Interaction of NPR1 with basic leucine zipper protein transcription factors that bind sequences required for salicylic acid induction of the PR–1 gene

The Arabidopsis thaliana NPR1 has been shown to be a key regulator of gene expression during the onset of a plant disease-resistance response known as systemic acquired resistance. The npr1 mutant plants fail to respond to systemic acquired resistance-inducing signals such as salicylic acid (SA), or express SA-induced pathogenesis-related (PR) genes. Using NPR1 as bait in a yeast two-hybrid screen, we identified a subclass of transcription factors in the basic leucine zipper protein family (AHBP-1b and TGA6) and showed that they interact specifically in yeast and in vitro with NPR1. Point mutations that abolish the NPR1 function in A. thaliana also impair the interactions between NPR1 and the transcription factors in the yeast two-hybrid assay. Furthermore, a gel mobility shift assay showed that the purified transcription factor protein, AHBP-1b, binds specifically to an SA-responsive promoter element of the A. thaliana PR-1 gene. These data suggest that NPR1 may regulate PR-1 gene expression by interacting with a subclass of basic leucine zipper protein transcription factors.

Web searching interaction model based on user cognitive styles

As more and more information is available on the Web finding quality and reliable information is becoming harder. To help solve this problem, Web search models need to incorporate users’ cognitive styles. This paper reports the preliminary results from a user study exploring the relationships between Web users’ searching behavior and their cognitive style. The data was collected using a questionnaire, Web search logs and think-aloud strategy. The preliminary findings reveal a number of cognitive factors, such as information searching processes, results evaluations and cognitive style, having an influence on users’ Web searching behavior. Among these factors, the cognitive style of the user was observed to have a greater impact. Based on the key findings, a conceptual model of Web searching and cognitive styles is presented.

Closing the gap : the next steps for the interaction of corporate reputation and psychological contract development

With an increasing body of literature linking the human resource management and marketing fields, one area receiving increased academic attention is how an organisation’s corporate reputation can be managed to attract potential recruits and shape their employment expectations through their psychological contracts. This paper seeks to enhance current models which focus on the interrelationship of corporate reputation and psychological contract theory. It is argued that a number of factors need to be considered in order the build a firmer foundation for such a theory. Firstly, a common understanding of the psychological contract needs to be established such that the focus on either expectations or promises is clarified. Secondly, the included components of the psychological contract need to be considered in light of their empirical founding and their relationship with one another. Thirdly, the interrelationship of corporate reputation, employer branding, identity and image needs to be explicated within the context of how they both influence and interrelate with the psychological contract. The final consideration surrounds the opportunity for potential employees to be considered within the corporate reputation literature as a significant stakeholder group.

Interaction, privacy and profiling considerations in local mobile social software : a prototype agile ride share system

Agile ridesharing aims to utilise the capability of social networks and mobile phones to facilitate people to share vehicles and travel in real time. However the application of social networking technologies in local communities to address issues of personal transport faces significant design challenges. In this paper we describe an iterative design-based approach to exploring this problem and discuss findings from the use of an early prototype. The findings focus upon interaction, privacy and profiling. Our early results suggest that explicitly entering information such as ride data and personal profile data into formal fields for explicit computation of matches, as is done in many systems, may not be the best strategy. It might be preferable to support informal communication and negotiation with text search techniques.

Sapporo World Window : urban interaction through public and private screens

This paper introduces Sapporo World Window, a screen-based application that is currently under development for the new underway passage at the centre of Sapporo City. There are ten large public screens installed in the space, displaying user-generated videos about various aspects of the city and a real-time map that visualises users’ interaction with the city. The application aims to engage the general public by functioning as a unique ‘point of connection’ for socio-cultural and technological interactions, making the space a lively social place where people can have meaningful experiences of interacting with people and places of Sapporo through mobile phones (keitai) and the public screens in the space. This paper first outlines the contextual background and key concept for the application’s design. Then the paper discusses the user interaction processes, technical specifications, and interface design, followed by the conclusions and outlook.

Persistent ocean monitoring with underwater gliders : towards accurate reconstruction of dynamic ocean processes

Ocean processes are dynamic and complex events that occur on multiple different spatial and temporal scales. To obtain a synoptic view of such events, ocean scientists focus on the collection of long-term time series data sets. Generally, these time series measurements are continually provided in real or near-real time by fixed sensors, e.g., buoys and moorings. In recent years, an increase in the utilization of mobile sensor platforms, e.g., Autonomous Underwater Vehicles, has been seen to enable dynamic acquisition of time series data sets. However, these mobile assets are not utilized to their full capabilities, generally only performing repeated transects or user-defined patrolling loops. Here, we provide an extension to repeated patrolling of a designated area. Our algorithms provide the ability to adapt a standard mission to increase information gain in areas of greater scientific interest. By implementing a velocity control optimization along the predefined path, we are able to increase or decrease spatiotemporal sampling resolution to satisfy the sampling requirements necessary to properly resolve an oceanic phenomenon. We present a path planning algorithm that defines a sampling path, which is optimized for repeatability. This is followed by the derivation of a velocity controller that defines how the vehicle traverses the given path. The application of these tools is motivated by an ongoing research effort to understand the oceanic region off the coast of Los Angeles, California. The computed paths are implemented with the computed velocities onto autonomous vehicles for data collection during sea trials. Results from this data collection are presented and compared for analysis of the proposed technique.

Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend prelim- inary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

Towards improvement of autonomous glider navigation accuracy through the use of regional ocean models

Autonomous underwater gliders are robust and widely-used ocean sampling platforms that are characterized by their endurance, and are one of the best approaches to gather subsurface data at the appropriate spatial resolution to advance our knowledge of the ocean environment. Gliders generally do not employ sophisticated sensors for underwater localization, but instead dead-reckon between set waypoints. Thus, these vehicles are subject to large positional errors between prescribed and actual surfacing locations. Here, we investigate the implementation of a large-scale, regional ocean model into the trajectory design for autonomous gliders to improve their navigational accuracy. We compute the dead-reckoning error for our Slocum gliders, and compare this to the average positional error recorded from multiple deployments conducted over the past year. We then compare trajectory plans computed on-board the vehicle during recent deployments to our prediction-based trajectory plans for 140 surfacing occurrences.

Cooperative multi-AUV tracking of phytoplankton blooms based on ocean model predictions

In recent years, ocean scientists have started to employ many new forms of technology as integral pieces in oceanographic data collection for the study and prediction of complex and dynamic ocean phenomena. One area of technological advancement in ocean sampling if the use of Autonomous Underwater Vehicles (AUVs) as mobile sensor plat- forms. Currently, most AUV deployments execute a lawnmower- type pattern or repeated transects for surveys and sampling missions. An advantage of these missions is that the regularity of the trajectory design generally makes it easier to extract the exact path of the vehicle via post-processing. However, if the deployment region for the pattern is poorly selected, the AUV can entirely miss collecting data during an event of specific interest. Here, we consider an innovative technology toolchain to assist in determining the deployment location and executed paths for AUVs to maximize scientific information gain about dynamically evolving ocean phenomena. In particular, we provide an assessment of computed paths based on ocean model predictions designed to put AUVs in the right place at the right time to gather data related to the understanding of algal and phytoplankton blooms.

Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

Trajectory design and implementation for multiple autonomous underwater vehicles based on ocean model predictions

-

Autonomous underwater vehicles: development and implementation of time and energy efficient trajectories

Autonomous underwater vehicles (AUVs) are increasingly used, both in military and civilian applications. These vehicles are limited mainly by the intelligence we give them and the life of their batteries. Research is active to extend vehicle autonomy in both aspects. Our intent is to give the vehicle the ability to adapt its behavior under different mission scenarios (emergency maneuvers versus long duration monitoring). This involves a search for optimal trajectories minimizing time, energy or a combination of both. Despite some success stories in AUV control, optimal control is still a very underdeveloped area. Adaptive control research has contributed to cost minimization problems, but vehicle design has been the driving force for advancement in optimal control research. We look to advance the development of optimal control theory by expanding the motions along which AUVs travel. Traditionally, AUVs have taken the role of performing the long data gathering mission in the open ocean with little to no interaction with their surroundings, MacIver et al. (2004). The AUV is used to find the shipwreck, and the remotely operated vehicle (ROV) handles the exploration up close. AUV mission profiles of this sort are best suited through the use of a torpedo shaped AUV, Bertram and Alvarez (2006), since straight lines and minimal (0 deg - 30 deg) angular displacements are all that are necessary to perform the transects and grid lines for these applications. However, the torpedo shape AUV lacks the ability to perform low-speed maneuvers in cluttered environments, such as autonomous exploration close to the seabed and around obstacles, MacIver et al. (2004). Thus, we consider an agile vehicle capable of movement in six degrees of freedom without any preference of direction.

Genetic analysis reveals two stocks of skipjack tuna (Katsuwonus pelamis) in the north western Indian Ocean

Skipjack (SJT) (Katsuwonus pelamis) is a medium sized, pelagic, highly dispersive tuna species that occurs widely across tropical and subtropical waters. SJT constitute the largest tuna fishery in the Indian Ocean, and are currently managed as a single stock. Patterns of genetic variation in a mtDNA gene and 6 microsatellite loci were examined to test for stock structure in the northwestern Indian Ocean. 324 individuals were sampled from five major fishing grounds around Sri Lanka, and single sites in the Maldive Islands and the Laccadive Islands. Phylogenetic reconstruction of mtDNA revealed two coexisting divergent clades in the region. AMOVA (Analysis of Molecular Variance) of mtDNA data revealed significant genetic differentiation among sites (ΦST = 0.2029, P < 0.0001), also supported by SAMOVA results. AMOVA of microsatellite data also showed significant differentiation among most sampled sites (FST = 0.0256, P<0.001) consistent with the mtDNA pattern. STRUCTURE analysis of the microsatellite data revealed two differentiated stocks. While the both two marker types examined identified two genetic groups, microsatellite analysis indicates that the sampled SJT are likely to represent individuals sourced from discrete breeding grounds that are mixed in feeding grounds in Sri Lankan waters.