Managing user interface pattern collections : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

The research presented in this thesis describes the development of a comprehensive UI pattern management tool, MUIP, to support researchers and UI designers manipulate and explore a repository of UI pattern collections. The concept of patterns originated from Alexander's pattern language for the architecture domain. Later, the software development and HCI communities adopted the pattern concept. Many disparate UI pattern collections have been developed and published using various media, such as books, internet, etc. Various pattern formats were used in these collections. In 2003, to cope with this problem, a group of HCI researchers developed a standardised pattern form, called PLML. Researchers have authored patterns, investigated the characteristics of pattern collections and also identified many of the functions required to manage pattern collections. A framework for MUIP has been developed in the light of the analysis of the relevant literature and a survey of existing pattern tools. The framework supports the following features: pattern authoring, manipulating forces, browsing patterns, searching patterns, versioning and customising patterns, relating patterns, manipulating collections and importing or exporting patterns. Patterns are described using the standard pattern form (PLML). An enhanced version of PLML, called PLML vl.2, has been developed so that pattern contents can be organised more effectively. Based on this framework, a specification of a comprehensive pattern management system for manipulating pattern collections was developed and a prototype implemented accordingly. A formal evaluation confirmed the usefulness of the prototype

Immunoproteomic analysis of outer membrane proteins and extracellular proteins of Actinobacillus pleuropneumoniae JL03 serotype 3

<p>Abstract</p> <p>Background</p> <p><it>Actinobacillus pleuropneumoniae </it>is the causative agent of porcine contagious pleuropneumonia, a highly contagious respiratory infection in pigs, and all the 15 serotypes are able to cause disease. Current vaccines including subunit vaccines could not provide satisfactory protection against <it>A. pleuropneumoniae</it>. In this study, the immunoproteomic approach was applied to the analysis of extracellular and outer membrane proteins of <it>A. pleuropneumoniae </it>JL03 serotype 3 for the identification of novel immunogenic proteins for <it>A. pleuropneumoniae</it>.</p> <p>Results</p> <p>A total of 30 immunogenic proteins were identified from outer membrane and extracellular proteins of JL03 serotype 3, of which 6 were known antigens and 24 were novel immunogenic proteins for <it>A. pleuropneumoniae</it>.</p> <p>Conclusion</p> <p>These data provide information about novel immunogenic proteins for <it>A. pleuropneumoniae </it>serotype 3, and are expected to aid in development of novel vaccines against <it>A. pleuropneumoniae</it>.</p

Improving disparity estimation based on residual cost volume and reconstruction error volume

Recently, great progress has been made in formulating dense disparity estimation as a pixel-wise learning task to be solved by deep convolutional neural networks. However, most resulting pixel-wise disparity maps only show little detail for small structures. In this paper, we propose a two-stage architecture: we first learn initial disparities using an initial network, and then employ a disparity refinement network, guided by the initial results, which directly learns disparity corrections. Based on the initial disparities, we construct a residual cost volume between shared left and right feature maps in a potential disparity residual interval, which can capture more detailed context information. Then, the right feature map is warped with the initial disparity and a reconstruction error volume is constructed between the warped right feature map and the original left feature map, which provides a measure of correctness of the initial disparities. The main contribution of this paper is to combine the residual cost volume and the reconstruction error volume to guide training of the refinement network. We use a shallow encoder-decoder module in the refinement network and do learning from coarse to fine, which simplifies the learning problem. We evaluate our method on several challenging stereo datasets. Experimental results demonstrate that our refinement network can significantly improve the overall accuracy by reducing the estimation error by 30% compared with our initial network. Moreover, our network also achieves competitive performance compared with other CNN-based methods. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

A broad-spectrum gas sensor based on correlated two-dimensional electron gas

Designing a broad-spectrum gas sensor capable of identifying gas components in complex environments, such as mixed atmospheres or extreme temperatures, is a significant concern for various technologies, including energy, geological science, and planetary exploration. The main challenge lies in finding materials that exhibit high chemical stability and wide working temperature range. Materials that amplify signals through non-chemical methods could open up new sensing avenues. Here, we present the discovery of a broad-spectrum gas sensor utilizing correlated two-dimensional electron gas at a delta-doped LaAlO3/SrTiO3 interface with LaFeO3. Our study reveals that a back-gating on this two-dimensional electron gas can induce a non-volatile metal to insulator transition, which consequently can activate the two-dimensional electron gas to sensitively and quantitatively probe very broad gas species, no matter whether they are polar, non-polar, or inert gases. Different gas species cause resistance change at their sublimation or boiling temperature and a well-defined phase transition angle can quantitatively determine their partial pressures. Such unique correlated two-dimensional electron gas sensor is not affected by gas mixtures and maintains a wide operating temperature range. Furthermore, its readout is a simple measurement of electric resistance change, thus providing a very low-cost and high-efficient broad-spectrum sensing technique.</p

Mesenchymal Stem Cells Combined With Electroacupuncture Treatment Regulate the Subpopulation of Macrophages and Astrocytes to Facilitate Axonal Regeneration in Transected Spinal Cord

Objective Herein, we investigated whether mesenchymal stem cells (MSCs) transplantation combined with electroacupuncture (EA) treatment could decrease the proportion of proinflammatory microglia/macrophages and neurotoxic A1 reactive astrocytes and inhibit glial scar formation to enhance axonal regeneration after spinal cord injury (SCI). Methods Adult rats were divided into 5 groups after complete transection of the spinal cord at the T10 level: a control group, a nonacupoint EA (NA-EA) group, an EA group, an MSC group, and an MSCs+EA group. Immunofluorescence labeling, quantitative real-time polymerase chain reaction, enzyme-linked immunosorbent assay, and Western blots were performed. Results The results showed that MSCs+EA treatment reduced the proportion of proinflammatory M1 subtype microglia/macrophages, but increased the differentiation of anti-inflammatory M2 phenotype cells, thereby suppressing the mRNA and protein expression of proinflammatory cytokines (tumor necrosis factor-α and IL-1β) and increasing the expression of an anti-inflammatory cytokine (interleukin [IL]-10) on days 7 and 14 after SCI. The changes in expression correlated with the attenuated neurotoxic A1 reactive astrocytes and glial scar, which in turn facilitated the axonal regeneration of the injured spinal cord. In vitro, the proinflammatory cytokines increased the level of proliferation of astrocytes and increased the expression levels of C3, glial fibrillary acidic protein, and chondroitin sulfate proteoglycan. These effects were blocked by administering inhibitors of ErbB1 and signal transducer and activator of transcription 3 (STAT3) (AG1478 and AG490) and IL-10. Conclusion These findings showed that MSCs+EA treatment synergistically regulated the microglia/macrophage subpopulation to reduce inflammation, the formation of neurotoxic A1 astrocytes, and glial scars. This was achieved by downregulating the ErbB1-STAT3 signal pathway, thereby providing a favorable microenvironment conducive to axonal regeneration after SCI