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12th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2010

作者： Teixeira, César A. Alvarenga, A.V. Von Krüger, M.A. Pereira, W.C.A. Centre for Informatics and Systems University of Coimbra 3030-290 Coimbra Portugal Duque de Caxias Brazil Biomedical Eng. Program COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9783642130380

This paper presents the first results of a method to estimate temperature change by monitoring the gray-scale average value of standard ultrasound (US) images. It was carried out an experiment with a bovine muscle sample with four thermocouples along its depth, spaced of 1 cm. The sample was immersed in a reservoir to which water at 50°C was added. Images were recorded with a commercial ultrasound equipment during sample heating and cooling (2.5 h experiment). Temperature was acquired every 10 s. Each image was then divided in four horizontal regions-of-interest (ROIs), corresponding to each thermocouple positioning. Average gray-scale values versus temperature change were obtained and have shown a clear linear pattern within the range 0.5 to 8°C for all ROIs. A linear model was proposed to fit the curves with a maximum prediction error of approximately 0.6°C, which is close to the accepted error for hyperthermia treatment (0.5°C). The next step is to use ultrasound also as the heating source. © 2010 International Federation for Medical and Biological Engineering.

关键词： Thermocouples

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News Event Modeling and Tracking in the Social Web with Ontological Guidance

News Event Modeling and Tracking in the Social Web with Onto...

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International Conference on Semantic Computing

作者： Viet Ha-Thuc Yelena Mejova Christopher Harris Padmini Srinivasan Computer Science Department University of Iowa IA USA Informatics Program University of Iowa IA USA

News event modeling and tracking in the social web is the task of discovering which news events individuals in social communities are most interested in, how much discussion these events generate and tracking these discussions over time. The task could provide informative summaries on what has happened in the real world, yield important knowledge on what are the most important events from the crowd's perspective and reveal their temporal evolutionary trends. Latent Dirichlet Allocation (LDA) has been used intensively for modeling and tracking events (or topics) in text streams. However, the event models discovered by this bottom-up approach have limitations such as a lack of semantic correspondence to real world events. Besides, they do not scale well to large datasets. This paper proposes a novel latent Dirichlet framework for event modeling and tracking. Our approach takes into account ontological knowledge on events that exist in the real world to guide the modeling and tracking processes. Therefore, event models extracted from the social web by our approach are always meaningful and semantically match with real world events. Practically, our approach requires only a single scan over the dataset to model and track events and hence scales well with dataset size.

关键词： Internet Information services Web sites Data models Computational modeling Nominations and elections Training

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Dermatomyositis and type 1 interferons

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Current Rheumatology Reports 2010年第3期12卷 198-203页

作者： Greenberg, Steven A. Department of Neurology Division of Neuromuscular Disease Brigham and Women's Hospital Boston MA 02115 75 Francis Street United States Children's Hospital Informatics Program Harvard Medical School Boston MA 02115 75 Francis Street United States

Dermatomyositis is a poorly understood multisystem disease predominantly affecting skin and muscle. This review focuses on the potential role of a group of related cytokines, the type 1 interferons, in the pathogenesis of dermatomyositis. Type 1 interferon-inducible transcripts and proteins are uniquely elevated in dermatomyositis muscle compared with all other muscle diseases studied to date. The endothelial cell tubuloreticular inclusions present in affected dermatomyositis muscle are biomarkers of type 1 interferon exposure. The cell-poor lichenoid reaction in skin with predominant involvement of the basal epidermal cell layer and its topologic equivalent in muscle, perifascicular atrophy, may be lesions that develop directly in response to type 1 interferon signaling. © Springer Science+Business Media, LLC 2010.

关键词： Dermatomyositis Interferon

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Inferring nonstationary gene networks from temporal gene expression data

Inferring nonstationary gene networks from temporal gene exp...

