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Sex-Specific Effects of Prenatal Exposure to Endocrine Disrupting Chemicals on Adverse Birth Outcomes: Results from the Korean Children's Environmental Health Study (Ko-Chens)

SSRN

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SSRN 2025年

作者： Oh, Jongmin Shah, Surabhi Lee, Kyung A. Park, Eunkyo Lee, Dong-Wook Hong, Yun-Chul Song, Sanghwan Kim, Sung Yeon Park, Huibyeol Ha, Eunhee Kim, Suejin Park, Choonghee Song, Sanghwan Lee, Jueun Park, Huibyeol Jo, JungHeum Jung, A-Ra Yu, Seung Do Kim, Hyun Jeong Jung, Soon-Won Hong, Sooyeon Seo, Heung Won Hwang, Namkyoung Kang, Tack Shin Jeong, Dong Jin Lee, Seungjune Kim, Hwan-Cheol Jeong, Kyoung Sook Kim, Byungmi Lee, Seulbi Kim, Hyesook Kook, Hoon Baek, Hee Jo Moon, Jai Dong Park, Won Ju Shin, Myung-Geun Ha, Mina Paik, Ki-Chung Kwon, Ho-Jang Lim, Myung-Ho Yoo, Seung Jin Bae, Sanghyuk Kim, Jin Kyung Bae, Jisuk Hong, Young-Seoub Kim, Yu-Mi Son, Hyunjin Park, Mi-Hye Lim, Dae Hyun Kim, Jeong Hee Hong, Sung-Chul Lee, Keun Hwa Kim, Su-Young Kim, Woo Jin Na, Sunghun Lim, Myoung-Nam Yoo, Young Song, Dae Jin Lee, Jue Seong Yoon, Wonsuck Choe, Seung-Ah Kim, Seunghyun Choung, Ji Tae Kim, Sukyung Kim, Jihyun Ahn, Kangmo Hong, Yun-Chul Choi, Baek-Yong Ryoo, Seung-Woo Son, Seok-Yoon Lee, Ji-Hyeon Lee, Dong-Wook Woo, KuckHyeun Yoon, Seong Yong Hur, Jongil Choi, Kyung-Hwa Park, Myung-Sook Lee, Seung-Hwa Chu, Yeonhee Kim, Jung-Ah Kim, Yangho Lee, Jiho Sim, Chang Sun Kim, A. Ram Oh, Inbo Department of Environmental Medicine School of Medicine Ewha Womans University Seoul Korea Republic of College of Medicine Ewha Womans University Seoul Korea Republic of Department of Human Systems Medicine College of Medicine Seoul National University Seoul Korea Republic of Department of Obstetrics and Gynecology Ewha Womans University College of Medicine Seoul Korea Republic of Department of Occupational and Environmental Medicine Inha University Hospital Inha University Incheon Korea Republic of Institute of Environmental Medicine Medical Research Center Seoul National University Korea Republic of Environmental Health Research Division National Institute of Environmental Research Incheon Korea Republic of Graduate Program in System Health Science and Engineering Ewha Womans University Ewha Medical Research Institute College of Medicine Seoul Korea Republic of Environmental Health Research Division Department of Environmental Health Research National Institute of Environmental Research Ministry of Environment Incheon Korea Republic of Environmental Health Policy Division Office of Environmental Health Ministry of Environment Sejong Korea Republic of Department of Occupational and Environmental Medicine Inha University School of Medicine Incheon Korea Republic of Department of Occupational and Environmental Medicine Wonju Severance Christian Hospital Wonju College of Medicine Yonsei University Wonju Korea Republic of Division of Cancer Prevention National Cancer Control Institute National Cancer Center Goyang Korea Republic of Department of Big Data Strategy National Health Insurance Service Wonju Korea Republic of Department of Food and Nutrition Wonkwang University Iksan Korea Republic of Environmental Health Center for Childhood Leukemia and Cancer Department of Pediatrics Chonnam National University Hwasun Hospital Hwasun Korea Republic of Environmental Health Center for Childhood Leukemia and Cancer Department of Occupational and

