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The Japanese Vision for the Black Hole Explorer Mission

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arXiv 2024年

作者： Akiyama, Kazunori Niinuma, Kotaro Hada, Kazuhiro Doi, Akihiro Hagiwara, Yoshiaki Higuchi, Aya E. Honma, Mareki Kawashima, Tomohisa Kolev, Dimitar Koyama, Shoko Masui, Sho Ohsuga, Ken Sano, Hidetoshi Takami, Hideki Tsunetoe, Yuh Uzawa, Yoshinori Akahori, Takuya Akiyama, Yuto Galison, Peter Hayashi, Takayuki J. Hirota, Tomoya Inoue, Makoto Iwata, Yuhei Johnson, Michael D. Kino, Motoki Kofuji, Yutaro Mizuno, Yosuke Moriyama, Kotaro Nagai, Hiroshi Nakamura, Kenta Notsu, Shota Ono, Fumie Oya, Yoko Oyama, Tomoaki Rana, Hannah Saida, Hiromi Saito, Ryo Saito, Yoshihiko Sasada, Mahito Sawada-Satoh, Satoko Takahashi, Mikiya M. Takamura, Mieko Tong, Edward Tsuji, Hiroyuki Yoshioka, Shogo Watanabe, Yoshimasa Massachusetts Institute of Technology Haystack Observatory WestfordMA01886 United States Mizusawa VLBI Observatory National Astronomical Observatory of Japan Iwate023-0861 Japan Black Hole Initiative Harvard University CambridgeMA02138 United States Graduate School of Sciences and Technology for Innovation Yamaguchi University Yamaguchi753-8512 Japan The Graduate School of Science Nagoya City University Aichi467-0001 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Department of Space and Astronautical Science The Graduate University for Advanced Studies SOKENDAI Kanagawa229-8510 Japan Natural Science Laboratory Toyo University Tokyo112-8606 Japan Division of Science School of Science and Engineering Tokyo Denki University Saitama350-0394 Japan Department of Astronomy Graduate School of Science The University of Tokyo Tokyo113-0033 Japan Institute for Cosmic Ray Research The University of Tokyo Chiba277-8582 Japan Space Communication Systems Laboratory Wireless Networks Research Center National Institute of Information and Communications Technology Tokyo184-8795 Japan Graduate School of Science and Technology Niigata University Niigata950-2181 Japan Advanced Technology Center National Astronomical Observatory of Japan Tokyo181-8588 Japan Center for Computational Sciences University of Tsukuba Ibaraki305-8577 Japan Faculty of Engineering Gifu University Gifu501-1193 Japan Harvard University CambridgeMA02138 United States Azabu Junior and Senior High School Tokyo106-0046 Japan Astronomical Science Program The Graduate University for Advanced Studies SOKENDAI Tokyo181-8588 Japan Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan Center for Astrophysics Harvard & Smithsonian CambridgeMA02138 United States Kogakuin University of Technology & Engineering Academic Support Center 2665-1 Nakano Hachioji Tokyo192-0015 Japan Tsung-Dao Lee Institut

The Black Hole Explorer (BHEX) is a next-generation space very long baseline interferometry (VLBI) mission concept that will extend the ground-based millimeter/submillimeter arrays into space. The mission, closely aligned with the science priorities of the Japanese VLBI community, involves an active engagement of this community in the development of the mission, resulting in the formation of the Black Hole Explorer Japan Consortium. Here we present the current Japanese vision for the mission, ranging from scientific objectives to instrumentation. The Consortium anticipates a wide range of scientific investigations, from diverse black hole physics and astrophysics studied through the primary VLBI mode, to the molecular universe explored via a potential single-dish observation mode in the previously unexplored 50-70 GHz band that would make BHEX the highest-sensitivity explorer ever of molecular oxygen. A potential major contribution for the onboard instrument involves supplying essential elements for its high-sensitivity dual-band receiving system, which includes a broadband 300 GHz SIS mixer and a space-certified multi-stage 4.5K cryocooler akin to those used in the Hitomi and XRISM satellites by the Japan Aerospace Exploration Agency. Additionally, the Consortium explores enhancing and supporting BHEX operations through the use of millimeter/submillimeter facilities developed by the National Astronomical Observatory of Japan, coupled with a network of laser communication stations operated by the National Institute of Information and Communication Technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio astronomy

Current md forcefields fail to capture key features of protein structure and fluctuations: a case study of cyclophilin a and t4 lysozyme

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arXiv 2020年

作者： Mei, Zhe Grigas, Alex T. Treado, John D. Corres, Gabriel Melendez Vuorte, Maisa Sammalkorpi, Maria Regan, Lynne Levine, Zachary A. O’Hern, Corey S. Department of Chemistry Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Biology UPR Humacao Puerto RicoHumacao00792 Puerto Rico Department of Chemistry and Materials Science School of Chemical Engineering Aalto University Aalto Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Department of Pathology Yale School of Medicine New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores. © 2020, CC BY.

