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2. PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022

Author

H. Hao, K. Wang, G. Wu, J. Liu, and J. Li

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Long-term PM2.5 data are essential for the atmospheric environment, human health, and climate change. PM2.5 measurements are sparsely distributed and of short duration. In this study, daily PM2.5 concentrations are estimated using a machine learning method for the period from 1959 to 2022 in the Northern Hemisphere based on near-surface atmospheric visibility. They are extracted from the Integrated Surface Database (ISD). Daily continuous monitored PM2.5 concentration is set as the target, and near-surface atmospheric visibility and other related variables are used as the inputs. A total of 80 % of the samples of each site are the training set, and 20 % are the testing set. The training result shows that the slope of linear regression with a 95 % confidence interval (CI) between the estimated PM2.5 concentration and the monitored PM2.5 concentration is 0.955 [0.955, 0.955], the coefficient of determination (R2) is 0.95, the root mean square error (RMSE) is 7.2 µg m−3, and the mean absolute error (MAE) is 3.2 µg m−3. The test result shows that the slope within a 95 % CI between the predicted PM2.5 concentration and the monitored PM2.5 concentration is 0.864 [0.863, 0.865], the R2 is 0.79, the RMSE is 14.8 µg m−3, and the MAE is 7.6 µg m−3. Compared with a global PM2.5 concentration dataset derived from a satellite aerosol optical depth product with 1 km resolution, the slopes of linear regression on the daily (monthly) scale are 0.817 (0.854) from 2000 to 2021, 0.758 (0.821) from 2000 to 2010, and 0.867 (0.879) from 2011 to 2022, indicating the accuracy of the model and the consistency of the estimated PM2.5 concentration on the temporal scale. The interannual trends and spatial patterns of PM2.5 concentration on the regional scale from 1959 to 2022 are analyzed using a generalized additive mixed model (GAMM), suitable for situations with an uneven spatial distribution of monitoring sites. The trend is the slope of the Theil–Sen estimator. In Canada, the trend is −0.10 µg m−3 per decade, and the PM2.5 concentration exhibits an east–high to west–low pattern. In the United States, the trend is −0.40 µg m−3 per decade, and PM2.5 concentration decreases significantly after 1992, with a trend of −1.39 µg m−3 per decade. The areas of high PM2.5 concentration are in the east and west, and the areas of low PM2.5 concentration are in the central and northern regions. In Europe, the trend is −1.55 µg m−3 per decade. High-concentration areas are distributed in eastern Europe, and the low-concentration areas are in northern and western Europe. In China, the trend is 2.09 µg m−3 per decade. High- concentration areas are distributed in northern China, and the low-concentration areas are distributed in southern China. The trend is 2.65 µg m−3 per decade up to 2011 and −22.23 µg m−3 per decade since 2012. In India, the trend is 0.92 µg m−3 per decade. The concentration exhibits a north–high to south–low pattern, with high-concentration areas distributed in northern India, such as the Ganges Plain and Thar Desert, and the low-concentration area in the Deccan Plateau. The trend is 1.41 µg m−3 per decade up to 2013 and −23.36 µg m−3 per decade from 2014. The variation in regional PM2.5 concentrations is closely related to the implementation of air quality laws and regulations. The daily site-scale PM2.5 concentration dataset from 1959 to 2022 in the Northern Hemisphere is available at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.301127) (Hao et al., 2024).

Published
2024
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3. The China Active Faults Database (CAFD) and its web system

