DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention.

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving the model performance via increasing the depth or width of convolution. The learning ability of Convolutional Neural Network (CNN) structure is still under-explored. In this paper, a Detail-Enhanced Attention Block (DEAB) consisting of Detail-Enhanced Convolution (DEConv) and Content-Guided Attention (CGA) is proposed to boost the feature learning for improving the dehazing performance. Specifically, the DEConv contains difference convolutions which can integrate prior information to complement the vanilla one and enhance the representation capacity. Then by using the re-parameterization technique, DEConv is equivalently converted into a vanilla convolution to reduce parameters and computational cost. By assigning the unique Spatial Importance Map (SIM) to every channel, CGA can attend more useful information encoded in features. In addition, a CGA-based mixup fusion scheme is presented to effectively fuse the features and aid the gradient flow. By combining above mentioned components, we propose our Detail-Enhanced Attention Network (DEA-Net) for recovering high-quality haze-free images. Extensive experimental results demonstrate the effectiveness of our DEA-Net, outperforming the state-of-the-art (SOTA) methods by boosting the PSNR index over 41 dB with only 3.653 M parameters. (The source code of our DEA-Net is available at https://github.com/cecret3350/DEA-Net.).

Covert Communication through Robust Fragment Hiding in a Large Number of Images.

For covert communication in lossy channels, it is necessary to consider that the carrier of the hidden watermark will undergo multiple image-processing attacks. In order to ensure that secret information can be extracted without distortion from the watermarked images that have undergone attacks, in this paper, we design a novel fragmented secure communication system. The sender will fragment the secret data to be transmitted and redundantly hide it in a large number of multimodal carriers of messenger accounts on multiple social platforms. The receiver receives enough covert carriers, extracts each fragment, and concatenates the transmitted secret data. This article uses the image carrier as an example to fragment the text file intended for transmission and embeds it into a large number of images, with each fragment being redundant and embedded into multiple images. In this way, at the receiving end, only enough stego images need to be received to extract the information in each image, and then concatenate the final secret file. In order to resist various possible attacks during image transmission, we propose a strong robust image watermarking method. This method adopts a watermark layer based on DFT, which has high embedding and detection efficiency and good invisibility. Secondly, a watermark layer based on DCT is adopted, which can resist translation attacks, JPEG attacks, and other common attacks. Experiments have shown that our watermarking method is very fast; both the embedding time and the extraction time are less than 0.15 s for images not larger than 2000×2000. Our watermarking method has very good invisibility with 41dB PSNR on average. And our watermarking method is more robust than existing schemes and robust to nearly all kinds of attacks. Based on this strong robust image watermarking method, the scheme of fragmenting and hiding redundant transmission content into a large number of images is effective and practical. Our scheme can 100% restore the secret file completely under different RST or hybrid attacks, such as rotation by 1 degree and 5 degrees, scaling by 1.25 and 0.8, and cropping by 10% and 25%. Our scheme can successfully restore the secret file completely even if 30% of received images are lost. When 80% of received images are lost, our scheme can still restore 61.1% of the secret file. If all stego images can be obtained, the original text file can be completely restored.

Resampling-Detection-Network-Based Robust Image Watermarking against Scaling and Cutting.

Watermarking is an excellent solution to protect multimedia privacy but will be damaged by attacks such as noise adding, image filtering, compression, and especially scaling and cutting. In this paper, we propose a watermarking scheme to embed the watermark in the DWT-DCT composite transform coefficients, which is robust against normal image processing operations and geometric attacks. To make our scheme robust to scaling operations, a resampling detection network is trained to detect the scaling factor and then rescale the scaling-attacked image before watermark detection. To make our scheme robust to cutting operations, a template watermark is embedded in the Y channel to locate the cutting position. Experiments for various low- and high-resolution images reveal that our scheme has excellent performance in terms of imperceptibility and robustness.

Robust PDF Watermarking against Print-Scan Attack.

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print-scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print-scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print-scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution.

DSSS Signal Detection Based on CNN.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.

Period Estimation of Spread Spectrum Codes Based on ResNet.

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.

Helmet Wearing State Detection Based on Improved Yolov5s.

