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Author Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第6期55卷 1078页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Samsung Genome Institute Samsung Medical Center Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Samsung Advanced Institute for Health Sciences and Technology Sungkyunkwan University School of Medicine Seoul South Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Department of Haematology University of Cambridge Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson.org. Quantitative and Comp

Distributed linear regression by averaging

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arXiv 2018年

作者： Dobriban, Edgar Sheng, Yue Wharton Statistics Department University of Pennsylvania United States Graduate Group in Applied Mathematics and Computational Science Department of Mathematics University of Pennsylvania United States

Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communicate short messages. Communication is often the bottleneck. In this paper, we study one-step and iterative weighted parameter averaging in statistical linear models under data parallelism. We do linear regression on each machine, send the results to a central server, and take a weighted average of the parameters. Optionally, we iterate, sending back the weighted average and doing local ridge regressions centered at it. How does this work compared to doing linear regression on the full data? Here we study the performance loss in estimation, test error, and confidence interval length in high dimensions, where the number of parameters is comparable to the training data size. We find the performance loss in one-step weighted averaging, and also give results for iterative averaging. We also find that different problems are affected differently by the distributed framework. Estimation error and confidence interval length increase a lot, while prediction error increases much less. We rely on recent results from random matrix theory, where we develop a new calculus of deterministic equivalents as a tool of broader interest. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear regression

Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor

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arXiv 2020年

作者： Hashim, Akel Naik, Ravi K. Morvan, Alexis Ville, Jean-Loup Mitchell, Bradley Kreikebaum, John Mark Davis, Marc Smith, Ethan Iancu, Costin O'Brien, Kevin P. Hincks, Ian Wallman, Joel J. Emerson, Joseph Siddiqi, Irfan Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Graduate Group in Applied Science and Technology University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada Institute for Quantum Computing and Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error which has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors, and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally-measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

3D Tortuosity: a Morphological Characterization of the Central Sulcus to Differentiate Patients with Alzheimer's Disease and Controls

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3D Tortuosity: a Morphological Characterization of the Centr...

IEEE Symposium on Nuclear science (NSS/MIC)

作者： Maria Julieta Mateos Jorge Márquez Ernesto Bribiesca Universidad Nacional Autónoma de México Graduate Program in Computer Science and Engineering Ciudad Universitaria Coyoacán CDMX México Instituto de Ciencias Aplicadas y Tecnología Universidad Nacional Autónoma de México Circuito Exterior S/N Ciudad Universitaria Mexico City Universidad Nacional Autónoma de México Institute of Research in Applied Mathematics and Systems (IIMAS) Circuito Escolar 3000 Ciudad Universitaria Coyoacán CDMX

Magnetic Resonance Images of the brain provide detailed anatomical information that allows morphological analysis of the different brain structures. The analysis of the cortical folding patterns variation is inspiring great interest, as this relates to cognitive function. The sulci are a depression in the cerebral cortex and represent ≈ 70% of the cortical surface. The sulcal width, depth and cortical thickness are the most frequent morphological descriptors applied to the sulci. In this work, the 3D tortuosity is proposed as new descriptor to capture information about the twist and turns of the sulci. 1 The 3D tortuosity of the central sulcus of both hemispheres was measured using the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution MRI of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. As it is known AD causes significant gray matter loss which cause morphological changes in the cortical structure. It is expected for the tortuosity to capture these changes and serve as a biomarker to differentiate between populations. The result of the Wilcoxon tests show that the tortuosity values of the central sulci are significantly larger for the AD patients (p < 0.05) for the left hemisphere, which is consistent with the hypothesis. The evaluation of the proposed tortuosity measurement of the Central Sulcus as a potential biomarker was performed and the results indicate its effectiveness to extract additional anatomical information and discriminate between AD patients and Controls.

关键词： Brain Three-dimensional displays Shape Shape measurement Sociology Surface morphology Skeleton

EMPRESS. XII. Statistics on the Dynamics and Gas Mass Fraction of Extremely-Metal Poor Galaxies

