Catalytic mechanism of the tryptophan activation reaction revealed by crystal structures of human tryptophanyl-tRNA synthetase in different enzymatic states

Human tryptophanyl-tRNA synthetase (hTrpRS) differs from its bacterial counterpart at several key positions of the catalytic active site and has an extra N-terminal domain, implying possibly a different catalytic mechanism. We report here the crystal structures of hTrpRS in complexes with Trp, tryptophanamide and ATP and tryptophanyl-AMP, respectively, which represent three different enzymatic states of the Trp activation reaction. Analyses of these structures reveal the molecular basis of the mechanisms of the substrate recognition and the activation reaction. The dimeric hTrpRS is structurally and functionally asymmetric with half-of-the-sites reactivity. Recognition of Trp is by an induced-fit mechanism involving conformational change of the AIDQ motif that creates a perfect pocket for the binding and activation of Trp and causes coupled movements of the N-terminal and C-terminal domains. The KMSAS loop appears to have an inherent flexibility and the binding of ATP stabilizes it in a closed conformation that secures the position of ATP for catalysis. Our structural data indicate that the catalytic mechanism of the Trp activation reaction by hTrpRS involves more moderate conformational changes of the structural elements at the active site to recognize and bind the substrates, which is more complex and fine-tuned than that of bacterial TrpRS

Impacts of bus overtaking policies on the capacity of bus stops

Long bus queues at busy stops plague bus systems in many cities. Since berths are laid-out in tandem, buses’ overtaking maneuvers are often prohibited or restricted, which can significantly reduce a bus stop’s discharge capacity. When overtaking is allowed, aggressive drivers may perform disruptive oblique insertion maneuvers that would undermine stop capacity and compromise safety. This paper develops parsimonious yet realistic simulation models to examine the impacts of different overtaking policies on bus-stop capacity. Key realistic features are considered, including the oblique insertions resulting from overtaking, impacts of a nearby traffic signal, and bus traffic characteristics (reaction and move-up times). Extensive numerical experiments unveil many new findings. Some are at odds with those reported by previous studies. In addition, we examine two strategies that can improve the stop capacity without incurring disruptive oblique insertions. Practical implications of our findings are discussed, especially on choosing the most productive overtaking policy and means to minimize the capacity lost to buses’ mutual blockage at stops. These implications have broad applications to various types of bus stops

A Deep-Learning Approach for Network Traffic Assignment with Incomplete Data

International audienceThe Origin-Destination (OD) data collection often relies on the questionnaire surveys which is inevitably incomplete. With incomplete input data, the traditional traffic assignment models (e.g., mathematical programming) cannot generate reasonable results. Alternatively, we propose a deep-learning approach employing Feed-Forward Neural Network (FFNN) for the traffic assignment that respects incomplete data. Experiments are conducted in the Braess's paradox network, Sioux Falls network, and Chicago sketch network. In the first two networks, training data for the FFNN is obtained by randomly generating 10000 OD scenarios and running mathematical assignment models for link flows. For Chicago sketch network, a mesoscopic tool is employed to generate the training data. The feasibility of using FFNN to learn traffic assignment mechanics is verified by using complete OD data and full link flow data with accuracy over 90% in three networks. In case of partially observed OD data, our idea is to learn the mapping between incomplete OD data and full link flow data. Experiments are conducted under different OD data incompleteness levels. The results demonstrate that the accuracy of FFNN model remains over 90% even losing 50% OD data and overwhelms that of the mathematical assignment model in three networks. Practically, the reported model can be trained for a certain network with easily-obtained partial OD data (e.g., observed cellular mobile data) and traffic flow data in the field (e.g., loop data and video data). Once well trained, when inputting voluminous incomplete OD data, the data-driven approach can provide accurate full link flows efficiently

Preparation Method of Lunar Soil Simulant and Experimental Verification of the Performance of an Impact Penetrator for Lunar Soil Exploration

The exploration and investigation of lunar soil can provide necessary information for human beings to understand the Moon’s geological evolution history and solar activity, and is also of great significance for human beings to search for new energy sources. The impact penetrator can dive to a certain depth below the lunar surface, depending on soil compaction effect, and obtain lunar soil detection data by using the onboard sensors. The penetrator has the advantages of small size, light weight, low power consumption and long-term detection ability. In order to verify the diving performance of the developed impact penetrator, a great deal of lunar soil simulant, with physical and mechanical properties similar to a real lunar soil sample, was prepared, which lay the foundation for experimental research. Experiments on the influences of mass–stiffness parameters and dynamic parameters were conducted to obtain reasonable parameter-matching effects and driving parameters. The penetrating experiments in lunar soil simulant, with different relative compaction parameters, indicated that the penetrator could penetrate the simulated lunar soil with high relative compaction, and the penetration depth could reach to 545 mm after 894 shocks in lunar soil, with a relative compaction of 85%. This study on the impact penetrator can provide a feasible approach for in-situ exploration of lunar soil

Preparation Method of Lunar Soil Simulant and Experimental Verification of the Performance of an Impact Penetrator for Lunar Soil Exploration

The exploration and investigation of lunar soil can provide necessary information for human beings to understand the Moon’s geological evolution history and solar activity, and is also of great significance for human beings to search for new energy sources. The impact penetrator can dive to a certain depth below the lunar surface, depending on soil compaction effect, and obtain lunar soil detection data by using the onboard sensors. The penetrator has the advantages of small size, light weight, low power consumption and long-term detection ability. In order to verify the diving performance of the developed impact penetrator, a great deal of lunar soil simulant, with physical and mechanical properties similar to a real lunar soil sample, was prepared, which lay the foundation for experimental research. Experiments on the influences of mass–stiffness parameters and dynamic parameters were conducted to obtain reasonable parameter-matching effects and driving parameters. The penetrating experiments in lunar soil simulant, with different relative compaction parameters, indicated that the penetrator could penetrate the simulated lunar soil with high relative compaction, and the penetration depth could reach to 545 mm after 894 shocks in lunar soil, with a relative compaction of 85%. This study on the impact penetrator can provide a feasible approach for in-situ exploration of lunar soil

Capacity approximations for near- and far-side bus stops in dedicated bus lanes

We develop analytical approximations for the bus-carrying capacities at near- and far-side stops with one or multiple curbside berths where buses operate in a dedicated bus lane. The approximations are derived using time-space diagrams of bus trajectories and probabilistic methods. They correctly account for the effects of key operating factors that were ignored or incorrectly addressed by previous methods. These factors include the signal timing and the distance between stop and signal. Comparison against computer simulation shows that our models furnish much more accurate estimates for near- and far-side stop capacities than previous methods in the literature. Numerical case studies are performed to examine how the stop capacity is affected by various operating factors. New findings and their practical implications are discussed