Risk factors for habitual snoring among children aged 2-14 years in Chengdu, Sichuan.

PURPOSE: Habitual snoring (HS) is a prominent symptom of sleep-disordered breathing; thus, it is also important to consider the associated, multidimensional risk factors for HS in children. This study was aimed to identify risk factors for HS in children. METHODS: A cross-sectional survey was performed in Chengdu. Children aged 2-14 years from four districts were randomly chosen to participate. Questionnaires were voluntarily completed by the children's guardians. RESULTS: The survey included 926 boys and 622 girls, who were an average of 6.4 ± 3.0 years old. The sample included 463 habitual snorers (30.4%), 683 occasional snorers (44.8%), and 402 nonsnorers (26.4%). HS was found in 51.8% of children under 7 years old and 26.6% of children aged 7 years old or older. The prevalence of pregnancy complications was significantly lower in mothers who bore children with HS (p = .006). Among the HS group, 86.6% had an immediate family member who snores. Breastfeeding duration among the HS group was significantly less than among the occasional snoring and nonsnoring groups. History of symptoms of allergic rhinitis, rhinosinusitis, tonsillitis, and pneumonia/bronchitis in the past 6 months was associated with HS. Likewise, maternal smoking during pregnancy, maternal exposure to secondhand smoke during pregnancy, and child exposure to secondhand smoke were also associated with HS. CONCLUSION: The prevalence of HS was higher in children under 7 years old. Having a mother with more education, a family history of snoring, a shorter period of breastfeeding, upper respiratory tract inflammation, and passive smoking are important risk factors for HS. Pregnancy complications may be associated with lower prevalence of snoring in childhood.

Behavioral and cognitive outcomes of habitual snoring in children aged 2-14 years in Chengdu, Sichuan.

PURPOSE: Habitual snoring is associated with cognitive, behavioral, and other physiological problems of children. Few studies have reported specifically on the relationships between snoring and those problems in children as noticed by their parents. We aimed to identify the cognitive, behavioral, and sleep-related nocturnal problems in children with HS as noted by their parents. MATERIALS AND METHODS: A cross-sectional survey was performed in Chengdu, Sichuan, China. Children aged 2-14 years from four districts were randomly chosen to participate. Questionnaires were completed voluntarily by the children's parents/guardians. RESULTS: A total of 1548 questionnaires were analyzed and classified those children as 463 habitual snorers (HS group, 30.4 %), 683 occasional snorers (OS group, 44.8 %), and 402 non-snorers (NS group, 26.4 %). The percentages of children with sleep-related nocturnal symptoms were 94.6 %, 87.3 %, and 66.9 % in the HS, OS, and NS groups. Percentages of children with cognitive problems were 76.2 %, 74.6 %, and 64.9 % in the HS, OS, and NS groups, respectively (P < 0.001). The frequencies of daytime behavioral problems were 68.3 %, 61.5 %, and 46.8%in the HS, OS, and NS groups, respectively (P < 0.001).The average number of sleep-related nocturnal symptoms, cognitive symptoms and daytime behavioral problems was higher in the HS group than in the OS and NS groups. CONCLUSIONS: HS is a significant contributor to sleep-related nocturnal symptoms and daytime cognitive and behavioral problems in children, as reported by their parents/guardians. HS and OS are important contributors to poor sleep quality and daytime cognitive and behavioral problems in children.

Second-Order Side-Channel Analysis Based on Orthogonal Transform Nonlinear Regression.

In recent years, side-channel analysis technology has been one of the greatest threats to information security. SCA decrypts the key information in the encryption device by establishing an appropriate leakage model. As one of many leakage models, the XOR operation leakage proposed by linear regression has typical representative significance in side-channel analysis. However, linear regression may have the problem of irreversibility of a singular matrix in the modeling stage of template analysis and the problem of poor data fit in the template analysis after the cryptographic algorithm is masked. Therefore, this paper proposes a second-order template analysis method based on orthogonal transformation nonlinear regression. The irreversibility of a singular matrix and the inaccuracy of the model are solved by orthogonal transformation and adding a negative direction to the calculation of the regression coefficient matrix. In order to verify the data fitting effect of the constructed template, a comparative experiment of template analysis based on regression, Gaussian, and clustering was carried out on SAKURA-G. The experimental results show that the second-order template analysis based on orthogonal transformation nonlinear regression can complete key recovery without sacrificing the performance of regression estimation. Under the condition of high noise and high order template analysis, the established template has good universality.

A Three-Dimensional Infinite Collapse Map with Image Encryption.

Chaos is considered as a natural candidate for encryption systems owing to its sensitivity to initial values and unpredictability of its orbit. However, some encryption schemes based on low-dimensional chaotic systems exhibit various security defects due to their relatively simple dynamic characteristics. In order to enhance the dynamic behaviors of chaotic maps, a novel 3D infinite collapse map (3D-ICM) is proposed, and the performance of the chaotic system is analyzed from three aspects: a phase diagram, the Lyapunov exponent, and Sample Entropy. The results show that the chaotic system has complex chaotic behavior and high complexity. Furthermore, an image encryption scheme based on 3D-ICM is presented, whose security analysis indicates that the proposed image encryption scheme can resist violent attacks, correlation analysis, and differential attacks, so it has a higher security level.

Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm.

Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.

Role for Class I histone deacetylases in multidrug resistance.

Recent reports have showed that histone deacetylase (HDAC) inhibitor resulted in multidrug resistance (MDR) to other chemotherapeutic agents. However, the molecular mechanisms of Class I HDACs on MDR regulation are poorly understood. In this study, HDAC1 and HDAC2 acted as enhancers to intensify the chemosensitivities of anti-cancer drugs via reducing the expression levels of P-gp, MRP1 and MRP2. Furthermore, the dissociation of HDAC1 and HDAC2 led to transcriptional regulation of P-gp expression via the recruitment of p300, PCAF and NF-Y to the P-gp promoter region, which subsequently increased the level of the active gene marker, hyperacetylated histone H3. In parallel, selective inhibition of HDAC1 and HDAC2 induced the recruitment of p300, PCAF, NF-Y via acetylation of Sp1. Thus, our findings showed HDAC1 and 2 regulated P-gp expression through dynamic changes in chromatin structure and transcription factor association within the promoter region.

Self-supervised learning mechanism for identification of eyelid malignant melanoma in pathologic slides with limited annotation.

Purpose: The lack of finely annotated pathologic data has limited the application of deep learning systems (DLS) to the automated interpretation of pathologic slides. Therefore, this study develops a robust self-supervised learning (SSL) pathology diagnostic system to automatically detect malignant melanoma (MM) in the eyelid with limited annotation. Design: Development of a self-supervised diagnosis pipeline based on a public dataset, then refined and tested on a private, real-world clinical dataset. Subjects: A. Patchcamelyon (PCam)-a publicly accessible dataset for the classification task of patch-level histopathologic images. B. The Second Affiliated Hospital, Zhejiang University School of Medicine (ZJU-2) dataset - 524,307 patches (small sections cut from pathologic slide images) from 192 H&E-stained whole-slide-images (WSIs); only 72 WSIs were labeled by pathologists. Methods: Patchcamelyon was used to select a convolutional neural network (CNN) as the backbone for our SSL-based model. This model was further developed in the ZJU-2 dataset for patch-level classification with both labeled and unlabeled images to test its diagnosis ability. Then the algorithm retrieved information based on patch-level prediction to generate WSI-level classification results using random forest. A heatmap was computed for visualizing the decision-making process. Main outcome measures: The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the algorithm in identifying MM. Results: ResNet50 was selected as the backbone of the SSL-based model using the PCam dataset. This algorithm then achieved an AUC of 0.981 with an accuracy, sensitivity, and specificity of 90.9, 85.2, and 96.3% for the patch-level classification of the ZJU-2 dataset. For WSI-level diagnosis, the AUC, accuracy, sensitivity, and specificity were 0.974, 93.8%, 75.0%, and 100%, separately. For every WSI, a heatmap was generated based on the malignancy probability. Conclusion: Our diagnostic system, which is based on SSL and trained with a dataset of limited annotation, can automatically identify MM in pathologic slides and highlight MM areas in WSIs by a probabilistic heatmap. In addition, this labor-saving and cost-efficient model has the potential to be refined to help diagnose other ophthalmic and non-ophthalmic malignancies.

