The NMDA agonist D-cycloserine facilitates fear memory consolidation in humans

Animal research suggests that the consolidation of fear and extinction memories depends on N-methyl D-aspartate (NMDA)- type glutamate receptors. Using a fear conditioning and extinction paradigm in healthy normal volunteers, we show that postlearning administration of the NMDA partial agonist D-cycloserine (DCS) facilitates fear memory consolidation, evidenced behaviorally by enhanced skin conductance responses, relative to placebo, for presentations of a conditioned stimulus (CS) at a memory test performed 72 h later. DCS also enhanced CS-evoked neural responses in a posterior hippocampus/collateral sulcus region and in the medial prefrontal cortex at test. Our data suggest a role for NMDA receptors in regulating fear memory consolidation in humans

Polarization of coalitions in an agent-based model of political discourse

Political discourse is the verbal interaction between political actors in a policy domain. This article explains the formation of polarized advocacy or discourse coalitions in this complex phenomenon by presenting a dynamic, stochastic, and discrete agent-based model based on graph theory and local optimization. In a series of thought experiments, actors compute their utility of contributing a specific statement to the discourse by following ideological criteria, preferential attachment, agenda-setting strategies, governmental coherence, or other mechanisms. The evolving macro-level discourse is represented as a dynamic network and evaluated against arguments from the literature on the policy process. A simple combination of four theoretical mechanisms is already able to produce artificial policy debates with theoretically plausible properties. Any sufficiently realistic configuration must entail innovative and path-dependent elements as well as a blend of exogenous preferences and endogenous opinion formation mechanisms

Ethylene negatively regulates transcript abundance of ROP-GAP rheostat-encoding genes and affects apoplastic reactive oxygen species homeostasis in epicarps of cold stored apple fruits

Apple (Malus 7domestica Borkh) fruits are stored for long periods of time at low temperatures (1 \ub0C) leading to the occurrence of physiological disorders. 'Superficial scald' of Granny Smith apples, an economically important ethylene-dependent disorder, was used as a model to study relationships among ethylene action, the regulation of the ROP-GAP rheostat, and maintenance of H2O2 homeostasis in fruits during prolonged cold exposure. The ROP-GAP rheostat is a key module for adaptation to low oxygen in Arabidopsis through Respiratory Burst NADPH Oxidase Homologs (RBOH)-mediated and ROP GTPase-dependent regulation of reactive oxygen species (ROS) homeostasis. Here, it was shown that the transcriptional expression of several components of the apple ROP-GAP machinery, including genes encoding RBOHs, ROPs, and their ancillary proteins ROP-GEFs and ROP-GAPs, is coordinately and negatively regulated by ethylene in conjunction with the progressive impairment of apoplastic H2O2 homeostatic levels. RNA sequencing analyses showed that several components of the known ROP- and ROS-associated transcriptional networks are regulated along with the ROP-GAP rheostat in response to ethylene perception. These findings may extend the role of the ROP-GAP rheostat beyond hypoxic responses and suggest that it may be a functional regulatory node involved in the integration of ethylene and ROS signalling pathways in abiotic stress

Coherent Keyphrase Extraction via Web Mining

Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. A limitation of previous keyphrase extraction algorithms is that the selected keyphrases are occasionally incoherent. That is, the majority of the output keyphrases may fit together well, but there may be a minority that appear to be outliers, with no clear semantic relation to the majority or to each other. This paper presents enhancements to the Kea keyphrase extraction algorithm that are designed to increase the coherence of the extracted keyphrases. The approach is to use the degree of statistical association among candidate keyphrases as evidence that they may be semantically related. The statistical association is measured using web mining. Experiments demonstrate that the enhancements improve the quality of the extracted keyphrases. Furthermore, the enhancements are not domain-specific: the algorithm generalizes well when it is trained on one domain (computer science documents) and tested on another (physics documents).Comment: 6 pages, related work available at http://purl.org/peter.turney