Structural insights into Clostridium perfringens delta toxin pore formation

Clostridium perfringens Delta toxin is one of the three hemolysin-like proteins produced by C. perfringens type C and possibly type B strains. One of the others, NetB, has been shown to be the major cause of Avian Nectrotic Enteritis, which following the reduction in use of antibiotics as growth promoters, has become an emerging disease of industrial poultry. Delta toxin itself is cytotoxic to the wide range of human and animal macrophages and platelets that present GM2 ganglioside on their membranes. It has sequence similarity with Staphylococcus aureus β-pore forming toxins and is expected to heptamerize and form pores in the lipid bilayer of host cell membranes. Nevertheless, its exact mode of action remains undetermined. Here we report the 2.4 Å crystal structure of monomeric Delta toxin. The superposition of this structure with the structure of the phospholipid-bound F component of S. aureus leucocidin (LukF) revealed that the glycerol molecules bound to Delta toxin and the phospholipids in LukF are accommodated in the same hydrophobic clefts, corresponding to where the toxin is expected to latch onto the membrane, though the binding sites show significant differences. From structure-based sequence alignment with the known structure of staphylococcal α-hemolysin, a model of the Delta toxin pore form has been built. Using electron microscopy, we have validated our model and characterized the Delta toxin pore on liposomes. These results highlight both similarities and differences in the mechanism of Delta toxin (and by extension NetB) cytotoxicity from that of the staphylococcal pore-forming toxins

New Radar Interferometric Time Series Analysis Toolbox Released

Interferometric synthetic aperture radar (InSAR) has become an important geodetic tool for measuring deformation of Earth’s surface due to various geophysical phenomena, including slip on earthquake faults, subsurface migration of magma, slow‐moving landslides, movement of shallow crustal fluids (e.g., water and oil), and glacier flow. Airborne and spaceborne synthetic aperture radar (SAR) instruments transmit microwaves toward Earth’s surface and detect the returning reflected waves. The phase of the returned wave depends on the distance between the satellite and the surface, but it is also altered by atmospheric and other effects. InSAR provides measurements of surface deformation by combining amplitude and phase information from two SAR images of the same location taken at different times to create an interferogram. Several existing open‐source analysis tools [Rosen et al., 2004; Rosen et al., 2011; Kampes et al., 2003 ; Sandwell et al., 2011] enable scientists to exploit observations from radar satellites acquired at two different epochs to produce a surface displacement map

Satellite Radiation Products for Ocean Biology and Biogeochemistry: Needs, State-of-the-Art, Gaps, Development Priorities, and Opportunities

Knowing the spatial and temporal distribution of the underwater light field, i.e., the spectral and angular structure of the radiant intensity at any point in the water column, is essential to understanding the biogeochemical processes that control the composition and evolution of aquatic ecosystems and their impact on climate and reaction to climate change. At present, only a few properties are reliably retrieved from space, either directly or via water-leaving radiance. Existing satellite products are limited to planar photosynthetically available radiation (PAR) and ultraviolet (UV) irradiance above the surface and diffuse attenuation coefficient. Examples of operational products are provided, and their advantages and drawbacks are examined. The usefulness and convenience of these products notwithstanding, there is a need, as expressed by the user community, for other products, i.e., sub-surface planar and scalar fluxes, average cosine, spectral fluxes (UV to visible), diurnal fluxes, absorbed fraction of PAR by live algae (APAR), surface albedo, vertical attenuation, and heating rate, and for associating uncertainties to any product on a pixel-by-pixel basis. Methodologies to obtain the new products are qualitatively discussed in view of most recent scientific knowledge and current and future satellite missions, and specific algorithms are presented for some new products, namely sub-surface fluxes and average cosine. A strategy and roadmap (short, medium, and long term) for usage and development priorities is provided, taking into account needs and readiness level. Combining observations from satellites overpassing at different times and geostationary satellites should be pursued to improve the quality of daily-integrated radiation fields, and products should be generated without gaps to provide boundary conditions for general circulation and biogeochemical models. Examples of new products, i.e., daily scalar PAR below the surface, daily average cosine for PAR, and sub-surface spectral scalar fluxes are presented. A procedure to estimate algorithm uncertainties in the total uncertainty budget for above-surface daily PAR, based on radiative simulations for expected situations, is described. In the future, space-borne lidars with ocean profiling capability offer the best hope for improving our knowledge of sub-surface fields. To maximize temporal coverage, space agencies should consider placing ocean-color instruments in L1 orbit, where the sunlit part of the Earth can be frequently observed

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons

How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A model-based argumentation

