Rosuvastatin Can Block Pro-Inflammatory Actions of Transgenic Human C-Reactive Protein Without Reducing its Circulating Levels

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Modeling the Effects of Politics Based on a Sociological Reference Scheme for Self-organizing Systems

Many publications discuss how some region became the smartest one in the world. One of the underlying issues is how certain sociological mechanisms lead to the emergence of patterns according to what sometimes are called self-organizing systems. To analyse this, this paper exploits computational modeling based on (multi-order) adaptive network models and inspired by fields addressing self-organizing systems

Modeling Context-Sensitive Metacognitive Control of Focusing on a Mental Model During a Mental Process

Focusing on a proper mental model during mental processes is often crucial. Metacognition is used to control such focusing in a context-sensitive manner. In this paper, a second-order adaptive mental network model is introduced for this form of metacognitive control. The second-order adaptive network model obtained is illustrated by a case scenario concerning social interaction

An Adaptive Cognitive Temporal-Causal Model for Extreme Emotion Extinction Using Psilocybin

In this paper, an adaptive cognitive temporal-causal model using psilocybin for a reduction in extreme emotion is presented. Extreme emotion has an effect on some brain components such as visual cortex, auditory cortex, gustatory cortex, and somatosensory cortex as well as motor cortex such as primary motor cortex, and premotor cortex. Neuroscientific literature reviews show that using psilocybin has a significant effect mostly on two brain components, cerebral cortex, and thalamus. Network-oriented modeling via temporal-causal network-oriented modeling is presented to show the influences of using psilocybin on the cognitive part of the body, same as the brain components. Hebbian learning used to show the adaptivity and learning section of the presented model

Integrating Multilevel Adaptive Models to Develop Systematic, Transparent, and Participatory EIA Practice

European Directive 85/337/EC introduced Environmental Impact Assessment (EIA) as a process governed by administrative rules with the aim of reducing environmental degradation and associated health problems generated by projects. Generally, the EIA process involves analyses and evaluations of the potential impacts that human activities may have on the environment by considering approaches such as the precautionary principle, prevention of conflicts, loss of natural resources, and environmental degradation. EIA influences decision-making at local, national, and transboundary levels, with the following overall objectives: potentially screen out environmentally harmful projects, predict significant adverse impacts, suggest measures to reduce or prevent major impacts, identify feasible alternatives, and engage communities or individuals potentially affected by the implementation of the project. Several issues obstructing the proper implementation of the EIA process are common in developing countries: low quality of assessment reports, lack of public participation, insufficient equipment, and trained staff, inadequate institutional framework, and low cooperation between policymakers, researchers, and stakeholders. The number of research studies focused on the investigation of EIA collaboration process through network analysis and multilevel adaptive models is worryingly limited, considering that the implementation of EIA procedures is deficient in most developing countries, and the contribution of science that envisages the collaboration between the actors involved to the process is minor at best. This paper aims to use Multilevel Network Reification to create Higher-order Adaptive Network Models. The results of multilevel network analysis will contribute to reshape impact assessment procedures and create opportunities for better communication and transparency between practitioners, researchers, policymakers, and other stakeholders. Therefore, integrating Multilevel Adaptive Models in EIA helps to raise the policy efficiency and define the dynamic interplay between EIA actors and diagnose the organizational structures that strongly influence this procedure. Thus, by using an adaptive computational network model, we aim to understand the roles of each actor and the connections established in different EIA networks. The findings will provide innovative information to find solutions and design a collaborative EIA procedure to improve projects under evaluation considering the current threats to society and the environment

An Adaptive Network Model for the Changes in Human Behaviour in Response to the Spread of COVID-19

The aim of the study reported here was to develop a model that can simulate the changes in human behaviour in response to the COVID-19 outbreak. To achieve this, a second-order adaptive social network model was designed integrating mental network models for each person. The model is based on adaptation principles such as the first-order Hebbian learning adaptation principle and the second-order ‘adaptation accelerates with increasing exposure’ adaptation principle

A Cognitive Architecture for Mental Processes Involving Mental Models Analysed from a Self-modeling Network Viewpoint

This paper contributes an analysis of how in mental and social processes, humans often apply specific mental models and learn and adapt them in a controlled manner. It is discussed how controlled adaptation relates to the Plasticity Versus Stability Conundrum in neuroscience. From the analysis an informal three-level cognitive architecture for controlled adaptation was obtained. It is discussed here from a self-modeling network viewpoint how this cognitive architecture can be modeled as a self-modeling network. Making use of the specific network characteristics offered by the self-modeling network structure format, a large number of options for different types of adaptation of mental models and different types of control over adaptation of mental models were obtained. Many of these options were illustrated by a several realistic examples that were formalized by self-modeling networks. Other options that were distinguished from the analysis here, are offered as interesting options for future research

Overproduction of translation elongation factor 1-alpha (eEF1A) suppresses the peroxisome biogenesis defect in a Hansenula polymorpha pex3 mutant via translational read-through

In eukaryotes, elongation factor 1-alpha (eEF1A) is required during the elongation phase of translation. We observed that a portion of the cellular eEF1A colocalizes with purified peroxisomes from the methylotrophic yeast Hansenula polymorpha. We have isolated two genes (TEF1 and TEF2) that encode eEF1A, and which are constitutively expressed. We observed that overproduction of eEF1A suppressed the peroxisome deficient phenotype of an H. polymorpha pex3-1 mutant, which was not observed in a strain deleted for PEX3. The pex3-1 allele contains a UGG to UGA mutation, thereby truncating Pex3p after amino acid 242, suggesting that the suppression effect might be the result of translational read-through. Consistent with this hypothesis, overexpression of the pex3-1 gene itself (including its now untranslated part) partly restored peroxisome biogenesis in a PEX3 null mutant. Subsequent co-overexpression of TEF2 in this strain fully restored its peroxisome biogenesis defect and resulted in the formation of major amounts of full-length Pex3p, presumably via translational read-through