检索结果-内蒙古大学图书馆

Electronic Near Point of Convergence: A Tool to Aid the Assessment of Concussion

学校读者我要写书评

暂无评论

Electronic Near Point of Convergence: A Tool to Aid the Asse...

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liang, Bo Prichep, Leslie S. Kanakia, Saloni Bazarian, Jeffrey J. Covassin, Tracey Elbin, Robert J. Hotz, Gillian A. Struder, Jeb F. Armanañzas, Rubén BrainScope Company Inc. R&d Department Chevy ChaseMD United States University of Rochester School of Medicine Department of Emergency Medicine RochesterNY United States Michigan State University Department of Kinesiology East LansingMI United States University of Arkansas Office Sports Concussion Research FayettevilleAR United States University of Miami UHealth Concussion Program MiamiFL United States University of Connecticut Korey Stringer Institute StorrsCT United States University of Navarra Institute of Data Science and Artificial Intelligence Tecnun School of Engineering Pamplona Spain

ISBN: (纸本)9798350337488

Near Point of Convergence (NPC) is a widely used test when assessing oculomotor dysfunction in patients with mild Traumatic Brain Injury (mTBI) or concussion. A new electronic version of the Near Point Convergence test (eNPC) was developed to automatically measure NPC distance, addressing the need to improve the administration and precision of NPC testing. Age-adjusted normative values were achieved by using goodness-of-fit and generalized linear regression models. Target subjects included males and females aged 13 to 50 years. Normative values were computed as a function of age for records from 230 healthy volunteers. eNPC values from an independent cohort of 237 patients clinically deemed to have concussion and 245 matched controls were transformed into z-scores relative to age-expected normal eNPC values and then compared to demonstrate performance in a clinical population. Mean differences between matched controls and concussed patients showed a highly significant difference for eNPC z-scores between groups. The maximum Odds-Ratio (OR) value for z-scores was reached in the cutoff interval 0.75 to 1 with a mean of 2.614 and a median of 2.627. Usability for the eNPC test was high with a rate of obtaining measurements between 92% and 98%. Using z-scores helped reduce variance of the measurement and accounted for the effect of age. Significant separation between concussed patients and matched controls validated the use of the eNPC mathematical model. Further, high OR values using z-scores demonstrated potential improved clinical utility of the eNPC in the prediction of oculomotor dysfunction. © 2023 IEEE.

关键词： Concussion Medical Device Mild Traumatic Brain Injury Near Point of Convergence

graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS Results with Integration of Functional Annotation data

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Deng, Qiaolan Nam, Jin Hyun Yilmaz, Ayse Selen Chang, Won Pietrzak, Maciej Li, Lang Kim, Hang J. Chung, Dongjun The Interdisciplinary PhD program in Biostatistics The Ohio State University ColumbusOH United States Division of Big Data Science Korea University Sejong Campus Sejong Korea Republic of Department of Biomedical Informatics The Ohio State University ColumbusOH United States Division of Statistics and Data Science University of Cincinnati CincinnatiOH United States Pelotonia Institute for Immuno-Oncology The James Comprehensive Cancer Center The Ohio State University ColumbusOH United States

Motivation: Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Results: First, we conducted simulation studies to evaluate GGPA 2.0. The results indicate that incorporating functional annotation data using GGPA 2.0 does not only improve detection of disease-associated variants, but also allows to identify more accurate relationships among diseases. Second, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus and the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood, especially B cells and regulatory T cells across multiple diseases. Psychiatric disorders were enriched for brain, especially prefrontal cortex and inferior temporal lobe for bipolar disorder (BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfully identified the pleiotropy between BIP and SCZ. These results demonstrate that GGPA 2.0 can be a powerful tool to identify associated variants associated with each phenotype or those shared across multiple phenotypes, while also promoting understanding of functional mechanisms underlying the associated variants. Availability: R package ‘GGPA2’ is available at https://***/GGPA2/. © 2022, CC BY.

