Antigen depot is not required for alum adjuvanticity

Alum adjuvants have been in continuous clinical use for more than 80 yr. While the prevailing theory has been that depot formation and the associated slow release of antigen and/or inflammation are responsible for alum enhancement of antigen presentation and subsequent T- and B-cell responses, this has never been formally proven. To examine antigen persistence, we used the chimeric fluorescent protein EαGFP, which allows assessment of antigen presentation in situ, using the Y-Ae antibody. We demonstrate that alum and/or CpG adjuvants induced similar uptake of antigen, and in all cases, GFP signal did not persist beyond 24 h in draining lymph node antigen-presenting cells. Antigen presentation was first detectable on B cells within 6–12 h of antigen administration, followed by conventional dendritic cells (DCs) at 12–24 h, then finally plasmacytoid DCs at 48 h or later. Again, alum and/or CpG adjuvants did not have an effect on the magnitude or sequence of this response; furthermore, they induced similar antigen-specific T-cell activation in vivo. Notably, removal of the injection site and associated alum depot, as early as 2 h after administration, had no appreciable effect on antigen-specific T- and B-cell responses. This study clearly rules out a role for depot formation in alum adjuvant activity

Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations

Repeating Spatial-Temporal Motifs of CA3 Activity Dependent on Engineered Inputs from Dentate Gyrus Neurons in Live Hippocampal Networks.

Anatomical and behavioral studies, and in vivo and slice electrophysiology of the hippocampus suggest specific functions of the dentate gyrus (DG) and the CA3 subregions, but the underlying activity dynamics and repeatability of information processing remains poorly understood. To approach this problem, we engineered separate living networks of the DG and CA3 neurons that develop connections through 51 tunnels for axonal communication. Growing these networks on top of an electrode array enabled us to determine whether the subregion dynamics were separable and repeatable. We found spontaneous development of polarized propagation of 80% of the activity in the native direction from DG to CA3 and different spike and burst dynamics for these subregions. Spatial-temporal differences emerged when the relationships of target CA3 activity were categorized with to the number and timing of inputs from the apposing network. Compared to times of CA3 activity when there was no recorded tunnel input, DG input led to CA3 activity bursts that were 7× more frequent, increased in amplitude and extended in temporal envelope. Logistic regression indicated that a high number of tunnel inputs predict CA3 activity with 90% sensitivity and 70% specificity. Compared to no tunnel input, patterns of >80% tunnel inputs from DG specified different patterns of first-to-fire neurons in the CA3 target well. Clustering dendrograms revealed repeating motifs of three or more patterns at up to 17 sites in CA3 that were importantly associated with specific spatial-temporal patterns of tunnel activity. The number of these motifs recorded in 3 min was significantly higher than shuffled spike activity and not seen above chance in control networks in which CA3 was apposed to CA3 or DG to DG. Together, these results demonstrate spontaneous input-dependent repeatable coding of distributed activity in CA3 networks driven by engineered inputs from DG networks. These functional configurations at measured times of activation (motifs) emerge from anatomically accurate feed-forward connections from DG through tunnels to CA3

Kinetics and mechanism of proton transport across membrane nanopores

We use computer simulations to study the kinetics and mechanism of proton passage through a narrow-pore carbon-nanotube membrane separating reservoirs of liquid water. Free energy and rate constant calculations show that protons move across the membrane diffusively in single-file chains of hydrogen-bonded water molecules. Proton passage through the membrane is opposed by a high barrier along the effective potential, reflecting the large electrostatic penalty for desolvation and reminiscent of charge exclusion in biological water channels. At neutral pH, we estimate a translocation rate of about 1 proton per hour and tube.Comment: 4 pages, 4 figure

The Einstein Ring 0047-2808 Revisited: A Bayesian Inversion

In a previous paper, we outlined a new Bayesian method for inferring the properties of extended gravitational lenses, given data in the form of resolved images. This method holds the most promise for optimally extracting information from the observed image, whilst providing reliable uncertainties in all parameters. Here, we apply the method to the well studied optical Einstein ring 0047-2808. Our results are in broad agreement with previous studies, showing that the density profile of the lensing galaxy is aligned within a few degrees of the light profile, and suggesting that the source galaxy (at redshift 3.6) is a binary system, although its size is only of order 1-2 kpc. We also find that the mass of the elliptical lensing galaxy enclosed by the image is (2.91$\pm$0.01)$\times10^{11}$ M_{\sun}. Our method is able to achieve improved resolution for the source reconstructions, although we also find that some of the uncertainties are greater than has been found in previous analyses, due to the inclusion of extra pixels and a more general lens model.Comment: Accepted for publication in Ap

Dissecting magnetar variability with Bayesian hierarchical models

Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behaviour, ranging from extremely bright, rare giant flares to numerous, less energetic recurrent bursts. The exact trigger and emission mechanisms for these bursts are not known; favoured models involve either a crust fracture and subsequent energy release into the magnetosphere, or explosive reconnection of magnetic field lines. In the absence of a predictive model, understanding the physical processes responsible for magnetar burst variability is difficult. Here, we develop an empirical model that decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. The cascades of spikes that we model might be formed by avalanches of reconnection, or crust rupture aftershocks. Using Markov Chain Monte Carlo (MCMC) sampling augmented with reversible jumps between models with different numbers of parameters, we characterise the posterior distributions of the model parameters and the number of components per burst. We relate these model parameters to physical quantities in the system, and show for the first time that the variability within a burst does not conform to predictions from ideas of self-organised criticality. We also examine how well the properties of the spikes fit the predictions of simplified cascade models for the different trigger mechanisms.Comment: accepted for publication in The Astrophysical Journal; code available at https://bitbucket.org/dhuppenkothen/magnetron, data products at http://figshare.com/articles/SGR_J1550_5418_magnetron_data/129242