Aspects of the analysis of variance for classifactory data

This thesis is directed toward data analysis and analysis of variance for data in a classificatory structure. Different approaches to looking at data by different authors are examined and some of their results extended;An analysis of variance of data is a partial description of the data. The fitted model is an approximating description of the data. P. D. Finch has worked on the quantification of the quality of a description, in particular, the description of a strong ordering by an ordered dichotomy. His ideas have been extended to ordered polychotomous numerical data and data in some basic ANOVA type structures;D. R. Cox looked at one-way, two-way and three-way classifications of data and obtained the expected mean squares under random permutation. These expected mean squares are expressed simply in terms of the quantities (SIGMA) which are defined as certain combinations of the variance components (sigma). The (SIGMA) are easily derived whether or not there is unit-treatment additivity. The derivations of the (SIGMA) are extended, in this thesis, to the general n-way classification of data;Many authors have written about the mixed model. The controversy with respect to the proper error term when testing for the random factor in the mixed model is examined from several viewpoints;References;Cox, D. R. 1958. The interpretation of the effects of non-additivity in the Latin square. Biometrika 45:69-73;Finch, P. D. 1979. Description and analogy in the practice of Statistics and Probability; Biometrics 3:1-21

Joyce Performance on a Multiprocessor

Joyce is a parallel programming language based on CSP and Pascal. The language has been moved from the IBM PC to the Encore Multimax. The paper explains how the multiprocessor implementation of Joyce was guided by performance evaluation. The measurements show that the speed-up of Joyce programs follows Amdahl’s law

Are Granulins Copper Sequestering Proteins?

Granulins (GRN 1-7) are short (~6 kDa), cysteine-rich proteins that are generated upon the proteolytic processing of progranulin (PGRN). These peptides, along with their precursor, have been implicated in multiple pathophysiological roles, especially in neurodegenerative diseases. Previously we showed that GRN-3 and GRN-5 are fully disordered in the reduced form implicating redox sensitive attributes to the proteins. Redox-based modulations are often carried out by metalloproteins in mitigating oxidative stress and maintaining metal-homeostasis within cells. To probe whether GRNs play a role in metal sequestration, we tested the metal binding propensity of the reduced forms of GRNs −3 and − 5 under neutral and acidic pH mimicking cytosolic and lysosomal conditions, respectively. We found, at neutral pH, both GRNs selectively bind Cu and no other divalent metal cations, with a greater specificity for Cu(I). Binding of Cu did not result in a disorder-to-order structural transition but partly triggered the multimerization of GRNs via uncoordinated cystines at both pH conditions. Overall, the results indicate that GRNs −3 and − 5 have surprisingly strong affinity for Cu in the pM range, comparable to other known copper sequestering proteins. The results also hint at a potential of GRNs to reduce Cu(II) to Cu(I), a process that has significance in mitigating Cu-induced ROS cytotoxicity in cells. Together, this report uncovers metal-coordinating property of GRNs for the first time, which may have profound significance in their structure and pathophysiological functions