Detecting event-related recurrences by symbolic analysis: Applications to human language processing

Quasistationarity is ubiquitous in complex dynamical systems. In brain dynamics there is ample evidence that event-related potentials reflect such quasistationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study we elaborate a recent approach for detecting quasistationary states as recurrence domains by means of recurrence analysis and subsequent symbolisation methods. As a result, recurrence domains are obtained as partition cells that can be further aligned and unified for different realisations. We address two pertinent problems of contemporary recurrence analysis and present possible solutions for them.Comment: 24 pages, 6 figures. Draft version to appear in Proc Royal Soc

Stability and bifurcations in neural fields with axonal delay and general connectivity

A stability analysis is presented for neural field equations in the presence of axonal delays and for a general class of connectivity kernels and synaptic properties. Sufficient conditions are given for the stability of equilibrium solutions. It is shown that the delays play a crucial role in non-stationary bifurcations of equilibria, whereas the stationary bifurcations depend only on the kernel. Bounds are determined for the frequencies of bifurcating periodic solutions. A perturbative scheme is used to calculate the types of bifurcations leading to spatial patterns, oscillatory solutions, and traveling waves. For high transmission speeds a simple method is derived that allows the determination of the bifurcation type by visual inspection of the Fourier transforms of the connectivity kernel and its first moment. Results are numerically illustrated on a class of neurologically plausible second order systems with combinations of Gaussian excitatory and inhibitory connections

Detection of fixed points in spatiotemporal signals by clustering method

We present a method to determine fixed points in spatiotemporal signals. A 144-dimensioanl simulated signal, similar to a Kueppers-Lortz instability, is analyzed and its fixed points are reconstructed.Comment: 3 pages, 3 figure

Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process.

To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual

Stochastic modulation of oscillatory neural activity.

Rhythmic neural activity plays a central role in neural computation. Oscillatory activity has been associated with myriad functions such as homeostasis, attention, and cognition [1] as well as neurological and psychiatric disorders, including Parkinson’s disease, schizophrenia, and depression [2]. Despite this pervasiveness, little is known about the dynamic mechanisms by which the frequency and power of ongoing cyclical neural activity can be modulated either externally (e.g. external stimulation) or via internally-driven modulatory drive of nearby neurons. While numerous studies have focused on neural rhythms and synchrony, it remains unresolved what mediates frequency transitions whereby the predominant power spectrum shifts from one frequency to another. Here, we provide computational perspectives regarding responses of cortical networks to fast stochastic fluctuations (hereafter “noise”) at frequencies in the range of 10-500 Hz that are mimicked using Poisson shot-noise. Using a sparse and randomly connected network of neurons with time delay, we determine the functional impact of these fluctuations on network topology using mean-field approximations. We show how noise can be used to displace the equilibrium activity state of the population: the noise smoothly shifts the mean activity of the modeled neurons from a regime dominated by inhibition to a regime dominated by excitation. Moreover, we show that noise alone may support frequency transition via a non-nonlinear mechanism that operates in addition to resonance. Surprisingly, stochastic fluctuations non-monotonically modulate network’s oscillations, which are in the beta band. The system’s frequency is first slowed down and then accelerated as the stimulus intensity and/or rate increases. This non-linear effect is caused by combined input-induced linearization of the dynamics and enhanced network susceptibility. Our results provide insights regarding a potentially significant mechanism at play in synchronous neural systems; ongoing activity rhythms can be externally and dynamically modulated, and moreover indicate a candidate mechanism supporting frequency transitions. By altering the oscillation frequency of the network, power can be displaced from one frequency band to another. As such, the action of noise on oscillating neural systems must be regarded as strongly non-linear; its action recruiting more than resonance alone to operate on ongoing dynamics

Seismic Loss and Downtime Assessment of Existing Tall Steel-Framed Buildings and Strategies for Increased Resilience

