Ethylene (ET) plays an important role as signal transducer in plants in response to environmental stimuli. Under water deficient conditions, fine adjustment of osmosis and redox states through phytohormones is vital for plant resistance to drought. Here, physiological and cellular responses of ET insensitive mutants (ein2-5 and ein3-1) were analyzed under water deficiency stimulated by polyethylene glycol 6000 (PEG-6000), and compared with wild type plants (Col-0) as controls. The accumulation of compatible solutes was higher in Col-0 than in ein2-5 and ein3-1. In Col-0 plants, water stress also increased transcription of P5CS1, which encoded a key rate-limiting enzyme in proline biosynthesis. These results suggested that ET signaling is involved in increasing the up-regulation of soluble sugar and proline accumulation to adjust to osmotic stress. In addition, oxidative stress was higher in ET defective mutants than in Col-0 wild-type plants. Meanwhile, increased activities of the reactive oxygen species (ROS)-scavenging enzymes superoxide dismutase (SOD) and peroxidase (POD) were observed in ET insensitive mutants, indicating aggravated oxidative stress in ET-defective plants under drought. In conclusion, ET signaling is involved in modulating plant oxidative stress under drought conditions.
Traditional accessibility evaluation fails to fully capture the travel costs, especially the external costs, of travel. This study develops a full cost accessibility (FCA) framework by combining the internal and external cost components of travel time, safety, emissions, and money. The example illustrated compares FCA by automobile and bicycle on a toy network to demonstrate the potential and practicality of applying the FCA framework on real networks. This method provides an efficient evaluation tool for transport planning projects.
Accessibility, measuring the ease of reaching potential destinations, is increasingly being considered as an effective indicator to evaluate the performance of transport and land use interactions. Primal accessibility, a generalization of the first accessibility formulation proposed by Hansen (1959), has been widely used in many studies and demonstrated to be a reliable tool for project, program, and policy evaluation. The dual of accessibility, measuring the time required to reach a given number of opportunities, is less often considered but can be used for optimization in location-covering type problems. This paper, hence, clarifies the definitions of primal and dual access, and applies both measures to the Minneapolis-St. Paul metropolitan area for auto and transit to demonstrate their practicality as a metropolitan-level measurement. We explore the correlations and differences between the primal and dual access to better understand the relative strengths of the measures. It is found that, as with primal accessibility, dual accessibility is an efficient approach to evaluate accessibility, which is straightforward to calculate and to explain to policy-makers and the public.
On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.
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