Multicellular rosettes drive fluid-solid transition in epithelial tissues

Models for confluent biological tissues often describe the network formed by cells as a triple-junction network, similar to foams. However, higher order vertices or multicellular rosettes are prevalent in developmental and {\it in vitro} processes and have been recognized as crucial in many important aspects of morphogenesis, disease, and physiology. In this work, we study the influence of rosettes on the mechanics of a confluent tissue. We find that the existence of rosettes in a tissue can greatly influence its rigidity. Using a generalized vertex model and effective medium theory we find a fluid-to-solid transition driven by rosette density and intracellular tensions. This transition exhibits several hallmarks of a second-order phase transition such as a growing correlation length and a universal critical scaling in the vicinity a critical point. Further, we elucidate the nature of rigidity transitions in dense biological tissues and other cellular structures using a generalized Maxwell constraint counting approach. This answers a long-standing puzzle of the origin of solidity in these systems.Comment: 11 pages, 5 figures + 8 pages, 7 figures in Appendix. To be appear in PR

Gate-Tunable Tunneling Resistance in Graphene/Topological Insulator Vertical Junctions

Graphene-based vertical heterostructures, particularly stacks incorporated with other layered materials, are promising for nanoelectronics. The stacking of two model Dirac materials, graphene and topological insulator, can considerably enlarge the family of van der Waals heterostructures. Despite well understanding of the two individual materials, the electron transport properties of a combined vertical heterojunction are still unknown. Here we show the experimental realization of a vertical heterojunction between Bi2Se3 nanoplate and monolayer graphene. At low temperatures, the electron transport through the vertical heterojunction is dominated by the tunneling process, which can be effectively tuned by gate voltage to alter the density of states near the Fermi surface. In the presence of a magnetic field, quantum oscillations are observed due to the quantized Landau levels in both graphene and the two-dimensional surface states of Bi2Se3. Furthermore, we observe an exotic gate-tunable tunneling resistance under high magnetic field, which displays resistance maxima when the underlying graphene becomes a quantum Hall insulator

Absence of a transport signature of spin-orbit coupling in graphene with indium adatoms

Enhancement of the spin-orbit coupling in graphene may lead to various topological phenomena and also find applications in spintronics. Adatom absorption has been proposed as an effective way to achieve the goal. In particular, great hope has been held for indium in strengthening the spin-orbit coupling and realizing the quantum spin Hall effect. To search for evidence of the spin-orbit coupling in graphene absorbed with indium adatoms, we carry out extensive transport measurements, i.e., weak localization magnetoresistance, quantum Hall effect and non-local spin Hall effect. No signature of the spin-orbit coupling is found. Possible explanations are discussed.Comment: 5 pages, 4 figures, with supplementary material

Approximate Accuracy Approaches to Attribute Reduction for Information Systems

The key problem for attribute reduction to information systems is how to evaluate the importance of an attribute. The algorithms are challenged by the variety of data forms in information system. Based on rough sets theory we present a new approach to attribute reduction for incomplete information systems and fuzzy valued information systems. In order to evaluate the importance of an attribute effectively, a novel algorithm with rigorous theorem is proposed. Experiments show the effect of proposed algorithm

Cluster Analysis Based on Bipartite Network

Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy c-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster number c in advance. The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method

Hydrogen assisted growth of high quality epitaxial graphene on the C-face of 4H-SiC

We demonstrate hydrogen assisted growth of high quality epitaxial graphene on the C-face of 4H-SiC. Compared with the conventional thermal decomposition technique, the size of the growth domain by this method is substantially increased and the thickness variation is reduced. Based on the morphology of epitaxial graphene, the role of hydrogen is revealed. It is found that hydrogen acts as a carbon etchant. It suppresses the defect formation and nucleation of graphene. It also improves the kinetics of carbon atoms via hydrocarbon species. These effects lead to increase of the domain size and the structure quality. The consequent capping effect results in smooth surface morphology and suppression of multilayer growth. Our method provides a viable route to fine tune the growth kinetics of epitaxial graphene on SiC.Comment: 9 pages, 4 figure