The emerging role of small non-coding RNA in renal cell carcinoma

Noncoding RNAs are transcribed in the most regions of the human genome, divided into small noncoding RNAs (less than 200 nt) and long noncoding RNAs (more than 200 nt) according to their size. Compelling evidences suggest that small noncoding RNAs play critical roles in tumorigenesis and tumor progression, especially in renal cell carcinoma. MiRNA, the most famous small noncoding RNA, has been comprehensively explored for its fundamental role in cancer. And several miRNA-based therapeutic strategies have been applied to several ongoing clinical trials. However, piRNAs and tsRNAs, have not received as much research attention, because of several technological limitations. Nevertheless, some studies have revealed the presence of aberration of piRNAs and tsRNAs in renal cell carcinoma, highlighting a potentially novel mechanism for tumor onset and progression. In this review, we provide an overview of three classes of small noncoding RNA: miRNAs, piRNAs and tsRNAs, that have been reported dysregulation in renal cell carcinoma and have the potential for advancing diagnosis, prognosis and therapeutic applications of this disease.     

Modeling neural activity using the generalized inverse Gaussian distribution

Spike trains from neurons are often used to make inferences about the underlying processes that generate the spikes. Random walks or diffusions are commonly used to model these processes; in such models, a spike corresponds to the first passage of the diffusion to a boundary, or firing threshold. An important first step in such a study is to fit families of densities to the trains' interspike interval histograms; the estimated parameters, and the families' goodness of fit can then provide information about the process leading to the spikes. In this paper, we propose the generalized inverse Gaussian family because its members arise as first passage time distributions of certain diffusions to a constant boundary. We provide some theoretical support for the use of these diffusions in neural firing models. We compare this family with the lognormal family, using spike trains from retinal ganglion cells of goldfish, and simulations from an integrate-and-fire and a dynamical model for generating spikes. We show that the generalized inverse Gaussian family is closer to the true model in all these cases. Received: 16 September 1996 / Accepted in revised form: 2 July 1997