The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4-9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.

We present a braided circuit topology framework for investigating topology and structural phase transitions in aggregates of semiflexible polymers. In the conventional approach to circuit topology, which specifically applies to single isolated folded linear chains, the number and arrangement of contacts within the circuitry of a folded chain give rise to increasingly complex fold topologies. Another avenue for achieving complexity is through the interaction and entanglement of two or more folded linear chains. The braided circuit topology approach describes the topology of such multiple-chain systems and offers topological measures such as writhe, complexity, braid length, and isotopy class. This extension of circuit topology to multichains reveals the interplay between collapse, aggregation, and entanglement. In this work, we show that circuit topological motif fractions are ideally suited order parameters to characterize structural phase transitions in entangled systems that can detect structural re-ordering other measures cannot.

Assisted reproductive technology (ART) is an important invention for the treatment of human infertility, and the isolation of high-quality sperm with progressive motility is one of the most critical steps that eventually affect the fertilization rate. Conventional sperm separation approaches include the swim-up method and density gradient centrifugation. However, the quality of isolated sperm obtained from both approaches can still be improved by improving sorted sperm motility, minimizing the DNA fragmentation rate, and removing abnormal phenotypes. Here, we report a Progressive Sperm Sorting Chip (PSSC) for high-quality sperm isolation. Based on the rheotaxis behavior of sperm, a gradient flow field is created in the chip for progressive sperm sorting. Clinical experiment results for 10 volunteers showed that greater than 90% of isolated sperm exhibit high motility (> 25 μm/s), high linearity (0.8), and a very low DNA fragmentation rate (< 5%). In addition, the whole process is label and chemical free. These features aid in gentle sperm sorting to obtain healthy sperm. This device uniquely enables the selection of high-quality sperm with progressive motility and might be clinically applied for infertility treatment in the near future.

By using true random number (TRN) generators, encoding with the highest security can be realized. However, a completely secure strategy to transfer these TRNs has not yet been devised. Quantum key distribution (QKD) has attempted to establish secure key distribution methodology of this kind; however, several quantum cracking strategies have been predicted and experimentally demonstrated. In this work, COSMOCAT was invented as a solution for next-generation ultrahigh security near-field communications. With COSMOCAT, TRNs are generated from naturally occurring and ubiquitous cosmic-ray muons and the generated cosmic keys are distributed by these muons with an unprecedented level of security. The successful results of this experiment indicate that our prototype and the new key-generation-and-distribution standard can be utilized for practical encoding and near-field data transfer at rates of 10-100 Mbps. It is anticipated that COSMOCAT will be one of key techniques for future high security, near-field communication management.

Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e., if the mean number of synapses per neuron K is large and synaptic efficacy is of the order of 1 / K . When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of the order of 1/ log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine-tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.

Condensation of humid air is an important process in thermal and process engineering and a subject of many currently research-intensive scientific domains, such as atmospheric water harvesting and seawater desalination. The nature of (water) vapor condensation in the presence of non-condensable gas (NCG) such as air differs significantly from the case with the pure, quiescent vapor condensation. In the literature, simple models that describe the forced flow condensation of water vapor in the presence of air on a series of vertical flat plates are hard to find. Here we present a simple and computationally efficient semi-empirical correlation describing forced flow condensation from humid air inside vertical channels formed by flat plates. The correlation accounts air as a non-condensing gas, different heights of vertical plates, and different thermal-hydraulic parameters. The correlation has been experimentally validated and shows excellent agreement, as 90% of theoretically predicted values are within ±12% of experimental data.

An effective approach based on the principle of maximum entropy is developed to analyze reliability in systems with dynamics of electric circuits and infectious diseases like coronavirus disease 2019 (COVID-19).

One of the outstanding problems in complexity science and engineering is the study of high-dimensional networked systems and of their susceptibility to transitions to undesired states as a result of changes in external drivers or in the structural properties. Because of the incredibly large number of parameters controlling the state of such complex systems and the heterogeneity of its components, the study of their dynamics is extremely difficult. Here we propose an analytical framework for collapsing complex N-dimensional networked systems into an S+1-dimensional manifold as a function of S effective control parameters with S << N. We test our approach on a variety of real-world complex problems showing how this new framework can approximate the system\'s response to changes and correctly identify the regions in the parameter space corresponding to the system\'s transitions. Our work offers an analytical method to evaluate optimal strategies in the design or management of networked systems.

Cointegration focuses on whether the long-term linear relationship between two or more time series is stationary even if this linear relationship does not exist or is not strong for the short term. Identifying the potential cointegration is important for economics, ecology, meteorology, neuroscience, and much more. Classic methods only considered or restricted in cointegration where the order of integration of all time series is 1. We introduce a method based on searching the vector to minimize the absolute correlation of convergent cross-mapping that can explore the universal cointegration and its extent. The proposed method can be applied to time series whose order of integration is not 1, cases that are not covered by classic cointegration. The proposed method is first illustrated and validated through time series generated by mathematical models in which the underlying relationships are known and then applied to three real-world examples.

The generation of abnormal excitations in pathological regions of the heart is a main trigger for lethal cardiac arrhythmias. Such abnormal excitations, also called ectopic activity, often arise from areas with local tissue heterogeneity or damage accompanied by localized depolarization. Finding the conditions that lead to ectopy is important to understand the basic biophysical principles underlying arrhythmia initiation and might further refine clinical procedures. In this study, we are the first to address the question of how geometry of the abnormal region affects the onset of ectopy using a combination of experimental, in silico, and theoretical approaches. We paradoxically find that, for any studied geometry of the depolarized region in optogenetically modified monolayers of cardiac cells, primary ectopic excitation originates at areas of maximal curvature of the boundary, where the stimulating electrotonic currents are minimal. It contradicts the standard critical nucleation theory applied to nonlinear waves in reaction-diffusion systems, where a higher stimulus is expected to produce excitation more easily. Our in silico studies reveal that the nonconventional ectopic activity is caused by an oscillatory instability at the boundary of the damaged region, the occurrence of which depends on the curvature of that boundary. The onset of this instability is confirmed using the Schrödinger equation methodology proposed by Rinzel and Keener [SIAM J. Appl. Math. 43, 907 (1983)]. Overall, we show distinctively novel insight into how the geometry of a heterogeneous cardiac region determines ectopic activity, which can be used in the future to predict the conditions that can trigger cardiac arrhythmias.