Tamoxifen is a drug commonly used in the treatment of breast cancer, especially for postmenopausal patients. However, its efficacy is limited by the development of drug resistance. Downregulation of estrogen receptor alpha (ERα) is an important mechanism of tamoxifen resistance. In recent years, with progress in research into the protective autophagy of drug-resistant cells and cell cycle regulators, major breakthroughs have been made in research on tamoxifen resistance. For a better understanding of the mechanism of tamoxifen resistance, protective autophagy, cell cycle regulators, and some transcription factors and enzymes regulating the expression of the estrogen receptor are summarized in this review. In addition, recent progress in reducing resistance to tamoxifen is reviewed. Finally, we discuss the possible research directions into tamoxifen resistance in the future to provide assistance for the clinical treatment of breast cancer.

High-density tactile sensing has been pursued for humanoid robotic hands to obtain contact force information while the elastomer skin cover is traditionally considered to impair the force discrimination. In this work, we try to utilize the diffusion effect of the elastomer cover to identify an arbitrary contact force load just based on a sparse tactile sensor array. By numerical analysis, we proved the monotonous relation between the Pearson’s correlation coefficient and the relative distance of two single-force loads. Then, we meshed the elastomer surface and conducted the calibration load process to establish the calibration database of the sensing outputs. Afterwards, we applied the correlation method to the database and the sensing output of the unknown load to determine its location and intensity. For validation tests of the proposed method, we designed and fabricated a 3 × 3 sparse tactile sensor array with flat elastomer cover and established an automatic three-axis loading system. The validation tests were implemented including 100 random points with force intensity ranging from 0.1 to 1 N. The test results show that the method has good accuracy of detecting force load with the mean location error of 0.46 mm and the mean intensity error of 0.043 N, which meets the basic requirements of tactile sensing. Therefore, it is feasible for the sparse tactile sensor array to realize high-density load detection.

In this letter, an ellipsoid-spherical combined light source structure is presented for the purpose of uniform flux and color mixing, when considering heat dissipation. To allow more rays couple into spherical chamber while keeping illumination uniformity, algebraic equations to estimate light spot size focused by ellipsoidal reflector are derived and then employed to decide input slot size of the chamber. Then Monte Carlo ray tracing simulation suggests that the most preferable output slot size is between 15% and 30% of the chamber radius when considering both uniformity and available test area. A prototype with a 150-W Xenon arc lamp is fabricated to demonstrate heat distribution for the system, and experiment about uniformity indicates that 89.4% of the test circle radius can reach 2% nonuniformity. Color mixing performance for the system is studied by equipped with three ellipsoidal reflectors to collect rays from different color LEDs, and experiment shows that the maximum root-mean-square error of the spectrum across 90% of output circle radius is 1.4%. Results demonstrate it is a structure with simplified design process and having less dependence with light source type.

In order to address the unreasonable distributed corners in single threshold Harris detection and expensive computation cost incurred from image region matching performed by normalized cross correlation (NCC) algorithm, multi-threshold corner detection and region matching algorithm based on texture classification are proposed. Firstly, the input image is split into sub-blocks which are classified into four different categories based on the specific texture: flat, weak, middle texture and strong regions. Subsequently, an algorithm is suggested to decide threshold values for different texture type, and interval calculation for the sub-blocks is performed to improve operation efficiency in the algorithm implementation. Finally, based on different texture characteristics, Census, interval-sampled NCC, and complete NCC are employed to perform image matching. As demonstrated by the experimental results, corner detection based on texture classification is capable to obtain a reasonable corner number as well as a more uniform spatial distribution, when compared to the traditional Harris algorithm. If combined with the interval classification, speedup for texture classification is approximately 30%. In addition, the matching algorithm based on texture classification is capable to improve the speed of 26.9%∼29.9% while maintaining the comparable accuracy of NCC. In general, for better splicing quality, the overall stitching speed is increased by 14.1%∼18.4%. Alternatively, for faster speed consideration, the weak texture region which accounts for a large proportion of an image and provides less effective information can be ignored, for which 23.9%∼28.4% speedup can be achieved at the cost of a 1.9%∼3.9% reduction in corner points. Therefore, the proposed algorithm is made potentially suited to uniformly distributed corner point calculation and high computation efficiency requirement scenarios.

No abstract