Objectives Although previous epidemiological studies have reported the effects of fetal exposure to phthalates and phenols on birth outcomes, evidence is limited. The objective of this study was to investigate whether prenatal exposure to endocrine-disrupting chemicals [EDCs;phthalates and bisphenol A (BPA)] is associated with birth outcomes and whether there are sex-specific *** We used data from the Korean CHildren’s ENvironmental health Study (Ko-CHENS) cohort. Exposure to phthalates and BPA was measured by urine tests, and the information on outcomes and confounders was obtained from delivery records, biomarker assessment, and a self-reported questionnaire. We performed multivariate logistic regression to estimate the association between exposure to individual EDCs and each birth outcome. Additionally, to evaluate the effects of EDC mixture exposure, we performed weighted quantile sum regression analysis. Subgroup analysis stratified by maternal age and sex at birth was performed to examine potential differences in *** This study included 2,176 mother-child pairs from the Ko-CHENS. We observed a significant association between the risk of PTB and exposure to BPA during early pregnancy and mono-carboxyoctyl phthalate (MCOP) during late pregnancy, with odds ratios of 1.23 (95% CI: 1.01, 1.49) and 1.29 (95% CI: 1.03, 1.62), respectively. Additionally, there was a positive association between exposure to MCOP during early pregnancy and the risk of LBW (OR: 1.39, 95% CI: 1.03, 1.89). For male infants, exposure to MCOP during early pregnancy was associated with the risk of LBW (OR: 2.44, 95% CI: 1.43, 4.15), and exposure to MCOP during late pregnancy was associated with the risk of PTB (OR 1.45, 95% CI: 1.05, 2.02).Conclusions This study presents quantitative evidence for the association between prenatal exposure to EDCs, especially BPA and MCOP, and PTB/LBW. Moreover, the effect was particularly pronounced for older mothers (≥35 years) an

关键词： Obstetrics

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Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Contour Detection from Ultrasound Kidney Images with A Coars...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Tao Gu, Yidong Xu, Yanqing Wang, Caishan Zhang, Lei Cai, Jing School of Future Science and Engineering Soochow University Suzhou China UT Southwestern Medical Center Department of Radiation Oncology DallasTX United States The Affiliated Suzhou Hospital of Nanjing Medical University Department of Medical Ultrasound Suzhou Municipal Hospital Jiangsu Suzhou China University of Texas Southwestern Medical Center McDermott Center for Human Growth and Development DallasTX United States The Second Affiliated Hospital of Soochow University Department of Ultrasound Suzhou China Duke Kunshan University Graduate Program of Medical Physics and Data Science Research Center Jiangsu Kunshan China The Hong Kong Polytechnic University Department of Health Technology and Informatics Hong Kong Hong Kong

ISBN: (纸本)9798350337020

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation. © 2023 IEEE.

关键词： Artificial intelligence Explainable mathematical model Ultrasound kidney segmentation

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Interactive Ultrasound Prostate Cancer Segmentation using Deep Learning with Principal Curve-based Fine-tuning

Interactive Ultrasound Prostate Cancer Segmentation using De...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Tao Mao, Hui Wang, Caishan Di, Gongye Hu, Ting Zhang, Lei Soochow University School of Future Science and Engineering Suzhou China Ut Southwestern Medical Center Department of Radiation Oncology DallasTX United States Huangshi Central Hospital Department of Dermatology Hubei China Second Affiliated Hospital of Soochow University Department of Ultrasound Suzhou China Medical University Taizhou People's Hospital Affiliated to Nanjing Department of Ultrasonic Taizhou China The First Affiliated Hospital of Soochow University Department of Obstetrics and Gynaecology Ultrasonography Suzhou China Duke Kunshan University Graduate Program of Medical Physics and Data Science Research Center Kunshan China

ISBN: (纸本)9798350337488

Segmentation of prostate cancer on ultrasound (US) images is challenging because of the low-intensity contrast around the region of interest (ROI) contour caused by the intestinal gas. To the best of our knowledge, our work is one of the extremely rare groups to segment prostate cancer on US images, which designs a coarse-to-refinement segmentation framework with four merits: 1) it fuses the advantages of the deep learning- and principal curve-based algorithms to locate the ROI and fit the data center automatically, respectively;2) a principal curve-based polygon searching method is designed by newly adding some constraints so that the performance of our method could be enhanced;3) the quantum characteristics are added into evolution network, while newly adding numerous- operator scheme and global optimum search schemes;4) an interpretable mathematical mapping model is developed to denote the ROI contour. Segmentation results show that our network outperforms other medical image segmentation models. © 2023 IEEE.

