Comparative study of gp130 cytokine effects on corticotroph AtT-20 cells - Redundancy or specificity of neuroimmunoendocrine modulators?

Objective: This comparative in vitro study examined the effects of all known gp130 cytokines on murine corticotroph AtT-20 cell function. Methods: Cytokines were tested at equimolar concentrations from 0.078 to 10 nM. Tyrosine phosphorylation of the signal transducer and activator of transcription ( STAT) 3 and STAT1, the STAT-dependent suppressor of cytokine signaling (SOCS)-3 promoter activity, SOCS-3 gene expression, STAT-dependent POMC promoter activity and adrenocorticotropic hormone ( ACTH) secretion were determined. Results: Leukemia inhibitory factor (LIF), human oncostatin M (OSM) and cardiotrophin (CT)-1 (LIFR/gp130 ligands), as well as ciliary neurotrophic factor ( CNTF) and novel neurotrophin1/B-cell stimulating factor-3 (CNTFRalpha/LIFR/gp130 ligands) are potent stimuli of corticotroph cells in vitro. In comparison, interleukin (IL)-6 (IL-6R/gp130 ligand) and IL-11 (IL-11R/gp130 ligand) exhibited only modest direct effects on corticotrophs, while murine OSM (OSMR/gp130 ligand) showed no effect. Conclusion: (i) CNTFR complex ligands are potent stimuli of corticotroph function, comparable to LIFR complex ligands; (ii) IL-6 and IL-11 are relatively weak direct stimuli of corticotroph function; (iii) differential effects of human and murine OSM suggest that LIFR/gp130 (OSMR type I) but not OSMR/gp130 (OSMR type II) are involved in corticotroph signaling. (iv) CT-1 has the hitherto unknown ability to stimulate corticotroph function, and (v) despite redundant immuno-neuroendocrine effects of different gp130 cytokines, corticotroph cells are preferably activated through the LIFR and CNTFR complexes. Copyright (C) 2004 S. Karger AG, Basel

Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation

Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural networks as function approximators is typically considered a black box with little explainability and often suffers from suboptimal performance, especially for autonomous navigation in highly interactive multi-agent environments. To address these issues, we propose three auxiliary tasks with spatio-temporal relational reasoning and integrate them into the standard DRL framework, which improves the decision making performance and provides explainable intermediate indicators. We propose to explicitly infer the internal states (i.e., traits and intentions) of surrounding agents (e.g., human drivers) as well as to predict their future trajectories in the situations with and without the ego agent through counterfactual reasoning. These auxiliary tasks provide additional supervision signals to infer the behavior patterns of other interactive agents. Multiple variants of framework integration strategies are compared. We also employ a spatio-temporal graph neural network to encode relations between dynamic entities, which enhances both internal state inference and decision making of the ego agent. Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents. To validate the proposed method, we design an intersection driving simulator based on the Intelligent Intersection Driver Model (IIDM) that simulates vehicles and pedestrians. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics and provides explainable intermediate indicators (i.e., internal states, and interactivity scores) for decision making.Comment: 18 pages, 14 figure

Linking geographic vocabularies through WordNet

The linked open data (LOD) paradigm has emerged as a promising approach to structuring and sharing geospatial information. One of the major obstacles to this vision lies in the difficulties found in the automatic integration between heterogeneous vocabularies and ontologies that provides the semantic backbone of the growing constellation of open geo-knowledge bases. In this article, we show how to utilize WordNet as a semantic hub to increase the integration of LOD. With this purpose in mind, we devise Voc2WordNet, an unsupervised mapping technique between a given vocabulary and WordNet, combining intensional and extensional aspects of the geographic terms. Voc2WordNet is evaluated against a sample of human-generated alignments with the OpenStreetMap (OSM) Semantic Network, a crowdsourced geospatial resource, and the GeoNames ontology, the vocabulary of a large digital gazetteer. These empirical results indicate that the approach can obtain high precision and recall

Attempting to understand (and control) the relationship between structure and magnetism in an extended family of Mn-6 single-molecule magnets

International audienceThe synthesis and characterisation of a large family of hexametallic [Mn-6(III)] Single-Molecule Magnets of general formula [(Mn6O2)-O-III(R-sao)(6)(X)(2)(Sol)(4-6)] (where R = H, Me, Et; X = -O2CR'(R' = H, Me, Ph etc) or Hal(-); sol = EtOH, MeOH and/or H2O) are presented. We show how deliberate structural distortions of the [Mn3O] trinuclear moieties within the [Mn-6] complexes are used to tune their magnetic properties. These findings highlight a qualitative magneto-structural correlation whereby the type (anti- or ferromagnetic) of each Mn-2 pairwise magnetic exchange is dominated by the magnitude of each individual Mn-N-O-Mn torsion angle. The observation of magneto-structural correlations on Such large polymetallic complexes is rare and represents one of the largest studies of this kind