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arXiv 2022年

作者： Huang, Jiahao Fang, Yingying Nan, Yang Wu, Huanjun Wu, Yinzhe Gao, Zhifan Li, Yang Wang, Zidong Lio, Pietro Rueckert, Daniel Eldar, Yonina C. Yang, Guang The National Heart and Lung Institute Imperial College London Guy Scadding Building Dovehouse St LondonSW3 6LY United Kingdom The Department of Bioengineering Imperial College London South Kensington LondonSW7 2AZ United Kingdom The School of Biomedical Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China The School of Automation Sciences and Electrical Engineering Beihang University Beijing China The Department of Computer Science Brunel University London LondonUB8 3PH United Kingdom The Computer Laboratory University of Cambridge CambridgeCB3 0FD United Kingdom The Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning. However, deep learning models are not the sovereign remedy for medical image analysis when the upstream imaging is not being conducted properly (with artefacts). This has been manifested in MRI studies, where the scanning is typically slow, prone to motion artefacts, with a relatively low signal to noise ratio, and poor spatial and/or temporal resolution. Recent studies have witnessed substantial growth in the development of deep learning techniques for propelling fast MRI. This article aims to (1) introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods, (2) survey the attention and transformer based models for speeding up MRI reconstruction, and (3) detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies, and discuss common pitfalls in current research and recommendations for future research directions. © 2022, CC BY-NC-ND.

关键词： Signal to noise ratio

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Vaccination and SARS-CoV-2 variants: how much containment is still needed? A quantitative assessment

arXiv

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arXiv 2021年

作者： Giordano, Giulia Colaneri, Marta Filippo, Alessandro Di Blanchini, Franco Bolzern, Paolo de Nicolao, Giuseppe Sacchi, Paolo Bruno, Raffaele Colaneri, Patrizio Department of Industrial Engineering University of Trento Trento Italy Division of Infectious Diseases I Fondazione IRCCS Policlinico San Matteo Pavia Italy Dipartimento di Scienze Matematiche Informatiche e Fisiche University of Udine Udine Italy Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Clinical Surgical Diagnostic and Paediatric Sciences University of Pavia Pavia Italy IEIIT-CNR Milano Italy

Despite the progress in medical care1, combined population-wide interventions (such as physical distancing, testing and contact tracing) are still crucial to manage the SARS-CoV-2 pandemic, aggravated by the emergence of new highly transmissible variants. We combine the compartmental SIDARTHE model2, predicting the course of COVID-19 infections, with a new data-based model that projects new cases onto casualties and healthcare system costs. Based on the Italian case study, we outline several scenarios: mass vaccination campaigns with different paces, different transmission rates due to new variants, and different enforced countermeasures, including the alternation of opening and closure phases. Our results demonstrate that non-pharmaceutical interventions (NPIs) have a higher impact on the epidemic evolution than vaccination, which advocates for the need to keep containment measures in place throughout the vaccination campaign. We also show that, if intermittent open-close strategies are adopted, deaths and healthcare system costs can be drastically reduced, without any aggravation of socioeconomic losses, as long as one has the foresight to start with a closing phase rather than an opening one. © 2021, CC BY-NC-ND.

关键词： Vaccines

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Theoretical modeling of tunable vibrations of three-dimensional serpentine structures for simultaneous measurement of adherent cell mass and modulus

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MRS Bulletin 2020年

作者： Zhao, Jianzhong Li, Weican Guo, Xingming Wang, Heling Rogers, John A. Huang, Yonggang Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States School of Engineering Brown University Rhode Island United States Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Departments of Materials Science and Engineering Biomedical Engineering Chemistry Mechanical Engineering Electrical Engineering and Computer Science and Neurological Surgery Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Northwestern University Illinois United States Querrey-Simpson Institute for Bioelectronics Northwestern University Illinois United States

Vibration-based methods can be used effectively to characterize the physical properties of biological materials, with an increasing interest focused on the mechanics of individual, living cells. Real-time measurements of cell properties, such as mass and Young's modulus, can yield important insights into many aspects of cell growth and metabolism as well as the interaction of cells with external stimuli (e.g., drugs). Vibrational test structures designed for the study of such cell properties often use fixed configurations and operational modes, with associated limitations in determining multiple characteristics of the cell, simultaneously. Recent development of mechanics-guided techniques for deterministic assembly of three-dimensional (3D) microstructures provides a route to vibrational frameworks that offer tunable configurations, vibration modes, and resonant frequencies. Here we propose a method that exploits such tunable vibrational structures to simultaneously determine the mass and modulus of a single adherent cell, or of other biological materials or small-scale living systems (e.g., organoids), through theoretical modeling and finite element analysis. The idea involves a 3D architecture that supports two different vibrational structures and can be converted from one to the other through application of strain to an elastomeric substrate. Specifically, tailored designs for serpentine ribbons in these systems enable a decoupling of the dependence of the resonant frequencies of the two structures to the cell mass and modulus, with an associated ability to measure these two properties accurately and independently. These same concepts can be scaled to apply to various types of cells, as well as to organoids (3D clusters of cells) and other biological materials with small geometries, across a range of values of mass and modulus. This method could serve as the foundation for microelectromechanical systems capable of monitoring mass and modulus in real time for use

