The neotropical rodent genus 'Rhipidomys' (Cricetidae: Sigmodontinae) A taxonomic revision

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Aspects of Neural Networks in Intelligent Collision Avoidance Systems for Prometheus

This paper presents our work on adaptive driver modelling and obstacle classification applications, which will be incorporated into an intelligent collision avoidance system (ICAS) for road vehicles. The reliability of the ICAS is largely determined by the accuracy of these models. Multi-layered-Perceptron and Cerebellar-Model-Articulated-Controller neural networks were used in constructing the driver and obstacle classification models, and were evaluated using a car-following scenario for the driver model and a two-class obstacle (car or pedestrian) for the classification model. In the driver modelling application where the input dimension was low and training samples were rich, the CMAC network was found to achieve better accuracy than the MLP network. On the other hand, in the obstacle classification application where the input dimension was high and training samples were sparse, the MLP network was found to have fewer classification errors than the CMAC network. In both cases, the CMAC network converged significantly faster than the MLP network

Action Planning for the collision Avoidance System Using Neural Networks

An understanding of the scenario in complex traffic situations is essential in order to give an early warning, or in an autonomous system, to intervene in the urban or motorway environment. A collision avoidance system needs both to predict possible collisions or hazards and to plan a less hazardous move in a critical situation. A crucial factor in the success of the system is the use of a priori knowledge. The classical problem with a knowledge-based decision making system is the acquisition and representation of the knowledge. It is difficult to design and develop a system for real time auto-piloting in varied traffic environments. Neural networks are ideally suited for applications where a large training set is available because they can apply human decision making criteria in different situations. The learning processes encapsulate a wide variety of drivers' reactions to various scenarios. Neural networks' abilities to generalise their training to new scenarios in the light of driving experience and to make emotion-free decisions leads to a system that is adaptive and closely which resembles human action strategy. Recognition of a scenario is achieved by acquiring data about a scene from a variety of sensors. Visual data is preprocessed and features are extracted using a real-time image processing system, while microwave radar provides obstacle information and distances. This paper described an early warning system and suggests possible responses to various traffic situations. The paper focuses on various learning algorithms for decision making which is based on the current model and immediate history only. It would help if we could always recognise the dominant threat at every instant and avoid it by either slowing down or changing direction. In our analysis of situations using neural networks, the test cases show that reasonably such behaviour can be generated. In order to validate the auto pilot it is tested in parallel with expert drivers to assess the drivers' action in a number of scenarios. The network's intervention control is verified by independent observers. The intervention strategies are based on a number of rules by which an intervention controller is trained to generate various actions. These rules are fine tuned on-line to achieve reliable and repeatable actions

Successful treatment of severe Rh iso-immunization with immunosuppression and plasmapheresis

Nine patients with severe Rh iso-immunization were treated by plasma exchange combined with immunosuppression. Apart from 3 abortions, previous pregnancy losses consisted of 7 intra-uterine and 5 neonatal deaths. Only 2 patients had had no previous pregnancy loss. Differences in the optical density of the amniotic fluid of 8 patients fell into the upper Liley zone. There was one intra-uterine death due to abruptio placentae but no neonatal deaths. When the outcome of the pregnancy immediately preceding the treatment pregnancy was compared to the treatment pregnancy, the fetal loss was reduced from 6 to 1. No adverse fetal effects were encountered.Articl

Tyrosine Latching of a Regulatory Gate Affords Allosteric Control of Aromatic Amino Acid Biosynthesis*

The first step of the shikimate pathway for aromatic amino acid biosynthesis is catalyzed by 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase (DAH7PS). Thermotoga maritima DAH7PS (TmaDAH7PS) is tetrameric, with monomer units comprised of a core catalytic (β/α)8 barrel and an N-terminal domain. This enzyme is inhibited strongly by tyrosine and to a lesser extent by the presence of phenylalanine. A truncated mutant of TmaDAH7PS lacking the N-terminal domain was catalytically more active and completely insensitive to tyrosine and phenylalanine, consistent with a role for this domain in allosteric inhibition. The structure of this protein was determined to 2.0 Å. In contrast to the wild-type enzyme, this enzyme is dimeric. Wild-type TmaDAH7PS was co-crystallized with tyrosine, and the structure of this complex was determined to a resolution of 2.35 Å. Tyrosine was found to bind at the interface between two regulatory N-terminal domains, formed from diagonally located monomers of the tetramer, revealing a major reorganization of the regulatory domain with respect to the barrel relative to unliganded enzyme. This significant conformational rearrangement observed in the crystal structures was also clearly evident from small angle X-ray scattering measurements recorded in the presence and absence of tyrosine. The closed conformation adopted by the protein on tyrosine binding impedes substrate entry into the neighboring barrel, revealing an unusual tyrosine-controlled gating mechanism for allosteric control of this enzyme