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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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A Framework for Interpretability in Machine Learning for Medical Imaging

arXiv

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arXiv 2023年

作者： Wang, Alan Q. Karaman, Batuhan K. Kim, Heejong Rosenthal, Jacob Saluja, Rachit Young, Sean I. Sabuncu, Mert R. School of Electrical and Computer Engineering Cornell University Cornell Tech New YorkNY10044 United States Department of Radiology Weill Cornell Medical School New YorkNY10065 United States Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New YorkNY10065 United States Martinos Center for Biomedical Imaging Harvard Medical School BostonMA02129 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research. © 2023, CC0.

关键词： Machine learning

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Thermalization of fluorescent protein exciton-polaritons at room temperature

arXiv

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arXiv 2022年

作者： Satapathy, Sitakanta Liu, Bin Deshmukh, Prathmesh Molinaro, Paul M. Dirnberger, Florian Khatoniar, Mandeep Koder, Ronald L. Menon, Vinod M. Department of Physics Center for Discovery and Innovation The City College of New York 85 St. Nicholas Terrace New YorkNY10031 United States The PhD Program in Physics The Graduate Center of the City University of New York 365 5th Ave New YorkNY10016 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Fluorescent proteins (FPs) have recently emerged as a serious contender for realizing ultra-low threshold room temperature exciton-polariton condensation and lasing. Our contribution investigates the thermalization of FP microcavity exciton-polaritons upon optical pumping under ambient conditions. We realize polariton cooling using a new FP molecule, called mScarlet, coupled strongly to the optical modes in a Fabry-Pérot cavity. Interestingly, at the threshold excitation energy (fluence) of ~9 nJ/pulse (15.6 mJ/cm2), we observe an effective temperature, Teff ~350 ± 35 K close to the lattice temperature indicative of strongly thermalized exciton-polaritons at equilibrium. This efficient thermalization results from the interplay of radiative pumping facilitated by the energetics of the lower polariton branch and the cavity Qfactor. Direct evidence for dramatic switching from an equilibrium state into a metastable state is observed for the organic cavity polariton device at room temperature via deviation from the Maxwell-Boltzmann statistics at k1 = 0 above the threshold. Thermalized polariton gases in organic systems at equilibrium hold substantial promise for designing room temperature polaritonic circuits, switches, and lattices for analog simulation. © 2022, CC BY-NC-SA.

关键词： Polariton

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AN ALGEBRAIC CHARACTERIZATION OF k–COLORABILITY

arXiv

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arXiv 2020年

作者： Flores, Ramón Kahrobaei, Delaram Koberda, Thomas Department of Geometry and Topology University of Seville Spain Department of Computer Science University of York UK New York University Tandon School of Engineering PhD Program in Computer Science CUNY Graduate Center Mathematics Department University of Virginia CharlottesvilleVA22904

We characterize k–colorability of a simplicial graph via the intrinsic algebraic structure of the associated right-angled Artin group. As a consequence, we show that a certain problem about the existence of homomorphisms from right-angled Artin groups to products of free groups is NP–complete. Copyright © 2020, The Authors. All rights reserved.

关键词： Algebra

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Expanders and right-angled Artin groups

arXiv

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arXiv 2020年

作者： Flores, Ramón Kahrobaei, Delaram Koberda, Thomas Department of Geometry and Topology University of Seville Spain Department of Computer Science University of York United Kingdom New York University Tandon School of Engineering PhD Program in Computer Science CUNY Graduate Center Department of Mathematics University of Virginia CharlottesvilleVA22904 United States

The purpose of this article is to give a characterization of families of expander graphs via right-angled Artin groups. We prove that a sequence of simplicial graphs {Γi}i∈ forms a family of expander graphs if and only if a certain natural mini-max invariant arising from the cup product in the cohomology rings of the groups {A(Γi)}i∈ agrees with the Cheeger constant of the sequence of graphs, thus allowing us to characterize expander graphs via cohomology. This result is proved in the more general framework of vector space expanders, a novel structure consisting of sequences of vector spaces equipped with vector-space-valued bilinear pairings which satisfy a certain mini-max condition. These objects can be considered to be analogues of expander graphs in the realm of linear algebra, with a dictionary being given by the cup product in cohomology, and in this context represent a different approach to expanders that those developed by Lubotzky-Zelmanov and Bourgain-Yehudayoff. Copyright © 2020, The Authors. All rights reserved.

