检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models

arXiv

引用

arXiv 2020年

作者： Luo, Yun Zhu, Li-Zhen Wan, Zi-Yu Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China

The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models. Our proposed methods are based on two deep generative models, variational autoencoder (VAE) and generative adversarial network (GAN), and two data augmentation strategies. For the full usage strategy, all of the generated data are augmented to the training dataset without judging the quality of the generated data, while for partial usage, only high-quality data are selected and appended to the training dataset. These three methods are called conditional Wasserstein GAN (cWGAN), selective VAE (sVAE), and selective WGAN (sWGAN). To evaluate the effectiveness of these methods, we perform a systematic experimental study on two public EEG datasets for emotion recognition, namely, SEED and DEAP. We first generate realistic-like EEG training data in two forms: power spectral density and differential entropy. Then, we augment the original training datasets with a different number of generated realistic-like EEG data. Finally, we train support vector machines and deep neural networks with shortcut layers to build affective models using the original and augmented training datasets. The experimental results demonstrate that the augmented training datasets produced by our methods enhance the performance of EEG-based emotion recognition models and outperform the existing data augmentation methods such as conditional VAE, Gaussian noise, and rotational data augmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Electrophysiology

来源：评论

学校读者我要写书评

暂无评论

[email protected]: Theoretical and Methodological Challenges in Intelligent Conversational User Interface interactions

[email protected]: Theoretical and Methodological Challenges...

引用

26th International Conference on Intelligent User Interfaces - Companion

作者： Philip R Doyle Daniel John Rough Justin Edwards Benjamin R. Cowan Leigh Clark Martin Porcheron Stephan Schlögl María Inés Torres Cosmin Munteanu Christine Murad Jaisie Sin Minha Lee Matthew Peter Aylett Heloisa Candello School of Information & Communication Studies University College Dublin Ireland School of Science and Engineering University of Dundee United Kingdom Information and Communication Studies University College Dublin Ireland Computer Science Swansea University United Kingdom Dept. Management Communication & IT MCI Management Center Innsbruck Austria Speech Interactive Research Group Universidad del Pais Vasco UPV/EHU Spain Institute for Communication Culture Information and Technology University of Toronto Mississauga Canada University of Toronto Canada Human Technology Interaction Technical University of Eindhoven Netherlands CereProc Ltd. United Kingdom IBM Research Brazil

ISBN: (纸本)9781450380188

This workshop aims to bring together the Intelligent User Interface (IUI) and Conversational User Interface (CUI) communities to understand the theoretical and methodological challenges in designing, deploying and evaluating CUIs. CUIs have continued to prosper with the increased use and technological developments in both text-based chatbots and speech-based systems. However, challenges remain in creating established theoretical and methodological approaches for CUIs, and how these can be used with recent engineering advances. These include assessing the impact of interface design on user behaviours and perceptions, developing design guidelines, understanding the role of personalisation and issues of ethics and privacy. Our half-day multidisciplinary workshop brings together researchers and practitioners from the IUI and CUI communities in academia and industry. We aim to (1) identify and map out key focus areas and research challenges to address these critical theoretical and methodological gaps and (2) foster strong relationships between disciplines within and related to Artificial Intelligence (AI) and human-computer interaction (HCI).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dispensed transformer network for unsupervised domain adaptation

arXiv

引用

arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth labels for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

引用

LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

关键词：

来源：评论

学校读者我要写书评

暂无评论

Disease diagnostics using machine learning of B cell and T cell receptor sequences

引用

science (New York, N.Y.) 2025年第6736期387卷 eadp2407页

作者： Zaslavsky, Maxim E. Craig, Erin Michuda, Jackson K. Sehgal, Nidhi Ram-Mohan, Nikhil Lee, Ji-Yeun Nguyen, Khoa D. Hoh, Ramona A. Pham, Tho D. Röltgen, Katharina Lam, Brandon Parsons, Ella S. Macwana, Susan R. DeJager, Wade Drapeau, Elizabeth M. Roskin, Krishna M. Cunningham-Rundles, Charlotte Moody, M Anthony Haynes, Barton F. Goldman, Jason D. Heath, James R. Chinthrajah, R Sharon Nadeau, Kari C. Pinsky, Benjamin A. Blish, Catherine A. Hensley, Scott E. Jensen, Kent Meyer, Everett Balboni, Imelda Utz, Paul J. Merrill, Joan T. Guthridge, Joel M. James, Judith A. Yang, Samuel Tibshirani, Robert Kundaje, Anshul Boyd, Scott D. Department of Computer Science Stanford University Stanford CA United States Department of Biomedical Data Science Stanford University Stanford CA United States Department of Genetics Stanford University Stanford CA United States Department of Pathology Stanford University Stanford CA United States Department of Emergency Medicine Stanford University Stanford CA United States Stanford Blood Center Stanford CA United States Department of Medical Parasitology and Infection Biology Swiss Tropical and Public Health Institute Allschwil Switzerland University of Basel Basel Switzerland Sean N. Parker Center for Allergy and Asthma Research Stanford University Stanford CA United States Department of Arthritis and Clinical Immunology Oklahoma Medical Research Foundation Oklahoma City OK United States Department of Microbiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Divisions of Biomedical Informatics and Immunobiology Cincinnati Children's Hospital Medical Center Cincinnati OH United States Icahn School of Medicine at Mount Sinai New York NY USA Department of Pediatrics Duke University Durham NC United States Duke Human Vaccine Institute Duke University Durham NC United States Department of Immunology Duke University Durham NC United States Department of Medicine Duke University Durham NC United States Swedish Center for Research and Innovation Swedish Medical Center Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Institute for Systems Biology Seattle WA United States Department of Bioengineering University of Washington Seattle WA United States Department of Environmental Health Harvard T.H. Chan School of Public Health Boston MA United States Division of Allergy and Inflammation Beth Israel Deaconess Medical Center Bosto

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Learning Model to Improve Learning Outcome on Experiential Learning in a Multi-Phase Internship: A Case Study of the Internship Program of a Thai University. 8

A Learning Model to Improve Learning Outcome on Experiential...

引用

8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019

作者： Kingkaew, Chanakarn Supnithi, Thepchai Theeramunkong, Thanaruk Morita, Kai Tanaka, Koji Ikeda, Mitsuru School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand School of Knowledge Science Japan Advanced Institute of Science and Technology Nomi Japan Department of Psychological Science College of Informatics and Human Communication Kanazawa Institute of Technology Nonoichi Japan

ISBN: (纸本)9781728126272

The internship is one of the crucial learning processes that provide a unique experience to combine what they learned in university courses and how to use it in the real society during their university lives as it provides the opportunity to learn through work experience offered by the external organization. Although learner can learn career skills through the internship, most learner cannot make good reflection and cannot learn how to learn from experience. We think the cause of this problem is that most learner cannot reflect their experience and cannot clear the abstract concept and apply it to the next trial. This implies that those learners can improve their learning through internship by acquiring experiential learning skills. In the internship in general, the mentor has the important education roles. One is to foster career skills learning, and the other is to promote experiential learning. However, in most case, the mentor is very good at career skill teaching, but most of the mentor is not well prepared to support experiential learning. In response to the above problems, to support learners to realized good learning, we provide a learning environment for the learner to support experiential learning. A guideline for the learner to let them aware of the critical concept of internship and experiential learning. Also, we provide a web system to practice reflective writing to understand and able to apply experiential learning concepts. In this paper, as the preliminary research to develop learning environment, the analysis of the learner learning activity of the current internship program and show the prototype of the conceptualization of the educational concepts to support the experiential learning. © 2019 IEEE.

关键词： Knowledge management

来源：评论

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021年 12856 LNCS卷 v页

作者： Hernández-Leo, Davinia Hishiyama, Reiko Zurita, Gustavo Weyers, Benjamin Nolte, Alexander Ogata, Hiroaki Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Department of Management Control and Information Systems University of Chile Santiago Chile Institute of Computer Science University of Tartu Tartu Estonia School of Creative Science Waseda University Tokyo Japan Human-Computer Interaction Group University of Trier Trier Germany Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

来源：评论

学校读者我要写书评

暂无评论

The Impact of Genomic Variation on Function (IGVF) Consortium

arXiv

引用

arXiv 2023年

作者： Shendure, Jay Ahituv, Nadav Kircher, Martin Agarwal, Vikram Blair, Andrew Chalkiadakis, Theofilos Chardon, Florence M. Dash, Pyaree M. Deng, Chengyu Hamazaki, Nobuhiko Keukeleire, Pia Kubo, Connor Lalanne, Jean-Benoît Maass, Thorben Martin, Beth McDiarmid, Troy Nobuhara, Mai Page, Nicholas F. Regalado, Sam Schubach, Max Sims, Jasmine Ushiki, Aki Zhao, Jingjing Starita, Lea M. Fowler, Douglas M. Best, Sabrina M. Boyle, Gabe Camp, Nathan Casadei, Silvia Da, Estelle Y. Dawood, Moez Dawson, Samantha C. Fayer, Shawn Hamm, Audrey James, Richard G. Jarvik, Gail P. McEwen, Abbye E. Moore, Nick Muffley, Lara A. Pendyala, Sriram Popp, Nicholas A. Post, Mason Rubin, Alan F. Shendure, Jay Smith, Nahum T. Rubin, Alan F. Stone, Jeremy Tejura, Malvika Wang, Ziyu R. Wheelock, Melinda K. Woo, Ivan Zapp, Brendan D. Engreitz, Jesse M. Quertermous, Thomas Amgalan, Dulguun Aradhana, Aradhana Arana, Sophia M. Bassik, Michael C. Bauman, Julia R. Bhattacharya, Asmita Cai, Xiangmeng Shawn Chen, Ziwei Conley, Stephanie Deshpande, Salil Doughty, Benjamin R. Du, Peter P. Gifford, Casey Greenleaf, William J. Gschwind, Andreas R. Guo, Katherine Isobe, Sarasa Jagoda, Evelyn Jain, Nimit Jones, Hank Kang, Helen Y. Kim, Samuel H. Kim, YeEun Klemm, Sandy Kundaje, Anshul Kundu, Soumya Lago-Docampo, Mauro Lee-Yow, Yannick C. Levin-Konigsberg, Roni Li, Daniel Y. Lindenhofer, Dominik Rosa Ma, X. Marinov, Georgi K. Martyn, Gabriella E. Metzl-Raz, Eyal Monteiro, Joao P. Montgomery, Michael T. Mualim, Kristy S. Munger, Chad Munson, Glen Nguyen, Tri C. Nguyen, Trieu Palmisano, Brian T. Pampari, Anusri Park, Chong Y. Rabinovitch, Marlene Ramste, Markus Ray, Judhajeet Roy, Kevin R. Rubio, Oriane M. Schaepe, Julia M. Schnitzler, Gavin Schreiber, Jacob Sharma, Disha Sheth, Maya U. Shi, Huitong Singh, Vasundhara Sinha, Riya Steinmetz, Lars M. Tan, Jason Tan, Anthony Tycko, Josh Valbuena, Raeline C. Pour Amiri, Valeh Valiollah van Kooten, Mariëlle J.F.M. Vaughan-Jackson, Alun Venida, Anthony Weldy, Chad S. Yao, David Zeng, Tony Zhou, Ronghao Vidal, Marc Calderw Department of Genome Sciences University of Washington SeattleWA United States Department of Bioengineering and Therapeutic Sciences Institute for Human Genetics University of California San Francisco San FranciscoCA United States Institute of Human Genetics University Medical Center Schleswig-Holstein University of Lübeck Lübeck23562 Germany Exploratory Diagnostic Sciences Berlin Institute of Health Charité-Universitätsmedizin Berlin Berlin10117 Germany Brotman Baty Institute for Precision Medicine SeattleWA United States Center of immunotherapy and Immunity Seattle Children's Research Institute SeattleWA United States Bioinformatics Division WEHI ParkvilleVIC Australia Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Laboratory Medicine and Pathology University of Washington SeattleWA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Basic Science and Engineering Initiative Stanford Children’s Health Betty Irene Moore Children’s Heart Center StanfordCA United States The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard CambridgeMA United States Division of Cardiovascular Medicine School of Medicine Stanford University United States Department of Computer Science Stanford University School of Medicine StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine StanfordCA United States Division of Pediatric Cardiology Cardiovascular Institute Stanford University School of Medicine Stanford University United States Genome Biology Unit Heidelberg Germany Department of Biology Stanford University StanfordCA United States Stanford Genome Techn

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations. © 2023, CC BY.

关键词： Genome

来源：评论

学校读者我要写书评

暂无评论

Revolutionising engineering pedagogy: The role of 3D printing in modern engineering education

引用

PLET: Programmed Learning & Educational Technology 2025年第2期62卷

作者： Muhammad Tajammal Munir Prashant K. Jamwal Bing Li Susan Carter Shahid Hussain a College of Engineering and Technology American University of the Middle East Egaila Kuwait b Department of Electrical and Computer Engineering School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan c Water Research Center Shenzhen International Graduate School Tsinghua University Shenzhen China d School of Critical Studies in Higher Education The University of Auckland Auckland New Zealand e Human-Centred Technology Research Center Faculty of Science and Technology University of Canberra Canberra Australia

ABSTRACTThis paper presents a comprehensive study on the integration of 3D printing technology in biomedical robotics education with undergraduate students learning about the design of robotic systems. The research investigated the effectiveness of this new teaching strategy in enhancing students’ understanding of complex engineering principles by comparing their learning experiences, and academic performance with results from traditional teaching methods. The students’ learning and performance were evaluated through a survey and grade analysis. The Mann – Whitney U test was used to analyse the data, revealing statistically significant improvements in both student satisfaction and grades. The Mann-Whitney U test results show a clear preference for the 2017 3D printing and project-based learning strategy over the 2016 traditional method. With a U-value of 0 and a critical U-value atp< 0.05 being 23, coupled with a z-score of −3.742, the data significantly indicates enhanced student outcomes with the newer teaching approach compared to conventional methods. The study also found that 3D printing technology fostered creativity, critical thinking, and enhanced spatial visualisation skills among students. However, we also identify potential challenges, including equipment maintenance, safety concerns, and the need for faculty expertise. The study concludes by providing potential strategies to overcome these challenges. This research offers valuable insights for educators and policy-makers in leveraging technology for improved learning outcomes in engineering education.

关键词： 3D printing engineering education pedagogical theories curriculum integration

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：