Author

X. Wu, X. Xu, G. Yu, J. Ren, X. Yang, G. Chen, C. Xu, K. Du, X. Huang, H. Yang, K. Li, and H. Hao

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Active faults serve as potential sources of destructive earthquakes. Studies and investigations of active faults are necessary for earthquake disaster prevention. This study presents a nation-scale database of active faults in China and its adjacent regions, in tandem with an associated web-based query system. This database is an updated version of the active faults data included in the Seismotectonic Map of China and its Adjacent Regions (1:4 000 000), which is one of the four essential maps of the mandatory Chinese standard GB 18306-2015 Seismic Ground Motion Parameter Zonation Maps of China. The data update and integration stem from regional-scale studies and surveys conducted over the past 2 decades (at reference scales from 1:250 000 to 1:50 000). The information amassed from these regional-scale studies and surveys encompasses geophysical probing, drill logging, measurement of offset landforms, sample dating, as well as geometric and kinematic parameters of exposed and blind faults, paleo-earthquake sequences, and recurrence intervals. These data have been acquired and analyzed utilizing a uniform technical standard framework and reviewed by expert panels in both field and laboratory settings. Our system hosts this nation-scale database accessible through a Web Geographic Information System (GIS) application, enabling browsing, querying, and downloading functionalities via a web browser. The system we built also publishes the Open Geospatial Consortium (OGC) Web Feature Service and the OGC Web Map Service of active faults data. Users can incorporate map layers and obtain fault data in OGC-compliant GIS software for further analysis through these services. The Chinese government, research institutions, and companies have widely used the active faults data from the previous versions of the database. The database is available at https://doi.org/10.12031/activefault.china.400.2023.db (Xu, 2023) and via the web system (https://data.activetectonics.cn/arcportal/apps/webappviewer/index.html?id=684737e8849c4170bbca14447608c451, CEFIS, 2023; http://data.activetectonics.cn/arcserver/services/Hosted/CAFD400_2022_WFS/MapServer/WFSServer, CAFD WFS, 2024).

Published
2024
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4. Visibility-derived aerosol optical depth over global land from 1959 to 2021

Author

H. Hao, K. Wang, C. Zhao, G. Wu, and J. Li

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Long-term and high spatial resolution aerosol optical depth (AOD) data are essential for climate change detection and attribution. Global ground-based AOD observations are sparsely distributed, and satellite AOD retrievals have a low temporal frequency as well low accuracy before 2000 over land. In this study, AOD at 550 nm is derived from visibility observations collected at more than 5000 meteorological stations over global land regions from 1959 to 2021. The AOD retrievals (550 nm) of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua Earth observation satellite are used to train the machine learning model, and the ERA5 reanalysis boundary layer height is used to convert the surface visibility to AOD. Comparisons with an independent dataset (AERONET ground-based observations) show that the predicted AOD has a correlation coefficient of 0.55 at the daily scale. The correlation coefficients are higher at monthly and annual scales, which are 0.61 and 0.65, respectively. The evaluation shows consistent predictive ability prior to 2000, with correlation coefficients of 0.54, 0.66, and 0.66 at the daily, monthly, and annual scales, respectively. Due to the small number and sparse visibility stations prior to 1980, the global and regional analysis in this study is from 1980 to 2021. From 1980 to 2021, the mean visibility-derived AOD values over global land areas, the Northern Hemisphere, and the Southern Hemisphere are 0.177, 0.178, and 0.175, with a trend of −0.0029 per 10 years, −0.0030 per 10 years, and −0.0021 per 10 years from 1980 to 2021. The regional means (trends) of AOD are 0.181 (−0.0096 per 10 years), 0.163 (−0.0026 per 10 years), 0.146 (−0.0017 per 10 years), 0.165 (−0.0027 per 10 years), 0.198 (−0.0075 per 10 years), 0.281 (−0.0062 per 10 years), 0.182 (−0.0016 per 10 years), 0.133 (−0.0028 per 10 years), 0.222 (0.0007 per 10 years), 0.244 (−0.0009 per 10 years), 0.241 (0.0130 per 10 years), and 0.254 (0.0119 per 10 years) in Eastern Europe, Western Europe, Western North America, Eastern North America, Central South America, Western Africa, Southern Africa, Australia, Southeast Asia, Northeast Asia, Eastern China, and India, respectively. However, the trends decrease significantly in Eastern China (−0.0572 per 10 years) and Northeast Asia (−0.0213 per 10 years) after 2014, with the larger increasing trend found after 2005 in India (0.0446 per 10 years). The visibility-derived daily AOD dataset at 5032 stations over global land from 1959 to 2021 is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Atmos.tpdc.300822) (Hao et al., 2023).

