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Population Wise Variation of Breast and Ovarian Cancer-A Pharmacogenomic Approach

Materials Today: Proceedings 2018年第8期5卷 16106-16110页

作者： C. Lakshmi Anand A.M. Hima Vyshnavi Gilu Francis S. Karthikeyan P. Sanjay Kumar P.K. Krishnan Namboori Computational Chemistry Group (CCG) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Amrita University India Center for Computational Engineering & Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Amrita University India Dept. of Electronics and Communications Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Amrita University India

The somatic mutations in TP53 may be considered as one of the major genomic reasons behind breast and ovarian cancers. These two types of cancers have been reported as the major terminal diseases among women, with breast cancer as the second and ovarian cancer as the fifth cause of death as per the WHO reports last year. However, the proneness of the disease varies from trait to trait and even from person to person. A population-wise analysis of the risk factors associated with the variations results in the study of pharmacogenomic components such as epigenetics, metagenomics, environmental genomics and genomics . The single nucleotide polymorphism (SNP) has been identified as the effective genetic signature to include these variations. For TP53, 27 non-synonymous, pathogenic and deleterious SNPs have been identified in the world populations. Among Indian populations, the SNPs identified are rs1642785, rs1042522, rs8064946, rs12951053, rs1641549, rs1625895 and rs2078486. The frequency of TP53 is found to be high in New Zealand and Australian populations. The methylation probability of TP53 has been predicted through linear SVM using 10 classes of attributes keeping a total of 847 features. The prominent features identified during the epigenetic analysis useful for predicting the possibility of methylation of the gene are the constituents in the DNA sequence, epigenome/chromatin structure and presence of regulatory regions.

关键词： Epigenetics Metagenomics Environmental factors Genetic signature Pharmacogenomics SVM

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Spin image of an atomic vapor cell with a resolution smaller than the diffusion crosstalk free distance

arXiv

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arXiv 2019年

作者： Dong, Hai-Feng Chen, Jing-Ling Li, Ji-Min Li, Chen Li, Ai-Xian Zhao, Nan Guo, Fen-Zhuo School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100083 China Quantum Sensing and Information Sensing Lab Graduate School of China Academy of Engineering Physics Beijing100193 China Quantum Physics and Quantum Information Division Beijing Computational Science Research Center Beijing100193 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Science Beijing University of Posts and Telecommunication Beijing100876 China

The diffusion crosstalk free distance is an important parameter for spin images in atomic vapor cells and is also regarded as a limit on the spatial resolution. However, by modulating the pumping light both spatially and temporally using a digital micromirror device, a spin image of a vapor cell has been obtained with a distinguishable stripe width of 13.7 μm, which is much smaller than the corresponding diffusion crosstalk free distance of ∼138 μm. The fundamental limit on the spatial resolution as determined by diffusion and the uncertainty principle is analyzed. Copyright © 2019, The Authors. All rights reserved.

关键词： Crosstalk

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deepCybErNet at EmoInt-2017: Deep emotion intensities in tweets 8

deepCybErNet at EmoInt-2017: Deep emotion intensities in twe...

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8th Workshop on computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017, in conjunction with the Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Vinayakumar, R. Premjith, B. Sachin Kumar, S. Soman, K.P. Poornachandran, Prabaharan Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University India Center for Cyber Security Systems and Networks Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University India

ISBN: (纸本)9781945626951

This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods is apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type. © 2017 Association for computational Linguistics.

关键词： recurrent network Emotions embedding TWITTER Word NETWORK STRUCTURE Database

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Fabrication of Paper Microfluidics POCT Device for the Colorimetric Assay of Alkaline Phosphatase

Fabrication of Paper Microfluidics POCT Device for the Color...

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IEEE India Conference (INDICON)

作者： Resmi P.E. John Stanley Sachin Kumar K.P. Soman Ramachandran T. Satheesh Babu T.G. Amrita Biosensor Research Lab Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Center for Excellence in Computational Engineering and Networking Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Sciences Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781538682364

Alkaline phosphatase (ALP) serves as an important biomarker for the diagnosis of various diseases including liver malfunction, bone diseases, breast and prostate cancers, and diabetes. This paper discusses the fabrication and testing of a paper-based Point-of-care testing (POCT) device for the colorimetric detection of alkaline phosphatase (ALP). The device was fabricated using a commercial wax printer followed by reagent casting on the patterned paper. In the presence of ALP concentration of 30 - 500 U/L, the reagent paranitrophenylphosphate (PNPP) changes to yellow color with increasing intensity. The different color intensities observed for different ALP concentrations were captured and a color band was developed for the visual interpretation. The RGB and HSV of the captured color intensities were processed using MATLAB to obtain the corresponding ALP concentration.

关键词： Paper-based POCT devices Para-nitrophenyl phosphate Alkaline phosphatase RGB and HSV values Alkaline Phosphatase fabrication Color COLOR STRENGTH Prostate Cancer Microfluidics HSV Simplexvirus

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Vector Space Model as Cognitive Space for Text Classification

arXiv

引用

arXiv 2017年

作者： Ganesh, Barathi H.B. Kumar, Anand M. Soman, K. Center for Computational Engineering and Networking Amrita School of Engineering Coimbatore

In this era of digitization, knowing the user’s sociolect aspects have become essential features to build the user specific recommendation systems. These sociolect aspects could be found by mining the user’s language sharing in the form of text in social media and reviews. This paper describes about the experiment that was performed in PAN Author Profiling 2017 shared task. The objective of the task is to find the sociolect aspects of the users from their tweets. The sociolect aspects considered in this experiment are user’s gender and native language information. Here user’s tweets written in a different language from their native language are represented as Document - Term Matrix with document frequency as the constraint. Further classification is done using the Support Vector Machine by taking gender and native language as target classes. This experiment attains the average accuracy of 73.42% in gender prediction and 76.26% in the native language identification *** Codes 68T50 Copyright © 2017, The Authors. All rights reserved.

关键词： Vector spaces

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Improving the Performance of Stress Detection Systems by Minimizing Person Specific Variations

Improving the Performance of Stress Detection Systems by Min...

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IEEE India Conference (INDICON)

作者： H. Haritha C. Santhosh Kumar A. Anand Kumar P. C. Kesavankutty Nayar Siby Gopinath K. I. Ramachandran Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Coimbatore India Center for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India Department of Neurology Amrita Vishwa Vidyapeetham Kochi India Department of Psychiatry Amrita Vishwa Vidyapeetham Kochi India

ISBN: (纸本)9781538682364

In this paper, we explore several approaches for improving the performance of a stress detection system (SDS) with electrocardiogram (ECG) signal as its input. Time and frequency domain features extracted from the heart rate variability (HRV), is input to a support vector machine (SVM) backend classifier, for the baseline SVM-SDS. We used ECG data from normative population and patients with severe stress disorders for validating the effectiveness of SDS. We observed that there are variations in the HRV parameters among the normative population, and the patients with stress disorder, usually referred to as person specific variations. To minimize the person specific variations, we explored three methods: 1. Locality-constrained linear coding (LLC) - input feature vectors are expressed in terms of person independent basis vectors. 2. Fisher vector encoding (FVE) -input features are expressed in a person independent feature space using Gaussian mixture models (GMM) trained patient independently. 3. Covariance normalization (CVN) - minimizes the person specific variations in the input vector through feature normalization. For the CVN-SDS, we obtained a sensitivity of 87.50% absolute, specificity of 100.00% absolute and accuracy of 91.66% absolute, which is 31.43%, 31.43% and 33.33% absolute improvement over the SVM-SDS.

关键词： Stress Electrocardiography Heart rate variability Feature extraction Covariance matrices Support vector machines Encoding

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ECG-Based Monitoring of Electrolyte Fluctuations During the Long Interdialytic Interval

ECG-Based Monitoring of Electrolyte Fluctuations During the ...

引用

Computers in Cardiology (CinC)

作者： Ana Rodrigues Andrius Petrėnas Vaidotas Marozas Neda Kušleikaite-Pere Pablo Laguna Biomedical Engineering Institute Kaunas Lithuania Biomedical Engineering Institute Kaunas University of Technology Kaunas Lithuania Lithuanian University of Health Sciences Kaunas Lithuania Biomedical Signal Interpretation and Computational Simulation (BSICoS) group Biomedical Research Networking Center (CIBER) Zaragoza Spain

ISBN: (纸本)9781728109244

Hemodialysis (HD) patients have a higher risk of sudden death due to cardiac arrhythmias, which commonly occur during the long interdialytic interval (LII) as a result of electrolyte fluctuations (EFs). Noninvasive monitoring of EFs would enable restoring normal serum electrolyte levels (SELs) by performing early HD before the onset of arrhythmias. In this study, we propose an ECG-derived descriptor, θ μ , that is noise robust and capable of capturing EFs during HD and the LII. To investigate the variation of θ μ , ECG and blood samples of 3 patients were acquired continuously, starting at Friday's HD and ending at Monday's HD. Results show that the increase of θ μ during Friday's HD is correlated with the decrease of SELs. Moreover, θ μ tends to decrease during the LII (no blood samples were obtained) and further increases during Monday's HD. If results in larger databases are confirmed, θ μ might be suitable for noninvasive monitoring of EFs during the LII.

关键词： Electrocardiography Blood Monitoring Electrolytes Potassium Biomedical monitoring Morphology

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Least Square Denoising in Spectral Domain for Hyperspectral Images

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Procedia Computer Science 2017年 115卷 399-406页

作者： Jayaprakash Chippy Naveen Varghese Jacob R.K. Renu V Sowmya K.P. Soman Center for Computational Engineering and Networking(CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidhyapeetham Amrita University India

Denoising is one of the fundamental pre-processing tasks in image processing that improves the quality of the information in the image. Processing of hyperspectral images requires high computational power and time. In this paper, a denoising technique based on least square weighted regularization in the spectral domain is proposed. The proposed technique is experimented on standard hyperspectral datasets and also, the performance of the proposed least square denoising in spectral domain is compared with least square weighted regularization in the spatial domain and total variation based denoising method. The obtained results in terms of computational time, Signal-to-Noise Ratio calculations and visual interpretation depicts that the proposed technique performs comparably better than the existing methods such as least square and total variation based hyperspectral image denoising.

关键词： Pre-processing technique Least square weighted regularization Spectral domain Hyperspectral Image Denoising SNR Total variation

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Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