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Workshop on Signal Processing Systems

作者： Chang, Hsun-Hsien Smith, Jonathan J. Ramoni, Marco F. Children's Hospital Informatics Program Harvard-MIT Division of Health Sciences and Technology Harvard Medical School Boston MA 02115 United States Department of Mathematics Massachusetts Institute of Technology Cambridge MA 02142 United States

ISBN: (纸本)9781424489336

Reverse-engineering transcriptional networks from longitudinal expression profiles is a crucial step towards the study of gene regulatory mechanisms. Genes dynamically orchestrate to each other, the stationarity assumption made by existing methods of transcriptional interaction inference is no longer adequate. As such, we need a new approach to handle the nonstationary behavior in gene expression. On the other hand, microarrays for human studies are equipped with a large number of probe sets, leading the inference of dynamic networks to a computationally intensive task. Hence, there is a need to design the inference algorithm in a tractable manner. This paper develops a Bayesian network approach to inferring the nonstationary transcriptional interactions. The applications of our approach to a clinical study of mechanical periodontal therapy demonstrates a significant improvement over stationary models. Our nonstationary network model also explains the anti-inflammatory effect of mechanical periodontal therapy. ©2010 IEEE.

关键词： Bayesian networks

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Energy efficient mapping and voltage islanding for regular NoC under design constraints

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International Journal of High Performance Systems Architecture 2010年第3-4期2卷 132-144页

作者： Ghosh, Pavel Sen, Arunabha Computer Science Program School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85281 United States

Computational energy consumption of the processing elements (PEs) of a NoC can be significantly reduced by scaling down their voltage levels. This creates clusters of adjacent PEs operating at the same voltage level, known as voltage islands. Excessive number of voltage islands is undesirable from the physical design perspective and due to the overhead of level shifter energy consumption between adjacent voltage islands. Considering these issues during mapping of the PEs to the NoC routers, can potentially lead to acceptable solutions with reduced overall energy consumption. In this paper, we formulate the mapping problem as an optimisation problem. We present both optimal solution, obtained by solving a mixed integer linear program (MILP), and heuristic solution based on random greedy selection. Experimental results using benchmark and real applications show that the heuristic finds near-optimal solution in almost all cases in a very small fraction of the time required to achieve the optimal solution. Copyright © 2010 Inderscience Enterprises Ltd.

关键词： Network-on-chip

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Learning to predict expression efficacy of vectors in recombinant protein production

Learning to predict expression efficacy of vectors in recomb...

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8th Asia-Pacific Bioinformatics Conference, APBC 2010

作者： Chan, Wen-Ching Liang, Po-Huang Shih, Yan-Ping Yang, Ueng-Cheng Lin, Wen-Chang Hsu, Chun-Nan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biological Chemistry Academia Sinica Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan

Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression. Results: In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production

关键词： Vectors

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Active learning for human protein-protein interaction prediction

Active learning for human protein-protein interaction predic...

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8th Asia-Pacific Bioinformatics Conference, APBC 2010

作者： Mohamed, Thahir P. Carbonell, Jaime G. Ganapathiraju, Madhavi K. Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA United States Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States Language Technologies Institute Carnegie Mellon University Pittsburgh PA United States

Background: Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive;only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. Results: Random forest (RF) has previously been shown to be effective for predicting proteinprotein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data. Conclusion: Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification. © 2010 Yeturu et al;licensee BioMed Central Ltd.

关键词： Proteins

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A data mining approach to MPGN type II renal survival analysis 10

A data mining approach to MPGN type II renal survival analys...