Published
2024
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7. Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management

Author

Y. Yuexin Huang, S. Suihuai Yu, J. Jianjie Chu, H. Hao Fan, and B. Bin Du

Subjects
Cultural heritage, Chinese ceramics, Knowledge graph, Deep learning, Knowledge extraction, Knowledge completion, Fine Arts, Analytical chemistry, QD71-142
Abstract

Abstract Cultural heritage management poses significant challenges for museums due to fragmented data, limited intelligent frameworks, and insufficient applications. In response, a digital cultural heritage management approach based on knowledge graphs and deep learning algorithms is proposed to address the above challenges. A joint entity-relation triple extraction model is proposed to automatically identify entities and relations from fragmented data for knowledge graph construction. Additionally, a knowledge completion model is presented to predict missing information and improve knowledge graph completeness. Comparative simulations have been conducted to demonstrate the effectiveness and accuracy of the proposed approach for both the knowledge extraction model and the knowledge completion model. The efficacy of the knowledge graph application is corroborated through a case study utilizing ceramic data from the Palace Museum in China. This method may benefit users since it provides automated, interconnected, visually appealing, and easily accessible information about cultural heritage.

Published
2023
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13. P1047: REAL-WORLD SAFETY OF RUXOLITINIB IN PATIENTS WITH INTERMEDIATE OR HIGH RISK OF PRIMARY MYELOFIBROSIS, POST-POLYCYTHEMIA VERA MYELOFIBROSIS OR POST-ESSENTIAL THROMBOCYTHEMIA MYELOFIBROSIS IN CHINA

Author

Z. Xu, M. Duan, Q. Jiang, Q. Leng, N. Xu, Y. Zhang, C. Zhao, W. Wu, Q. Zhang, J. Fu, J. Zhang, R. Fu, Z. Yan, C. Lin, G. Ouyang, Z. Wang, L. Ma, H. Hao, X. Li, S. Ran, Y. Chen, T. Li, and Z. Xiao

Subjects
Diseases of the blood and blood-forming organs, RC633-647.5
Published
2022
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16. MicroRNA-195a-5p Regulates Blood Pressure by Inhibiting NKCC2A

Author

Shoujin Hao, Hong Zhao, David H. Hao, and Nicholas R. Ferreri

Subjects
Internal Medicine
Abstract

Background: Previous studies showed that miR-195a-5p was among the most abundant microRNAs (miRNAs) expressed in the kidney. Methods: Lentivirus silencing of tumor necrosis factor-α (TNF) was performed in vivo and in vitro. Luciferase reporter assays confirmed that bumetanide-sensitive Na + -K + -2Cl − cotransporter isoform A (NKCC2A) mRNA is targeted and repressed by miR-195a-5p. Radiotelemetry was used to measure mean arterial pressure. Results: TNF upregulates mmu-miR-195a-5p, and -203 and downregulates mmu-miR-30c and -100 in the medullary thick ascending limb of male mice. miR-195a-5p was >3-fold higher in the renal outer medulla of mice given an intrarenal injection of murine recombinant TNF, whereas silencing TNF inhibited miR-195a-5p expression by ≈51%. Transient transfection of a miR-195a-5p mimic into medullary thick ascending limb cells suppressed NKCC2A mRNA by ≈83%, whereas transfection with Anti-miR-195a-5p increased NKCC2A mRNA. Silencing TNF in medullary thick ascending limb cells prevented increases in miR-195 induced by 400 mosmol/kg H 2 O medium, an effect reversed by transfection with a miR-195a-5p mimic. Expression of phosphorylated NKCC2 increased 1.5-fold in medullary thick ascending limb cells transfected with Anti-miR-195a-5p and a miR-195a-5p mimic prevented the increase, which was induced by silencing TNF in cells exposed to 400 mosmol/kg H 2 O medium after osmolality was increased by adding NaCl. Intrarenal injection of TNF suppressed NKCC2A mRNA, whereas injection of miR-195a-5p prevented the increase of NKCC2A mRNA abundance and phosphorylated NKCC2 expression when TNF was silenced. Intrarenal injection with miR-195a-5p markedly attenuated MAP after renal silencing of TNF in mice given 1% NaCl. Conclusions: The study identifies miR-195a-5p as a salt-sensitive and TNF-inducible miRNA that attenuates NaCl-mediated increases in blood pressure by inhibiting NKCC2A.

