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11th Asia Joint Conference on information Security, AsiaJCIS 2016

作者： Kuo, Wen-Chung Li, Jyun-Jia Wang, Chun-Cheng Wuu, Lih-Chyau Huang, Yu-Chih Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Taiwan Graduate School of Engineering Science and Technology Doctoral Program National Yunlin University of Science and Technology Taiwan Department of Information Management Tainan University of Technology Taiwan

ISBN: (纸本)9781509022854

Recently, a modified data hiding scheme based on pixel value differencing and improving exploiting modification directions is proposed by Shen and Huang. There are two major contributions in this scheme. One is to enhance the embedding rate and good embedding capacity and the other is an optimization problem used to solve the overflow/ underflow problem. In fact, they propose the similar brute force method to solve overflow/underflow problem in their proposed scheme. To overcome the overflow/underflow problem exactly, we will propose the close form to solve this problem in this paper. Then, an improvement data hiding scheme based on FFEMD (Formula Fully Exploiting Modification Directions) and pixel value differencing method is proposed. According to our analysis, it is not only to keep the advantages of Shen-Huang scheme but also to use the close form to solve the overflow/ underflow problem. © 2016 IEEE.

关键词： Pixels

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Designing an Institutional Research Management System Framework for Higher Education

Designing an Institutional Research Management System Framew...

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International Conference on information Management and technology (ICIMTech)

作者： Noerlina Siti Elda Hiererra Bahtiar Saleh Abbas Brilly Andro Makalew Desi Maya Kristin Tirta Nugraha Mursitama David Information Systems Department Bina Nusantara University Jakarta Indonesia Industrial Engineering Department Bina Nusantara University Jakarta Indonesia Mobile Application & Technology Program Bina Nusantara University Jakarta Indonesia International Relations Department Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

The following topics are dealt with: Internet; organisational aspects; data analysis; educational institutions; information systems; knowledge management; statistical analysis; further education; computer aided instru... 详细信息

关键词： Manuals Education Proposals information systems Requirements engineering System analysis and design Conferences

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Role of Integrating Diffusion Mr Image-Markers with Clinical-Biomarkers For Early Assessment of Renal Transplants

Role of Integrating Diffusion Mr Image-Markers with Clinical...

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IEEE International Conference on Image Processing

作者： Mohammad Shehata Mohammed Ghazal Garth Beache Mohamed Abou EI-Ghar Amy Dwyer Hassan Hajjdiab Ashraf Khalil Ayman EI-Baz Bioengineering Department University of Louisville Louisville KY USA Radiology Department University of Louisville Louisville KY USA Radiolozy Department Mansoura University Mansoura Egypt Kidney Disease Program University of Louisville Louisville KY USA Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi UAE Computer Science and Information Technology Department Abu Dhabi University Abu Dhabi UAE

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.

关键词： Kidney Diseases Three-dimensional displays Image segmentation Magnetic resonance imaging Machine learning Radio frequency

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Genetic variant analysis of boys with Autism: A pilot study on linking facial phenotype to genotype

Genetic variant analysis of boys with Autism: A pilot study ...

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2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

作者： Obafemi-Ajayi, Tayo Settles, Luke Su, Yuqing Germeroth, Cynthia Olbricht, Gayla R. Wunsch, Donald C. Takahashi, T. Nicole Miles, Judith Engineering Program Missouri State University Springfield MO United States Mathematics and Statistics Dept. Missouri University of Science and Technology RollaMO United States Information Technology Missouri University of Science and Technology RollaMO United States Electrical and Computer Eng. Dept. Missouri University of Science and Technology RollaMO United States Thompson Center for Autism and Neurodevelopmental Disorders ColumbiaMO United States

ISBN: (纸本)9781509030491

This work examines the validity of facial phenotypes as Autism Spectrum Disorders (ASD) biomarkers in boys with essential autism. A family-based association analysis framework is presented that uses previously identified facially-delineated (FD) clusters to examine relationship between FD clusters and known ASD genes. The hypothesis is that there are certain genetic variants, single nucleotide polymorphisms (SNP), specific to the FD clusters. Although statistical significance was not established, the results identified some candidate SNPs unique to each of the FD clusters that could indicate an underlying etiological difference. Further, recommendations are provided for larger-scale studies that could utilize the analysis framework presented. © 2017 IEEE.

