No abstract

To elucidate the genetic events associated with gastric cancer, 124,704 cDNA clones were collected from 37 human gastric cDNA libraries, including 20 full-length enriched cDNA libraries of gastric cancer cell lines and tissues from Korean patients. An analysis of the collected ESTs revealed that 97,930 high-quality ESTs coalesced into 13,001 clusters, of which 11,135 clusters (85.6%) were annotated to known ESTs. The analysis of the full-length cDNAs also revealed that 4862 clusters (51.7%) contained at least one putative full-length cDNA clone with an initiation codon, with the average length of the 5' UTR of 140 bp. A large number appear to have a diverse transcription start site (TSS). An examination of the TSS of some genes, such as TEGT and GAPD, using 5' RACE revealed that the predicted TSSs are actually found in human gastric cancer cells and that several TSSs differ depending on the specific gastric cell line. Furthermore, of the human gastric ESTs, 766 genes (9.5%) were present as putative alternatively spliced variants. Confirmation of the predicted spliced isoforms using RT-PCR showed that the predicted isoforms exist in gastric cancer cells and some isoforms coexist in gastric cell lines. These results provide potentially useful information for elucidating the molecular mechanisms associated with gastric oncogenesis.

We revisit the widely investigated problem of maximizing the centralized sum-rate capacity in a cognitive radio network. We consider an interference-limited multi-user multi-channel environment, with a transmit sum-power constraint over all channels as well as an aggregate average interference constraint towards multiple primary users. Until very recently only sub-optimal algorithms were proposed due to the inherent non-convexity of the problem. Yet, the problem at hand has been neglected in the large-scale setting (i.e., number of nodes and channels) as usually encountered in practical scenarios. To tackle this issue, we first propose an exact mathematical adaptation of the well-known successive convex geometric programming with condensation approximations (SCVX) to better cope with large systems while keeping the convergence proof intact. Alternatively, we also propose a novel efficient low-complexity heuristic algorithm, ELCI. ELCI is an iterative approach, where the constraints are handled alternately based on the special property of the optimal solution, with a particular power update formulation based on the KKT conditions of the problem. In order to demonstrate ELCI's efficiency we compare it to two state-of-the-art algorithms, SCVX, and the recently proposed global optimum approach, MARL. The salient highlight of ELCI is the relatively fast and very good sub-optimal performance in large-scale CR systems.