Modeling postpartum depression in rats: theoretic and methodological issues

The postpartum period is when a host of changes occur at molecular, cellular, physiological and behavioral levels to prepare female humans for the challenge of maternity. Alteration or prevention of these normal adaptions is thought to contribute to disruptions of emotion regulation, motivation and cognitive abilities that underlie postpartum mental disorders, such as postpartum depression. Despite the high incidence of this disorder, and the detrimental consequences for both mother and child, its etiology and related neurobiological mechanisms remain poorly understood, partially due to the lack of appropriate animal models. In recent decades, there have been a number of attempts to model postpartum depression disorder in rats. In the present review, we first describe clinical symptoms of postpartum depression and discuss known risk factors, including both genetic and environmental factors. Thereafter, we discuss various rat models that have been developed to capture various aspects of this disorder and knowledge gained from such attempts. In doing so, we focus on the theories behind each attempt and the methods used to achieve their goals. Finally, we point out several understudied areas in this field and make suggestions for future directions

Examining The Relationship Between Experiential Avoidance And Attentional Bias Using Eye-Tracking

Cognitive models of anxiety disorders propose that attentional biases serve as a key contributor to the development and maintenance of anxiety pathology, and a large body of research has accumulated demonstrating that anxious individuals exhibit consistent attentional biases for threat-relevant information. Recent research has also suggested that individuals with known cognitive vulnerabilities for anxiety disorders exhibit similar attentional biases for threat. Experiential avoidance (EA), or the unwillingness to experience uncomfortable cognitive, emotional, or sensory experiences, has been proposed to serve as a core vulnerability factor for emotional disorders in some recent models of psychopathology, and several lines of correlational and longitudinal research appears to support this assertion. Although preliminary research suggests that EA is characterized by biased processing, researchers have yet to examine the association between EA and attentional biases. Using eye tracking technology, the present study examined whether EA predicted attentional vigilance to, fixation on, and subsequent avoidance of negative-emotion and anxiety-related stimuli in 141 undergraduate students. Contrary to hypotheses, EA was not significantly related to any eye-tracking outcomes beyond a negative association with vigilance to neutral stimuli. Results are framed within the context of the anxiety attentional bias literature and directions for future research are discussed

Architecting Efficient Data Centers.

Data center power consumption has become a key constraint in continuing to scale Internet services. As our society’s reliance on “the Cloud” continues to grow, companies require an ever-increasing amount of computational capacity to support their customers. Massive warehouse-scale data centers have emerged, requiring 30MW or more of total power capacity. Over the lifetime of a typical high-scale data center, power-related costs make up 50% of the total cost of ownership (TCO). Furthermore, the aggregate effect of data center power consumption across the country cannot be ignored. In total, data center energy usage has reached approximately 2% of aggregate consumption in the United States and continues to grow. This thesis addresses the need to increase computational efficiency to address this grow- ing problem. It proposes a new classes of power management techniques: coordinated full-system idle low-power modes to increase the energy proportionality of modern servers. First, we introduce the PowerNap server architecture, a coordinated full-system idle low- power mode which transitions in and out of an ultra-low power nap state to save power during brief idle periods. While effective for uniprocessor systems, PowerNap relies on full-system idleness and we show that such idleness disappears as the number of cores per processor continues to increase. We expose this problem in a case study of Google Web search in which we demonstrate that coordinated full-system active power modes are necessary to reach energy proportionality and that PowerNap is ineffective because of a lack of idleness. To recover full-system idleness, we introduce DreamWeaver, architectural support for deep sleep. DreamWeaver allows a server to exchange latency for full-system idleness, allowing PowerNap-enabled servers to be effective and provides a better latency- power savings tradeoff than existing approaches. Finally, this thesis investigates workloads which achieve efficiency through methodical cluster provisioning techniques. Using the popular memcached workload, this thesis provides examples of provisioning clusters for cost-efficiency given latency, throughput, and data set size targets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91499/1/meisner_1.pd

Intraindividual Variability in Reaction Time Predicts Cognitive Outcomes 5 Years Later

Objective: Building on results suggesting that intraindividual variability in reaction time (inconsistency) is highly sensitive to even subtle changes in cognitive ability, this study addressed the capacity of inconsistency to predict change in cognitiv

Performance Modeling of Multithreaded Distributed Memory Architectures

In multithreaded distributed memory architectures, long—latency memory operations and synchronization delays are tolerated by suspending the execution of the current thread and switching to another thread, which is executed concurrently with the long—latency operation of the suspended thread. Timed Petri nets are used to model several multithreaded architectures at the instruction and thread levels. Model evaluation results are presented to illustrate the influence of different model parameters on the performance of the system

Priming the Data: Examining Self-Potentiation in a Word Fragment Completion Task

The studies presented assessed the presence and severity of self-potentiation effects in a word fragment completion task commonly used to evaluate priming effects. Priming effects have suffered a plethora of replication issues, and the field is currently under intense scrutiny. By analyzing and refining the methodology used, we will be able to more effectively evaluate the significance and strength of these effects in future research, and increase the reliability of results under replication. In these experiments, outcomes on a word fragment completion task were examined under a variety of conditions. In the first study, responses were collected in a free-response style similarly to previous research without using an induction. The second study evaluated these outcomes under mortality salience induction, subtle mortality salience induction, and no induction, using restricted timing for stimulus presentation and response. Responses revealed evidence suggesting a potentiating effect. Analyses for effects between conditions were non-significant, regardless of method of analysis used

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks