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2023 IEEE EMBS R9 Conference, EMBS R9 2023

作者： Lopez-Medina, Lorena Malinalli Paredes, Omar Lora-Castro, S. Velez-Perez, H. Romo-Vazquez, R. Morales, J. Alejandro CUCEI University of Guadalajara Biodigital Innovation Lab Mexico CUCEI University of Guadalajara Translational Bioengineering Department Mexico Science in Bioengineering and Intelligent Computing Program CUCEI Campus at University of Guadalajara JAL 44430 Mexico

ISBN: (纸本)9798350381092

As individuals age, the brain undergoes structural, functional and connectivity changes. Brain activity and anatomy do not always correspond, then evolving differently. In this regard, Independent Component Analysis (ICA) for brain parcellation proves advantageous in studying brain function with a comprehensive and flexible approach that spans across all stages of life. We proposed a method based on ICA that estimates coherent anatomical networks based on functional connectivity patterns. Clinical Relevance - The analysis of brain function across various age groups will aid in the investigation of conditions or pathologies related to alterations in functional connectivity, such as autism spectrum disorder, Alzheimers disease, major depressive disorder and epilepsy © 2023 IEEE.

关键词： Independent component analysis

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Evaluation of Pharmacological Treatment in Acute Osteo Arthrosis Model by Means of Kinematic Analysis and Pain Threshold

Evaluation of Pharmacological Treatment in Acute Osteo Arthr...

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2023 IEEE EMBS R9 Conference, EMBS R9 2023

作者： Gonzalez-Lujan, F.J. Osuna-Carrasco, L.P. De La Torre-Valdovinos, B. Aguilar-Garcia, I.G. Toro-Castillo, C. Trevino, M. CUCEI University of Guadalajara Translational Bioengineering Department Mexico Master of Science in Bioengineering and Intelligent Computing Program CUCEI Campus at University of Guadalajara Jalisco44430 Mexico CUCS Molecular Biology and Genomics Department University of Guadalajara Mexico

ISBN: (纸本)9798350381092

The proposed study proves the option to have a different treatment displayed on quantitative and qualitative parameters of kinematics and pain tests, by experiments with mice groups during walk and mobility analysis with kinematics parameters and pain sensation using Von Frey filaments in acute osteoarthritis knee (AOAK) model. This would allow us to have enough information to recommend or not recommend a new based treatment that can be compared to another successful treatment for osteoarthritis. Clinical Relevance - Therefore, with this study it is proposed to be able to analyze different compounds from opioids, steroids, and non-steroid anti-inflammatories (NSAIDs) like cannabinoid components, avoiding with that the dependance pathology and renal damage in patients treated with one of this kind of common treatments and thus be able to determine their viability on a in vivo model to recommend their study in instances closer to a clinical model. © 2023 IEEE.

关键词： Kinematics

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Evaluation of Pharmacological Treatment in Acute Osteo Arthrosis Model by Means of Kinematic Analysis and Pain Threshold

Evaluation of Pharmacological Treatment in Acute Osteo Arthr...

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EMBS R9 Conference, IEEE

作者： F. J. Gonzalez-Lujan L. P. Osuna-Carrasco B. De la Torre-Valdovinos I. G. Aguilar-García C. Toro-Castillo M. Treviño Translational Bioengineering Department CUCEI University of Guadalajara Master of Science in Bioengineering and Intelligent Computing program CUCEI campus at University of Guadalajara Jalisco Molecular Biology and Genomics Department CUCS University of Guadalajara Neuroscience Institute CUCBA University of Guadalajara

ISBN: (数字)9798350381092

ISBN: (纸本)9798350381108

The proposed study proves the option to have a different treatment displayed on quantitative and qualitative parameters of kinematics and pain tests, by experiments with mice groups during walk and mobility analysis with kinematics parameters and pain sensation using Von Frey filaments in acute osteoarthritis knee (AOAK) model. This would allow us to have enough information to recommend or not recommend a new based treatment that can be compared to another successful treatment for osteoarthritis. Clinical Relevance — Therefore, with this study it is proposed to be able to analyze different compounds from opioids, steroids, and non-steroid anti-inflammatories (NSAIDs) like cannabinoid components, avoiding with that the dependance pathology and renal damage in patients treated with one of this kind of common treatments and thus be able to determine their viability on a in vivo model to recommend their study in instances closer to a clinical model.