At many construction sites, whether to wear a helmet is directly related to the safety of the workers. Therefore, the detection of helmet use has become a crucial monitoring tool for construction safety. However, most of the current helmet wearing detection algorithms are only dedicated to distinguishing pedestrians who wear helmets from those who do not. In order to further enrich the detection in construction scenes, this paper builds a dataset with six cases: not wearing a helmet, wearing a helmet, just wearing a hat, having a helmet, but not wearing it, wearing a helmet correctly, and wearing a helmet without wearing the chin strap. On this basis, this paper proposes a practical algorithm for detecting helmet wearing states based on the improved YOLOv5s algorithm. Firstly, according to the characteristics of the label of the dataset constructed by us, the K-means method is used to redesign the size of the prior box and match it to the corresponding feature layer to increase the accuracy of the feature extraction of the model; secondly, an additional layer is added to the algorithm to improve the ability of the model to recognize small targets; finally, the attention mechanism is introduced in the algorithm, and the CIOU_Loss function in the YOLOv5 method is replaced by the EIOU_Loss function. The experimental results indicate that the improved algorithm is more accurate than the original YOLOv5s algorithm. In addition, the finer classification also significantly enhances the detection performance of the model.

A Real-Time Cup-Detection Method Based on YOLOv3 for Inventory Management.

Inventory is the basis of business activities; inventory management helps industries keep their inventories stocked with reasonable quantities, which ensures consumers demand while minimizing storage costs. The traditional manual inventory management has low efficiency and a high labor cost. In this paper, we used improved YOLOv3 to detect the cups stored on the warehouse shelves and counted their numbers to realize automated inventory management. The warehouse images are collected by the camera and transmitted to the industrial computer, which runs the YOLOv3 network. There are three feature maps in YOLOv3, the two smaller feature maps and the structure behind them are removed, and the k-means algorithm is used to optimize the default anchor size. Moreover, the detection range is limited to a specified area. Experiments show that, by eliminating those two feature maps, the network parameter is reduced from 235 MB to 212 MB, and detection FPS is improved from 48.15 to 54.88 while mAP is improved from 95.65% to 96.65% on our test dataset. The new anchors obtained by the k-means algorithm further improve the mAP to 96.82%. With those improvements, the average error rate of detection is reduced to 1.61%. Restricted detection areas eliminate irrelevant items to ensure the high accuracy of the detection result. The accurately counted number of cups and its change provide significant data for inventory management.

An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network.

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer-Convolution fusion dehazing network (TCFDN), which uses Transformer's global modeling ability and convolutional neural network's local modeling ability to improve the dehazing ability. In the Transformer-Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer-Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer-Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.

Genetically Modified T Cells for Esophageal Cancer Therapy: A Promising Clinical Application.

Esophageal cancer is an exceedingly aggressive and malignant cancer that imposes a substantial burden on patients and their families. It is usually treated with surgery, chemotherapy, radiotherapy, and molecular-targeted therapy. Immunotherapy is a novel treatment modality for esophageal cancer wherein genetically engineered adoptive cell therapy is utilized, which modifies immune cells to attack cancer cells. Using chimeric antigen receptor (CAR) or T cell receptor (TCR) modified T cells yielded demonstrably encouraging efficacy in patients. CAR-T cell therapy has shown robust clinical results for malignant hematological diseases, particularly in B cell-derived malignancies. Natural killer (NK) cells could serve as another reliable and safe CAR engineering platform, and CAR-NK cell therapy could be a more generalized approach for cancer immunotherapy because NK cells are histocompatibility-independent. TCR-T cells can detect a broad range of targeted antigens within subcellular compartments and hold great potential for use in cancer therapy. Numerous studies have been conducted to evaluate the efficacy and feasibility of CAR and TCR based adoptive cell therapies (ACT). A comprehensive understanding of genetically-modified T cell technologies can facilitate the clinical translation of these adoptive cell-based immunotherapies. Here, we systematically review the state-of-the-art knowledge on genetically-modified T-cell therapy and provide a summary of preclinical and clinical trials of CAR and TCR-transgenic ACT.

Chimeric Antigen Receptor T-Cell Therapy in Lung Cancer: Potential and Challenges.

