The majority of plant disease resistance (R) genes encode proteins that share common structural features. However, the transcription activator-like effector (TALE) associated executor type R genes show no considerable sequence homology to any known R genes. We adopted a map-based cloning approach and TALE-based technology to isolate and characterize Xa23, a new executor R gene derived from the wild rice (Oryza rufipogon) that confers an extremely broad spectrum of resistance to bacterial blight caused by Xanthomonas oryzae pv. oryzae (Xoo). Xa23 encodes a 113-amino acid protein that shares 50% identity to the known executor R protein XA10. The predicted transmembrane helices in XA23 also overlap with those of XA10. Unlike Xa10, however, Xa23 transcription is specifically activated by AvrXa23, a TALE present in all examined Xoo field isolates. Moreover, the susceptible xa23 allele has an identical open reading frame of Xa23, but differs in promoter region by lacking the TALE binding-element (EBE) for AvrXa23. XA23 can trigger strong hypersensitive response in rice, tobacco and tomato. Our results provide the first evidence that plant genomes have an executor R gene family in which members execute their function and spectrum of disease resistance by recognizing the cognate TALEs in pathogen.
The development of the terahertz frequency range (1-10 THz, λ ≈ 30-300 µm) has long been impeded by the relative dearth of compact, coherent radiation sources of reasonable power. This thesis details the development of quantum cascade lasers (QCLs) that operate in the terahertz with photon energies below the semiconductor Reststrahlen band. Photons are emitted via electronic intersubband transitions that take place entirely within the conduction band, where the wavelength is chosen by engineering the well and barrier widths in multiple-quantum-well heterostructures. Fabrication of such long wavelength lasers has traditionally been challenging, since it is difficult to obtain a population inversion between such closely spaced energy levels (hω ≈ 4-40 meV), and because traditional dielectric waveguides become extremely lossy due to free carrier absorption.This thesis reports the development of terahertz QCLs in which the lower radiative state is depopulated via resonant longitudinal-optical phonon scattering. This mechanism is efficient and temperature insensitive, and provides protection from thermal backfilling due to the large energy separation (∼36 meV) between the lower radiative state and the injector. Both properties are important in allowing higher temperature operation at longer wavelengths. Lasers using a surface plasmon based waveguide grown on a semi-insulating (SI) GaAs substrate were demonstrated at 3.4 THz (λ ≈ 88 µm) in pulsed mode up to 87 K, with peak collected powers of 14 mW at 5 K, and 4 mW at 77 K.Additionally, the first terahertz QCLs have been demonstrated that use metalmetal waveguides, where the mode is confined between metal layers placed immediately above and below the active region. These devices have confinement factors close to unity, and are expected to be advantageous over SI-surface-plasmon waveguides, especially at long wavelengths. Such a waveguide was used to obtain lasing at 3.8 THz (λ ≈ 79 µm) in pulsed mode up to a record high temperature of 137 K, whereas similar devices fabricated in SI-surface-plasmon waveguides had lower maximum lasing temperatures (∼92 K) due to the higher losses and lower confinement factors.This thesis describes the theory, design, fabrication, and testing of terahertz quantum cascade laser devices. A summary of theory relevant to design is presented, 3 including intersubband radiative transitions and gain, intersubband scattering, and coherent resonant tunneling transport using a tight-binding density matrix model. Analysis of the effects of the complex heterostructure phonon spectra on terahertz QCL design are considered. Calculations of the properties of various terahertz waveguides are presented and compared with experimental results. Various fabrication methods have been developed, including a robust metallic wafer bonding technique used to fabricate metal-metal waveguides. A wide variety of quantum cascade structures, both lasing and non-lasing, have been experimentally characterized, which yield valuable information about the transport ...
This paper presents a novel actuation technology for robotically assisted MRI-guided interventional procedures. Compact and wireless, the actuators are both powered and controlled by the MRI scanner. The design concept and performance limits are described and derived analytically. Simulation and experiments in a clinical MR scanner are used to validate the analysis and to demonstrate the capability of the approach for needle biopsies. The concepts of actuator locking mechanisms and multi-axis control are also introduced.
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [1], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of day to predict the travel time. The beauty of ST-NN is that it uses only the raw trips data without requiring further feature engineering and provides a joint estimate of travel time and distance. We compare the performance of ST-NN to that of state-of-the-art travel time estimation methods, and we observe that the proposed approach generalizes better than state-of-the-art methods. We show that ST-NN approach significantly reduces the mean absolute error for both predicted travel time and distance, about 17% for travel time prediction. We also observe that the proposed approach is more robust to outliers present in the dataset by testing the performance of ST-NN on the datasets with and without outliers.
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