Identification of Cj1051c as a Major determinant for the restriction barrier of Campylobacter jejuni StrainNCTC11168

Campylobacter jejuni is a leading cause of human diarrheal illness in the world, and research on it has benefitted greatly by the completion of several genome sequences and the development of molecular biology tools. However, many hurdles remain for a full understanding of this unique bacterial pathogen. One of the most commonly used strains for genetic work with C. jejuni is NCTC11168. While this strain is readily transformable with DNA for genomic recombination, transformation with plasmids is problematic. In this study, we have identified a determinant of this to be cj1051c, predicted to encode a restriction-modification type IIG enzyme. Knockout mutagenesis of this gene resulted in a strain with a 1,000-fold-enhanced transformation efficiency with a plasmid purified from a C. jejuni host. Additionally, this mutation conferred the ability to be transformed by plasmids isolated from an Escherichia coli host. Sequence analysis suggested a high level of variability of the specificity domain between strains and that this gene may be subject to phase variation. We provide evidence that cj1051c is active in NCTC11168 and behaves as expected for a type IIG enzyme. The identification of this determinant provides a greater understanding of the molecular biology of C. jejuni as well as a tool for plasmid work with strain NCTC11168. © 2012, American Society for Microbiology.

Comparing crowding in human and ideal observers.

A visual target is more difficult to recognize when it is surrounded by other, similar objects. This breakdown in object recognition is known as crowding. Despite a long history of experimental work, computational models of crowding are still sparse. Specifically, few studies have examined crowding using an ideal-observer approach. Here, we compare crowding in ideal observers with crowding in humans. We derived an ideal-observer model for target identification under conditions of position and identity uncertainty. Simulations showed that this model reproduces the hallmark of crowding, namely a critical spacing that scales with viewing eccentricity. To examine how well the model fits quantitatively to human data, we performed three experiments. In Experiments 1 and 2, we measured observers' perceptual uncertainty about stimulus positions and identities, respectively, for a target in isolation. In Experiment 3, observers identified a target that was flanked by two distractors. We found that about half of the errors in Experiment 3 could be accounted for by the perceptual uncertainty measured in Experiments 1 and 2. The remainder of the errors could be accounted for by assuming that uncertainty (i.e., the width of internal noise distribution) about stimulus positions and identities depends on flanker proximity. Our results provide a mathematical restatement of the crowding problem and support the hypothesis that crowding behavior is a sign of optimality rather than a perceptual defect.

Identification of the steering control behaviour of five test subjects following a randomly curving path in a driving simulator

The paper is concerned with the identification of theoretical preview steering controllers using data obtained from five test subjects in a fixed-base driving simulator. An understanding of human steering control behaviour is relevant to the design of autonomous and semi-autonomous vehicle controls. The driving task involved steering a linear vehicle along a randomly curving path. The theoretical steering controllers identified from the data were based on optimal linear preview control. A direct-identification method was used, and the steering controllers were identified so that the predicted steering angle matched as closely as possible the measured steering angle of the test subjects. It was found that identification of the driver's time delay and noise is necessary to avoid bias in identification of the controller parameters. Most subjects' steering behaviour was predicted well by a theoretical controller based on the lateral/yaw dynamics of the vehicle. There was some evidence that an inexperienced driver's steering action was better represented by a controller based on a simpler model of the vehicle dynamics, perhaps reflecting incomplete learning by the driver. Copyright © 2014 Inderscience Enterprises Ltd.