关键词： Molecular dynamics

RETRACTED ARTICLE: Classification of severe autism in fMRI using functional connectivity and conditional random forests

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Neural Computing and Applications 2019年第12期32卷 8415-8415页

作者： Jac Fredo, A. R. Jahedi, Afrooz Reiter, Maya A. Müller, Ralph-Axel Brain Development Imaging Lab (BDIL) Psychology San Diego State University (SDSU) San Diego USA Computational Science San Diego State University/Claremont Graduate University’s Joint Doctoral Program San Diego USA Clinical Psychology San Diego State University/UC San Diego’s Joint Doctoral Program San Diego USA

Response of aqueous dichromate and nanoclay dichromate gel dosimeters to carbon ion irradiation

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Journal of Physics: Conference Series 2013年第1期444卷

作者： T Maeyama N Fukunishi K L Ishikawa K Fukasaku T Furuta S Takagi S Noda R Himeno S Fukuda RIKEN/Nishina Center for Accelerator-Based Science Wako Japan RIKEN/Computational Science Research Program Wako Japan The University of Tokyo/Department of Nuclear Engineering and Management Graduate School of Engineering Tokyo Japan The University of Tokyo/Photon Science Center Graduate School of Engineering Tokyo Japan Himon'ya Hospital/ Department of Neurosurgery Tokyo Japan RIKEN/Advanced Center for Computing and Communication Wako Japan The University of Tokyo/Department of Mechanical Engineering Graduate School of Engineering Tokyo Japan National Institute of Radiological Sciences/ Research Center for Charged Particle Therapy Chiba Japan

We have recently reported the significant reduction of radiation product diffusion by the incorporation of clay nanoparticles into dichromate gel (DCG) dosimeters. In this work, we investigate the influence of the nanoclay addition and gelation on the MRI R1 (1/T1) image response of the dichromate dosimeter to the therapeutic carbon ion beam (12C6+ 290 MeV/u). The MRI R1 distribution in the aqueous dichromate solution well reproduces physical dose-depth distribution with a high linear-energy-transfer (LET) efficiency. The nanoclay DCG dosimeters, on the other hand, exhibit composition-dependent LET efficiency degradation, while a sharp Bragg peak can still be detected. These results indicate that the nanocomposite gel addition may induce change in the radiation-induced reaction mechanism.

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors

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Protein science 2024年第6期33卷 e5007-e5007页

作者： Lin, Tony Eight Yen, Dyan HuangFu, Wei-Chun Wu, Yi-Wen Hsu, Jui-Yi Yen, Shih-Chung Sung, Tzu-Ying Hsieh, Jui-Hua Pan, Shiow-Lin Yang, Chia-Ron Huang, Wei-Jan Hsu, Kai-Cheng Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan Warshel Institute for Computational Biology The Chinese University of Hong Kong (Shenzhen) Guangdong Shenzhen China Biomedical Translation Research Center Academia Sinica Taipei Taiwan Division of Translational Toxicology National Institute of Environmental Health Sciences National Institutes of Health Durham NC United States School of Pharmacy College of Medicine National Taiwan University Taipei Taiwan Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei Taiwan Cancer Center Wan Fang Hospital Taipei Medical University Taipei Taiwan

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC₅₀ value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8. © 2024 The Authors. Protein science published by Wiley Periodicals LLC on behalf of The Protein Society.

关键词： CDK8 fragment machine learning molecular docking structure-based virtual screening

Iterative quantum optimization of spin glass problems with rapidly oscillating transverse fields

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arXiv 2024年

作者： Barton, Brandon Sagal, Jacob Feeney, Sean Grattan, George Patnaik, Pratik Oganesyan, Vadim Carr, Lincoln D. Kapit, Eliot Quantum Engineering Program Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Computer Science Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Physics and Astronomy College of Staten Island City University of New York Staten IslandNY10314 United States Physics program and Initiative for the Theoretical Sciences The Graduate Center City University of New York New YorkNY10016 United States

In this work, we introduce a new iterative quantum algorithm, called Iterative Symphonic Tunneling for Satisfiability problems (IST-SAT), which solves quantum spin glass optimization problems using high-frequency oscillating transverse fields. IST-SAT operates as a sequence of iterations, in which bitstrings returned from one iteration are used to set spin-dependent phases in oscillating transverse fields in the next iteration. Over several iterations, the novel mechanism of the algorithm steers the system toward the problem ground state. We benchmark IST-SAT on sets of hard MAX-3-XORSAT problem instances with exact state vector simulation, and report polynomial speedups over trotterized adiabatic quantum computation (TAQC) and the best known semi-greedy classical algorithm. When IST-SAT is seeded with a sufficiently good initial approximation, the algorithm converges to exact solution(s) in a polynomial number of iterations. Our numerical results identify a critial Hamming radius(CHR), or quality of initial approximation, where the time-to-solution crosses from exponential to polynomial scaling in problem size. By combining IST-SAT with future classical or quantum approximation algorithms, larger gains may be achieved. The mechanism we present in this work thus presents a new path toward achieving quantum advantage in optimization. © 2024, CC BY.

