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Converting directly real-world images into high-quality anime styles using generative adversarial networks is one of the research hotspots in computer vision. The current popular AnimeGAN and WhiteBox anime generative adversarial networks are problematic when distortion of image features, loss of details on lines and textures are concerned. To address these problems, we introduce AnimationGAN. To preserve the details of images, we use the linear bottlenecks in the residual network, what is more, we also employ the hybrid attention mechanism to capture the salient information of images. In addition, we adopt optimized normalizations to improve the accuracy and learning rate of the model. The experimental results show that compared with AnimeGAN and Whitebox, the proposed AnimationGAN has smaller FID to cartoon(61.73), better IS(6.79) and faster network training speed(405 s per epoch). In summary, the generated animation image significantly improves line texture details and image feature retention with much faster network training speed. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC), a highly aggressive and heterogeneous subtype of breast cancer, lacks effective treatment options. Sophora flavescens Aiton, a Chinese medicinal plant, is often used in traditional Chinese medicine to treat cancer. Matrine (MAT) is an alkaloid extracted from Sophora flavescens. It has good anticancer effects, and thus can be explored as a new therapeutic agent in TNBC research. We performed bioinformatics analysis to analyze the differentially expressed genes between normal breast tissues and TNBC tissues, and comprehensive network pharmacology analyses. The activity and invasion ability of TNBC cells treated with MAT were analyzed. Apoptosis and cell cycle progression were determined using cytometry. We used Monodansylcadaverine (MDC) staining to determine the condition of autophagosomes. Finally, the expression levels of the key target proteins of the PI3K/AKT pathway were determined using western blotting. The proliferation and invasion ability of MDA-MB-231 and MDA-MB-468 can be effectively inhibited by MAT. The results of flow cytometry indicated that MAT arrested the TNBC cell cycle and induced apoptosis. In addition, we confirmed that MAT inhibited the expression of BCL-2 while up-regulating the expression of cleaved caspase-3. Moreover, enhanced intensity of MDC staining and high LC3-II expression were observed, which confirmed that MAT induced autophagy in TNBC cells. Western blotting showed that MAT inhibited the PI3K/AKT pathway and downregulated the expressions of PI3K, AKT, p-AKT, and PGK1. This study provides feasible methods, which include bioinformatics analysis and in vitro experiments, for the identification of compounds with anti-TNBC properties. MAT inhibited the PI3K/AKT signaling pathway, arrested cell cycle, as well as promoted cell apoptosis and autophagy. These experiments provide evidence for the anti-TNBC effect of MAT and identified potential targets against TNBC. [ABSTRACT FROM AUTHOR]

The nuclear system is a promising area for demonstrating practical quantum advantage. A comprehensive computation of a nuclear system in a classical computer is beyond the capacity of current classical computers. With the rapid development of hardware, the prospect of using quantum computers in nuclear physics is close at hand. In this paper, we report a full quantum package, QCSH, for solving a nuclear shell model in a quantum computer. QCSH uses the linear combination of the unitary formalism of quantum computing and performs all calculations in a quantum computer. The complexities of qubit resource and the number of basic gates of QCSH are both polynomials to the number of nucleons in nuclei. For example, QCSH is used to calculate the binding energies of 12 light nuclei (i.e., 2H, 3H, 3He, 4He, 6Li, 7Li, 12C, 14N, 16O, 17O, 23Na, and 40Ca). Moreover, we experimentally demonstrate the calculation of deuteron binding energy using a superconducting quantum processor. The result indicates that QCSH can provide meaningful results already in near-term quantum devices. [ABSTRACT FROM AUTHOR]