关键词： Biomarkers

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Medical image denoising using sparse representations

Medical image denoising using sparse representations

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International Conference on Awareness science and technology (iCAST)

作者： Fatma S. Abousaleh Neng-Hao Yu Kai-Lung Hua Wen-Huang Cheng Social Networks and Human Centered Computing Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan Department of Computer Science and Information Engineering Taiwan University of Science and Technology Taipei Taiwan

Image noise reduction, or denoising, became an attractive research domain in the recent years. There are several published approaches in the image denoising field and each one has its presumptions, advantages, and limitations. In this paper, we address the problem of eliminating or even decreasing the noise from medical images. Some schemes for image noise reduction are proposed. The schemes include DCT_OMP, DCT_BOMP, Log Gabor_BOMP, DCT_OCMP and Wavelet_OMP. In these schemes, the image content is represented as a sparse linear combination of a set of atoms that can be obtained from some trained dictionaries. In literature, several algorithms are proposed to build up these kinds of dictionaries. In our study, the K-SVD algorithm is utilized to obtain a dictionary that can effectively describe the image. Also, some greedy algorithms are used to perform the sparse-coding of the signal. The proposed schemes have been tested on two different medical images with different levels of an additive noise. The Log Gabor_BOMP scheme showed a significant outperformance, in both the de-noising results and computational time, on all the other schemes.

关键词： Matching pursuit algorithms Dictionaries Approximation algorithms Image denoising Noise reduction Discrete cosine transforms Algorithm design and analysis

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Universal research index: An inclusive metric to quantify scientific research output

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Journal of Academic Librarianship 2023年第3期49卷

作者： Keshavarz-Fathi, Mahsa Yazdanpanah, Niloufar Kolahchi, Sajad Ziaei, Heliya Darmstadt, Gary L. Dorigo, Tommaso Dochy, Filip Levin, Lisa Thongboonkerd, Visith Ogino, Shuji Chen, Wei-Hsin Perc, Matjaz Tremblay, Mark S. Olusanya, Bolajoko O. Rao, Idupulapati M. Hatziargyriou, Nikos Moradi-Lakeh, Maziar Bella, Federico Rosivall, Laszlo Gandomi, Amir H. Sorooshian, Armin Gupta, Manoj Gal, Ciprian Lozano, Andres M. Weaver, Connie Tanzer, Michael Poggi, Alessandro Sepanlou, Sadaf G. Weiskirchen, Ralf Jambrak, Anet Režek Torres, Pedro J. Capanoglu, Esra Barba, Francisco J. Ernest, Chua Kian Jon Sigman, Mariano Pluchino, Stefano Gharehpetian, Gevork B. Fereshtehnejad, Seyed-Mohammad Yang, Muh-Hwa Thomas, Sabu Cai, Wenju Comini, Elisabetta Scolding, Neil J. Myles, Paul S. Nieto, Juan J. Perry, George Sedikides, Constantine Rezaei, Nima School of Medicine Tehran University of Medical Sciences Tehran Iran Universal Scientific Education and Research Network (USERN) Iran Department of Pediatrics Stanford University School of Medicine Stanford CA United States Istituto Nazionale di Fisica Nucleare (INFN) Via Francesco Marzolo Sezione di Padova Italy High Impact Learning Academy Learning and Development Department Brussels Belgium Center for Marine Biodiversity and Conservation Integrative Oceanography Division Scripps Institution of Oceanography University of California San Diego San Diego CA United States Medical Proteomics Unit Office for Research and Development Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Program in MPE Molecular Pathological Epidemiology Department of Pathology Brigham and Women's Hospital and Harvard Medical School Boston MA United States Broad Institute of MIT and Harvard Cambridge MA United States Department of Aeronautics and Astronautics National Cheng Kung University Tainan Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung 407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung 411 Taiwan Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Complexity Science Hub Vienna Vienna Austria Alma Mater Europaea Maribor Slovenia Children's Hospital of Eastern Ontario Research Institute Department of Pediatrics University of Ottawa Ottawa ON Canada Centre for Healthy Start Initiative Ikoyi Lagos Nigeria Alliance of Bioversity International International Center for Tropical Agriculture Cali Colombia International Centre of Insect Physiology and Ecology (ICIPE) Nairobi Kenya School of Electrical and Computer Engineering Natio

Scientometrics and bibliometrics, the subfields of library and information science, deal with the quantity and quality of research outputs. Currently, various scientometric indices are being used to quantify and compare research outputs. The most widely known is the h-index. However, this index and its derivatives suffer from dependence on the mere count of a scholar's highly cited publications. To remedy this deficiency, we developed a novel index, the Universal Research Index (UR-Index) (https://***/UR-Index/) by which every single publication has its own impact on the total score. We developed this index by surveying international top 1 % cited scientists in various disciplines and included additional component variables such as publication type, leading role of a scholar, co-author count, and source metrics to this scientometric index. We acknowledge that unconscious biases built into the component variables included in the UR-Index might put research from specific groups at a disadvantage, thus continued efforts to improve equitable scholarly impact in science and academia are encouraged. © 2023 Elsevier Inc.

