An orthodox blup approach to generalized linear mixed models

We introduce a new class of generalized linear mixed models assuming Tweedie exponential dispersion model distributions for both the response and the random effects. This class of models accommodates a wide range of discrete, continuous and mixed data. By letting the random effects enter as weights as well as means in the conditional distributions, the variance matrix may be expressed as a sum of variance components. We consider an orthodox BLUP approach to parameter estimation and random effects prediction for this new class of models based on a predictor of the random effects that is truly best linear and unbiased, in contrast to the conventional BLUP which is the conditional mode. We obtain an optimal estimating equation based on the orthodox BLUP, which is solved by a modified Newton algorithm. This approach facilitates analysis of residuals and allows justification of asymptotic results under realistic conditions through standard estimating equation theory. An important feature of this approach is that the principal results depend only on the first and second moment assumptions of unobserved random effects. The common fitting algorithm based on orthodox BLUP enables us to study this new class of models as a single class, rather than as a collection of unrelated different models. This approach is illustrated with the analyses of seed germination data, epilepsy data and cake baking data. By means of asymptotic justifications, simulations and worked examples, we conclude that the orthodox BLUP approach is of practical value for analysis of clustered non-normal data.Science, Faculty ofStatistics, Department ofGraduat

Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study

Numerous methods have been developed for longitudinal binomial data in the literature. These traditional methods are reasonable for longitudinal binomial data with a negative association between the number of successes and the number of failures over time; however, a positive association may occur between the number of successes and the number of failures over time in some behaviour, economic, disease aggregation and toxicological studies as the numbers of trials are often random. In this paper, we propose a joint Poisson mixed modelling approach to longitudinal binomial data with a positive association between longitudinal counts of successes and longitudinal counts of failures. This approach can accommodate both a random and zero number of trials. It can also accommodate overdispersion and zero inflation in the number of successes and the number of failures. An optimal estimation method for our model has been developed using the orthodox best linear unbiased predictors. Our approach not only provides robust inference against misspecified random effects distributions, but also consolidates the subject-specific and population-averaged inferences. The usefulness of our approach is illustrated with an analysis of quarterly bivariate count data of stock daily limit-ups and limit-downs

Tweedie Compound Poisson Models with Covariate-Dependent Random Effects for Multilevel Semicontinuous Data

Multilevel semicontinuous data occur frequently in medical, environmental, insurance and financial studies. Such data are often measured with covariates at different levels; however, these data have traditionally been modelled with covariate-independent random effects. Ignoring dependence of cluster-specific random effects and cluster-specific covariates in these traditional approaches may lead to ecological fallacy and result in misleading results. In this paper, we propose Tweedie compound Poisson model with covariate-dependent random effects to analyze multilevel semicontinuous data where covariates at different levels are incorporated at relevant levels. The estimation of our models has been developed based on the orthodox best linear unbiased predictor of random effect. Explicit expressions of random effects predictors facilitate computation and interpretation of our models. Our approach is illustrated through the analysis of the basic symptoms inventory study data where 409 adolescents from 269 families were observed at varying number of times from 1 to 17 times. The performance of the proposed methodology was also examined through the simulation studies

Overexpression of RKIP Inhibits Cell Invasion in Glioma Cell Lines through Upregulation of miR-98

Raf-1 kinase inhibitor protein (RKIP) is a tumor and metastasis suppressor in cancer cells. MicroRNAs (miRNAs) have been suggested to play a vital role in tumor initiation and progression by negatively regulating oncogenes and tumor suppressors. Quite recently, studies have identified some miRNAs operating to promote or suppress tumor invasion or metastasis via regulating metastasis-related genes, providing potential therapeutic targets on antimetastasis strategy. In this study, we found that the expression of RKIP and miR-98 in glioma tissues were significantly lower than that in normal brain tissues. Overexpression of RKIP upregulated miR-98 expression and inhibited glioma cell invasion and miR-98 target gene HMGA2 but had no effect in glioma cell proliferation. Moreover, forced expression of miR-98 accelerated the inhibition of glioma cell invasion and the expression of HMGA2 also had no effect in glioma cell proliferation. Our findings newly described RKIP/miR-98 to HMGA2 link and provided a potential mechanism for glioma cell invasion. RKIP and miR-98 may illustrate the potential therapeutic utility of signaling pathway signatures

Novel harmine derivatives as potent acetylcholinesterase and amyloid beta aggregation dual inhibitors for management of Alzheimer’s disease

AbstractIn this study, a series of potential ligands for the treatment of AD were synthesised and characterised as novel harmine derivatives modified at position 9 with benzyl piperazinyl. In vitro studies revealed that the majority of the derivatives exhibited moderate to potent inhibition against hAChE and Aβ1 − 42 aggregation. Notably, compounds 13 and 17d displayed potent drug − likeness and ADMET properties, demonstrating remarkable inhibitory activities towards AChE (IC50 = 58.76 nM and 89.38 nM, respectively) as well as Aβ aggregation (IC50 = 9.31 μM and 13.82 μM, respectively). More importantly, compounds 13 and 17d showed exceptional neuroprotective effects against Aβ1 − 42−induced SH − SY5Y damage, while maintaining low toxicity in SH − SY5Y cells. Further exploration of the mechanism through kinetic studies and molecular modelling confirmed that compound 13 could interact with both the CAS and the PAS of AChE. These findings suggested that harmine derivatives hold great potential as dual − targeted candidates for treating AD