To investigate the potential mechanism of resveratrol in anti-fatigue by network pharmacology and molecular docking, and to investigate the anti-fatigue efficacy of resveratrol through in vitro animal experiments. Resveratrol action targets and fatigue-related targets were obtained using various databases. The anti-fatigue targets of resveratrol were obtained using the Venn diagram, uploaded to the String database, imported into Cytoscape 3.7.1, and constructed into a Protein-protein interaction network. The target genes were then subjected to Gene ontology and Kyoto encyclopedia of gene and genome enrichment analysis. Molecular docking verification was performed on the binding ability of the core target to resveratrol. Using swimming-trained mice as exercise models, exhaustive swimming time and fatigue-related biochemical parameters were used as indicators to investigate the effects of resveratrol on exercise endurance and energy metabolism. 104 anti-fatigue targets and 10 core target genes of resveratrol were obtained. KEGG analysis enrichment included AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, and Pathways in cancer. Molecular docking showed that the core target genes TP53, PIK3R1, AKT1, PIK3CA, and MAPK1 had good binding activity to resveratrol. Animal experiments showed that resveratrol could prolong the exhaustive swimming time of endurance-trained mice (P < 0.01), decrease aspartate aminotransferase, alanine aminotransferase, uric acid, blood lactate (P < 0.01), decrease blood urea nitrogen (P < 0.05), increase the liver glycogen, muscle glycogen (P < 0.01). Conclusion: Resveratrol has the characteristics of multiple targets and multiple pathways in anti-fatigue; resveratrol can enhance exercise endurance in mice. [ABSTRACT FROM AUTHOR]

We propose a variational quantum attack algorithm (VQAA) for classical advanced encryption standard (AES)-like symmetric cryptography, as exemplified by the simplified-data encryption standard (S-DES). In the VQAA, the known ciphertext is encoded as the ground state of a Hamiltonian that is constructed through a regular graph, and the ground state can be found using a variational approach. We designed the ansatz and cost function for the S-DES’s variational quantum attack. It is surprising that sometimes the VQAA is even faster than Grover’s algorithm as demonstrated by our simulation results. The relationships of the entanglement entropy, concurrence, and the cost function are investigated, which indicate that entanglement plays a crucial role in the speedup. [ABSTRACT FROM AUTHOR]

In order to gain comprehensive knowledge of an arbitrary unknown quantum state, one feasible way is to reconstruct it, which can be realized by finding a series of quantum operations that can refactor the unitary evolution producing the unknown state. We design an adaptive framework that can reconstruct unknown quantum states at high fidelities, which utilizes SWAP test, parameterized quantum circuits (PQCs) and layerwise learning strategy. We conduct benchmarking on the framework using numerical simulations and reproduce states of up to six qubits at more than 96% overlaps with original states on average using PQCs trained by our framework, revealing its high applicability to quantum systems of different scales theoretically. Moreover, we perform experiments on a five-qubit IBM Quantum hardware to reconstruct random unknown single qubit states, illustrating the practical performance of our framework. For a certain reconstructing fidelity, our method can effectively construct a PQC of suitable length, avoiding barren plateaus of shadow circuits and overuse of quantum resources by deep circuits, which is of much significance when the scale of the target state is large and there is no a priori information on it. This advantage indicates that it can learn credible information of unknown states with limited quantum resources, giving a boost to quantum algorithms based on parameterized circuits on near-term quantum processors. [ABSTRACT FROM AUTHOR]

The quantum amplitude amplification algorithms based on Grover's rotation operator need to perform phase flips for both the initial state and the target state. When the initial state is oblivious, the phase flips will be intractable, and we need to adopt oblivious amplitude amplification algorithm to handle. Without knowing exactly how many target items there are, oblivious amplitude amplification also suffers the "soufflé problem", in which iterating too little "undercooks" the state and too much "overcooks" the state, both resulting in a mostly non-target final state. In this work, we present a fixed-point oblivious quantum amplitude-amplification (FOQA) algorithm by introducing damping based on methods proposed by A. Mizel. Moreover, we construct the quantum circuit to implement our algorithm under the framework of duality quantum computing. Our algorithm can avoid the "soufflé problem", meanwhile keep the square speedup of quantum search, serving as a subroutine to improve the performance of quantum algorithms containing oblivious amplitude amplification procedure. [ABSTRACT FROM AUTHOR]

