Receptor-like protein tyrosine phosphatases: alike and yet so different

Reversible phosphorylation on tyrosine residues is an extremely rapid and powerful posttranslational modification that is used in signalling pathways for the regulation of cell growth and differentiation. Over the past several years an impressive number of receptor-like protein tyrosine phosphatase (RPTPase) family members have been identified by molecular cloning, and undoubtedly many more will follow. This review provides an overview of the molecular data that are available for the currently identified RPTPases and discusses their possible biological implications.     

Host microhabitat location by stem-borer parasitoidCotesia flavipes: the role of herbivore volatiles and locally and systemically induced plant volatiles

The origin of olfactory stimuli involved in the host microhabitat location inCotesia flavipes, a parasitoid of stem-borer larvae, was investigated in a Y-tube olfactometer. The response of femaleC. flavipes towards different components of the plant-host complex, consisting of a maize plant infested with two or more larvae of the stem borerChilo partellus, was tested in dualchoice tests. The concealed lifestyle of the stem-borer larvae did not limit the emission of volatiles attractive to a parasitoid. A major source of the attractive volatiles from the plant-host complex was the stem-borer-injured stem, including the frass produced by the feeding larvae. Moreover, the production of volatiles attractive to a parasitoid was not restricted to the infested stem part but occurs systemically throughout the plant. The uninfested leaves of a stem-borer-infested plant were found to emit volatiles that attract femaleC. flavipes. We further demonstrate that an exogenous elicitor of this systemic plant response is situated in the regurgitate of a stem-borer larva. When a minor amount of regurgitate is inoculated into the stem of an uninfested plant, the leaves of the treated plant emit volatiles that attract femaleC. flavipes.     

On restraining the production of small scales of motion in a turbulent channel flow

Since most turbulent flows cannot be computed directly from the incompressible Navier-Stokes equations, a dynamically less complex mathematical formulation is sought. In the quest for such a formulation, we consider nonlinear approximations of the convective term that preserve the symmetry and conservation properties. In particularly, the energy, enstrophy (in 2D) and helicity are conserved. The underlying idea is to restrain the convective production of small scales in an unconditional stable manner, meaning that the approximate solution cannot blow up in the energy-norm (in 2D also: enstrophy-norm). The numerical algorithm used to solve the governing equations preserves the symmetry and conservation properties too. The resulting simulation method is successfully tested for a turbulent channel flow (Re_τ = 180 and 395).     

How a cockpit calculates its speeds and why errors while doing this are so hard to detect

Recent incidents have shown that the production of take-off speeds is an activity vulnerable to miscalculations with a potential for disastrous outcomes. The aim of this paper is to analyze the calculation of the take-off speeds in a modern airline cockpit as a distributed cognitive activity in order to identify possible vulnerabilities in this process. We took the cockpit as the joint cognitive system under analysis and conducted an ethnographic study based on documental analysis, flight observations, interviews, and the analysis of 22 events involving failures related to the calculation of take-off speeds. The main argument is that the cognitive systems engineering perspective, with less focus on the human contribution than it is common in investigations, levels people and artifacts in the system as equal contributors to its eventual performance. Our analysis identified four assertions regarding vulnerabilities in the process of take-off speeds calculation: (1) representations at the level of the cockpit are always partial and incomplete; (2) some interactions require interpretation rather than institution; (3) interactions of agents do not follow a canonical process of coordination; (4) the control of the prevention of failures is accurate but inadequate. These vulnerabilities are a matter of interactions among cognitive systems in the cockpit, rather than vulnerabilities of individual agents, such as humans or artifacts.     

Exact algorithms for a generalization of the order acceptance and scheduling problem in a single-machine environment

This paper studies a generalization of the order acceptance and scheduling problem in a single-machine environment where a pool consisting of firm planned orders as well as potential orders is available from which an over-demanded company can select. The capacity available for processing the accepted orders is limited and each order is characterized by a known processing time, delivery date, revenue and a weight representing a penalty per unit-time delay beyond the delivery date. We prove that the existence of a constant-factor approximation algorithm for this problem is unlikely. We propose two linear formulations that are solved using an IP solver and we devise two exact branch-and-bound procedures able to solve instances with up to 50 jobs within reasonable CPU times. We compare the efficiency and quality of the results obtained using the different solution approaches.     

Relationships between driving simulator performance and driving test results

This article is considered relevant because: 1) car driving is an everyday and safety-critical task; 2) simulators are used to an increasing extent for driver training (related topics: training, virtual reality, human-machine interaction); 3) the article addresses relationships between performance in the simulator and driving test results--a relevant topic for those involved in driver training and the virtual reality industries; 4) this article provides new insights about individual differences in young drivers' behaviour. Simulators are being used to an increasing extent for driver training, allowing for the possibility of collecting objective data on driver proficiency under standardised conditions. However, relatively little is known about how learner drivers' simulator measures relate to on-road driving. This study proposes a theoretical framework that quantifies driver proficiency in terms of speed of task execution, violations and errors. This study investigated the relationships between these three measures of learner drivers' (n=804) proficiency during initial simulation-based training and the result of the driving test on the road, occurring an average of 6 months later. A higher chance of passing the driving test the first time was associated with making fewer steering errors on the simulator and could be predicted in regression analysis with a correlation of 0.18. Additionally, in accordance with the theoretical framework, a shorter duration of on-road training corresponded with faster task execution, fewer violations and fewer steering errors (predictive correlation 0.45). It is recommended that researchers conduct more large-scale studies into the reliability and validity of simulator measures and on-road driving tests.     

FAO’s AVHRR-based Agricultural Stress Index System (ASIS) for global drought monitoring

Agricultural production is highly dependent on climate variability in many parts of the world. In particular, drought may severely reduce crop yields, potentially affecting food availability at local, regional, and global scales. The Food and Agriculture Organization of the United Nations (FAO) operates the Global Early Warning System (GIEWS), which monitors global food supply and demand. One of the key challenges is to obtain synoptic information on a recurrent and timely basis about drought-affected agricultural zones. This is needed to quickly identify areas requiring immediate attention. The Agricultural Stress Index System (ASIS), based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) sensors on board the National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational Satellite (METOP) satellites, was specifically developed to meet this need. The system is based on a methodology developed by Rojas, Vrieling, and Rembold over the African continent. This approach has been modified and adapted to the global scale to produce an agricultural stress index (ASI) representing, per administrative unit, the percentage of cropland (or pasture) areas affected by drought over the growing season. The vegetation health index (VHI), based on normalized difference vegetation index (NDVI) and temperature anomalies, is used as a drought indicator. A fused time series of AVHRR data from METOP and NOAA was used to produce a consistent time series of VHI at 1 km resolution. Global phenology maps, indicating the number of growing seasons and their start and end dates, were derived from a multi-annual image set of SPOT-Vegetation (1999–2011). The VHI time series and phenology maps were then combined to produce the ASI for the years 1984 to the present. This allowed evaluation of the suitability of the ASIS to identify drought using historical reports and ancillary data. As a result of this analysis, ASIS was positively evaluated to support the FAO early warning system.