Jim Starnes' Contributions to Residual Strength Analysis Methods for Metallic Structures

A summary of advances in residual strength analyses methods for metallic structures that were realized under the leadership of Dr. James H. Starnes, Jr., is presented. The majority of research led by Dr. Starnes in this area was conducted in the 1990's under the NASA Airframe Structural Integrity Program (NASIP). Dr. Starnes, respectfully referred to herein as Jim, had a passion for studying complex response phenomena and dedicated a significant amount of research effort toward advancing damage tolerance and residual strength analysis methods for metallic structures. Jim's efforts were focused on understanding damage propagation in built-up fuselage structure with widespread fatigue damage, with the goal of ensuring safety in the aging international commercial transport fleet. Jim's major contributions in this research area were in identifying the effects of combined internal pressure and mechanical loads, and geometric nonlinearity, on the response of built-up structures with damage. Analytical and experimental technical results are presented to demonstrate the breadth and rigor of the research conducted in this technical area. Technical results presented herein are drawn exclusively from papers where Jim was a co-author

Synchronization hubs may arise from strong rhythmic inhibition during gamma oscillations in primary visual cortex

Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. Parallel multiunit recordings from V1 in anesthetized cat were collected during the presentation of random sequences of drifting sinusoidal gratings at 12 fixed orientations while gamma oscillations were present. In agreement with the seminal work [1], most units were orientation selective to varying degrees and synchronization was evident in spike train crosscorrelograms computed between units with similar preferred orientations, particularly during the presentation of optimal stimuli. Interestingly, a subset of units, which we refer to as synchronization hubs, were additionally found to synchronize with units having differing preferred orientations which was consistent with a previous study [2]. Moreover, oscillatory patterning in spike train autocorrelograms was also found to be strongest in units denoted as synchronization hubs, and synchronization hubs also tended to have narrower tuning curves relative to other units. We used simplified computational models of small networks of V1 neurons to demonstrate that neurons subject to a sufficiently strong level of inhibitory input can function as synchronization hubs. Neurons were endowed either with integrate-and-fire or conductance-based dynamics and each neuron received a combination of excitatory (AMPA) synaptic inputs that were Poisson-distributed and inhibitory (GABA) inputs that were coherent at a gamma-frequency range. If the strength of rhythmic inhibition was increased for a subset of neurons in the network, and excitation was increased simultaneously to maintain a fixed firing rate, then these neurons produced stronger oscillatory patterning in their discharge probabilities. The oscillations in turn synchronized these neurons with other neurons in the network. Importantly, the strength of synchronization increased with neurons of differing orientation preferences even though no direct synaptic coupling existed between the hubs and the other neurons. Enhanced levels of inhibition account for the emergence of synchronization hubs in the following way: Inhibitory inputs exhibiting a gamma rhythm determine a time window within which a cell is likely to discharge. Increased levels of inhibition narrow down this window further simultaneously leading to (i) even stronger oscillatory patterning of the neuron's activity and (ii) enhanced synchronization with other neurons. This enables synchronization even between cells with differing orientation preferences. Additionally, the same increased levels of inhibition may be responsible for the narrow tuning curves of hub neurons. In conclusion, synchronization hubs may be the cells that interact most strongly with the network of inhibitory interneurons during gamma oscillations in primary visual cortex

Macroscopic coherent structures in a stochastic neural network: from interface dynamics to coarse-grained bifurcation analysis

We study coarse pattern formation in a cellular automaton modelling a spatially-extended stochastic neural network. The model, originally proposed by Gong and Robinson (Phys Rev E 85(5):055,101(R), 2012), is known to support stationary and travelling bumps of localised activity. We pose the model on a ring and study the existence and stability of these patterns in various limits using a combination of analytical and numerical techniques. In a purely deterministic version of the model, posed on a continuum, we construct bumps and travelling waves analytically using standard interface methods from neural field theory. In a stochastic version with Heaviside firing rate, we construct approximate analytical probability mass functions associated with bumps and travelling waves. In the full stochastic model posed on a discrete lattice, where a coarse analytic description is unavailable, we compute patterns and their linear stability using equation-free methods. The lifting procedure used in the coarse time-stepper is informed by the analysis in the deterministic and stochastic limits. In all settings, we identify the synaptic profile as a mesoscopic variable, and the width of the corresponding activity set as a macroscopic variable. Stationary and travelling bumps have similar meso- and macroscopic profiles, but different microscopic structure, hence we propose lifting operators which use microscopic motifs to disambiguate them. We provide numerical evidence that waves are supported by a combination of high synaptic gain and long refractory times, while meandering bumps are elicited by short refractory times

The dynamics of neural fields on bounded domains: an interface approach for Dirichlet boundary conditions

Continuum neural field equations model the large scale spatio-temporal dynamics of interacting neurons on a cortical surface. They have been extensively studied, both analytically and numerically, on bounded as well as unbounded domains. Neural field models do not require the specification of boundary conditions. Relatively little attention has been paid to the imposition of neural activity on the boundary, or to its role in inducing patterned states. Here we redress this imbalance by studying neural field models of Amari type (posed on one- and two-dimensional bounded domains) with Dirichlet boundary conditions. The Amari model has a Heaviside nonlinearity that allows for a description of localised solutions of the neural field with an interface dynamics. We show how to generalise this reduced but exact description by deriving a normal velocity rule for an interface that encapsulates boundary effects. The linear stability analysis of localised states in the interface dynamics is used to understand how spatially extended patterns may develop in the absence and presence of boundary conditions. Theoretical results for pattern formation are shown to be in excellent agreement with simulations of the full neural field model. Furthermore, a numerical scheme for the interface dynamics is introduced and used to probe the way in which a Dirichlet boundary condition can limit the growth of labyrinthine structures

