Imaging radar investigations of the Sudbury structure

This paper reports preliminary results of airborne imaging radar studies of the Sudbury structure carried out in preparation for a CCRS European Remote Sensing Satellite (ERS-1) investigation. The data used were synthetic aperture radar (SAR) C-band (5.66 cm) images acquired from about 6 km altitude in 1987. They cover the Sudbury area in both wide and narrow swath modes, with east-west flight paths and north-south illumination directions. Narrow swath resolution is 6 m in range and azimuth; wide swath resolution is 20 m in range and 10 m in azimuth. The STAR imagery has proven highly effective for field use, providing excellent rendition of topography and topographically expressed structure. Reasons for this include the illumination geometry, notably the look azimuth normal to the long axis of the Sudbury structure and Penokean fold axes, the good spatial resolution, and the short wavelength. Forested areas in the Sudbury area tend to be uniformly rough at C-band wavelength, with backscatter dominated by local incidence angle (i.e., topography). Field work using the SAR imagery has to date been concentrated in the North Range and Superior Province as far north as the Benny greenstone belt. This area was chosen for initial investigation of the original size and shape of the Sudbury structure because the effects of the Penokean Orogeny were minimal there. Field work using SAR indicates that there has been little postimpact deformation of the North Range or adjacent Superior Province rock. There appears to be no evidence for an outer ring concentric with the North Range as indicated by early Landsat imagery. The apparent ring shown by Landsat is visible on the SAR imagery as the intersection of two regional fracture patterns not related to the Sudbury structure. There is no outer ring visible southwest of the structure. This can reasonably be explained by Penokean deformation, but there is no outer ring to the northeast cutting the relatively undeformed Huronian sediments of the Cobalt Embayment

Low-Flow Push-Pull Perfusion for Measuring Neurotransmitters with High Spatial and Temporal Resolution within the Living Brain.

Low-flow push-pull perfusion is a technique for measuring neurotransmitters within the brain with ~200 μm resolution. Activity of neurotransmitters can vary on this size scale; therefore, low-flow push-pull may offer new insights into physiology. Flow rates used by this technique (50 nL/min) may present challenges for sample handling and assay sensitivity due to nL sample fractions. In this work, the temporal resolution of low-flow push-pull was advanced to 7 s in vivo, several different neurochemical assays were implemented, and gradients of neurotransmitters were mapped across sub-mm distances. To address collection and manipulation of 7 s fractions collected in vivo, push-pull samples were stored as 6 nL plugs in an oil carrier phase. A tee was developed to address each fraction discretely for reagent addition. L-glutamate was measured within the striatum of anesthetized rats by using a fluorogenic enzyme assay. Microinjection of a potassium solution at the probe tip evoked L-glutamate concentration transients that had maxima of 4.5 ± 1.1 μM and rise times of 22 ± 2 s. Nanospray ionization mass spectrometry was used to simultaneously measure three neurochemicals in plug samples. After microinjection of neostigmine at the push-pull probe tip, rapid extracellular concentration increases of neostigmine (14 ± 3 s), acetylcholine (35 ± 4 s) and a gradual decrease in choline (60 ± 13 s) were observed. This experiment highlights the ability of low-flow push-pull perfusion to observe drug-neurotransmitter dynamics in vivo. A GABA enzyme assay and capillary electrophoresis were demonstrated for analysis of push-pull perfusion plugs. A miniaturized push-pull probe was adapted for awake, freely moving animals and used to measure 13 neurotransmitters and metabolites. Concentration gradients were observed between proximate brain regions. For example, dopamine in the ventral tegmental area was 4.8 ± 1.5 nM, but in the red nucleus (200 µm apart) was 0.5 ± 0.2 nM. This collection of work illustrates that low-flow push-pull perfusion is a versatile tool for monitoring many different neurotransmitters within the brain with 200 μm spatial and 7 s or faster temporal resolution. Future research directions may include ms temporal resolution in vivo measurements and microfabricated probes.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97958/1/tslaney_1.pd

Network conduciveness with application to the graph-coloring and independent-set optimization transitions

We introduce the notion of a network's conduciveness, a probabilistically interpretable measure of how the network's structure allows it to be conducive to roaming agents, in certain conditions, from one portion of the network to another. We exemplify its use through an application to the two problems in combinatorial optimization that, given an undirected graph, ask that its so-called chromatic and independence numbers be found. Though NP-hard, when solved on sequences of expanding random graphs there appear marked transitions at which optimal solutions can be obtained substantially more easily than right before them. We demonstrate that these phenomena can be understood by resorting to the network that represents the solution space of the problems for each graph and examining its conduciveness between the non-optimal solutions and the optimal ones. At the said transitions, this network becomes strikingly more conducive in the direction of the optimal solutions than it was just before them, while at the same time becoming less conducive in the opposite direction. We believe that, besides becoming useful also in other areas in which network theory has a role to play, network conduciveness may become instrumental in helping clarify further issues related to NP-hardness that remain poorly understood

Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were asked to respond to a set of scenarios where they imagined achieving either perfect (success) or flawed results (failure). In both British and Japanese students, self-oriented perfectionism positively predicted pride after success and embarrassment after failure whereas socially prescribed perfectionism predicted embarrassment after success and failure. Moreover, in Japanese students, socially prescribed perfectionism positively predicted pride after success and self-oriented perfectionism negatively predicted pride after failure. The findings have implications for our understanding of perfectionism indicating that the perfectionism–pride relationship not only varies between perfectionism dimensions, but may also show cultural variations

Automatically generating streamlined constraint models with ESSENCE and CONJURE

Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously, effective streamlined models have been constructed by hand, requiring an expert to examine closely solutions to small instances of a problem class and identify regularities. We present a system that automatically generates many conjectured regularities for a given Essence specification of a problem class by examining the domains of decision variables present in the problem specification. These conjectures are evaluated independently and in conjunction with one another on a set of instances from the specified class via an automated modelling tool-chain comprising of Conjure, Savile Row and Minion. Once the system has identified effective conjectures they are used to generate streamlined models that allow instances of much larger scale to be solved. Our results demonstrate good models can be identified for problems in combinatorial design, Ramsey theory, graph theory and group theory - often resulting in order of magnitude speed-ups.Postprin