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associated with the broader literature on disease, aging, and cognition Cardiovascular Disease, Aging, and Cognitive Functioning Familiarity with the literature on cardiovascular disease or with risk factors for cardiovascular disease such as hypertension, obesity, diabetes, cigarette smoking, and high cholesterol and cognitive function is a prerequisite for understanding research in the area of cardiovascular disease and behavior (See the review by Waldstein & Elias, 2001) Hypertension, diabetes, smoking, and obesity have been associated with poorer cognitive functioning, although total cholesterol and alcohol consumption have been associated both with better and poorer cognition depending on dose relationships and the speci c cognitive measures employed (see P Elias, Elias, D Agostino, Silbershatz, & Wolf, 1999; Muldoon, Flory, & Ryan, 2001) Because of the signi cant volume of research on cardiovascular disease variables, we focus on studies of older populations and of interactions of disease factors with age (cross-sectional) or aging (longitudinal).



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3, we will show how graph labelings can be useful in finding algorithms for channel assignment problems with demands greater than 1 We will now proceed with a formal definition of the model described, and a review of other relevant models 511 Graph Models The definitions and notations used in this chapter are consistent with those introduced in the previous chapter [28] For a general background on graph theory, the reader is referred to [8] A constrained graph G = (V, E, c0, , ck) is a graph G = (V, E) and positive integer parameters c0, , ck, c0 c1 ck called its constraints The constraints represent the prescribed channel spacing for pairs of channels assigned to the same node or to different nodes More precisely, ci represents the constraint between nodes at graph distance i from each other.





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We restrict our review to hypertension because it has received the greatest amount of attention and because it serves as a model, or general paradigm, for studies of the cumulative impact of aging and disease, or risk for disease, on cognitive functioning Hypertension and Age: Main Effects It is well-known that age and aging are associated with declines in cognitive functioning It is also clear that hypertension and increments in systolic and diastolic blood pressure (DBP) are associated with lower levels of cognitive functioning across all ages Hypertension affects almost all areas of the cerebral vasculature A wide range of abilities are adversely affected, including psychomotor speed, visual constructive ability, learning memory, selective attention, uid ability, and executive function (M Elias & Robbins, 1991a; Waldstein, 1995; Waldstein & Katzel, 2001).

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The most recent summaries of hypothetical variables relating high blood pressure and cognitive performance in explanatory models have been provided in papers by Waldstein (1995) and Waldstein and Katzel (2001) These mechanisms include genetic and environmental factors, psychosocial variables, mood states and traits, and a long list of biological factors including cerebral metabolism, blood ow, changes in endothelial dysfunction, cellular dysfunction, neurochemical dysfunction, white matter disease, silent infarction, brain atrophy, and atherosclerosis An important aspect of these models is that they.

What you did was write a VBA Sub procedure (also known as a macro). When you issued the command to execute the macro, the VBE quickly compiled the code and executed it. In other words, each instruction was evaluated and Excel simply did what it was told to do. You can execute this macro any number of times, although it tends to lose its appeal after a while. For the record, this simple procedure uses the following concepts (all of which are covered later): Declaring a procedure (the first line) Assigning a value to variables (Msg and Ans)

The reuse distance of G equals k + 1, the minimum graph distance between nodes that can use the same channel For consistency, the constraint between nodes whose distance is at least the reuse distance is defined to be zero A constrained, weighted graph is a pair (G, w) where G is a constrained graph and w is a positive integral weight vector indexed by the nodes of G The component of w corresponding to node u is denoted by w(u) and called the weight of node u The weight of node u represents the number of calls to be serviced at node u We use wmax to denote max{w(v) | v V} and wmin to denote the corresponding minimum weight of any node in the graph For any set S V, we use w(S) to denote the sum of the weight of all nodes in S.

Source: CCITT Rec. G.822 (Ref. 14).

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