Collaboration of Werner syndrome protein and BRCA1 in cellular responses to DNA interstrand cross-links

Cells deficient in the Werner syndrome protein (WRN) or BRCA1 are hypersensitive to DNA interstrand cross-links (ICLs), whose repair requires nucleotide excision repair (NER) and homologous recombination (HR). However, the roles of WRN and BRCA1 in the repair of DNA ICLs are not understood and the molecular mechanisms of ICL repair at the processing stage have not yet been established. This study demonstrates that WRN helicase activity, but not exonuclease activity, is required to process DNA ICLs in cells and that WRN cooperates with BRCA1 in the cellular response to DNA ICLs. BRCA1 interacts directly with WRN and stimulates WRN helicase and exonuclease activities in vitro. The interaction between WRN and BRCA1 increases in cells treated with DNA cross-linking agents. WRN binding to BRCA1 was mapped to BRCA1 452–1079 amino acids. The BRCA1/BARD1 complex also associates with WRN in vivo and stimulates WRN helicase activity on forked and Holliday junction substrates. These findings suggest that WRN and BRCA1 act in a coordinated manner to facilitate repair of DNA ICLs

Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration.

Perceptual multistability is a phenomenon in which alternate interpretations of a fixed stimulus are perceived intermittently. Although correlates between activity in specific cortical areas and perception have been found, the complex patterns of activity and the underlying mechanisms that gate multistable perception are little understood. Here, we present a neural field competition model in which competing states are represented in a continuous feature space. Bifurcation analysis is used to describe the different types of complex spatio-temporal dynamics produced by the model in terms of several parameters and for different inputs. The dynamics of the model was then compared to human perception investigated psychophysically during long presentations of an ambiguous, multistable motion pattern known as the barberpole illusion. In order to do this, the model is operated in a parameter range where known physiological response properties are reproduced whilst also working close to bifurcation. The model accounts for characteristic behaviour from the psychophysical experiments in terms of the type of switching observed and changes in the rate of switching with respect to contrast. In this way, the modelling study sheds light on the underlying mechanisms that drive perceptual switching in different contrast regimes. The general approach presented is applicable to a broad range of perceptual competition problems in which spatial interactions play a role

Numerical simulation scheme of one-and two-dimensional neural fields involving space-dependent delays

International audienceNeural Fields describe the spatio-temporal dynamics of neural populations involving spatial axonal connections between neurons. These neuronal connections are delayed due to the finite axonal transmission speeds along the fibers inducing a distance-dependent delay between two spatial locations. The numerical simulation in 1-dimensional neural fields is numerically demanding but may be performed in a reasonable run time by implementing standard numerical techniques. However 2-dimensional neural fields demand a more sophisticated numerical technique to simulate solutions in a reasonable time. The work presented shows a recently developed numerical iteration scheme that allows to speed up standard implementations by a factor 10-20. Applications to some pattern forming systems illustrate the power of the technique

E2F1 Mediated Apoptosis Induced by the DNA Damage Response Is Blocked by EBV Nuclear Antigen 3C in Lymphoblastoid Cells

EBV latent antigen EBNA3C is indispensible for in vitro B-cell immortalization resulting in continuously proliferating lymphoblastoid cell lines (LCLs). EBNA3C was previously shown to target pRb for ubiquitin-proteasome mediated degradation, which facilitates G1 to S transition controlled by the major transcriptional activator E2F1. E2F1 also plays a pivotal role in regulating DNA damage induced apoptosis through both p53-dependent and -independent pathways. In this study, we demonstrate that in response to DNA damage LCLs knocked down for EBNA3C undergo a drastic induction of apoptosis, as a possible consequence of both p53- and E2F1-mediated activities. Importantly, EBNA3C was previously shown to suppress p53-induced apoptosis. Now, we also show that EBNA3C efficiently blocks E2F1-mediated apoptosis, as well as its anti-proliferative effects in a p53-independent manner, in response to DNA damage. The N- and C-terminal domains of EBNA3C form a stable pRb independent complex with the N-terminal DNA-binding region of E2F1 responsible for inducing apoptosis. Mechanistically, we show that EBNA3C represses E2F1 transcriptional activity via blocking its DNA-binding activity at the responsive promoters of p73 and Apaf-1 apoptosis induced genes, and also facilitates E2F1 degradation in an ubiquitin-proteasome dependent fashion. Moreover, in response to DNA damage, E2F1 knockdown LCLs exhibited a significant reduction in apoptosis with higher cell-viability. In the presence of normal mitogenic stimuli the growth rate of LCLs knockdown for E2F1 was markedly impaired; indicating that E2F1 plays a dual role in EBV positive cells and that active engagement of the EBNA3C-E2F1 complex is crucial for inhibition of DNA damage induced E2F1-mediated apoptosis. This study offers novel insights into our current understanding of EBV biology and enhances the potential for development of effective therapies against EBV associated B-cell lymphomas

How Structure Determines Correlations in Neuronal Networks

Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks