Ecological factors associated with child sexual abuse among 15- to 17-year-old adolescents in mainland China: implications for intervention.

Background: Child sexual abuse is a major public health problem with adverse consequences for victims' physical, mental, and reproductive health. This cross-sectional study aimed to determine the prevalence of child sexual abuse and its associated factors among 15- to 17-year-old adolescents in mainland China. Methods: From September 8, 2019 to January 17, 2020, a total of 48,660 participants were recruited by 58 colleges and universities across the whole country to complete the self-administered, structured, online questionnaire. This analysis was restricted to 3,215 adolescents aged between 15 and 17 years in mainland China. Chi-square tests and multivariate Logistic regression analyses were performed to identify individual, relationship, and community factors associated with child sexual abuse. Results: The overall prevalence of child sexual abuse was 12.0%. More specifically, 13.0% of girls and 10.6% of boys reported that they were sexually abused prior to 18 years of age. At the individual level, being female, sexual minority identity, younger age, and higher levels of knowledge, skills and self-efficacy regarding condom use were significantly related to increased odds of reporting sexual abuse. At the relationship and community level, adolescents from disrupted families and those entering into a marriage, having casual sexual partners, and having first intercourse at a younger age were more likely to report sexual abuse. On the contrary, those who had never discussed sex-related topics with their family members at home and were offered school-based sexuality education later (vs. earlier) were less likely to report sexual abuse. Conclusion: Multilevel prevention programs and strategies, including targeting adolescents with high-risk characteristics, educating young children and their parents about child sexual abuse prevention and optimizing the involvement of parents, school, community, society and government in comprehensive sexuality education, should be taken to reduce child sexual abuse among 15- to 17-year-old adolescents.

TransU²-Net: An Effective Medical Image Segmentation Framework Based on Transformer and U²-Net.

BACKGROUND: In the past few years, U-Net based U-shaped architecture and skip-connections have made incredible progress in the field of medical image segmentation. U2-Net achieves good performance in computer vision. However, in the medical image segmentation task, U2-Net with over nesting is easy to overfit. PURPOSE: A 2D network structure TransU2-Net combining transformer and a lighter weight U2-Net is proposed for automatic segmentation of brain tumor magnetic resonance image (MRI). METHODS: The light-weight U2-Net architecture not only obtains multi-scale information but also reduces redundant feature extraction. Meanwhile, the transformer block embedded in the stacked convolutional layer obtains more global information; the transformer with skip-connection enhances spatial domain information representation. A new multi-scale feature map fusion strategy as a postprocessing method was proposed for better fusing high and low-dimensional spatial information. RESULTS: Our proposed model TransU2-Net achieves better segmentation results, on the BraTS2021 dataset, our method achieves an average dice coefficient of 88.17%; Evaluation on the publicly available MSD dataset, we perform tumor evaluation, we achieve a dice coefficient of 74.69%; in addition to comparing the TransU2-Net results are compared with previously proposed 2D segmentation methods. CONCLUSIONS: We propose an automatic medical image segmentation method combining transformers and U2-Net, which has good performance and is of clinical importance. The experimental results show that the proposed method outperforms other 2D medical image segmentation methods. Clinical Translation Statement: We use the BarTS2021 dataset and the MSD dataset which are publicly available databases. All experiments in this paper are in accordance with medical ethics.

Prevalence of and factors associated with unintended pregnancies among sexually active undergraduates in mainland China.

BACKGROUND: Unintended pregnancies (UIP) among unmarried sexually active college students in mainland China have emerged as a major reproductive health issue with detrimental personal and socioeconomic consequences. This cross-sectional study aimed to determine the prevalence and factors associated with UIP among sexually active undergraduates in mainland China. METHODS: Between September 8, 2019 and January 17, 2020, a total of 48,660 participants were recruited across the Chinese mainland to complete the self-administered, structured, online questionnaire. This analysis was restricted to 6347 sexually experienced, never-married 15-26 year old undergraduates. Pearson's Chi square tests and multivariate Logistic regression analyses were performed to identify sociodemographic, familial and individual variables associated with UIP. RESULTS: The overall prevalence of UIP was 17.7%. More specifically, 19.5% of male college students reported they had unintentionally gotten a partner pregnant, while 14.9% of female college students became unintentionally pregnant. Students who experienced UIP were more likely to belong to the older age group (23-26 years), live with only one parent or live without parents at home, report that their family members approve of premarital sex, initiate sexual activity younger than 14 years old and have casual sex partners. Furthermore, females with multiple partners and males who came from low- income households, experienced sexual abuse, perceived difficulties in acquiring condoms and did not know how to use condoms correctly were also at higher risk of experiencing an unintended pregnancy. CONCLUSION: In order to prevent UIP, a comprehensive intervention measure should be taken to target older students and those engaging in risky sexual behaviors, work with young male students to improve condom use skills, improve the availability of free condoms, optimize the involvement of parents and other family members in their children's sex education.

