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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Jiaying Li Jiamei Wu Kangwen Yang Qiang Hao Minbiao Ji Ming Yan Kun Huang Heping Zeng Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and System Ministry of Education School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China State Key Laboratory of Surface Physics and Department of Physics Human Phenome Institute Multiscale Research Institute of Complex Systems Academy for Engineering and Technology Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) Fudan University Shanghai China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai China Chongqing Institute of East China Normal University Chongqing China Jinan Institute of Quantum Technology Jinan China

A pre-chirper free, core-pumped nonlinear fiber amplifier was demonstrated to generate parabolic pulse with spectral bandwidth of 53 nm, these pulses were filtered and passively synchronized for conducting coherent an...

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Diffusion-weighted MR spectroscopy: consensus, recommendations and resources from acquisition to modelling

arXiv

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arXiv 2023年

作者： Ligneul, Clémence Najac, Chloé Döring, André Beaulieu, Christian Branzoli, Francesca Clarke, William T. Cudalbu, Cristina Genovese, Guglielmo Jbabdi, Saad Jelescu, Ileana Karampinos, Dimitrios Kreis, Roland Lundell, Henrik Marjańska, Malgorzata Möller, Harald E. Mosso, Jessie Mougel, Eloïse Posse, Stefan Ruschke, Stefan Simsek, Kadir Szczepankiewicz, Filip Tal, Assaf Tax, Chantal Oeltzschner, Georg Palombo, Marco Ronen, Itamar Valette, Julien Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford United Kingdom C.J. Gorter MRI Center Department of Radiology Leiden University Medical Center Netherlands School of Psychology Cardiff University Cardiff United Kingdom Departments of Biomedical Engineering and Radiology University of Alberta EdmontonAB Canada Paris Brain Institute - ICM Sorbonne University UMR S 1127 Inserm U 1127 CNRS UMR 7225 ParisF-75013 France CIBM Center for Biomedical Imaging Switzerland Animal Imaging and Technology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Center for Magnetic Resonance Research Department of Radiology University of Minnesota MinneapolisMN United States Department of Radiology Lausanne University Hospital Lausanne Switzerland Faculty of Biology and Medicine University of Lausanne Lausanne Switzerland Department of Diagnostic and Interventional Radiology Technical University of Munich Munich Germany MR Methodology Department for Diagnostic and Interventional Neuroradiology University of Bern Switzerland Swiss Institute for Translational and Entrepreneurial Medicine Bern Switzerland Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and Research Copenhagen University Hospital - Amager anf Hvidovre Denmark Department for Health Technology Technical University of Denmark Lyngby Denmark NMR Methods & Development Group Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany LIFMET EPFL Lausanne Switzerland Université Paris-Saclay CEA CNRS MIRCen Laboratoires des Maladies Neurodégénératives Fontenay-aux-Roses France Department of Neurology University of New Mexico School of Medicine AlbuquerqueNM87131 United States Department of Physics and Astronomy University of New Mexico School of Medicine AlbuquerqueNM87131 United States School of Computer Science and Informatics Cardiff University Cardiff United Kin

Brain cell structure and function reflect neurodevelopment, plasticity and ageing, and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to non-invasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MR spectroscopy (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modelling and interpretation of results. These challenges were the motivation behind the organisation of the Lorentz Workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting and modelling dMRS data and provides links to useful resources. © 2023, CC BY-NC-ND.

关键词： Diffusion

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Walking-in-Place Characteristics-Based Geriatric Assessment Using Deep Convolutional Neural Networks

Walking-in-Place Characteristics-Based Geriatric Assessment ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Dawoon Jung Mau Dung Nguyen Mina Park Miji Kim Chang Won Won Jinwook Kim Kyung-Ryoul Mun Center for Imaging Media Research KIST Seoul South Korea Department of Human Computer Interface & Robotics Engineering University of Science and Technology Daejeon South Korea Department of Biomedical Science and Technology College of Medicine East-West Medical Research Institute Kyung Hee University Seoul South Korea Department of Family Medicine College of Medicine Kyung Hee University Seoul South Korea

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The world population is aging, and this phenomenon is expected to continue for the next decades. This study aimed to propose a simple and reliable method that can be used for daily in-home monitoring of frailty and cognitive dysfunction in the elderly based on their walking-in-place characteristics. Fifty-four community-dwelling elderly people aged 65 years or older participated in this study. The participants were categorized into the robust and the non-robust groups according to the FRAIL scale. The mini-mental state examination was used to classify the cognitive impairment and the non-cognitive impairment groups. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while each participant was walking in place for 20 seconds. The walking-in-place spectrograms were acquired by applying time-frequency analysis to the lower body movement signals measured in one stride. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The deep convolutional neural network-based classifiers trained with the walking-in-place spectrograms enabled to categorize the robust and the non-robust groups with 94.63% accuracy and classify the cognitive impairment and the non-cognitive impairment groups with 97.59% accuracy. This study suggests that the walking-in-place spectrograms, which can be obtained without spacious experimental space, cumbersome equipment, and laborious processes, are effective indicators of frailty and cognitive dysfunction in the elderly.

关键词： Spectrogram Foot Acceleration Angular velocity Atmospheric measurements Particle measurements Senior citizens

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KG-Hub - Building and Exchanging Biological Knowledge Graphs

arXiv

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arXiv 2023年

作者： Caufield, J. Harry Putman, Tim Schaper, Kevin Unni, Deepak R. Hegde, Harshad Callahan, Tiffany J. Cappelletti, Luca Moxon, Sierra A.T. Ravanmehr, Vida Carbon, Seth Chan, Lauren E. Cortes, Katherina Shefchek, Kent A. Elsarboukh, Glass Balhoff, James P. Fontana, Tommaso Matentzoglu, Nicolas Bruskiewich, Richard M. Thessen, Anne E. Harris, Nomi L. Munoz-Torres, Monica C. Haendel, Melissa A. Robinson, Peter N. Joachimiak, Marcin P. Mungall, Christopher J. Reese, Justin T. Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States University of Colorado Anschutz Medical Campus AuroraCO80045 United States Swiss Institute of Bioinformatics Basel Switzerland Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Department of Computer Science University of Milano Milan Italy Department of Lymphoma-Myeloma MD Anderson Cancer Center HoustonTX77030 United States College of Public Health and Human Sciences Oregon State University CorvallisOR97331 United States Helix Genomics Inc. San MateoCA94401 United States Renaissance Computing Institute University of North Carolina Chapel HillNC27517 United States Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Semanticly Ltd. Athens Greece STAR Informatics Delphinai Corporation SookeBCV9Z 0M3 Canada Department of Biomedical Informatics University of Colorado Anschutz Medical Campus AuroraCO80045 United States The Jackson Laboratory for Genomic Medicine FarmingtonCT06032 United States

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. © 2023, CC BY.

关键词： Data integration

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Author Correction: Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

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Nature genetics 2023年第3期55卷 519-520页

作者： Ceres Fernandez-Rozadilla Maria Timofeeva Zhishan Chen Philip Law Minta Thomas Stephanie Schmit Virginia Díez-Obrero Li Hsu Juan Fernandez-Tajes Claire Palles Kitty Sherwood Sarah Briggs Victoria Svinti Kevin Donnelly Susan Farrington James Blackmur Peter Vaughan-Shaw Xiao-Ou Shu Jirong Long Qiuyin Cai Xingyi Guo Yingchang Lu Peter Broderick James Studd Jeroen Huyghe Tabitha Harrison David Conti Christopher Dampier Mathew Devall Fredrick Schumacher Marilena Melas Gad Rennert Mireia Obón-Santacana Vicente Martín-Sánchez Ferran Moratalla-Navarro Jae Hwan Oh Jeongseon Kim Sun Ha Jee Keum Ji Jung Sun-Seog Kweon Min-Ho Shin Aesun Shin Yoon-Ok Ahn Dong-Hyun Kim Isao Oze Wanqing Wen Keitaro Matsuo Koichi Matsuda Chizu Tanikawa Zefang Ren Yu-Tang Gao Wei-Hua Jia John Hopper Mark Jenkins Aung Ko Win Rish Pai Jane Figueiredo Robert Haile Steven Gallinger Michael Woods Polly Newcomb David Duggan Jeremy Cheadle Richard Kaplan Timothy Maughan Rachel Kerr David Kerr Iva Kirac Jan Böhm Lukka-Pekka Mecklin Pekka Jousilahti Paul Knekt Lauri Aaltonen Harri Rissanen Eero Pukkala Johan Eriksson Tatiana Cajuso Ulrika Hänninen Johanna Kondelin Kimmo Palin Tomas Tanskanen Laura Renkonen-Sinisalo Brent Zanke Satu Männistö Demetrius Albanes Stephanie Weinstein Edward Ruiz-Narvaez Julie Palmer Daniel Buchanan Elizabeth Platz Kala Visvanathan Cornelia Ulrich Erin Siegel Stefanie Brezina Andrea Gsur Peter Campbell Jenny Chang-Claude Michael Hoffmeister Hermann Brenner Martha Slattery John Potter Konstantinos Tsilidis Matthias Schulze Marc Gunter Neil Murphy Antoni Castells Sergi Castellví-Bel Leticia Moreira Volker Arndt Anna Shcherbina Mariana Stern Bens Pardamean Timothy Bishop Graham Giles Melissa Southey Gregory Idos Kevin McDonnell Zomoroda Abu-Ful Joel Greenson Katerina Shulman Flavio Lejbkowicz Kenneth Offit Yu-Ru Su Robert Steinfelder Temitope Keku Bethany van Guelpen Thomas Hudson Heather Hampel Rachel Pearlman Sonja Berndt Richard Hayes Marie Elena Martinez Sushma Thomas Douglas Corley Paul Pharoah Susanna Larsson Yun Yen Heinz-Jo Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK. Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain. Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA. Division of Genetics and Epidemiology Institute of Cancer Research London UK. Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA. Genomic Medicine Institute Cleveland Clinic Cleveland OH USA. Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA. Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain. Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain. Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain. Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain. Department of Biostatistics School of Public Health University of Washington Seattle WA USA. Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK. Department of Public Health Richard Doll Building University of Oxford Oxford UK. Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA. Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA. Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA. De

