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Defect detection of petrochemical pipelines is an important task for industrial production safety. At present, pipeline defect detection mainly relies on closed circuit television method (CCTV) to take video of the pipeline inner wall and then detect the defective area manually, so the detection is very time-consuming and has a high rate of false and missed detections. To solve the above issues, we proposed an automatic defect detection system for petrochemical pipeline based on Cycle-GAN and improved YOLO v5. Firstly, in order to create the pipeline defect dataset, the original pipeline videos need pre-processing, which includes frame extraction, unfolding, illumination balancing, and image stitching to create coherent and tiled pipeline inner wall images. Secondly, aiming at the problems of small amount of samples and the imbalance of defect and non-defect classes, a sample enhancement strategy based on Cycle-GAN is proposed to generate defect images and expand the data set. Finally, in order to detect defective areas on the pipeline and improve the detection accuracy, a robust defect detection model based on improved YOLO v5 and Transformer attention mechanism is proposed, with the average precision and recall as 93.10% and 90.96%, and the F1-score as 0.920 on the test set. The proposed system can provide reference for operators in pipeline health inspection, improving the efficiency and accuracy of detection.

ObjectiveThe prevalence of chlamydia infections in China is rapidly increasing, which may lead to substantial economic burden. However, studies on the economic burden of chlamydia infections in China are limited. This study aims to measure the economic burden of outpatient chlamydia infections in Guangdong Province, China.DesignData on chlamydia outpatient costs were collected through outpatient medical record systems in healthcare facilities and a cross-sectional survey.SettingOutpatient departments of 15 hospitals in Guangdong Province, China.ParticipantsPatients diagnosed with chlamydia at the investigated medical institutions between 1 January 2021, and 31 December 2023.Main outcome measuresThe direct economic burden (medical costs+non-medical costs) was assessed using a bottom-up approach, productivity loss was evaluated through the human capital approach and the intangible burden was assessed by the contingency valuation method.ResultsThe annual per capita (per diagnosed outpatient case of chlamydia infection) medical costs were $37.74 (95% CI 35.38 to 40.10), with costs for patients with a co-diagnosis of pelvic inflammatory disease (PID) reaching $136.23 (95% CI 118.15 to 154.31). Annual per capita non-medical costs were $5.68 (95% CI 4.06 to 7.30) and productivity losses were $30.70 (95% CI 27.52 to 33.88). Ultimately, the per capita direct economic burden for outpatients with chlamydia was $43.42, and the total economic burden was $74.12. The per capita intangible cost was $132.49 (95% CI 110.12 to 154.86). Following the adjustment for covariates, medical costs were significantly associated with a co-diagnosis of PID (adjusted OR (aOR) = 8.07, 95% CI 5.23 to 12.31). The intangible cost was associated with urinary tract symptoms (aOR=1.56, 95% CI 1.01 to 2.42), abnormal vaginal bleeding (aOR=2.44, 95% CI 1.05 to 5.64), and high-risk sexual behaviour (aOR=5.63, 95% CI 1.63 to 17.63).ConclusionIn Southern China, chlamydia infections complicated by PID entail a markedly greater medical cost than uncomplicated infections. Effective prevention and treatment strategies are needed to prevent the progression of chlamydia infections to PID and thereby reduce the associated economic burden.

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.

Background Sexual uses of alcohol and drugs are pervasive among men who have sex with men (MSM) and associated with increased risk of HIV infection. However, there are limited studies related to sexual uses of alcohol and drugs among MSM in China. This study aims to describe the pattern of alcohol use, drug use, and multi-drug use during sex among Chinese MSM and to examine the association between condomless anal intercourse, group sex, commercial sex and HIV infection. Methods We conducted an online cross-sectional survey in China. Characteristics on social-demographic, sexual behaviors, and sexual uses of alcohol and drugs were collected. The associations with high-risk sexual behaviors and HIV infection were analyzed with multivariable logistic regression. Results A total of 699 MSM were included in this study. About 39.5% (230/582) of men reported sexual alcohol use in the past three months and 50.8% (355/699) reported sexual drug use. Of those reporting sexual drug use, around 10.7% (38/355) reported having multi-drug use. Factors associated with both sexual uses of alcohol and drugs included: reporting more male sexual partners (alcohol: adjusted odds ratio [ aOR ] = 1.77; drug: aOR  = 2.12), reporting condomless anal intercourse in the past three months (alcohol: aOR  = 2.08; drug: aOR  = 2.08), having ever engaged in group sex (alcohol: aOR  = 2.04; drug: aOR  = 5.22; multi-drug: aOR  = 3.52) and commercial sex (alcohol: aOR  = 4.43; drug: aOR  = 4.22 multi-drug: aOR  = 5.07). Sexual drug use was also correlated with reported HIV-positive status (drug: aOR  = 2.53, 95% CI :1.31–4.90). Conclusion Sexual uses of alcohol and drugs are prevalent among Chinese MSM. Interventions to reduce the sexual use of alcohol and other drugs may be warranted among MSM in China.

Background Chlamydia and gonorrhea notifications are rapidly rising in men who have sex with men (MSM). Currently, there are limited data on the prevalence of chlamydia and gonorrhea across various anatomical sites. Our study aimed to explore the prevalence, association and changing trends of urethral and rectal chlamydia and gonorrhea among MSM in Guangdong Province, China. Methods We analyzed data among MSM attending sexually transmitted infections (STI) clinics in the Guangdong governmental sentinel network between 2018 and 2022. Chi-square tests were used to compare the difference, Join-point regressions for analyzing changing trends, and multivariate logistic regressions for examining associated factors. Results We included 4856 men in the analysis. Rectal chlamydia significantly increased from 13.8% to 26.4% over the past 5 years (average annual percentage change [AAPC] 19.2%, 95%CI 1.0-40.6, p  = 0.043). After adjusting for covariates, chlamydia infection positively associated with main venue used to seek sexual partners (aOR = 2.31, 95%CI 1.17–4.55), having regular sexual partners in the past 6 months (aOR = 3.32, 95%CI 1.95–5.64), receiving HIV counselling and testing services (aOR = 2.94, 95%CI 1.67–5.17), receiving peer education (aOR = 1.80, 95%CI 1.14–2.83), infection with syphilis (aOR = 2.02, 95%CI 1.02–4.01) and infection with gonorrhea (aOR 7.04, 95% CI 3.01–16.48). Gonorrhea infection positively associated with having regular sexual partners in the past 6 months (aOR = 3.48.95%CI 1.16–10.49), and infection with chlamydia (aOR 7.03, 95% CI 2.99–16.51). Conclusions To conclude, our findings reveal a high prevalence of chlamydia infections among MSM, particularly in the rectal area. Comprehensive chlamydia and gonorrhea health services are necessary for MSM to improve sexual health.

Hyperspectral anomaly detection aims to separate anomalies and backgrounds without prior knowledge. The collaborative representation (CR)-based hyperspectral anomaly detection methods have gained significant interest and development because of their interpretability and high detection rate. However, the traditional CR presents a low utilization rate for deep latent features in hyperspectral images, making the dictionary construction and the optimization of weight matrix sub-optimal. Due to the excellent capacity of neural networks for generation, we formulate the deep learning-based method into CR optimization in both global and local streams, and propose a novel hyperspectral anomaly detection method based on collaborative representation neural networks (CRNN) in this paper. In order to gain a complete background dictionary and avoid the pollution of anomalies, the global dictionary is collected in the global stream by optimizing the dictionary atom loss, while the local background dictionary is obtained by using a sliding dual window. Based on the two dictionaries, our two-stream networks are trained to learn the global and local representation of hyperspectral data by optimizing the objective function of CR. The detection result is calculated by the fusion of residual maps of original and represented data in the two streams. In addition, an autoencoder is introduced to obtain the hidden feature considered as the dense expression of the original hyperspectral image, and a feature extraction network is concerned to further learn the comprehensive features. Compared with the shallow learning CR, the proposed CRNN learns the dictionary and the representation weight matrix in neural networks to increase the detection performance, and the fixed network parameters instead of the complex matrix operations in traditional CR bring a high inference efficiency. The experiments on six public hyperspectral datasets prove that our proposed CRNN presents the state-of-the-art performance.

Background Chlamydia is common among women of reproductive age and can cause serious health issues. This study aimed to examine the trends and factors linked to newly diagnosed and reported chlamydia cases in women aged 15–49 in Guangdong Province from 2006 to 2020. Methods We included all newly diagnosed and reported chlamydia cases from January 1, 2006, to December 31, 2020. Data from 21 cities in Guangdong Province were sourced from the National Notifiable Infectious Disease Reporting Information System in China. Temporal trends were analyzed using Joinpoint regression models. City-level factors (population density, net migration rate, and male-to-female sex ratio) were derived from the Guangdong Statistical Yearbook and the Guangdong Health and Family Planning Statistical Yearbook. Quasi-Poisson regression models were used to explore the relationship between sociodemographic factors and chlamydia incidence. Results From 2006 to 2020, 523,367 new chlamydia cases were reported among women of reproductive age in Guangdong. The mean reported rate was 122.6 per 100,000 population over 15 years, significantly increasing from 1.4 in 2006 to 179.7 in 2020 (average annual percent change [AAPC] = 47.4%, 95% CI: 42.8%-52.2%, P  < 0.05). The highest rate was 196.8 per 100,000 population in 2019. Among older women, 9,045 cases were reported, with a mean reported rate of 4.9 per 100,000, rising significantly from 0.01 in 2006 to 9.6 in 2020 (AAPC = 52.6%, 95% CI: 30.3%-78.8%, P  < 0.05). The reported rate among women of reproductive age correlated with the net migration rate ( RR  = 1.2; 95% CI: 1.2–1.3) and the ratio of those participating in child-bearing insurance to the permanent population ( RR  = 1.5; 95% CI: 1.4–1.6). Conclusion The reported rate of new chlamydia cases among women of reproductive age was significantly higher than among older adults and increased markedly from 2006 to 2020. These findings underscore the urgent need for targeted prevention strategies for women of reproductive age.

