Component Neural Systems for the Creation of Emotional Memories during Free Viewing of a Complex, Real-World Event.

To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI). During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets) or from non-viewed portions of the same game (foils). After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan's perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.

The synchronization of superparamagnetic beads driven by a micro-magnetic ratchet.

We present theoretical, numerical, and experimental analyses on the non-linear dynamic behavior of superparamagnetic beads exposed to a periodic array of micro-magnets and an external rotating field. The agreement between theoretical and experimental results revealed that non-linear magnetic forcing dynamics are responsible for transitions between phase-locked orbits, sub-harmonic orbits, and closed orbits, representing different mobility regimes of colloidal beads. These results suggest that the non-linear behavior can be exploited to construct a novel colloidal separation device that can achieve effectively infinite separation resolution for different types of beads, by exploiting minor differences in their bead's properties. We also identify a unique set of initial conditions, which we denote the "devil's gate" which can be used to expeditiously identify the full range of mobility for a given bead type.

Triangular flow in event-by-event ideal hydrodynamics in Au+Au collisions at √SNN = 200A GeV

The first calculation of triangular flow ν3 in Au+Au collisions at √sNN = 200A GeV from an event-by-event (3 + 1) d transport+hydrodynamics hybrid approach is presented. As a response to the initial triangularity Ie{cyrillic, ukrainian}3 of the collision zone, ν3 is computed in a similar way to the standard event-plane analysis for elliptic flow ν2. It is found that the triangular flow exhibits weak centrality dependence and is roughly equal to elliptic flow in most central collisions. We also explore the transverse momentum and rapidity dependence of ν2 and ν3 for charged particles as well as identified particles. We conclude that an event-by-event treatment of the ideal hydrodynamic evolution startingwith realistic initial conditions generates the main features expected for triangular flow. © 2010 The American Physical Society.

Task-driven adaptive statistical compressive sensing of gaussian mixture models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

The Justice Gap in Global Forest Governance

Claims of injustice in global forest governance are prolific: assertions of colonization, marginalization and disenfranchisement of forest-dependent people, and privatization of common resources are some of the most severe allegations of injustice resulting from globally-driven forest conservation initiatives. At its core, the debate over the future of the world's forests is fraught with ethical concerns. Policy makers are not only deciding how forests should be governed, but also who will be winners, losers, and who should have a voice in the decision-making processes. For 30 years, policy makers have sought to redress the concerns of the world's 1.6 billion forest-dependent poor by introducing rights-based and participatory approaches to conservation. Despite these efforts, however, claims of injustice persist. This research examines possible explanations for continued claims of injustice by asking: What are the barriers to delivering justice to forest-dependent communities? Using data collected through surveys, interviews, and collaborative event ethnography in Laos and at the Tenth Conference of Parties to the Convention on Biological Diversity, this dissertation examines the pursuit of justice in global forest governance across multiple scales of governance. The findings reveal that particular conceptualizations of justice have become a central part of the metanormative fabric of global environmental governance, inhibiting institutional evolution and therewith perpetuating the justice gap in global forest governance.

Genetic Studies Identify Critical Biomarkers and Refine the Classification of Malignant Gliomas

Gliomagenesis is driven by a complex network of genetic alterations and while the glioma genome has been a focus of investigation for many years; critical gaps in our knowledge of this disease remain. The identification of novel molecular biomarkers remains a focus of the greater cancer community as a method to improve the consistency and accuracy of pathological diagnosis. In addition, novel molecular biomarkers are drastically needed for the identification of targets that may ultimately result in novel therapeutics aimed at improving glioma treatment. Through the identification of new biomarkers, laboratories will focus future studies on the molecular mechanisms that underlie glioma development. Here, we report a series of genomic analyses identifying novel molecular biomarkers in multiple histopathological subtypes of glioma and refine the classification of malignant gliomas. We have completed a large scale analysis of the WHO grade II-III astrocytoma exome and report frequent mutations in the chromatin modifier, alpha thalassemia mental retardation x-linked (ATRX), isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), and mutations in tumor protein 53 (TP53) as the most frequent genetic mutations in low grade astrocytomas. Furthermore, by analyzing the status of recurrently mutated genes in 363 brain tumors, we establish that highly recurrent gene mutational signatures are an effective tool in stratifying homogeneous patient populations into distinct groups with varying outcomes, thereby capable of predicting prognosis. Next, we have established mutations in the promoter of telomerase reverse transcriptase (TERT) as a frequent genetic event in gliomas and in tissues with low rates of self renewal. We identify TERT promoter mutations as the most frequently mutated gene in primary glioblastoma. Additionally, we show that TERT promoter mutations in combination with IDH1 and IDH2 mutations are able to delineate distinct clinical tumor cohorts and are capable of predicting median overall survival more effectively than standard histopathological diagnosis alone. Taken together, these data advance our understanding of the genetic alterations that underlie the transformation of glial cells into neoplasms and we provide novel genetic biomarkers and multi – gene mutational signatures that can be utilized to refine the classification of malignant gliomas and provide opportunity for improved diagnosis.

Specialized motor-driven dusp1 expression in the song systems of multiple lineages of vocal learning birds.

Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1) was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits.