This paper addresses two questions in the context of neuronal networks dynamics, using methods from dynamical systems theory and statistical physics: (i) How to characterize the statistical properties of sequences of action potentials ("spike trains") produced by neuronal networks ? and; (ii) what are the effects of synaptic plasticity on these statistics ? We introduce a framework in which spike trains are associated to a coding of membrane potential trajectories, and actually, constitute a symbolic coding in important explicit examples (the so-called gIF models). On this basis, we use the thermodynamic formalism from ergodic theory to show how Gibbs distributions are natural probability measures to describe the statistics of spike trains, given the empirical averages of prescribed quantities. As a second result, we show that Gibbs distributions naturally arise when considering "slow" synaptic plasticity rules where the characteristic time for synapse adaptation is quite longer than the characteristic time for neurons dynamics.Comment: 39 pages, 3 figure

The 2013 M_w 7.7 Balochistan Earthquake: Seismic Potential of an Accretionary Wedge

Great earthquakes rarely occur within active accretionary prisms, despite the intense long‐term deformation associated with the formation of these geologic structures. This paucity of earthquakes is often attributed to partitioning of deformation across multiple structures as well as aseismic deformation within and at the base of the prism (Davis et al., 1983). We use teleseismic data and satellite optical and radar imaging of the 2013 M_w 7.7 earthquake that occurred on the southeastern edge of the Makran plate boundary zone to study this unexpected earthquake. We first compute a multiple point‐source solution from W‐phase waveforms to estimate fault geometry and rupture duration and timing. We then derive the distribution of subsurface fault slip from geodetic coseismic offsets. We sample for the slip posterior probability density function using a Bayesian approach, including a full description of the data covariance and accounting for errors in the elastic structure of the crust. The rupture nucleated on a subvertical segment, branching out of the Chaman fault system, and grew into a major earthquake along a 50° north‐dipping thrust fault with significant along‐strike curvature. Fault slip propagated at an average speed of 3.0  km/s for about 180 km and is concentrated in the top 10 km with no displacement on the underlying décollement. This earthquake does not exhibit significant slip deficit near the surface, nor is there significant segmentation of the rupture. We propose that complex interaction between the subduction accommodating the Arabia–Eurasia convergence to the south and the Ornach Nal fault plate boundary between India and Eurasia resulted in the significant strain gradient observed prior to this earthquake. Convergence in this region is accommodated both along the subduction megathrust and as internal deformation of the accretionary wedge

Strain budget of the Ecuador–Colombia subduction zone: A stochastic view

The 2016 Pedernales earthquake (M_W=7.8) ruptured a portion of the Colombia–Ecuador subduction interface where several large historical earthquakes have been documented since the great 1906 earthquake (M=8.6). Considering all significant ruptures that occurred in the region, it has been suggested that the cumulative moment generated co-seismically along this part of the subduction over the last century exceeds the moment deficit accumulated inter-seismically since 1906. Such an excess challenges simple models with earthquakes resetting the elastic strain accumulated inter-seismically in locked asperities. These inferences are however associated with large uncertainties that are generally unknown. The impact of spatial smoothing constraints on co-seismic and inter-seismic models also prevents any robust assessment of the strain budget. We propose a Bayesian kinematic slip model of the 2016 Pedernales earthquake using the most comprehensive dataset to date including InSAR and GPS offsets, tsunami waveforms, and kinematic records from high-rate GPS and strong-motions. In addition, we use inter-seismic geodetic velocities to produce a probabilistic inter-seismic coupling model of the subduction interface. Our stochastic co-seismic and inter-seismic solutions include the ensemble of all plausible models consistent with our prior information and that fit the observations within uncertainties. The analysis of these model ensembles indicates that an excess of co-seismic moment during the 1906–2016 period is likely in Central Ecuador only if we assume that 1942 and 2016 earthquakes are colocated. If this assumption is relaxed, we show that this conclusion no longer holds given uncertainties in co- and inter-seismic processes. The comparison of 1942 and 2016 teleseismic records reveals large uncertainties in the location of the 1942 event, hampering our ability to draw strong conclusions on the unbalanced moment budget in the region. Our results also show a heterogeneous coupling of the subduction interface that coincides with two slip asperities in our co-seismic model for the 2016 Pedernales earthquake and with the location of historical ruptures in 1958, 1979 and 1998. The spatial variability in coupling and complexity in earthquake history suggest strong heterogeneities in frictional properties of the subduction megathrust

A discrete time neural network model with spiking neurons II. Dynamics with noise

We provide rigorous and exact results characterizing the statistics of spike trains in a network of leaky integrate and fire neurons, where time is discrete and where neurons are submitted to noise, without restriction on the synaptic weights. We show the existence and uniqueness of an invariant measure of Gibbs type and discuss its properties. We also discuss Markovian approximations and relate them to the approaches currently used in computational neuroscience to analyse experimental spike trains statistics.Comment: 43 pages - revised version - to appear il Journal of Mathematical Biolog

A view of Neural Networks as dynamical systems

We consider neural networks from the point of view of dynamical systems theory. In this spirit we review recent results dealing with the following questions, adressed in the context of specific models. 1. Characterizing the collective dynamics; 2. Statistical analysis of spikes trains; 3. Interplay between dynamics and network structure; 4. Effects of synaptic plasticity.Comment: Review paper, 51 pages, 10 figures. submitte