关键词： Automatic segmentation explainability-guided mathematical expression polygon searching quantum evolution network ultrasound prostate cancer image

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Hybrid Intelligent-Annotation Organ Segmentation on medical datasets

Hybrid Intelligent-Annotation Organ Segmentation on Medical ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Tao Zhao, Jing Gu, Yidong Di, Gongye Zhang, Lei Cai, Jing School of Future Science and Engineering Soochow University Suzhou China Ut Southwestern Medical Center Department of Radiation Oncology DallasTX United States Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Department of Ultrasound Beijing China The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Department of Medical Ultrasound Jiangsu Suzhou China The Affiliated Taizhou People's Hospital of Nanjing Medical University Department of Ultrasonic Taizhou China Duke Kunshan University Graduate Program of Medical Physics Data Science Research Center Jiangsu Kunshan China The Hong Kong Polytechnic University Department of Health Technology and Informatics Hong Kong

ISBN: (纸本)9798350337020

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency. © 2023 IEEE.

关键词： distributed-evolution learning network Multi-organs segmentation principal curve SEL U-based explainable mathematical model ultrasound

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Delineation of Prostate Cancer Via Enhanced AI-Based Algorithm In Ultrasound Images

Delineation of Prostate Cancer Via Enhanced AI-Based Algorit...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yiwen Ruan Rui Jin Zhaorui Liu Caishan Wang Lei Zhang Tao Peng School of Future Science and Engineering Soochow University Suzhou China Department of Ultrasound Second Affiliated Hospital of Soochow University Suzhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Radiation Oncology UT Southwestern Medical Center Dallas Texas USA

Delineation of prostate cancer (PCa) on ultrasound images has become an essential technique for early PCa treatment, which still faces several challenges, such as low image contrast and blurred organ boundaries. Facing the aforementioned issues, a novel coarse-fine segmentation framework method is adopted in this work. First, a deep learning algorithm is used for positioning the region of interest (ROI); then, the principal curve-related technique is adopted to fine-tune initial results. Thirdly, an enhanced quantum evolutionary algorithm is used to hunt for the optimal initialization of the backpropagation neural network. Finally, an interpretable mathematical model is introduced for representing the ROI contour by using the parameters of the neural network. A comprehensive evaluation using the clinical data proves the superiority of our method, with Dice similarity coefficient (DSC) of 83.48+3.44%, Jaccard similarity coefficient (Ω) of 82.25+4.12%, and accuracy (ACC) of 83.4+3.57%, respectively.

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Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study

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Cancer science 2023年第10期114卷 4063-4072页

作者： Quynh Thi Nhu Nguyen Phung-Anh Nguyen Chun-Jung Wang Phan Thanh Phuc Ruo-Kai Lin Chin-Sheng Hung Nei-Hui Kuo Yu-Wen Cheng Shwu-Jiuan Lin Zong-You Hsieh Chi-Tsun Cheng Min-Huei Hsu Jason C Hsu School of Pharmacy College of Pharmacy Taipei Medical University Taipei City Taiwan. Clinical Data Center Office of Data Science Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei City Taiwan. Research Center of Health Care Industry Data Science College of Management Taipei Medical University Taipei City Taiwan. Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan. Oncology Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Data Science College of Management Taipei Medical University Taipei City Taiwan. International Ph.D. Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei City Taiwan.

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei medical University Clinical research database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and December 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.

关键词： TMUCRD breast cancer survival machine learning prediction models real-world data

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Accurate and precise quantum computation of valence two-neutron systems

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Physical Review C 2024年第6期109卷 064305-064305页

作者： Sota Yoshida (吉田聡太) Takeshi Sato (佐藤健) Takumi Ogata (緒方拓巳) Tomoya Naito (内藤智也) Masaaki Kimura (木村真明) Institute for Promotion of Higher Academic Education School of Data Science and Management for Accelerator-based Science RIKEN Wako 351-0198 Japan Graduate School of Engineering Photon Science Center School of Engineering Research Institute for Photon Science and Laser Technology RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) RIKEN 2-1 Hirosawa Wako 351-0198 Japan Department of Physics Graduate School of Science

