BACKGROUND: Extracting biological information from high-density Affymetrix arrays is a multi-step process that begins with the accurate annotation of microarray probes. Shortfalls in the original Affymetrix probe annotation have been described; however, few studies have provided rigorous solutions for routine data analysis. RESULTS: Using AceView, a comprehensive human transcript database, we have reannotated the probes by matching them to RNA transcripts instead of genes. Based on this transcript-level annotation, a new probe set definition was created in which every probe in a probe set maps to a common set of AceView gene transcripts. In addition, using artificial data sets we identified that a minimal probe set size of 4 is necessary for reliable statistical summarization. We further demonstrate that applying the new probe set definition can detect specific transcript variants contributing to differential expression and it also improves cross-platform concordance. CONCLUSION: We conclude that our transcript-level reannotation and redefinition of probe sets complement the original Affymetrix design. Redefinitions introduce probe sets whose sizes may not support reliable statistical summarization; therefore, we advocate using our transcript-level mapping redefinition in a secondary analysis step rather than as a replacement. Knowing which specific transcripts are differentially expressed is important to properly design probe/primer pairs for validation purposes. For convenience, we have created custom chip-description-files (CDFs) and annotation files for our new probe set definitions that are compatible with Bioconductor, Affymetrix Expression Console or third party software.

We present a system that lets a designer directly annotate a 3D model with strokes, imparting a personal aesthetic to the non-photorealistic rendering of the object. The artist chooses a "brush" style, then draws strokes over the model from one or more viewpoints. When the system renders the scene from any new viewpoint, it adapts the number and placement of the strokes appropriately to maintain the original look.

This paper studies the scheduling of tasks on a pool of identical workstations in a network where message passing is used for data transfer and communication between processors and where the precedence relations among tasks form a send-receive graph. Bur parallel computation model differs from previous models by including all of the following practical considerations: 1) the sending and receiving of multiple messages from one processor to another is performed sequentially, 2) communication overhead is proportional to the message size, and 3) the starting and ending task must be performed on the same machine. These factors are crucial when performing parallel task execution using a pool of workstations whose communication primitives are provided by off-the-shelf packages, such as PVM, and whose message sizes are nontrivial. Although our model is new, using reduction from other well-known scheduling results shows that finding a scheduling with the optimal makespan is NP-hard. Our focus, therefore, is on developing and analyzing approximation algorithms for this problem. When the number of workstations in the network is abundant, a linear approximation algorithm is given with a proven performance bound of two times the optimal. When the number of available workstations is a fixed constant k that is greater than 2, we show an O(nlogn)-time approximation algorithm which always performs better than (3+k-2/k) times the optimal. Simulation results show that, on the average, both of our approximation algorithms perform much better than the worst case analysis and both generate schedulings whose makespans are very close to optimal.