Chimeric antigen receptor T (CAR-T) cell therapy has exhibited a substantial clinical response in hematological malignancies, including B-cell leukemia, lymphoma, and multiple myeloma. Therefore, the feasibility of using CAR-T cells to treat solid tumors is actively evaluated. Currently, multiple basic research projects and clinical trials are being conducted to treat lung cancer with CAR-T cell therapy. Although numerous advances in CAR-T cell therapy have been made in hematological tumors, the technology still entails considerable challenges in treating lung cancer, such as on-target, of-tumor toxicity, paucity of tumor-specific antigen targets, T cell exhaustion in the tumor microenvironment, and low infiltration level of immune cells into solid tumor niches, which are even more complicated than their application in hematological tumors. Thus, progress in the scientific understanding of tumor immunology and improvements in the manufacture of cell products are advancing the clinical translation of these important cellular immunotherapies. This review focused on the latest research progress of CAR-T cell therapy in lung cancer treatment and for the first time, demonstrated the underlying challenges and future engineering strategies for the clinical application of CAR-T cell therapy against lung cancer.

DNA hydroxymethylation increases the susceptibility of reactivation of methylated P16 alleles in cancer cells.

It is well established that 5-methylcytosine (5mC) in genomic DNA of mammalian cells can be oxidized into 5-hydroxymethylcytosine (5hmC) and other derivates by DNA dioxygenase TETs. While conversion of 5mC to 5hmC plays an important role in active DNA demethylation through further oxidation steps, a certain proportion of 5hmCs remain in the genome. Although 5hmCs contribute to the flexibility of chromatin and protect bivalent promoters from hypermethylation, the direct effect of 5hmCs on gene transcription is unknown. In this present study, we have engineered a zinc-finger protein-based P16-specific DNA dioxygenase (P16-TET) to induce P16 hydroxymethylation and demethylation in cancer cells. Our results demonstrate, for the first time, that although the hydroxymethylated P16 alleles retain transcriptionally inactive, hydroxymethylation could increase the susceptibility of reactivation of methylated P16 alleles.

Web-based interactive drone control using hand gesture.

This paper develops a drone control prototype based on web technology with the aid of hand gesture. The uplink control command and downlink data (e.g., video) are transmitted by WiFi communication, and all the information exchange is realized on web. The control command is translated from various predetermined hand gestures. Specifically, the hardware of this friendly interactive control system is composed by a quadrotor drone, a computer vision-based hand gesture sensor, and a cost-effective computer. The software is simplified as a web-based user interface program. Aided by natural hand gestures, this system significantly reduces the complexity of traditional human-computer interaction, making remote drone operation more intuitive. Meanwhile, a web-based automatic control mode is provided in addition to the hand gesture control mode. For both operation modes, no extra application program is needed to be installed on the computer. Experimental results demonstrate the effectiveness and efficiency of the proposed system, including control accuracy, operation latency, etc. This system can be used in many applications such as controlling a drone in global positioning system denied environment or by handlers without professional drone control knowledge since it is easy to get started.

Attack Vulnerability of Network Controllability.

Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdos-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability.

Homeostatic maintenance of allele-specific p16 methylation in cancer cells accompanied by dynamic focal methylation and hydroxymethylation.

AIM: p16 Methylation frequently occurs in carcinogenesis. While it has been hypothesized that the p16 methylation states are dynamically maintained in cancer cells, direct evidence supporting this hypothesis has not been available until now. METHODS: A fusion cell model was established which reprogrammed the native DNA methylation pattern of the cells. The methylation status of the p16 alleles was then repeatedly quantitatively analyzed in the fusion monoclonal, parental cancer cell lines (p16-completely methylated-AGS and unmethylated-MGC803), and HCT116 non-fusion cell using DHPLC and bisulfite sequencing. Histone methylation was analyzed using chromatin immuno-precipitation (ChIP)-PCR. P16 expression status was determined using immuno-staining and RT-PCR. RESULTS: The methylation status for the majority of the p16 alleles was stably maintained in the fusion monoclonal cells after up to 60 passages. Most importantly, focal de novo methylation, demethylation, and hydroxymethylation were consistently observed within about 27% of the p16 alleles in the fusion monoclones, but not the homozygously methylated or unmethylated parental cells. Furthermore, subclones of the monoclones consistently maintained the same p16 methylation pattern. A similar phenomenon was also observed using the p16 hemi-methylated HCT116 non-fusion cancer cell line. Interestingly, transcription was not observed in p16 alleles that were hydroxymethylated with an antisense-strand-specific pattern. Also, the levels of H3K9 and H3K4 trimethylation in the fusion cells were found to be slightly lower than the parental AGS and MGC803 cells, respectively. CONCLUSION: The present study provides the first direct evidence confirming that the methylation states of p16 CpG islands is not only homeostatically maintained, but also accompanied by a dynamic process of transient focal methylation, demethylation, and hydroxymethylation in cancer cells.

Fast video shot boundary detection based on SVD and pattern matching.

Video shot boundary detection (SBD) is the first and essential step for content-based video management and structural analysis. Great efforts have been paid to develop SBD algorithms for years. However, the high computational cost in the SBD becomes a block for further applications such as video indexing, browsing, retrieval, and representation. Motivated by the requirement of the real-time interactive applications, a unified fast SBD scheme is proposed in this paper. We adopted a candidate segment selection and singular value decomposition (SVD) to speed up the SBD. Initially, the positions of the shot boundaries and lengths of gradual transitions are predicted using adaptive thresholds and most non-boundary frames are discarded at the same time. Only the candidate segments that may contain the shot boundaries are preserved for further detection. Then, for all frames in each candidate segment, their color histograms in the hue-saturation-value) space are extracted, forming a frame-feature matrix. The SVD is then performed on the frame-feature matrices of all candidate segments to reduce the feature dimension. The refined feature vector of each frame in the candidate segments is obtained as a new metric for boundary detection. Finally, cut and gradual transitions are identified using our pattern matching method based on a new similarity measurement. Experiments on TRECVID 2001 test data and other video materials show that the proposed scheme can achieve a high detection speed and excellent accuracy compared with recent SBD schemes.

Nucleosomes correlate with in vivo progression pattern of de novo methylation of p16 CpG islands in human gastric carcinogenesis.

BACKGROUND: The exact relationship between nucleosome positioning and methylation of CpG islands in human pathogenesis is unknown. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we characterized the nucleosome position within the p16 CpG island and established a seeding methylation-specific PCR (sMSP) assay based on bisulfite modification to enrich the p16 alleles containing methylated-CpG at the methylation "seeding" sites within its intron-1 in gastric carcinogenesis. The sMSP-positive rate in primary gastric carcinoma (GC) samples (36/40) was significantly higher than that observed in gastritis (19/45) or normal samples (7/13) (P<0.01). Extensive clone sequencing of these sMSP products showed that the density of methylated-CpGs in p16 CpG islands increased gradually along with the severity of pathological changes in gastric tissues. In gastritis lesions the methylation was frequently observed in the region corresponding to the exon-1 coding-nucleosome and the 5'UTR-nucleosome; the methylation was further extended to the region corresponding to the promoter-nucleosome in GC samples. Only few methylated-CpG sites were randomly detected within p16 CpG islands in normal tissues. The significantly inversed relationship between the p16 exon-1 methylation and its transcription was observed in GC samples. An exact p16 promoter-specific 83 bp-MSP assay confirms the result of sMSP (33/55 vs. 1/6, P<0.01). In addition, p16 methylation in chronic gastritis lesions significantly correlated with H. pylori infection; however, such correlation was not observed in GC specimens. CONCLUSIONS/SIGNIFICANCE: It was determined that de novo methylation was initiated in the coding region of p16 exon-1 in gastritis, then progressed to its 5'UTR, and ultimately to the proximal promoter in GCs. Nucleosomes may function as the basic extension/progression unit of de novo methylation of p16 CpG islands in vivo.

Methylation of GATA-4 and GATA-5 and development of sporadic gastric carcinomas.

AIM: To understand the implication of GATA-4 and GATA-5 methylation in gastric carcinogenesis. METHODS: Methylation status of GATA-4 and GATA-5 CpG islands in human gastric mucosa samples, including normal gastric biopsies from 45 outpatients, gastric dysplasia [low-grade gastric intraepithelial neoplasia (GIN), n = 30; indefinite, n = 77], and 80 paired sporadic gastric carcinomas (SGC) as well as the adjacent non-neoplastic gastric tissues was analyzed by methylation specific polymerase chain reaction (MSP) and confirmed by denatured high performance liquid chromatography (DHPLC). Immunohistochemical staining was used to detect protein expression. The correlation between GATA-4 and GATA-5 methylation and clinicopathological characteristics of patients including Helicobacter pylori (H. pylori) infection was analyzed. RESULTS: GATA-4 and GATA-5 methylation was frequently observed in SGCs (53.8% and 61.3%, respectively) and their corresponding normal tissues (41.3% and 46.3%) by MSP. The result of MSP was consistent with that of DHPLC. Loss of both GATA-4 and GATA-5 proteins was associated with their methylation in SGCs (P = 0.01). Moreover, a high frequency of GATA-4 and GATA-5 methylation was found in both gastric low-grade GIN (57.1% and 69.0%) and indefinite for dysplasia (42.9% and 46.7%), respectively. However, GATA-4 and GATA-5 methylation was detected only in 4/32 (12.5%) and 3/39 (7.7%) of normal gastric biopsies. GATA-4 methylation in both normal gastric mucosa and low-grade GIN was also significantly associated with H. pylori infection (P = 0.023 and 0.027, two-sides). CONCLUSION: Epigenetic inactivation of GATA-4 (and GATA-5) by methylation of CpG islands is an early frequent event during gastric carcinogenesis and is significantly correlated with H. pylori infection.

High efficient generation of replication-defective adenoviruses containing thymidine kinase by homogeneous recombination in bacteria.

BACKGROUND: Suicide gene therapy is a widely used molecular treatment for head and neck cancer. In this study, we try to use the method of homogenous recombination in bacteria to clone thymidine kinase gene (tk)-a kind of suicide gene to adenovirus backbone vectors for the construction of replication-defective adenoviruses. METHODS: pAdTrack-CMV/tk was constructed through subclone of a restriction endonuclease fragment including thymidine kinase gene from plasmid pCMV-tk to another plasmid pAdTrack-CMV, and then co-transfected with supercoiled pAdEasy-1, which was an adenoviral backbone vector except for deletions of E1 and E3, to competent E. coli BJ5183 for homogenous recombination using electroporation procedure. With the same method, pAdTrack-CMV was also co-transformed with pAdEasy-1 for homogenous recombination in BJ5183. Identified with restriction endonuclease PacI and polymerase chain reaction (PCR), plasmids pAd-GFP/tk and pAd-GFP were successfully constructed. Each of them was digested with PacI and sequently transfected into human embryo kidney 293 cells (HEK293) using Lipofectamine 2000. RESULTS: Comet-like adenovirus-producing foci of Ad-GFP/tk and Ad-GFP were observed after 5 to 7 days of cell culture. After twelve days of packaging, the replication-defective adenoviruses were collected. Identified with PCR, thymidine kinase gene was successfully constructed into Ad-GFP/tk. CONCLUSION: The replication-defective adenoviruses containing thymidine kinase can be constructed more easily by homogenous recombination in bacteria than conventional techniques.

Multipurpose image watermarking algorithm based on multistage vector quantization.

The rapid growth of digital multimedia and Internet technologies has made copyright protection, copy protection, and integrity verification three important issues in the digital world. To solve these problems, the digital watermarking technique has been presented and widely researched. Traditional watermarking algorithms are mostly based on discrete transform domains, such as the discrete cosine transform, discrete Fourier transform (DFT), and discrete wavelet transform (DWT). Most of these algorithms are good for only one purpose. Recently, some multipurpose digital watermarking methods have been presented, which can achieve the goal of content authentication and copyright protection simultaneously. However, they are based on DWT or DFT. Lately, several robust watermarking schemes based on vector quantization (VQ) have been presented, but they can only be used for copyright protection. In this paper, we present a novel multipurpose digital image watermarking method based on the multistage vector quantizer structure, which can be applied to image authentication and copyright protection. In the proposed method, the semi-fragile watermark and the robust watermark are embedded in different VQ stages using different techniques, and both of them can be extracted without the original image. Simulation results demonstrate the effectiveness of our algorithm in terms of robustness and fragility.