On the Maximum Likelihood estimation of a linear structural relationship when the intercept is known

AbstractThis paper considers the Maximum Likelihood (ML) estimation of the five parameters of a linear structural relationship y = α + βx when α is known. The parameters are β, the two variances of observation errors on x and y, the mean and variance of x. When the ML estimates of the parameters cannot be obtained by solving a simple simultaneous system of five equations, they are found by maximizing the likelihood function directly. Some asymptotic properties of the estimates are also obtained

Analysis of Robust Parameter Designs

The analysis of robust parameter design is discussed via a model incorporating mean-variance relationship which, when ignored as in the classical regression approach, can be problematic. The model is also capable of alleviating the difficulties of the regression approach in the search of the minimum variance occurring region

Inactivation of Chk2 and Mus81 Leads to Impaired Lymphocytes Development, Reduced Genomic Instability, and Suppression of Cancer

Chk2 is an effector kinase important for the activation of cell cycle checkpoints, p53, and apoptosis in response to DNA damage. Mus81 is required for the restart of stalled replication forks and for genomic integrity. Mus81Δex3-4/Δex3-4 mice have increased cancer susceptibility that is exacerbated by p53 inactivation. In this study, we demonstrate that Chk2 inactivation impairs the development of Mus81Δex3-4/Δex3-4 lymphoid cells in a cell-autonomous manner. Importantly, in contrast to its predicted tumor suppressor function, loss of Chk2 promotes mitotic catastrophe and cell death, and it results in suppressed oncogenic transformation and tumor development in Mus81Δex3-4/Δex3-4 background. Thus, our data indicate that an important role for Chk2 is maintaining lymphocyte development and that dual inactivation of Chk2 and Mus81 remarkably inhibits cancer

Histone Demethylase JMJD2B Functions as a Co-Factor of Estrogen Receptor in Breast Cancer Proliferation and Mammary Gland Development

Estrogen is a key regulator of normal function of female reproductive system and plays a pivotal role in the development and progression of breast cancer. Here, we demonstrate that JMJD2B (also known as KDM4B) constitutes a key component of the estrogen signaling pathway. JMJD2B is expressed in a high proportion of human breast tumors, and that expression levels significantly correlate with estrogen receptor (ER) positivity. In addition, 17-beta-estradiol (E2) induces JMJD2B expression in an ERα dependent manner. JMJD2B interacts with ERα and components of the SWI/SNF-B chromatin remodeling complex. JMJD2B is recruited to ERα target sites, demethylates H3K9me3 and facilitates transcription of ER responsive genes including MYB, MYC and CCND1. As a consequence, knockdown of JMJD2B severely impairs estrogen-induced cell proliferation and the tumor formation capacity of breast cancer cells. Furthermore, Jmjd2b-deletion in mammary epithelial cells exhibits delayed mammary gland development in female mice. Taken together, these findings suggest an essential role for JMJD2B in the estrogen signaling, and identify JMJD2B as a potential therapeutic target in breast cancer

p73: A Multifunctional Protein in Neurobiology

p73, a transcription factor of the p53 family, plays a key role in many biological processes including neuronal development. Indeed, mice deficient for both TAp73 and ΔNp73 isoforms display neuronal pathologies, including hydrocephalus and hippocampal dysgenesis, with defects in the CA1-CA3 pyramidal cell layers and the dentate gyrus. TAp73 expression increases in parallel with neuronal differentiation and its ectopic expression induces neurite outgrowth and expression of neuronal markers in neuroblastoma cell lines and neural stem cells, suggesting that it has a pro-differentiation role. In contrast, ΔNp73 shows a survival function in mature cortical neurons as selective ΔNp73 null mice have reduced cortical thickness. Recent evidence has also suggested that p73 isoforms are deregulated in neurodegenerative pathologies such as Alzheimer’s disease, with abnormal tau phosphorylation. Thus, in addition to its increasingly accepted contribution to tumorigenesis, the p73 subfamily also plays a role in neuronal development and neurodegeneration

NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP).mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs.Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level

On the Maximum Likelihood estimation of a linear structural relationship when the intercept is known

This paper considers the Maximum Likelihood (ML) estimation of the five parameters of a linear structural relationship y = [alpha] + [beta]x when [alpha] is known. The parameters are [beta], the two variances of observation errors on x and y, the mean and variance of x. When the ML estimates of the parameters cannot be obtained by solving a simple simultaneous system of five equations, they are found by maximizing the likelihood function directly. Some asymptotic properties of the estimates are also obtained.linear structural relationship known intercept maximum likelihood estimation