关键词： Vector spaces

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Experience of Truth-Telling From the Viewpoints of Family in the Intensive Care Units

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Hu li za zhi The journal of nursing 2020年第4期67卷 50-60页

作者： Kung, Chen-Ning Yeh, Chun-Yin Lai, Wei-Shu Chen, Chin-Ming Ko, Nai-Ying 1MSN RN Department of Nursing Chi Mei Medical Center Taiwan ROC PhD RN Postdoctoral Research Fellow Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science National Cheng Kung University Taiwan ROC PhD RN Assistant Professor Department of Nursing College of Medicine National Cheng Kung University Taiwan ROC MD Attending Physician and Professor Department of Intensive Care Medicine Chi Mei Medical Center ROC Taiwan PhD RN Distinguished Professor Department of Nursing College of Medicine National Cheng Kung University Taiwan ROC

TITLE: 從加護病房家屬觀點探究重症病情告知的經驗. 背景: 病情告知是減少醫病認知落差的重要步驟，且能減輕家屬心理壓力。現階段研究仍缺乏家屬對重症病情告知經驗及需求之探討。. 目的: 瞭解重症家屬病情告知經驗及過程需求。. 方... 详细信息

TITLE: 從加護病房家屬觀點探究重症病情告知的經驗. 背景: 病情告知是減少醫病認知落差的重要步驟，且能減輕家屬心理壓力。現階段研究仍缺乏家屬對重症病情告知經驗及需求之探討。. 目的: 瞭解重症家屬病情告知經驗及過程需求。. 方法: 採描述現象學，立意取樣5位加護病房疾病嚴重度≥ 20分的病人家屬進行深度訪談。以Giorgi現象學分析法，利用Nvivo 11分析歸納資料。. 結果: 整體經驗包含四大主題：（一）訊息聽了無法懂，希望疑問詳細解、（二）無助找尋為解答，期望護理來幫忙、（三）託付專業卻無奈，渴望醫療多些愛、（四）重重難關多牽絆，盼望見解來割斷。. 結論／實務應用: 病情告知著重傳遞結果，家屬無法理解病情變化原因。本研究建議醫療專業人員應體認家屬認知程度，並顧及其情緒反應，納入家屬需求及期待，以病人為中心提供個別化告知。.;BACKGROUND: Truth-telling is an important step toward reducing the cognitive gap between physicians and patients as well as reducing the psychological pressures applied to physicians by family members. There is a lack of research on the truth-telling experience and needs in the intensive care unit from the perspective of patient family members. PURPOSE: This study is designed to explore the experiences and needs of families in the intensive care unit. METHODS: A descriptive phenomenology method was used in this study. In-depth interviews were conducted with five participants who had family members assessed with acute physiology and chronic health evaluation II scores ≥ 20. Data were analyzed using Giorgi's phenomenological methods and Nvivo 11. RESULTS: Four experience themes were examined, including (1) nothing is clear, requires explanation;(2) helpless to find answers, need a nurse to resolve this issue;(3) professional conduct makes us feel helpless, longing for love from the medical team;(4) decisions are very difficult, hoping to get more help. CONCLUSIONS / IMPLICATIONS FOR PRACTICE: The family members expressed that they were unable to understand the underlying causes of the progression in patient condition because the medical team only presented outcomes to the family and did not discuss related causes. Thus, it is recommended that medical teams learn to recognize the cognitive processes of patient family members and consider their emotions, including their needs and expectations, in order to provide individualized explanations based on a patient's status and progress.

关键词： family needs intensive care unit phenomenology truth-telling

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Comprehensive study of Multi heuristic image segmentation techniques

Comprehensive study of Multi heuristic image segmentation te...

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Intelligent Computing and Control Systems (ICCS), International Conference on

作者： Gurbakash Phonsa Ruchi Ruchi Department of computer Science & Engineering Phd Scholar of Mewar University Rajasthan India Department of computer Science & Engineering Lovely Professional University Phagwara India

ISBN: (数字)9781538681138

ISBN: (纸本)9781538681145

Image segmentation is the process in which image is partitioned into multiple regions which results into number of fragments of objects. The main purpose of image segmentation is to make modifications and simplifications in an image to make it more clear and meaningful. The main feature of segmentation is to find boundaries and objects in an image .Resultant of image segmentation composed of multiple regions that entitrely cover the whole image. Pixels in a common area are alike to each other on the basis of some computed property such as color, texture.

关键词： Image edge detection Image segmentation Clustering algorithms Thresholding (Imaging) Histograms Classification algorithms Magnetic resonance imaging

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Supervised Machine-Learning Algorithms in Real-time Prediction of Hypotensive Events*

Supervised Machine-Learning Algorithms in Real-time Predicti...