Developing methods to solve nuclear many-body problems with quantum computers is an imperative pursuit within the nuclear physics community. Here, we introduce a quantum algorithm to accurately and precisely compute the ground state of valence two-neutron systems leveraging presently available noisy intermediate-scale quantum devices. Our focus lies on the nuclei having a doubly magic core plus two valence neutrons in the p, sd, and pf shells, i.e., He6, O18, and Ca42, respectively. Our ansatz, quantum circuit, is constructed in the pair-wise form, taking into account the symmetries of the system in an explicit manner, and enables us to reduce the number of qubits and the number of CNOT gates required. The results on a real quantum hardware by IBM Quantum Platform show that the proposed method gives very accurate results of the ground-state energies, which are typically within 0.1% error in the energy for He6 and O18 and at most 1% error for Ca42. Furthermore, our experiments using real quantum devices also show the pivotal role of the circuit layout design, attuned to the connectivity of the qubits, in mitigating errors.

关键词： Nuclear many-body theory Quantum simulation Shell model

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis (vol 53, pg 403, 2021)

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NATURE GENETICS 2021年第6期53卷 935-935页

作者： Granja, Jeffrey M. Corces, M. Ryan Pierce, Sarah E. Bagdatli, S. Tansu Choudhry, Hani Chang, Howard Y. Greenleaf, William J. Department of Genetics Stanford University School of Medicine Stanford CA USA Program in Biophysics Stanford University Stanford CA USA Center for Personal Dynamic Regulomes Stanford University Stanford CA USA Department of Pathology Stanford University School of Medicine Stanford CA USA Gladstone Institute of Neurological Disease Gladstone Institute of Data Science and Biotechnology San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA Program in Cancer Biology Stanford University School of Medicine Stanford CA USA Department of Biochemistry Faculty of Science Cancer and Mutagenesis Unit King Fahd Center for Medical Research King Abdulaziz University Jeddah Saudi Arabia Howard Hughes Medical Institute Stanford University Stanford CA USA Department of Applied Physics Stanford University Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://***/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

关键词： Epigenetics Epigenomics Gene regulation

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Characterization of Markarian 421 during its most violent year: Multiwavelength variability and correlations

arXiv

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arXiv 2025年

作者： Abe, K. Abe, S. Abhir, J. Abhishek, A. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Engels, A. Arbet Arcaro, C. Asano, K. Baack, D. Babić, A. de Almeida, U. Barres Barrio, J.A. Batković, I. Bautista, A. Baxter, J. González, J. Becerra Bednarek, W. Bernardini, E. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Bronzini, E. Burelli, I. Campoy-Ordaz, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Cerasole, D. Ceribella, G. Chai, Y. Cifuentes, A. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Ammando, F. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. de Menezes, R. Delfino, M. Delgado, J. Mendez, C. Delgado Di Pierro, F. Di Tria, R. Di Venere, L. Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsaesser, D. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. López, R.J. García Garczarczyk, M. Gasparyan, S. Gaug, M. Paiva, J.G. Giesbrecht Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Gradetzke, T. Grau, R. Green, D. Green, J.G. Günther, P. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Llorente, J. Herrera Hrupec, D. Imazawa, R. Ishio, K. Martínez, I. Jiménez Jormanainen, J. Kankkunen, S. Kayanoki, T. Kerszberg, D. Kluge, G.W. Kouch, P.M. Kubo, H. Kushida, J. Láinez, M. Lamastra, A. Leone, F. Lindfors, E. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Majumdar, P. Makariev, M. Maneva, G. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. González, M. Molero Molina, E. Mondal, H.A. Moralejo, A. Morcuende, D. Nakamori, T. Nanci, C. Neustroev, V. Nickel, L. Nigro, C. Nikolić, L. Nilsson, K. Nishijima, K. Ekoume, T. Njoh Noda, K. Nozaki, S. Okumura, A. Otero-Santos, J. Paiano, S. Japanese MAGIC Group Department of Physics Tokai University Kanagawa Hiratsuka259-1292 Japan The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Università di Siena INFN Pisa SienaI-53100 Italy Bellaterra BarcelonaE-08193 Spain Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain RomeI-00136 Italy Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik GarchingD-85748 Germany Università di Padova INFN PadovaI-35131 Italy Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia URCA RJ Rio de Janeiro22290-180 Brazil IPARCOS Institute EMFTEL Department Universidad Com-plutense de Madrid MadridE-28040 Spain Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna La Laguna TenerifeE-38200 Spain University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Università di Pisa INFN Pisa PisaI-56126 Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari BariI-70125 Italy Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy Croatian MAGIC Group University of Rijeka Faculty of Physics Rijeka51000 Croatia Universität Würzburg WürzburgD-97074 Germany Japanese MAGIC Group: Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan ZeuthenD-15738 Germany Armenian MAGIC Group: ICRANet-Armenia Yerevan0019 Armenia Split21000 Croatia Croatian MAGIC Group J

