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Provoking Tweets by Indonesia Media Twitter in the Initial Month of Coronavirus Disease Hit

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Provoking Tweets by Indonesia Media Twitter in the Initial M...

International Conference on Information Management and Technology (ICIMTech)

作者： Ika Nurlaila Reza Rahutomo Kartika Purwandari Bens Pardamean BINUS Online Learning Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

After months in denial, on 2 nd March 2020 the first case of COVID-19 in Indonesia was announced and soon was declared as a national pandemic. COVID-19 was since being the headlines of the news offline and online. This trigger either positive or negative responses from the readers, which in turn affect at least the depth of understanding and furthermore the effectiveness of any enforced intervention taken with the purpose to control the transmission of the COVID-19. In order to assess how likely the COVID-19-containing news being forwarded to reach more readers and how the pertinent news bridge further virtual interaction, we crawled data from Twitter as the major online microblogging platform to analyze how likely COVID-19-containing tweets were echoed and what typical tweet about COVID-19 that gained attentions. This is critical to evaluate how people virtually responded against the tweets. Our analyzed data is visualized using Matplotlib by Python and Graph Prism8 accordingly. We figured out that timing and number of followers are not determinative for the tweets being retweeted. Instead, provoking headline add likelihood for the tweets to be moving forward. Also appeared in our analysis that the top two media (Detik and Kompas) shared the same proportion on their tweet type fractions on where information dominated the fraction of their tweets.

关键词： Twitter Media Corona Tagging Diseases data visualization

Factors Associated With Cost Barriers to Asthma Management by Insurance Coverage Status Among US Adults

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Journal of Allergy and Clinical Immunology: In Practice 2025年第6期13卷 1412-1420.e1页

作者： Xie, Luyu Tang, Yixin Felix-George, Rebecca I. Joo, Joseph H. Chen, Yutong Rivera-Sanchez, Yadira Liao, Joshua M. Quantitative Biomedical Research Center Department of Health Data Science and Biostatistics Peter O'Donnell Jr. School of Public Health The University of Texas Southwestern Medical Center Dallas TX United States Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX United States Program on Policy Evaluation and Learning Dallas TX United States Department of Epidemiology School of Public Health University of Texas Health Science Center at Houston Dallas TX United States Department of Medicine University of Washington School of Medicine Seattle Wash Department of Economics University of Texas at Arlington Arlington TX United States Department of Pediatrics University of Texas Southwestern Medical Center Dallas TX United States

Background: Asthma is a common chronic condition in the United States, yet despite insurance coverage, patients with asthma can potentially face cost barriers and limit access to care. Objective: To investigate the predictors of cost barriers in asthma care and explore variations based on insurance coverage status. Methods: We used 2019 to 2021 Behavioral Risk Factor Surveillance System Survey data and multivariable logistic regression to evaluate the relationship between insurance coverage (full year vs partial or no coverage) as study exposure and self-reported cost barriers (to primary care, asthma specialists, and asthma medications) as study outcomes. Results: Among 25,996 patients, 86.3% had full-year insurance coverage whereas 13.7% had partial or no insurance coverage. Almost half of patients with partial or no coverage reported cost barriers (28.4% to primary care, 13.9% to asthma specialists, and 33% to asthma medications) compared with 10.6% fully insured individuals (3.3% to primary care, 2.7% to asthma specialists, and 8.3% to asthma medications;P < .001). Non-Hispanic Black people with full-year insurance were more likely to face primary care cost barriers than were non-Hispanic White people (odds ratio = 1.91;95% CI, 1.19-3.07). Males were less likely to have asthma medication cost barriers than were females regardless of insurance status. In addition, among fully insured participants, older age, higher income, and longer time since last asthma symptoms reduced any cost barriers, whereas recent emergency department visits or hospitalizations increased them (odds ratio = 2.23;95% CI, 1.74-2.86). In partially or uninsured participants, higher income and time since symptoms were protective, but emergency department visits or hospitalizations also increased cost barriers. Conclusions: Although partially or uninsured individuals face greater cost challenges, fully insured individuals also experience significant barriers, particularly among specific subgrou

