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Metastasis accounts for almost 90% of breast cancer-related fatalities, making it frequent malignancy and the main reason of tumor mortality globally among women. LSD1 is a histone demethylase, which plays an important role in breast cancer. In order to explore the effect of LSD1 on invasion and migration of breast cancer, we treated breast cancer cells with MCF7 and T47D exosomes knocked down by LSD1, and the invasion and migration of breast cancer cells were significantly enhanced. This phenomenon indicates that LSD1 can inhibit the invasion and migration of breast cancer cells. miR-1290 expression was downregulated in LSD1 knockdown MCF7 exosomes. By analyzing the database of miR-1290 target gene NAT1, we verified that miR-1290 could regulate the expression of NAT1. These data provide fresh insights into the biology of breast cancer therapy by demonstrating how the epigenetic factor LSD1 stimulates the breast cancer cells' invasion and migration via controlling exosomal miRNA. [ABSTRACT FROM AUTHOR]

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds. [ABSTRACT FROM AUTHOR]

To enhance the microhardness and wear resistance of low-carbon steel, laser cladding was employed to create A100-Y2O3 cladding coatings that remained free of cracks. The phase composition, microstructure, and element distribution of these coatings were examined using XRD and SEM analyses, respectively. The microhardness and wear resistance of the A100-Y2O3 cladding coatings were tested by an HXS-1000 A type digital liquid crystal intelligent microhardness tester and an ML-10 friction and wear tester, respectively. The XRD results show that the addition of Y2O3 did not change the phase composition of the A100-Y2O3 cladding coatings. With the addition of Y2O3, the grains of the A100-Y2O3 cladding coatings are finer compared with those of the A100-0%Y2O3 cladding coating. The upper part of the A100-Y2O3 cladding coatings were composed of fine equiaxed grains. The average microhardness of the A100-0%Y2O3 cladding coatings was 532.489 HV. With the addition of Y2O3, the microhardness of the A100-Y2O3 cladding coatings was obviously improved, and the average microhardness of A100-1.5%Y2O3 coating reached 617.290 HV. The A100-Y2O3 cladding coatings were reduced, and the worn surface became relatively smooth owing to the addition of Y2O3. The addition of Y2O3 significantly improved the wear resistance of the A100-Y2O3 cladding coatings. [ABSTRACT FROM AUTHOR]

The waveguide structure made of lithium niobate crystal (LiNbO3, LN) can further improve the device integration. It has been widely used in electro-optical modulator, frequency conversion, acousto-optic Q-switching and other optoelectronic devices. It has important application prospects in optical fiber communication, photoelectric sensing, lidar, aerospace and other fields. The LN waveguide made by traditional Ti diffusion method has poor resistance to photorefractive damage in short wave applications. The LN waveguide made by annealing proton exchange method only support TM mode (transverse magnetic mode) single polarization transmission, and its application field is limited. In this paper, a new Zn diffusion method is proposed to fabricate magnesium doped LN ridge waveguide. By establishing the diffusion model and simulation of the waveguide, the technological conditions were explored and tested. The lowest transmission loss of LN waveguide is 0.86 dB/cm, and the photorefractive damage threshold can reach 184 kW/cm². It will provide a better preparation way for the research and development of high power lithium niobate waveguide integrated optoelectronic devices. [ABSTRACT FROM AUTHOR]

CT and MR are currently the most common imaging techniques for pancreatic cancer diagnosis. Accurate segmentation of the pancreas in CT and MR images can provide significant help in the diagnosis and treatment of pancreatic cancer. Traditional supervised segmentation methods require a large number of labeled CT and MR training data, which is usually time-consuming and laborious. Meanwhile, due to domain shift, traditional segmentation networks are difficult to be deployed on different imaging modality datasets. Cross-domain segmentation can utilize labeled source domain data to assist unlabeled target domains in solving the above problems. In this paper, a cross-domain pancreas segmentation algorithm is proposed based on Moment-Consistent Contrastive Cycle Generative Adversarial Networks (MC-CCycleGAN). MC-CCycleGAN is a style transfer network, in which the encoder of its generator is used to extract features from real images and style transfer images, constrain feature extraction through a contrastive loss, and fully extract structural features of input images during style transfer while eliminate redundant style features. The multi-order central moments of the pancreas are proposed to describe its anatomy in high dimensions and a contrastive loss is also proposed to constrain the moment consistency, so as to maintain consistency of the pancreatic structure and shape before and after style transfer. Multi-teacher knowledge distillation framework is proposed to transfer the knowledge from multiple teachers to a single student, so as to improve the robustness and performance of the student network. The experimental results have demonstrated the superiority of our framework over state-of-the-art domain adaptation methods.