Monolithic electro-optically modulated VCSEL suitable for radio over fibre applications to 20 GHz

An integrated EOM VCSELs is shown to offer high linearity (92dB/Hz 2/3 at 6GHz) and by extrapolation ∼90dB/Hz2/3 up to 20GHz. Successful modulation with IEEE 802.11g signals is demonstrated at 6GHz with a 12dB dynamic range. © 2011 Optical Society of America.

1.55-μm VCSEL transmission performance up to 20 Gb/s for access networks

We experimentally demonstrate for the first time 1.55μm vertical-cavity surface-emitting laser (VCSEL) transmission over 6.5 km single mode fiber (SMF) at 20 Gb/s for optical access networks. Characterization of the device is also investigated. © 2009 IEEE.

32 Gb/s multilevel modulation of an 850 nm VCSEL for next-generation datacommunication standards

An 850 nm vertical-cavity surface-emitting laser is modulated at 32 Gb/s using pulse-amplitude modulation with four levels. Transmitter predistortion generates an optimized modulation waveform, which requires a receiver bandwidth of only 15 GHz. © 2011 OSA.

Discovering transcriptional modules by Bayesian data integration.

MOTIVATION: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. RESULTS: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs. AVAILABILITY: If interested in the code for the work presented in this article, please contact the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization and Comparison of the Tissue-Related Modules in Human and Mouse

Background: Due to the advances of high throughput technology and data-collection approaches, we are now in an unprecedented position to understand the evolution of organisms. Great efforts have characterized many individual genes responsible for the interspecies divergence, yet little is known about the genome-wide divergence at a higher level. Modules, serving as the building blocks and operational units of biological systems, provide more information than individual genes. Hence, the comparative analysis between species at the module level would shed more light on the mechanisms underlying the evolution of organisms than the traditional comparative genomics approaches. Results: We systematically identified the tissue-related modules using the iterative signature algorithm (ISA), and we detected 52 and 65 modules in the human and mouse genomes, respectively. The gene expression patterns indicate that all of these predicted modules have a high possibility of serving as real biological modules. In addition, we defined a novel quantity, "total constraint intensity,'' a proxy of multiple constraints (of co-regulated genes and tissues where the co-regulation occurs) on the evolution of genes in module context. We demonstrate that the evolutionary rate of a gene is negatively correlated with its total constraint intensity. Furthermore, there are modules coding the same essential biological processes, while their gene contents have diverged extensively between human and mouse. Conclusions: Our results suggest that unlike the composition of module, which exhibits a great difference between human and mouse, the functional organization of the corresponding modules may evolve in a more conservative manner. Most importantly, our findings imply that similar biological processes can be carried out by different sets of genes from human and mouse, therefore, the functional data of individual genes from mouse may not apply to human in certain occasions.