Published
2023
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17. Design and Research of CAS‐CIG for Earth System Models

Author

T. Wang, J. Jiang, M. Zhang, H. Zhang, J. He, H. Hao, and X. Chi

Subjects
coupling interface generator, automatic generation, high performance experiments, Astronomy, QB1-991, Geology, QE1-996.5
Abstract

Abstract The Chinese Academy of Sciences Coupling Interface Generator (CAS‐CIG) is designed to address the complexities of the development and coupling of different component models in Earth System Models based on the Coupler 7 of the Community Earth System Model (CESM). Its application in the Chinese Academy of Sciences Earth System Model (CAS‐ESM) is described. The CAS‐CIG automatically generates the coupler code through a simple configuration file when a component model is accessed, enabling different component models to be easily ported to CPL7 to create simulation cases. Combined with the automatic generation of compile scripts, the precompilation and run directories are directly formed. The component model integration, model selection, experimental setup, and platform migration can be all accomplished in the CAS‐CIG. Verification of the CAS‐CIG is presented to show that the automatically generated codes can identically reproduce the simulation results of CAS‐ESM. CAS‐CIG presents a software tool for modeling centers to investigate the impact of component model selections on simulations of climate and weather.

Published
2020
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18. A NEW MULTI-SPECTRAL THRESHOLD NORMALIZED DIFFERENCE WATER INDEX (MST-NDWI) WATER EXTRACTION METHOD – A CASE STUDY IN YANHE WATERSHED

Author

Y. Zhou, H. Zhao, H. Hao, and C. Wang

Subjects
Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract

Accurate remote sensing water extraction is one of the primary tasks of watershed ecological environment study. Since the Yanhe water system has typical characteristics of a small water volume and narrow river channel, which leads to the difficulty for conventional water extraction methods such as Normalized Difference Water Index (NDWI). A new Multi-Spectral Threshold segmentation of the NDWI (MST-NDWI) water extraction method is proposed to achieve the accurate water extraction in Yanhe watershed. In the MST-NDWI method, the spectral characteristics of water bodies and typical backgrounds on the Landsat/TM images have been evaluated in Yanhe watershed. The multi-spectral thresholds (TM1, TM4, TM5) based on maximum-likelihood have been utilized before NDWI water extraction to realize segmentation for a division of built-up lands and small linear rivers. With the proposed method, a water map is extracted from the Landsat/TM images in 2010 in China. An accuracy assessment is conducted to compare the proposed method with the conventional water indexes such as NDWI, Modified NDWI (MNDWI), Enhanced Water Index (EWI), and Automated Water Extraction Index (AWEI). The result shows that the MST-NDWI method generates better water extraction accuracy in Yanhe watershed and can effectively diminish the confusing background objects compared to the conventional water indexes. The MST-NDWI method integrates NDWI and Multi-Spectral Threshold segmentation algorithms, with richer valuable information and remarkable results in accurate water extraction in Yanhe watershed.

Published
2018
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19. Damping signatures at JUNO, a medium-baseline reactor neutrino oscillation experiment

Author

Wang, J. (Jun), Liao, J. (Jiajun), Wang, W. (Wei), Abusleme, A. (Angel), Adam, T. (Thomas), Ahmad, S. (Shakeel), Ahmed, R. (Rizwan), Aiello, S. (Sebastiano), Akram, M. (Muhammad), An, F. (Fengpeng), An, Q. (Qi), Andronico, G. (Giuseppe), Anfimov, N. (Nikolay), Antonelli, V. (Vito), Antoshkina, T. (Tatiana), Asavapibhop, B. (Burin), de Andre, J. P. (Joao Pedro Athayde Marcondes), Auguste, D. (Didier), Babic, A. (Andrej), Balashov, N. (Nikita), Baldini, W. (Wander), Barresi, A. (Andrea), Basilico, D. (Davide), Baussan, E. (Eric), Bellato, M. (Marco), Bergnoli, A. (Antonio), Birkenfeld, T. (Thilo), Blin, S. (Sylvie), Blum, D. (David), Blyth, S. (Simon), Bolshakova, A. (Anastasia), Bongrand, M. (Mathieu), Bordereau, C. (Clement), Breton, D. (Dominique), Brigatti, A. (Augusto), Brugnera, R. (Riccardo), Bruno, R. (Riccardo), Budano, A. (Antonio), Buscemi, M. (Mario), Busto, J. (Jose), Butorov, I. (Ilya), Cabrera, A. (Anatael), Cai, H. (Hao), Cai, X. (Xiao), Cai, Y. (Yanke), Cai, Z. (Zhiyan), Callegari, R. (Riccardo), Cammi, A. (Antonio), Campeny, A. (Agustin), Cao, C. (Chuanya), Cao, G. (Guofu), Cao, J. (Jun), Caruso, R. (Rossella), Cerna, C. (Cedric), Chang, J. (Jinfan), Chang, Y. (Yun), Chen, P. (Pingping), Chen, P.-A. (Po-An), Chen, S. (Shaomin), Chen, X. (Xurong), Chen, Y.-W. (Yi-Wen), Chen, Y. (Yixue), Chen, Y. (Yu), Chen, Z. (Zhang), Cheng, J. (Jie), Cheng, Y. (Yaping), Chetverikov, A. (Alexey), Chiesa, D. (Davide), Chimenti, P. (Pietro), Chukanov, A. (Artem), Claverie, G. (Gerard), Clementi, C. (Catia), Clerbaux, B. (Barbara), Di Lorenzo, S. C. (Selma Conforti), Corti, D. (Daniele), Dal Corso, F. (Flavio), Dalager, O. (Olivia), De la Taille, C. (Christophe), Deng, J. (Jiawei), Deng, Z. (Zhi), Deng, Z. (Ziyan), Depnering, W. (Wilfried), Diaz, M. (Marco), Ding, X. (Xuefeng), Ding, Y. (Yayun), Dirgantara, B. (Bayu), Dmitrievsky, S. (Sergey), Dohnal, T. (Tadeas), Dolzhikov, D. (Dmitry), Donchenko, G. (Georgy), Dong, J. (Jianmeng), Doroshkevich, E. (Evgeny), Dracos, M. (Marcos), Druillole, F. (Frederic), Du, R. (Ran), Du, S. (Shuxian), Dusini, S. (Stefano), Dvorak, M. (Martin), Enqvist, T. (Timo), Enzmann, H. (Heike), Fabbri, A. (Andrea), Fajt, L. (Lukas), Fan, D. (Donghua), Fan, L. (Lei), Fang, J. (Jian), Fang, W. (Wenxing), Fargetta, M. (Marco), Fedoseev, D. (Dmitry), Fekete, V. (Vladko), Feng, L.-C. (Li-Cheng), Feng, Q. (Qichun), Ford, R. (Richard), Fournier, A. (Amelie), Gan, H. (Haonan), Gao, F. (Feng), Garfagnini, A. (Alberto), Gavrikov, A. (Arsenii), Giammarchi, M. (Marco), Giaz, A. (Agnese), Giudice, N. (Nunzio), Gonchar, M. (Maxim), Gong, G. (Guanghua), Gong, H. (Hui), Gornushkin, Y. (Yuri), Goettel, A. (Alexandre), Grassi, M. (Marco), Grewing, C. (Christian), Gromov, V. (Vasily), Gu, M. (Minghao), Gu, X. (Xiaofei), Gu, Y. (Yu), Guan, M. (Mengyun), Guardone, N. (Nunzio), Gul, M. (Maria), Guo, C. (Cong), Guo, J. (Jingyuan), Guo, W. (Wanlei), Guo, X. (Xinheng), Guo, Y. (Yuhang), Hackspacher, P. (Paul), Hagner, C. (Caren), Han, R. (Ran), Han, Y. (Yang), Hassan, M. S. (Muhammad Sohaib), He, M. (Miao), He, W. (Wei), Heinz, T. (Tobias), Hellmuth, P. (Patrick), Heng, Y. (Yuekun), Herrera, R. (Rafael), Hor, Y. (YuenKeung), Hou, S. (Shaojing), Hsiung, Y. (Yee), Hu, B.