Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.

BackgroundAntibiotic resistance represents a significant public health problem. When resistance genes are mobile, being carried on plasmids or phages, their spread can be greatly accelerated. Plasmids in particular have been implicated in the spread of antibiotic resistance genes. However, the selective pressures which favour plasmid-carried resistance genes have not been fully established. Here we address this issue with mathematical models of plasmid dynamics in response to different antibiotic treatment regimes.ResultsWe show that transmission of plasmids is a key factor influencing plasmid-borne antibiotic resistance, but the dosage and interval between treatments is also important. Our results also hold when plasmids carrying the resistance gene are in competition with other plasmids that do not carry the resistance gene. By altering the interval between antibiotic treatments, and the dosage of antibiotic, we show that different treatment regimes can select for either plasmid-carried, or chromosome-carried, resistance.ConclusionsOur research addresses the effect of environmental variation on the evolution of plasmid-carried antibiotic resistance.

The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.DOI: http://dx.doi.org/10.7554/eLife.24364.001

Spinal interneurons coordinate the activity of motoneurons to generate the spatiotemporal patterns of muscle contractions required for vertebrate locomotion. It is controversial to what degree the orderly, gradual recruitment of motoneurons is determined by biophysical differences among them rather than by specific connections from presynaptic interneurons to subsets of motoneurons. To answer this question, we mapped all connections from two types of interneurons onto all motoneurons in a larval zebrafish spinal cord hemisegment, using serial block-face electron microscopy (SBEM). We found specific synaptic connectivity from dorsal but not from ventral excitatory ipsilateral interneurons, with large motoneurons, active only when strong force is required, receiving specific inputs from dorsally located interneurons, active only during fast swims. By contrast, the connectivity between inhibitory commissural interneurons and motoneurons lacks any discernible pattern. The wiring pattern is consistent with a recruitment mechanism that depends to a considerable extent on specific connectivity.

Secondary ion mass spectrometry (SIMS) is a desorption/ionization method in which ions are generated by the impact of a primary ion beam on a sample. Classic matrix assisted laser desorption and ionization (MALDI) matrices can be used to increase secondary ion yields and decrease fragmentation in a SIMS experiment, which is referred to as matrix enhanced SIMS (ME-SIMS). Contrary to MALDI, the choice of matrices for ME-SIMS is not constrained by their photon absorption characteristics. This implies that matrix compounds that exhibit an insufficient photon absorption coefficient have the potential of working well with ME-SIMS. Here, we evaluate a set of novel derivatives of the classical MALDI matrices α-cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB) for usability in ME-SIMS. This evaluation was carried out using peptide mixtures of different complexity and demonstrates significant improvements in signal intensity for several compounds with insufficient UV absorption at the standard MALDI laser wavelengths. Our study confirms that the gas-phase proton affinity of a matrix compound is a key physicochemical characteristic that determines its performance in a ME-SIMS experiment. As a result, these novel matrices improve the performance of matrix enhanced secondary ion mass spectrometry experiments on complex peptide mixtures.