Functional anatomy of the masking level difference, an fMRI study

Introduction: Masking level differences (MLDs) are differences in the hearing threshold for the detection of a signal presented in a noise background, where either the phase of the signal or noise is reversed between ears. We use N0/Nπ to denote noise presented in-phase/out-of-phase between ears and S0/Sπ to denote a 500 Hz sine wave signal as in/out-of-phase. Signal detection level for the noise/signal combinations N0Sπ and NπS0 is typically 10-20 dB better than for N0S0. All combinations have the same spectrum, level, and duration of both the signal and the noise. Methods: Ten participants (5 female), age: 22-43, with N0Sπ-N0S0 MLDs greater than 10 dB, were imaged using a sparse BOLD fMRI sequence, with a 9 second gap (1 second quiet preceding stimuli). Band-pass (400-600 Hz) noise and an enveloped signal (.25 second tone burst, 50% duty-cycle) were used to create the stimuli. Brain maps of statistically significant regions were formed from a second-level analysis using SPM5. Results: The contrast NπS0- N0Sπ had significant regions of activation in the right pulvinar, corpus callosum, and insula bilaterally. The left inferior frontal gyrus had significant activation for contrasts N0Sπ-N0S0 and NπS0-N0S0. The contrast N0S0-N0Sπ revealed a region in the right insula, and the contrast N0S0-NπS0 had a region of significance in the left insula. Conclusion: Our results extend the view that the thalamus acts as a gating mechanism to enable dichotic listening, and suggest that MLD processing is accomplished through thalamic communication with the insula, which communicate across the corpus callosum to either enhance or diminish the binaural signal (depending on the MLD condition). The audibility improvement of the signal with both MLD conditions is likely reflected by activation in the left inferior frontal gyrus, a late stage in the what/where model of auditory processing. © 2012 Wack et al

Endothelial Cell Cortactin Phosphorylation by Src Contributes to Polymorphonuclear Leukocyte Transmigration In Vitro

The underlying mechanisms that regulate leukocyte transendothelial migration through the vascular endothelium remain unclear. Cortactin is a substrate of Src tyrosine kinases and a regulator of cytoskeletal dynamics. Previous studies demonstrated a role for Src phosphorylation of cortactin in clustering of E-selectin and intercellular cell adhesion molecule-1 around adherent leukocytes. In the current study, we used an in vitro flow model to investigate the role of Src-induced cortactin phosphorylation in endothelium during polymorphonuclear leukocyte (PMN) transmigration through human umbilical vein endothelium (HUVEC) monolayers preactivated with tumor necrosis factor-{alpha}. Inhibition of Src in HUVEC using Src kinase inhibitors PP2 and SU6656 reduced PMN transmigration by 45±8% and 36±6%, respectively. Live cell imaging of green fluorescent protein–tagged cortactin in HUVEC revealed redistribution of cortactin in the region surrounding transmigrating PMN. Knockdown of cortactin in HUVEC by small interfering RNA also impaired transmigration to a similar degree, and this phenotype was rescued by reexpression of wild-type cortactin. Analysis of the location of initial arrest and locomotion of PMN adherent to HUVEC demonstrated that inhibition of Src tyrosine kinases or pretreatment with cortactin small interfering RNA reduced PMN transmigration at endothelial cell-to-cell junctions and not adhesion. Tyrosine phosphorylation of cortactin was important for transmigration, because expression of a mutant, in which the tyrosine phosphorylation sites were mutated to phenylalanine (cortactin3F), failed to rescue PMN transmigration. Moreover, expression of cortactin3F alone partially blocked PMN transmigration. These data suggest a model whereby tyrosine phosphorylation of cortactin by Src family kinases regulates PMN transmigratio

Random Sierpinski network with scale-free small-world and modular structure

In this paper, we define a stochastic Sierpinski gasket, on the basis of which we construct a network called random Sierpinski network (RSN). We investigate analytically or numerically the statistical characteristics of RSN. The obtained results reveal that the properties of RSN is particularly rich, it is simultaneously scale-free, small-world, uncorrelated, modular, and maximal planar. All obtained analytical predictions are successfully contrasted with extensive numerical simulations. Our network representation method could be applied to study the complexity of some real systems in biological and information fields.Comment: 7 pages, 9 figures; final version accepted for publication in EPJ

Computing the Loewner driving process of random curves in the half plane

We simulate several models of random curves in the half plane and numerically compute their stochastic driving process (as given by the Loewner equation). Our models include models whose scaling limit is the Schramm-Loewner evolution (SLE) and models for which it is not. We study several tests of whether the driving process is Brownian motion. We find that just testing the normality of the process at a fixed time is not effective at determining if the process is Brownian motion. Tests that involve the independence of the increments of Brownian motion are much more effective. We also study the zipper algorithm for numerically computing the driving function of a simple curve. We give an implementation of this algorithm which runs in a time O(N^1.35) rather than the usual O(N^2), where N is the number of points on the curve.Comment: 20 pages, 4 figures. Changes to second version: added new paragraph to conclusion section; improved figures cosmeticall

Distributed acoustic sensing for seismic activity monitoring

Continuous, real-time monitoring of surface seismic activity around the globe is of great interest for acquiring new insight into global tomography analyses and for recognition of seismic patterns leading to potentially hazardous situations. The already-existing telecommunication fiber optic network arises as an ideal solution for this application, owing to its ubiquity and the capacity of optical fibers to perform distributed, highly sensitive monitoring of vibrations at relatively low cost (ultra-high density of point sensors available with minimal deployment of new equipment). This perspective article discusses early approaches on the application of fiber-optic distributed acoustic sensors (DASs) for seismic activity monitoring. The benefits and potential impact of DAS technology in these kinds of applications are here illustrated with new experimental results on teleseism monitoring based on a specific approach: the so-called chirped-pulse DAS. This technology offers promising prospects for the field of seismic tomography due to its appealing properties in terms of simplicity, consistent sensitivity across sensing channels, and robustness. Furthermore, we also report on several signal processing techniques readily applicable to chirped-pulse DAS recordings for extracting relevant seismic information from ambient acoustic noise. The outcome presented here may serve as a foundation for a novel conception for ubiquitous seismic monitoring with minimal investment

Distributed acoustic sensing for seismic activity monitoring

Continuous, real-time monitoring of surface seismic activity around the globe is of great interest for acquiring new insight into global tomography analyses and for recognition of seismic patterns leading to potentially hazardous situations. The already-existing telecommunication fiber optic network arises as an ideal solution for this application, owing to its ubiquity and the capacity of optical fibers to perform distributed, highly sensitive monitoring of vibrations at relatively low cost (ultra-high density of point sensors available with minimal deployment of new equipment). This perspective article discusses early approaches on the application of fiber-optic distributed acoustic sensors (DASs) for seismic activity monitoring. The benefits and potential impact of DAS technology in these kinds of applications are here illustrated with new experimental results on teleseism monitoring based on a specific approach: the so-called chirped-pulse DAS. This technology offers promising prospects for the field of seismic tomography due to its appealing properties in terms of simplicity, consistent sensitivity across sensing channels, and robustness. Furthermore, we also report on several signal processing techniques readily applicable to chirped-pulse DAS recordings for extracting relevant seismic information from ambient acoustic noise. The outcome presented here may serve as a foundation for a novel conception for ubiquitous seismic monitoring with minimal investment

Revealing the CO Coverage Driven C-C Coupling Mechanism for Electrochemical CO₂ Reduction on Cu₂O Nanocubes via Operando Raman Spectroscopy

