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For the first time, extract, reverse transcribe, preamplify, and ultimately detect and analyze cell activity using just one technology:
Single-cell gene expression profiling emerged as a powerful method to reveal heterogeneity in cell populations and Fluidigm has developed a streamlined automated workflow based on microfluidic technology for capturing and analyzing single cells. It includes the C1Single-Cell Auto Prep System and the BioMark HD System for real-time PCR. The C1System, based on innovative microfluidic technology from Fluidigm allows prompt and reliable individual cells isolation and process for genomic analysis. For the first time, a researcher can isolate cells, extract RNA, and then reverse transcribe and preamplify mRNA transcripts automatically to detect and analyze cell activity. One technology reduces variability caused by multi-platform technical errors. Single-cell workflow facilitates expression assess of hundreds of genes in a few hours compared to experiments that would normally take days by traditional practices. Unique attributes of individual cells can be rapidly explored without the technical variability of a standard gene expression workflow. Single-cell genomic applications, includes single-cell mRNA sequencing, and provides a streamlined workflow for generating large volumes of data in cellular subpopulations.

Realizing and engineering optical non-linearity at the level of single photons is a goal of scientific and technological significance. We obtain strong interaction between propagating photons by coherently coupling them to Rydberg atoms in a cold gas. While slowly traversing the medium, the "Rydberg polaritons” interact via the Van-der-Waals force, owing to their large electric dipole-moment. We are able to vary the dynamics of the
two-photon wavefunction from dispersive (Schrodinger-like) to dissipative (diffusion-like) and observe strong bunching, anti-bunching, and a conditional phase-shifts of 1 radian for two individual photons.

Human behavior was shown to be predictable from neural activity before subjects reported having decided on the contents or onset of their actions. This was suggested to challenges the role of consciousness intentions in decision making, and through it have far reaching implications for the notions of free will and moral responsibility. However, these studies focused on non-ecological, arbitrary decisions – like raising the left or right hand for no reason or purpose and with no consequences – which are not typically held to be the hallmarks of freedom, not to mention assigning blame or praise. We wanted to understand to what degree this ability generalizes to deliberate actions, so we analyzed intracranial, human brain activity in a competitive environment – a matching-pennies game. We were increasingly better able to predict such actions with time before movement onset, retrospectively and even online and in real time. We further discovered specific neural differences between deliberate and arbitrary decision making using EEG. And we also found bias activity in monkey DLPFC and striatum that may help explain the early predictions in arbitrary decisions. I will sketch some features of a model of action selection that is congruent with the above findings.