Paradoxical enhancement of fear extinction memory and synaptic plasticity by inhibition of the histone acetyltransferase p300

It is well established that the coordinated regulation of activity-dependent gene expression by the histone acetyltransferase (HAT) family of transcriptional coactivators is crucial for the formation of contextual fear and spatial memory, and for hippocampal synaptic plasticity. However, no studies have examined the role of this epigenetic mechanism within the infralimbic prefrontal cortex (ILPFC), an area of the brain that is essential for the formation and consolidation of fear extinction memory. Here we report that a postextinction training infusion of a combined p300/CBP inhibitor (Lys-CoA-Tat), directly into the ILPFC, enhances fear extinction memory in mice. Our results also demonstrate that the HAT p300 is highly expressed within pyramidal neurons of the ILPFC and that the small-molecule p300-specific inhibitor (C646) infused into the ILPFC immediately after weak extinction training enhances the consolidation of fear extinction memory. C646 infused 6 h after extinction had no effect on fear extinction memory, nor did an immediate postextinction training infusion into the prelimbic prefrontal cortex. Consistent with the behavioral findings, inhibition of p300 activity within the ILPFC facilitated long-term potentiation (LTP) under stimulation conditions that do not evoke long-lasting LTP. These data suggest that one function of p300 activity within the ILPFC is to constrain synaptic plasticity, and that a reduction in the function of this HAT is required for the formation of fear extinction memory.

Toward an object-based semantic memory for long-term operation of mobile service robots

Throughout a lifetime of operation, a mobile service robot needs to acquire, store and update its knowledge of a working environment. This includes the ability to identify and track objects in different places, as well as using this information for interaction with humans. This paper introduces a long-term updating mechanism, inspired by the modal model of human memory, to enable a mobile robot to maintain its knowledge of a changing environment. The memory model is integrated with a hybrid map that represents the global topology and local geometry of the environment, as well as the respective 3D location of objects. We aim to enable the robot to use this knowledge to help humans by suggesting the most likely locations of specific objects in its map. An experiment using omni-directional vision demonstrates the ability to track the movements of several objects in a dynamic environment over an extended period of time.

Nonvolatile multilevel data storage memory device from controlled ambipolar charge trapping mechanism

The capability of storing multi-bit information is one of the most important challenges in memory technologies. An ambipolar polymer which intrinsically has the ability to transport electrons and holes as a semiconducting layer provides an opportunity for the charge trapping layer to trap both electrons and holes efficiently. Here, we achieved large memory window and distinct multilevel data storage by utilizing the phenomena of ambipolar charge trapping mechanism. As fabricated flexible memory devices display five well-defined data levels with good endurance and retention properties showing potential application in printed electronics.

Emotion recognition of static and dynamic faces in autism spectrum disorder

There is substantial evidence for facial emotion recognition (FER) deficits in autism spectrum disorder (ASD). The extent of this impairment, however, remains unclear, and there is some suggestion that clinical groups might benefit from the use of dynamic rather than static images. High-functioning individuals with ASD (n = 36) and typically developing controls (n = 36) completed a computerised FER task involving static and dynamic expressions of the six basic emotions. The ASD group showed poorer overall performance in identifying anger and disgust and were disadvantaged by dynamic (relative to static) stimuli when presented with sad expressions. Among both groups, however, dynamic stimuli appeared to improve recognition of anger. This research provides further evidence of specific impairment in the recognition of negative emotions in ASD, but argues against any broad advantages associated with the use of dynamic displays.

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Towards pose-robust face recognition on video

This thesis investigates face recognition in video under the presence of large pose variations. It proposes a solution that performs simultaneous detection of facial landmarks and head poses across large pose variations, employs discriminative modelling of feature distributions of faces with varying poses, and applies fusion of multiple classifiers to pose-mismatch recognition. Experiments on several benchmark datasets have demonstrated that improved performance is achieved using the proposed solution.

Predicting transactive memory systems in multidisciplinary teams : The interplay between team and professional identities

In explaining how communication quality predicts TMS in multidisciplinary teams, we drew on the social identity approach to investigate the mediating role of team identification and the moderating role of professional identification. Recognizing that professional identification could trigger intergroup biases among professional subgroups, or alternatively, could bring resources to the team, we explored the potential moderating role of professional identification in the relationship between team identification and TMS. Using data collected from 882 healthcare personnel working in 126 multidisciplinary hospital teams, results supported our hypothesis that perceived communication quality predicted TMS through team identification. Furthermore, findings provided support for a resource view of professional subgroup identities with results indicating that high levels of professional identification compensated for low levels of team identification in predicting TMS. We provide recommendations on how social identities may be used to promote TMS in multidisciplinary teams.

