Measurement-induced nonlocality in arbitrary dimensions in terms of the inverse approximate joint diagonalization

Here we focus on the measurement induced nonlocality and present a redefinition in terms of the skew information subject to a broken observable. It is shown that the obtained quantity possesses an obvious operational meaning, can tackle the noncontractivity of the measurement induced nonlocality and has analytic expressions for many quantum states. Most importantly, an inverse approximate joint diagonalization algorithm, due to its simplicity, high efficiency, stability, and state independence, is presented to provide almost analytic expressions for any quantum state, which can also shed light on other aspects in physics

Decoherence and purity of a driven solid-state qubit in Ohmic bath

In this paper we study the decoherence and purity of a driven solid-state qubit in the Ohmic bath by using the method based on the master equation. At first, instead of solving the master equation we investigate the coefficients of the equation which describe the shift in frequency, diffusive, decoherence, and so on. It is shown that one of the coefficients (we called it decoherence coefficient) is crucial to the decoherence of the qubit in the model. Then we investigate the evolution of the purity of the state in the model. From the analysis of the purity we see that the decoherence time of the qubit decrease with the increase of the amplitude of the driven fields and it is increase with the increase of the frequency of the driven fields.Comment: 9 pages, 8 figure

The mouse and ferret models for studying the novel avian-origin human influenza A (H7N9) virus.

BackgroundThe current study was conducted to establish animal models (including mouse and ferret) for the novel avian-origin H7N9 influenza virus.FindingsA/Anhui/1/2013 (H7N9) virus was administered by intranasal instillation to groups of mice and ferrets, and animals developed typical clinical signs including body weight loss (mice and ferrets), ruffled fur (mice), sneezing (ferrets), and death (mice). Peak virus shedding from respiratory tract was observed on 2 days post inoculation (d.p.i.) for mice and 3-5 d.p.i. for ferrets. Virus could also be detected in brain, liver, spleen, kidney, and intestine from inoculated mice, and in heart, liver, and olfactory bulb from inoculated ferrets. The inoculation of H7N9 could elicit seroconversion titers up to 1280 in ferrets and 160 in mice. Leukopenia, significantly reduced lymphocytes but increased neutrophils were also observed in mouse and ferret models.ConclusionsThe mouse and ferret model enables detailed studies of the pathogenesis of this illness and lay the foundation for drug or vaccine evaluation

Isolation and characterization of ZZ1, a novel lytic phage that infects Acinetobacter baumannii clinical isolates

BACKGROUND: Acinetobacter baumannii, a significant nosocomial pathogen, has evolved resistance to almost all conventional antimicrobial drugs. Bacteriophage therapy is a potential alternative treatment for multidrug-resistant bacterial infections. In this study, one lytic bacteriophage, ZZ1, which infects A. baumannii and has a broad host range, was selected for characterization. RESULTS: Phage ZZ1 and 3 of its natural hosts, A. baumanni clinical isolates AB09V, AB0902, and AB0901, are described in this study. The 3 strains have different sensitivities to ZZ1, but they have the same sensitivity to antibiotics. They are resistant to almost all of the antibiotics tested, except for polymyxin. Several aspects of the life cycle of ZZ1 were investigated using the sensitive strain AB09V under optimal growth conditions. ZZ1 is highly infectious with a short latent period (9 min) and a large burst size (200 PFU/cell). It exhibited the most powerful antibacterial activity at temperatures ranging from 35°C to 39°C. Moreover, when ZZ1 alone was incubated at different pHs and different temperatures, the phage was stable over a wide pH range (4 to 9) and at extreme temperatures (between 50°C and 60°C). ZZ1 possesses a 100-nm icosahedral head containing double-stranded DNA with a total length of 166,682 bp and a 120-nm long contractile tail. Morphologically, it could be classified as a member of the Myoviridae family and the Caudovirales order. Bioinformatic analysis of the phage whole genome sequence further suggested that ZZ1 was more likely to be a new member of the Myoviridae phages. Most of the predicted ORFs of the phage were similar to the predicted ORFs from other Acinetobacter phages. CONCLUSION: The phage ZZ1 has a relatively broad lytic spectrum, high pH stability, strong heat resistance, and efficient antibacterial potential at body temperature. These characteristics greatly increase the utility of this phage as an antibacterial agent; thus, it should be further investigated

