Letter, George A. McCall to Jefferson Davis, February 14, 1855; Letter, Jefferson Davis to James C. Dobbin, February 19, 1855

This handwritten letter, dated February 14, is from George A. McCall to Jefferson Davis as an introduction and endorsement of Mr. Falkner M. Murtrie who is an applicant for a naval post. The back of the letter includes Davis\u27s endorsement of Murtrie addressed to Hon. Mr. Dobbin, dated February 19, 1855.https://scholarsjunction.msstate.edu/fvw-manuscripts-original-manuscripts/1133/thumbnail.jp

Artificial reaction networks.

In this paper we present a novel method of simulating cellular intelligence, the Artificial Reaction Network (ARN). The ARN can be described as a modular S-System, with some properties in common with other Systems Biology and AI techniques, including Random Boolean Networks, Petri Nets, Artificial Biochemical Networks and Artificial Neural Networks. We validate the ARN against standard biological data, and successfully apply it to simulate cellular intelligence associated with the well-characterized cell signaling network of Escherichia coli chemotaxis. Finally, we explore the adaptability of the ARN, as a means to develop novel AI techniques, by successfully applying the simulated E. coli chemotaxis to a general optimization problem

Combining biochemical network motifs within an ARN-agent control system.

The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony

Artificial chemistry approach to exploring search spaces using artificial reaction network agents.

The Artificial Reaction Network (ARN) is a cell signaling network inspired representation belonging to the branch of A-Life known as Artificial Chemistry. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, Random Boolean Networks and S-Systems. The ARN has been previously applied to control of limbed robots and simulation of biological signaling pathways. In this paper, multiple instances of independent distributed ARN controlled agents function to find the global minima within a set of simulated environments characterized by benchmark problems. The search behavior results from the internal ARN network, but is enhanced by collective activities and stigmergic interaction of the agents. The results show that the agents are able to find best fitness solutions in all problems, and compare well with results of cell inspired optimization algorithms. Such a system may have practical application in distributed or swarm robotics

Exploring aspects of cell intelligence with artificial reaction networks.

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence

Mechanistic Study of the Effect of Endothelin SNPs in Microvascular Angina – protocol of the PRIZE Endothelin Sub-Study

Introduction Microvascular angina (MVA) is a common cause of ischemia with non-obstructive coronary arteries (INOCA) and limited therapeutic options are available to those affected. Endothelin-1 (ET-1) is a potent vasoconstrictor implicated in the pathophysiology of MVA. A large randomised, double blinded, placebo controlled crossover trial, the PRecIsion medicine with ZibotEntan in microvascular angina (PRIZE) trial is currently underway, investigating an endothelin receptor antagonist – Zibotentan, as a new drug treatment for microvascular angina. The trial uses a 'precision medicine' approach by preferential selection of those with higher ET-1 expression conferred by the PHACTR1 minor G allele single nucleotide polymorphism (SNP). The incidence of this SNP occurs in approximately one third of the population therefore a considerable number of screened patients will be ineligible for randomisation and the treatment phase of the trial. Methods In the PRIZE Endothelin (ET) Sub-Study, patients screened out of the PRIZE trial will be genotyped for other genetic variants in the ET-1 pathway. These will be correlated with phenotypic characteristics including exercise tolerance, angina severity and quantitative measures of microvascular function on cardiovascular MRI as well as mechanistic data on endothelin pathway signalling. Conclusions The study will provide a comprehensive genotype and phenotype bio-resource identifying novel ET-1 genotypes to inform the potential wider use of endothelin receptor antagonists for this indication.Wellcome Trust [WT107715/Z/15/Z, APD], British Heart Foundation [CB, RE/18/6134217], Medical Research Council [CB, MR/S018905/1], ].Jon Moulton Charity Trust [GRA, SPH

Applications and design of cooperative multi-agent ARN-based systems.

The Artificial Reaction Network (ARN) is an Artificial Chemistry inspired by Cell Signalling Networks (CSNs). Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In previous work, the ARN was applied to the simulation of the chemotaxis pathway of E. coli and to the control of quadrupedal robotic gaits. In this paper, the design and application of ARN-based cell-like agents termed Cytobots are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognizing environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics

Hippocampal long-term potentiation is disrupted during expression and extinction but is restored after reinstatement of morphine place preference

Learned associations between environmental cues and morphine use play an important role in the maintenance and/or relapse of opioid addiction. Although previous studies suggest that context-dependent morphine treatment alters glutamatergic transmission and synaptic plasticity in the hippocampus, their role in morphine conditioned place preference (CPP) and reinstatement remains unknown. We investigated changes in synaptic plasticity and NMDAR expression in the hippocampus after the expression, extinction, and reinstatement of morphine CPP. Here we report that morphine CPP is associated with increased basal synaptic transmission, impaired hippocampal long-term potentiation (LTP), and increased synaptic expression of the NR1 and NR2b NMDAR subunits. Changes in synaptic plasticity, synaptic NR1 and NR2b expression, and morphine CPP were absent when morphine was not paired with a specific context. Furthermore, hippocampal LTP was impaired and synaptic NR2b expression was increased after extinction of morphine CPP, indicating that these alterations in plasticity may be involved in the mechanisms underlying the learning of drug–environment associations. After extinction of morphine CPP, a priming dose of morphine was sufficient to reinstate morphine CPP and was associated with LTP that was indistinguishable from saline control groups. In contrast, morphine CPP extinguished mice that received a saline priming dose did not show CPP and had disrupted hippocampal LTP. Finally, we found that reinstatement of morphine CPP was prevented by the selective blockade of the NR2b subunit in the hippocampus. Together, these data suggest that alterations in synaptic plasticity and glutamatergic transmission play an important role in the reinstatement of morphine CPP