An Evaluation of Alzheimer\u27s Disease-related Pathology in Two Different Models of Diabetes in Immune-challenged Mice

Obesity, type 2 diabetes mellitus (T2DM), and metabolic syndrome are related disorders with wide-ranging and devastating effects that can be observed throughout the body. One important and understudied organ of damage is the brain. Clinical and epidemiological studies have found that T2DM, and more specifically hyperinsulinemia, significantly increases the risk of cognitive decline and increases the likelihood of Alzheimer’s disease (AD) and other forms of dementia in the elderly. Insulin has slightly different functions in the peripheral body than in the central nervous system and the dysregulation of these functions may contribute to the onset and progression of late-life neurodegenerative disease. These experiments were designed to investigate cognitive function and AD-related disease pathology in two different models of diabetes, one model resulting from a diabetogenic compound that selectively targets insulin-producing pancreatic β-cells and the other model based on diet-induced obesity. Additionally, these diabetic models were combined with a genetic mouse model of inflammation to explore the compounding effects of multiple AD risk factors. We found that diabetic-status, regardless of whether it was drug- or diet-induced, resulted in profound impairments in learning and memory and subtle alterations to AD-related histopathology within the hippocampus. Additionally, impairments were most dramatic in male mice; whereas females appeared to be more resistant to metabolic disturbances

The Effect of Acute LPS-Induced Immune Activation and Brain Insulin Signaling Disruption in a Diabetic Model of Alzheimer\u27s Disease

Alzheimer\u27s disease (AD) is a neurodegenerative disorder marked by progressive cognitive impairments and pathological hallmarks that include amyloid plaques, neurofibrillary tangles, and neuronal loss. Several well-known mutations exist that lead to early-onset familial AD (fAD). However, these cases only account for a small percentage of total AD cases. The vast majority of AD cases are sporadic in origin (sAD) and are less clearly influenced by a single mutation but rather some combination of genetic and environmental risk. The etiology of sAD remains unclear but numerous risk factors have been identified that increase the chance of developing AD. Among these risk factors, Type II Diabetes Mellitus (DM) and chronic inflammation of the brain have been implicated as two leading risk factors. Longitudinal studies have identified that patients with T2DM have nearly twice the risk of developing AD. DM is a common metabolic disorder that affects a quarter of the elderly population with symptoms that include insulin dysregulation and altered glucose metabolism. Numerous studies link insulin resistance in the brain with an increased risk of AD. Intracerebroventricular (ICV) administration of the diabetogenic drug streptozotocin (STZ) leads to brain insulin resistance and several AD-like pathologies including progressive deterioration of memory, increased Aβ load and hyperphosphorylated tau. STZ has been proposed to be a relevant animal model of sAD. Additionally, neuroinflammation has been implicated in playing a fundamental role in the progression of the neuropathological changes observed in AD brains. Neuroinflammation is typically thought to be a result of one or more of the other AD pathologies and serves to rapidly progress the disease. Lipopolysaccharide (LPS) is capable of mounting an immune response through the activation of Toll-like receptor 4 (TLR4). Studies involving transgenic models routinely activate the immune system by administering LPS to exacerbate AD-like deficits to better understand the role of neuroinflammation in AD. The majority of AD models rely on genetic mutations and provide valuable information regarding the role of Aβ and tau pathologies but do not represent the prevailing sAD. Considerable research has been conducted to help elucidate the risk factors associated with sAD, including DM and neuroinflammation. However, there is a lack of research regarding the role of neuroinflammation in this particular model of sAD. The purpose of this study was to investigate the effects of a one-time immune activation in the STZ model on learning and memory and proteins associated both with AD hallmarks and with various neurotransmitter systems. Results indicated that an acute inflammatory response played a beneficial role in spatial learning and in several of the investigated proteins. These data may help shed light on the role of brain inflammation in AD

Market failures, consumer preferences, and transaction costs inenergy efficiency purchase decisions

Several factors limit the energy savings potential and increase the costs of energy-efficient technologies to consumers. These factors may usefully be placed into two categories; one category is what economists would define as market failures and the other is related to consumer preferences. This paper provides a conceptual framework for understanding the roles of these factors, and develops a methodology to quantify their effects on costs and potentials of two energy efficient end uses - residential lighting and clothes washers. It notes the significant roles played by the high implicit cost of obtaining information about the benefits of the two technologies and the apparent inability to process and utilize information. For compact fluorescent lamps, this report finds a conservative estimate of the cost of conserved energy of 3.1 cents per kWh. For clothes washers, including water savings reduces the cost of conserved energy from 13.6 cents to 4.3 cents per equivalent kWh. Despite these benefits, market share remains low. About 18 million tons of CO2 could be saved cost effectively from 2005 sales of these two technologies alone. The paper also notes that trading of carbon emissions will incur transaction costs that will range from less than 10 cents per metric ton of CO2 for larger size projects and programs to a few dollars per metric ton of carbon for the smaller ones

Methodological and Practical Considerations for DevelopingMultiproject Baselines for Electric Power and Cement Industry Projects inCentral America

The Lawrence Berkeley National Laboratory (Berkeley Lab) andthe Center for Sustainable Development in the Americas (CSDA) conductedtechnical studies and organized two training workshops to developcapacity in Central America for the evaluation of climate changeprojects. This paper describes the results of two baseline case studiesconducted for these workshops, one for the power sector and one for thecement industry, that were devised to illustrate certain approaches tobaseline setting. Multiproject baseline emission rates (BERs) for themain Guatemalan electricity grid were calculated from 2001 data. Inrecent years, the Guatemalan power sector has experienced rapid growth;thus, a sufficient number of new plants have been built to estimateviable BERs. We found that BERs for baseload plants offsetting additionalbaseload capacity ranged from 0.702 kgCO2/kWh (using a weighted averagestringency) to 0.507 kgCO2/kWh (using a 10th percentile stringency),while the baseline for plants offsetting load-followingcapacity is lowerat 0.567 kgCO2/kWh. For power displaced from existing load-followingplants, the rate is higher, 0.735 kgCO2/kWh, as a result of the age ofsome plants used for meeting peak loads and the infrequency of their use.The approved consolidated methodology for the Clean Development Mechanismyields a single rate of 0.753 kgCO2/kWh. Due to the relatively smallnumber of cement plants in the region and the regional nature of thecement market, all of Central America was chosen as the geographicboundary for setting cement industry BERs. Unfortunately, actualoperations and output data were unobtainable for most of the plants inthe region, and many data were estimated. Cement industry BERs rangedfrom 205 kgCO2 to 225 kgCO2 per metric ton of cement

Opportunities, Barriers and Actions for Industrial Demand Response in California

In 2006 the Demand Response Research Center (DRRC) formed an Industrial Demand Response Team to investigate opportunities and barriers to implementation of Automated Demand Response (Auto-DR) systems in California industries. Auto-DR is an open, interoperable communications and technology platform designed to: Provide customers with automated, electronic price and reliability signals; Provide customers with capability to automate customized DR strategies; Automate DR, providing utilities with dispatchable operational capability similar to conventional generation resources. This research began with a review of previous Auto-DR research on the commercial sector. Implementing Auto-DR in industry presents a number of challenges, both practical and perceived. Some of these include: the variation in loads and processes across and within sectors, resource-dependent loading patterns that are driven by outside factors such as customer orders or time-critical processing (e.g. tomato canning), the perceived lack of control inherent in the term 'Auto-DR', and aversion to risk, especially unscheduled downtime. While industry has demonstrated a willingness to temporarily provide large sheds and shifts to maintain grid reliability and be a good corporate citizen, the drivers for widespread Auto-DR will likely differ. Ultimately, most industrial facilities will balance the real and perceived risks associated with Auto-DR against the potential for economic gain through favorable pricing or incentives. Auto-DR, as with any ongoing industrial activity, will need to function effectively within market structures. The goal of the industrial research is to facilitate deployment of industrial Auto-DR that is economically attractive and technologically feasible. Automation will make DR: More visible by providing greater transparency through two-way end-to-end communication of DR signals from end-use customers; More repeatable, reliable, and persistent because the automated controls strategies that are 'hardened' and pre-programmed into facility's software and hardware; More affordable because automation can help reduce labor costs associated with manual DR strategies initiated by facility staff and can be used for long-term

Energy savings and structural changes in the U.S. economy: Evidence from disaggregated data using decomposition techniques

During the period 1973 to 1985, the U.S. economy saved energy in virtually every sector. Much of this period of energy saving was also marked by a significant drop in the ratio of energy use to GDP. However, since 1985 there has been a slowdown in the rate of energy saving, as key energy intensities (space heating, automobile driving, etc.) have declined less rapidly since 1985 than before. This paper examines delivered (or final) energy consumption trends from the early 1970s to 1994 and provides a framework for measuring key changes that affect U.S. energy use. Starting with estimates of outputs or activity levels for thirty major energy end uses, and energy intensities of each end use, we use the Adaptive Weighted Divisia decomposition to measure the impact of changes in the structure of the U.S. economy. In contrast to many similar decomposition studies, we define measures of structural changes for both households and branches of transportation. We find that between 1973 and 1985, lower energy intensities (corrected to average winter heating demand) reduced U.S. energy uses by about 1.7 percent per year, while structural changes reduced energy uses by 0.4 percent per year. After 1985, when oil prices declined markedly, intensities fell by only 0.8 percent per year and structural changes actually increased energy use by 0.4 percent per year. In the 1990s energy intensities in some industries have even edged upward. Changes in the ratio of energy to GDP (E/GDP) are affected both by intensities and the changes in the demand for energy services relative to GDP. During the first period, from 1973 to 1985, GDP increased faster than the growth in key structural and activity parameters that determine demand for energy services (such as home area, appliance ownership, and motor vehicle use) by 1.5 percent per year. From 1985 to 1994 the difference dropped to less than 0.3 percent per year, largely due to the reversal of structural trends. Thus, the sharp fall in the rate of decline in E/GDP from -3.1 percent to -1.1 percent per year was due almost as much to structural changes as it was to the slowdown in energy intensity reduction. The analysis presented here shows why the E/GDP is an increasingly unreliable yardstick for making measurements of how the energy-economy relationship is changing: effects not related to energy efficiency per se may have roughly the same impact on that ratio as energy saving itself. Since these effects have different causes, and potentially different impacts over the long run, looking at them in the aggregate by considering only the ratio of energy use to GDP is misleading

Quantifying the Effect of the Principal-Agent Problem on US Residential Energy Use

The International Energy Agency (IEA) initiated and coordinated this project to investigate the effects of market failures in the end-use of energy that may isolate some markets or portions thereof from energy price signals in five member countries. Quantifying the amount of energy associated with market failures helps to demonstrate the significance of energy efficiency policies beyond price signals. In this report we investigate the magnitude of the principal-agent (PA) problem affecting four of the major energy end uses in the U.S. residential sector: refrigeration, water heating, space heating, and lighting. Using data from the American Housing Survey, we develop a novel approach to classifying households into a PA matrix for each end use. End use energy values differentiated by housing unit type from the Residential Energy Consumption Survey were used to estimate the final and primary energy use associated with the PA problem. We find that the 2003 associated site energy use from these four end uses totaled over 3,400 trillion Btu, equal to 35 percent of the site energy consumed by the residential sector

Quantifying the Effect of the Principal-Agent Problem on US Residential Energy Use

The International Energy Agency (IEA) initiated and coordinated this project to investigate the effects of market failures in the end-use of energy that may isolate some markets or portions thereof from energy price signals in five member countries. Quantifying the amount of energy associated with market failures helps to demonstrate the significance of energy efficiency policies beyond price signals. In this report we investigate the magnitude of the principal-agent (PA) problem affecting four of the major energy end uses in the U.S. residential sector: refrigeration, water heating, space heating, and lighting. Using data from the American Housing Survey, we develop a novel approach to classifying households into a PA matrix for each end use. End use energy values differentiated by housing unit type from the Residential Energy Consumption Survey were used to estimate the final and primary energy use associated with the PA problem. We find that the 2003 associated site energy use from these four end uses totaled over 3,400 trillion Btu, equal to 35 percent of the site energy consumed by the residential sector