Aims. Mrk 421 was in its most active state around early 2010, which led to the highest TeV gamma-ray flux ever recorded from any active galactic nuclei (AGN). We aim to characterize the multiwavelength behavior during this exceptional year for Mrk 421, and evaluate whether it is consistent with the picture derived with data from other less exceptional years. Methods. We investigated the period from November 5, 2009, (MJD 55140) until July 3, 2010, (MJD 55380) with extensive coverage from very-high-energy (VHE;E > 100 GeV) gamma rays to radio with MAGIC, VERITAS, Fermi-LAT, RXTE, Swift, GASP-WEBT, VLBA, and a variety of additional optical and radio telescopes. We characterized the variability by deriving fractional variabilities as well as power spectral densities (PSDs). In addition, we investigated images of the jet taken with VLBA and the correlation behavior among different energy bands. Results. Mrk 421 was in widely different states of activity throughout the campaign, ranging from a low-emission state to its highest VHE flux ever recorded. We find the strongest variability in X-rays and VHE gamma rays, and PSDs compatible with power-law functions with indices around 1.5. We observe strong correlations between X-rays and VHE gamma rays at zero time lag with varying characteristics depending on the exact energy band. We also report a marginally significant (∼ 3σ) positive correlation between high-energy (HE;E > 100 MeV) gamma rays and the ultraviolet band. We detected marginally significant (∼ 3σ) correlations between the HE and VHE gamma rays, and between HE gamma rays and the X-ray, that disappear when the large flare in February 2010 is excluded from the correlation study, hence indicating the exceptionality of this flaring event in comparison with the rest of the campaign. The 2010 violent activity of Mrk421 also yielded the first ejection of features in the VLBA images of the jet of Mrk 421. Yet the large uncertainties in the ejection times of these unprece

关键词： Active Galactic Nuclei

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SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests (vol 54, pg 1446, 2022)

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NATURE GENETICS 2022年第11期54卷 1755-1755页

作者： Zhou, Wei Bi, Wenjian Zhao, Zhangchen Dey, Kushal K. Jagadeesh, Karthik A. Karczewski, Konrad J. Daly, Mark J. Neale, Benjamin M. Lee, Seunggeun Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA USA Department of Medical Genetics School of Basic Medical Sciences Peking University Beijing China Center for Statistical Genetics University of Michigan School of Public Health Ann Arbor MI USA Department of Biostatistics University of Michigan School of Public Health Ann Arbor MI USA Department of Epidemiology Harvard T. H. Chan School of Public Health Boston MA USA Institute for Molecular Medicine Finland Helsinki Institute of Life Sciences University of Helsinki Helsinki Finland Graduate School of Data Science Seoul National University Seoul Korea

Several biobanks, including UK Biobank (UKBB), are generating large-scale sequencing data. An existing method, SAIGE-GENE, performs well when testing variants with minor allele frequency (MAF) ≤ 1%, but inflation is observed in variance component set-based tests when restricting to variants with MAF ≤ 0.1% or 0.01%. Here, we propose SAIGE-GENE+ with greatly improved type I error control and computational efficiency to facilitate rare variant tests in large-scale data. We further show that incorporating multiple MAF cutoffs and functional annotations can improve power and thus uncover new gene–phenotype associations. In the analysis of UKBB whole exome sequencing data for 30 quantitative and 141 binary traits, SAIGE-GENE+ identified 551 gene–phenotype associations.

关键词： Genome-wide association studies Software

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