayes net could provide a classi cation decision using the highest posterior probability among p(winter | coat), p(spring | coat), p(summer | coat), and p(fall | coat) (see the exercises). A Bayesian network represents the joint probability distribution for a given set of variables. What is a joint probability distribution Let X 1 , X 2 , . . . , X m represent a set of m random variables, with each random variable X i de ned on space S X i . For example, a normal random variable X is de ned on space S X , where S X is the real number line. Then the joint space of X 1 , X 2 , . . . , X m is de ned as the cross-product S X 1 S X 2 S X m . That is, each joint observation consists of the vector of length m of observed eld values x1 , x2 , . . . , xm . The distribution of these observations over the joint space is called the joint probability distribution. The Bayesian network represents the joint probability distribution by providing (1) a speci ed set of assumptions regarding the conditional independence of the variables, and (2) the probability tables for each variable given its direct predecessors. For each variable, information regarding both is provided. For a subset of variables X 1 , X 2 , . . . , X m , the joint probability may be found thus:

Dominating nonnegative matrix: Given an (m x m) matrix B = [bij], a nonnegative (m x m) matrix A = [aij] is said to dominate B if Ibjjlabs ~ aij, i,j = 1, ... , m, that is, IBlabs ~ A. (See Section 9.9 for

Total standard cost 10,260.20 = 100 = 38.7% Total standard revenue 26,487.00 = 38.8% 0.1%

2 .3

Select the value > 0 such that (1 + )E[Y] = 7 ln[4f (s + 1)] Notice that by (19) and (20) combined, we have 1+ = 7 ln[4f (s + 1)] E[Y] 7 ln[4f (s + 1)] 7 = 2 ln[4f (s + 1)] 2 (10.20)