Higgs amplitude mode in massless Dirac fermion systems

The Higgs amplitude mode in superconductors is the condensed matter analogy of Higgs bosons in particle physics. We investigate the time evolution of Higgs amplitude mode in massless Dirac systems, induced by a weak quench of an attractive interaction. We find that the Higgs amplitude mode in the half-filling honeycomb lattice has a logarithmic decaying behaviour, qualitatively different from the $1/\sqrt{t}$ decay in the normal superconductors. Our study is also extended to the doped cases in honeycomb lattice. As for the 3D Dirac semimetal at half filling, we obtain an undamped oscillation of the amplitude mode. Our finding is not only an important supplement to the previous theoretical studies on normal fermion systems, but also provide an experimental signature to characterize the superconductivity in 2D or 3D Dirac systems.Comment: 6 pages, 8 figure

Competing orders and inter-layer tunnelling in cuprate superconductors: A finite temperature Landau theory

We propose a finite temperature Landau theory that describes competing orders and interlayer tunneling in cuprate superconductors as an important extension to a corresponding theory at zero temperature [Nature {\bf 428}, 53 (2004)], where the superconducting transition temperature $T_c$ is defined in three possible ways as a function of the zero temperature order parameter. For given parameters, our theory determines $T_c$ without any ambiguity. In mono- and double-layer systems we discuss the relation between zero temperature order parameter and the associated transition temperature in the presence of competing orders, and draw a connection to the puzzling experimental fact that the pseudo-gap temperature is much higher than the corresponding energy scale near optimum doping. Applying the theory to multi-layer systems, we calculate the layer-number dependence of $T_c$. In a reasonable parameter space the result turns out to be in agreement with experiments.Comment: 5 pages, 3 figure

Leaf-Encapsulated Vaccines: Agroinfiltration and Transient Expression of the Antigen Staphylococcal Endotoxin B in Radish Leaves.

Transgene introgression is a major concern associated with transgenic plant-based vaccines. Agroinfiltration can be used to selectively transform nonreproductive organs and avoid introgression. Here, we introduce a new vaccine modality in which Staphylococcal enterotoxin B (SEB) genes are agroinfiltrated into radishes (Raphanw sativus L.), resulting in transient expression and accumulation of SEB in planta. This approach can simultaneously express multiple antigens in a single leaf. Furthermore, the potential of high-throughput vaccine production was demonstrated by simultaneously agroinfiltrating multiple radish leaves using a multichannel pipette. The expression of SEB was detectable in two leaf cell types (epidermal and guard cells) in agroinfiltrated leaves. ICR mice intranasally immunized with homogenized leaves agroinfiltrated with SEB elicited detectable antibody to SEB and displayed protection against SEB-induced interferon-gamma (IFN-γ) production. The concept of encapsulating antigens in leaves rather than purifying them for immunization may facilitate rapid vaccine production during an epidemic disease

Impact of Fibronectin Knockout on Proliferation and Differentiation of Human Infrapatellar Fat Pad-Derived Stem Cells

Fibronectin plays an essential role in tissue development and regeneration. However, the effects of fibronectin knockout (FN1-KO) on stem cells’ proliferation and differentiation remain unknown. In this study, CRISPR/Cas9 generated FN1-KO in human infrapatellar fat pad-derived stem cells (IPFSCs) was evaluated for proliferation ability including cell cycle and surface markers as well as stemness gene expression and for differentiation capacity including chondrogenic and adipogenic differentiation. High passage IPFSCs were also evaluated for proliferation and differentiation capacity after expansion on decellularized ECM (dECM) deposited by FN1-KO cells. Successful FN1-KO in IPFSCs was confirmed by Sanger sequencing and Inference of CRISPR Edits analysis (ICE) as well as immunostaining for fibronectin expression. Compared to the GFP control, FN1-KO cells showed an increase in cell growth, percentage of cells in the S and G2 phases, and CD105 and CD146 expression but a decrease in expression of stemness markers CD73, CD90, SSEA4, and mesenchymal condensation marker CDH2 gene. FN1-KO decreased both chondrogenic and adipogenic differentiation capacity. Interestingly, IPFSCs grown on dECMs deposited by FN1-KO cells exhibited a decrease in cell proliferation along with a decline in CDH2 expression. After induction, IPFSCs plated on dECMs deposited by FN1-KO cells also displayed decreased expression of both chondrogenic and adipogenic capacity. We concluded that FN1-KO increased human IPFSCs’ proliferation capacity; however, this capacity was reversed after expansion on dECM deposited by FN1-KO cells. Significance of fibronectin in chondrogenic and adipogenic differentiation was demonstrated in both FN1-KO IPFSCs and FN(–) matrix microenvironment

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page