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Kinetic simulations of relativistic turbulence have significantly advanced our understanding of turbulent particle acceleration. Recent progress has highlighted the need for an updated acceleration theory that can account for particle acceleration within the plasma’s coherent structures. Here, we investigate how intermittency modeling connects statistical fluctuations in turbulence to regions of high-energy dissipation. This connection is established by employing a generalized She–Leveque model to characterize the exponentsζ_pfor the structure functions$S^p\propto l^{{\zeta }_p}$. The fitting of the scaling exponents provides us with a measure of the codimension of the dissipative structures, for which we subsequently determine the filling fraction. We perform our analysis for a range of magnetizationsσand relative fluctuation amplitudesδB₀/B₀. We find that increasing values ofσandδB₀/B₀allow the turbulent cascade to break sheetlike structures into smaller regions of dissipation that resemble chains of flux ropes. However, as their dissipation measure increases, the dissipative regions become less volume filling. With this work, we aim to inform future turbulent acceleration theories that incorporate particle energization from interactions with coherent structures within relativistic turbulence.

 

Abstract Recent analyses have found waves of neural activity traveling across entire visual cortical areas in awake animals. These traveling waves modulate the excitability of local networks and perceptual sensitivity. The general computational role of these spatiotemporal patterns in the visual system, however, remains unclear. Here, we hypothesize that traveling waves endow the visual system with the capacity to predict complex and naturalistic inputs. We present a network model whose connections can be rapidly and efficiently trained to predict individual natural movies. After training, a few input frames from a movie trigger complex wave patterns that drive accurate predictions many frames into the future solely from the network’s connections. When the recurrent connections that drive waves are randomly shuffled, both traveling waves and the ability to predict are eliminated. These results suggest traveling waves may play an essential computational role in the visual system by embedding continuous spatiotemporal structures over spatial maps. 

The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well-studied cortical regions. Further, the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike–field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs, but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical. 

The goal of tissue decellularization is to efficiently remove unwanted cellular components, such as DNA and cellular debris, while retaining the complex structural and molecular milieu within the extracellular matrix (ECM). Decellularization protocols to date are centered on customized tissue-specific and lab-specific protocols that involve consecutive manual steps which results in variable and protocol-specific ECM material. The differences that result from the inconsistent protocols between decellularized ECMs affect consistency across batches, limit comparisons between results obtained from different laboratories, and could limit the transferability of the material for consistent laboratory or clinical use. The present study is the first proof-of-concept towards the development of a standardized protocol that can be used to derive multiple ECM biomaterials (powders and hydrogels) via a previously established automated system. The automated decellularization method developed by our group was used due to its short decellularization time (4 hours) and its ability to reduce batch-to-batch variability. The ECM obtained using this first iteration of a unified protocol was able to produce ECM hydrogels from skin, lung, muscle, tendons, cartilage, and laryngeal tissues. All hydrogels formed in this study were cytocompatible and showed gelation and rheological properties consistent with previous ECM hydrogels. The ECMs also showed unique proteomic composition. The present study represents the first step towards developing standardized protocols that can be used on multiple tissues in a fast, scalable, and reproducible manner. 

Recently, particle-in-cell (PIC) simulations have shown that relativistic turbulence in collisionless plasmas can result in an equilibrium particle distribution function where turbulent heating is balanced by radiative cooling of electrons. Strongly magnetized plasmas are characterized by higher energy peaks and broader particle distributions. In relativistically moving astrophysical jets, it is believed that the flow is launched Poynting flux dominated and that the resulting magnetic instabilities may create a turbulent environment inside the jet, i.e. the regime of relativistic turbulence. In this paper, we extend previous PIC simulation results to larger values of plasma magnetization by linearly extrapolating the diffusion and advection coefficients relevant for the turbulent plasmas under consideration. We use these results to build a single-zone turbulent jet model that is based on the global parameters of the blazar emission region, and consistently calculate the particle distribution and the resulting emission spectra. We then test our model by comparing its predictions with the broad-band quiescent emission spectra of a dozen blazars. Our results show good agreement with observations of low synchrotron peaked (LSP) sources and find that LSPs are moderately Poynting flux dominated with magnetization 1 ≲ σ ≲ 5, have bulk Lorentz factor Γj ∼ 10–30, and that the turbulent region is located at the edge, or just beyond the broad-line region (BLR). The turbulence is found to be driven at an area comparable to the jet cross-section.

 

Abstract Studies of sensory-evoked neuronal responses often focus on mean spike rates, with fluctuations treated as internally-generated noise. However, fluctuations of spontaneous activity, often organized as traveling waves, shape stimulus-evoked responses and perceptual sensitivity. The mechanisms underlying these waves are unknown. Further, it is unclear whether waves are consistent with the low rate and weakly correlated “asynchronous-irregular” dynamics observed in cortical recordings. Here, we describe a large-scale computational model with topographically-organized connectivity and conduction delays relevant to biological scales. We find that spontaneous traveling waves are a general property of these networks. The traveling waves that occur in the model are sparse, with only a small fraction of neurons participating in any individual wave. Consequently, they do not induce measurable spike correlations and remain consistent with locally asynchronous irregular states. Further, by modulating local network state, they can shape responses to incoming inputs as observed in vivo. 

Click chemistry reactions have become an important tool for synthesizing user-defined hydrogels consisting of poly(ethylene glycol) (PEG) and bioactive peptides for tissue engineering. However, because click crosslinking proceeds via a step-growth mechanism, multi-arm telechelic precursors are required, which has some disadvantages. Here, we report for the first time that this requirement can be circumvented to create PEG–peptide hydrogels solely from linear precursors through the use of two orthogonal click reactions, the thiol–maleimide Michael addition and thiol–norbornene click reaction. The rapid kinetics of both click reactions allowed for quick formation of norbornene-functionalized PEG–peptide block copolymers via Michael addition, which were subsequently photocrosslinked into hydrogels with a dithiol linker. Characterization and in vitro testing demonstrated that the hydrogels have highly tunable physicochemical properties and excellent cytocompatibility. In addition, stoichiometric control over the crosslinking reaction can be leveraged to leave unreacted norbornene groups in the hydrogel for subsequent hydrogel functionalization via bioorthogonal inverse-electron demand Diels–Alder click reactions with s -tetrazines. After selectively capping norbornene groups in a user-defined region with cysteine, this feature was leveraged for protein patterning. Collectively, these results demonstrate that our novel chemical strategy is a simple and versatile approach to the development of hydrogels for tissue engineering that could be useful for a variety of applications. 

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three‐ or four‐node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information‐seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.