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In silico analysis of tetrahydroxycurcumin compound in tumeric (Curcuma longa) as inhibitor of SARS-CoV-2 virus replication

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AIP Conference Proceedings 2023年第1期2748卷

作者： Mohamad Amin Nanda Hilda Khikmawati Suryadi Suryadi Rifcha Fachrunnisa Ihya Fakhrurizal Amin Dina Maulina Ahya Zhilalikbar Amin Nik Ahmad Nizam Nik Malek Restu Fitriani Department of Biology Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia Special Education Department Faculty of Education Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia Faculty of Medicine Universitas Indonesia Jakarta Indonesia Biology Education Study Program Faculty of Teacher Training and Education Universitas Lampung Lampung Indonesia Department of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia Study Program of Informatics Engineering Faculty of Computer Sciencies Universitas Brawijaya Malang Indonesia Biotechnology Program Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang No.5 Malang 65145 Indonesia

There are many uses of natural ingredients to find out their potential in overcoming Coronavirus Disease 2019. One of the plants that is often used to treat various diseases, including SARS-Cov2 infection is turmeric. Turmeric contains Tetrahydroxycurcumin compounds. The aim of this research was to study the potential of Tetrahydroxycurcumin in preventing infection with the SARS-CoV-2 virus. The results showed that Tetrahydroxicurcumin has the potential to inhibit Tyrosine protein kinase ABL1, which is a protein that plays a role in the preparation of the cytoskeleton during viral infection. The binding affinity of Tetrahydroxycurcumin for this protein is -8.4 kcal/mol.

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Load balancing factor using greedy algorithm in the routing protocol for improving internet access 2

Load balancing factor using greedy algorithm in the routing ...

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2nd Nommensen International Conference on Technology and engineering, NICTE 2018

作者： Satria, Ady Sihombing, Poltak Sawaluddin Informatics Engineering Study Program Magister Faculty of Computer Science and Information Technology University of Sumatera Utara Indonesia

Load Balancing is very well applied to the distribution of internet access at any point of the Wi-Fi area so that the use of limited devices can be optimal and on target. The proper use of the Greedy Algorithms when applied to an access point device is very capable in resolving excessive loading in a single resource to take the best choice at every stage in an optimum process. Access point device is also a success factor in running the optimization of the distribution of Wi-Fi access on the Access Point is strongly influenced by the parameters set. In this research, user access factor in one very high Wi-Fi area also influences the possibility of incoming access failure. For the application of the results of this study can be utilized on all access point devices that have limits, but for speed internet access remains at the capacity provided bandwidth. © Published under licence by IOP Publishing Ltd.

关键词： Wireless local area networks (WLAN)

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Determination of Giving 'Tips' Using Fuzzy Logic To Determine Restaurant Customer Service Satisfaction

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Dadang Priyanto Muhammad Zarlis Anthony Anggrawan Graduate Program Of Computer Science Department Of Computer Science Faculty Of Computer Science And Information Technology Universitas Sumatera Utara Medan Indonesia. Informatics Engineering Universitas Bumigora Mataram Indonesia Department of Computer Science Faculty of computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia.

Fuzzy logic can be used to represent obscurity based on terms such as high, old, hot, cold, and so on. Fuzzy logic can extend the range of truth values to all real numbers in intervals between 0 and 1. Numbers in this interval represent the possibility that the statement given is 'true' or 'wrong'. The use of fuzzy logic can be used in everyday life, such as in the fields of medicine, engineering, agriculture, transportation, environmental science and also in economic and business applications. As in this study, it was used to calculate the ideal amount of 'tips' for restaurant waiters based on the service provided by restaurant waiters. The method used in this study is to use Mamdani fuzzy logic with the process of defuzzyfication using the Centroid of area (COA). The results of this study are that it can be determined the value crisp of giving tips to restaurant waiters is 45 thousand rupiah provided that the service provided for service = 7 (Excellent) and food service = 8 (Delicious)..

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Optimization of short message media in the Arduino and Bluetooth-based blank spot areas

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IOP Conference Series: Materials science and engineering 2021年第4期1098卷

作者： I Sulistiyowati F A Hanif M Nasar M P T Sulistyanto M Ahsan Faculty of Science and Technology University of Muhammadiyah Sidoarjo Sidoarjo Indonesia Electrical Engineering University of Muhammadiyah Malang Malang Indonesia Informatics Study Program Faculty of Science and Technology Universitas Kanjuruhan Malang Malang Indonesia

Communication is important in human life because communication can bridge all forms of ideas to be conveyed by someone. And the communication media commonly used today are via mobile phones, which can be via SMS messages, WhatsApp, or telephone. The problem is, in some areas the absence of signals or blank spots is a problem for those who rely on communication via mobile phones. For this reason, it is necessary to optimize the short message media in the Arduino and Bluetooth-based blank spot areas. Here the main goal is to make it easier for humans to communicate via mobile phones even in difficult conditions and without a data packet or WIFI signal. The tools used are android, the Lo-Ra module as a long-distance wireless transmission technology platform that can reach distances of 10km, and then also use the Arduino Pro Mini as a microcontroller board and Bluetooth terminal that functions to transmit data from android to Arduino and then forward it to Lo Ra. From the test results, it was found that this tool works well in the blank spot area or difficult to signal, without the need for data packages or WIFI we can still communicate well through the short message media on the cell phone. From the test results, it was found that this tool works well in the blank spot area or difficult to signal, without the need for data packages or WIFI we can still communicate well through the short message media on the cell phone. So, with the invention of this tool, we can still communicate through the medium of short messages even though we are in the blank spot area, and even when we do not have a data package or WIFI.

