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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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The C_(6)D_(6)detector system on the Back-n beam line of CSNS

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Radiation Detection technology and Methods 2019年第3期3卷 274-282页

作者： Jie Ren Xichao Ruan Jie Bao Guangyuan Luan Wei Jiang Qi An Huaiyong Bai Ping Cao Qiping Chen Yonghao Chen Pinjing Cheng Zengqi Cui Ruirui Fan Changqing Feng Minhao Gu Fengqin Guo Changcai Han Zijie Han Guozhu He Yongcheng He Yuefeng He Hanxiong Huang Weiling Huang Xiru Huang Xiaolu Ji Xuyang Ji Haoyu Jiang Hantao Jing Ling Kang Mingtao Kang Bo Li Lun Li Qiang Li Xiao Li Yang Li Yang Li Rong Liu Shubin Liu Xingyan Liu Yinglin Ma Changjun Ning Binbin Qi Zhaohui Song Hong Sun Xiaoyang Sun Zhijia Sun Zhixin Tan Hongqing Tang Jingyu Tang Pengcheng Wang Qi Wang Taofeng Wang Yanfeng Wang Zhaohui Wang Zheng Wang Jie Wen Zhongwei Wen Qingbiao Wu Xiaoguang Wu Xuan Wu Likun Xie Yiwei Yang Han Yi Li Yu Tao Yu Yongji Yu Guohui Zhang Jing Zhang Linhao Zhang Liying Zhang Qingmin Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Yingtan Zhao Liang Zhou Zuying Zhou Danyang Zhu Kejun Zhu Peng Zhu Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Dongguan Neutron Science Center Dongguan 523803China State Key Laboratory of Particle Detection and Electronics HefeiChina Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China University of South China Hengyang 421001China Northwest Institute of Nuclear Technology Xi’an 710024China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China Xi’an Jiaotong University Xi’an 710049China Beihang University Beijing 100083China

Introduction The neutron capture cross sections are very important in the field of nuclear device design and basic physics ***-free liquid scintillator such as C_(6)D_(6)detectors are widely used in the neutron captur... 详细信息

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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach

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Heliyon 2025年第1期11卷 e41404页

作者： Dalmonte, Sara Cocozza, Maria Adriana Cuicchi, Dajana Remondini, Daniel Faggioni, Lorenzo Castellucci, Paolo Farolfi, Andrea Fortunati, Emilia Cappelli, Alberta Biondi, Riccardo Cattabriga, Arrigo Poggioli, Gilberto Fanti, Stefano Castellani, Gastone Coppola, Francesca Curti, Nico IRCCS Rizzoli Orthopedic Institute Medical Technology Laboratory Bologna 40138 Italy Medical Physics Specialization School University of Bologna Bologna 40127 Italy Department of Radiology IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna 40138 Italy Medical and Surgical Department of Digestive Hepatic and Endocrine-Metabolic Diseases IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna 40138 Italy Department of Physics and Astronomy University of Bologna Bologna 40127 Italy Nuclear Medicine IRCCS Azienda Ospedaliero-Universitaria di Bologna Bologna 40138 Italy IRCCS Institute of Neurological Sciences of Bologna Data Science and Bioinformatics Laboratory Bologna 40139 Italy Department of Medical and Surgical Sciences University of Bologna Bologna 40138 Italy Dipartimento Diagnostica per Immagini AUSL Romagna UOC Radiologia Faenza Faenza 48018 Italy Academic Radiology Department of Translational Research and of New Surgical and Medical Technologies University of Pisa Pisa 56126 Italy

Background: The modern approach to treating rectal cancer, which involves total mesorectal excision directed by imaging assessments, has significantly enhanced patient outcomes. However, locally recurrent rectal cancer (LRRC) continues to be a significant clinical issue. Identifying LRRC through imaging is complex, due to the mismatch between fibrosis and inflammatory pelvic tissue. This work aimed to develop a machine learning model for predicting LRRC using radiomic features extracted from 18F-FDG Positron Emission Tomography/Computed Tomography (PET/CT). Methods: CT and PET images of PET/CT examinations were retrospectively collected from 44 patients, with 29 cases of recurrence (66 %) and 15 cases with no local recurrence (34 %). The whole analysis was conducted separately for CT and PET images to evaluate their different predictive power. Radiomic features were extracted from suspected lesion volumes identified by physicians and the most relevant radiomic features were selected to predict the presence or absence of LRRC. Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. The prediction was done using a Support Vector Classifier (SVC) with a 10-fold cross-validation. The efficiency of the pipeline in predicting LRRC was evaluated according to the sensitivity, specificity, Balanced Accuracy Score (BAS) and Matthews's Correlation Coefficient (MCC). Results: CT features were found to be the most predictive, showing a sensitivity of 0.80, a specificity of 0.82, a BAS of 0.81 and an MCC of 0.61. PET features obtained a sensitivity of 0.93, a specificity of 0.61, a BAS of 0.77 and a MCC of 0.52. The combination of PET and CT features obtained a sensitivity of 0.80, a specificity of 0.75, a BAS of 0.77 and a MCC of 0.53. Conclusions: To the best of our knowledge, the DNetPRO algorithm was applied for the first time to medical image analysis and proved suitable for the selection of radiomic featu

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