关键词： Asthma Cost barrier Health insurance

Pan-cancer integrative histology-genomic analysis via interpretable multimodal deep learning

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arXiv 2021年

作者： Chen, Richard J. Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Lipkova, Jana Shaban, Muhammad Shady, Maha Williams, Mane Joo, Bumjin Noor, Zahra Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Computer Science Harvard University CambridgeMA United States

The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are either based on histology or genomics alone and do not address how histology and genomics can be integrated to develop joint image-omic prognostic models. Additionally identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We used multimodal deep learning to integrate gigapixel whole slide pathology images, RNA-seq abundance, copy number variation, and mutation data from 5,720 patients across 14 major cancer types. Our interpretable, weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities for predicting outcomes and discover prognostic features from these modalities that corroborate with poor and favorable outcomes via multimodal interpretability. We compared our model with unimodal deep learning models trained on histology slides and molecular profiles alone, and demonstrate performance increase in risk stratification on 9 out of 14 cancers. In addition, we analyze morphologic and molecular markers responsible for prognostic predictions across all cancer types. All analyzed data, including morphological and molecular correlates of patient prognosis across the 14 cancer types at a disease and patient level are presented in an interactive open-access database (http://***) to allow for further exploration and prognostic biomarker discovery. To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied. Copyright © 2021, The Authors. All rights reserved.

关键词： Diseases

Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders

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arXiv 2021年

作者： Gajamannage, Kelum Park, Yonggi Paffenroth, Randy Jayasumana, Anura P. Department of Mathematics and Statistics Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States Department of Mathematical Sciences Department of Computer Science Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80525 United States

Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the multi-object methods tracking such dynamics might lose track of the agents where that might result fragmentation in the constructed trajectories. Here, we present an extended deep autoencoder (DA) that we train only on fully observed segments of the trajectories by defining its loss function as the Hadamard product of a binary indicator matrix with the absolute difference between the outputs and the labels. The trajectories of the agents practicing collective motion is low-rank due to mutual interactions and dependencies between the agents that we utilize as the underlying pattern that our Hadamard deep autoencoder (HDA) codes during its training. The performance of our HDA is compared with that of a low-rank matrix completion scheme in the context of fragmented trajectory reconstruction. © 2021, CC BY.

关键词： Learning systems

Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models

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arXiv 2024年

作者： Pelletier, Alexander R. Ramirez, Joseph Adam, Irsyad Sankar, Simha Yan, Yu Wang, Ding Steinecke, Dylan Wang, Wei Ping, Peipei Department of Physiology UCLA School of Medicine Suite 1-609 MRL Building 675 Charles E. Young Dr. LACA90095-1760 United States Department of Computer Science UCLA School of Engineering Los AngelesCA90095 United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Suite 1-609 MRL Building 675 Charles E. Young Dr. South Los AngelesCA90095-1760 United States CA90095 United States UCLA School of Medicine Suite 1-609 MRL Building 675 Charles E. Young Dr. South Los AngelesCA90095-1760 United States

The vast amount of biomedical information available today presents a significant challenge for investigators seeking to digest, process, and understand these findings effectively. Large Language Models (LLMs) have emerged as powerful tools to navigate this complex and challenging data landscape. However, LLMs may lead to hallucinatory responses, making Retrieval Augmented Generation (RAG) crucial for achieving accurate information. In this protocol, we present RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation, identifying validated paths forward. Relevant biomedical information from publications and knowledge bases are reviewed, integrated, and extracted via text-mining association analysis and explainable graph prediction models on disease nodes, forecasting potential links among drugs and diseases. These analyses, along with biomedical texts, are integrated into a framework that facilitates user-directed mechanism elucidation as well as hypothesis exploration through RAG-enabled LLMs. A clinical use-case demonstrates RUGGED's ability to evaluate and recommend therapeutics for Arrhythmogenic Cardiomyopathy (ACM) and Dilated Cardiomyopathy (DCM), analyzing prescribed drugs for molecular interactions and unexplored uses. The platform minimizes LLM hallucinations, offers actionable insights, and improves the investigation of novel therapeutics. © 2024, CC BY.

