PubMed · 10148517
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M W Kita. 1991. Methodologic notes.. https://pubmed.ncbi.nlm.nih.gov/10148517/
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HIV-1 gene expression relies upon a complex machinery that is primarily controlled by two viral regulatory proteins, Tat and Rev. Rev is involved in regulating post-transcriptional events of HIV-1 gene expression. The Tat protein transactivates transcription from the HIV-1 5' long terminal repeat (LTR) and acts in synergy with specific cellular factors. Recently, it has been shown that one set of these cellular factors is a protein kinase activity termed TAK (Tat-associated kinase), which activates transcription by hyperphosphorylation of the carboxyl-terminal domain (CTD) of the large subunit of RNA polymerase II. TAK also enhances transcription of HIV-2, together with the retroviral transactivator, Tat-2. The TAK activity appears to be related to the CTD kinase P-TEFb, which stabilizes transcription elongation of many genes and was originally isolated from Drosophila extracts. Both TAK and P-TEFb contain at least two subunits: the cyclin-dependent kinase, CDK9 (PITALRE), the catalytic subunit, and the regulatory subunit, cyclin T1. CDK9 and cyclin T1 are ubiquitous factors that affects many cellular processes, including cell differentiation and apoptosis. The involvement of TAK in HIV-1 and HIV-2 gene expression is an important aspect in the biology of these two retroviruses, and may lead to the development of novel antiretroviral drugs and/or gene therapy approaches for the treatment of patients with AIDS.
We derive a non-parametric maximum likelihood estimator for bivariate interval censored data using standard techniques for constrained convex optimization. Our approach extends those taken for univariate interval censored data. We illustrate the estimator with bivariate data from an AIDS study.
Non-parametric tests of independence, as well as accompanying measures of association, are essential tools for the analysis of bivariate data. Such tests and measures have been developed for uncensored and right censored failure time data, but have not been developed for interval censored failure time data. Bivariate interval censored data arise in AIDS studies in which screening tests for early signs of viral and bacterial infection are done at clinic visits. Because of missed clinic visits, the actual times of first positive screening tests are interval censored. To handle such data, we propose an extension of Kendall's coefficient of concordance. We apply it to data from an AIDS study that recorded times of shedding of cytomegalovirus (CMV) and times of colonization of mycobacterium avium complex (MAC). We examine the performance of our proposed measure through a simulation study.