Transformable Super-Isostatic Crystals Self-Assembled from Segment Colloidal Rods

Colloidal particles can spontaneously self-assemble into ordered structures, which not only can manipulate the propagation of light, but also vibration or phonons. Using Monte Carlo simulation, we study the self-assembly of perfectly aligned segment rod particles with lateral flat cutting. Under the help of surface attractions, we find that particles with different cutting degree can self-assemble into different crystal phases characterized by bond coordination $z$ that varies from 3 to 6. Importantly, we identify a transformable super-isostatic structures with \emph{pgg} symmetry and redundant bonds ($z=5$). We find that this structure can support either the soft bulk model or soft edge model depending on its Poisson's ratio which can be tuned from positive to negative by a uniform soft deformation. Importantly, the bulk soft modes are associated with states of self-stress along the direction of zero strain during the uniform soft deformation. This self-assembled transformable super-isostatic structure may act as mechanical metamaterials with potential application in micro-mechanical engineering.Comment: 11pages,5 figure

Zinc isotope characteristics in the biogeochemical cycle as revealed by analysis of suspended particulate matter(SMP) in Aha Lake and Hongfeng Lake, Guizhou, China

Zn isotope is a useful tool for tracing biogeochemical processes as zinc plays important roles in the biogeochemistry of natural systems. However, the Zn isotope composition in the lake ecosystems has not been well characterized. In order to resolve this problem, we investigate the Zn isotope compositions of suspended particulate matter (SPM) and biological samples collected from the Aha Lake and Hongfeng Lake, and their tributaries in summer and winter, aiming to explore the potential of this novel isotope system as a proxy for biogeochemical processes in aqueous environments. Concentration of dissolved Zn ranges from 0.65 to 5.06 μg/L and 0.74 to 12.04 μg/L for Aha Lake and Hongfeng Lake, respectively, while Zn (SPM) ranges from 0.18 to 0.70 mg/g and 0.24 to 0.75 mg/g for Aha Lake and Hongfeng Lake, respectively. The Zn isotope composition in SPM from Aha Lake and its main tributaries ranges from -0.18‰ to 0.27‰ and -0.17‰ to 0.46‰, respectively, and it varies from -0.29‰ to 0.26‰ and -0.04‰ to 0.48‰, respectively in Hongfeng Lake and its main tributaries, displaying a wider range in tributaries than lakes. These results imply that Zn isotope compositions are mainly affected by tributaries inputting into Aha Lake, while adsorption process by algae is the major factor for the Zn isotope composition in Hongfeng Lake, and ZnS precipitation leads to the light Zn isotope composition of SPM in summer. These data and results provide the basic information of the Zn isotope for the lake ecosystem, and promote the application of Zn isotope in biogeochemistry

DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation

Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images. However, detecting power lines in aerial images is difficult,as the foreground data(i.e, power lines) is small and the background information is abundant.To tackle this problem, we introduce DUFormer, a semantic segmentation algorithm explicitly designed to detect power lines in aerial images. We presuppose that it is advantageous to train an efficient Transformer model with sufficient feature extraction using a convolutional neural network(CNN) with a strong inductive bias.With this goal in mind, we introduce a heavy token encoder that performs overlapping feature remodeling and tokenization. The encoder comprises a pyramid CNN feature extraction module and a power line feature enhancement module.After successful local feature extraction for power lines, feature fusion is conducted.Then,the Transformer block is used for global modeling. The final segmentation result is achieved by amalgamating local and global features in the decode head.Moreover, we demonstrate the importance of the joint multi-weight loss function in power line segmentation. Our experimental results show that our proposed method outperforms all state-of-the-art methods in power line segmentation on the publicly accessible TTPLA dataset