Clozapine and olanzapine, but not haloperidol, suppress serotonin efflux in the medial prefrontal cortex elicited by phencyclidine and ketamine

N-methyl-D-aspartate (NMDA) receptor antagonists such as phencyclidine (PCP) and ketamine can evoke psychotic symptoms in normal individuals and schizophrenic patients. Here, we have examined the effects of PCP (5 mg/kg) and ketamine (25 mg/kg) on the efflux of serotonin (5-HT) in the medial prefrontal cortex (mPFC) and their possible blockade by the antipsychotics, clozapine, olanzapine and haloperidol, as well as ritanserin (5-HT2A/2C receptor antagonist) and prazosin (alpha1-adrenoceptor antagonist). The systemic administration, but not the local perfusion, of the two NMDA receptor antagonists markedly increased the efflux of 5-HT in the mPFC. The atypical antipsychotics clozapine (1 mg/kg) and olanzapine (1 mg/kg), and prazosin (0.3 mg/kg), but not the classical antipsychotic haloperidol (1 mg/kg), reversed the PCP- and ketamine-induced increase in 5-HT efflux. Ritanserin (5 mg/kg) was able to reverse only the effect of PCP. These findings indicate that an increased serotonergic transmission in the mPFC is a functional consequence of NMDA receptor hypofunction and this effect is blocked by atypical antipsychotic drugs.Peer reviewe

Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques

Cliques (or quasi-cliques) are frequently used to model communities: a set of nodes where each pair is (equally) likely to be connected. However, when observing real-world communities, we see that most communities have more structure than that. In particular, the nodes can be ordered in such a way that (almost) all edges in the community lie below a hyperbola. In this paper we present three new models for communities that capture this phenomenon. Our models explain the structure of the communities differently, but we also prove that they are identical in their expressive power. Our models fit to real-world data much better than traditional block models, and allow for more in-depth understanding of the structure of the data

How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?

Graph neural networks (GNNs) have emerged as a powerful machine learning approach for the prediction of molecular properties. In particular, recently proposed advanced GNN models promise quantum chemical accuracy at a fraction of the computational cost. While the capabilities of such advanced GNNs have been extensively demonstrated on benchmark datasets, there have been few applications in real atomistic simulations. Here, we therefore put the robustness of GNN interatomic potentials to the test, using the recently proposed GemNet architecture as a testbed. Models are trained on the QM7-x database of organic molecules and used to perform extensive MD simulations. We find that low test set errors are not sufficient for obtaining stable dynamics and that severe pathologies sometimes only become apparent after hundreds of ps of dynamics. Nonetheless, highly stable and transferable GemNet potentials can be obtained with sufficiently large training sets

Inferring Strange Behavior from Connectivity Pattern in Social Networks

Abstract. Given a multimillion-node social network, how can we sum-marize connectivity pattern from the data, and how can we find unex-pected user behavior? In this paper we study a complete graph from a large who-follows-whom network and spot lockstep behavior that large groups of followers connect to the same groups of followees. Our first contribution is that we study strange patterns on the adjacency matrix and in the spectral subspaces with respect to several flavors of lockstep. We discover that (a) the lockstep behavior on the graph shapes dense “block ” in its adjacency matrix and creates “ray ” in spectral subspaces, and (b) partially overlapping of the behavior shapes “staircase ” in the matrix and creates “pearl ” in the subspaces. The second contribution is that we provide a fast algorithm, using the discovery as a guide for practi-tioners, to detect users who offer the lockstep behavior. We demonstrate that our approach is effective on both synthetic and real data.

Measuring player’s behaviour change over time in public goods game

An important issue in public goods game is whether player's behaviour changes over time, and if so, how significant it is. In this game players can be classified into different groups according to the level of their participation in the public good. This problem can be considered as a concept drift problem by asking the amount of change that happens to the clusters of players over a sequence of game rounds. In this study we present a method for measuring changes in clusters with the same items over discrete time points using external clustering validation indices and area under the curve. External clustering indices were originally used to measure the difference between suggested clusters in terms of clustering algorithms and ground truth labels for items provided by experts. Instead of different cluster label comparison, we use these indices to compare between clusters of any two consecutive time points or between the first time point and the remaining time points to measure the difference between clusters through time points. In theory, any external clustering indices can be used to measure changes for any traditional (non-temporal) clustering algorithm, due to the fact that any time point alone is not carrying any temporal information. For the public goods game, our results indicate that the players are changing over time but the change is smooth and relatively constant between any two time points

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations