Cement-Organobentonite Admixtures for Stabilization/Solidification of PAH-Contaminated Soil: A Laboratory Study

The feasibility of using Portland cement and organobentonite to stabilize and solidify Polycyclic Aromatic Hydrocarbons (PAH) contaminated soil was examined. Naphthalene and phenanthrene in solid and dissolved phases were selected as PAHs compounds to represent organic contaminants in the soil. Different tests including Toxicity Characteristics Leaching Procedure (TCLP), Unconfined Compressive Strength (UCS), and permeability tests were conducted on the stabilization/solidification (S/S) contaminated soils. The leaching test results confirmed a significant reduction in the leaching of naphthalene and phenanthrene from the stabilized soil specimen by adding 2%, 5%, and 10% of organoclay during solidification/stabilization. Based on the results for the tested ranges of cement and organoclay for S/S contaminated soil, the optimum mix design includes 5% of cement and 2% of organoclay. The observation in this study confirmed that organoclay particles sorbed the organic contaminates and therefore naphthalene and phenanthrene leachate concentration will be reduced. Moreover, results show that increasing the curing time of S/S products reduces the naphthalene and phenanthrene leachate concentration.     

Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.     

Statistics and geometry of orientation selectivity in primary visual cortex

Orientation maps are a prominent feature of the primary visual cortex of higher mammals. In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains. However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion. Although subject of intense investigation for half a century now, it is still not entirely clear how these maps emerge and what function they might serve. Here, we suggest a new model of orientation selectivity that combines the geometry and statistics of clustered thalamocortical afferents to explain the emergence of orientation maps. We show that the model can generate spatial patterns of orientation selectivity closely resembling the maps found in cats or monkeys. Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.