Neurons in cerebral cortical circuits interact by sending and receiving electrical impulses called spikes. The ongoing spiking activity of cortical circuits is fundamental to many cognitive functions including sensory processing, working memory, and decision making. London et al. [Sensitivity to Perturbations In Vivo Implies High Noise and Suggests Rate Coding in Cortex, Nature (London) 466, 123 (2010).] recently argued that even a single additional spike can cause a cascade of extra spikes that rapidly decorrelate the microstate of the network. Here, we show theoretically in a minimal model of cortical neuronal circuits that single-spike perturbations trigger only a very weak rate response. Nevertheless, single-spike perturbations are found to rapidly decorrelate the microstate of the network, although the dynamics is stable with respect to small perturbations. The coexistence of stable and unstable dynamics results from a system of exponentially separating dynamic flux tubes around stable trajectories in the network’s phase space. The radius of these flux tubes appears to decrease algebraically with neuron number $N$ and connectivity $K$, which implies that the entropy of the circuit’s repertoire of state sequences scales as $N\ln (KN)$.

An effective approach to separating shaped particles is needed to isolate disease-causing cells for diagnostics or to aid in purifying nonspherical particles in applications ranging from food science to drug delivery. However, the separation of shaped particles is generally challenging, since nonspherical particles can freely rotate and present different faces while being sorted. We experimentally and numerically show that inertial fluid-dynamic effects allow for shape-dependent separation of flowing particles. (Spheres and rods with aspect ratios of $3:1$ and $5:1$ have all been separable.) Particle rotation around a conserved axis following Jeffery orbits is found to be a necessary component in producing different equilibrium positions across the channel that depend on particle rotational diameter. These differences are large enough to enable passive, continuous, high-purity, high-throughput, and shape-based separation downstream. Furthermore, we show that this shape-based separation can be applied to a large range of particle sizes and types, including small, artificially made $3\mathrm{\text{−}}\mu \mathrm{m}$ particles as well as bioparticles such as yeast. This practical approach for sorting particles by a previously inaccessible geometric parameter opens up a new capability that should find use in a range of fields.

The study of human interactions is of central importance for understanding the behavior of individuals, groups, and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links, and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We show that an accurate estimation of these probabilistic tendencies can be achieved only by following the time evolution of the network. Inferences about the reason for the existence of links using statistical analysis of network snapshots must therefore be made with great caution. Here, we start by characterizing every single link when the tie was established in the network. This information allows us to describe the probabilistic tendencies of tie formation and extract meaningful sociological conclusions. We also find significant differences in behavioral traits in the social tendencies among individuals according to their degree of activity, gender, age, popularity, and other attributes. For instance, in the particular data sets analyzed here, we find that women reciprocate connections 3 times as much as men and that this difference increases with age. Men tend to connect with the most popular people more often than women do, across all ages. On the other hand, triangular tie tendencies are similar, independent of gender, and show an increase with age. These results require further validation in other social settings. Our findings can be useful to build models of realistic social network structures and to discover the underlying laws that govern establishment of ties in evolving social networks.

When an infectious disease strikes a population, the number of newly reported cases is often the only available information during the early stages of the outbreak. An important goal of early outbreak analysis is to obtain a reliable estimate for the basic reproduction number, ${\mathcal{R}}_0$. Over the past few years, infectious disease epidemic processes have gained attention from the physics community. Much of the work to date, however, has focused on the analysis of an epidemic process in which the disease has already spread widely within a population; conversely, very little attention has been paid, in the physics literature or elsewhere, to formulating the initial phase of an outbreak. Careful analysis of this phase is especially important as it could provide policymakers with insight on how to effectively control an epidemic in its initial stage. We present a novel method, based on the principles of network theory, that enables us to obtain a reliable real-time estimate of the basic reproduction number at an early stage of an outbreak. Our method takes into account the possibility that the infectious period has a wide distribution and that the degree distribution of the underlying contact network is heterogeneous. We validate our analytical framework with numerical simulations.

To better understand the organization of overlapping modules in large networks with respect to flow, we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy-search algorithm, we find that some networks, for example, the neural network of the nematode Caenorhabditis elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European-roads network, have an organization of highly overlapping modules.

A new paradigm of resistive pulse sensing (Coulter counting) is developed using a liquid bridge in lieu of a solid pore as the sensing aperture, whereby the flexible liquid aperture circumvents the clogging issue of conventional Coulter counters. The electrohydrodynamic bridge is formed between two opposing Taylor cones and stabilized by radial polarization stresses. Passage of a colloidal particle through the upstream conical apex triggers a current oscillation at the resonant frequency of the cone-jet bridge. The relative current change is indicative of the particle-to-jet diameter ratio.

We propose a dynamical theory of market liquidity that predicts that the average supply/demand profile is V shaped and vanishes around the current price. This result is generic, and only relies on mild assumptions about the order flow and on the fact that prices are, to a first approximation, diffusive. This naturally accounts for two striking stylized facts: First, large metaorders have to be fragmented in order to be digested by the liquidity funnel, which leads to a long memory in the sign of the order flow. Second, the anomalously small local liquidity induces a breakdown of the linear response and a diverging impact of small orders, explaining the “square-root” impact law, for which we provide additional empirical support. Finally, we test our arguments quantitatively using a numerical model of order flow based on the same minimal ingredients.

Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cell-phone locations, [C. Song et al., Science 327, 1018 (2010)] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one’s conversation partners is defined as the degree to which one’s next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by long-tailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual’s predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community—in the sense of an abundance of surrounding triangles—tend to have low predictability, and those bridging different communities tend to have high predictability.

It is demonstrated that acoustic levitation is able to produce amorphous forms from a variety of organic molecular compounds with different glass forming abilities. This can lead to enhanced solubility for pharmaceutical applications. High-energy x-ray experiments show that several viscous gels form from saturated pharmaceutical drug solutions after 10–20 min of levitation at room temperature, most of which can be frozen in solid form. Laser heating of ultrasonically levitated drugs can also result in the vitrification of molecular liquids, which is not attainable using conventional amorphization methods.

We investigate a model for spatial epidemics explicitly taking into account bidirectional movements between base and destination locations on individual mobility networks. We provide a systematic analysis of generic dynamical features of the model on regular and complex metapopulation network topologies and show that significant dynamical differences exist to ordinary reaction-diffusion and effective force of infection models. On a lattice we calculate an expression for the velocity of the propagating epidemic front and find that, in contrast to the diffusive systems, our model predicts a saturation of the velocity with an increasing traveling rate. Furthermore, we show that a fully stochastic system exhibits a novel threshold for the attack ratio of an outbreak that is absent in diffusion and force of infection models. These insights not only capture natural features of human mobility relevant for the geographical epidemic spread, they may serve as a starting point for modeling important dynamical processes in human and animal epidemiology, population ecology, biology, and evolution.