关键词： Knee Analytical models Pathology In vivo Pain Kinematics Opioids

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eVision: Forecasting the spread of Tuberculosis in India with Deep Learning

eVision: Forecasting the spread of Tuberculosis in India wit...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Juan Zuluaga Michael Castillo Divya Syal Andres Calle Navid Shaghaghi Department of Bioengineering (BIOE) Computer Science & Engineering (CSEN) Ethical Pragmatic & Intelligent Computing (EPIC) Laboratory in collaboration with the Healthcare Innovation & Design (HID) Program Information Systems & Analytics (ISA) and Mathematics & Computer Science (MCS) Santa Clara University Santa Clara California USA

Humanity has battled tuberculosis for all of recorded history. Some studies estimate that Mycobacterium tuberculosis may have been around as long as 3 million years but it was only in 1834 when Johann Schonlein officially presented the characteristics of it. Even though the TB epidemic has touched all corners of the world, Africa and Asia are the regions that currently suffer the worst consequences. The purpose of this study is to construct a model within the eVision forecasting environment, capable of forecasting the trend of Tuberculosis cases in India, as India is the country that accounts for the largest percentage of TB cases and deaths worldwide. And being able to make predictions for India may also lead to successful results for other regions in Asia and Africa. In order to do so, this study presents different test cases that show the effectiveness of the model, varying the number of steps for each one of the data sets created. It's important to note, that these data sets are combinations of data gathered from the states with the most TB cases in India in the last years, as well as the total data for India, and supplemental data from Google Trends, as a way to facilitate the machine learning model. Even though the final results were respectable compared to past research done on India and other countries, the model nevertheless has a limitation on the number of weeks ahead which the predictions are still considered to be good; with 7 weeks being the optimal result.

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Data gap/outlier correction and treatment

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AIP Conference Proceedings 2024年第1期3034卷

作者： Juan Zuluaga Michael Castillo Divya Syal Andres Calle Navid Shaghaghi Ethical Pragmatic and Intelligent Computing (EPIC) Research Laboratory and the Healthcare Innovation and Design (HID) Program Departments of Computer Science and Engineering (CSEN) and Bioengineering (BIOE) of the School of Engineering (SoE) at Santa Clara University (SCU) in Santa Clara California USA

The prevalence of gaps and outliers within datasets presents substantial challenges, particularly in the realm of time series fore-casting and various other predictive machine learning (ML) tasks. This paper, introduces an effective technique for correcting gaps and outliers in data and validates the approach by applying it to datasets with outlier zones drawn from three diverse contexts. This innovative technique holds promising potential to enhance the performance of machine learning models by treating the data to alleviate the complications posed by these issues and in doing so contributes a valuable tool to the data science toolbox.

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Mapping the Human Body at Cellular Resolution -- The NIH Common Fund Human BioMolecular Atlas program

arXiv

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arXiv 2019年

作者： Snyder, Michael P. Lin, Shin Posgai, Amanda Atkinson, Mark Regev, Aviv Rood, Jennifer Rozenblatt-Rosen, Orit Gaffney, Leslie Hupalowska, Anna Satija, Rahul Gehlenborg, Nils Shendure, Jay Laskin, Julia Harbury, Pehr Nystrom, Nicholas A. Bar-Joseph, Ziv Zhang, Kun Börner, Katy Lin, Yiing Conroy, Richard Procaccini, Dena Roy, Ananda L. Pillai, Ajay Brown, Marishka Galis, Zorina S. Cai, Long Shendure, Jay Trapnell, Cole Lin, Shin Jackson, Dana Snyder, Michael P. Nolan, Garry Greenleaf, William James Lin, Yiing Plevritis, Sylvia Ahadi, Sara Lee, Hayan Nevins, Stephanie Esplin, Ed Horning, Aaron Bahmani, Amir Zhang, Kun Sun, Xin Jain, Sanjay Hagood, James Kharchenko, Peter Atkinson, Mark Bodenmiller, Bernd Brusko, Todd Clare-Salzler, Michael Nick, Harry Otto, Kevin Posgai, Amanda Wasserfall, Clive Maffioletti, Sergio Caprioli, Richard M. Spraggins, Jeffrey M. Gutierrez, Danielle Patterson, Nathan Heath Neumann, Elizabeth K. Harris, Raymond deCaestecker, Mark de Plas, Raf Van Lau, Ken Cai, Long Yuan, Guo-Cheng Zhu, Qian Dries, Ruben Yin, Peng Saka, Sinem K. Kishi, Jocelyn Y. Wang, Yu Goldaracena, Isabel Laskin, Julia Ye, DongHye Burnum-Johnson, Kristin E. Piehowski, Paul D. Ansong, Charles Zhu, Ying Harbury, Pehr Desai, Tushar Mulye, Jay Chou, Peter Nagendran, Monica Bar-Joseph, Ziv Teichmann, Sarah A. Paten, Benedict Murphy, Robert F. Ma, Jian Kiselev, Vladimir Yu Kingsford, Carl Ricarte, Allyson Ruffalo, Matthew Gehlenborg, Nils Kharchenko, Peter Vella, Margaret McCallum, Chuck Börner, Katy Cross, Leonard E. Friedman, Samuel H. Heiland, Randy Herr, Bruce Macklin, Paul Quardokus, Ellen M. Record, Lisel Sluka, James P. Weber, Griffin M. Nystrom, Nicholas A. Silverstein, Jonathan C. Blood, Philip D. Ropelewski, Alexander J. Shirey, William E. Mabee, Paula Lenhardt, W. Christopher Robasky, Kimberly Michailidis, Stavros Satija, Rahul Marioni, John Regev, Aviv Butler, Andrew Stuart, Tim Fisher, Eyal Ghazanfar, Shila Rood, Jennifer Gaffney, Leslie Ersalan, Gokcen Biancalani, Tommaso Conroy, Richard Procaccini, Dena Roy, Anan School of Medicine Stanford University 300 Pasteur Drive United States Department of Genetics Stanford School of Medicine StanfordCA94305 United States Department of Medicine University of Washington SeattleWA98195 United States Department of Pathology University of Florida Diabetes Institute GainesvilleFL32610 United States Klarman Cell Observatory Broad Institute of MIT and Harvard CambridgeMA02142 United States Howard Hughes Medical Institute Koch Institute of Integrative Cancer Research Department of Biology Massachusetts Institute of Technology CambridgeMA02140 United States New York Genome Center New York CityNY10013 United States New York University New YorkNY10012 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Genome Sciences University of Washington SeattleWA98105 United States Department of Chemistry Purdue University West LafayetteIN47907 United States Department of Biochemistry Stanford University School of Medicine StanfordCA94305 United States Pittsburgh Supercomputing Center Carnegie Mellon University PittsburghPA15213 United States Department of Computational Biology School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Department of Bioengineering University of California San Diego La JollaCA92093 United States Department of Intelligent Systems Engineering School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Surgery Washington University School of Medicine St LouisMO63110 United States Division of Program Coordination Planning and Strategic Initiatives National Institutes of Health BethesdaMD20892 United States National Human Genome Research Institute National Institutes of Health BethesdaMD20892 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Caltech-UW TMC Departmen

Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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