Size, surface charge, and material compositions are known to influence cell uptake of nanoparticles. However, the effect of particle geometry, i.e., the interplay between nanoscale shape and size, is less understood. Here we show that when shape is decoupled from volume, charge, and material composition, under typical in vitro conditions, mammalian epithelial and immune cells preferentially internalize disc-shaped, negatively charged hydrophilic nanoparticles of high aspect ratios compared with nanorods and lower aspect-ratio nanodiscs. Endothelial cells also prefer nanodiscs, however those of intermediate aspect ratio. Interestingly, unlike nanospheres, larger-sized hydrogel nanodiscs and nanorods are internalized more efficiently than their smallest counterparts. Kinetics, efficiency, and mechanisms of uptake are all shape-dependent and cell type-specific. Although macropinocytosis is used by both epithelial and endothelial cells, epithelial cells uniquely internalize these nanoparticles using the caveolae-mediated pathway. Human umbilical vein endothelial cells, on the other hand, use clathrin-mediated uptake for all shapes and show significantly higher uptake efficiency compared with epithelial cells. Using results from both upright and inverted cultures, we propose that nanoparticle internalization is a complex manifestation of three shape-and size-dependent parameters: particle surface-to-cell membrane contact area, i.e., particle-cell adhesion, strain energy for membrane deformation, and sedimentation or local particle concentration at the cell membrane. These studies provide a fundamental understanding on how nanoparticle uptake in different mammalian cells is influenced by the nanoscale geometry and is critical for designing improved nanocarriers and predicting nanomaterial toxicity.cell-uptake mechanism | nanoimprint lithography | drug delivery P olymeric nanoparticles are widely studied for delivering therapeutic and imaging payloads to cells. Understanding how particle properties affect cell uptake is not only critical for designing improved therapeutic and diagnostic agents (1, 2) but also essential for efficient in vitro cell manipulation (3) and evaluating nanomaterial toxicity (4). Nanoparticle uptake by cells has been shown to depend on particle size, surface charge, and material compositions (5-7). Recent advances in fabrication technologies have enabled generation of shape-specific microparticles and nanoparticles (8-12). These particles, inspired by the diverse, evolutionarily conserved shapes of pathogens and cells, are being used to study the role of carrier shape on cellular internalization, in vivo transport, and organ distribution (6,11,(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23).Despite these pioneering studies, there remains a significant knowledge gap in our fundamental understanding of the interplay between nanoscale shape and size on cellular internalization, especially for clinically relevant polymer-based hydrophilic nanoparticles. Most in vivo drug delivery and imaging applicati...

We show how the prevailing majority opinion in a population can be rapidly reversed by a small fraction p of randomly distributed committed agents who consistently proselytize the opposing opinion and are immune to influence. Specifically, we show that when the committed fraction grows beyond a critical value pc ≈ 10%, there is a dramatic decrease in the time, Tc, taken for the entire population to adopt the committed opinion. In particular, for complete graphs we show that when p < pc, Tc ∼ exp(α(p)N ), while for p > pc, Tc ∼ ln N . We conclude with simulation results for Erdős-Rényi random graphs and scale-free networks which show qualitatively similar behavior.

Entorhinal grid cells in mammals fire as a function of animal location, with spatially periodic response patterns. This nonlocal periodic representation of location, a local variable, is unlike other neural codes. There is no theoretical explanation for why such a code should exist. We examined how accurately the grid code with noisy neurons allows an ideal observer to estimate location and found this code to be a previously unknown type of population code with unprecedented robustness to noise. In particular, the representational accuracy attained by grid cells over the coding range was in a qualitatively different class from what is possible with observed sensory and motor population codes. We found that a simple neural network can effectively correct the grid code. To the best of our knowledge, these results are the first demonstration that the brain contains, and may exploit, powerful error-correcting codes for analog variables.

We consider the effect of network topology on the optimality of packet routing which is quantified by gammac, the rate of packet insertion beyond which congestion and queue growth occurs. We show that for any network, there exists an absolute upper bound, expressed in terms of vertex separators, for the scaling of gammac with network size N, irrespective of the static routing protocol used. We then derive an estimate to this upper bound for scale-free networks and introduce a static routing protocol, the "hub avoidance protocol," which, for large packet insertion rates, is superior to the shortest path routing protocol.

A classical model for social-influence-driven opinion change is the threshold model. Here we study cascades of opinion change driven by threshold model dynamics in the case where multiple initiators trigger the cascade, and where all nodes possess the same adoption threshold ϕ. Specifically, using empirical and stylized models of social networks, we study cascade size as a function of the initiator fraction p. We find that even for arbitrarily high value of ϕ, there exists a critical initiator fraction pc(ϕ) beyond which the cascade becomes global. Network structure, in particular clustering, plays a significant role in this scenario. Similarly to the case of single-node or single-clique initiators studied previously, we observe that community structure within the network facilitates opinion spread to a larger extent than a homogeneous random network. Finally, we study the efficacy of different initiator selection strategies on the size of the cascade and the cascade window.

