Cholinergic vasoconstrictor effects in the rabbit eye: vasomotor effects of pentobarbital anesthesia

Oculomotor nerve stimulation causes vasoconstriction in the anterior uvea, which is due partly to a muscarinic mechanism and partly to a non-sympathetic aminergic mechanism. The labelled microsphere method was used to analyze the effect of pentobarbital anesthesia on the resting cholinergic vasomotor tone in the anterior uvea and to determine the relationship between stimulation frequency and vasomotor response. An attempt was made also to ascertain whether the aminergic part of the vasoconstriction is caused by release of 5-hydroxytryptamine or norepinephrine. Induction of pentobarbital anesthesia caused a marked vasodilation in the iris and the ciliary processes and a subsequent muscarinic blockade had no effect on the blood flow. A similar result was obtained in the optic nerve. In the choroid plexus, heart muscle, pineal body and coecum, pentobarbital anesthesia caused vasodilation and a subsequent muscarinic blockade caused vasoconstriction. In the brain pentobarbital anesthesia caused a marked reduction in the blood flow of the grey matter and a moderate reduction in the white matter. After a muscarinic blockade there was some increase in the blood flow of the grey matter. Stimulation of the oculomotor nerve caused near maximum vasomotor responses at 10-20 Hz; maximum effect on the pupil size was obtained at 40-50 Hz. Depletion of 5-hydroxytryptamine with fenfluramine did not prevent the aminergic part of the vasoconstriction and a marked vasoconstriction was also observed after pretreatment with reserpine. The results indicate the pentobarbital anesthesia abolishes most of the spontaneous cholinergic vasoconstrictor tone of the anterior uvea and that the aminergic part of the oculomotor nerve induced vasoconstriction is caused by the stimulation of phentolamine-sensitive receptors by a mechanism probably not involving release of norepinephrine or 5-hydroxytryptamine.     

Effect of alpha1,2-mannosidic linkage located in a alpha1,3-branch of Man6GlcNAc2 oligosaccharide on enzyme activity of recombinant human Man9-mannosidase produced in Escherichia coli

We produced the human kidney Man9-mannosidase in Escherichia coli and studied the effect of the alpha-1,2-mannosidic linkage located in the alpha branch of Manalpha1-6(Manalpha1-2Manalpha1-3)Manalpha1-6(Manalpha1-3)Manbeta1-4GlcNAcbeta1-4-GlcNAc, an N-linked oligosaccharide, on the enzyme activity. The alpha1,2-mannose residue influenced the rate of hydrolysis of and substrate preference for other alpha-1,2-mannosidic linkages.     

Genomic mining for complex disease traits with “random chemistry”

Our rapidly growing knowledge regarding genetic variation in the human genome offers great potential for understanding the genetic etiology of disease. This, in turn, could revolutionize detection, treatment, and in some cases prevention of disease. While genes for most of the rare monogenic diseases have already been discovered, most common diseases are complex traits, resulting from multiple gene–gene and gene-environment interactions. Detecting epistatic genetic interactions that predispose for disease is an important, but computationally daunting, task currently facing bioinformaticists. Here, we propose a new evolutionary approach that attempts to hill-climb from large sets of candidate epistatic genetic features to smaller sets, inspired by Kauffman’s “random chemistry” approach to detecting small auto-catalytic sets of molecules from within large sets. Although the algorithm is conceptually straightforward, its success hinges upon the creation of a fitness function able to discriminate large sets that contain subsets of interacting genetic features from those that don’t. Here, we employ an approximate and noisy fitness function based on the ReliefF data mining algorithm. We establish proof-of-concept using synthetic data sets, where individual features have no marginal effects. We show that the resulting algorithm can successfully detect epistatic pairs from up to 1,000 candidate single nucleotide polymorphisms in time that is linear in the size of the initial set, although success rate degrades as heritability declines. Research continues into seeking a more accurate fitness approximator for large sets and other algorithmic improvements that will enable us to extend the approach to larger data sets and to lower heritabilities.     

An Enskog based Monte Carlo method for high Knudsen number non-ideal gas flows

A Monte Carlo method based on the Enskog equation for dense gas is developed by considering high density effect on collision rates and both repulsive and attractive molecular interactions for a Lennard-Jones fluid. The appropriate internal energy exchange model is introduced with consistency with the collision model. The equation of state for a non-ideal gas is therefore derived involving the finite density effect and the van der Waals intermolecular force, changing from the Clapeyron equation to the van der Waals equation. In contrast to previous Monte Carlo approaches, the present predictions agree better with experimental data for the gas transport properties at high densities and in a wide temperature region. The numerical modeling of non-ideal gas flow in micro and nanochannels show that the high gas density affects greatly flow behavior and heat transfer characteristics. The high density of gas leads to a lower skin friction coefficient on the wall surfaces than the predictions by the perfect gas assumption.     

The roles of textural images in improving land-cover classification in the Brazilian Amazon

Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l’Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving land-cover classification. The classification accuracy can be improved by 5.2–13.4% as the pixel size changes from 30 to 0.6 m.     

Reduction and non-linear controllability of symmetric distributed systems

This paper considers the ‘reduction’ problem for distributed control systems. In particular, we consider controllability of systems containing multiple instances of diffeomorphic components where the overall system dynamics is invariant with respect to a discrete group action. A subclass of such systems are systems with a set of identical components where the overall system dynamics are invariant with respect to physically interchanging these components. The main result is a proposition which shows that for an equivalence class of symmetric systems of this type, controllability of the entire class of systems can be determined by analysing the member of the equivalence class with the smallest state space. The reduction methods developed are illustrated by considering the controllability of a team of mobile robots and a platoon of underwater vehicles.     

Aggression and the psychic self

In this paper we explore the idea of aggression as a defence against threats to the psychological self. This aspect of the self allows reflection about people in psychological terms and develops, in the first three years of life, through appreciation of mental states in the other. When the object is unpredictable or hostile, recognition of this is painful to the child, and his reflective function will not be adequately established. The defences of aggression or avoidance will be invoked very frequently. In time, aggression may become an organising influence in the construction of the self; pathological destructiveness then takes the place of emotional relatedness and concern for the other. Psychoanalytic treatment no longer works primarily by addressing conflict. Instead, particularly through interpretations of transference and countertransference, the analyst recreates an intersubjective process which enhances the patient's reflective self, this time in the safety of a benign relationship.     

Monte Carlo Simulations of High-Performance Implant Free In0.3Ga 0.7As Nano-MOSFETs for Low-Power CMOS Applications

The potential performance of implant free heterostructure In_0.3Ga_0.7As channel MOSFETs with gate lengths of 30, 20, and 15 nm is investigated using state-of-the-art Monte Carlo (MC) device simulations. The simulations are carefully calibrated against the electron mobility and sheet density measured on fabricated III-V MOSFET structures with a high-kappa dielectric. The MC simulations show that the 30 nm gate length implant free MOSFET can deliver a drive current of 2174 muA/mum at 0.7 V supply voltage. The drive current increases to 2542 muA/mum in the 20 nm gate length device, saturating at 2535 muA/mum in the 15 nm gate length one. When quantum confinement corrections are included into MC simulations, they have a negligible effect on the drive current in the 30 and 20 nm gate length transistors but lower the 15 nm gate length device drive current at 0.7 V supply voltage by 10%. When compared to equivalent Si based MOSFETs, the implant free heterostructure MOSFETs can deliver a very high performance at low supply voltage, making them suitable for low-power high-performance CMOS applications     

Hodograph transformation for crack-tip fields in hyperelastic sheets: higher order eigenmodes and asymptotic path-independent integrals

Hodograph transformations can be used to linearize a nonlinear partial differential equation by judicious use of physical quantities (e.g. velocities or displacement gradients) as coordinate variables in the hodograph plane. This approach has been found useful for obtaining the leading order terms of eigenproblems that govern asymptotic singular crack fields in nonlinear materials. There is little work on the use of the hodograph transformation for obtaining higher order terms in the asymptotic expansion of the crack tip fields. In this paper, we develop a framework to obtain such higher order terms using the hodograph transformation. The method relies heavily on the representation of physical quantities of interest in terms of hodograph plane variables. We demonstrate the method via application to a generalized neo-Hookean material. In addition, asymptotic path-independent J-integrals are expressed in terms of either physical or hodograph variables and are used to compute the leading-order amplitude coefficients. A relationship between the asymptotic J-integrals and the energy release rate is established for a mixed crack mode. The asymptotic results are compared with numerical results from finite element computation and excellent agreement is obtained.