Consider a large wireless ad hoc network that facilitates communication between random pairs of network nodes. This paper investigates the size of routing tables as the number of nodes in the network increases. A routing protocol in a flat-label network is information-efficient if the amount of information at individual nodes required to route packets does not increase with the network size. It is shown that the shortest-path and straight-line routing algorithms are not information-efficient, i.e., these protocols can be implemented only when nodes' memory increases with the network size. On the other hand, it is established that there exists an information-efficient algorithm that routes packets correctly even if each node in the network is capable of storing information on a fixed number of destinations only.

BACKGROUND: Gene expression profiling using microarrays has become an important genetic tool. Spotted arrays prepared in academic labs have the advantage of low cost and high design and content flexibility, but are often limited by their susceptibility to quality control (QC) issues. Previously, we have reported a novel 3-color microarray technology that enabled array fabrication QC. In this report we further investigated its advantage in spot-level data QC. RESULTS: We found that inadequate amount of bound probes available for hybridization led to significant, gene-specific compression in ratio measurements, increased data variability, and printing pin dependent heterogeneities. The impact of such problems can be captured through the definition of quality scores, and efficiently controlled through quality-dependent filtering and normalization. We compared gene expression measurements derived using our data processing pipeline with the known input ratios of spiked in control clones, and with the measurements by quantitative real time RT-PCR. In each case, highly linear relationships (R2 > 0.94) were observed, with modest compression in the microarray measurements (correction factor < 1.17). CONCLUSION: Our microarray analytical and technical advancements enabled a better dissection of the sources of data variability and hence a more efficient QC. With that highly accurate gene expression measurements can be achieved using the cDNA microarray technology.