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Proceedings of the 1st ACM International Health informatics Symposium

作者： Yang, Chen Street, W. Nick Lu, Der-Fa Lanning, Lynne Health Informatics Program University of Iowa Iowa City IA 52242 United States Management Sciences Department University of Iowa Iowa City IA 52242 United States College of Nursing University of Iowa Iowa City IA 52242 United States

ISBN: (纸本)9781450300308

There are three recognized types of Membranoproliferative glomerulonephritis (MPGN). Type II or Dense Deposit Disease (DDD)has a renal survival of 50% at 10 years. The goal of this study was to better identify patients at high risk of early renal failure,and to understand the factors that lead to fast progression of the disease. We identified six diagnostic features on the 98 DDD patients who responed to a Web-based survey, and examined the prognostic performance of these features in isolation and simple combinations. We then combined the features to build predictive models using both Cox proportional hazards regression (CHR), a standard statistical approach, and support vector machines (SVMs), a classification technique from the data mining literature. While the age and gender features showed some prognostic ability, the combined models - particularly the SVM - were superior in identifying cases with fast disease *** approach can be applied to disease survival analysis and prognosis, and might be useful to healthcare providers and patients in making healthcare decisions. © 2010 ACM.

关键词： Data mining

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A pilot study for genome wide association study (GWAS) in patients with juvenile idiopathic arthritis (JIA) and their parents

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Pediatric Rheumatology 2011年第1期9卷 1-1页

作者： Seza Ozen Yelda Bilginer Nesrin Besbas Yesim Aydin Son Yener Tuncel Erkan Demirkaya Cengizhan Acikel Bilge Volkan Salancı Mehmet Alikasifoglu Ozgur Kasapcopur Erbil Unsal Hacettepe University Faculty of Medicine Department of Pediatrics Pediatric Nephrology and Rheumatology Unit Ankara Turkey Department of Health Informatics METU Informatics Institute Ankara Turkey METU Informatics Institute Bioinformatics Graduate Program Ankara Turkey Gulhane Military Medical Academy Pediatric Nephrology & Rheumatology Unit Ankara Turley Department of Public Health Division of Epidemiology Gulhane Military Medical Faculty Ankara Turkey Department of Pediatrics Clinical Genetics Unit Hacettepe University Faculty of Medicine Ankara Turkey Istanbul University Cerrahpasa Medical School Department of Pediatric Rheumatology Istanbul Turley Department of Pediatrics Division of Immunology- Rheumatology Dokuz Eylul University Faculty of Medicine Izmir Turkey

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Intergration or Information Technology in Nursing Simulation Training

Intergration or Information Technology in Nursing Simulation...

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2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Jennie Q.Lou Elizabeth Oviawe Wendy Thomson Master of Science in Biomedical Informatics Program College of Osteopathic Medicine Nova Southeastern University Ft.Lauderdale Florida USA

ISBN: (纸本)9781424463671;9781424463688;9781424463701

The healthcare workforce is increasingly moving towards the global adoption of computer applications and technology for effective and efficient delivery of healthcare services. This emphasizes the need to integrate the use of information technology especially the electronic medical records (EMR) into the learning process of the health professions student, thereby exposing them to the use of EMR at each level of their studies;therefore, the medical students would be familiar with and better prepared for the use of the complex technologies for effective medical operations as they enter the workforce. Despite the growing importance of EMR, health professions students in medical schools in the US have been given little or no hands-on training on electronic medical records during their course of training. Nova Southeastern University (NSU) in Florida USA has six healthcare professions colleges, including medicine, nursing, dentistry, pharmacy, and other allied health professionals;and has been an ideal ground for collaborative projects in teaching and training. This NSU-Nursing Simulation Lab EMR project, with an initial focus on the nursing students, was a collaborative effort between the graduate students in the Medical informatics program and Nursing program at NSU. As a practicum requirement, the medical informatics graduate students assessed the needs of the nursing simulation laboratory for the electronic medical records;based on the analysis, the graduate students developed and implemented the customized EMR for the nursing students and faculty training. The platforms of Microsoft Access, SQL statements and Visual Basic Application language were used to provide a practical and interactive electronic medical record for the nursing simulation experience. A functional and secure EMR was developed and implemented in the NSU nursing simulation laboratory, and it is being integrated as a part of daily training of using current information technology for the nurses in tr

关键词： electronic medical recrds simulation training information technology

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