关键词： Combinatorial optimization

Discovery of dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors using an artificial intelligence model and their effects on tau and tubulin dynamics

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Biomedicine and Pharmacotherapy 2024年 181卷 117688-117688页

作者： Chen, Jun-Hong Tu, Huang-Ju Lin, Tony Eight Peng, Zhao-Xiang Wu, Yi-Wen Yen, Shih-Chung Sung, Tzu-Ying Hsieh, Jui-Hua Lee, Hsueh-Yun Pan, Shiow-Lin HuangFu, Wei-Chun Hsu, Kai-Cheng Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Warshel Institute for Computational Biology The Chinese University of Hong Kong Guangdong Shenzhen China Division of Translational Toxicology National Institute of Environmental Health Sciences National Institutes of Health Durham NC United States School of Pharmacy College of Pharmacy Taipei Medical University Taipei Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan Cancer Center Wan Fang Hospital Taipei Medical University Taipei Taiwan

The dual-specificity tyrosine-phosphorylation-regulated kinase 1 A (DYRK1A) presents a promising therapeutic target for neurological diseases. However, current inhibitors lack selectivity, which can lead to unexpected side effects and increase the difficulty of studying DYRK1A. Therefore, identifying selective inhibitors targeting DYRK1A is essential for reducing side effects and facilitating neurological disease research. This study aimed to discover DYRK1A inhibitors through a screening pipeline incorporating a deep neural network (DNN) model. Herein, we report an optimized model with an accuracy of 0.93 on a testing set. The pipeline was then performed to identify potential DYRK1A inhibitors from the National Cancer Institute (NCI) library. Four novel DYRK1A inhibitors were identified, and compounds NSC657702 and NSC31059 were noteworthy for their potent inhibition, with IC₅₀ values of 50.9 and 39.5 nM, respectively. NSC31059 exhibited exceptional selectivity across 70 kinases. The compounds also significantly reduced DYRK1A-induced tau phosphorylation at key sites associated with the pathology of neurodegenerative diseases. Moreover, they promoted tubulin polymerization, suggesting a role in microtubule stabilization. Cytotoxicity assessments further confirmed the neuronal safety of the compounds. Together, the results demonstrated a promising screening pipeline and novel DYRK1A inhibitors as candidates for further optimization and development. © 2024 The Authors

关键词： Deep Learning DYRK1A Tau

GW190521 as a black-hole merger coincident with the ZTF19abanrhr flare

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arXiv 2021年

作者： Bustillo, Juan Calderón Leong, Samson H.W. Chandra, Koustav McKernan, Barry Ford, K.E.S. Instituto Galego de Física de Altas Enerxías Universidade de Santiago de Compostela Galicia Santiago de Compostela15782 Spain Department of Physics The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Physics Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India Department of Science CUNY-BMCC 199 Chambers St. New YorkNY10007 United States Department of Astrophysics American Museum of Natural History Central Park West New YorkNY10028 United States Physics Program The Graduate Center CUNY New YorkNY10016 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States

We present an analysis that reconciles the gravitational wave signal GW190521 observed by the Advanced LIGO and Advanced Virgo detectors with the electromagnetic flare ZTF19abanrhr observed by the Zwicky Transient Facility. We analyze GW190521 under a mass-ratio prior uniform in Q ∈ [1, 4] using the state-of-the-art waveform model for black-hole mergers NRSur7dq4. We find a 90% credible region for the black-hole masses extending far outside what originally reported by [1], where our maximum likelihood masses reside. We find a 15% probability that both black holes avoid the pair-instability supernova gap. We infer a three-dimensional sky-location highly consistent with ZTF19abanrhr, obtaining an odds-ratio OC/R = 72: 1 that strongly favors the hypothesis of a true coincidence over a random one. Combining this event with the neutron-star merger GW170817, we estimate a Hubble constant H0 = 72.1-6.4+10.6km s-1 Mpc-1 at the 68% credible level. Copyright © 2021, The Authors. All rights reserved.

关键词： Merging