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

作者： Mina Chookhachizadeh Moghadam Ehsan Masoumi Nader Bagherzadeh Davinder Ramsingh Zeev N. Kain PhD Candidate of Computer Engineering University of California Irvine CA USA Research Collaborator with the Department of Electrical Engineering and computer science University of California Irvine CA USA Faculty of Electrical Engineering and Computer Science University of California Irvine CA USA Faculty of School of Medicine Loma Linda University Medical Center Loma Linda CA USA Chancellor’s Professor and Executive Director University of California Irvine CA USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Hypotension is common in critically ill patients. Early prediction of hypotensive events in the Intensive Care Units (ICUs) allows clinicians to pre-emptively treat the patient and avoid possible organ damage. In this study, we investigate the performance of various supervised machine-learning classification algorithms along with a real-time labeling technique to predict acute hypotensive events in the ICU. It is shown that logistic regression and SVM yield a better combination of specificity, sensitivity and positive predictive value (PPV). Logistic regression is able to predict 85% of events within 30 minutes of their onset with 81% PPV and 96% specificity, while SVM results in 96% specificity, 83% sensitivity and 82% PPV. To further reduce the false alarm rate, we propose a high-level decision-making algorithm that filters isolated false positives identified by the machine-learning algorithms. By implementing this technique, 24% of the false alarms are filtered. This saves 21 hours of medical staff time through 2,560 hours of monitoring and significantly reduces the disturbance caused by alarming monitors.

关键词： Prediction algorithms Sensitivity Support vector machines Decision making Labeling Logistics Monitoring

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Object Identification Model using Deep Reinforcement Machine Learning Concept for Image

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IOP Conference Series: Materials science and engineering 2021年第1期1085卷

作者： Saurabh Tiwari S Veenadhari Sanjeev K Gupta Phd Research Scholar Department of Computer Science and Engineering RNTU Bhopal 462003 Madhya Pradesh India Associate Professor RNTU Department of Computer Science and Engineering RNTU Bhopal 462003 Madhya Pradesh India Professor RNTU Department of Electronics and Communication Engineering RNTU Bhopal 462003 Madhya Pradesh India

This paper presents a model which gives the detailed process of object identification. We need to identify class and location of object in image for completing process of objet identification. Proposed model works on the principal of reinforcement learning which takes action on the basis of rewards and experiences. Normally methods in literature uses sliding window which moves in same direction but proposed algorithm provides a variable mask which moves 360 degree for identifying object using action history vector proposed with RL also not only this work focuses on localization like other work but also used class information with Softmax classification able to classify multiple object in single image with efficient time which is novel. Proposed mask acts as agent and focuses on proposed candidate reason this saves time and works in efficient manner for identification. Agent depends on transformation action and by applying top down reasoning it gives location of object. Classification is done using Softmax classification as we are having features of image by CNN. Reinforcement learning concept used for training of agent and Pascal voc dataset used for testing. Analysis of only 10 to 25 regions is sufficient with proposed work to identify first instance of object. Experiment and performance evaluation shows the efficiency of proposed work.

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The Influence of Using Responsive Façade as a Tool for Improving the Built Environment: Case study: Attaba – Opera square

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IOP Conference Series: Earth and Environmental science 2022年第1期992卷

作者： M F Eltanboly M M Afify Architecture Department Teaching Assistant Cairo Higher Institute for Engineering Computer Science and Management No.2 Banafseg Center2 first Settlement Cairo Postcode 11477 Egypt and PhD. Student Environmental Design and Energy Efficiency Department Cairo University Faculty of Engineering Giza Architecture department Professor in Environmental Design and Energy Efficiency Department Cairo University Faculty of Engineering Giza

The path we are currently following to improve the physics of construction due to the destruction of our environment through the negative impact of the built environment around us. Current efforts to decrease carbon and energy are noted by modifying and improving modern 'layered' envelopes and improving a misunderstanding of thermal comfort that has become much less effective than expected. Understanding construction physics is an important factor in improving the quality of life indoor and outdoor, relying primarily on theoretical and computational aspects, then applying the practical part to reduce excessive use of carbon and energy and hinder the elaboration of a sustainable and built environment. This paper shows how the problem stems from neglecting to think about the first principles and basic construction physics. Similarly, we show how combining good construction physics with reassessing old methodologies to construction and structure use provides some powerful and effective tools to address the climate emergency. The use of construction physics methods supports the search for new solutions to express the most important factors affecting thermal conduction and minimum surface temperatures. However, buildings have integrated concepts in which advanced systems work together to optimize energy, comfort, and health performance. Convergence between various engineering fields and the overlapping field of construction techniques and services has great potential in the future to achieve the next step in energy saving. As a result, the research highlights new methods that make our buildings interact with the external environment using climate data, responsive envelope design, simulation software, thereby achieving architectural response to surrounding variables, improving building physics to reduce environmental pollution, thermal comfort, and achieving the highest energy efficiency and performance in buildings. This is illustrated by the presentation of a case study

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