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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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The Online Test application uses Telegram Bots Version 1.0

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Journal of Physics: Conference Series 2020年第2期1569卷

作者： Rizky Parlika Arista Pratama Informatics Engineering Study Program Faculty of Computer Science Information Systems Study Program Faculty of Computer Science National Development University 'Veteran' East Java Raya Rungkut Madya Road Gunung Anyar Surabaya

The development of the smartphones that rapidly increasing, and high smartphone Users as a tool to make a lot of information, make emergence of instant messaging applications with a variety of options for use. Telegram, as one of the instant messaging application that is fairly new, offering in its various features compared to other instant messaging applications, so it can grow rapidly and much in demand in only 2 years. One of the interesting features is the Bot telegram, a special account without number that can handle Command from the User and gives answers appropriate to Command functionality. The account serves as the interface of the running system that communicate via Telegram Bot API. This study seeks to build a Bot telegram with database to built The Online Test application uses Telegram Bots Version 1.0. In each lecture meeting, a Lecturer can make a short test in the form of multiple choices to review the material at the previous meeting and get information about what percentage of students understand the material that has been taught before. We can use the application to send realtime messages to facilitate the test. Of course, which supports the creation of bots easily to add features that did not yet exist. Of the many we chose Telegram to be an online test environment by utilizing the Bot creation facilities provided by Telegram. This article will be a guide that makes it easier for lecturers to give a simple test at the beginning of the meeting, so that they can attract students' daily grades. And in order to be more interactive there are features of student value progress reports that can also be accessed individually so that students can know the progress of their learning outcomes.

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Influence of blended learning on learning result of algorithm and programming 3

Influence of blended learning on learning result of algorith...

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3rd International Conference on informatics and Computing, ICIC 2018

作者： Anggrawan, Anthony Ibrahim, Nurdin Suyitno, M. Satria, Christofer Informatics Engineering Study Program of College of Informatics Management and Computer Science Bumigora West Nusa Tenggara Indonesia Education Technology Study Program of Jakarta State University Jakarta Indonesia Education Technology Study Program of Jakarta State University Jakarta Indonesia Visual Communication Design Study Program of College of Informatics Management and Computer Science Bumigora West Nusa Tenggara Indonesia

ISBN: (纸本)9781538669204

The advancement of today's learning technology makes the learning process no longer fully bases on face to face learning, but can achieve by online learning. Online learning is not only useful in overcoming deficiencies in conventional classroom learning, but also a means to meet the learning styles of students who are less fond of and/or dislike face-to-face learning. On the other hand, the main disadvantage of online learning is the lack of face-to-face interaction, while the strength of face-to-face learning is the intensity of student interaction with teachers, the ease of cooperative learning and the clarity of learning materials. In turn, the superiorities on face-to-face learning and online self-learning can be acquired by blended learning. However, blended learning is a learning solution by taking the best advantage of face-to-face classroom learning and online learning. Blended learning has been a learning solution of today's learning. In this study, two blended learning that has different learning content portions between the portion of face-to-face learning materials and online self-learning was conducted to find out which blended learning results produce better pedagogy between two blended learning, and how well the learning outcomes of blended learning are, especially for Algorithm and programming subject. The research results showed the blended learning with 60% face-to-face learning content and 40% online self-learning content delivers learning cognitive competency outcomes worse than 40% face-to-face learning content and 60% online self-learning content. The learning outcome of both blended learning is good, that is greater than 60% from the ideal number. © 2018 IEEE.

关键词： Students

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Large language models for diabetes training:a prospective study

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science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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Literature Review of Religion Video Game

Literature Review of Religion Video Game

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2019 International Congress on Applied Information Technology, AIT 2019

作者： Wibawa, Setya Chendra Rahayu, Yuni Sri Wrahatnolo, Tri Vindy, V. Warnars, Harco Leslie Hendric Spits Hashimoto, Kiyota Universitas Negeri Surabaya Jawa Timur Informatics Department 60213 Indonesia Universitas Negeri Surabaya Jawa Timur Biology Department 60213 Indonesia Universitas Negeri Surabaya Jawa Timur Electrical Engineering Department 60213 Indonesia Game Application Technology Program School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Technology and Environment Prince Songkla University Thailand

ISBN: (纸本)9781728158624

In this current digital era, the video game is favorite especially among young people. Besides widely known as a form of entertainment, some messages or lessons can be learned from a video game that may affect the players. Conscious or unconscious, games have influenced many sectors of life, and it has been recognized as a serious game and no exception to religion. Human as a living creature, like or dislike or even as an atheist should believe with something out there, and religion is as a medium which connects human with something out there. The game as a representation of entertaining, intelligent application will represent joy and happiness in human life's activities, and inevitably games will influence human religion. In the end, religion game will help the human to increase their faith in those their belief based on their religion. Similar to Information Technology which is implemented in business to increase revenue, the game as an entertaining intelligent application will increase human's faith. One kind of video game that provides it is religion video games which are the result of the incorporation between religion and video games. In this paper, we do a literature review about religion video games. We try to summarize how religious elements can be incorporated into a video game which can be seen from the parts, components, and approaches of the religion video game itself. We also try to summarize the positive side and controversies in religion video games. © 2019 IEEE.

关键词： Serious games

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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