Schema-driven construction of future autobiographical traumatic events: the future is much more troubling than the past.

Research on future episodic thought has produced compelling theories and results in cognitive psychology, cognitive neuroscience, and clinical psychology. In experiments aimed to integrate these with basic concepts and methods from autobiographical memory research, 76 undergraduates remembered past and imagined future positive and negative events that had or would have a major impact on them. Correlations of the online ratings of visual and auditory imagery, emotion, and other measures demonstrated that individuals used the same processes to the same extent to remember past and construct future events. These measures predicted the theoretically important metacognitive judgment of past reliving and future "preliving" in similar ways. On standardized tests of reactions to traumatic events, scores for future negative events were much higher than scores for past negative events. The scores for future negative events were in the range that would qualify for a diagnosis of posttraumatic stress disorder (PTSD); the test was replicated (n = 52) to check for order effects. Consistent with earlier work, future events had less sensory vividness. Thus, the imagined symptoms of future events were unlikely to be caused by sensory vividness. In a second experiment, to confirm this, 63 undergraduates produced numerous added details between 2 constructions of the same negative future events; deficits in rated vividness were removed with no increase in the standardized tests of reactions to traumatic events. Neuroticism predicted individuals' reactions to negative past events but did not predict imagined reactions to future events. This set of novel methods and findings is interpreted in the contexts of the literatures of episodic future thought, autobiographical memory, PTSD, and classic schema theory.

Event memory: A theory of memory for laboratory, autobiographical, and fictional events.

An event memory is a mental construction of a scene recalled as a single occurrence. It therefore requires the hippocampus and ventral visual stream needed for all scene construction. The construction need not come with a sense of reliving or be made by a participant in the event, and it can be a summary of occurrences from more than one encoding. The mental construction, or physical rendering, of any scene must be done from a specific location and time; this introduces a "self" located in space and time, which is a necessary, but need not be a sufficient, condition for a sense of reliving. We base our theory on scene construction rather than reliving because this allows the integration of many literatures and because there is more accumulated knowledge about scene construction's phenomenology, behavior, and neural basis. Event memory differs from episodic memory in that it does not conflate the independent dimensions of whether or not a memory is relived, is about the self, is recalled voluntarily, or is based on a single encoding with whether it is recalled as a single occurrence of a scene. Thus, we argue that event memory provides a clearer contrast to semantic memory, which also can be about the self, be recalled voluntarily, and be from a unique encoding; allows for a more comprehensive dimensional account of the structure of explicit memory; and better accounts for laboratory and real-world behavioral and neural results, including those from neuropsychology and neuroimaging, than does episodic memory.

The centrality of event scale: a measure of integrating a trauma into one's identity and its relation to post-traumatic stress disorder symptoms.

We introduce a new scale that measures how central an event is to a person's identity and life story. For the most stressful or traumatic event in a person's life, the full 20-item Centrality of Event Scale (CES) and the short 7-item scale are reliable (alpha's of .94 and .88, respectively) in a sample of 707 undergraduates. The scale correlates .38 with PTSD symptom severity and .23 with depression. The present findings are discussed in relation to previous work on individual differences related to PTSD symptoms. Possible connections between the CES and measures of maladaptive attributions and rumination are considered along with suggestions for future research.

Heat balances and thermally driven lagoon-ocean exchangeson a tropical coral reef system (Moorea, French Polynesia)

© 2015. American Geophysical Union. All Rights Reserved.The role of surface and advective heat fluxes on buoyancy-driven circulation was examined within a tropical coral reef system. Measurements of local meteorological conditions as well as water temperature and velocity were made at six lagoon locations for 2 months during the austral summer. We found that temperature rather than salinity dominated buoyancy in this system. The data were used to calculate diurnally phase-averaged thermal balances. A one-dimensional momentum balance developed for a portion of the lagoon indicates that the diurnal heating pattern and consistent spatial gradients in surface heat fluxes create a baroclinic pressure gradient that is dynamically important in driving the observed circulation. The baroclinic and barotropic pressure gradients make up 90% of the momentum budget in part of the system; thus, when the baroclinic pressure gradient decreases 20% during the day due to changes in temperature gradient, this substantially changes the circulation, with different flow patterns occurring during night and day. Thermal balances computed across the entire lagoon show that the spatial heating patterns and resulting buoyancy-driven circulation are important in maintaining a persistent advective export of heat from the lagoon and for enhancing ocean-lagoon exchange.

Wave transformation and wave-driven flow across a steep coral reef

Observations of waves, setup, and wave-driven mean flows were made on a steep coral forereef and its associated lagoonal system on the north shore of Moorea, French Polynesia. Despite the steep and complex geometry of the forereef, and wave amplitudes that are nearly equal to the mean water depth, linear wave theory showed very good agreement with data. Measurements across the reef illustrate the importance of including both wave transport (owing to Stokes drift), as well as the Eulerian mean transport when computing the fluxes over the reef. Finally, the observed setup closely follows the theoretical relationship derived from classic radiation stress theory, although the two parameters that appear in the model-one reflecting wave breaking, the other the effective depth over the reef crest-must be chosen to match theory to data. © 2013 American Meteorological Society.