关键词： Bioinformatics

Estimation of nonparametric ordinal logistic regression model using generalized additive models (GAM) method based on local scoring algorithm

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AIP Conference Proceedings 2022年第1期2668卷

作者： Marisa Rifada Nur Chamidah Ratih Ardiati Ningrum 1Statistics Study Program Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia 3Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia 2Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia

One of the statistical methods used to describe the relationship between categorical scale response variable and categorical or continuous variable of predictors is logistic regression analysis. If the response variable has ordinal scale, it is called ordinal logistic regression. The ordinal response variables is common used in scientific research. There are two approaches to the regression model, i.e. parametric and nonparametric approach. We develop the nonparametric ordinal logistic regression which is an expansion of the ordinal logistic regression model where the regression function is estimated using a nonparametric approach. The aim of this study is determine the regression function estimators of the nonparametric ordinal logistic regression model using Generalized Additive Models (GAM) method based on local scoring algorithm. The GAM method assumes that the regression function is expressed as the sum of the regression functions of each component predictor variables. The local scoring algorithm consists of two loops, namely the scoring step (outer loop) which is iterated until the deviance value converges and the backfitting step (inner loop) is iterated until the Residual Sum of Squares (RSS) value converges.

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A new covariant formalism for kinetic plasma simulations in curved spacetimes

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arXiv 2023年

作者： Trent, Tyler Christian, Pierre Chan, Chi-Kwan Psaltis, Dimitrios Özel, Feryal School of Physics Georgia Institute of Technology 837 State St NW AtlantaGA30332 United States Departments of Astronomy and Physics University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Physics Department Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Data Science Institute and Program in Applied Mathematics University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States

Low density plasmas are characterized by a large scale separation between the gyromotion of particles around local magnetic fields and the macroscopic scales of the system, often making global kinetic simulations computationally intractable. The guiding center formalism has been proposed as a powerful tool to bridge the gap between these scales. Despite its usefulness, the guiding center approach has been formulated successfully only in flat spacetimes, limiting its applicability in astrophysical settings. Here, we present a new covariant formalism that leads to kinetic equations in the guiding center limit that are valid in arbitrary spacetimes. Through a variety of experiments, we demonstrate that our equations capture all known gyro-center drifts while overcoming one severe limitation imposed on numerical algorithms by the fast timescales of the particle gyromotion. This formalism will enable explorations of a variety of global plasma kinetic phenomena in the curved spacetimes around black holes and neutron stars. © 2023, CC BY.

关键词： Integral equations

Greater than the parts: A review of the information decomposition approach to causal emergence

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arXiv 2021年

作者： Mediano, Pedro A.M. Rosas, Fernando E. Luppi, Andrea I. Jensen, Henrik J. Seth, Anil K. Barrett, Adam B. Carhart-Harris, Robin L. Bor, Daniel Department of Psychology University of Cambridge Department of Psychology Queen Mary University of London Centre for Psychedelic Research Imperial College London Data Science Institute Imperial College London Centre for Complexity Science Imperial College London University Division of Anaesthesia University of Cambridge Department of Clinical Neurosciences University of Cambridge Leverhulme Centre for the Future of Intelligence University of Cambridge Department of Mathematics Imperial College London Institute of Innovative Research Tokyo Institute of Technology Sackler Centre for Consciousness Science University of Sussex CIFAR Program on Brain Mind and Consciousness Toronto Canada The Data Intensive Science Centre University of Sussex Psychedelics Division Neuroscape Department of Neurology University of California San Francisco

Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons. Despite the broad interest that exists on this concept, the study of emergence has suffered from a lack of formalisms that could be used to guide discussions and advance theories. Here we summarise, elaborate on, and extend a recent formal theory of causal emergence based on information decomposition, which is quantifiable and amenable to empirical testing. This theory relates emergence with information about a system’s temporal evolution that cannot be obtained from the parts of the system separately. This article provides an accessible but rigorous introduction to the framework, discussing the merits of the approach in various scenarios of interest. We also discuss several interpretation issues and potential misunderstandings, while highlighting the distinctive benefits of this formalism. © 2021, CC BY.

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