关键词：

Grid-uniform copulas and rectangle exchanges: Bayesian model and inference for a rich class of copula functions

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kuschinski, Nicolás Jara, Alejandro ANID Millennium Science Initiative Program – Millennium Nucleus Center for the Discovery of Structures in Complex Data Casilla 306 Correo 22 Santiago Chile Department of Statistics Pontificia Universidad Católica de Chile and ANID Millennium Science Initiative Program – Millennium Nucleus Center for the Discovery of Structures in Complex Data Casilla 306 Correo 22 Santiago Chile

Copula-based models provide a great deal of flexibility in modelling multivariate distributions, allowing for the specifications of models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. Choosing a class of copula models is not a trivial task and its misspecification can lead to wrong conclusions. We introduce a novel class of grid-uniform copula functions, which is dense in the space of all continuous copula functions in a Hellinger sense. We propose a Bayesian model based on this class and develop an automatic Markov chain Monte Carlo algorithm for exploring the corresponding posterior distribution. The methodology is illustrated by means of simulated data and compared to the main existing *** Codes 62G05 © 2021, CC BY-NC-SA.

关键词： Bayesian networks

High‑Performance Thick Cathode Based on Polyhydroxyalkanoate Binder for Li Metal Batteries

学校读者我要写书评

暂无评论

Advanced Fiber Materials 2024年第1期6卷 214-228页

作者： Dong Hyuk Kang Minhyuck Park Jeonghun Lee Chan Yeol Kim Jimin Park Youn‑Ki Lee Jong Chan Hyun Son Ha Jin Hwan Kwak Juhee Yoon Hyemin Kim Hyun Soo Kim Do Hyun Kim Sangmin Kim Ji Yong Park Robin Jang Seung Jae Yang Hee‑Dae Lim Se Youn Cho Hyoung‑Joon Jin Seungjin Lee Yunil Hwang Young Soo Yun KU‑KIST Graduate School of Converging Science and Technology Korea University145 Anam‑roSeongbuk‑guSeoul 02841South Korea Carbon Composite Materials Research Center Korea Institute of Science and Technology(KIST)92 Chundong‑roBongdong‑eupWanju‑gunJeollabuk‑do 55324South Korea Energy Storage Research Center Korea Institute of Science and Technology(KIST)514‑gil Hwaraong‑roSeoul 02792South Korea Program in Environmental and Polymer Engineering Inha University100 Inha‑ro Michuhol‑guIncheon 22212South Korea Advanced Analysis Data Center Korea Institute of Science and Technology(KIST)514‑gil Hwaraong‑roSeoul 02792South Korea Advanced Nanohybrids Laboratory Department of Chemistry and Chemical EngineeringInha University100 Inha‑roMichuhol‑guIncheon 22212South Korea CJ CheilJedang Corporation 55 Gwanggyo‑ro 42Beon‑gilYeongtong‑guSuwon 16495South Korea Department of Chemical Engineering Hanyang University222 Wangsimni‑roSeongdong‑guSeoul 04763South Korea Department of Integrative Energy Engineering Korea University145 Anam‑roSeongbuk‑guSeoul 02841South Korea

Thick cathodes can overcome the low capacity issues,which mostly hamper the performance of the conventional active cathode materials,used in rechargeable Li ***,the typical slurry-based method induces cracking and flaking during the fabrication of thick *** addition,a significant increase in the charge-transfer resistance and local cur-rent overload results in poor rate capabilities and cycling stabilities,thereby limiting electrode *** this study,a synergistic dual-network combination strategy based on a conductive nanofibrillar network(CNN)and a nano-bridging amor-phous polyhydroxyalkanoate(aPHA)binder is used to demonstrate the feasibility of constructing a high-performance thick *** CNN and aPHA dual network facilitates the fabrication of a thick cathode(≥250μm thickness and≥90 wt%active cathode material)by a mass-producible slurry *** thick cathode exhibited a high rate capability and excel-lent cycling *** addition,the thick cathode and thin Li metal anode pair(Li//t-NCM)exhibited an optimal energy performance,affording high-performance Li metal batteries with a high areal energy of~25.3 mW h cm^(-2),a high volumetric power density of~1720 W L^(-1),and an outstanding specific energy of~470 W h kg^(-1)at only 6 mA h cm^(-2).

