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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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Cell2Sentence: Teaching Large Language Models the Language of Biology 41

Cell2Sentence: Teaching Large Language Models the Language o...

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41st International Conference on Machine Learning, ICML 2024

作者： Levine, Daniel Rizvi, Syed Asad Lévy, Sacha Pallikkavaliyaveetil, Nazreen Zhang, David Chen, Xingyu Ghadermarzi, Sina Wu, Ruiming Zheng, Zihe Vrkic, Ivan Zhong, Anna Raskin, Daphne Han, Insu de Oliveira Fonseca, Antonio Henrique Caro, Josue Ortega Karbasi, Amin Dhodapkar, Rahul M. van Dijk, David Department of Computer Science Yale University New HavenCT United States School of Engineering Applied Science University of Pennsylvania PhiladelphiaPA United States School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Neuroscience Yale School of Medicine New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Google United States Yale Institute for Foundations of Data Science New HavenCT United States Yale School of Engineering and Applied Science New HavenCT United States Roski Eye Institute University of Southern California Los AngelesCA United States Yale School of Medicine New HavenCT United States Cardiovascular Research Center Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into"cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S finetuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications. Copyright 2024 by the author(s)

关键词： Gene expression

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data 1

DEA and Fuzzy Simple Additive Weighting for Benchmarking Qua...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Hartono, H. Ongko, Erianto Erliana, Cut Ita Irwansyah, Defi Iskandar, Akbar Sari, Ayu Esteka Abdussakir Yuniningsih Siregar, Nurintan Asyiah Kahar, Muhammad Syahrul Waliulu, Raditya Faisal Amalia, Ainna Kurniasih, Nuning Arifin, Muhammad Muzid, Syafiul Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Economic and Business Universitas Pembangunan Nasional Veteran Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Bonifacius Vicky Indriyono Natalinda Pamungkas Fatima Hashim Abbas Ery Mintorini Mohammed Almohamadi Sarmad Nozad Mahmood Sameer Alani Ahmed H. Alkhayyat Eko Hari Rachmawanto Information System University of Dian Nuswantoro Kediri Indonesia Medical Laboratories Techniques Department Al-Mustaqbal University College Hillah Babil Iraq Visual Communication Design University of Dian Nuswantoro Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq Medical Laboratories Techniques University of Mashreq Research center Baghdad Iraq College of technical engineering University of Mashreq The Islamic University Najaf Iraq Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments; they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD.

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GEOAIDA applied to SPOT satellite image interpretation

GEOAIDA applied to SPOT satellite image interpretation

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GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas

作者： S. Muller R.Q. Feitosa G.L. Abelha Mota D.P. da Costa V.V. da Silva K. Tanisaki Institute of Communication Theory and Signal Processing University of Hanover Hanover Germany Department of Electrical Engineering Catholic University of Rio de Janeiro Rio de Janeiro Brasil Department of Computer Engineering State University of Rio de Janeiro Rio de Janeiro Brasil Applied Geology Department State University of Rio de Janeiro Rio de Janeiro Brasil Radioecology and Global Change Laboratory and Researcher of Geomatic Program State University of Rio de Janeiro Rio de Janeiro Brasil

This paper investigates the application of a knowledge-based approach, founded on semantic networks, to the automatic land use mapping assisted by low resolution satellite images. Like the visual photo-interpretation, the automatic image interpretation considers scene and sensors knowledge, delivered by an expert photo-interpreter, as well as additional information about the region like the digital elevation model, the position of the emergent rocks, the mapping of the water bodies and the road-network. By this means, the analysis of a scene can be automatically performed, mimicking the reasoning of the photo-interpreter. The implementation of such proposal employed the GEOAIDA [J. Buckner et al., June 2001] system, a flexible environment for image interpretation developed at the University of Hanover, which exploits semantic networks to structure the domain specific knowledge. In the reported experiments, a multispectral SPOT 3 XS image was analysed resulting were evaluated and compared with a manually made reference map of the investigated scene. The automatically obtained results were evaluated and compared with a manually made reference map of the investigated scene. The experimental results demonstrate the potential of a knowledge-based approach for low resolution satellite images interpretation.

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A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach

A Platform for Monitoring Student Commuting in the Use of Sc...

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International Conference on Smart communications and Networking (SmartNets)

作者： Jessé Da Costa Rocha Marcela Alves de Souza Evelin Helena Silva Cardoso Nandamudi Vijaykumar Jasmine Priscyla Leite de Araújo Renato Lisboa Francês Graduate Program in Electrical Engineering Institute of Technology Federal University of Pará (UFPA) Belém Pará Brazil Information and Communication Technology Center Federal University of the South and Southeast of Para (Unifesspa) Marabá Pará Brazil Department of Computer Science Federal Rural University of the Amazon (UFRA) Capitão Poço Pará Brazil Coordination of Applied Research and Technological Development National Institute for Space Research (INPE) São Paulo Brazil

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics.

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A Multidisciplinary Effort for Smart Health Assistive Technology in a Children's Psychiatric Unit

A Multidisciplinary Effort for Smart Health Assistive Techno...

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IEEE International Conference on Microwaves, communications, Antennas and Electronic Systems, COMCAS

作者： Itai Dabran Meira Levy Michal Pauzner Mariela Mosheva Tom Sofer Tal Shilton Eitan Eldar Doron Gothelf Computer Science Faculty Technion - Israel Institute of Technology Haifa Israel Department of Industrial Engineering and Management Shenkar College Ramat Gan Israel Department of Visual Communication Azrieli Faculty of Design Shenkar College Ramat Gan Israel The Edmond and Lily Safra Children's Hospital Chaim Sheba Medical Centre Tel HaShomer Israel Applied Behavior Analysis in Psychiatry Program Chaim Sheba Medical Centre Tel HaShomer Israel

ISBN: (数字)9798350348187

ISBN: (纸本)9798350348194

This paper demonstrates the importance of interdisciplinary collaboration between teams of medical researchers, product design researchers, and computer science students. Their combined work resulted in both a concept and a product, a Smart Health Assistive Bag for children being hospitalized. The Child Psychiatry Inpatient Unit at Sheba Medical Center provides mental health services to children suffering from a variety of psychopathological conditions. The multidisciplinary staff takes a family-oriented approach where parents and caregivers are encouraged to be active participants in the treatment team throughout their child's stay. Our work focused on the design and development of a Smart Bag, which supports this family-oriented approach, encourages the involvement of the family during the child's stay and collects new data for future functional analysis. engineering and design students from both Shenkar and the Technion worked with Sheba's clinicians to create a conceptual prototype that advances the defined theoretical needs and build a working prototype. Our Smart Bag aimed at aiding child hospitalizations in the Psychiatrist Inpatient Unit is an important innovative tool for Sheba's Smart Health facility. It is designed to help the child in adjusting to the unit and be an active participant in their daily routine while in the unit. Additionally, because it has IoT sensors and is linked to a network through a digital platform, it supports the child's family and caregivers in staying updated regarding the child's progress. The Smart Bag can apprise the family about the child's daily achievements by informing them of tokens given to the child for desirable behavior. It updates both family and caregivers regarding the current stage in the child's treatment. Such on-going communication involving the child, caregivers and clinicians reduces the stress and worry that families often feel when their child is hospitalized in the Psychiatrist Unit and improves the therap

关键词： Microwave antennas computer science Pediatrics Hospitals Databases Smart healthcare Authentication

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Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

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computers in Biology and Medicine 2024年 178卷 108664-108664页

作者： Nguyen, Van-Nui Ho, Trang-Thi Doan, Thu-Dung Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen University Thai Nguyen Viet Nam Department of Computer Science and Information Engineering TamKang University New Taipei251301 Taiwan International Degree Program in Animal Vaccine Technology International College National Pingtung University of Science and Technology Pingtung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei110 Taiwan

N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities. © 2024 Elsevier Ltd

关键词： Embeddings

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