A Cognitive Theory-based Opportunistic Resource-Pooling Scheme for Ad hoc Networks

Resource pooling in ad hoc networks deals with accumulating computing and network resources to implement network control schemes such as routing, congestion, traffic management, and so on. Pooling of resources can be accomplished using the distributed and dynamic nature of ad hoc networks to achieve collaboration between the devices. Ad hoc networks need a resource-pooling technique that offers quick response, adaptability, and reliability. In this context, we are proposing an opportunistic resource pooling scheme that uses a cognitive computing model to accumulate the resources with faster resource convergence rate, reliability, and lower latency. The proposed scheme is implemented using the behaviors observations beliefs cognitive model, in which the resource pooling decisions are made based on accumulated knowledge over various behaviors exhibited by nodes in ad hoc networks.Comment: 22 pages, 16 figures

Deletion of Genes Encoding Arginase Improves Use of "Heavy" Isotope-Labeled Arginine for Mass Spectrometry in Fission Yeast

<div><p>The use of “heavy” isotope-labeled arginine for stable isotope labeling by amino acids in cell culture (SILAC) mass spectrometry in the fission yeast <i>Schizosaccharomyces pombe</i> is hindered by the fact that under normal conditions, arginine is extensively catabolized <i>in vivo</i>, resulting in the appearance of “heavy”-isotope label in several other amino acids, most notably proline, but also glutamate, glutamine and lysine. This “arginine conversion problem” significantly impairs quantification of mass spectra. Previously, we developed a method to prevent arginine conversion in fission yeast SILAC, based on deletion of genes involved in arginine catabolism. Here we show that although this method is indeed successful when ¹³C₆-arginine (Arg-6) is used for labeling, it is less successful when ¹³C₆¹⁵N₄-arginine (Arg-10), a theoretically preferable label, is used. In particular, we find that with this method, “heavy”-isotope label derived from Arg-10 is observed in amino acids other than arginine, indicating metabolic conversion of Arg-10. Arg-10 conversion, which severely complicates both MS and MS/MS analysis, is further confirmed by the presence of ¹³C₅¹⁵N₂-arginine (Arg-7) in arginine-containing peptides from Arg-10-labeled cells. We describe how all of the problems associated with the use of Arg-10 can be overcome by a simple modification of our original method. We show that simultaneous deletion of the fission yeast arginase genes <i>car1+</i> and <i>aru1+</i> prevents virtually all of the arginine conversion that would otherwise result from the use of Arg-10. This solution should enable a wider use of heavy isotope-labeled amino acids in fission yeast SILAC.</p></div

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45751/1/11336_2005_Article_BF02294647.pd