Imaging and tuning polarity at SrTiO3 domain walls.

Electrostatic fields tune the ground state of interfaces between complex oxide materials. Electronic properties, such as conductivity and superconductivity, can be tuned and then used to create and control circuit elements and gate-defined devices. Here we show that naturally occurring twin boundaries, with properties that are different from their surrounding bulk, can tune the LaAlO3/SrTiO3 interface 2DEG at the nanoscale. In particular, SrTiO3 domain boundaries have the unusual distinction of remaining highly mobile down to low temperatures, and were recently suggested to be polar. Here we apply localized pressure to an individual SrTiO3 twin boundary and detect a change in LaAlO3/SrTiO3 interface current distribution. Our data directly confirm the existence of polarity at the twin boundaries, and demonstrate that they can serve as effective tunable gates. As the location of SrTiO3 domain walls can be controlled using external field stimuli, our findings suggest a novel approach to manipulate SrTiO3-based devices on the nanoscale

Acute rejection is associated with antibodies to non-Gal antigens in baboons using Gal-knockout pig kidneys

We transplanted kidneys from α1,3-galactosyltransferase knockout (GalT-KO) pigs into six baboons using two different immunosuppressive regimens, but most of the baboons died from severe acute humoral xenograft rejection. Circulating induced antibodies to non-Gal antigens were markedly elevated at rejection, which mediated strong complement-dependent cytotoxicity against GalT-KO porcine target cells. These data suggest that antibodies to non-Gal antigens will present an additional barrier to transplantation of organs from GalT-KO pigs to humans. © 2005 Nature Publishing Group

An associative network with spatially organized connectivity

We investigate the properties of an autoassociative network of threshold-linear units whose synaptic connectivity is spatially structured and asymmetric. Since the methods of equilibrium statistical mechanics cannot be applied to such a network due to the lack of a Hamiltonian, we approach the problem through a signal-to-noise analysis, that we adapt to spatially organized networks. The conditions are analyzed for the appearance of stable, spatially non-uniform profiles of activity with large overlaps with one of the stored patterns. It is also shown, with simulations and analytic results, that the storage capacity does not decrease much when the connectivity of the network becomes short range. In addition, the method used here enables us to calculate exactly the storage capacity of a randomly connected network with arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA

Zippin’ up my boots, goin’ back to my roots: Radical left parties in Southern Europe

Radical left parties actively encourage the participation of their members in internal decision-making and insist on promoting organised links to trade unions and social movements. As a party family, they deviate from what is considered to be the trend in which Western political parties have turned their backs on their social roots. Drawing on the experience of South European radical left parties from the fall of the Berlin Wall until the recent financial crisis, we argue that ideology, electoral incentives, party competition and external events explain the radical left's pronounced emphasis on linkage, while organisational trajectory explains variation within the party family in terms of the linkage strategies pursued

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships

Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems

Correlations in spiking neuronal networks with distance dependent connections

Can the topology of a recurrent spiking network be inferred from observed activity dynamics? Which statistical parameters of network connectivity can be extracted from firing rates, correlations and related measurable quantities? To approach these questions, we analyze distance dependent correlations of the activity in small-world networks of neurons with current-based synapses derived from a simple ring topology. We find that in particular the distribution of correlation coefficients of subthreshold activity can tell apart random networks from networks with distance dependent connectivity. Such distributions can be estimated by sampling from random pairs. We also demonstrate the crucial role of the weight distribution, most notably the compliance with Dales principle, for the activity dynamics in recurrent networks of different types

Role of Cell-to-Cell Variability in Activating a Positive Feedback Antiviral Response in Human Dendritic Cells

In the first few hours following Newcastle disease viral infection of human monocyte-derived dendritic cells, the induction of IFNB1 is extremely low and the secreted type I interferon response is below the limits of ELISA assay. However, many interferon-induced genes are activated at this time, for example DDX58 (RIGI), which in response to viral RNA induces IFNB1. We investigated whether the early induction of IFNBI in only a small percentage of infected cells leads to low level IFN secretion that then induces IFN-responsive genes in all cells. We developed an agent-based mathematical model to explore the IFNBI and DDX58 temporal dynamics. Simulations showed that a small number of early responder cells provide a mechanism for efficient and controlled activation of the DDX58-IFNBI positive feedback loop. The model predicted distributions of single cell responses that were confirmed by single cell mRNA measurements. The results suggest that large cell-to-cell variation plays an important role in the early innate immune response, and that the variability is essential for the efficient activation of the IFNB1 based feedback loop

Continuous Attractors with Morphed/Correlated Maps

Continuous attractor networks are used to model the storage and representation of analog quantities, such as position of a visual stimulus. The storage of multiple continuous attractors in the same network has previously been studied in the context of self-position coding. Several uncorrelated maps of environments are stored in the synaptic connections, and a position in a given environment is represented by a localized pattern of neural activity in the corresponding map, driven by a spatially tuned input. Here we analyze networks storing a pair of correlated maps, or a morph sequence between two uncorrelated maps. We find a novel state in which the network activity is simultaneously localized in both maps. In this state, a fixed cue presented to the network does not determine uniquely the location of the bump, i.e. the response is unreliable, with neurons not always responding when their preferred input is present. When the tuned input varies smoothly in time, the neuronal responses become reliable and selective for the environment: the subset of neurons responsive to a moving input in one map changes almost completely in the other map. This form of remapping is a non-trivial transformation between the tuned input to the network and the resulting tuning curves of the neurons. The new state of the network could be related to the formation of direction selectivity in one-dimensional environments and hippocampal remapping. The applicability of the model is not confined to self-position representations; we show an instance of the network solving a simple delayed discrimination task

The reductive glycine pathway allows autotrophic growth of Desulfovibrio desulfuricans

Supplementary informationis available for this paper athttps://doi.org/10.1038/s41467-020-18906-7Six CO2 fixation pathways are known to operate in photoautotrophic and chemoautotrophic microorganisms. Here, we describe chemolithoautotrophic growth of the sulphate-reducing bacterium Desulfovibrio desulfuricans (strain G11) with hydrogen and sulphate as energy substrates. Genomic, transcriptomic, proteomic and metabolomic analyses reveal that D. desulfuricans assimilates CO2 via the reductive glycine pathway, a seventh CO2 fixation pathway. In this pathway, CO2 is first reduced to formate, which is reduced and condensed with a second CO2 to generate glycine. Glycine is further reduced in D. desulfuricans by glycine reductase to acetyl-P, and then to acetyl-CoA, which is condensed with another CO2 to form pyruvate. Ammonia is involved in the operation of the pathway, which is reflected in the dependence of the autotrophic growth rate on the ammonia concentration. Our study demonstrates microbial autotrophic growth fully supported by this highly ATP-efficient CO2 fixation pathway.We acknowledge Änne-Michaelis and William Newell for assistance with the LC-MS forthe metabolomics experiments and Daniel Amador-Noguez for access to the LC-MS usedfor13C intracellular metabolomic analysis. We thank Ines Cardoso Pereira and John vander Oost for critically reading the manuscript. This research was funded by the Neth-erlands Organisation for Scientific Research (NWO) through SIAM Gravitation Grant024.002.002 and the Innovation Program Microbiology (WUR), NJC acknowledgesfunding from NWO through a Rubicon Grant (019.163LW.035) and a Veni Grant(VI.Veni.192.156).info:eu-repo/semantics/publishedVersio