There are strong evidences that Mycobacterium tuberculosis survives in a non-replicating state in the absence of oxygen in closed lesions and granuloma in vivo. In addition, M. tuberculosis is acid-resistant, allowing mycobacteria to survive in acidic, inflamed lesions. The ability of M. tuberculosis to resist to acid was recently shown to contribute to the bacillus virulence although the mechanisms involved have yet to be deciphered. In this study, we report that M. tuberculosis resistance to acid is oxygen-dependent; whereas aerobic mycobacteria were resistant to a mild acid challenge (pH 5.5) as previously reported, we found microaerophilic and hypoxic mycobacteria to be more sensitive to acid. In hypoxic conditions, mild-acidity promoted the dissipation of the protonmotive force, rapid ATP depletion and cell death. Exogenous nitrate, the most effective alternate terminal electron acceptor after molecular oxygen, protected hypoxic mycobacteria from acid stress. Nitrate-mediated resistance to acidity was not observed for a respiratory nitrate reductase NarGH knock-out mutant strain. Furthermore, we found that nitrate respiration was equally important in protecting hypoxic non-replicating mycobacteria from radical nitrogen species toxicity. Overall, these data shed light on a new role for nitrate respiration in protecting M. tuberculosis from acidity and reactive nitrogen species, two environmental stresses likely encountered by the pathogen during the course of infection.

The Canton Tower (formerly named Guangzhou New TV Tower) of 610 m high has been instrumented with a long-term structural health monitoring (SHM) system consisting of over 700 sensors of sixteen types. Under the auspices of the Asian-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST), an SHM benchmark problem for high-rise structures has been developed by taking the instrumented Canton Tower as a host structure. This benchmark problem aims to provide an international platform for direct comparison of various SHM-related methodologies and algorithms with the use of realworld monitoring data from a large-scale structure, and to narrow the gap that currently exists between the research and the practice of SHM. This paper first briefs the SHM system deployed on the Canton Tower, and the development of an elaborate three-dimensional (3D) full-scale finite element model (FEM) and the validation of the model using the measured modal data of the structure. In succession comes the formulation of an equivalent reduced-order FEM which is developed specifically for the benchmark study. The reducedorder FEM, which comprises 37 beam elements and a total of 185 degrees-of-freedom (DOFs), has been elaborately tuned to coincide well with the full-scale FEM in terms of both modal frequencies and mode shapes. The field measurement data (including those obtained from 20 accelerometers, one anemometer and one temperature sensor) from the Canton Tower, which are available for the benchmark study, are subsequently presented together with a description of the sensor deployment locations and the sensor specifications.

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This article focuses on the discrete double-curl operator arising in the Maxwell equation that models three-dimensional photonic crystals with face-centered cubic lattice. The discrete double-curl operator is the degenerate coefficient matrix of the generalized eigenvalue problems (GEVP) due to the Maxwell equation. We derive an eigendecomposition of the degenerate coefficient matrix and explore an explicit form of orthogonal basis for the range and null spaces of this matrix. To solve the GEVP, we apply these theoretical results to project the GEVP to a standard eigenvalue problem (SEVP), which involves only the eigenspace associated with the nonzero eigenvalues of the GEVP, and therefore the zero eigenvalues are excluded and will not degrade the computational efficiency. This projected SEVP can be solved efficiently by the inverse Lanczos method. The linear systems within the inverse Lanczos method are well-conditioned and can be solved efficiently by the conjugate gradient method without using a preconditioner. We also demonstrate how two forms of matrix-vector multiplications, which are the most costly part of the inverse Lanczos method, can be computed by fast Fourier transformation due to the eigendecomposition to significantly reduce the computation cost. Integrating all of these findings and techniques, we obtain a fast eigenvalue solver. The solver has been implemented by MATLAB and successfully solves each of a set of 5.184 million dimension eigenvalue problems within 50 to 104 minutes on a workstation with two Intel Quad-Core Xeon X5687 3.6 GHz CPUs.

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SUMMARYSeveral Jacobi-Davidson type methods are proposed for computing interior eigenpairs of large-scale cubic eigenvalue problems. To successively compute the eigenpairs, a novel explicit non-equivalence de ation method with low-rank updates is developed and analysed. Various techniques such as locking, search direction transformation, restarting, and preconditioning are incorporated into the methods to improve stability and e ciency. A semiconductor quantum dot model is given as an example to illustrate the cubic nature of the eigenvalue system resulting from the ÿnite di erence approximation. Numerical results of this model are given to demonstrate the convergence and e ectiveness of the methods. Comparison results are also provided to indicate advantages and disadvantages among the various methods.