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  • Hindawi Limited  (14)
  • Tang, Chao  (14)
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  • Hindawi Limited  (14)
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  • 1
    Online Resource
    Online Resource
    Chitosan-Based Nanogel Enhances Chemotherapeutic Efficacy of 10-Hydroxycamptothecin against Human Breast Cancer Cells
    Hindawi Limited ; 2019
    In:  International Journal of Polymer Science Vol. 2019 ( 2019-04-04), p. 1-6
    Details
    In: International Journal of Polymer Science, Hindawi Limited, Vol. 2019 ( 2019-04-04), p. 1-6
    Abstract: Chitosan (CS), the second most abundant polysaccharide in nature, has been widely developed as a nanoscopic drug delivery vehicle due to its intriguing characteristics. In this work, a positively charged CS-based nanogel was designed and synthesized to inhibit the proliferation of breast cancer cell lines. The model drug of 10-hydroxycamptothecin (HCPT) was entrapped into the core via a facile diffusion to form CS/HCPT. The characteristics of CS/HCPT were evaluated by assessing particle size, drug loading content, and drug loading efficiency. Furthermore, cell internalization, cytotoxicity, and apoptosis of CS/HCPT were also investigated in vitro . The present investigation indicated that the positively charged CS-based nanogel could be potentially used as a promising drug delivery system.
    Type of Medium: Online Resource
    ISSN: 1687-9422 , 1687-9430
    URL: Article
    DOI: 10.1155/2019/1914976
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2520688-6
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  • 2
    Online Resource
    Online Resource
    Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information
    Hindawi Limited ; 2019
    In:  Computational and Mathematical Methods in Medicine Vol. 2019 ( 2019-12-07), p. 1-11
    Details
    In: Computational and Mathematical Methods in Medicine, Hindawi Limited, Vol. 2019 ( 2019-12-07), p. 1-11
    Abstract: The widespread application of X-ray computed tomography (CT) in clinical diagnosis has led to increasing public concern regarding excessive radiation dose administered to patients. However, reducing the radiation dose will inevitably cause server noise and affect radiologists’ judgment and confidence. Hence, progressive low-dose CT (LDCT) image reconstruction methods must be developed to improve image quality. Over the past two years, deep learning-based approaches have shown impressive performance in noise reduction for LDCT images. Most existing deep learning-based approaches usually require the paired training dataset which the LDCT images correspond to the normal-dose CT (NDCT) images one-to-one, but the acquisition of well-paired datasets requires multiple scans, resulting the increase of radiation dose. Therefore, well-paired datasets are not readily available. To resolve this problem, this paper proposes an unpaired LDCT image denoising network based on cycle generative adversarial networks (CycleGAN) with prior image information which does not require a one-to-one training dataset. In this method, cyclic loss, an important trick in unpaired image-to-image translation, promises to map the distribution from LDCT to NDCT by using unpaired training data. Furthermore, to guarantee the accurate correspondence of the image content between the output and NDCT, the prior information obtained from the result preprocessed using the LDCT image is integrated into the network to supervise the generation of content. Given the map of distribution through the cyclic loss and the supervision of content through the prior image loss, our proposed method can not only reduce the image noise but also retain critical information. Real-data experiments were carried out to test the performance of the proposed method. The peak signal-to-noise ratio (PSNR) improves by more than 3 dB, and the structural similarity (SSIM) increases when compared with the original CycleGAN without prior information. The real LDCT data experiment demonstrates the superiority of the proposed method according to both visual inspection and quantitative evaluation.
    Type of Medium: Online Resource
    ISSN: 1748-670X , 1748-6718
    URL: Article
    DOI: 10.1155/2019/8639825
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2256917-0
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  • 3
    Online Resource
    Online Resource
    The Performance of Artificial Intelligence in Cervical Colposcopy: A Retrospective Data Analysis
    Hindawi Limited ; 2022
    In:  Journal of Oncology Vol. 2022 ( 2022-1-5), p. 1-6
    Details
    In: Journal of Oncology, Hindawi Limited, Vol. 2022 ( 2022-1-5), p. 1-6
    Abstract: Objective. We aimed to evaluate the performance of artificial intelligence (AI) system in detecting high-grade precancerous lesions. Methods. A retrospective and diagnostic study was conducted in Chongqing Cancer Hospital. Anonymized medical records with cytology, HPV testing, colposcopy findings with images, and the histopathological results were selected. The sensitivity, specificity, and areas under the curve (AUC) in detecting CIN2+ and CIN3+ were evaluated for the AI system, the AI-assisted colposcopy, and the human colposcopists, respectively. Results. Anonymized medical records from 346 women were obtained. The images captured under colposcopy of 194 women were found positive by the AI system; 245 women were found positive either by human colposcopists or the AI system. In detecting CIN2+, the AI-assisted colposcopy significantly increased the sensitivity (96.6% vs. 88.8%, p = 0.016 ). The specificity was significantly lower for AI-assisted colposcopy (38.1%), compared with human colposcopists (59.5%, p 〈 0.001 ) or the AI system (57.6%, p 〈 0.001 ). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.741, 0.765, and 0.674, respectively. In detecting CIN3+, the sensitivities of the AI system and AI-assisted colposcopy were not significantly higher than human colposcopists (97.5% vs. 92.6%, p = 0.13 ). The specificity was significantly lower for AI-assisted colposcopy (37.4%) compared with human colposcopists (59.2%, p 〈 0.001 ) or compared with the AI system (56.6%, p 〈 0.001 ). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.759, 0.674, and 0.771, respectively. Conclusions. The AI system provided equally matched sensitivity to human colposcopists in detecting CIN2+ and CIN3+. The AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+.
    Type of Medium: Online Resource
    ISSN: 1687-8469 , 1687-8450
    URL: Article
    DOI: 10.1155/2022/4370851
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2461349-6
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  • 4
    Online Resource
    Online Resource
    Random Fuzzy Granular Decision Tree
    Hindawi Limited ; 2021
    In:  Mathematical Problems in Engineering Vol. 2021 ( 2021-6-9), p. 1-17
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2021 ( 2021-6-9), p. 1-17
    Abstract: In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of interclass standard deviation to intraclass standard deviation, and avoid falling into local optimal solution; second, on the basis of AGRC, a parallel model is designed for fuzzy granulation of data to construct granular space, which can greatly enhance the efficiency compared with serial granulation of data; third, in the fuzzy granular space, we design RFGDT to classify the fuzzy granules, which can select important features as tree nodes based on information gain ratio and avoid the problem of overfitting based on the pruning algorithm proposed. Finally, we employ the dataset from UC Irvine Machine Learning Repository for verification. Theory and experimental results prove that RFGDT has high efficiency and accuracy and is robust in solving classification problems.
    Type of Medium: Online Resource
    ISSN: 1563-5147 , 1024-123X
    URL: Article
    DOI: 10.1155/2021/5578682
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 5
    Online Resource
    Online Resource
    The Reaction Pathway of miR-30c-5p Activates Lipopolysaccharide Promoting the Course of Traumatic and Hemorrhagic Shock Acute Lung Injury
    Hindawi Limited ; 2022
    In:  BioMed Research International Vol. 2022 ( 2022-4-13), p. 1-7
    Details
    In: BioMed Research International, Hindawi Limited, Vol. 2022 ( 2022-4-13), p. 1-7
    Abstract: Acute lung injury (ALI) is an acute hypoxic respiratory failure caused by diffuse inflammatory injury in alveolar epithelial cells during severe infection, trauma, and shock. Among them, trauma/hemorrhagic shock (T/HS) is the main type of indirect lung injury. Despite a great number of clinical studies, indirect factor trauma/hemorrhagic shock to the function and the mechanism in acute lung injury is not clear yet. Therefore, it is still necessary to carry on relevant analysis in order to thoroughly explore its molecular and cellular mechanisms and the pathway of disease function. In our research, we aimed to identify potential pathogenic genes and do modular analysis by downloading disease-related gene expression profile data. And our dataset is from the NCBI-GEO database. Then, we used the Clusterprofiler R package, GO function, and KEGG pathway enrichment analysis to analyze the core module genes. In addition, we also identified key transcription factors and noncoding RNAs. Based on the high degree of interaction of potential pathogenic genes and their involved functions and pathways, we identified 17 dysfunction modules. Among them, up to 9 modules significantly regulate the response to bacterial-derived molecules, and the response to lipopolysaccharide and other related functional pathways that mediate disease development. In addition, miR-290, miR-30c-5p, miR-195-5p, and miR-1-3p-based ncRNA and Jun, Atf1, and Atf3-based transcription factors have a total of 80 transcription drivers for functional modules. In summary, this study confirmed that miR-30c-5p activates lipopolysaccharide response pathway to promote the pathogenesis of ALI induced by hemorrhagic shock. This result can be an important direction for further research on related deepening diseases such as acute respiratory distress syndrome (ARDS). It further provides a piece of scientific medical evidence for revealing the pathogenic principle and cure difficulty of acute lung injury and also provides important guidance for the design of therapeutic strategies and drug development.
    Type of Medium: Online Resource
    ISSN: 2314-6141 , 2314-6133
    URL: Article
    DOI: 10.1155/2022/3330552
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2698540-8
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  • 6
    Online Resource
    Online Resource
    Sequence Fusion Algorithm of Tumor Gene Sequencing and Alignment Based on Machine Learning
    Hindawi Limited ; 2021
    In:  Computational Intelligence and Neuroscience Vol. 2021 ( 2021-12-31), p. 1-17
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2021 ( 2021-12-31), p. 1-17
    Abstract: With the rapid development of DNA high-throughput testing technology, there is a high correlation between DNA sequence variation and human diseases, and detecting whether there is variation in DNA sequence has become a hot research topic at present. DNA sequence variation is relatively rare, and the establishment of DNA sequence sparse matrix, which can quickly detect and reason fusion variation point, has become an important work of tumor gene testing. Because there are differences between the current comparison software and mutation detection software in detecting the same sample, there are errors between the results of derivative sequence comparison and the detection of mutation. In this paper, SNP and InDel detection methods based on machine learning and sparse matrix detection are proposed, and VarScan 2, Genome Analysis Toolkit (GATK), BCFtools, and FreeBayes are compared. In the research of SNP and InDel detection with intelligent reasoning, the experimental results show that the detection accuracy and recall rate are better when the depth is increasing. The reasoning fusion method proposed in this paper has certain advantages in comparison effect and discovery in SNP and InDel and has good effect on swelling and pain gene detection.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2021/9444194
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2388208-6
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  • 7
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    Online Resource
    Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-1-10), p. 1-18
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-1-10), p. 1-18
    Abstract: The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/1877464
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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  • 8
    Online Resource
    Online Resource
    Boosted Fuzzy Granular Regression Trees
    Hindawi Limited ; 2021
    In:  Mathematical Problems in Engineering Vol. 2021 ( 2021-7-21), p. 1-16
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2021 ( 2021-7-21), p. 1-16
    Abstract: The regression problem is a valued problem in the domain of machine learning, and it has been widely employed in many fields such as meteorology, transportation, and material. Granular computing (GrC) is a good approach of exploring human intelligent information processing, which has the superiority of knowledge discovery. Ensemble learning is easy to execute parallelly. Based on granular computing and ensemble learning, we convert the regression problem into granular space equivalently to solve and proposed boosted fuzzy granular regression trees (BFGRT) to predict a test instance. The thought of BFGRT is as follows. First, a clustering algorithm with automatic optimization of clustering centers is presented. Next, in terms of the clustering algorithm, we employ MapReduce to parallelly implement fuzzy granulation of the data. Then, we design new operators and metrics of fuzzy granules to build fuzzy granular rule base. Finally, a fuzzy granular regression tree (FGRT) in the fuzzy granular space is presented. In the light of these, BFGRT can be designed by parallelly combing multiple FGRTs via random sampling attributes and MapReduce. Theory and experiments show that BFGRT is accurate, efficient, and robust.
    Type of Medium: Online Resource
    ISSN: 1563-5147 , 1024-123X
    URL: Article
    DOI: 10.1155/2021/9958427
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 9
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    Online Resource
    Using a Multilearner to Fuse Multimodal Features for Human Action Recognition
    Hindawi Limited ; 2020
    In:  Mathematical Problems in Engineering Vol. 2020 ( 2020-08-27), p. 1-18
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2020 ( 2020-08-27), p. 1-18
    Abstract: The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.
    Type of Medium: Online Resource
    ISSN: 1024-123X , 1563-5147
    URL: Article
    DOI: 10.1155/2020/4358728
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 10
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    Online Resource
    Optimal Granularity Selection for Indoor Localization Detection with Wireless IoT Networks
    Hindawi Limited ; 2022
    In:  Wireless Communications and Mobile Computing Vol. 2022 ( 2022-8-10), p. 1-9
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2022 ( 2022-8-10), p. 1-9
    Abstract: Indoor localization detection acts as an important issue and has wide applications with wireless Internet of Things (IoT) networks. In recent years, the WiFi-based localization by using the latest artificial intelligence methods for improving the detection accuracy has attracted attention of many researchers. Granular computing is a newly emerged computing paradigm in artificial intelligence, which focuses on the structured thinking based on multiple levels of granularity. Thus, we introduce granular computing approaches to the task of wireless indoor localization detection, and a novel heuristic data discretization method is proposed based on the binary ant colony optimization and rough set (BACORS) for the selection of optimal granularity. For BACORS, the global optimal cut point set is searched based on the binary ant colony optimization to simultaneously discretize multiple attributes. Meanwhile, the accuracy of approximation classifications coined from rough sets is used to determine the consistent of multiple attribute data. To validate the effectiveness of BACORS, it is applied to a wireless indoor localization data set, and the experimental results indicate that it has promising performance.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2022/8156917
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045240-8
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