Electrochemical reduction of carbon dioxide (CO2RR) is an attractive route to close the carbon cycle and potentially turn CO2 into valuable chemicals and fuels. However, the highly selective generation of multicarbon products remains a challenge, suffering from poor mechanistic understanding. Herein, we used operando Raman spectroscopy to track the potential-dependent reduction of Cu2O nanocubes and the surface coverage of reaction intermediates. In particular, we discovered that the potential-dependent intensity ratio of the Cu–CO stretching band to the CO rotation band follows a volcano trend similar to the CO2RR Faradaic efficiency for multicarbon products. By combining operando spectroscopic insights with Density Functional Theory, we proved that this ratio is determined by the CO coverage and that a direct correlation exists between the potential-dependent CO coverage, the preferred C–C coupling configuration, and the selectivity to C2+ products. Thus, operando Raman spectroscopy can serve as an effective method to quantify the coverage of surface intermediates during an electrocatalytic reaction

DNMT gene expression and methylome in Marek’s disease resistant and susceptible chickens prior to and following infection by MDV

Marek’s disease (MD) is characterized as a T cell lymphoma induced by a cell-associated α-herpesvirus, Marek’s disease virus type 1 (MDV1). As with many viral infectious diseases, DNA methylation variations were observed in the progression of MD; these variations are thought to play an important role in host-virus interactions. We observed that DNA methyltransferase 3a (DNMT3a) and 3b (DNMT3b) were differentially expressed in chicken MD-resistant line 6(3) and MD-susceptible line 7(2) at 21 d after MDV infection. To better understand the role of methylation variation induced by MDV infection in both chicken lines, we mapped the genome-wide DNA methylation profiles in each line using Methyl-MAPS (methylation mapping analysis by paired-end sequencing). Collectively, the data sets collected in this study provide a more comprehensive picture of the chicken methylome. Overall, methylation levels were reduced in chickens from the resistant line 6(3) after MDV infection. We identified 11,512 infection-induced differential methylation regions (iDMRs). The number of iDMRs was larger in line 7(2) than in line 6(3), and most of iDMRs found in line 6(3) were overlapped with the iDMRs found in line 7(2). We further showed that in vitro methylation levels were associated with MDV replication, and found that MDV propagation in the infected cells was restricted by pharmacological inhibition of DNA methylation. Our results suggest that DNA methylation in the host may be associated with disease resistance or susceptibility. The methylation variations induced by viral infection may consequentially change the host transcriptome and result in diverse disease outcomes

2D library beyond graphene and transition metal dichalcogenides: A focus on photodetection

Two-dimensional layered materials (2DLMs) have attracted a tremendous amount of attention as photodetectors due to their fascinating features, including high potentials in new-generation electronic devices, wide coverage of bandgaps, ability to construct van der Waals heterostructures, extraordinary light-mass interaction, strong mechanical flexibility, and the capability of enabling synthesis of 2D nonlayered materials. Until now, most attention has been focused on the well-known graphene and transition metal dichalcogenides (TMDs). However, a growing number of functional materials (more than 5619) with novel optoelectronic and electronic properties are being re-discovered, thereby widening the horizon of 2D libraries. In addition to showing common features of 2DLMs, these new 2D members may bring new opportunities to their well-known analogues, like wider bandgap coverage, direct bandgaps independence with thickness, higher mechanical flexibility, and new photoresponse phenomena. The impressive results communicated so far testify that they have shown high potentials with photodetections covering THz, IR, visible, and UV ranges with comparable or even higher performances than well-known TMDs. Here, we give a comprehensive review on the state-of-the-art photodetections of two-dimensional materials beyond graphene and TMDs. The review is organized as follows: fundamentals of photoresponse first are discussed, followed by detailed photodetections of new 2D members including both layered and non-layered ones. After that, photodiodes and hybrid structures based on these new 2D materials are summarized. Then, the integration of these 2D materials with flexible substrates is reviewed. Finally, we conclude with the current research status of this area and offer our perspectives on future developments. We hope that, through reading this manuscript, readers will quickly have a comprehensive view on this research area

Discovering universal statistical laws of complex networks

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their generalisation power, which we identify with large structural variability and absence of constraints imposed by the construction scheme. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This allows, for instance, to infer global features from local ones using regression models trained on networks with high generalisation power. Our results confirm and extend previous findings regarding the synchronisation properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks with good approximation. Finally, we demonstrate on three different data sets (C. elegans' neuronal network, R. prowazekii's metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models