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Copy detection is a key task of image copyright protection. This article proposes a robust image hashing algorithm by CP decomposition and discrete cosine transform (DCT) for copy detection. The first contribution is the third-order tensor construction with low-frequency coefficients in the DCT domain. Since the low-frequency DCT coefficients contain most of the image energy, they can reflect the basic visual content of the image and are less disturbed by noise. Hence, the third-order tensor construction with the low-frequency DCT coefficients can ensure robustness of our algorithm. Another contribution is the application of the CP decomposition to the third-order tensor for learning a short binary hash. As the factor matrices learned from the CP decomposition can preserve the topology of the original tensor, the binary hash derived from the factor matrices can reach good discrimination. Lots of experiments and comparisons are done to validate effectiveness and advantage of our algorithm. The results demonstrate that our algorithm has superior classification and copy detection performances than several baseline algorithms. In addition, our algorithm is also better than some baseline algorithms with regard to hash length and computational time. [ABSTRACT FROM AUTHOR]

This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection. [ABSTRACT FROM AUTHOR]

Bacterial infections trigger a multifaceted interplay between inflammatory mediators and redox regulation. Recently, accumulating evidence has shown that redox signaling plays a significant role in immune initiation and subsequent immune cell functions. This review addresses the crucial role of the thioredoxin (Trx) system in the initiation of immune reactions and regulation of inflammatory responses during bacterial infections. Downstream signaling pathways in various immune cells involve thiol-dependent redox regulation, highlighting the pivotal roles of thiol redox systems in defense mechanisms. Conversely, the survival and virulence of pathogenic bacteria are enhanced by their ability to counteract oxidative stress and immune attacks. This is achieved through the reduction of oxidized proteins and the modulation of redox-sensitive signaling pathways, which are functions of the Trx system, thereby fortifying bacterial resistance. Moreover, some selenium/sulfur-containing compounds could potentially be developed into targeted therapeutic interventions for pathogenic bacteria. Taken together, the Trx system is a key player in redox regulation during bacterial infection, and contributes to host–pathogen interactions, offering valuable insights for future research and therapeutic development. [ABSTRACT FROM AUTHOR]

Microcrystalline cellulose (MCC) fibers are co mmonly used to reinforce polymers; however, their freeze‐drying process consumes a lot of energy. In this study, MCC dried at different temperatures, oven‐dried at 25, 50, 75, and 103°C and freeze‐dried, was evaluated based on their effect on the reinforced composites. The dried MCC was compounded with polylactic acid (PLA) by melt extrusion and compressed into an MCC/PLA composite film. The morphology, structure, and crystallinity of the dried MCCs and MCC/PLA composites were analyzed using scanning electron microscopy, laser particle size analysis, x‐ray diffraction, mechanical testing, and Fourier transform infrared spectroscopy. It was proved that MCC dried at 103°C was more uniformly dispersed and tightly bound to the PLA matrix than freeze‐dried MCC. Tensile measurements showed that the MCC dried at 103°C had nearly the same tensile strength (58.35 MPa) as that of the freeze‐dried MCC (58.7 MPa) and was higher than that of the other oven‐dried MCC. Energy consumption evaluation revealed that oven‐drying consumed much less energy than freeze‐drying MCC for 24 h to obtain the same final moisture content. MCC dried at 103°C has significantly better properties than MCC dried at 25, 50, and 75°C, similar to freeze‐drying. Concerning performance and energy consumption, drying at 103°C is determined the optimal choice to support the preparation of MCC‐reinforced PLA composites. Highlights: Drying temperature effects aggregation of MCC.MCC Dried at 103°C can present similar properties to freeze‐dried MCC but save more energy.Oven‐drying can be a good way to dry MCC for the preparation of MCC/PLA composite. [ABSTRACT FROM AUTHOR]

Image hashing is an effective technology for extensive image applications, such as retrieval, authentication and copy detection. This paper designs a new image hashing scheme based on saliency map and sparse model. The major contributions are twofold. The first contribution is the construction of a weighted image representation by combining a visual attention model called Itti model and the matrix of color vector angle (CVA). Since the Itti model can efficiently detect saliency map and CVA fully captures color information of image, they contribute to a visually robust and discriminative image representation. The second contribution is the hash extraction from the weighted image representation via sparse model. A classical sparse model called robust principal component analysis is exploited to decompose the weighted image representation into a low-rank component and a sparse component. As the low-rank component can describe intrinsic structure of image, hash calculation with low-rank component can achieve good discrimination. The efficiencies of the proposed scheme are validated by extensive experiments with open databases. The results demonstrate that the proposed scheme is superior to some state-of-the-art schemes in terms of classification performance between robustness and discrimination. [ABSTRACT FROM AUTHOR]

