ASU Scholarship Showcase

Illicit psychostimulant addiction remains a significant problem worldwide, despite decades of research into the neural underpinnings and various treatment approaches. The purpose of this review is to provide a succinct overview of the neurocircuitry involved in drug addiction, as well as the acute and chronic effects of cocaine and amphetamines within this circuitry in humans. Investigational pharmacological treatments for illicit psychostimulant addiction are also reviewed. Our current knowledge base clearly demonstrates that illicit psychostimulants produce lasting adaptive neural and behavioral changes that contribute to the progression and maintenance of addiction. However, attempts at generating pharmacological treatments for psychostimulant addiction have historically focused on intervening at the level of the acute effects of these drugs. The lack of approved pharmacological treatments for psychostimulant addiction highlights the need for new treatment strategies, especially those that prevent or ameliorate the adaptive neural, cognitive, and behavioral changes caused by chronic use of this class of illicit drugs.

Positive allosteric modulators (PAMs) of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors are a diverse class of compounds that increase fast excitatory transmission in the brain. AMPA PAMs have been shown to facilitate long-term potentiation, strengthen communication between various cortical and subcortical regions, and some of these compounds increase the production and release of brain-derived neurotrophic factor (BDNF) in an activity-dependent manner. Through these mechanisms, AMPA PAMs have shown promise as broad spectrum pharmacotherapeutics in preclinical and clinical studies for various neurodegenerative and psychiatric disorders. In recent years, a small collection of preclinical animal studies has also shown that AMPA PAMs may have potential as pharmacotherapeutic adjuncts to extinction-based or cue-exposure therapies for the treatment of drug addiction. The present paper will review this preclinical literature, discuss novel data collected in our laboratory, and recommend future research directions for the possible development of AMPA PAMs as anti-addiction medications.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

The group I metabotropic glutamate receptors (mGluR1a and mGluR5) are important modulators of neuronal structure and function. Although these receptors share common signaling pathways, they are capable of having distinct effects on cellular plasticity. We investigated the individual effects of mGluR1a or mGluR5 activation on dendritic spine density in medium spiny neurons in the nucleus accumbens (NAc), which has become relevant with the potential use of group I mGluR based therapeutics in the treatment of drug addiction. We found that systemic administration of mGluR subtype-specific positive allosteric modulators had opposite effects on dendritic spine densities. Specifically, mGluR5 positive modulation decreased dendritic spine densities in the NAc shell and core, but was without effect in the dorsal striatum, whereas increased spine densities in the NAc were observed with mGluR1a positive modulation. Additionally, direct activation of mGluR5 via CHPG administration into the NAc also decreased the density of dendritic spines. These data provide insight on the ability of group I mGluRs to induce structural plasticity in the NAc and demonstrate that the group I mGluRs are capable of producing not just distinct, but opposing, effects on dendritic spine density.

Studies utilizing selective pharmacological antagonists or targeted gene deletion have demonstrated thattype 5 metabotropic glutamate receptors (mGluR5) are critical mediators and potential therapeutic targets for the treatment of numerous disorders of the central nervous system (CNS), including depression, anxiety, drug addiction, chronic pain, Fragile X syndrome, Parkinson’s disease, and gastroesophageal reflux disease. However, in recent years, the development of positive allosteric modulators (PAMs) of the mGluR5 receptor have revealed that allosteric activation of this receptor may also be of potential therapeutic benefit for the treatment of other CNS disorders, including schizophrenia, cognitive deficits associated with chronic drug use, and deficits in extinction learning. Here we summarize the discovery and characterization of various mGluR5 PAMs, with an emphasis on those that are systemically active. We will also review animal studies showing that these molecules have potential efficacy as novel antipsychotic agents. Finally, we will summarize findings that suggest that mGluR5 PAMs have pro-cognitive effects such as the ability toenhance synaptic plasticity, improve performance in various learning and memory tasks, including extinction of drug-seeking behavior, and reverse cognitive deficits produced by chronic drug use.

Glutamate plays a pivotal role in drug addiction, and the N-methyl-D-aspartate (NMDA) glutamate receptor subtype serves as a molecular target for several drugs of abuse. In this review, we will provide an overview of NMDA receptor structure and function, followed by a review of the mechanism of action, clinical efficacy, and side effect profile of NMDA receptor ligands that are currently in use or being explored for the treatment of drug addiction. These ligands include the NMDA receptor modulators memantine and acamprosate, as well as the partial NMDA agonist D-cycloserine. Data collected to date suggest that direct NMDA receptor modulators have relatively limited efficacy in the treatment of drug addiction, and that partial agonism of NMDA receptors may have some efficacy with regards to extinction learning during cue exposure therapy. However, the lack of consistency in results to date clearly indicates that additional studies are needed, as are studies examining novel ligands with indirect mechanisms for altering NMDA receptor function.

Background: Use of synthetic cathinones, which are designer stimulants found in “bath salts,” has increased dramatically in recent years. Following governmental bans of methylenedioxypyrovalerone, mephedrone, and methylone, a second generation of synthetic cathinones with unknown abuse liability has emerged as replacements.

Methods: Using a discrete trials current intensity threshold intracranial self-stimulation procedure, the present study assessed the effects of 2 common second-generation synthetic cathinones, α‐pyrrolidinopentiophenone (0.1–5mg/kg) and 4-methyl-N-ethcathinone (1–100mg/kg) on brain reward function. Methamphetamine (0.1–3mg/kg) was also tested for comparison purposes.

Results: Results revealed both α‐pyrrolidinopentiophenone and 4-methyl-N-ethcathinone produced significant intracranial self-stimulation threshold reductions similar to that of methamphetamine. α‐Pyrrolidinopentiophenone (1mg/kg) produced a significant maximal reduction in intracranial self-stimulation thresholds (~19%) most similar to maximal reductions produced by methamphetamine (1mg/kg, ~20%). Maximal reductions in intracranial self-stimulation thresholds produced by 4-methyl-N-ethcathinone were observed at 30mg/kg (~15%) and were comparable with those observed with methamphetamine and α‐pyrrolidinopentiophenone tested at the 0.3-mg/kg dose (~14%). Additional analysis of the ED50 values from log-transformed data revealed the rank order potency of these drugs as methamphetamine ≈ α‐pyrrolidinopentiophenone>4-methyl-N-ethcathinone.

Conclusions: These data suggest that the newer second-generation synthetic cathinones activate the brain reward circuitry and thus may possess a similar degree of abuse potential as prototypical illicit psychostimulants such as methamphetamine as well as the first generation synthetic cathinone methylenedioxypyrovalerone, as previously reported.