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Correction to: Analytical modelling and numerical simulations for microfluidic chipenabling accurate sheathless impedance spectroscopy

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Research on Biomedical Engineering 2024年第1期40卷 181-181页

作者： Sherif, Sameh Ziko, Laila Abdelraheem, Omnia Mahmoud Elgamal, Muhammad Ghallab, Yehya H. Ismail, Yahya School of Electronics Communications & Computer Engineering Egypt-Japan University of Science & Technology Alexandria Egypt Biomedical Engineering Department Faculty of Engineering Helwan University Cairo Egypt Center of Nanoelectronics and Devices (CND) Zewail City of Science and Technology and The American University in Cairo (AUC) Giza Egypt Department of Biochemistry School of Life and Medical Sciences University of Hertfordshire Hosted by Global Academic Foundation R5 New Garden City New Administrative Capital Egypt Institute of Global Health and Human Ecology The American University in Cairo New Cairo Egypt Center of Nanoelectronics and Devices (CND) School of Sciences and Engineering (SSE) American University in Cairo (AUC) Cairo Egypt

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Towards universal end-to-end affect recognition from multilingual speech by convnets

arXiv

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arXiv 2019年

作者： Bertero, Dario Kampman, Onno Fung, Pascale Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We propose an end-to-end affect recognition approach using a Convolutional Neural Network (CNN) that handles multiple languages, with applications to emotion and personality recognition from speech. We lay the foundation of a universal model that is trained on multiple languages at once. As affect is shared across all languages, we are able to leverage shared information between languages and improve the overall performance for each one. We obtained an average improvement of 12.8% on emotion and 10.1% on personality when compared with the same model trained on each language only. It is endto- end because we directly take narrow-band raw waveforms as input. This allows us to accept as input audio recorded from any source and to avoid the overhead and information loss of feature extraction. It outperforms a similar CNN using spectrograms as input by 12.8% for emotion and 6.3% for personality, based on Fscores. Analysis of the network parameters and layers activation shows that the network learns and extracts significant features in the first layer, in particular pitch, energy and contour variations. Subsequent convolutional layers instead capture language-specific representations through the analysis of supra-segmental features. Our model represents an important step for the development of a fully universal affect recognizer, able to recognize additional descriptors, such as stress, and for the future implementation into affective interactive systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Older adults’ reasons to participate in digital skills learning: An interdisciplinary, multiple case study from Austria, Finland, and Germany

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Studies in Adult Education 2023年第1期55卷

作者： Kaisa Pihlainen Anja Ehlers Rebekka Rohner Katerina Cerna Eija Kärnä Moritz Hess Lisa Hengl Lotta Aavikko Susanne Frewer-Graumann Vera Gallistl Claudia Müller a School of Educational Sciences and Psychology University of Eastern Finland Joensuu Finland b Institute of Gerontology at Technical University Dortmund Research Association for Gerontology Technical University Dortmund Dortmund Germany c Department of General Health Studies Karl Landsteiner University Krems an der Donau Austria d Division of Human-Computer Interaction University of Gothenburg Gothenburg Sweden e Hochschule Niederrhein Krefeld Germany f FOM University of Applied Science Münster Germany g Institute for Information Systems University of Siegen Siegen Germany

