Advances towards reliable identification and concentration determination of rare cells in peripheral blood

Through further development, integration and validation of micro-nano-bio and biophotonics systems FP7 CanDo is developing an instrument that will permit highly reproducible and reliable identification and concentration determination of rare cells in peripheral blood for two key societal challenges, early and low cost anti-cancer drug efficacy determination and cancer diagnosis/monitoring. A cellular link between the primary malignant tumour and the peripheral metastases, responsible for 90% of cancerrelated deaths, has been established in the form of circulating tumour cells (CTCs) in peripheral blood. Furthermore, the relatively short survival time of CTCs in peripheral blood means that their detection is indicative of tumour progression thereby providing in addition to a prognostic value an evaluation of therapeutic efficacy and early recognition of tumour progression in theranostics. In cancer patients however blood concentrations are very low (=1 CTC/1E9 cells) and current detection strategies are too insensitive, limiting use to prognosis of only those with advanced metastatic cancer. Similarly, problems occur in therapeutics with anti-cancer drug development leading to lengthy and costly trials often preventing access to market. The novel cell separation/Raman analysis technologies plus nucleic acid based molecular characterization of the CanDo platform will provide an accurate CTC count with high throughput and high yield meeting both key societal challenges. Being beyond the state of art it will lead to substantial share gains not just in the high end markets of drug discovery and cancer diagnostics but due to modular technologies also in others. Here we present preliminary DNA hybridization sensing results

Emergent self-adaptation in an integrated photonic neural network for backpropagation-free learning

Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt and process information similarly to the way biological brains do. In this work, we experimentally demonstrate these properties occurring in arrays of photonic neurons. Importantly, this is realised autonomously in an emergent fashion, without the need for an external controller setting weights and without explicit feedback of a global reward signal. Using a hierarchy of such arrays coupled to a backpropagation-free training algorithm based on simple logistic regression, we are able to achieve a performance of 98.2% on the MNIST task, a popular benchmark task looking at classification of written digits. The plastic nodes consist of silicon photonics microring resonators covered by a patch of phase-change material that implements nonvolatile memory. The system is compact, robust, and straightforward to scale up through the use of multiple wavelengths. Moreover, it constitutes a unique platform to test and efficiently implement biologically plausible learning schemes at a high processing speed