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arXiv 2023年

作者： Xu, Yi Ouchi, Masami Isobe, Yuki Nakajima, Kimihiko Ozaki, Shinobu Bouché, Nicolas F. Wise, John H. Emsellem, Eric Kusakabe, Haruka Hattori, Takashi Nagao, Tohru Chiaki, Gen Fukushima, Hajime Harikane, Yuichi Hayashi, Kohei Hirai, Yutaka Kim, Ji Hoon Maseda, Michael V. Nagamine, Kentaro Shibuya, Takatoshi Sugahara, Yuma Yajima, Hidenobu Aoyama, Shohei Fujimoto, Seiji Fukushima, Keita Hatano, Shun Inoue, Akio K. Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Moriya, Takashi J. Motohara, Kentaro Murai, Kai Nishigaki, Moka Onodera, Masato Ono, Yoshiaki Rauch, Michael Saito, Tomoki Sasaki, Rin Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Zhang, Yechi Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Univ Lyon Univ Lyon1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-LavalF-69230 France Center for Relativistic Astrophysics School of Physics Georgia Institute of Technology AtlantaGA30332 United States European Southern Observatory Karl-Schwarzschild-Straße 2 Garching85748 Germany Observatoire de Genéve Université de Genéve 51 Ch. des Maillettes Versoix1290 Switzerland 650 North A’ohoku Place HiloHI96720 United States Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho 2-5 Ehime Matsuyama790-8577 Japan Center for Computational Sciences University of Tsukuba Ten-nodai 1-1-1 Tsukuba Ibaraki305-8577 Japan Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom National Institute of Technology Ichinoseki College Hagisho Ichinoseki021-8511 Japan Astronomical Institute Tohoku University 6-3 Aoba Aramaki Aoba-ku Miyagi Sendai980-8578 Japan Department of Physics and Astronomy University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Astronomy University of Wisconsin-Madison 475 N. Charter Street MadisonWI53706 United States Theoretical Astrophysics Department of Earth & Space S

We present demography of the dynamics and gas-mass fraction of 33 extremely metal-poor galaxies (EMPGs) with metallicities of 0.015 − 0.195 Zo and low stellar masses of 104 − 108 Mo in the local universe. We conduct deep optical integral-field spectroscopy (IFS) for the low-mass EMPGs with the medium high resolution (R = 7500) grism of the 8m-Subaru FOCAS IFU instrument by the EMPRESS 3D survey, and investigate Hα emission of the EMPGs. Exploiting the resolution high enough for the low-mass galaxies, we derive gas dynamics with the Hα lines by the fitting of 3-dimensional disk models. We obtain an average maximum rotation velocity (vrot) of 15 ± 3 km s−1 and an average intrinsic velocity dispersion (σ0) of 27 ± 10 km s−1 for 15 spatially resolved EMPGs out of the 33 EMPGs, and find that all of the 15 EMPGs have vrot/σ0 rot/σ0 with the decreasing stellar mass and metallicity. We derive the gas mass fraction (fgas) for all of the 33 EMPGs, and find no clear dependence on stellar mass and metallicity. These vrot/σ0 and fgas trends should be compared with young high-z galaxies observed by the forthcoming JWST IFS programs to understand the physical origins of the EMPGs in the local universe. © 2023, CC BY.

关键词： Galaxies

Development of a do-it-yourself (D.I.Y.) gel electrophoresis apparatus for Grade-12 STEM general biology students

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Journal of Physics: Conference Series 2021年第1期1835卷

作者： Janne Noelle L. Gamale Dharel P. Acut Karla Marie Frances P. Niere Gwen Stephany S. Silagan Ethel P. Curaraton Guadalupe C. Latonio Rhett Anthony C. Latonio Joy R. Magsayo High School Department Sotero B. Cabahug FORUM for Literacy Consolacion Cebu 6001 Philippines Applied Physics Program National Institute of Physics College of Science University of the Philippines – Diliman Roxas Ave. Diliman Quezon City 1101 Philippines Science Education Program Graduate School College of Teacher Education Cebu Normal University Osmeña Blvd. Cebu City Cebu 6000 Philippines Department of Science and Mathematics Education College of Education Mindanao State University – Iligan Institute of Technology Iligan City 9200 Philippines

A hurdle to effective science education in schools is often the lack of teaching materials. Teaching science education in the 21st century requires direct visualization and manipulation with effective use and implementation for meaningful learning to occur, thus, unleashing students' potential towards the lesson, boosting their engagement in learning, and applying the knowledge into skills. This study was carried out to develop a do-it-yourself gel electrophoresis apparatus. This study was conducted in Cebu Normal University, Philippines with nineteen (19) teachers from the Methods of Teaching science and Instructional Trends and Issues Class who evaluated the apparatus in terms of usability and functionality. The respondents tested the Gel Electrophoresis apparatus by conducting the experiment and they evaluated the apparatus' functionality and usability by using an evaluation sheet which is composed of eleven (11) usability questions, four (4) functionality questions. Suggestions were also asked to improve the said apparatus. After gathering data, the suggestions made by the respondents were incorporated which resulted to the improved D.I.Y. Gel Electrophoresis Apparatus. A manual with the procedures was also designed to further guide students and teachers on how to build the apparatus. With the findings and results obtained in the study, the respondents recommended the usage of the developed do-it-yourself gel electrophoresis apparatus in their respective classes and agreed that it is a good source of learning the concept of gel electrophoresis as a 21st century biological technique. The apparatus was easy to set-up and is of low-cost. During experimentation, the apparatus yielded with a positive result – the separation of food dyes which follows the principle of gel electrophoresis. Moreover, the experiment encourages creativity and it helps with the understanding of the nature of DNA and the concept of gel electrophoresis as a 21st century biological skills.

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Smooth graph signal interpolation for big data

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arXiv 2018年

作者： Heimowitz, Ayelet Eldar, Yonina C. Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel

In this paper we present the Markov variation, a smoothness measure which offers a probabilistic interpretation of graph signal smoothness. This measure is then used to develop an optimization framework for graph signal interpolation. Our approach is based on diffusion embedding vectors and the connection between diffusion maps and signal processing on graphs. As diffusion embedding vectors may be expensive to compute for large graphs, we present a computationally efficient method, based on the Nyström extension, for interpolation of signals over a graph. We demonstrate our approach on the MNIST dataset and a dataset of daily average temperatures around the US. We show that our method outperforms state of the art graph signal interpolation methods on both datasets, and that our computationally efficient reconstruction achieves slightly reduced accuracy with a large speedup. Copyright © 2018, The Authors. All rights reserved.

关键词： computational efficiency

Synthesis of Electrophilic Cyclopent-2-enone Conjugates With Modulatory Effects on Inflammatory Responses Mediated by Nrf2/Keap1, NF-κB and IL-6

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ChemistrySelect 2024年第30期9卷

作者： Gabriel Luis L. Neri Yi-Siao Chen Hans Christian P. Arturo Mark Tristan J. Quimque Warren S. Vidar Young-Sam Keum Chen-Chung Liao Chia-Hung Yen Allan Patrick G. Macabeo Laboratory for Organic Reactivity Discovery and Synthesis (LORDS) Research Center for the Natural and Applied Sciences University of Santo Tomas 1015 España Manila Philippines The Graduate School University of Santo Tomas 1015 España Manila Philippines Ph.D. Program in Environmental and Occupational Medicine College of Medicine Kaohsiung Medical University and National Health Research Institutes Kaohsiung 80708 Taiwan Department of Chemistry College of Science and Mathematics Mindanao State University-Iligan Institute of Technology 9200 Tibanga Iligan City Philippines College of Pharmacy and Integrated Research Institute for Drug Development Dongguk University 32 Dongguk-ro 10326 Goyang Gyeonggi-do Korea Cancer Progression Research Center National Yang Ming Chiao Tung University Taipei 112 Taiwan Graduate Institute of Natural Products College of Pharmacy Kaohsiung Medical University Kaohsiung 80708 Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung Taiwan

Cyclopent-2-enone bearing natural products possess anti-inflammatory, cytotoxic, antiproliferative, and antimicrobial activities attributed to the presence of a Michael acceptor enone group acting as a bait for protein targets. To explore the biological activity of synthetic small molecules bearing the cyclopent-2-enone moiety, a collection of 4-substituted cyclopent-2-enones ( 3 a – 3 i ) was prepared synthetically using Lewis acid-catalyzed Mukaiyama-Michael reaction (MMR) in modest to high yields. These derivatives were screened for their effect on inflammatory response mediators, namely Nrf2, NF-κB, and IL-6 through cell-based reporter assays. The results show that 5-(4-oxocyclopent-2-en-1-yl)furan-2(5 H )-one ( 3 g ) is a potent Nrf2 activator in HaCaT cells. The mechanism of Nrf2 activation by 3 g was investigated through an MS/MS-directed proteomic analysis and have shown the formation of a Michael adduct via cysteine-613 on Keap1, an inhibitor of Nrf2. Density functional theory calculations show favorable Michael adduct formation between 3 g and the truncated Keap1 tripeptide Pro-Cys613-Ala at the β-carbon of the cyclopent-2-enone moiety. On the other hand, the derivative 4-(2-oxo-2-phenylethyl)cyclopent-2-en-1-one ( 3 a ) exhibited selective Nrf2 inhibition in the cancer cell line Huh7, and inhibitory activity along with 3 g against NF-κB, and IL-6 in RAW 264.7 cells. This points to the cyclopent-2-enone group being an effective scaffold for anti-inflammatory drug designs.

关键词： Cyclopentenones Nrf2 Keap1 Michael acceptor Drug discovery

Ten quick tips for deep learning in biology

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms