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The PALM-COEIN classification for causes of abnormal uterine bleeding (AUB) was proposed by the International Federation of Gynecology and Obstetrics (FIGO) in 2011, which has been gradually applied in the diagnosis of AUB in the past 2 years in China. However, there are no reports yet on the causes of chronic AUB among Chinese women with this new classification system.The purpose of this study was to describe the prevalence of the causes of chronic AUB among Chinese women of reproductive age using the PALM-COIEN classification system.This is a cross-sectional study. Beijing Shijitan Hospital, Capital Medical University.A total of 1053 women aged 15 to 55 years with chronic AUB were evaluated between November 2016 and May 2017.Prevalence of the causes of chronic AUB using the PALM-COEIN classification. AUB-O was the most frequent finding in women with chronic AUB, accounting for 608 (57.7%) cases. AUB-P was found in 171 (16.2%) women, AUB-L in 130 (12%) women, AUB-A in 52 (4.94%) women, AUB-E in 28 (2%) women, AUB-I in 23 (2%) women, AUB-M in 20 (1.9%) women, AUB-C in 10 (1%) women, and AUB-N in 10 (0.9%) women.Ovulatory dysfunction (AUB-O) is the most common cause of AUB among the nonstructural causes. Endometrial polyps (AUB-P) are the most common among the structural causes, followed by uterine fibroids (AUB-L) and uterine adenomyosis (AUB-A).

The three-dimensional high-resolution imaging of rock samples is the basis for pore-scale characterization of reservoirs. Micro X-ray computed tomography (µ-CT) is considered the most direct means of obtaining the three-dimensional inner structure of porous media without deconstruction. The micrometer resolution of µ-CT, however, limits its application in the detection of small structures such as nanochannels, which are critical for fluid transportation. An effective strategy for solving this problem is applying numerical reconstruction methods to improve the resolution of the µ-CT images. In this paper, a convolutional neural network reconstruction method is introduced to reconstruct high-resolution porous structures based on low-resolution µ-CT images and high-resolution scanning electron microscope (SEM) images. The proposed method involves four steps. First, a three-dimensional low-resolution tomographic image of a rock sample is obtained by µ-CT scanning. Next, one or more sections in the rock sample are selected for scanning by SEM to obtain high-resolution two-dimensional images. The high-resolution segmented SEM images and their corresponding low-resolution µ-CT slices are then applied to train a convolutional neural network (CNN) model. Finally, the trained CNN model is used to reconstruct the entire low-resolution three-dimensional µ-CT image. Because the SEM images are segmented and have a higher resolution than the µ-CT image, this algorithm integrates the super-resolution and segmentation processes. The input data are low-resolution µ-CT images, and the output data are high-resolution segmented porous structures. The Yuzhu Wang

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The speech signal is different from the typical audio in terms of spectral bandwidth, intensity distribution, and signal continuity, thus how to achieve high imperceptibility and strong robustness for speech steganography is a big challenge. In this paper, we present a speech steganography scheme based on the parity-segmented method and the differential singular value decomposition (SVD). The selected discrete cosine transform (DCT) coefficients are divided into two segments according to parity order. In this way, the energy of the paired segments is approximately equal, therefore the changes in the singular values caused by data embedding are reduced, and high imperceptibility is achieved. Unlike the common SVD-based steganography, the differential SVD scheme can effectively remove the impact of amplitude scaling attack by embedding the secret message into the difference between the singular values. Experimental results show that the proposed method achieves high imperceptibility and strong robustness while resisting the state-of-the-art steganalytic methods. INDEX TERMS Steganography, differential SVD, paired segments, imperceptibility, amplitude scaling.