Extracellular vesicles transfer nuclear Abl-dependent and radiation-induced miR-34c into unirradiated cells to cause bystander effects.

Ionizing radiation (IR) not only activates DNA damage response (DDR) in irradiated cells but also induces bystander effects (BE) in cells not directly targeted by radiation. How DDR pathways activated in irradiated cells stimulate BE is not well understood. We show here that extracellular vesicles secreted by irradiated cells (EV-IR), but not those from unirradiated controls (EV-C), inhibit colony formation in unirradiated cells by inducing reactive oxygen species (ROS). We found that µEV-IR from Abl nuclear localization signal-mutated ( Abl-µNLS) cells could not induce ROS, but expression of wild-type Abl restored that activity. Because nuclear Abl stimulates miR-34c biogenesis, we measured miR-34c in EV and found that its levels correlated with the ROS-inducing activity of EV. We then showed that EV from miR-34c minigene-transfected, but unirradiated cells induced ROS; and transfection with miR-34c-mimic, without radiation or EV addition, also induced ROS. Furthermore, EV-IR from miR34-family triple-knockout cells could not induce ROS, whereas EV-IR from wild-type cells could cause miR-34c increase and ROS induction in the miR-34 triple-knockout cells. These results establish a novel role for extracellular vesicles in transferring nuclear Abl-dependent and radiation-induced miR-34c into unirradiated cells to cause bystander oxidative stress

A New State-Regularized QRRLS Algorithm with Variable Forgetting Factor

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A novel sliding-mode observer for indirect position sensing of switched reluctance motor drives

A switched reluctance motor (SRM) drive generally requires a rotor position sensor for commutation and current control. However, the use of this position sensor increases both cost and size of the motor drive and causes limitations for industrial applications. In this paper, a novel indirect position sensing technique, namely, the sliding-mode observer, is proposed for SRM drives. The corresponding design approach and operating performance are provided to illustrate the fast convergence and high robustness of the observer against disturbances and variations.published_or_final_versio

A new transform-domain regularized recursive least M-estimate algorithm for a robust linear estimation

This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR-decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better estimation accuracy than direct application of regularization techniques to estimate system coefficients when they are correlated. Furthermore, a QR-based algorithm and an effective adaptive method for selecting regularization parameters are developed for recursive implementation of the TD-R-ME algorithm. Simulation results show that the proposed TD regularized QR recursive least M-estimate (TD-R-QRRLM) algorithm offers improved performance over its least squares counterpart in an impulsive noise environment. Moreover, a TD smoothly clipped absolute deviation R-QRRLM was found to give a better steady-state excess mean square error than other QRRLM-related methods when regression coefficients are correlated. © 2006 IEEE.published_or_final_versio

Stability analysis of fuzzy sliding mode controlled switched reluctance motor drives

This paper presents a small deviant dynamic-state model and the system transfer functions for SRM drive system under different control modes with fuzzy sliding mode controller. With the help of the system open-loop and dosed-loop transfer functions, the operation performances of SRM drive system are discussed. From the characteristics of the transfer functions, it is concluded that the FSMC speed regulator can be regarded as an adaptive PI regulator. The proposed scheme is applied to a 4 kW SRM drive and the system performance is verified by computer simulation results.published_or_final_versio

A new regularized transform-domain NLMS adaptive filtering algorithm

The transform domain normalized LMS (TD-NLMS)-adaptive filtering algorithm is an efficient adaptive filter with fast convergence speed and reasonably low arithmetic complexity. However, it is sensitive to the level of the excitation signal, which may vary significantly over time in speech and audio signals. This paper proposes a new regularized transform domain NLMS (R-TDNLMS) algorithm and studies its mean and mean square convergence performance. The proposed algorithm extends the conventional TDNLMS algorithm by imposing a regularization term on the coefficients to reduce the variance of the estimator. The mean and mean square convergence behaviors of the proposed algorithm are studied to characterize its convergence condition and steady-state excess mean squares error (MSE). It shows that regularization can help to reduce the MSE for coloured inputs by trading slight bias for variance. Moreover, the immunity to varying input signal level is significantly reduced. Computer simulations are conducted to examine the effectiveness of the proposed algorithm and they are in good agreement with the theoretical analysis. © 2010 IEEE.published_or_final_versionThe 2010 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS 2010), Kuala Lumpur, Malaysia, 6-9 December 2010. In Proceedings of APCCAS, 2010, p. 696-69

A new regularized QRD recursive least M-estimate algorithm: Performance analysis and applications

Proceedings of the International Conference on Green Circuits and Systems, 2010, p. 190-195This paper proposes a new regularized QR decomposition based recursive least M-estimate (R-QRRLM) adaptive filtering algorithm and studies its mean and mean square convergence performance and application to acoustic echo cancellation (AEC). The proposed algorithm extends the conventional RLM algorithm by imposing a weighted L2 regularization term on the coefficients to reduce the variance of the estimator. Moreover, a QRD-based algorithm is employed for efficient recursive implementation and improved numerical property. The mean convergence analysis shows that a bias solution to the classical Wiener solution will be introduced due to the regularization. The steady-state excess mean square error (EMSE) is derived and it suggests that the variance will decrease while the bias will increase with the regularization parameter. Therefore, regularization can help to trade bias for variance. In this study, the regularization parameter can be adaptively selected and the resultant variable regularization parameter QRRLM (VR-QRRLM) algorithm can obtain both high immunity to input variation and low steady-state EMSE values. The theoretical results are in good agreement with simulation results. Computer simulation results on AEC show that the R-QRRLM and VR-QRRLM algorithms considerably outperform the traditional RLS algorithm when the input signal level is low or during double talk. © 2010 IEEE.published_or_final_versio

A novel position and velocity observer for robust control ofswitched reluctance motors

A new position and velocity sensorless control algorithm for the switched reluctance motor (SRM) drive is presented, which employs a sliding mode observer technique to estimate the motor speed and rotor position. This paper describes the construction of the observer, proposes the detailed method for designing the feedback gains of the observer and provides a quantitative robustness analysis against disturbances. The proposed sliding observer scheme is applied to a 4 kW SRM drive and the system performance is verified by computer simulation and experimental results.published_or_final_versio

Effects of secondary loudspeaker properties on broadband feedforward active duct noise control

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Preventing cervical cancer: primary and secondary measures

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