关键词： Serpentine

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Reducing Line-of-block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods

arXiv

引用

arXiv 2021年

作者： Schuler, Steffen Schaufelberger, Matthias Bear, Laura R. Bergquist, Jake A. Cluitmans, Matthijs J.M. Coll-Font, Jaume Onak, Önder N. Zenger, Brian Loewe, Axel MacLeod, Rob S. Brooks, Dana H. Dössel, Olaf Institute of Biomedical Engineering Karlsruhe Institute of Technology Karlsruhe Germany IHU-LIRYC University of Bordeaux Inserm U1045 CRCTB Bordeaux France SCI Institute University of Utah Salt Lake City United States CARIM Maastricht University Medical Centre Maastricht Netherlands CBI Massachusetts General Hospital Harvard Medical School Boston¨ United States Institute of Applied Mathematics METU Ankara Turkey Department of Electrical & Computer Engineering Northeastern University Boston United States

Objective: To investigate cardiac activation maps estimated using electrocardiographic imaging and to find methods reducing line-of-block (LoB) artifacts, while preserving real LoBs. Methods: Body surface potentials were computed for 137 simulated ventricular excitations. Subsequently, the inverse problem was solved to obtain extracellular potentials (EP) and transmembrane voltages (TMV). From these, activation times (AT) were estimated using four methods and compared to the ground truth. This process was evaluated with two cardiac mesh resolutions. Factors contributing to LoB artifacts were identified by analyzing the impact of spatial and temporal smoothing on the morphology of source signals. Results: AT estimation using a spatiotemporal derivative performed better than using a temporal derivative. Compared to deflection-based AT estimation, correlation-based methods were less prone to LoB artifacts but performed worse in identifying real LoBs. Temporal smoothing could eliminate artifacts for TMVs but not for EPs, which could be linked to their temporal morphology. TMVs led to more accurate ATs on the septum than EPs. Mesh resolution had a negligible effect on inverse reconstructions, but small distances were important for cross-correlation-based estimation of AT delays. Conclusion: LoB artifacts are mainly caused by the inherent spatial smoothing effect of the inverse reconstruction. Among the configurations evaluated, only deflection-based AT estimation in combination with TMVs and strong temporal smoothing can prevent LoB artifacts, while preserving real LoBs. Significance: Regions of slow conduction are of considerable clinical interest and LoB artifacts observed in non-invasive ATs can lead to misinterpretations. We addressed this problem by identifying factors causing such artifacts and methods to reduce them. © 2021, CC BY-NC-ND.

关键词： Signal reconstruction

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Heart Rate Variability Monitoring Using a Wearable Armband

Heart Rate Variability Monitoring Using a Wearable Armband

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2019 Computing in Cardiology, CinC 2019

作者： Lázaro, Jesús Reljin, Natasa Noh, Yeonsik Laguna, Pablo Chon, Ki H. University of Connecticut Department of Biomedical Engineering Unit 3247 260 Gleenbrook Rd StorrsCT06269 United States IIS Aragón University of Zaragoza Zaragoza Spain Madrid Spain College of Nursing Department of Electrical and Computer Engineering University of Massachusetts AmherstMA United States

ISBN: (纸本)9781728169361

A wearable electrocardiogram (ECG) monitor is evaluated as heart rate variability (HRV) monitor. The device consists of an armband designed to be worn on the left upper arm which provides 3 ECG channels based on 3 pairs of dry (no hydrogel) electrodes. Armband-ECG and conventional-Holter-ECG signals were simultaneously recorded from 14 subjects during 5 minutes in supine position. Spacial principal component analysis was used to obtain a unique armband ECG signal in which the electromyogram contribution is attenuated. QRS complexes were automatically detected. Five traditional HRV parameters were derived: SDNN, RMSSD, pNN50, and powers within low frequency (LF, [0.04, 0.15] Hz) and high frequency (HF, [0.15, 0.4] Hz) bands. The Pearson's correlation coefficient between the measurements from the armband device and the measures from the Holter device was computed. Results show very high correlations (1.0000, 0.9999, 0.9984, 1.0000, and 0.9999 for SDNN, RMSSD, pNN50, and powers at LF and HF, respectively), suggesting that the quality of armband-ECG signals is enough to estimate HRV parameters during stationary movement restricted conditions. © 2019 Creative Commons.

