Identification And Functional Characterization Of Plant Small Secreted Proteins During Arbuscular Mycorrhizal Symbiosis

Plant small secreted proteins (SSPs) are sequences of 50 – 250 amino acids in size which are transported out of cells to fulfill multiple functions related to plant growth and development and response to various stresses. With the development of more accurate and affordable genome sequencing technology, an increasing number of SSPs have been predicted using diverse computational tools based on machine learning. Although experimentally validated plant SSPs are still limited, some studies have reported that plant SSPs can be induced and involved in mutualistic relationships between plants and microbes. In Chapter I, known SSPs and their functions in various plant species are reviewed. Additionally, current computational tools and experimental methods that have been widely applied to identify plant SSPs are summarized. A new, robust, and integrated pipeline to discover plant SSPs is proposed. Furthermore, strategies for elucidating the biological functions of SSPs in plants are discussed in Chapter I. Chapter II presents predicted SSPs from 60 plant species and elucidates the evolutionary convergence of changes in SSP sequences. Furthermore, the expression of SSPs induced by arbuscular mycorrhizal fungi (AMF) which correspond to the convergent abilityfor different plants to form mutualistic association with AMF are explored. Overall, this study provides insightful ideas to understand functions of plant SSPs that occur during symbiosis between plants and fungi

Fundamental Diagram Estimation Based on Random Probe Pairs on Sub-Segments

A new statistical algorithm is proposed in this paper with the aim of estimating fundamental diagram (FD) in actual traffic and dividing the traffic state. Traditional methods mainly focus on sensor data, but this paper takes random probe pairs as research objects. First, a mathematical method is proposed by using probe pairs data and the jam density to determine the FD on a stationary segment. Second, we applied it to the near-stationary probe traffic state set through linear regression and expectation maximisation iterative algorithm, estimating the free flow speed and the backward wave speed and dividing the traffic state based on the 95% confidence interval of the estimated FD. Finally, simulation and empirical analyses are used to verify the new algorithm. The simulation analysis results show that the estimation error corresponding to the free flow speed and the backward wave speed are 1.0668 km/h and 0.0002 km/h respectively. The empirical analysis calculates the maximum capacity of the road and divides the traffic into three states (free flow state, breakdown state, and congested state), which demonstrates the accuracy and practicability of the research in this paper, and provides a reference for urban traffic monitoring and government decision-making

Macroscopic Fundamental Diagram Estimation Considering Traffic Flow Condition of Road Network

A macroscopic fundamental diagram (MFD) is an important basis for road network research. It describes the functional relationship between the average flow and average density of the road network. We proposed an MFD estimation method based on the traffic flow condition. Firstly, according to statistical theories, the road network data are divided into three traffic flow conditions (free flow, chaotic and congested) bounded by a 95% confidence interval of the maximum traffic capacity of each intersection in the road network. Then, in each condition, we combined principal component analysis and the Jolliffe B4 method to reduce dimension for extracting critical intersections. Finally, the full-scale dataset of the road network was reconstructed to estimate the road network MFD. Through numerical simulation and empirical research, it is found that the root mean square error and absolute percentage error between estimated MFD and true MFD considering the traffic flow condition are smaller than those without considering the traffic flow condition. The MFD estimation and the division of the traffic states of the road network were completed at the same time. The proposed method effectively saves the time cost of road network research and is highly accurate

Effect of Substrate Support on Dynamic Graphene/Metal Electrical Contacts.

Recent advances in graphene and other two-dimensional (2D) material synthesis and characterization have led to their use in emerging technologies, including flexible electronics. However, a major challenge is electrical contact stability, especially under mechanical straining or dynamic loading, which can be important for 2D material use in microelectromechanical systems. In this letter, we investigate the stability of dynamic electrical contacts at a graphene/metal interface using atomic force microscopy (AFM), under static conditions with variable normal loads and under sliding conditions with variable speeds. Our results demonstrate that contact resistance depends on the nature of the graphene support, specifically whether the graphene is free-standing or supported by a substrate, as well as on the contact load and sliding velocity. The results of the dynamic AFM experiments are corroborated by simulations, which show that the presence of a stiff substrate, increased load, and reduced sliding velocity lead to a more stable low-resistance contact

The Morphotropic Phase Boundary in the (1-x)PbZrO3–x[0.3Bi(Zn1/2Ti1/2)O3–0.7PbTiO3] Perovskite Solid Solution

Ceramics in the (1-x)PbZrO3–x[0.3Bi(Zn1/2Ti1/2)O3–0.7PbTiO3] solid solution system with 0.48 x 0.56 were investigated. A morphotropic phase boundary separating rhombohedral and tetragonal perovskite phases was identified at x = 0.52. This composition displays the maximum remanent polarization Pr of 40.7 μC/cm2 and the best piezoelectric coefficient d33 of 311 pC/N in the pseudo-binary system. However, the Curie temperature Tc for this MPB composition is 291 °C, much lower than initially expected