In this study, we aimed to determine the prevalence and factors associated with UIP among sexually active undergraduates in mainland China. Between September 8, 2019 and January 17, 2020, a total of 48,660 participants were recruited from the Chinese mainland to complete the self-administered, structured, online questionnaire. This analysis was restricted to 6347 sexually experienced, never-married 15­26 year old undergraduates. Based on a social-ecological theoretical framework, we ran separate multivariate Logistic regression models for men and women to identify sociodemographic, familial and individual variables associated with UIP. Our findings indicate that the overall prevalence of UIP was 17.7%. More specifically, 19.5% of male college students reported they had unintentionally gotten a partner pregnant, while 14.9% of female college students became unintentionally pregnant. Students who experienced UIP were more likely to belong to the older age group, live with only one parent or live without parents at home, report that their family members approve of premarital sex, initiate sexual activity younger than 14 years old and have casual sex partners. Furthermore, females with multiple partners and males who came from low-income households, experienced sexual abuse, perceived difficulties in acquiring condoms and did not know how to use condoms correctly were also at higher risk of having an unintended pregnancy. In order to prevent UIP, a comprehensive intervention measure should be taken to target older students and those engaging in risky sexual behaviors, work with young male students to improve condom use skills, improve the availability of free condoms, optimize the involvement of parents and other family members in their children's sex education.

Generating adversarial examples without specifying a target model.

Adversarial examples are regarded as a security threat to deep learning models, and there are many ways to generate them. However, most existing methods require the query authority of the target during their work. In a more practical situation, the attacker will be easily detected because of too many queries, and this problem is especially obvious under the black-box setting. To solve the problem, we propose the Attack Without a Target Model (AWTM). Our algorithm does not specify any target model in generating adversarial examples, so it does not need to query the target. Experimental results show that it achieved a maximum attack success rate of 81.78% in the MNIST data set and 87.99% in the CIFAR-10 data set. In addition, it has a low time cost because it is a GAN-based method.

Adversarial example defense based on image reconstruction.

The rapid development of deep neural networks (DNN) has promoted the widespread application of image recognition, natural language processing, and autonomous driving. However, DNN is vulnerable to adversarial examples, such as an input sample with imperceptible perturbation which can easily invalidate the DNN and even deliberately modify the classification results. Therefore, this article proposes a preprocessing defense framework based on image compression reconstruction to achieve adversarial example defense. Firstly, the defense framework performs pixel depth compression on the input image based on the sensitivity of the adversarial example to eliminate adversarial perturbations. Secondly, we use the super-resolution image reconstruction network to restore the image quality and then map the adversarial example to the clean image. Therefore, there is no need to modify the network structure of the classifier model, and it can be easily combined with other defense methods. Finally, we evaluate the algorithm with MNIST, Fashion-MNIST, and CIFAR-10 datasets; the experimental results show that our approach outperforms current techniques in the task of defending against adversarial example attacks.

An intrusion detection algorithm for sensor network based on normalized cut spectral clustering.

Sensor network intrusion detection has attracted extensive attention. However, previous intrusion detection methods face the highly imbalanced attack class distribution problem, and they may not achieve a satisfactory performance. To solve this problem, we propose a new intrusion detection algorithm based on normalized cut spectral clustering for sensor network in this paper. The main aim is to reduce the imbalance degree among classes in an intrusion detection system. First, we design a normalized cut spectral clustering to reduce the imbalance degree between every two classes in the intrusion detection data set. Second, we train a network intrusion detection classifier on the new data set. Finally, we do extensive experiments and analyze the experimental results in detail. Simulation experiments show that our algorithm can reduce the imbalance degree among classes and reserves the distribution of the original data on the one hand, and improve effectively the detection performance on the other hand.

Efficacy of Ezetimibe/Simvastatin (10/10 mg) versus High Dose Statin in Dyslipidemia Patients: A Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: The monotherapies of statin and ezetimibe had not successfully achieved their objectives in the management of lipid levels of dyslipidemia patients. We aimed to compare the effects of combined low-dose simvastatin and ezetimibe versus high-dose statin on the lipid-lowering treatment of dyslipidemia patients. METHODS: We searched five databases published before May 2018, namely PubMed, EMBASE, Cochrane, Web of Science, and Clinicaltrials.gov. Completely published randomized controlled trials (RCTs) comparing the effect of high-dose statin (S) with ezetimibe/simvastatin (10/10 mg; E/S) on the management of dyslipidemia patients were included. RESULTS: A total of ten RCTs met the inclusion criteria, including 1,624 patients (E/S:691, S:933). Six outcomes underwent pooled analysis, including weighted mean difference (WMD) from baseline in total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), high sensitivity C-reactive protein (hs-CRP), triglyceride (TG), and non-high-density lipoprotein cholesterol (non-HDL-C). No significant gap was found between high-dose statin and ezetimibe/simvastatin (10/10 mg) in LDL-C (-1.55; 95% confidence interval [CI]: -4.42∼1.31, P=0 .29), HDL-C (1.05; 95%CI: -0.21∼2.3, P=0 .1), TG (4.03; 95%CI: -4.53∼12.58, P=0.36), and hs-CRP (0.14; 95%CI: -0.50∼0.78, P=0.67). However, there was significant difference found between the two lipid-lowering treatments in TC (-0.45; 95%CI: -9.07∼-0.83, P=0.02) and non-HDL-C (-4.97; 95%CI -8.46∼-1.49, P=0.005). CONCLUSION: Ezetimibe co-administered with simvastatin (10 mg) and high-dose statin monotherapy may show similar effects in reducing LDL-C, TG, and hs-CRP levels and in increasing HDL-C levels. However, the results suggest that there was greater TC and non-HDL-C lowering through high-dose statin monotherapy as compared with ezetimibe/simvastatin co-administration.