Sub-Doppler spectroscopy of the Cs atom 6S1/2-7P1/2 transition at 459 nm in a microfabricated vapor cell.

We report on the characterization of sub-Doppler resonances detected by probing the 6S1/2 - 7P1/2 transition of the Cs atom at 459 nm in a microfabricated vapor cell. The dependence of the sub-Doppler resonance (linewidth, amplitude) on some key experimental parameters, including the laser intensity and the cell temperature, is investigated. These narrow atomic resonances are of interest for high-resolution spectroscopy and instrumentation and may constitute the basis of a high-stability microcell optical standard.

Stand-Off Magnetometry with Directional Emission from Sodium Vapors.

Stand-off magnetometry allows measuring magnetic field at a distance, and can be employed in geophysical research, hazardous environment monitoring, and security applications. Stand-off magnetometry based on resonant scattering from atoms or molecules is often limited by the scarce amounts of detected light. The situation would be dramatically improved if the light emitted by excited atoms were to propagate towards the excitation light source in a directional manner. Here, we demonstrate that this is possible by means of mirrorless lasing. In a tabletop experiment, we detect free-precession signals of ground-state sodium spins under the influence of an external magnetic field by measuring backward-directed light. This method enables scalar magnetometry in the Earth field range, that can be extended to long-range remote sensing.

Cellular connectomes as arbiters of local circuit models in the cerebral cortex.

With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. Here, we investigated whether cellular-resolution connectomic data can in principle allow model discrimination for local circuit modules in layer 4 of mouse primary somatosensory cortex. We used approximate Bayesian model selection based on a set of simple connectome statistics to compute the posterior probability over proposed models given a to-be-measured connectome. We find that the distinction of the investigated local cortical models is faithfully possible based on purely structural connectomic data with an accuracy of more than 90%, and that such distinction is stable against substantial errors in the connectome measurement. Furthermore, mapping a fraction of only 10% of the local connectome is sufficient for connectome-based model distinction under realistic experimental constraints. Together, these results show for a concrete local circuit example that connectomic data allows model selection in the cerebral cortex and define the experimental strategy for obtaining such connectomic data.

Proof of the feasibility of a nanocell-based wide-range optical magnetometer.

We present an experimental scheme performing scalar magnetometry based on the fitting of Rb $ {{\rm D}_2} $D2 line spectra recorded by derivative selective reflection spectroscopy from an optical nanometric-thick cell. To demonstrate its efficiency, the magnetometer is used to measure the inhomogeneous magnetic field produced by a permanent neodymium--iron-boron alloy ring magnet at different distances. The computational tasks are realized by relatively cheap electronic components: an Arduino Due board for external control of the laser and acquisition of spectra, and a Raspberry Pi computer for the fitting. The coefficient of variation of the measurements remains under 5% in the magnetic field range of 40-200 mT, limited only by the size of the oven and translation stage used in our experiment. The proposed scheme is expected to operate with high measurement precision also for stronger magnetic fields ($ {\gt} {500}\;{\rm mT}$>500mT) in the hyperfine Paschen-Back regime, where the evolution of atomic transitions can be calculated with high accuracy.

pyABC: distributed, likelihood-free inference.

Summary: Likelihood-free methods are often required for inference in systems biology. While approximate Bayesian computation (ABC) provides a theoretical solution, its practical application has often been challenging due to its high computational demands. To scale likelihood-free inference to computationally demanding stochastic models, we developed pyABC: a distributed and scalable ABC-Sequential Monte Carlo (ABC-SMC) framework. It implements a scalable, runtime-minimizing parallelization strategy for multi-core and distributed environments scaling to thousands of cores. The framework is accessible to non-expert users and also enables advanced users to experiment with and to custom implement many options of ABC-SMC schemes, such as acceptance threshold schedules, transition kernels and distance functions without alteration of pyABC's source code. pyABC includes a web interface to visualize ongoing and finished ABC-SMC runs and exposes an API for data querying and post-processing. Availability and Implementation: pyABC is written in Python 3 and is released under a 3-clause BSD license. The source code is hosted on https://github.com/icb-dcm/pyabc and the documentation on http://pyabc.readthedocs.io. It can be installed from the Python Package Index (PyPI). Supplementary information: Supplementary data are available at Bioinformatics online.