The physical adsorption of nanosized plastic beads onto a model cellulose film and two living algal species, Chlorella and Scenedesmus, has been studied. This adsorption has been found to ubiquitously favor positively charged over negatively charged plastic beads due to the electrostatic attraction between the beads and the cellulose constituent of the model and living systems. Such a charge preference is especially pronounced for Chlorella and Scenedesmus, whose binding with the plastic beads also depended upon algal morphology and motility, as characterized by the Freundlich coefficients. Using a CO 2 depletion assay, we show that the adsorption of plastic beads hindered algal photosynthesis, possibly through the physical blockage of light and air flow by the nanoparticles. Our ROS assay further indicated that plastic adsorption promoted algal ROS production. Such algal responses to plastic exposure may have implications on the sustainability of the aquatic food chain.
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.
Computational searches for novel ligands for a given protein binding site have become ubiquitous in the pharmaceutical industry, and are potentially equally useful in helping identify small-molecule tools for biology. Here we describe the steps needed to carry out a high-throughput docking (HTD) or three-dimensional (3D) pharmacophore virtual screen starting with a model of the target protein's structure. The advice given is, in most cases, software independent but some tips are provided which apply only to certain popular programs. Useful work can be carried out using free programs on a modest workstation. Of course, any resultant "hits" remain in the virtual world until they are experimentally tested.
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