Comparative Transcriptome Analysis of Resistant and Susceptible Tomato Lines in Response to Infection by Xanthomonas perforans Race T3

Bacterial spot, incited by several Xanthomonas sp., is a serious disease in tomato (Solanum lycopersicum L.). Although genetics of resistance has been widely investigated, the interactions between the pathogen and tomato plants remain unclear. In this study, tanscriptomes of X. perforans race T3 infected tomato lines were compared to those of controls. An average of 7 million reads were generated with approximately 21,526 genes mapped in each sample post-inoculation at 6h (6 HPI) and 6d (6 DPI) using RNA-sequencing technology. Overall, the numbers of differentially expressed genes (DEGs) were higher in the resistant tomato line PI 114490 than in the susceptible line OH 88119, and the numbers of DEGs were higher at 6 DPI than at 6 HPI. Fewer genes (78 in PI 114490 and 15 in OH 88119) were up-regulated and most DEGs were down-regulated, suggesting that the inducible defense response might not be fully activated at 6 HPI. Accumulation expression levels of 326 co-up regulated genes in both tomato lines at 6 DPI might be involved in basal defense, while the specific and strongly induced genes at 6 DPI might be correlated with the resistance in PI114490. Most DEGs were involved in plant hormone signal transduction, plant-pathogen interaction and phenylalanine metabolism, and the genes significantly up-regulated in PI114490 at 6 DPI were associated with defense response pathways. DEGs containing NBS-LRR domain or defense-related WRKY transcription factors were also identified. The results will provide a valuable resource for understanding the interactions between X. perforans and tomato plants

Self-Healing Control Framework Against Actuator Fault of Single-Rotor Unmanned Helicopters

Unmanned helicopters (UHs) develop quickly because of their ability to hover and low speed flight. Facing different work conditions, UHs require the ability to safely operate under both external environment constraints, such as obstacles, and their own dynamic limits, especially after faults occurrence. To guarantee the postfault UH system safety and maximum ability, a self‐healing control (SHC) framework is presented in this chapter which is composed of fault detection and diagnosis (FDD), fault‐tolerant control (FTC), trajectory (re‐)planning, and evaluation strategy. More specifically, actuator faults and saturation constraints are considered at the same time. Because of the existence of actuator constraints, usable actuator efficiency would be reduced after actuator fault occurrence. Thus, the performance of the postfault UH system should be evaluated to judge whether the original trajectory and reference is reachable, and the SHC would plan a new trajectory to guarantee the safety of the postfault system under environment constraints. At last, the effectiveness of proposed SHC framework is illustrated by numerical simulations

High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The mainstream edge learning approach, federated learning, has been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for updating a global AI model. Since each stochastic gradient is typically high-dimensional (with millions to billions of coefficients), communication overhead becomes a bottleneck for edge learning. To address this issue, we propose in this work a novel framework of hierarchical stochastic gradient quantization and study its effect on the learning performance. First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively. Subsequently, the quantized normalized block gradients are scaled and cascaded to yield the quantized normalized stochastic gradient using a so-called hinge vector designed under the criterion of minimum distortion. The hinge vector is also efficiently compressed using another low-dimensional Grassmannian quantizer. The other feature of the framework is a bit-allocation scheme for reducing the quantization error. The scheme determines the resolutions of the low-dimensional quantizers in the proposed framework. The framework is proved to guarantee model convergency by analyzing the convergence rate as a function of the quantization bits. Furthermore, by simulation, our design is shown to substantially reduce the communication overhead compared with the state-of-the-art signSGD scheme, while both achieve similar learning accuracies

A STEM PROFILE MODEL CALIBRATED BY NONLINEAR MIXED-EFFECTS MODELING

A stem profile model was developed for black spruce (Picea mariana (Mill.) B.S.P.) trees in Alberta, Canada using a nonlinear mixed model approach. The model included two random parameters to capture between-subject variation and a general covariance structure to model within-subject residual autocorrelation. After evaluating various covariance structures, the 4-banded toeplitz and the spatial power structures were chosen for further evaluation. The 4-banded toeplitz structure provided a better fit. The model was further evaluated using an independent data set to examine its validation accuracy. Model validation results showed that the model was able to accurately predict stem diameters at the population and subject-specific levels. Both covariance structures produced reliable model predictions, but the spatial power structure was superior to the 4-banded toeplitz structure. One to four stem diameters were used to predict random parameters and to subsequently generate subject-specific predictions. At least three stem diameters were needed to achieve better subject-specific predictions than population-average predictions

Molecular dynamics investigation of interfacial adhesion between oxidised bitumen and mineral surfaces

The interfacial adhesion between oxidised bitumen and mineral surfaces at dry and wet conditions was investigated using molecular dynamics (MD) simulations. Molecular models were built for virgin and oxidised bitumen components including saturate, aromatic, resin and asphaltenes. The bitumen models and four representative mineral substrates (namely quartz, calcite, albite and microcline) were employed to construct bitumen-mineral interface systems. These models were validated by the experimental results and MD simulations reported in the literature. The hardening mechanism of the aged bitumen was analysed by comparing the density, cohesive energy density and fraction of free volume between the virgin and oxidised bitumen. Work of adhesion was computed to quantify the adhesive bonding property of the bitumen-mineral interface systems for the virgin, lightly oxidised and heavily oxidised bitumen models under dry and wet conditions. Results show that the oxidised products (carbonyl and sulfoxide) strengthen the intermolecular bonding, resulting in molecular aggregation and physical hardening of the aged bitumen. When bitumen becomes oxidised at the dry condition, the interfacial adhesion of bitumen-acidic minerals (quartz) is dominated by van der Waals interaction which decreases due to the increased bitumen-quartz intermolecular distance caused by the aggregated bitumen molecules during aging. In comparison, the interfacial adhesion of bitumen-strong alkali minerals (albite and microcline) is dominated by electrostatic energy which increases due to higher polarity introduced by the oxidised products. For the bitumen-weak alkali mineral (calcite), the interfacial adhesion is attributed to both electrostatic energy and van der Waals energy, where compared to the virgin bitumen, the electrostatic energy becomes lower for the lightly-oxidised bitumen due to the increased bitumen-mineral distance but becomes higher for the heavily-oxidised bitumen due to higher polarity. At wet condition, water is the dominating factor that affects (weakens) the interfacial adhesion between the bitumen and the acidic minerals (quartz), and the oxidative aging of bitumen is the major factor that affects (strengthens) the interfacial adhesion between the bitumen and the strongly alkaline minerals (albite and microcline). For the weak alkali minerals such as calcite, both water and bitumen aging can significantly affect the interfacial adhesion