Belief change and spread have been studied in many disciplines-from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics-but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.In this paper, a representative of chain-oxidized sterols, 25-hydroxycholesterol (25-OH), has been studied in Langmuir monolayers mixed with the sphingolipids sphingomyelin (SM) and ganglioside (GM1) to build lipid rafts. A classical Langmuir monolayer approach based on thermodynamic analysis of interactions was complemented with microscopic visualization of films (Brewster angle microscopy), surface-sensitive spectroscopy (polarization modulation-infrared reflection-absorption spectroscopy) and theoretical calculations (density functional theory modelling and molecular dynamics simulations). https://www.selleckchem.com/products/ly333531.html Strong interactions between 25-OH and both investigated sphingolipids enabled the formation of surface complexes. As known from previous studies, 25-OH in pure monolayers can be anchored to the water surface with a hydroxyl group at either C(3) or C(25). In this study, we investigated how the presence of additional strong interactions with sphingolipids modifies the surface arrangement of 25-OH. Results have shown that, in the 25-OH/GM1 system, there are no preferences regarding the orientation of the 25-OH molecule in surface complexes and two types of complexes are formed. On the other hand, SM enforces one specific orientation of 25-OH being anchored with the C(3)-OH group to the water. The strength of interactions between the studied sphingolipids and 25-OH versus cholesterol is similar, which indicates that cholesterol may well be replaced by oxysterol in the lipid raft system. In this way, the composition of lipid rafts can be modified, changing their rheological properties and, as a consequence, influencing their proper functioning.
Pavlovian-to-instrumental transfer (PIT) quantifies the extent to which a stimulus that has been associated with reward or punishment alters operant behaviour. In alcohol dependence (AD), the PIT effect serves as a paradigmatic model of cue-induced relapse. Preclinical studies have suggested a critical role of the opioid system in modulating Pavlovian-instrumental interactions. The A118G polymorphism of the
gene affects opioid receptor availability and function. Furthermore, this polymorphism interacts with cue-induced approach behaviour and is a potential biomarker for pharmacological treatment response in AD. In this study, we tested whether the
polymorphism is associated with the PIT effect and relapse in AD.
Using a PIT task, we examined three independent samples young healthy subjects (
= 161), detoxified alcohol-dependent patients (
= 186) and age-matched healthy controls (
= 105). We used data from a larger study designed to assess the role of learning mechanisms in the development and maintenance of AD. Subjects were genotyped for the A118G (rs1799971) polymorphism of the
gene. Relapse was assessed after three months.
In all three samples, participants with the minor
G-Allele (G+ carriers) showed increased expression of the PIT effect in the absence of learning differences. Relapse was not associated with the
polymorphism. Instead, G+ carriers displaying increased PIT effects were particularly prone to relapse.
These results support a role for the opioid system in incentive salience motivation. Furthermore, they inform a mechanistic model of aberrant salience processing and are in line with the pharmacological potential of opioid receptor targets in the treatment of AD.
These results support a role for the opioid system in incentive salience motivation. Furthermore, they inform a mechanistic model of aberrant salience processing and are in line with the pharmacological potential of opioid receptor targets in the treatment of AD.https://www.selleckchem.com/products/ly333531.html
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