The rapid development of digital technologies and their increasing application in many areas of everyday life challenge all citizens to continuously learn digital skills. This also applies to older adults, among whom digital literacy is on average less well-developed than among younger adults. This article investigates why retired older adults participate in opportunities to learn digital skills. Multiple case design with both qualitative and quantitative methods was used to include the views of older adults from Austria, Finland, and Germany. The results of this interdisciplinary study indicated individual, social and technical reasons for their participation in digital skills training. Practical implications and recommendations for future studies are suggested.

关键词： Older adults participation digital skills multiple case study

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Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation

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Npj mental health research 2024年第1期3卷 12页

作者： Elizabeth C Stade Shannon Wiltsey Stirman Lyle H Ungar Cody L Boland H Andrew Schwartz David B Yaden João Sedoc Robert J DeRubeis Robb Willer Johannes C Eichstaedt Dissemination and Training Division National Center for PTSD VA Palo Alto Health Care System Palo Alto CA USA. betsystade@stanford.edu. Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. betsystade@stanford.edu. Institute for Human-Centered Artificial Intelligence & Department of Psychology Stanford University Stanford CA USA. betsystade@stanford.edu. Dissemination and Training Division National Center for PTSD VA Palo Alto Health Care System Palo Alto CA USA. Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA. Department of Computer Science Stony Brook University Stony Brook NY USA. Department of Psychiatry and Behavioral Sciences Johns Hopkins University School of Medicine Baltimore MD USA. Department of Technology Operations and Statistics New York University New York NY USA. Department of Psychology University of Pennsylvania Philadelphia PA USA. Department of Sociology Stanford University Stanford CA USA. Institute for Human-Centered Artificial Intelligence & Department of Psychology Stanford University Stanford CA USA. johannes.stanford@***.

Large language models (LLMs) such as Open AI's GPT-4 (which power ChatGPT) and Google's Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.

关键词： Psychology Psychiatric disorders Psychology Technology

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Mitigating urban heat islands: Enhancing outdoor comfort with retro-reflective building materials through computational-fluid-dynamics analysis

Next Research

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Next Research 2025年第2期2卷

作者： Jihui Yuan Da Yan Xiong Xiao Yasuhiro Shimazaki Yupeng Wang Zhichao Jiao Zhengsong Lin Marko Bizjak Craig Farnham Kazuo Emura Department of Living Environment Design Grad. School of Human Life and Ecology Osaka Metropolitan University Osaka Japan Department of Architecture and Civil Eng. Grad. School of Eng. Toyohashi University of Technology Aichi Japan Building Energy Research Center School of Architecture Tsinghua University Beijing China School of Human Settlements and Civil Engineering Xi'an Jiaotong University No.28 Xianning West Road Xi'an Shaanxi Province China College of Art & Design Nanjing Forestry University Nanjing 210037 China Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 SI 2000 Maribor Slovenia

This study aims to delineate the origin, purpose, process, and results of the research, focusing on investigating the impact of different exterior wall materials on outdoor thermal indicators in Nagoya, Japan. The ultimate goal is to mitigate the Urban Heat Island (UHI) effect and improve urban outdoor thermal comfort. Computational Fluid Dynamics (CFD) is employed to evaluate two types of exterior wall materials: diffuse highly reflective (DHR) and retro-reflective (RR) materials. The research process involves assessing various outdoor thermal indices, such as wet-bulb globe temperature (WBGT), standard effective temperature (SET*), and universal thermal climate index (UTCI), in order to comprehend the thermal performance of each material. The results of the study indicate that the RR material consistently outperforms the DHR material in terms of outdoor thermal comfort, despite having slightly lower solar reflectance. This is supported by lower surface temperatures and surface heat flow associated with the RR material, suggesting reduced indoor cooling loads and significant energy savings for cooling purposes. These findings highlight the potential of RR material in mitigating the UHI effect and enhancing urban outdoor thermal conditions. By establishing a critical relationship between exterior wall materials and outdoor thermal indicators, this study proposes the adoption of RR materials for urban building exteriors as a sustainable and economically viable solution to improve urban outdoor thermal comfort and enhance the quality of life for residents.

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