Quantum optimization algorithms can outperform their classical counterpart and are key in modern technology. The second-order optimization algorithm (the Newton algorithm) is a critical optimization method, speeding up the convergence by employing the second-order derivative of loss functions in addition to their first derivative. Here, we propose a new quantum second-order optimization algorithm for general polynomials with a computational complexity of O(poly(log d)). We use this algorithm to solve the nonlinear equation and learning parameter problems in factorization machines. Numerical simulations show that our new algorithm is faster than its classical counterpart and the first-order quantum gradient descent algorithm. While existing quantum Newton optimization algorithms apply only to homogeneous polynomials, our new algorithm can be used in the case of general polynomials, which are more widely present in real applications. [ABSTRACT FROM AUTHOR]

The gradient descent method is central to numerical optimization and is the key ingredient in many machine learning algorithms. It promises to find a local minimum of a function by iteratively moving along the direction of the steepest descent. Since for high-dimensional problems the required computational resources can be prohibitive, it is desirable to investigate quantum versions of the gradient descent, such as the recently proposed (Rebentrost et al.1). Here, we develop this protocol and implement it on a quantum processor with limited resources. A prototypical experiment is shown with a four-qubit nuclear magnetic resonance quantum processor, which demonstrates the iterative optimization process. Experimentally, the final point converged to the local minimum with a fidelity >94%, quantified via full-state tomography. Moreover, our method can be employed to a multidimensional scaling problem, showing the potential to outperform its classical counterparts. Considering the ongoing efforts in quantum information and data science, our work may provide a faster approach to solving high-dimensional optimization problems and a subroutine for future practical quantum computers. [ABSTRACT FROM AUTHOR]

In this paper, the hot deformation behavior of A357 alloy was investigated by hot compression tests. Isothermal hot compression simulation tests for A357 alloy were carried out at the conditions of deformation temperature of 350-470 °C, strain rate of 0.001-10 s−1 and engineering strain of 50%. Based on the Prasad instability criterion and dynamic material model, the hot processing maps of A357 alloy were constructed. The microstructure of compressed samples at different areas of the hot processing map was characterized by metallographic and scanning electron microscope and transmission electron microscope. The experimental results showed that the unstable processing zone of A357 alloy was mainly distributed in the high strain rate (10 s−1) and low-temperature (350, 380 °C) region. With an increase in the strain, the unstable zone expanded from low temperature to high temperature (470 °C). The ideal deformation conditions were deformation temperatures of 380-410 °C and strain rates of 0.001-0.01 s−1, and deformation temperatures of 440-470 °C and strain rates of 0.01-1 s−1. When the A357 alloy was extruded with the optimal hot processing parameters, the results suggested that hot extrusion could make the microstructure uniform and fine, thereby improving mechanical properties, especially when the elongation was up to 19.5% higher than the as-cast A357 alloy. [ABSTRACT FROM AUTHOR]

Although Durr and Hoyer have proposed state-of-the-art quantum algorithm (DHA) for searching minimum value, the lower limit of DHA's successful probability is 1/2. Also, DHA requires approximately (log 2 N) 2 copies of the initial state. In this paper, we propose a new quantum maximum or minimum searching algorithm (QUMMSA). In big data scenarios, according to sparse sampling with different densities, we can estimate the corresponding precision parameters. QUMMSA can improve the successful probability close to 100 % . Furthermore, with the quantum exact search algorithm, QUMMSA only requires approximately log 2 N copies of the initial state to solve this problem. Since preparing an arbitrary quantum state is a problem with exponential complexity, our algorithm has a greater advantage with the increasing database size. In addition, we first propose a general method for circuits construction, which can be used in any database. An experiment implemented in an IBM superconducting processor and a numerical simulation of a 6-qubit system to solve a real issue indicate the feasibility and efficiency of QUMMSA. QUMMSA can serve as a subroutine in various quantum algorithms which involves searching maximum or minimum. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare the microbial community structure and diversity in powdery mildew-infected and noninfected strawberry plant rhizosphere soils in the greenhouse based on variations in the 16S rRNA gene V3–V4 and fungal ITS2 regions by Illumina amplicon sequencing. Powdery mildew infection reduced the number of operational taxonomic units (OTUs) and prokaryotic and fungal community richness/diversity indexes in the rhizosphere soils compared with those in healthy plant soils. Furthermore, 3543 prokaryotic and 581 fungal OTUs were obtained at the 97% similarity level. Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Chloroflexi were the dominant bacterial phyla; Woesearchaeota_DHVEG-6, Bathyarchaeota, and Thaumarchaeota were the dominant archaea; and Ascomycota, Basidiomycota, unclassified_fungi, and Zygomycota were the dominant fungal phyla. Their proportions differed significantly among samples. Wolbachia, Devosia, Pseudolabrys, Streptomyces, and Rhizomicrobium were the most abundant bacterial genera; their proportions differed significantly among samples. Most Pseudomonas, Streptomyces, and 'norank' group members might be potential antagonistic microorganisms of powdery mildew pathogens, and Wolbachia and Rickettsia might be pathogen-transmitting vectors. Microascus, Clitopilus, and Ciliophora were the dominant fungi, and their community structures and abundances significantly differed among samples. Microascus, Talaromyces, Zopfiella, and Cryptococcus were relatively more abundant in the powdery mildew-infected strawberry plant rhizosphere soils. Fusarium, Trichoderma, Clitopilus, and 'unclassified' group members may be potential antagonistic populations. The results suggested that powdery mildew-infected strawberry fruits and plants cannot be consumed. This report is the first study to illustrate differences in the rhizosphere soil prokaryotic and fungal communities between powdery mildew-infected and noninfected strawberry plants in a greenhouse. [ABSTRACT FROM AUTHOR]

The recently theoretical and experimental researches on PT -symmetric system have attracted unprecedented attention because of the various interesting features and potential applications in extending canonical quantum mechanics. However, as the counterpart of PT -symmetry, there are few experimental researches on the anti- PT -symmetric quantum system because of the challenges in simulating anti- PT -symmetry in experiment. Here, we propose an algorithm for simulating the generalized anti- PT -symmetric system in the quantum circuit model. Utilizing the algorithm, we demonstrated the quantum simulation of anti- PT system experimentally, and an oscillation of information flow is observed in a nuclear magnetic resonance quantum simulator. The experiment showed that the information could recover from the environment completely when the anti- PT -symmetry is broken, whereas no information can be retrieved in the symmetry-unbroken phase. Our work opens the gate for the practical quantum simulation and experimental investigation of universal anti- PT -symmetric system in a quantum computer. [ABSTRACT FROM AUTHOR]

The Yang-Baxter equation has become a significant theoretical tool in a variety of areas of physics. It is desirable to investigate the quantum simulation of the Yang-Baxter equation itself, exploring the connections between quantum integrability and quantum information processing, in which the unity of both the Yang-Baxter equation system and its quantum entanglement should be kept as a whole. In this work, we propose a duality quantum simulation algorithm of the Yang-Baxter equation, which contains the Yang-Baxter system and an ancillary qubit. Contrasting to conventional methods in which the two hand sides of the equation are simulated separately, they are simulated simultaneously in this proposal. Consequently, it opens up a way to further investigate entanglements in a Yang-Baxter equation. [ABSTRACT FROM AUTHOR]

The antimicrobial drug metronidazole (MTZ) was covalently conjugated to the secondary hydroxyl groups of β-cyclodextrin through ester linkage using sodium hydride as the deproton reagent. The preliminary release behavior of MTZ in rat gastrointestinal tract contents was studied at 37 °C within 24 h. In the contents of stomach, the conjugates did hardly release MTZ, released MTZ only 9.5 % in the contents of small intestine, and released MTZ significantly up to 43.6 and 40.2 % in the contents of cecum and colon, respectively. These results indicate that the conjugate activation took place site-specifically in the rat cecal and colonic contents, probably via the biodegradation by glycosidases and hydrolases. The present MTZ-appended cyclodextrin conjugate may be of value as an orally administered delayed-release and/or colon-specific prodrug. [ABSTRACT FROM AUTHOR]