We review results on the scaling of the optimal path length ℓopt in random networks with weighted links or nodes. We refer to such networks as "weighted" or "disordered" networks. The optimal path is the path with minimum sum of the weights. In strong disorder, where the maximal weight along the path dominates the sum, we find that ℓopt increases dramatically compared to the known small-world result for the minimum distance ℓ min ~ log N, where N is the number of nodes. For Erdős–Rényi (ER) networks ℓ opt ~ N1/3, while for scale free (SF) networks, with degree distribution P(k) ~ k-λ, we find that ℓopt scales as N(λ - 3)/(λ - 1) for 3 < λ < 4 and as N1/3 for λ ≥ 4. Thus, for these networks, the small-world nature is destroyed. For 2 < λ < 3 in contrary, our numerical results suggest that ℓopt scales as ln λ-1 N, representing still a small world. We also find numerically that for weak disorder ℓ opt ~ ln N for ER models as well as for SF networks. We also review the transition between the strong and weak disorder regimes in the scaling properties of ℓopt for ER and SF networks and for a general distribution of weights τ, P(τ). For a weight distribution of the form P(τ) = 1/(aτ) with (τ min < τ < τ max ) and a = ln τ max /τ min , we find that there is a crossover network size N* = N*(a) at which the transition occurs. For N ≪ N* the scaling behavior of ℓopt is in the strong disorder regime, while for N ≫ N* the scaling behavior is in the weak disorder regime. The value of N* can be determined from the expression ℓ∞(N*) = apc, where ℓ∞ is the optimal path length in the limit of strong disorder, A ≡ apc → ∞ and pc is the percolation threshold of the network. We suggest that for any P(τ) the distribution of optimal path lengths has a universal form which is controlled by the scaling parameter Z = ℓ∞/A where [Formula: see text] plays the role of the disorder strength and τc is defined by [Formula: see text]. In case P(τ) ~ 1/(aτ), the equation for A is reduced to A = apc. The relation for A is derived analytically and supported by numerical simulations for Erdős–Rényi and scale-free graphs. We also determine which form of P(τ) can lead to strong disorder A → ∞. We then study the minimum spanning tree (MST), which is the subset of links of the network connecting all nodes of the network such that it minimizes the sum of their weights. We show that the minimum spanning tree (MST) in the strong disorder limit is composed of percolation clusters, which we regard as "super-nodes", interconnected by a scale-free tree. The MST is also considered to be the skeleton of the network where the main transport occurs. We furthermore show that the MST can be partitioned into two distinct components, having significantly different transport properties, characterized by centrality — number of times a node (or link) is used by transport paths. One component the superhighways, for which the nodes (or links) with high centrality dominate, corresponds to the largest cluster at the percolation threshold (incipient infinite percolation cluster) which is a subset of the MST. The other component, roads, includes the remaining nodes, low centrality nodes dominate. We find also that the distribution of the centrality for the incipient infinite percolation cluster satisfies a power law, with an exponent smaller than that for the entire MST. We demonstrate the significance identifying the superhighways by showing that one can improve significantly the global transport by improving a very small fraction of the network, the superhighways.

This article discusses the transition of a form of nanoimprint lithography technology, known as Jet and Flash Imprint Lithography (J-FIL), from research to a commercial fabrication infrastructure for leading-edge semiconductor integrated circuits (ICs). Leading-edge semiconductor lithography has some of the most aggressive technology requirements, and has been a key driver in the 50-year history of semiconductor scaling. Introducing a new, disruptive capability into this arena is therefore a case study in a “high-risk-high-reward” opportunity. This article first discusses relevant literature in nanopatterning including advanced lithography options that have been explored by the IC fabrication industry, novel research ideas being explored, and literature in nanoimprint lithography. The article then focuses on the J-FIL process, and the interdisciplinary nature of risk, involving nanoscale precision systems, mechanics, materials, material delivery systems, contamination control, and process engineering. Next, the article discusses the strategic decisions that were made in the early phases of the project including: (i) choosing a step and repeat process approach; (ii) identifying the first target IC market for J-FIL; (iii) defining the product scope and the appropriate collaborations to share the risk-reward landscape; and (iv) properly leveraging existing infrastructure, including minimizing disruption to the widely accepted practices in photolithography. Finally, the paper discusses the commercial J-FIL stepper system and associated infrastructure, and the resulting advances in the key lithographic process metrics such as critical dimension control, overlay, throughput, process defects, and electrical yield over the past 5 years. This article concludes with the current state of the art in J-FIL technology for IC fabrication, including description of the high volume manufacturing stepper tools created for advanced memory manufacturing.

Efficient penetration and uniform distribution of nanoparticles (NPs) inside solid tissues and tumors is paramount to their therapeutic and diagnostic success. While many studies have reported the effect of NP size and charge on intratissue distribution, role of shape, and aspect ratio on NP transport inside solid tissues remain unclear. Here experimental and theoretical studies are reported on how nanoscale geometry of Jet and Flash Imprint Lithography-fabricated, polyethylene-glycol-based anionic nanohydrogels affect their penetration and distribution inside 3D spheroids, a model representing the intervascular region of solid, tumor-like tissues. Unexpectedly, low aspect ratio cylindrical NPs (H/D ≈0.3; disk-like particles, 100 nm height, and 325 nm diameter) show maximal intratissue delivery (>50% increase in total cargo delivered) and more uniform penetration compared to nanorods or smaller NPs of the same shape. This is in contrast to spherical NPs where smaller NP size resulted in deeper, more uniform penetration. Our results provide fundamental new knowledge on NP transport inside solid tissues and further establish shape and aspect ratio as important design parameters in developing more efficient, better penetrating, nanocarriers for drug, or contrast-agent delivery.