169 GHz repetition rate passively harmonically mode-locked VECSEL emitting 265 fs pulses

High repetition rate passively mode-locked sources are of significant interest due to their potential for applications including optical clocking, optical sampling, communications and others. Due to their short excited state lifetimes mode-locked VECSELs are ideally suited to high repetition rate operation, however fundamentally mode-locked quantum well-based VECSELs have not achieved repetition rates above 10 GHz due to the limitations placed on the cavity geometry by the requirement that the saturable absorber saturates more quickly than the gain. This issue has been overcome by the use of quantum dot-based saturable absorbers with lower saturation fluences leading to repetition rates up to 50 GHz, but sub-picosecond pulses have not been achieved at these repetition rates. We present a passively harmonically mode-locked VECSEL emitting pulses of 265 fs duration at a repetition rate of 169 GHz with an output power of 20 mW. The laser is based around an antiresonant 6 quantum well gain sample and is mode-locked using a semiconductor saturable absorber mirror. Harmonic modelocking is achieved by using an intracavity sapphire etalon. The sapphire then acts as a coupled cavity, setting the repetition rate of the laser while still allowing a tight focus on the saturable absorber. RF spectra of the laser output show no peaks at harmonics of the fundamental repetition rate up to 26 GHz, indicating stable harmonic modelocking. Autocorrelations reveal groups of pulses circulating in the cavity as a result of an increased tendency towards Q-switched modelocking due to the low pulse energies.

Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data

We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/