关键词： Citations CiteScore h-index Impact factor Leading author Research impact Scholarly impact Scholarly output Scientometrics UR-Index USERN

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The pathogenicity of COVID-19 and the role of pentraxin-3: An updated review study

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Pathology - Research and Practice 2022年 238卷

作者： Ria Margiana Satish Kumar Sharma Bilal Irshad Khan Ameer A. Alameri Maria Jade Catalan Opulencia Ali Thaeer Hammid Thulfeqar Ahmed Hamza Sharaf Khamrakulovich Babakulov Walid Kamal Abdelbasset Zanko Hassan Jawhar Department of Anatomy Faculty of Medicine Universitas Indonesia Jakarta Indonesia Master's Programme Biomedical Sciences Faculty of Medicine Universitas Indonesia Jakarta Indonesia Andrology Program Faculty of Medicine Universitas Airlangga Surabaya Indonesia Dr. Soetomo General Academic Hospital Surabaya Indonesia Department of Pharmacology Glocal School of Pharmacy The Glocal University Saharanpur India Mbbs Rawalpindi Medical University Rawalpindi Pakistan Department of Chemistry University of Babylon Iraq College of Business Administration Ajman University Ajman United Arab Emirates Computer Engineering Techniques Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad Iraq Medical laboratory techniques department Al-Mustaqbal University College Babylon Iraq Tashkent State Dental Institute Makhtumkuli Street 103 Tashkent 100047 Uzbekistan Research scholar Department of Scientific affairs Samarkand State Medical Institute Amir Temur Street 18 Samarkand Uzbekistan Department of Health and Rehabilitation Sciences College of Applied Medical Sciences Prince Sattam bin Abdulaziz University Al Kharj Saudi Arabia Department of Physical Therapy Kasr Al-Aini Hospital Cairo University Giza Egypt Department of Medical Laboratory Science College of Health Science Lebanese French University Kurdistan Region Iraq

In recent years, the COVID-19 pandemic has become one of the most crucial scientific issues in the world, and efforts to eradicate the disease are still ongoing. The acute inflammatory reaction associated with this disease is associated with several complications such as cytokine storm, multiple organ damage, lung fibrosis, and blood clots. PTX3, as part of the humoral innate immune systems, is one of the acute-phase proteins that perform various functions, such as modulating inflammation, repairing tissue, and recruiting immune cells. PTX3 is increased in people with SARS-CoV-2, and its level decreases with proper treatment. Therefore, it can be regarded as a suitable marker for the prognosis of the COVID-19 and evaluating the effectiveness of the treatment method applied. However, some studies have shown that PTX3 can be a double-edged sword and develop tumors by providing an immunosuppressive environment.

关键词： COVID-19 SARS-CoV-2 Pentraxin-3 Acute-phase proteins

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Analysis backpropagation methods with neural network for prediction of children's ability in psychomotoric

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Journal of Physics: Conference Series 2018年第1期978卷 012085-012085页

作者： Izhari, F Dhany, H W Zarlis, M Sutarman Informatics Engineering Study Program Master Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

A good age in optimizing aspects of development is at the age of 4-6 years, namely with psychomotor development. Psychomotor is broader, more difficult to monitor but has a meaningful value for the child's life because it directly affects his behavior and deeds. Therefore, there is a problem to predict the child's ability level based on psychomotor. This analysis uses backpropagation method analysis with artificial neural network to predict the ability of the child on the psychomotor aspect by generating predictions of the child's ability on psychomotor and testing there is a mean squared error (MSE) value at the end of the training of 0.001. There are 30% of children aged 4-6 years have a good level of psychomotor ability, excellent, less good, and good enough.

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Elastic restricted Boltzmann machines for cancer data analysis

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Frontiers of Electrical and Electronic Engineering in China 2017年第2期5卷 159-172页

作者： Sai Zhang Muxuan Liang Zhongjun Zhou Chen Zhang Ning Chen Ting Chen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Department of Statistics University of Wisconsin-Madison Madison WI 53706-1685 USA Bioinformatics Division TNLIST Department of Computer Science and Technology Tsinghua University Beijing 100084 China Program in Computational Biology and Bioinformatics Univesity of Southern California Los Angeles CA 90089 USA

Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.

