Effect of size, shape, and surface modification on cytotoxicity of gold nanoparticles to human Hep-2 and canine MDCK cells

There have been increasing interests in applying gold nanoparticles in biological research, drug delivery, and therapy. As the interaction of gold nanoparticles with cells relies on properties of nanoparticles, the cytotoxicity is complex and still under debating. In this work, we investigate the cytotoxicity of gold nanoparticles of different encapsulations, surface charge states, sizes and shapes to both human HEp-2 and canine MDCK cells. We found that cetyltrimethylammonium-bromide- (CTAB-) encapsulated gold nanorods (GNRs) were relatively higher cytotoxic than GNRs undergone further polymer coating and citrate stabilized gold nanospheres (GNSs). The toxicity of CTAB-encapsulated GNRs was mainly caused by CTAB on GNRs’ surface but not free CTAB in the solution. No obvious difference was found among GNRs of different aspect ratios. Time-lapse study revealed that cell death caused by GNRs occurred predominately within one hour through apoptosis, whereas cell death by free CTAB was in a time- and dose-dependent manner. Both positively and negatively surface-charged polymer-coated GNRs (PSS-GNRs and PAH-PSS-GNRs) showed similar levels of cytotoxic, suggesting the significance of surface functionality rather than surface charge in this case

Effect of size, shape, and surface modification on cytotoxicity of gold nanoparticles to human Hep-2 and canine MDCK cells

There have been increasing interests in applying gold nanoparticles in biological research, drug delivery, and therapy. As the interaction of gold nanoparticles with cells relies on properties of nanoparticles, the cytotoxicity is complex and still under debating. In this work, we investigate the cytotoxicity of gold nanoparticles of different encapsulations, surface charge states, sizes and shapes to both human HEp-2 and canine MDCK cells. We found that cetyltrimethylammonium-bromide- (CTAB-) encapsulated gold nanorods (GNRs) were relatively higher cytotoxic than GNRs undergone further polymer coating and citrate stabilized gold nanospheres (GNSs). The toxicity of CTAB-encapsulated GNRs was mainly caused by CTAB on GNRs’ surface but not free CTAB in the solution. No obvious difference was found among GNRs of different aspect ratios. Time-lapse study revealed that cell death caused by GNRs occurred predominately within one hour through apoptosis, whereas cell death by free CTAB was in a time- and dose-dependent manner. Both positively and negatively surface-charged polymer-coated GNRs (PSS-GNRs and PAH-PSS-GNRs) showed similar levels of cytotoxic, suggesting the significance of surface functionality rather than surface charge in this case

Effect of size, shape, and surface modification on cytotoxicity of gold nanoparticles to human Hep-2 and canine MDCK cells

There have been increasing interests in applying gold nanoparticles in biological research, drug delivery, and therapy. As the interaction of gold nanoparticles with cells relies on properties of nanoparticles, the cytotoxicity is complex and still under debating. In this work, we investigate the cytotoxicity of gold nanoparticles of different encapsulations, surface charge states, sizes and shapes to both human HEp-2 and canine MDCK cells. We found that cetyltrimethylammonium-bromide- (CTAB-) encapsulated gold nanorods (GNRs) were relatively higher cytotoxic than GNRs undergone further polymer coating and citrate stabilized gold nanospheres (GNSs). The toxicity of CTAB-encapsulated GNRs was mainly caused by CTAB on GNRs’ surface but not free CTAB in the solution. No obvious difference was found among GNRs of different aspect ratios. Time-lapse study revealed that cell death caused by GNRs occurred predominately within one hour through apoptosis, whereas cell death by free CTAB was in a time- and dose-dependent manner. Both positively and negatively surface-charged polymer-coated GNRs (PSS-GNRs and PAH-PSS-GNRs) showed similar levels of cytotoxic, suggesting the significance of surface functionality rather than surface charge in this case

Simulating dynamical quantum Hall effect with superconducting qubits

We propose an experimental scheme to simulate the dynamical quantum Hall effect and the related interaction-induced topological transition with a superconducting-qubit array. We show that a one-dimensional Heisenberg model with tunable parameters can be realized in an array of superconducting qubits. The quantized plateaus, which is a feature of the dynamical quantum Hall effect, will emerge in the Berry curvature of the superconducting qubits as a function of the coupling strength between nearest neighbor qubits. We numerically calculate the Berry curvatures of two-, four- and six-qubit arrays, and find that the interaction-induced topological transition can be easily observed with the simplest two-qubit array. Furthermore, we analyze some practical conditions in typical experiments for observing such dynamical quantum Hall effect.Comment: 9 pages, 6 figures, version accepted by PR

Distributed Learning over Unreliable Networks

Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guarantee the delivery of the message. At the same time, recent work exhibits the impressive tolerance of machine learning algorithms to errors or noise arising from relaxed communication or synchronization. In this paper, we connect these two trends, and consider the following question: {\em Can we design machine learning systems that are tolerant to network unreliability during training?} With this motivation, we focus on a theoretical problem of independent interest---given a standard distributed parameter server architecture, if every communication between the worker and the server has a non-zero probability $p$ of being dropped, does there exist an algorithm that still converges, and at what speed? The technical contribution of this paper is a novel theoretical analysis proving that distributed learning over unreliable network can achieve comparable convergence rate to centralized or distributed learning over reliable networks. Further, we prove that the influence of the packet drop rate diminishes with the growth of the number of \textcolor{black}{parameter servers}. We map this theoretical result onto a real-world scenario, training deep neural networks over an unreliable network layer, and conduct network simulation to validate the system improvement by allowing the networks to be unreliable

Graphical Nonbinary Quantum Error-Correcting Codes

In this paper, based on the nonbinary graph state, we present a systematic way of constructing good non-binary quantum codes, both additive and nonadditive, for systems with integer dimensions. With the help of computer search, which results in many interesting codes including some nonadditive codes meeting the Singleton bounds, we are able to construct explicitly four families of optimal codes, namely, $[[6,2,3]]_p$, $[[7,3,3]]_p$, $[[8,2,4]]_p$ and $[[8,4,3]]_p$ for any odd dimension $p$ and a family of nonadditive code $((5,p,3))_p$ for arbitrary $p>3$. In the case of composite numbers as dimensions, we also construct a family of stabilizer codes $((6,2\cdot p^2,3))_{2p}$ for odd $p$, whose coding subspace is {\em not} of a dimension that is a power of the dimension of the physical subsystem.Comment: 12 pages, 5 figures (pdf