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6. The Human Phenotype Ontology in 2024: phenotypes around the world

Author

Gargano, Michael A, Matentzoglu, Nicolas, Coleman, Ben, Addo-Lartey, Eunice B, Anagnostopoulos, Anna V, Anderton, Joel, Avillach, Paul, Bagley, Anita M, Bakštein, Eduard, Balhoff, James P, Baynam, Gareth, Bello, Susan M, Berk, Michael, Bertram, Holli, Bishop, Somer, Blau, Hannah, Bodenstein, David F, Botas, Pablo, Boztug, Kaan, Čady, Jolana, Callahan, Tiffany J, Cameron, Rhiannon, Carbon, Seth J, Castellanos, Francisco, Caufield, J Harry, Chan, Lauren E, Chute, Christopher G, Cruz-Rojo, Jaime, Dahan-Oliel, Noémi, Davids, Jon R, de Dieuleveult, Maud, de Souza, Vinicius, de Vries, Bert BA, de Vries, Esther, DePaulo, J Raymond, Derfalvi, Beata, Dhombres, Ferdinand, Diaz-Byrd, Claudia, Dingemans, Alexander JM, Donadille, Bruno, Duyzend, Michael, Elfeky, Reem, Essaid, Shahim, Fabrizzi, Carolina, Fico, Giovanna, Firth, Helen V, Freudenberg-Hua, Yun, Fullerton, Janice M, Gabriel, Davera L, Gilmour, Kimberly, Giordano, Jessica, Goes, Fernando S, Moses, Rachel Gore, Green, Ian, Griese, Matthias, Groza, Tudor, Gu, Weihong, Guthrie, Julia, Gyori, Benjamin, Hamosh, Ada, Hanauer, Marc, Hanušová, Kateřina, He, Yongqun, Hegde, Harshad, Helbig, Ingo, Holasová, Kateřina, Hoyt, Charles Tapley, Huang, Shangzhi, Hurwitz, Eric, Jacobsen, Julius OB, Jiang, Xiaofeng, Joseph, Lisa, Keramatian, Kamyar, King, Bryan, Knoflach, Katrin, Koolen, David A, Kraus, Megan L, Kroll, Carlo, Kusters, Maaike, Ladewig, Markus S, Lagorce, David, Lai, Meng-Chuan, Lapunzina, Pablo, Laraway, Bryan, Lewis-Smith, David, Li, Xiarong, Lucano, Caterina, Majd, Marzieh, Marazita, Mary L, Martinez-Glez, Victor, McHenry, Toby H, McInnis, Melvin G, McMurry, Julie A, Mihulová, Michaela, Millett, Caitlin E, Mitchell, Philip B, Moslerová, Veronika, Narutomi, Kenji, Nematollahi, Shahrzad, and Nevado, Julian

Subjects
Biological Sciences, Genetics, Human Genome, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), 4.1 Discovery and preclinical testing of markers and technologies, Generic health relevance, Good Health and Well Being, Humans, Biological Ontologies, Phenotype, Genomics, Algorithms, Rare Diseases, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Published
2024

7. Role of electrodes in study of hydrovoltaic effects

Author

Zheng, Chunxiao, Fang, Sunmiao, Chu, Weicun, Tan, Jin, Tian, Bingkun, Jiang, Xiaofeng, and Guo, Wanlin

Subjects
Physics - Chemical Physics
Abstract

The last decade has witnessed the emergence of hydrovoltaic technology, which can harvest electricity from different forms of water movement, such as raindrops, waves, flows, moisture, and natural evaporation. In particular, the evaporation-induced hydrovoltaic effect received great attention since its discovery in 2017 due to its negative heat emission property. Nevertheless, the influence of electrode reactions in evaporation-induced power generation is not negligible due to the chemical reaction between active metal electrodes and water, which leads to " exceptional " power generation. Herein, we designed a series of experiments based on air-laid paper devices with electrodes of different activities as the top and bottom electrodes. To verify the contribution of electrodes, we compared the output performance of different electrode combinations when the device is partially-wetted and fully-wetted. The device hydrophilicity, salt concentration, and acidity or basicity of solutions are also comprehensively investigated. It is demonstrated that the chemical reaction of active metals (Zn, Cu, Ag, etc.) with different aqueous solutions can generate considerable electrical energy and significantly distort the device performance, especially for Zn electrodes with an output voltage from ~1.26 to ~1.52 V and current from ~1.24 to ~75.69 {\mu}A. To promote the long-term development of hydrovoltaic technology, we recommend use of inert electrodes in hydrovoltaic studies, such as Au and Pt, especially in water and moisture environment.

Published
2023

9. Integrated Optimization for Train Rescheduling and Rolling Stock Circulation with Passenger Demand Under Disruptions of Metro Line

Author

Jiang, Xiaofeng, Yuan, Yin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yang, Jianwei, editor, Yao, Dechen, editor, Liu, Zhigang, editor, and Diao, Lijun, editor

Published
2024
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36. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

Author

Jiang, Xiaofeng, Hoffmeister, Michael, Brenner, Hermann, Muti, Hannah Sophie, Yuan, Tanwei, Foersch, Sebastian, West, Nicholas P, Brobeil, Alexander, Jonnagaddala, Jitendra, Hawkins, Nicholas, Ward, Robyn L, Brinker, Titus J, Saldanha, Oliver Lester, Ke, Jia, Müller, Wolfram, Grabsch, Heike I, Quirke, Philip, Truhn, Daniel, and Kather, Jakob Nikolas

Published
2024
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