A Computational Cognitive Model Integrating Different Emotion Regulation Strategies

AbstractIn this paper a cognitive model is introduced which integrates a model for emotion generation with models for three different emotion regulation strategies. Given a stressful situation, humans often apply multiple emotion regulation strategies. The presented computational model has been designed based on principles from recent neurological theories based on brain imaging, and psychological and emotion regulation theories. More specifically, the model involves emotion generation and integrates models for the emotion regulation strategies reappraisal, expressive suppression, and situation modification. The model was designed as a dynamical system. Simulation experiments are reported showing the role of the emotion regulation strategies. The simulation results show how a potential stressful situation in principle could lead to emotional strain and how this can be avoided by applying the emotion regulation strategies decreasing the stressful effects

Differential signaling networks of Bcr-Abl p210 and p190 kinases in leukemia cells defined by functional proteomics

The two major isoforms of the oncogenic Bcr-Abl tyrosine kinase, p210 and p190, are expressed upon the Philadelphia chromosome translocation. p210 is the hallmark of chronic myelogenous leukemia, whereas p190 occurs in the majority of B-cell acute lymphoblastic leukemia. Differences in protein interactions and activated signaling pathways that may be associated with the different diseases driven by p210 and p190 are unknown. We have performed a quantitative comparative proteomics study of p210 and p190. Strong differences in the interactome and tyrosine phosphoproteome were found and validated. Whereas the AP2 adaptor complex that regulates clathrin-mediated endocytosis interacts preferentially with p190, the phosphatase Sts1 is enriched with p210. Stronger activation of the Stat5 transcription factor and the Erk1/2 kinases is observed with p210, whereas Lyn kinase is activated by p190. Our findings provide a more coherent understanding of Bcr-Abl signaling, mechanisms of leukemic transformation, resulting disease pathobiology and responses to kinase inhibitors.Leukemia accepted article preview online, 23 January 2017. doi:10.1038/leu.2017.36

The Dual PI3K/mTOR Inhibitor NVP-BEZ235 Induces Tumor Regression in a Genetically Engineered Mouse Model of PIK3CA Wild-Type Colorectal Cancer

To examine the in vitro and in vivo efficacy of the dual PI3K/mTOR inhibitor NVP-BEZ235 in treatment of PIK3CA wild-type colorectal cancer (CRC).PIK3CA mutant and wild-type human CRC cell lines were treated in vitro with NVP-BEZ235, and the resulting effects on proliferation, apoptosis, and signaling were assessed. Colonic tumors from a genetically engineered mouse (GEM) model for sporadic wild-type PIK3CA CRC were treated in vivo with NVP-BEZ235. The resulting effects on macroscopic tumor growth/regression, proliferation, apoptosis, angiogenesis, and signaling were examined.In vitro treatment of CRC cell lines with NVP-BEZ235 resulted in transient PI3K blockade, sustained decreases in mTORC1/mTORC2 signaling, and a corresponding decrease in cell viability (median IC(50) = 9.0-14.3 nM). Similar effects were seen in paired isogenic CRC cell lines that differed only in the presence or absence of an activating PIK3CA mutant allele. In vivo treatment of colonic tumor-bearing mice with NVP-BEZ235 resulted in transient PI3K inhibition and sustained blockade of mTORC1/mTORC2 signaling. Longitudinal tumor surveillance by optical colonoscopy demonstrated a 97% increase in tumor size in control mice (p = 0.01) vs. a 43% decrease (p = 0.008) in treated mice. Ex vivo analysis of the NVP-BEZ235-treated tumors demonstrated a 56% decrease in proliferation (p = 0.003), no effects on apoptosis, and a 75% reduction in angiogenesis (p = 0.013).These studies provide the preclinical rationale for studies examining the efficacy of the dual PI3K/mTOR inhibitor NVP-BEZ235 in treatment of PIK3CA wild-type CRC

Combination PI3K/MEK inhibition promotes tumor apoptosis and regression in PIK3CA wild-type, KRAS mutant colorectal cancer

PI3K inhibition in combination with other agents has not been studied in the context of PIK3CA wild-type, KRAS mutant cancer. In a screen of phospho-kinases, PI3K inhibition of KRAS mutant colorectal cancer cells activated the MAPK pathway. Combination PI3K/MEK inhibition with NVP-BKM120 and PD-0325901 induced tumor regression in a mouse model of PIK3CA wild-type, KRAS mutant colorectal cancer, which was mediated by inhibition of mTORC1, inhibition of MCL-1, and activation of BIM. These findings implicate mitochondrial-dependent apoptotic mechanisms as determinants for the efficacy of PI3K/MEK inhibition in the treatment of PIK3CA wild-type, KRAS mutant cancer. Keywords: PI3K; MEK; KRAS; Colorectal cancer; Mouse model of cance

Machine Autonomy : Definition, Approaches, Challenges and Research Gaps

Postprin

Causality Reconstruction by an Autonomous Agent

International audienceMost AI algorithms consider input data as "percepts" that the agent receives from the environment. Constructivist epistemology, however, suggests an alternative approach that considers the algorithm's input data as feedback resulting from the agent's actions. This paper introduces a constructivist algorithm to let an agent learn regularities of actions and feedback. The agent organizes its behaviors to fulfill a form of intentionality defined independently of a specific task. The experiment shows that this algorithm constructs a Petri net whose nodes represent hypothetical stable states afforded by the agent/environment coupling, and arcs represent transitions between such states. Since this Petri net allows the algorithm to predict the consequences of the agent's actions, we argue that it constitutes a rudimentary causal model of the "world" (agent+environment) learned by the agent through experience of interaction. This work opens the way to studying how an autonomous agent car learn more complex causal models of more complex worlds, in particular by explaining regularities of interaction through the presence of objects in the agent's surrounding space

Causality Reconstruction by an Autonomous Agent

International audienceMost AI algorithms consider input data as "percepts" that the agent receives from the environment. Constructivist epistemology, however, suggests an alternative approach that considers the algorithm's input data as feedback resulting from the agent's actions. This paper introduces a constructivist algorithm to let an agent learn regularities of actions and feedback. The agent organizes its behaviors to fulfill a form of intentionality defined independently of a specific task. The experiment shows that this algorithm constructs a Petri net whose nodes represent hypothetical stable states afforded by the agent/environment coupling, and arcs represent transitions between such states. Since this Petri net allows the algorithm to predict the consequences of the agent's actions, we argue that it constitutes a rudimentary causal model of the "world" (agent+environment) learned by the agent through experience of interaction. This work opens the way to studying how an autonomous agent car learn more complex causal models of more complex worlds, in particular by explaining regularities of interaction through the presence of objects in the agent's surrounding space

Kinase-templated abiotic reaction

Protein kinases are quintessential regulators of cellular function. Numerous pathologies are intimately linked to the dysregulated activity of a particular protein kinase. Herein we report a technology based on a proximity-induced chemical transformation that enables the detection and imaging of specific kinases. Using two probes that target the nucleotide-binding site and substrate binding site of a target kinase respectively, the reagents appended on the probes are brought within reactive distance thereby enabling the chemical transformation. The reaction used for sensing is a ruthenium-photocatalyzed reduction of a pyridinium immolative linker, which uncages a fluorophore (rhodamine). We demonstrate that this technology can be used to discriminate between closely related kinases with a high signal to noise ratio. We further demonstrate that the technology operates within the complexity of a cellular context with a good correlation between the level of kinase activity and fluorescence output