材料信息学平台的研究现状发展趋势与作用

调查分析了材料信息学平台研究与应用的现状,指出了材料信息学平台已成为材料研发的最重要的设备之一,展望了新一代材料信息学平台的发展趋势, 概述了材料信息平台对于材料研究与发展的重要促进作用,认为材料信息学平台的应用将加速材料科学与工程的发展,引起材料研究模式的变革.     

A web-based e-learning application for wound diagnosis and treatment

Pressure ulcers (PrU) are considered as one of the most challenging problems that Nursing professionals have to deal with in their daily practice. Nowadays, the education on PrUs is mainly based on traditional lecturing, seminars and face-to-face instruction, sometimes with the support of photographs of wounds being used as teaching material. This traditional educational methodology suffers from some important limitations, which could affect the efficacy of the learning process. This current study has been designed to introduce information and communication technologies (ICT) in the education on PrU for undergraduate students, with the main objective of evaluating the advantages an disadvantages of using ICT, by comparing the learning results obtained from using an e-learning tool with those from a traditional teaching methodology. In order to meet this major objective, a web-based learning system named ePULab has been designed and developed as an adaptive e-learning tool for the autonomous acquisition of knowledge on PrU evaluation. This innovative system has been validated by means of a randomized controlled trial that compares its learning efficacy with that from a control group receiving a traditional face-to-face instruction. Students using ePULab gave significantly better (p  <  0.01) learning acquisition scores (from pre-test mean 8.27 (SD 1.39) to post-test mean 15.83 (SD 2.52)) than those following traditional lecture-style classes (from pre-test mean 8.23 (SD 1.23) to post-test mean 11.6 (SD 2.52)). In this article, the ePULab software is described in detail and the results from that experimental educational validation study are also presented and analyzed.     

SemantEco: A semantically powered modular architecture for integrating distributed environmental and ecological data

We aim to inform the development of decision support tools for resource managers who need to examine large complex ecosystems and make recommendations in the face of many tradeoffs and conflicting drivers. We take a semantic technology approach, leveraging background ontologies and the growing body of linked open data. In previous work, we designed and implemented a semantically enabled environmental monitoring framework called SemantEco and used it to build a water quality portal named SemantAqua. Our previous system included foundational ontologies to support environmental regulation violations and relevant human health effects. In this work, we discuss SemantEco’s new architecture that supports modular extensions and makes it easier to support additional domains. Our enhanced framework includes foundational ontologies to support modeling of wildlife observation and wildlife health impacts, thereby enabling deeper and broader support for more holistically examining the effects of environmental pollution on ecosystems. We conclude with a discussion of how, through the application of semantic technologies, modular designs will make it easier for resource managers to bring in new sources of data to support more complex use cases.     

Principled exploitation of behavioural coupling

In robot building, attention tends to focus on internal signal processing and the way desired motor signals are generated. But equally important is the selection and configuration of the robot's sensory resources. The degree to which sensory input informs the process of output generation directly impacts internal complexity since the more informative input signals are, the less internal processing is required. In the extreme case, input signals may identify motor signals exactly and internal processing may then be eliminated completely. The paper presents an information theoretic model for measuring this tradeoff being input information and internal processing complexity, and establishes the conditions under which it is feasible to utilize the processing-free strategy of ‘direct connection’. The model is then used for purposes of analysing five robots, including two commercial products (Sony AIBO and WowWee's Robosapiens). The principled exploitation of very informative sensory input is then shown to be effective in an unexpectedly wide range of situations.     

Analytic differential approach for robust registration of rat brain histological images

Image registration is an important topic in medical image analysis. It is usually used to reconstruct 3D structure of tissues from a series of microscopic images. However, a variety of inherent factors may result in great differences between acquired slices during imaging even if they are adjacent. The common differences include the color difference and geometry discrepancy, which make the registration problem a difficult challenge. In this study, we propose a robust registration method to automatically reconstruct 3D volume data of the rat brain. It mainly consists of three procedures, including multiscale wavelet-based feature extraction, analytic robust point matching (ARPM), and registration refinement with feature-based modified Levenberg-Marquardt algorithm (FMLM). The product of gradient moduli in multi-scales is used to decide if extracted feature points are true according to the characteristic that features could exist in multiscale. The ARPM registration algorithm is proposed to speedily accomplish the registration of two point sets with different size by simultaneously evaluating the spatial correspondence and geometrical transformation. In addition, a FMLM method is also proposed to further refine registration results and achieve subpixel accuracy. The FMLM method converges much faster than most other methods due to its feature-based and nonlinear characteristic. The performance of proposed method is evaluated by comparing it with well-known thin-plate spline robust point matching (TPS-RPM) algorithm. The results indicate that ARPM-FMLM algorithm is a robust and fast method in image registration.     

医学信息学课程群立体化教学模式研究

根据医学信息学课程概念多、理论抽象、知识涉及面广,学生对整体内容很难完全把握的特点,在建构主义学习理论的指导下,笔者构建了从平面教材建设到网络资源提供、从课堂知识讲解到实验实践操练、从必修内容学习到课外兴趣小组知识拓展的立体化教学模式,形成了重自学、创环境、重实践、多维度评价的课程教学体系,有效提高了教学质量和学习效果。     

Improved automated detection of glaucoma by correlating fundus and SD-OCT image analysis

Glaucoma is a multifactorial ocular disease. Ophthalmologists mostly use fundus or optical coherence tomography (OCT) for diagnosis of glaucoma. In this study, a hybrid computer-aided-diagnosis (H-CAD) system has been proposed that integrates both fundus and OCT imaging technologies for reliable diagnosis of glaucoma. Fundus module inspects the outer layer of eye's posterior part. It considers a variety of structural and textural features and makes a decision using support vector machine (SVM). In OCT module, the cup to disc ratio (CDR) has been computed by examining the internal layers of the retina. The cup contour has been extracted from inner-limiting-membrane (ILM) layer using a set of novel techniques for the calculation of cup diameter. Similarly, in the disc diameter calculation the retinal-pigment-epithelium (RPE) layer termination points have been identified by a number of innovative strategies to locate disc margin. Furthermore, a new criterion based on the mean value of RPE-layer end points has been proposed for the determination of cup edges. A local-dataset annotated by four ophthalmologists has been used for evaluation of proposed H-CAD system. The evaluations and results have shown that the final result of H_CAD system is more trustable than its contemporary automated models.