-Z. (Bei-Zhen), Hu, H. (Hang), Hu, J. (Jianrun), Hu, J. (Jun), Hu, S. (Shouyang), Hu, T. (Tao), Hu, Z. (Zhuojun), Huang, C. (Chunhao), Huang, G. (Guihong), Huang, H. (Hanxiong), Huang, W. (Wenhao), Huang, X. (Xin), Huang, X. (Xingtao), Huang, Y. (Yongbo), Hui, J. (Jiaqi), Huo, L. (Lei), Huo, W. (Wenju), Huss, C. (Cedric), Hussain, S. (Safeer), Ioannisian, A. (Ara), Isocrate, R. (Roberto), Jelmini, B. (Beatrice), Jen, K.-L. (Kuo-Lun), Jeria, I. (Ignacio), Ji, X. (Xiaolu), Ji, X. (Xingzhao), Jia, H. (Huihui), Jia, J. (Junji), Jian, S. (Siyu), Jiang, D. (Di), Jiang, W. (Wei), Jiang, X. (Xiaoshan), Jin, R. (Ruyi), Jing, X. (Xiaoping), Jollet, C. (Cecile), Joutsenvaara, J. (Jari), Jungthawan, S. (Sirichok), Kalousis, L. (Leonidas), Kampmann, P. (Philipp), Kang, L. (Li), Karaparambil, R. (Rebin), Kazarian, N. (Narine), Khosonthongkee, K. (Khanchai), Korablev, D. (Denis), Kouzakov, K. (Konstantin), Krasnoperov, A. (Alexey), Kruth, A. (Andre), Kutovskiy, N. (Nikolay), Kuusiniemi, P. (Pasi), Lachenmaier, T. (Tobias), Landini, C. (Cecilia), Leblanc, S. (Sebastien), Lebrin, V. (Victor), Lefevre, F. (Frederic), Lei, R. (Ruiting), Leitner, R. (Rupert), Leung, J. (Jason), Li, D. (Demin), Li, F. (Fei), Li, F. (Fule), Li, H. (Haitao), Li, H. (Huiling), Li, J. (Jiaqi), Li, M. (Mengzhao), Li, M. (Min), Li, N. (Nan), Li, Q. (Qingjiang), Li, R. (Ruhui), Li, S. (Shanfeng), Li, T. (Tao), Li, W. (Weidong), Li, W. (Weiguo), Li, X. (Xiaomei), Li, X. (Xiaonan), Li, X. (Xinglong), Li, Y. (Yi), Li, Y. (Yufeng), Li, Z. (Zhaohan), Li, Z. (Zhibing), Li, Z. (Ziyuan), Liang, H. (Hao), Liebau, D. (Daniel), Limphirat, A. (Ayut), Limpijumnong, S. (Sukit), Lin, G.-L. (Guey-Lin), Lin, S. (Shengxin), Lin, T. (Tao), Ling, J. (Jiajie), Lippi, I. (Ivano), Liu, F. (Fang), Liu, H. (Haidong), Liu, H. (Hongbang), Liu, H. (Hongjuan), Liu, H. (Hongtao), Liu, H. (Hui), Wang, J. (Jun), Liao, J. (Jiajun), Wang, W. (Wei), Abusleme, A. (Angel), Adam, T. (Thomas), Ahmad, S. (Shakeel), Ahmed, R. (Rizwan), Aiello, S. (Sebastiano), Akram, M. (Muhammad), An, F. (Fengpeng), An, Q. (Qi), Andronico, G. (Giuseppe), Anfimov, N. (Nikolay), Antonelli, V. (Vito), Antoshkina, T. (Tatiana), Asavapibhop, B. (Burin), de Andre, J. P. (Joao Pedro Athayde Marcondes), Auguste, D. (Didier), Babic, A. (Andrej), Balashov, N. (Nikita), Baldini, W. (Wander), Barresi, A. (Andrea), Basilico, D. (Davide), Baussan, E. (Eric), Bellato, M. (Marco), Bergnoli, A. (Antonio), Birkenfeld, T. (Thilo), Blin, S. (Sylvie), Blum, D. (David), Blyth, S. (Simon), Bolshakova, A. (Anastasia), Bongrand, M. (Mathieu), Bordereau, C. (Clement), Breton, D. (Dominique), Brigatti, A. (Augusto), Brugnera, R. (Riccardo), Bruno, R. (Riccardo), Budano, A. (Antonio), Buscemi, M. (Mario), Busto, J. (Jose), Butorov, I. (Ilya), Cabrera, A. (Anatael), Cai, H. (Hao), Cai, X. (Xiao), Cai, Y. (Yanke), Cai, Z. (Zhiyan), Callegari, R. (Riccardo), Cammi, A. (Antonio), Campeny, A. (Agustin), Cao, C. (Chuanya), Cao, G. (Guofu), Cao, J. (Jun), Caruso, R. (Rossella), Cerna, C. (Cedric), Chang, J. (Jinfan), Chang, Y. (Yun), Chen, P. (Pingping), Chen, P.