arXiv

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arXiv 2023年

作者： Alexeev, Yuri Amsler, Maximilian Barroca, Marco Antonio Bassini, Sanzio Battelle, Torey Camps, Daan Casanova, David Choi, Young Jai Chong, Frederic T. Chung, Charles Codella, Christopher Córcoles, Antonio D. Cruise, James Di Meglio, Alberto Duran, Ivan Eckl, Thomas Economou, Sophia Eidenbenz, Stephan Elmegreen, Bruce Fare, Clyde Faro, Ismael Fernández, Cristina Sanz Ferreira, Rodrigo Neumann Barros Fuji, Keisuke Fuller, Bryce Gagliardi, Laura Galli, Giulia Glick, Jennifer R. Gobbi, Isacco Gokhale, Pranav de la Puente Gonzalez, Salvador Greiner, Johannes Gropp, Bill Grossi, Michele Gull, Emanuel Healy, Burns Hermes, Matthew R. Huang, Benchen Humble, Travis S. Ito, Nobuyasu Izmaylov, Artur F. Javadi-Abhari, Ali Jennewein, Douglas Jha, Shantenu Jiang, Liang Jones, Barbara de Jong, Wibe Albert Jurcevic, Petar Kirby, William Kister, Stefan Kitagawa, Masahiro Klassen, Joel Klymko, Katherine Koh, Kwangwon Kondo, Masaaki Kürkçüoglu, Doga Murat Kurowski, Krzysztof Laino, Teodoro Landfield, Ryan Leininger, Matt Leyton-Ortega, Vicente Li, Ang Lin, Meifeng Liu, Junyu Lorente, Nicolas Luckow, Andre Martiel, Simon Martin-Fernandez, Francisco Martonosi, Margaret Marvinney, Claire Medina, Arcesio Castaneda Merten, Dirk Mezzacapo, Antonio Michielsen, Kristel Mitra, Abhishek Mittal, Tushar Moon, Kyungsun Moore, Joel Mostame, Sarah Motta, Mario Na, Young-Hye Nam, Yunseong Narang, Prineha Ohnishi, Yu-Ya Ottaviani, Daniele Otten, Matthew Pakin, Scott Pascuzzi, Vincent R. Pednault, Edwin Piontek, Tomasz Pitera, Jed Rall, Patrick Ravi, Gokul Subramanian Robertson, Niall Rossi, Matteo A.C. Rydlichowski, Piotr Ryu, Hoon Samsonidze, Georgy Sato, Mitsuhisa Saurabh, Nishant Sharma, Vidushi Sharma, Kunal Shin, Soyoung Slessman, George Steiner, Mathias Sitdikov, Iskandar Suh, In-Saeng Switzer, Eric D. Tang, Wei Thompson, Joel Todo, Synge Tran, Minh C. Trenev, Dimitar Trott, Christian Tseng, Huan-Hsin Tubman, Norm M. Tureci, Esin Valiñas, David García Vallecorsa, Sofia Wever, Christopher Wojciechowski, Konrad Wu, Xiaodi Yoo, Shinjae Yoshioka, Argonne National Laboratory LemontIL60439 United States Corporate Sector Research and Advance Engineering Robert Bosch GmbH Robert-Bosch-Campus 1 RenningenD-71272 Germany IBM Research RJ Rio de Janeiro20031-170 Brazil Centro Brasileiro de Pesquisas Físicas RJ Rio de Janeiro22290-180 Brazil CINECA via Magnanelli 6/3 BO Casalecchio di Reno40033 Italy Arizona State University TempeAZ United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Euskadi Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Physics Yonsei University Seoul03722 Korea Republic of University of Chicago ChicagoIL United States IBM Quantum IBM T.J. Watson Research Center Yorktown Heights NY10598 United States Cambridge Consultants part of Capgemini Invent Cambridge United Kingdom Geneva1211 Switzerland Virginia Tech BlacksburgVA24061 United States Los Alamos National Laboratory Los AlamosNM87545 United States Osaka University Osaka560-8531 Japan Department of Chemistry Chicago Center for Theoretical Chemistry University of Chicago ChicagoIL United States Fraunhofer ITWM Rheinland-Pfalz Kaiserslautern67663 Germany Infleqtion ChicagoIL60622 United States University of Illinois Urbana-Champaign United States University of Michigan Ann ArborMI48109 United States Research Office Oak Ridge National Laboratory One Bethel Valley Road Oak RidgeTN37831 United States Hyogo Kobe650-0047 Japan Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoONM5S 3H6 Canada Department of Physical and Environmental Sciences University of Toronto Scarborough TorontoONM1C 1A4 Canada Brookhaven National Laboratory UptonNY United States Rutgers University New BrunswickNJ United States IBM Quantum Almaden Research Center San JoseCA95120 United States Applied Mathematics and Computational Research Division

computational models are an essential tool for the design, characterization, and discovery of novel materials. computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Characterization (materials science)

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computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

引用

SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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