As oncogenes and tumor suppressor genes, long non-coding RNAs (lncRNAs) regulate the biological behavior of gastric cancer (GC) cells such as proliferation, invasion, and metastasis through various signal pathways. At present, although numerous lncRNAs that significantly influence the development and progression of GC have been identified, a considerable number of them have not been found and studied yet. In this study, we identified a new lncRNA derived from pseudogenes WFDC21P, which have not been reported in any previous GC study. LncRNA WFDC21P was significantly upregulated in GC cells and tissues, and clinically associated with the pathological stages of advanced GC. WFDC21P promoted proliferation and metastasis of GC cells both in vitro and in vivo. LncRNA WFDC21P was directly bound to GTPase Ran and it promoted the activity of the Akt/GSK3β/β-catenin pathway. Forkhead Box P3 (FOXP3), as a transcription factor of WFDC21P, was directly bound to the promoter region and it positively regulated the transcription of WFDC21P. This finding may provide a novel biomarker and therapeutic target for GC.

Hyperspectral anomaly detection is a popular research direction for hyperspectral images; however, it is problematic because it separates the background and anomaly without prior target information. Currently, deep neural networks are used as an extractor to mine intrinsic features in hyperspectral images, which can be fed into separate anomaly detection methods to improve their performances. However, this hybrid approach is suboptimal because the subsequent detector is unable to drive the data representation in hidden layers, which makes it a challenge to maximize the capabilities of deep neural networks when extracting the underlying features customized for anomaly detection. To address this issue, a novel unsupervised, self-attention-based, one-class neural network (SAOCNN) is proposed in this paper. SAOCNN consists of two components: a novel feature extraction network and a one-class SVM (OC-SVM) anomaly detection method, which are interconnected and jointly trained by the OC-SVM-like loss function. The adoption of co-training updates the feature extraction network together with the anomaly detector, thus improving the whole network’s detection performance. Considering that the prominent feature of an anomaly lies in its difference from the background, we designed a deep neural extraction network to learn more comprehensive hyperspectral image features, including spectral, global correlation, and local spatial features. To accomplish this goal, we adopted an adversarial autoencoder to produce the residual image with highlighted anomaly targets and a suppressed background, which is input into an improved non-local module to adaptively select the useful global information in the whole deep feature space. In addition, we incorporated a two-layer convolutional network to obtain local features. SAOCNN maps the original hyperspectral data to a learned feature space with better anomaly separation from the background, making it possible for the hyperplane to separate them. Our experiments on six public hyperspectral datasets demonstrate the state-of-the-art performance and superiority of our proposed SAOCNN when extracting deep potential features, which are more conducive to anomaly detection.

Background Gastric cancer (GC) is a leading cause of cancer deaths, and an increased number of GC patients adopt to next-generation sequencing (NGS) to identify tumor genomic alterations for precision medicine. Methods In this study, we established a hybridization capture-based NGS panel including 612 cancer-associated genes, and collected sequencing data of tumors and matched bloods from 153 gastric cancer patients. We performed comprehensive analysis of these sequencing and clinical data. Results 35 significantly mutated genes were identified such as TP53 , AKAP9 , DRD2 , PTEN , CDH1 , LRP2 et al. Among them, 29 genes were novel significantly mutated genes compared with TCGA study. TP53 is the top frequently mutated gene, and tends to mutate in male (p = 0.025) patients and patients whose tumor located in cardia (p = 0.011). High tumor mutation burden (TMB) gathered in TP53 wild-type tumors (p = 0.045). TMB was also significantly associated with DNA damage repair (DDR) genes genotype (p = 0.047), Lauren classification (p = 1.5e−5), differentiation (1.9e−7), and HER2 status (p = 0.023). 38.31% of gastric cancer patients harbored at least one actionable alteration according to OncoKB database. Conclusions We drew a comprehensive mutational landscape of 153 gastric tumors and demonstrated utility of target next-generation sequencing to guide clinical management.