Automating Microfluidics: Reconfigurable Virtual Channels for Cell and Droplet Transport

The emerging field of digital microfluidics promises to solve many shortcomings of traditional continuous-flow fluidics. This technology has a few incarnations, including EWOD (eletrowetting on dielectric) and DEP (dielectrophoresis) chips. Both consist of large arrays of electrical pixels which move droplets and cells. They actuate fluids actively, have error feedback, are programmable, perform operations in parallel, and do not rely on external pumps. For these reasons we foresee the increased use of digital microfluidics in the near future. We also foresee a gradual shift away from purpose-built microfluidic devices, towards multi-purpose platforms with specific applications encoded in software. To this extent we present here a new paradigm of encoding and automating microfluidic operations using video files. We use this technology to create several configurations of virtual microfluidic channels and to play film clips using living cells on a DEP chip.Engineering and Applied SciencesPhysic

Fluctuation-dissipation ratios in the dynamics of self-assembly

We consider two seemingly very different self-assembly processes: formation of viral capsids, and crystallization of sticky discs. At low temperatures, assembly is ineffective, since there are many metastable disordered states, which are a source of kinetic frustration. We use fluctuation-dissipation ratios to extract information about the degree of this frustration. We show that our analysis is a useful indicator of the long term fate of the system, based on the early stages of assembly.Comment: 8 pages, 6 figure

widespread cross-infection of multiple RNA viruses across wild and managed bees

Declining populations of bee pollinators are a cause of concern, with major repercussions for biodiversity loss and food security. RNA viruses associated with honeybees represent a potential threat to other insect pollinators, but the extent of this threat is poorly understood. This study aims to attain a detailed understanding of the current and ongoing risk of emerging infectious disease (EID) transmission between managed and wild pollinator species across a wide range of RNA viruses. Within a structured large-scale national survey across 26 independent sites, we quantify the prevalence and pathogen loads of multiple RNA viruses in co-occurring managed honeybee (Apis mellifera) and wild bumblebee (Bombus spp.) populations. We then construct models that compare virus prevalence between wild and managed pollinators. Multiple RNA viruses associated with honeybees are widespread in sympatric wild bumblebee populations. Virus prevalence in honeybees is a significant predictor of virus prevalence in bumblebees, but we remain cautious in speculating over the principle direction of pathogen transmission. We demonstrate species-specific differences in prevalence, indicating significant variation in disease susceptibility or tolerance. Pathogen loads within individual bumblebees may be high and in the case of at least one RNA virus, prevalence is higher in wild bumblebees than in managed honeybee populations. Our findings indicate widespread transmission of RNA viruses between managed and wild bee pollinators, pointing to an interconnected network of potential disease pressures within and among pollinator species. In the context of the biodiversity crisis, our study emphasizes the importance of targeting a wide range of pathogens and defining host associations when considering potential drivers of population decline

Predicting atmospheric optical properties for radiative transfer computations using neural networks

The radiative transfer equations are well-known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parameterization (RRTMGP). To minimize computational costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimised BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within \SI{0.5}{\flux} compared to RRTMGP. Our neural network-based gas optics parametrization is up to 4 times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations whilst retaining high accuracy.Comment: 13 pages,5 figures, submitted to Philosophical Transactions

Contactless microwave sensors and their application in biological single use

In bioprocess technology, highly-sensitive robust sensors are required for operation in single use bioreactors (SUB) without direct contact to the fluid under analysis. Measuring the change of dielectric properties (permittivity and conductivity) at microwave frequencies allows the investigation of biological and chemical matter and processes, e.g., cell growth, cell metabolism and the concentration of large aqueous based molecules. This contribution describes a high frequency sensor that combines detection in macro- or microfluidic networks with quick and precise analysis. These kinds of sensors can be installed directly to the outer surface of the culture device (Figure 1) or can be clamped onto tubing (Figure 2). A clamped on sensor consists of a fluidic channel placed between a micro-strip line waveguide combined with resonant properties. Please click Additional Files below to see the full abstract

Unifying prospective and retrospective interval-time estimation: a fading-gaussian activation-based model of interval-timing

Hass and Hermann (2012) have shown that only variance-based processes will lead to the scalar growth of error that is characteristic of human time judgments. Secondly, a major meta-review of over one hundred studies (Block et al., 2010) reveals a striking interaction between the way in which temporal judgments are queried and cognitive load on participants’ judgments of interval duration. For retrospective time judgments, estimates under high cognitive load are longer than under low cognitive load. For prospective judgments, the reverse pattern holds, with increased cognitive load leading to shorter estimates. We describe GAMIT, a Gaussian spreading-activation model, in which the sampling rate of an activation trace is differentially affected by cognitive load. The model unifies prospective and retrospective time estimation, normally considered separately, by relating them to the same underlying process. The scalar property of time estimation arises naturally from the model dynamics and the model shows the appropriate interaction between mode of query and cognitive load