关键词： Thick cathode Polyhydroxyalkanoate binder Nano-bridging Conductive nano-fibrillar network Lithium metal battery

Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundational Models

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sankar, Baradwaj Simha Gilliland, Destiny Rincon, Jack Hermjakob, Henning Yan, Yu Adam, Irsyad Lemaster, Gwyneth Wang, Dean Watson, Karol Bui, Alex Wang, Wei Ping, Peipei Department of Physiology University of California Los AngelesCA United States NIH BRIDGE2AI Center UCLA NHLBI Integrated Cardiovascular Data Science Training Program at UCLA Los AngelesCA United States Cambridge United Kingdom Bioinformatics IDP University of California Los AngelesCA United States University of California Los AngelesCA United States Medical Informatics Home Area University of California Los AngelesCA United States Department of Computer Science University of California Los AngelesCA United States

Foundational Models (FMs) are gaining increasing attention in the biomedical AI ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders-especially those involved in or affected by these clinical and translational applications-are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem. © 2024, CC BY.

关键词：

Exploring the Relation between Blood Tests and Covid-19 Using Machine Learning

学校读者我要写书评

暂无评论

Exploring the Relation between Blood Tests and Covid-19 Usin...

data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Mohamed Almansoor Nabil M. Hewahi Big Data Science & Analytics Program University of Bahrain University of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

COVID-19 is a global pandemic that hit the world in 2019-2020 and caused massive losses. Every day, hundreds of thousands of tests are being done on possible infected cases. It usually takes several hours to get the results of virus test in advanced countries, whereas in other countries might take days. The aim of this study is to investigate whether normal blood medical tests help in detecting covid-19 using various machine learning approaches. If true, this would give an indication to people who should undergo the virus test. In this paper we independently use machine learning algorithms including support vector machines, adaptive boosting, random forest and k-nearest neighbors. These algorithms are then merged to form ensemble learning which leads to the classification. The results show that the ensemble learning is having the highest true positive rate of 30%. The obtained results show that normal blood tests do not help much in giving right indications about detecting COVID-19.

关键词： COVID-19 Biological system modeling Machine learning Support vector machines Blood Random forests Predictive models

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

Kohonen-SOM LOF Approach for Anomaly Detection

学校读者我要写书评

暂无评论

Kohonen-SOM LOF Approach for Anomaly Detection

Information Technology International Seminar (ITIS)

作者： Amri Muhaimin Kartika Fithriasari Data Science Program Study Universitas Pembangunan Nasional &#x201C Veteran&#x201D Jawa Timur Surabaya Indonesia Department of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781665408080

Water loss in distribution is a serious problem for water companies, as it can result in a loss of profit. Machine learning has a method for dealing with this, for example, anomaly detection. Anomalies in the study were detected using data from March 2017 to February 2018. The data used is water consumption, and the variables such as mean water use, maximum water use, and standard deviation of water usage were obtained. Kohonen Self-Organizing Map (SOM) and Local Outlier Factor (LOF) are used because they can create a good cluster, and LOF can detect which one of them is filled with anomalies. The Kohonen SOM algorithm produced 45 groups that were considered anomalous because their silhouette width criteria were less than the group's average silhouette width. There are 45 different groups of unexpected anomalies. The LOF generated 1229 unusual consumption events, with each incident involving 579 households or customers. The frequency calculation yields 41 customers who are suspected of being anomalous. While the baseline method from the company only captures 16 anomalies out of 41 customers. This is because the company method fails to capture unusual consumption behaviors, such as monthly consumption. The anomaly detected the customer's characteristic of having an average of more than average usage of classes and sub-zones.

关键词： Self-organizing feature maps Seminars Clustering algorithms Companies Machine learning Behavioral sciences Information technology

Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Kim, Heeseung Kim, Sungwon Yoon, Sungroh Data Science and AI Lab. Seoul National University Korea Republic of Department of ECE and Interdisciplinary Program in AI Seoul National University Korea Republic of

We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does not require any transcript of target speaker using classifier guidance. Guided-TTS combines an unconditional diffusion probabilistic model with a separately trained phoneme classifier for classifier guidance. Our unconditional diffusion model learns to generate speech without any context from untranscribed speech data. For TTS synthesis, we guide the generative process of the diffusion model with a phoneme classifier trained on a large-scale speech recognition dataset. We present a norm-based scaling method that reduces the pronunciation errors of classifier guidance in Guided-TTS. We show that Guided-TTS achieves a performance comparable to that of the state-of-the-art TTS model, Grad-TTS, without any transcript for LJSpeech. We further demonstrate that Guided-TTS performs well on diverse datasets including a long-form untranscribed dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset