Real-time, single-molecule observation of biomolecular interactions inside nanophotonic zero mode waveguides

Living cells rely on numerous protein-protein, RNA-protein and DNA-protein interactions for processes such as gene expression, biomolecular assembly, protein and RNA degradation. Single-molecule microscopy and spectroscopy are ideal tools for real-time observation and quantification of nucleic acids-protein and protein-protein interactions. One of the major drawbacks of conventional single-molecule imaging methods is low throughput. Methods such as sequencing by synthesis utilizing nanofabrication and single-molecule spectroscopy have brought high throughput into the realm of single-molecule biology. The Pacific Biosceinces RS2 sequencer utilizes sequencing by synthesis within nanophotonic zero mode waveguides. A number of years ago this instrument was unlocked by Pacific Biosciences for custom use by researchers allowing them to monitor biological interactions at the single-molecule level with high throughput. In this capability letter we demonstrate the use of the RS2 sequencer for real time observation of DNA-to-RNA transcription and RNA-protein interactions. We use a relatively complex model – transcription of structured ribosomal RNA from E. coli and interactions of ribosomal RNA with ribosomal proteins. We also show evidence of observation of transcriptional pausing without the application of an external force (as is required for single-molecule pausing studies using optical traps). Overall, in the unlocked, custom mode, the RS2 sequencer can be used to address a wide variety of biological assembly and interaction questions at the single-molecule level with high throughput. This instrument is available for use at the Center for Integrated Nanotechnologies Gateway located at Los Alamos National Laboratory.

Real-time observation of Cooper pair splitting showing strong non-local correlations

Controlled generation and detection of quantum entanglement between spatially separated particles constitute an essential prerequisite both for testing the foundations of quantum mechanics and for realizing future quantum technologies. Splitting of Cooper pairs from a superconductor provides entangled electrons at separate locations. However, experimentally accessing the individual split Cooper pairs constitutes a major unresolved issue as they mix together with electrons from competing processes. Here, we overcome this challenge with the first real-time observation of the splitting of individual Cooper pairs, enabling direct access to the time-resolved statistics of Cooper pair splitting. We determine the correlation statistics arising from two-electron processes and find a pronounced peak that is two orders of magnitude larger than the background. Our experiment thereby allows to unambiguously pinpoint and select split Cooper pairs with 99% fidelity. These results open up an avenue for performing experiments that tap into the spin-entanglement of split Cooper pairs.

PymoNNto: A Flexible Modular Toolbox for Designing Brain-Inspired Neural Networks

The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups. The behaviour of each group can be flexibly defined by exchangeable modules. The implementation of these modules is up to the user and only limited by Python itself. Behaviours can be implemented in Python, Numpy, Tensorflow, and other libraries to perform computations on CPUs and GPUs. PymoNNto comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2, and an adaptable modular Graphical User Interface for real-time observation and modification of the simulated network and its parameters.

Computer Image Recognition and Recovery Method for Distorted Underwater Images by Structural Light

When imaging through water surface, the random fluctuation of sea surface will cause the distortion of the target scene image, so the distorted image needs to be corrected and reconstructed. At present, distortion compensation mainly adopts iterative registration strategy based on image sequences which is difficult to satisfy the real-time observation. This paper presents a correction method based on active imaging of structured light for underwater image. Experimental results show that compared with the traditional iterative algorithm, the proposed algorithm cannot only improve the restoration accuracy, but also greatly shorten the processing time. Experimental test results demonstrate that the proposed algorithm has good recovery results.