A phytoplasma effector destabilizes chloroplastic glutamine synthetase inducing chlorotic leaves that attract leafhopper vectors.

Leaf yellowing is a well-known phenotype that attracts phloem-feeding insects. However, it remains unclear how insect-vectored plant pathogens induce host leaf yellowing to facilitate their own transmission by insect vectors. Here, we report that an effector protein secreted by rice orange leaf phytoplasma (ROLP) inhibits chlorophyll biosynthesis and induces leaf yellowing to attract leafhopper vectors, thereby presumably promoting pathogen transmission. This effector, designated secreted ROLP protein 1 (SRP1), first secreted into rice phloem by ROLP, was subsequently translocated to chloroplasts by interacting with the chloroplastic glutamine synthetase (GS2). The direct interaction between SRP1 and GS2 disrupts the decamer formation of the GS2 holoenzyme, attenuating its enzymatic activity, thereby suppressing the synthesis of chlorophyll precursors glutamate and glutamine. Transgenic expression of SRP1 in rice plants decreased GS2 activity and chlorophyll precursor accumulation, finally inducing leaf yellowing. This process is correlated with the previous evidence that the knockout of GS2 expression in rice plants causes a similar yellow chlorosis phenotype. Consistently, these yellowing leaves attracted higher numbers of leafhopper vectors, caused the vectors to probe more frequently, and presumably facilitate more efficient phytoplasma transmission. Together, these results uncover the mechanism used by phytoplasmas to manipulate the leaf color of infected plants for the purpose of enhancing attractiveness to insect vectors.

Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method.

Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.

A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control.

Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification.

The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions.

Modelling the reopen strategy from dynamic zero-COVID in China considering the sequela and reinfection.

Although the dynamic zero-COVID policy has effectively controlled virus spread in China, China has to face challenges in balancing social-economic burdens, vaccine protection, and the management of long COVID symptoms. This study proposed a fine-grained agent-based model to simulate various strategies for transitioning from a dynamic zero-COVID policy with a case study in Shenzhen. The results indicate that a gradual transition, maintaining some restrictions, can mitigate infection outbreaks. However, the severity and duration of epidemics vary based on the strictness of the measures. In contrast, a more direct transition to reopening may lead to rapid herd immunity but necessitate preparedness for potential sequelae and reinfections. Policymakers should assess healthcare capacity for severe cases and potential long-COVID symptoms and determine the most suitable approach tailored to local conditions.

Genome-wide identification and expression analysis of the phosphatase 2A family in rubber tree (Hevea brasiliensis).

The protein phosphatase 2As (PP2As) play a key role in manipulating protein phosphorylation. Although a number of proteins in the latex of laticifers are phosphorylated during latex regeneration in rubber tree, information about the PP2A family is limited. In the present study, 36 members of the HbPP2A family were genome-wide identified. They were clustered into five subgroups: the subgroup HbPP2AA (4), HbPP2AB' (14), HbPP2AB'' (6), HbPP2AB55 (4), and HbPP2AC (8). The members within the same subgroup shared highly conserved gene structures and protein motifs. Most of HbPP2As possessed ethylene- and wounding-responsive cis-acting elements. The transcripts of 29 genes could be detected in latex by using published high-throughput sequencing data. Of the 29 genes, seventeen genes were significantly down-regulated while HbPP2AA1-1 and HbPP2AB55α/Bα-1were up-regulated by tapping. Of the 17 genes, 14 genes were further significantly down-regulated by ethrel application. The down-regulated expression of a large number of HbPP2As may attribute to the enhanced phosphorylation of the proteins in latex from the tapped trees and the trees treated with ethrel application.