关键词： RBMs regularization cancer data analysis survival time prediction

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FinnGen provides genetic insights from a well-phenotyped isolated population (vol 613, pg 508, 2023)

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NATURE 2023年第7952期615卷 E19-E19页

作者： Kurki, Mitja I. Karjalainen, Juha Palta, Priit Sipila, Timo P. Kristiansson, Kati Donner, Kati M. Reeve, Mary P. Laivuori, Hannele Aavikko, Mervi Kaunisto, Mari A. Loukola, Anu Lahtela, Elisa Mattsson, Hannele Laiho, Paivi Parolo, Pietro Della Briotta Lehisto, Arto A. Kanai, Masahiro Mars, Nina Ramo, Joel Kiiskinen, Tuomo Heyne, Henrike O. Veerapen, Kumar Rueger, Sina Lemmela, Susanna Zhou, Wei Ruotsalainen, Sanni Parn, Kalle Hiekkalinna, Tero Koskelainen, Sami Paajanen, Teemu Llorens, Vincent Gracia-Tabuenca, Javier Siirtola, Harri Reis, Kadri Elnahas, Abdelrahman G. Sun, Benjamin Foley, Christopher N. Aalto-Setala, Katriina Alasoo, Kaur Arvas, Mikko Auro, Kirsi Biswas, Shameek Bizaki-Vallaskangas, Argyro Carpen, Olli Chen, Chia-Yen Dada, Oluwaseun A. Ding, Zhihao Ehm, Margaret G. Eklund, Kari Farkkila, Martti Finucane, Hilary Ganna, Andrea Ghazal, Awaisa Graham, Robert R. Green, Eric M. Hakanen, Antti Hautalahti, Marco Hedman, Asa K. Hiltunen, Mikko Hinttala, Reetta Hovatta, Iiris Hu, Xinli Huertas-Vazquez, Adriana Huilaja, Laura Hunkapiller, Julie Jacob, Howard Jensen, Jan-Nygaard Joensuu, Heikki John, Sally Julkunen, Valtteri Jung, Marc Junttila, Juhani Kaarniranta, Kai Kahonen, Mika Kajanne, Risto Kallio, Lila Kalviainen, Reetta Kaprio, Jaakko Kerimov, Nurlan Kettunen, Johannes Kilpelainen, Elina Kilpi, Terhi Klinger, Katherine Kosma, Veli-Matti Kuopio, Teijo Kurra, Venla Laisk, Triin Laukkanen, Jari Lawless, Nathan Liu, Aoxing Longerich, Simonne Magi, Reedik Makela, Johanna Makitie, Antti Malarstig, Anders Mannermaa, Arto Maranville, Joseph Matakidou, Athena Meretoja, Tuomo Mozaffari, Sahar V. Niemi, Mari E. K. Niemi, Marianna Niiranen, Teemu O'Donnell, Christopher J. Obeidat, Ma'En Okafo, George Ollila, Hanna M. Palomaki, Antti Palotie, Tuula Partanen, Jukka Paul, Dirk S. Pelkonen, Margit Pendergrass, Rion K. Petrovski, Slave Pitkaranta, Anne Platt, Adam Pulford, David Punkka, Eero Pussinen, Pirkko Raghavan, Neha Rahimov, Fedik Rajpal, Deepak Renaud, Nicole A. Riley-Gillis, Bridget Rodosthenous, Rodostheni Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Medical and Clinical Genetics University of Helsinki and Helsinki University Hospital Helsinki Finland Department of Obstetrics and Gynecology Tampere University Hospital Tampere Finland Faculty of Medicine and Health Technology Center for Child Adolescent and Maternal Health University of Tampere Tampere Finland Department of Biomedical Informatics Harvard Medical School Boston MA USA Digital Health Center Hasso Plattner Institute for Digital Engineering University of Potsdam Potsdam Potsdam Germany Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA Finnish Institute for Health and Welfare (THL) Helsinki Finland Department of Public Health University of Helsinki Helsinki Finland Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital Boston MA USA Abdominal Center Endocrinology Helsinki University Hospital Helsinki Finland Folkhalsan Research Center Helsinki Finland Research Program of Clinical and Molecular Metabolism University of Helsinki Helsinki Finland Biocenter Oulu University of Oulu Oulu Finland Computational Medicine Center for Life Course Health Research Faculty of Medicine University of Oulu Oulu Finland Turku University Hospital and University of Turku Turku Finland University of Turku Turku Finland Helsinki Biobank University of Helsinki and Hospital District of Helsinki and Uusimaa Helsinki Finland TAUCHI Research Center

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