169 research outputs found

    Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses

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    In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience (published

    A voice for change? Trust relationships between ombudsmen, individuals and public service providers

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    There has been a debate for years about what the role of the ombudsman is. This article examines a key component of the role, to promote trust in public services and government. To be able to do this, however, an ombudsman needs to be perceived as legitimate and be trusted by a range of stakeholders, including the user. This article argues that three key relationships in a person’s complaint journey can build trust in an institution, and must therefore be understood as a system. The restorative justice framework is adapted to conceptualize this trust model as a novel approach to understanding the institution from the perspective of its users. Taking two public sector ombudsmen as examples, the article finds that voice and trust need to be reinforced through the relationships in a consumer journey to manage individual expectations, prevent disengagement, and thereby promote trust in the institution, in public service providers, and in government

    A New Family of Lysozyme Inhibitors Contributing to Lysozyme Tolerance in Gram-Negative Bacteria

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    Lysozymes are ancient and important components of the innate immune system of animals that hydrolyze peptidoglycan, the major bacterial cell wall polymer. Bacteria engaging in commensal or pathogenic interactions with an animal host have evolved various strategies to evade this bactericidal enzyme, one recently proposed strategy being the production of lysozyme inhibitors. We here report the discovery of a novel family of bacterial lysozyme inhibitors with widespread homologs in gram-negative bacteria. First, a lysozyme inhibitor was isolated by affinity chromatography from a periplasmic extract of Salmonella Enteritidis, identified by mass spectrometry and correspondingly designated as PliC (periplasmic lysozyme inhibitor of c-type lysozyme). A pliC knock-out mutant no longer produced lysozyme inhibitory activity and showed increased lysozyme sensitivity in the presence of the outer membrane permeabilizing protein lactoferrin. PliC lacks similarity with the previously described Escherichia coli lysozyme inhibitor Ivy, but is related to a group of proteins with a common conserved COG3895 domain, some of them predicted to be lipoproteins. No function has yet been assigned to these proteins, although they are widely spread among the Proteobacteria. We demonstrate that at least two representatives of this group, MliC (membrane bound lysozyme inhibitor of c-type lysozyme) of E. coli and Pseudomonas aeruginosa, also possess lysozyme inhibitory activity and confer increased lysozyme tolerance upon expression in E. coli. Interestingly, mliC of Salmonella Typhi was picked up earlier in a screen for genes induced during residence in macrophages, and knockout of mliC was shown to reduce macrophage survival of S. Typhi. Based on these observations, we suggest that the COG3895 domain is a common feature of a novel and widespread family of bacterial lysozyme inhibitors in gram-negative bacteria that may function as colonization or virulence factors in bacteria interacting with an animal host

    When Two Become One: The Limits of Causality Analysis of Brain Dynamics

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    Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest

    Coherence Potentials: Loss-Less, All-or-None Network Events in the Cortex

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    Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks

    Water dynamics in Shewanella oneidensis at ambient and high pressure using quasi-elastic neutron scattering

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    Quasielastic neutron scattering (QENS) is an ideal technique for studying water transport and relaxation dynamics at pico- to nanosecond timescales and at length scales relevant to cellular dimensions. Studies of high pressure dynamic effects in live organisms are needed to understand Earth’s deep biosphere and biotechnology applications. Here we applied QENS to study water transport in Shewanella oneidensis at ambient (0.1 MPa) and high (200 MPa) pressure using H/D isotopic contrast experiments for normal and perdeuterated bacteria and buffer solutions to distinguish intracellular and transmembrane processes. The results indicate that intracellular water dynamics are comparable with bulk diffusion rates in aqueous fluids at ambient conditions but a significant reduction occurs in high pressure mobility. We interpret this as due to enhanced interactions with macromolecules in the nanoconfined environment. Overall diffusion rates across the cell envelope also occur at similar rates but unexpected narrowing of the QENS signal appears between momentum transfer values Q = 0.7–1.1 Å−1 corresponding to real space dimensions of 6–9 Å. The relaxation time increase can be explained by correlated dynamics of molecules passing through Aquaporin water transport complexes located within the inner or outer membrane structures

    Fetal brain tissue annotation and segmentation challenge results.

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    In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero
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