The monadic environment : evoking emotion and memory in participatory performance

This practice-led research project examined the audience experience of immersive environments in participatory performance. Drawing upon the work of artist Ilya Kabakov, Gaston Bachelard's Poetics of Space (1964) and Leibniz's theory of the monad, the study investigated how an immersive space can be constructed to evoke emotion and memory recall in participants. The research consisted of two cycles of creative experimentation resulting in the presentation of a final piece entitled Dulcet. The research contributes new terminology to the discourse surrounding the participant experience in immersive environments, specifically space-as-memory, the role of ambiguity in spatial design and the construct of the monadic environment.

Text recognition approaches for indoor robotics : a comparison

This paper evaluates the performance of different text recognition techniques for a mobile robot in an indoor (university campus) environment. We compared four different methods: our own approach using existing text detection methods (Minimally Stable Extremal Regions detector and Stroke Width Transform) combined with a convolutional neural network, two modes of the open source program Tesseract, and the experimental mobile app Google Goggles. The results show that a convolutional neural network combined with the Stroke Width Transform gives the best performance in correctly matched text on images with single characters whereas Google Goggles gives the best performance on images with multiple words. The dataset used for this work is released as well.

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Double-strand breaks and the concept of short- and long-term epigenetic memory

Double-strand breaks represent an extremely cytolethal form of DNA damage and thus pose a serious threat to the preservation of genetic and epigenetic information. Though it is well-known that double-strand breaks such as those generated by ionising radiation are among the principal causative factors behind mutations, chromosomal aberrations, genetic instability and carcino-genesis, significantly less is known about the epigenetic consequences of double-strand break formation and repair for carcinogenesis. Double-strand break repair is a highly coordinated process that requires the unravelling of the compacted chromatin structure to facilitate repair machinery access and then restoration of the original undamaged chromatin state. Recent experimental findings have pointed to a potential mechanism for double-strand break-induced epigenetic silencing. This review will discuss some of the key epigenetic regulatory processes involved in double-strand break (DSB) repair and how incomplete or incorrect restoration of chromatin structure can leave a DSB-induced epigenetic memory of damage with potentially pathological repercussions

Memory management and parallelization of data intensive all-to-all comparison in shared-memory systems

This thesis presents a novel program parallelization technique incorporating with dynamic and static scheduling. It utilizes a problem specific pattern developed from the prior knowledge of the targeted problem abstraction. Suitable for solving complex parallelization problems such as data intensive all-to-all comparison constrained by memory, the technique delivers more robust and faster task scheduling compared to the state-of-the art techniques. Good performance is achieved from the technique in data intensive bioinformatics applications.

In-memory business process management

In-memory databases have become a mainstay of enterprise computing offering significant performance and scalability boosts for online analytical and (to a lesser extent) transactional processing as well as improved prospects for integration across different applications through an efficient shared database layer. Significant research and development has been undertaken over several years concerning data management considerations of in-memory databases. However, limited insights are available on the impacts of applications and their supportive middleware platforms and how they need to evolve to fully function through, and leverage, in-memory database capabilities. This paper provides a first, comprehensive exposition into how in-memory databases impact Business Pro- cess Management, as a mission-critical and exemplary model-driven integration and orchestration middleware. Through it, we argue that in-memory databases will render some prevalent uses of legacy BPM middleware obsolete, but also open up exciting possibilities for tighter application integration, better process automation performance and some entirely new BPM capabilities such as process-based application customization. To validate the feasibility of an in-memory BPM, we develop a surprisingly simple BPM runtime embedded into SAP HANA and providing for BPMN-based process automation capabilities.

Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes

Vision-based place recognition involves recognising familiar places despite changes in environmental conditions or camera viewpoint (pose). Existing training-free methods exhibit excellent invariance to either of these challenges, but not both simultaneously. In this paper, we present a technique for condition-invariant place recognition across large lateral platform pose variance for vehicles or robots travelling along routes. Our approach combines sideways facing cameras with a new multi-scale image comparison technique that generates synthetic views for input into the condition-invariant Sequence Matching Across Route Traversals (SMART) algorithm. We evaluate the system’s performance on multi-lane roads in two different environments across day-night cycles. In the extreme case of day-night place recognition across the entire width of a four-lane-plus-median-strip highway, we demonstrate performance of up to 44% recall at 100% precision, where current state-of-the-art fails.

Visual place recognition for persistent robot navigation in changing environments

This thesis demonstrates that robots can learn about how the world changes, and can use this information to recognise where they are, even when the appearance of the environment has changed a great deal. The ability to localise in highly dynamic environments using vision only is a key tool for achieving long-term, autonomous navigation in unstructured outdoor environments. The proposed learning algorithms are designed to be unsupervised, and can be generated by the robot online in response to its observations of the world, without requiring information from a human operator or other external source.