In areas of high seismicity in the United States, the design of many existing tall buildings followed guidelines that do not provide an explicit understanding of performance during major earthquakes. This paper presents an assessment of the seismic performance of existing tall buildings and strategies for increased resilience for a case study city, San Francisco, where an archetype tall building is designed based on an inventory of the existing tall building stock. A 40-story moment-resisting frame system is selected as a representative tall building. The archetype building is rectangular in plan and represents the state of design and construction practice from the mid-1970s to the mid-1980s. Nonlinear response history analysis (NLRHA) are conducted with ground motions representative of the design earthquake hazard level defined in current building codes, with explicit consideration of near-fault directivity effects. Mean transient interstory drifts and story accelerations under the 10% in 50-year ground motion hazard range from 0.19 to 1.14% and 0.15 to 0.81 g, respectively. In order to influence decision making, performance is reported as the expected consequences in terms of direct economic losses and downtime. Furthermore, to achieve increased levels of resilience, a number of strategies are proposed including seismic improvements to structural and nonstructural systems as well as mitigation measures to minimize impeding factors. Expected direct economic losses for the archetype building are in the order of 34% of building cost and downtime estimates for functional recovery are 87 weeks. The strategies presented in this paper enable up to a 92% reduction in losses and minimize downtime for functional recovery to 1 day or less

The proteostasis boundary in misfolding diseases of membrane traffic

AbstractProtein function is regulated by the proteostasis network (PN) [Balch, W.E., Morimoto, R.I., Dillin, A. and Kelly, J.W. (2008) Adapting proteostasis for disease intervention. Science 319, 916–919], an integrated biological system that generates and protects the protein fold. The composition of the PN is regulated by signaling pathways including the unfolded protein response (UPR), the heat-shock response (HSR), the ubiquitin proteasome system (UPS) and epigenetic programs. Mismanagement of protein folding and function during membrane trafficking through the exocytic and endocytic pathways of eukaryotic cells by the PN is responsible for a wide range of diseases that include, among others, lysosomal storage diseases, myelination diseases, cystic fibrosis, systemic amyloidoses such as light chain myeloma, and neurodegenerative diseases including Alzheimer’s. Toxicity from misfolding can be cell autonomous (affect the producing cell) or cell non-autonomous (affect a non-producing cell) or both, and have either a loss-of-function or gain-of-toxic function phenotype. Herein, we review the role of the PN and its regulatory transcriptional circuitry likely to be operational in managing the protein fold and function during membrane trafficking. We emphasize the enabling principle of a ‘proteostasis boundary (PB)’ [Powers, E.T., Morimoto, R.T., Dillin, A., Kelly, J.W., and Balch, W.E. (2009) Biochemical and chemical approaches to diseases of proteostasis deficiency. Annu. Rev. Biochem. 78, 959–991]. The PB is defined by the combined effects of the kinetics and thermodynamics of folding and the kinetics of misfolding, which are linked to the variable and adjustable PN capacity found different cell types. Differences in the PN account for the versatility of protein folding and function in health, and the cellular and tissue response to mutation and environmental challenges in disease. We discuss how manipulation of the folding energetics or the PB through metabolites and pharmacological intervention provides multiple routes for restoration of biological function in trafficking disease

How clumpy is my image? Evaluating crowdsourced annotation tasks

13th UK Workshop on Computational Intelligence (UKCI), Guildford, UK, 9-11 September 2013This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.The use of citizen science to obtain annotations from multiple annotators has been shown to be an effective method for annotating datasets in which computational methods alone are not feasible. The way in which the annotations are obtained is an important consideration which affects the quality of the resulting consensus estimates. In this paper, we examine three separate approaches to obtaining scores for instances rather than merely classifications. To obtain a consensus score annotators were asked to make annotations in one of three paradigms: classification, scoring and ranking. A web-based citizen science experiment is described which implements the three approaches as crowdsourced annotation tasks. The tasks are evaluated in relation to the accuracy and agreement among the participants using both simulated and real-world data from the experiment. The results show a clear difference in performance between the three tasks, with the ranking task obtaining the highest accuracy and agreement among the participants. We show how a simple evolutionary optimiser may be used to improve the performance by reweighting the importance of annotators