Breast cancer metastasis involves complex mechanisms, particularly when patients are undergoing chemotherapy. In tissues, tumor cells encounter cell–cell interactions, cell–microenvironment interactions, complex nutrient, and drug gradients. Currently, two-dimensional cell culture systems and animal models are challenging to observe and analyze cell responses to microenvironments with various physical and bio-chemical conditions, and microfluidic technology has been systematically developed to address this dilemma. In this study, we have constructed a combined chemotherapy evaluation chip (CCEC) based on microfluidic technology. The chip possesses 192 diamond-shaped microchambers containing MDA-MB-231-RFP cells, and each microchamber is composed of collagen to mimic breast cancer and its surrounding microenvironment. In addition, by adding medium containing different drugs to the medium channels of CCEC, composite drug (paclitaxel+gemcitabine+7rh and paclitaxel+fluorouracil+PP2) concentration gradients, and single drug (paclitaxel, gemcitabine, 7rh, fluorouracil, PP2) concentration gradients have been established in the five collagen regions, respectively, so that each localized microchamber in the regions has a unique drug microenvironment. In this way, we evaluated the composite and single chemotherapy efficacy on the same chip by statistically analyzing their effects on the numbers and migration of the cell. The quantitative results in CCECs reveal that the inhibition effects on the numbers and migration of MDA-MB-231-RFP cell under the composite drug gradients are more optimal than those of the single drugs. Besides, the cancer cell inhibition effect between the groups composed of two drugs has also been compared, that is the paclitaxel+gemcitabine, paclitaxel+fluorouracil, and paclitaxel+PP2 have better cell numbers and migration inhibition effects than paclitaxel+7rh. The results indicate that the bio-mimetic and high-throughput combined chemotherapy evaluation platform can serve as a more efficient and accurate tool for preclinical drug development and screening. [ABSTRACT FROM AUTHOR]

This study serves the purpose of assisting users in selecting a comfortable seat surface material for office chairs and enhancing users' comfort while using office chairs. To address the issue that the selection of traditional seat surface material is too subjective and that the prediction effect is poor, an improved sparrow search algorithm (ISSA) optimized least squares support vector machine (LSSVM) method for office chair seat surface material comfort prediction has been proposed. Sparrow Search Algorithm (SSA) was optimized with Sobol sequences, nonlinear inertial weights, and a crisscross optimization algorithm to produce the Improved Sparrow Search Algorithm (ISSA), and then the relevant parameters of the LSSVM algorithm were optimized with the modified algorithm to improve its prediction performance. The prediction accuracy of the ISSA-LSSVM model is as high as 95.75% by combining the body pressure distribution experiments; the root mean square error (RMSE) is 0.29; the goodness of fit (R2) is 0.92; the mean absolute error (MAE) is 0.24; the standard deviation (RSD) is 5.99%. The ISSA-LSSVM model predicts seat surface material comfort more accurately and reliably. This strategy can assist consumers to narrow down their seat surface material choices and even suggest an optimal selection. In this way, it can boost users' pleasure with office chairs, which has great potential for wide application. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) is an important task of image processing and has diverse applications, such as image super-resolution reconstruction, image transmission and monitoring systems. This paper proposes a perceptual hashing algorithm with complementary color wavelet transform (CCWT) and compressed sensing (CS) for reduced-reference (RR) IQA. The CCWT is exploited to decompose input color image into different sub-bands. Since the calculation of CCWT uses all color channels without discarding any information, the distortions introduced by digital operations on color channels are preserved in the CCWT sub-bands. The block-based CS is used to extract features from the CCWT sub-bands. As the Euclidean distance between the block-based CS features is slightly influenced by content-preserving operations, perceptual features constructed by Euclidean distances are robust, discriminative and compact. Hash sequence is finally determined by quantifying the perceptual features. Effectiveness of the proposed hashing is verified by various experiments on four open image databases. Experimental results demonstrate that the proposed hashing is superior to some state-of-the-art algorithms in terms of classification and RR IQA application. [ABSTRACT FROM AUTHOR]

Hashing scheme is a high‐efficiency technique for processing massive images. Two critical metrics of the hashing scheme are discrimination and robustness, but most schemes do not get satisfied classification performance between them. This paper proposes a novel hashing scheme via image feature map and 2D PCA. First, the proposed scheme extracts local phase quantization (LPQ) features in the frequency domain and local ternary pattern (LTP) features in the spatial domain, and combines them to construct an image feature map. Second, the proposed scheme conducts dimension reduction via 2D PCA for learning features from the image feature map. Last, the learned features are compressed to generate the hash sequence. Performances are tested on open image datasets. The results demonstrate that the proposed scheme can make a good balance between discrimination and robustness. In addition, the classification and copy detection of the proposed scheme are both superior to those of some famous hashing schemes. [ABSTRACT FROM AUTHOR]

Video hashing is an efficient technique for tasks like copy detection and retrieval. This paper utilizes canonical polyadic (CP) decomposition and Hahn moments to design a robust video hashing. The first significant contribution is the secondary frame construction. It uses three weighted techniques to generate three secondary frames for each video group, which can effectively capture features of video frames from different aspects and thus improves discrimination. Another contribution is the deep feature extraction via the ResNet50 and CP decomposition. The use of the ResNet50 can provide rich features and the CP decomposition can learn a compact and discriminative representation from the rich features. In addition, the Hahn moments of secondary frames are taken to construct hash elements. Extensive experiments on the open video dataset demonstrate that the proposed algorithm surpasses several state-of-the-art algorithms in balancing discrimination and robustness. [ABSTRACT FROM AUTHOR]