关键词： Electrocardiograms

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MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

arXiv

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arXiv 2023年

作者： Fleming, Scott L. Lozano, Alejandro Haberkorn, William J. Jindal, Jenelle A. Reis, Eduardo Thapa, Rahul Blankemeier, Louis Genkins, Julian Z. Steinberg, Ethan Nayak, Ashwin Patel, Birju Chiang, Chia-Chun Callahan, Alison Huo, Zepeng Gatidis, Sergios Adams, Scott Fayanju, Oluseyi Shah, Shreya J. Savage, Thomas Goh, Ethan Chaudhari, Akshay S. Aghaeepour, Nima Sharp, Christopher Pfeffer, Michael A. Liang, Percy Chen, Jonathan H. Morse, Keith E. Brunskill, Emma P. Fries, Jason A. Shah, Nigam H. Department of Biomedical Data Science Stanford School of Medicine StanfordCA United States Department of Computer Science Stanford School of Engineering StanfordCA United States Department of Anesthesiology Peri-operative and Pain Medicine Stanford School of Medicine StanfordCA United States Department of Pediatrics Stanford School of Medicine StanfordCA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Department of Radiology Stanford School of Medicine StanfordCA United States Stanford University StanfordCA United States Hospital Israelita Albert Einstein SP Sao Paulo Brazil Technology and Digital Solutions Stanford Health Care Palo AltoCA United States Department of Electrical Enginering Stanford School of Engineering StanfordCA United States Department of Medicine Vanderbilt University School of Medicine NashvilleTN United States Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Medicine Stanford School of Medicine StanfordCA United States Department of Neurology Mayo Clinic RochesterMN United States Human-Centered Artificial Intelligence Institute Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Clinical Excellence Research Center Stanford School of Medicine StanfordCA United States

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. MedAlign is provided under a research data use agreement1 to enable LLM evaluations on tasks aligned with clinician needs and preferences. © 2023, CC BY.

关键词： Natural language processing systems

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Front Cover: Modification of ATP hydrolysis by Strong Coupling with O−H Stretching Vibration (ChemPhotoChem 4/2023)

引用

ChemPhotoChem 2023年第4期7卷

作者： Feng Gao Dr. Jun Guo Qiankang Si Prof. Liping Wang Prof. Feng Zhang Prof. Fan Yang School of Life Science Inner Mongolia Agricultural University Hohhot 010010 China Wenzhou Institute University of Chinese Academy of Sciences Wenzhou 325001 China Quantum Biophotonic Lab Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China School of Biomedical Engineering Guangzhou Medical University Guangzhou 511436 China

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Monitoring Cardiac Biomarkers with Aptamer-Based Molecular Pendulum Sensors

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Angewandte Chemie 2023年第20期135卷

作者： Alam Mahmud Dr. Dingran Chang Prof. Jagotamoy Das Dr. Surath Gomis Farid Foroutan Dr. Jenise B. Chen Laxman Pandey Connor D. Flynn Hanie Yousefi Armin Geraili Prof. Heather J. Ross Prof. Edward H. Sargent Prof. Shana O. Kelley Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto ON Canada Department of Pharmaceutical Sciences Leslie Dan Faculty of Pharmacy University of Toronto Toronto ON Canada Department of Chemistry Weinberg College of Arts and Sciences Northwestern University Evanston IL USA Ted Rogers Centre for Heart Research Toronto ON Canada Department of Chemistry Faculty of Arts & Science University of Toronto Toronto ON Canada Department of Biomedical Engineering McCormick School of Engineering Northwestern University Evanston IL USA Chan Zuckerberg Biohub Chicago Chicago IL USA

Reagent-free electronic biosensors capable of analyzing disease markers directly in unprocessed body fluids will enable the development of simple & affordable devices for personalized healthcare monitoring. Here we report a powerful and versatile nucleic acid-based reagent-free electronic sensing system. The signal transduction is based on the kinetics of an electrode-tethered molecular pendulum—a rigid double stranded DNA with one of the strands displaying an analyte-binding aptamer and the other featuring a redox probe—that exhibits field-induced transport modulated by receptor occupancy. Using chronoamperometry, which enables the sensor to circumvent the conventional Debye length limitation, the binding of an analyte can be monitored as these species increase the hydrodynamic drag. The sensing platform demonstrates a low femtomolar quantification limit and minimal cross-reactivity in analyzing cardiac biomarkers in whole blood collected from patients with chronic heart failure.

关键词： Aptamers Cardiac Diseases Molecular Pendulum Multiplexed Biosensing Reagent-Free Biosensing

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A Voltage Swing Self-Adjustable Match-Line Sensing Scheme for Content-Addressable Memories

A Voltage Swing Self-Adjustable Match-Line Sensing Scheme fo...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jianwei Zhang Xuefeng Cao Rui Yang Jinghu Li Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

A Voltage Swing Self-Adjustable (VSSA) Match-Line (ML) Sensing Scheme is presented for Content-Addressable Memories (CAMs). A simple ML voltage detector instead of a complicated fully differential one is proposed which enables ML voltage swing approaching to zero. Meanwhile, the scheme improves the robustness against process variation by means of voltage swing self-adjustment. Post-layout simulation results show the proposed 64-word × 144-bit ternary CAM, using 55-nm 1.2-V standard CMOS process, achieves 0.18fJ/bit/search for the ML power dissipation with less than 750ps search time. Normalized ML Energy-Delay-Product (EDP) achieves 0.14 fJ·ns/bit/search which accounts for 53.8% and 16.1% that of [7] and [8] separately.

关键词： Cams computer architecture Microprocessors Detectors Power dissipation Delays

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