The rapid development of digital cameras and smartphones makes it easy for people to record the information displayed in the media and obtain high-quality recaptured images, which would pose a serious threat to copyri...
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The rapid development of digital cameras and smartphones makes it easy for people to record the information displayed in the media and obtain high-quality recaptured images, which would pose a serious threat to copyright protection, identity authentication, and public social security. Therefore, detecting recaptured images is an urgent problem in the multimedia forensics community. Most existing methods for detecting recaptured images focus on mining specific traces left in the images during the recapture operation. However, these traces may be covered up in certain environmental settings. In order to address this issue, we explore the internal differences in image statistics between the original and recaptured images, which do not depend on specific traces, and construct a more robust feature for detecting recaptured images. Firstly, the most discriminative regions are extracted based on the measure of pixel dispersion. Secondly, a multi-scale residual feature is constructed by calculating the first-order statistics of residual images to enhance the robustness against various recapture environments. Lastly, binary grey wolf optimization and particle swarm optimization (BGWOPSO) feature selection method is used to reduce dimensions in the features space, which could keep a good balance between performance and computational complexity. Experimental results on three public databases demonstrate that our proposed method significantly improves detection performance, especially on the most difficult-to-detect ICL-COMMSP database.
Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on imag...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on images captured using single camera with default settings. However, with the advancement of imaging technology, there is a large number of smartphones equipped with multiple cameras and various shooting modes for acquiring images, which may pose a significant challenge to device source identification. Therefore, to assess the performance of image source identification algorithm for modern smartphones and promote further research, it is crucial to build a dataset of image and video captured by modern smartphones. In this paper, we present a large-scale image and video dataset for forensic analysis, ForensiCam-215K. The dataset includes over 215K media contents captured by 130 modern smartphones of 10 major brands. We used the latest equipment to capture images from the main, wide-angle, and telephoto cameras in six different shooting modes, and the media were collected under a strictly controlled procedure to reduce the bias caused by differences in the acquisition process between different devices. Additionally, we used the Photo Response Non-Uniformity (PRNU) method to perform device source identification tests on the dataset. The results indicate that device source identification is a challenging task especially for images and videos captured by smartphones with multiple cameras and various shooting modes. The dataset will be released as open-source and freely available for use by the multimedia forensics research community at https://***/dswdsw21072/ForensiCam-215K.
BackgroundWhile NduFAF6 is implicated in breast cancer, its specific role remains *** expression levels and prognostic significance of NduFAF6 in breast cancer were assessed using The Cancer Genome Atlas, Gene Express...
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BackgroundWhile NduFAF6 is implicated in breast cancer, its specific role remains *** expression levels and prognostic significance of NduFAF6 in breast cancer were assessed using The Cancer Genome Atlas, Gene Expression Omnibus, Kaplan-Meier plotter and cBio-Portal databases. We knocked down NduFAF6 in breast cancer cells using small interfering RNA and investigated its effects on cell proliferation and migration ability. We performed gene expression analysis and validated key findings using protein analysis. We also assessed mitochondrial activity and cellular ***6 was highly expressed in breast cancer, which was associated with a poorer prognosis. Knockdown of NduFAF6 reduced the proliferation and migration ability of breast cancer cells. Transcriptome analysis revealed 2,101 differentially expressed genes enriched in apoptosis and mitochondrial signaling pathways. Western blot results showed NduFAF6 knockdown enhanced apoptosis. In addition, differential gene enrichment analysis was related to mitochondrial signaling pathways, and western blot results verified that mitophagy was enhanced in NduFAF6 knockdown breast cancer cells. JC-1 assay also showed that mitochondrial dysfunction and reactive oxygen species content were increased after knocking down NduFAF6. In addition, basal and maximal mitochondrial oxygen consumption decreased, and intracellular glycogen content *** of NduFAF6 resulted in apoptosis and mitophagy in breast cancer cells and NduFAF6 may be a potential molecular target for breast cancer therapy.
BackgroundThe cytological diagnostic process of EUS-FNA smears is time-consuming and manpower-intensive, and the conclusion could be subjective and controversial. Moreover, the relative lack of cytopathologists has li...