A Self-Supervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images.

Eyelid malignant melanoma (MM) is a rare disease with high mortality. Accurate diagnosis of such disease is important but challenging. In clinical practice, the diagnosis of MM is currently performed manually by pathologists, which is subjective and biased. Since the heavy manual annotation workload, most pathological whole slide image (WSI) datasets are only partially labeled (without region annotations), which cannot be directly used in supervised deep learning. For these reasons, it is of great practical significance to design a laborsaving and high data utilization diagnosis method. In this paper, a self-supervised learning (SSL) based framework for automatically detecting eyelid MM is proposed. The framework consists of a self-supervised model for detecting MM areas at the patch-level and a second model for classifying lesion types at the slide level. A squeeze-excitation (SE) attention structure and a feature-projection (FP) structure are integrated to boost learning on details of pathological images and improve model performance. In addition, this framework also provides visual heatmaps with high quality and reliability to highlight the likely areas of the lesion to assist the evaluation and diagnosis of the eyelid MM. Extensive experimental results on different datasets show that our proposed method outperforms other state-of-the-art SSL and fully supervised methods at both patch and slide levels when only a subset of WSIs are annotated. It should be noted that our method is even comparable to supervised methods when all WSIs are fully annotated. To the best of our knowledge, our work is the first SSL method for automatic diagnosis of MM at the eyelid and has a great potential impact on reducing the workload of human annotations in clinical practice.

Inhibition of bone metastasis from breast carcinoma by rosmarinic acid.

Skeletal disorders are a common complication of breast cancer and will be found in the vast majority of women with metastatic disease. Our study showed that rosmarinic acid (RA) could inhibit the migration of MDA-MB-231BO human bone-homing breast cancer cells dose-dependently. Furthermore, in ST-2 murine bone marrow stromal cells cultured with RA there was a significant and dose-dependent increase in alkaline phosphatase (ALP) activity, with the number and size of mineralized nodules increasing. According to Western blot and quantitative real-time PCR assay, RA may inhibit bone metastasis from breast carcinoma mainly via the pathway of the receptor activator of NF kappaB ligand (RANKL)/RANK/osteoprotegerin (OPG) and by simultaneously suppressing the expression of interleukin-8 (IL-8). RA may thus be a good candidate for a new therapeutic approach in bone metastasis from breast carcinoma.

Effects of Six Commercial Saccharomyces cerevisiae Strains on Phenolic Attributes, Antioxidant Activity, and Aroma of Kiwifruit (Actinidia deliciosa cv.) Wine.

"Hayward" kiwifruit (Actinidia deliciosa cv.), widely planted all around the world, were fermented with six different commercial Saccharomyces cerevisiae strains (BM4×4, RA17, RC212, WLP77, JH-2, and CR476) to reveal their influence on the phenolic profiles, antioxidant activity, and aromatic components. Significant differences in the levels of caffeic acid, protocatechuate, and soluble solid content were found among wines with the six fermented strains. Wines fermented with RC212 strain exhibited the highest total phenolic acids as well as DPPH radical scavenging ability and also had the strongest ability to produce volatile esters. Wines made with S. cerevisiae BM 4×4 had the highest content of volatile acids, while the highest alcohol content was presented in CR476 wines. Scoring spots of wines with these strains were separated in different quadrants on the components of phenolics and aromas by principal component analyses. Kiwifruit wines made with S. cerevisiae RC212 were characterized by a rich fruity flavor, while CR476 strain and WLP77 strain produced floral flavors and green aromas, respectively. Altogether, the results indicated that the use of S. cerevisiae RC212 was the most suitable for the fermentation of kiwifruit wine with desirable characteristics.