Intra-colony channel morphology in Escherichia coli biofilms is governed by nutrient availability and substrate stiffness

Nutrient-transporting channels are found throughout mature Escherichia coli biofilms, however the influence of environmental conditions on intra-colony channel formation is poorly understood. We report the effect of different substrate nutrient concentrations and agar stiffness on the structure and distribution of intra-colony channels in mature E. coli colony biofilms using fluorescence mesoscopy and quantitative image analysis. Intra-colony channel width was observed to increase non-linearly with radial distance from the centre of the biofilm and channels were, on average, 50% wider at the centre of carbon-limited biofilms compared to nitrogen-limited biofilms. Channel density also differed in colonies grown on rich and minimal medium substrates, with the former creating a network of tightly packed channels and the latter leading to well-separated, wider channels with easily identifiable edges. We conclude that intra-colony channel morphology in E. coli biofilms is influenced by both substrate composition and nutrient availability.

Neuron Class and Target Variability in the Three-Dimensional Localization of SK2 Channels in Hippocampal Neurons as Detected by Immunogold FIB-SEM

Small-conductance calcium-activated potassium (SK) channels are crucial for learning and memory. However, many aspects of their spatial organization in neurons are still unknown. In this study, we have taken a novel approach to answering these questions combining a pre-embedding immunogold labeling with an automated dual-beam electron microscope that integrates focused ion beam milling and scanning electron microscopy (FIB/SEM) to gather 3D map ultrastructural and biomolecular information simultaneously. Using this new approach, we evaluated the number and variability in the density of extrasynaptic SK2 channels in 3D reconstructions from six dendritic segments of excitatory neurons and six inhibitory neurons present in the stratum radiatum of the CA1 region of the mouse. SK2 immunoparticles were observed throughout the surface of hippocampal neurons, either scattered or clustered, as well as at intracellular sites. Quantitative volumetric evaluations revealed that the extrasynaptic SK2 channel density in spines was seven times higher than in dendritic shafts and thirty-five times higher than in interneurons. Spines showed a heterogeneous population of SK2 expression, some spines having a high SK2 content, others having a low content and others lacking SK2 channels. SK2 immunonegative spines were significantly smaller than those immunopositive. These results show that SK2 channel density differs between excitatory and inhibitory neurons and demonstrates a large variability in the density of SK2 channels in spines. Furthermore, we demonstrated that SK2 expression was associated with excitatory synapses, but not with inhibitory synapses in CA1 pyramidal cells. Consequently, regulation of excitability and synaptic plasticity by SK2 channels is expected to be neuron class- and target-specific. These data show that immunogold FIB/SEM represent a new powerful EM tool to correlate structure and function of ion channels with nanoscale resolution.

GaN power IC normally-on and normally-off transistors technology and simulation

GaN technology has been waiting to be widely adopted because of its specific technical requirements. Integration of transistor and driver in a single die will enable to overcome problems with gate driving, high cost of circuit and low device reliability. This paper demonstrates technology of GaN-on-Si normally-on and normally-off transistor with different p-GaN cap-layer thickness as well as simulation of these devices. The simulation data confirm experimental results. P-GaN cap-layer thickness affects the current channel density: the more p-GaN thickness, the less channel density. The fabricated transistors have a maximum drain current in open state of about 800 mA/mm.

Electrophysiology using coaxial atom probe array: Live imaging reveals hidden circuits of a hippocampal neural network

Since the 1960s, it is held that when a neuron fires, a nerve spike passes only through the selective branches, the calculated choice is a key to learning by rewiring. It is argued by chemically estimating the membrane's ion channel density that different axonal branches get active to pass the spike -branches blink at firing at different time domains. Here, using a new time-lapse dielectric imaging, we visualize the classic branch selection process, hidden circuits operating at different time domains become visible. The fractal grid of coaxial probes captures wireless snapshots of material's vibration at various depths below the membrane by setting a suitable frequency. Thus far, branch selection observed emitted energy or particle but never the emitters, what they do. Since each dielectric material transmits & reflects signals of different frequencies, we image live how filaments search for many branch-made-circuits, choose an unique pathway 103 times faster than a single nerve spike. It reveals that neural branches and circuit visible in a microscope is not absolute, there coexist many circuits each operating in different dime domains, operating at a time.

Reduction of the Diffusion Equations of Ion Channel Clusters with Virtually No Increase in the Error Bound

A stochastic differential formulation for the collective dynamics of ion channel clusters in excitable membranes is developed from the so-called “reduced strong diffusion formulation”. In this error bound optimizing reduced formulation, the potassium channel states [Formula: see text] and [Formula: see text], and, the sodium channel states [Formula: see text] and [Formula: see text] are the retained states; consequently, the formulation accommodates only four channel variables and five white noises. The accuracy of the formulation is tested over the standard deviations and autocorrelation times of the channel density fluctuations. The findings are seen to be virtually identical to the corresponding results from the exact microscopic Markov simulations. The formulation arises as the most accurate model with that structural simplicity, thus making it an important model for both analytic analyses and numerical simulations in the study of finite-sized membranes.

Application of the Density Matrix Formalism for Obtaining the Channel Density of a Dual Gate Nanoscale Ultra-Thin MOSFET and its Comparison with the Semi-Classical Approach

Density Matrix Formalism using quantum methods has been used for determining the channel density of dual gate ultra-thin MOSFETs. The results obtained from the quantum methods have been compared with the semi-classical methods. This paper discusses in detail the simulation methods using self-consistent schemes and the discretization procedures for constructing the Hamiltonian Matrix for a dual gate MOSFET consisting of oxide/semiconductor/oxide interface and the self-consistent procedure involving the discretization of Poisson’s equation for satisfying the charge neutrality condition in the channel of different thicknesses. Under quantum methods, the channel densities are determined from the diagonal elements of the density matrix. This successfully simulates the size quantization effect for thin channels. For semi-classical methods, the Fermi–Dirac Integral function is used for the determination of the channel density. For thin channels, the channel density strongly varies with the values of the effective masses. This variation is simulated when we use Quantum methods. The channel density also varies with the asymmetric gate bias and this variation is more for thicker channels where the electrons get accumulated near the oxide/semiconductor interface. All the calculations are performed at room temperature (300[Formula: see text]K).