Copyright 2003 by The McGraw-Hill Companies, Inc. Click Here for Terms of Use. in .NET

Compose QR in .NET Copyright 2003 by The McGraw-Hill Companies, Inc. Click Here for Terms of Use.

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cases of idiopathic Parkinson disease. They were generally absent in postencephalitic cases, but neuro brillary tangles were the usual nigral cell abnormalities. Both of these cellular abnormalities appear occasionally in the substantia nigra of aged, nonparkinsonian individuals. Possibly the individuals with Lewy bodies would have developed Parkinson disease if had they lived a few more years. Noteworthy is the nding by McGeer et al that nigral cells normally diminish with age, from a maximal complement of about 425,000 to 200,000 at age 80. Tyrosine-hydroxylase, the rate-limiting enzyme for the synthesis of dopamine, diminishes correspondingly. However, these authors and others have found that in patients with Parkinson disease, the number of pigmented neurons was reduced to 30 percent or less of that in age-matched controls. Using more re ned counting techniques, Pakkenberg and coworkers estimated the average total number of pigmented neurons to be 550,000 and to be reduced in absolute numbers by 66 percent in Parkinson patients. (The number of nonpigmented neurons was reduced in Parkinson cases by only 24 percent.) Thus, aging contributes importantly to nigral cell loss, but the cell depletion is so much more marked in Parkinson disease that some factor other than aging must also be operative. Other depletions of cells are widespread as mentioned, but they have not been quantitatively evaluated and their signi cance is less clear. There is neuronal loss in the mesencephalic reticular formation, near the substantia nigra. These cells project to the thalamus and limbic lobes. In the sympathetic ganglia, there is slight neuronal loss and Lewy bodies are seen. This is also true of the pigmented nuclei of the lower brainstem as well as of neuronal populations in the putamen, caudatum, pallidum, and substantia innominata. On the other hand, dopaminergic neurons that project to cortical and limbic structures, to caudate nucleus and nucleus accumbens, and to periaqueductal gray matter and spinal cord are affected little or not at all. The lack of a consistent lesion in either the striatum or the pallidum is noteworthy in view of the reciprocal connections between the striatum and the substantia nigra and the depletion of striatal dopamine, based on the loss of nigral projections, that characterizes the disease.
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Figure 3-7
Information Metabolism Directions and Conventions DNA Replication Types of DNA Polymerase Recombination Regulation of Information Metabolism Transcription Regulation of Transcription Translation Use of High-Energy Phosphate Bonds During Translation
The keywords ByVal and ByRef will be discussed later in the section ByVal vs. ByRef. Next, parametername is a name, similar to a variable name, that will be used to refer to the parameter inside the body of the procedure. Finally, datatype is, as the word suggests, the data type of the parameter. N OTE As with variable names, you should name your parameters descriptively. For example, the parameter may be named num because that describes its purpose in the Power procedure, the number that will be raised to the second power.
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If each row of H is denoted as hi , then the rst three components of each hi de ne a unit vector pointing from the satellite to the point of linearization xk . For each iteration of eqn. (8.10), the matrix H should be recalculated. This recalculation is most important when pk p is large. The iteration of eqn. (8.10) assumes that the rows of H are linearly independent so that the required matrix inverse exists. The iteration of eqn. (8.10) is concluded when the change in the estimate xk given by 1 H ( ( k )) x (8.13) dxk = H H in a given iteration has magnitude dxk less than a prespeci ed threshold. The fact that dxk is small at the conclusion of the iteration does not imply that (8.14) xk = x xk is small. The estimate xk was selected to minimize the norm of the error between the computed pseudorange and measurements of the pseudorange. Therefore, the pseudorange measurement errors de ned in eqn. (8.7)
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43: Basic Modeling
Examples var a = -3; var b = Lang.abs(a); var c = Lang.max(a, b); // c = -3 var d = Lang.max(45.5, 76); // d = 76(integer) var e = Lang.max(45.0, 45); // e = 45.0
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