Serialelectron microscopy imaging is crucial for exploring the structure of cells and tissues. The development of block face scanning electron microscopy methods and their ability to capture large image stacks, some with near isotropic voxels, is proving particularly useful for the exploration of brain tissue. This has led to the creation of numerous algorithms and software for segmenting out different features from the image stacks. However, there are few tools available to view these results and make detailed morphometric analyses on all, or part, of these 3D models. We have addressed this issue by constructing a collection of software tools, called NeuroMorph, with which users can view the segmentation results, in conjunction with the original image stack, manipulate these objects in 3D, and make measurements of any region. This approach to collecting morphometric data provides a faster means of analysing the geometry of structures, such as dendritic spines and axonal boutons. This bridges the gap that currently exists between rapid reconstruction techniques, offered by computer vision research, and the need to collect measurements of shape and form from segmented structures that is currently done using manual segmentation methods.

This study has used dense reconstructions from serial EM images to compare the neuropil ultrastructure and connectivity of aged and adult mice. The analysis used models of axons, dendrites, and their synaptic connections, reconstructed from volumes of neuropil imaged in layer 1 of the somatosensory cortex. This shows the changes to neuropil structure that accompany a general loss of synapses in a well-defined brain region. The loss of excitatory synapses was balanced by an increase in their size such that the total amount of synaptic surface, per unit length of axon, and per unit volume of neuropil, stayed the same. There was also a greater reduction of inhibitory synapses than excitatory, particularly those found on dendritic spines, resulting in an increase in the excitatory/inhibitory balance. The close correlations, that exist in young and adult neurons, between spine volume, bouton volume, synaptic size, and docked vesicle numbers are all preserved during aging. These comparisons display features that indicate a reduced plasticity of cortical circuits, with fewer, more transient, connections, but nevertheless an enhancement of the remaining connectivity that compensates for a generalized synapse loss.

The connectedness percolation threshold (ηc) and critical coordination number (Zc) of systems of penetrable spherocylinders characterized by a length polydispersity are studied by way of Monte Carlo simulations for several aspect ratio distributions. We find that (i) ηc is a nearly universal function of the weight-averaged aspect ratio, with an approximate inverse dependence that extends to aspect ratios that are well below the slender rod limit and (ii) that percolation of impenetrable spherocylinders displays a similar quasiuniversal behavior. For systems with a sufficiently high degree of polydispersity, we find that Zc can become smaller than unity, in analogy with observations reported for generalized and complex networks.PACS numbers: 64.60.ah, 61.46.Fg, 82.70.Dd Idealized elongated objects such as perfectly rigid cylinders, spherocylinders and prolate spheroids are prototypical models for a wide array of technologically relevant systems that include liquid crystals, nanocomposites based on filamentous fillers as well as fiber-reinforced materials. Percolation phenomena involving dramatic increases in, e.g., structural rigidity and electrical and thermal conductivities of composites with increasing filler loading are currently of particular interest [1]. These increases are caused by the formation of an infinite cluster of in some sense connected particles at the critical loading, i.e., the percolation threshold.It has been established by analytical [1-3] and numerical [4][5][6][7][8][9][10][11] studies that for dispersions of sufficiently elongated objects of identical size and shape, i.e, "monodisperse" objects, the geometric percolation threshold expressed in terms of the critical volume fraction of particles is inversely proportional to the aspect ratio of the filler particles. This property is exploited in the fabrication of conducting polymeric composites with very low conducting filler contents. Depending on the production processes of the composites, however, the filler particles almost invariably exhibit a pronounced polydispersity in both size and shape [12,13]. Although it represents a possible factor behind huge quantitative discrepancies between theory and experiments [14,15], such polydispersity has received relatively little attention in terms of theoretical modeling until fairly recently [1,16,17], Achievement of a theoretical understanding of how the continuum percolation of fibrous fillers is affected by polydispersity is thus key to the controlled design of a large class of composite materials for practical particle size and shape distributions.Recent analytical results obtained from integral equation methods [16] and from an heuristic mapping onto a generalized Bethe lattice [17] predict that in the slender rod limit, where the particles have asymptotically large values of the aspect ratio, the volume fraction at the percolation threshold is inversely proportional to the weight average L w = L 2 / L of the rod lengths, where the brackets imply number averages over the distribution of...

In conductor-insulator nanocomposites in which conducting fillers are dispersed in an insulating matrix, the electrical connectedness is established by inter-particle tunneling or hopping processes. These systems are intrinsically non-percolative and a coherent description of the functional dependence of the conductivity σ on the filler properties, and in particular of the conductor-insulator transition, requires going beyond the usual continuum percolation approach by relaxing the constraint of a fixed connectivity distance. In this article, we consider dispersions of conducting spherical particles which are connected to all others by tunneling conductances and which are subjected to an effective attractive square-well potential. We show that the conductor-insulator transition at low contents φ of the conducting fillers does not determine the behavior of σ at larger concentrations, in striking contrast to what is predicted by percolation theory. In particular, we find that at low φ the conductivity is governed almost entirely by the stickiness of the attraction, while at larger φ values σ depends mainly on the depth of the potential well. As a consequence, by varying the range and depth of the potential while keeping the stickiness fixed, composites with similar conductor-insulator transitions may display conductivity variations of several orders of magnitude at intermediate and large φ values. By using a recently developed effective medium theory and the critical path approximation, we explain this behavior in terms of dominant tunneling processes which involve inter-particle distances spanning different regions of the square-well fluid structure as φ is varied. Our predictions could be tested in experiments by changing the potential profile with different depletants in polymer nanocomposites.

In conductor-insulator composites in which the conducting particles are dispersed in an insulating continuous matrix the electrical connectedness is established by interparticle quantum tunneling. A recent formulation of the transport problem in this kind of composites treats each conducting particle as electrically connected to all others via tunneling conductances to form a global tunneling network. Here, we extend this approach to nonhomogeneous composites with a segregated distribution of the conducting phase. We consider a model of segregation in which large random insulating spherical inclusions forbid small conducting particles to occupy homogeneously the volume of the composite, and allow tunneling between all pairs of the conducting objects. By solving numerically the corresponding tunneling resistor network, we show that the composite conductivity σ is enhanced by segregation and that it may remain relatively large also for very small values of the conducting filler concentration. We interpret this behavior by a segregation-induced reduction of the interparticle distances, which is confirmed by a critical path approximation applied to the segregated network. Furthermore, we identify an approximate but accurate scaling relation permitting to express the conductivity of a segregated systems in terms of the interparticle distances of a corresponding homogeneous system, and which provides an explicit formula for σ which we apply to experimental data on segregated RuO2-cermet composites.