Novel Retinal Imaging Technologies

Newly-developed imaging techniques show extensive promise and potential to improve early detection, accurate diagnosis, and management of retinal diseases. Optical coherernce tomography angiography (OCTA), photoacoustic imaging (PAI), and molecular imaging (MI) are all new and promising imaging modalities. As these imaging instruments have advanced, they have enabled visualization of the retina at an unprecedented resolution. Published studies have established the efficacy of these modalities in the assessment of common retinal diseases, such as age-related macular degeneration, diabetic retinopathy, and retinal vascular occlusions. Each of these systems is built upon different principles and all have different limitations. In addition, the three imaging modalities have complementary features and thus can be integrated in to a multimodal imaging system, which will be more powerful in future

A Framework for Teacher Verbal Feedback: Lessons from Chinese Mathematics Classrooms

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Analysis of scale effects in peer-to-peer networks

In this paper, we study both positive and negative scale effects on the operations of peer-to-peer (P2P) file sharing networks and propose the optimal sizing (number of peers) and grouping (number of directory intermediary) decisions. Using analytical models and simulation, we evaluate various performance metrics to investigate the characteristics of a P2P network. Our results show that increasing network scale has a positive effect on the expected content availability and transmission cost, but a negative effect on the expected provision and search costs. We propose an explicit expression for the overall utility of a content sharing P2P community that incorporates tradeoffs among all of the performance measures. This utility function is maximized numerically to obtain the optimal network size (or scale). We also investigate the impact of various P2P network parameters on the performance measures as well as optimal scaling decisions. Furthermore, we extend the model to examine the grouping decision in networks with symmetric interconnection structures and compare the performance between random- and location-based grouping policies. © 2008 IEEE.published_or_final_versio

A study of network-based kernel methods on protein-protein interaction for protein functions prediction

Predicting protein functions is an important issue in the post-genomic era. In this paper, we studied several network-based kernels including Local Linear Embedding (LLE) kernel method, Diffusion kernel and Laplacian Kernel to uncover the relationship between proteins functions and Protein-Protein Interactions (PPI). We first construct kernels based on PPI networks, we then apply Support Vector Machine (SVM) techniques to classify proteins into different functional groups. 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chisquare methods. Finally we made predictions of functions of some unknown genes and verified the preciseness of our prediction in part by the information of other data source.postprintThe 3rd International Symposium on Optimization and Systems Biology (OSB 2009), Zhangjiajie, China, 20-22 September 2009. In Lecture Notes in Operations Research, 2009, v. 11, p. 25-3

Influence of the indirect restoration design on the fracture resistance: a finite element study

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An Evolutionary Algorithm to Generate Real Urban Traffic Flows

In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle ows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being able to work with a traffic distribution close to reality. We have compared the results of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by project number 8.06/5.47.4142 in collaboration with the VSB-Technical University of Ostrava and Universidad de Málaga UMA/FEDER FC14-TIC36, programa de fortalecimiento de las capacidades de I+D+i en las universidades 2014-2015, de la Consejería de Economía, Innovación, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER). Also, partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). The authors would like to thank the FEDER of European Union for financial support via project Movilidad Inteligente: Wi-Fi, Rutas y Contaminación (maxCT) of the "Programa Operativo FEDER de Andalucía 2014-2020. We also thank all Agency of Public Works of Andalusia Regional Government staff and researchers for their dedication and professionalism. Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports

Haemorrhagic stroke vs ischaemic stroke: length of stay and functional outcome measures in stroke survivors

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Asymmetric hydroformylation of styrene catalyzed by pyranoside diphosphite-rh(Ⅰ) complexes

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Building a dense surface map incrementally from semi-dense point cloud and RGBimages

© 2015, Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg. Building and using maps is a fundamental issue for bionic robots in field applications. A dense surface map, which offers rich visual and geometric information, is an ideal representation of the environment for indoor/outdoor localization, navigation, and recognition tasks of these robots. Since most bionic robots can use only small light-weight laser scanners and cameras to acquire semi-dense point cloud and RGB images, we propose a method to generate a consistent and dense surface map from this kind of semi-dense point cloud and RGB images. The method contains two main steps: (1) generate a dense surface for every single scan of point cloud and its corresponding image(s) and (2) incrementally fuse the dense surface of a new scan into the whole map. In step (1) edge-aware resampling is realized by segmenting the scan of a point cloud in advance and resampling each sub-cloud separately. Noise within the scan is reduced and a dense surface is generated. In step (2) the average surface is estimated probabilistically and the non-coincidence of different scans is eliminated. Experiments demonstrate that our method works well in both indoor and outdoor semi-structured environments where there are regularly shaped objects

The effect of human mesenchymal stem cell on cigarette smoke-induced alterations of cardiac function and lipid metabolism in rat

Oral PresentationINTRODUCTION: Cigarette smoking is recognised as a major risk factor for cardiovascular diseases. Mesenchymal stem cells (MSC) were reported to attenuate cardiac injury of myocardial infarction. The aim of this study was to investigate the effect of bone marrow–derived MSCs (BM-MSC) and induced pluripotent stem …published_or_final_versio