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BackgroundThe cytological diagnostic process of EUS-FNA smears is time-consuming and manpower-intensive, and the conclusion could be subjective and controversial. Moreover, the relative lack of cytopathologists has limited the widespread implementation of Rapid on-site evaluation (ROSE) presently. Therefore, this study aimed to establish an AI system for the detection of pancreatic ductal adenocarcinoma (PDAC) based on EUS-FNA cytological *** collected 3213 unified magnification images of pancreatic cell clusters from 210 pancreatic mass patients who underwent EUS-FNA in four hospitals. A semi-supervised CNN (SSCNN) system was developed to distinguish PDAC from Non-PDAC. The internal and external verifications were adopted and the diagnostic accuracy was compared between different seniorities of cytopathologists. 33 images of "Atypical" diagnosed by expert cytopathologists were selected to analyze the consistency between the system and definitive *** segmentation indicators Mean Intersection over Union (mIou), precision, recall and F1-score of SSCNN in internal and external testing sets were 88.3%, 93.21%,94.24%, 93.68% and 87.75%, 93.81%, 93.14%, 93.48% successively. The PDAC classification indicators of the SSCNN model including area under the ROC curve (AUC), accuracy, sensitivity, specificity, PPV and NPV in the internal testing set were 0.97%, 0.95%, 0.94%, 0.97%, 0.98%, 0.91% respectively, and 0.99%, 0.94%, 0.94%, 0.95%, 0.99%, 0.75% correspondingly in the external testing set. The diagnostic accuracy of senior, intermediate and junior cytopathologists was 95.00%, 88.33% and 76.67% under the binary diagnostic criteria of PDAC and non-PDAC. In comparison, the accuracy of the SSCNN system was 90.00% in the dataset of man-machine competition. The accuracy of the SSCNN model was highly consistent with senior cytopathologists (Kappa = 0.853, P = 0.001). The accuracy, sensitivity and specificity of the system in the classification of "a
Bronchial asthma (asthma) is a chronic inflammatory disease of the airways that remains an unresolved problem. Reportedly M2 macrophages and exosomes play a role in inflammation, including asthma. We investigated the ...
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Bronchial asthma (asthma) is a chronic inflammatory disease of the airways that remains an unresolved problem. Reportedly M2 macrophages and exosomes play a role in inflammation, including asthma. We investigated the roles of M2 macrophage-derived exosomes (M2-Exos) effect in asthmatic progression by using ovalbumin (OVA) induced asthmatic mice model. M2-Exos significantly ameliorated the pulmonary inflammatory response and airway hyperresponsiveness in asthmatic mice and suppressed aberrant proliferation and transient receptor potential polycystic protein 2(TRPP2) expression in LPS-stimulated primary airway smooth muscle cells (ASMCs). Then, we found that miR-186-5p of M2-Exos could target TRPP2 through online database analysis. However, miR-186-5p downregulation by miR-186-5p inhibitors decreased the protective effect of M2-Exos in asthmatic mouse and cellular models. miR-186-5p was identified and selectively combined with the polycystin-2 gene encoding TRPP2 protein, inhibited TRPP2 protein production, and downregulated TRPP2 expression. A reduction in the number of TRPP2 calcium (Ca) channels formed on the cell membrane leads to a decreased intracellular Ca2+ concentration ([Ca2+] i), causing reduced ASMC contraction and proliferation, thereby improving airway hyperresponsiveness and airway remodeling in asthma. Collectively, we conclude that M2 exosomal miR-186-5p to alleviate asthma progression and airway hyperresponsiveness though downregulating TRPP2 expression. These results may offer a novel insight to the treatment and drug delivery of asthma.
We have proposed a new probabilistic inversion method to perform the joint inversion of receiver function and surface wave dispersion data. In this method, we apply the Hamiltonian dynamics in the Bayesian framework t...
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We have proposed a new probabilistic inversion method to perform the joint inversion of receiver function and surface wave dispersion data. In this method, we apply the Hamiltonian dynamics in the Bayesian framework to efficiently sample the posterior probability distribution of this joint inverse problem. This method will lead to nearly 100% acceptance of each sample in theory. Semianalytical derivatives of both the data -sets to the model parameters (including elastic parameters, density, and the thickness of each layer) are used to speed up this algorithm. Finally, we apply our method to both synthetic data and real data. The result shows that the velocity model can be recovered well within a much smaller number of samplings than the traditional Markov chain Monte Carlo method.
This paper considers the finite-time tracking control problem for the strict-feedback nonlinear continuous systems involving input saturation and output constraints. A sequence of desired and auxiliary virtual control...
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This paper considers the finite-time tracking control problem for the strict-feedback nonlinear continuous systems involving input saturation and output constraints. A sequence of desired and auxiliary virtual control signals and real control input is designed to derive a representation of the system estimation errors and stabilize the system. The proposed approach is further developed via a finite-time stability theory, barrier Lyapunov function, and neural network approximation scheme to achieve an expected performance of the considered system. According to the proposed scheme, we solve the finite-time tracking control problem of the nonlinear systems with input saturation. Then, a theorem is provided to address that all the signals and system states are bounded, and the system output is driven to track the reference signal in a finite time to a small neighborhood of zero and remains in the predefined compact sets. The effectiveness of the proposed scheme is confirmed via two simulation examples.
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