-A. (Po-An), Chen, S. (Shaomin), Chen, X. (Xurong), Chen, Y.-W. (Yi-Wen), Chen, Y. (Yixue), Chen, Y. (Yu), Chen, Z. (Zhang), Cheng, J. (Jie), Cheng, Y. (Yaping), Chetverikov, A. (Alexey), Chiesa, D. (Davide), Chimenti, P. (Pietro), Chukanov, A. (Artem), Claverie, G. (Gerard), Clementi, C. (Catia), Clerbaux, B. (Barbara), Di Lorenzo, S. C. (Selma Conforti), Corti, D. (Daniele), Dal Corso, F. (Flavio), Dalager, O. (Olivia), De la Taille, C. (Christophe), Deng, J. (Jiawei), Deng, Z. (Zhi), Deng, Z. (Ziyan), Depnering, W. (Wilfried), Diaz, M. (Marco), Ding, X. (Xuefeng), Ding, Y. (Yayun), Dirgantara, B. (Bayu), Dmitrievsky, S. (Sergey), Dohnal, T. (Tadeas), Dolzhikov, D. (Dmitry), Donchenko, G. (Georgy), Dong, J. (Jianmeng), Doroshkevich, E. (Evgeny), Dracos, M. (Marcos), Druillole, F. (Frederic), Du, R. (Ran), Du, S. (Shuxian), Dusini, S. (Stefano), Dvorak, M. (Martin), Enqvist, T. (Timo), Enzmann, H. (Heike), Fabbri, A. (Andrea), Fajt, L. (Lukas), Fan, D. (Donghua), Fan, L. (Lei), Fang, J. (Jian), Fang, W. (Wenxing), Fargetta, M. (Marco), Fedoseev, D. (Dmitry), Fekete, V. (Vladko), Feng, L.-C. (Li-Cheng), Feng, Q. (Qichun), Ford, R. (Richard), Fournier, A. (Amelie), Gan, H. (Haonan), Gao, F. (Feng), Garfagnini, A. (Alberto), Gavrikov, A. (Arsenii), Giammarchi, M. (Marco), Giaz, A. (Agnese), Giudice, N. (Nunzio), Gonchar, M. (Maxim), Gong, G. (Guanghua), Gong, H. (Hui), Gornushkin, Y. (Yuri), Goettel, A. (Alexandre), Grassi, M. (Marco), Grewing, C. (Christian), Gromov, V. (Vasily), Gu, M. (Minghao), Gu, X. (Xiaofei), Gu, Y. (Yu), Guan, M. (Mengyun), Guardone, N. (Nunzio), Gul, M. (Maria), Guo, C. (Cong), Guo, J. (Jingyuan), Guo, W. (Wanlei), Guo, X. (Xinheng), Guo, Y. (Yuhang), Hackspacher, P. (Paul), Hagner, C. (Caren), Han, R. (Ran), Han, Y. (Yang), Hassan, M. S. (Muhammad Sohaib), He, M. (Miao), He, W. (Wei), Heinz, T. (Tobias), Hellmuth, P. (Patrick), Heng, Y. (Yuekun), Herrera, R. (Rafael), Hor, Y. (YuenKeung), Hou, S. (Shaojing), Hsiung, Y. (Yee), Hu, B.-Z. (Bei-Zhen), Hu, H. (Hang), Hu, J. (Jianrun), Hu, J. (Jun), Hu, S. (Shouyang), Hu, T. (Tao), Hu, Z. (Zhuojun), Huang, C. (Chunhao), Huang, G. (Guihong), Huang, H. (Hanxiong), Huang, W. (Wenhao), Huang, X. (Xin), Huang, X. (Xingtao), Huang, Y. (Yongbo), Hui, J. (Jiaqi), Huo, L. (Lei), Huo, W. (Wenju), Huss, C. (Cedric), Hussain, S. (Safeer), Ioannisian, A. (Ara), Isocrate, R. (Roberto), Jelmini, B. (Beatrice), Jen, K.-L. (Kuo-Lun), Jeria, I. (Ignacio), Ji, X. (Xiaolu), Ji, X. (Xingzhao), Jia, H. (Huihui), Jia, J. (Junji), Jian, S. (Siyu), Jiang, D. (Di), Jiang, W. (Wei), Jiang, X. (Xiaoshan), Jin, R. (Ruyi), Jing, X. (Xiaoping), Jollet, C. (Cecile), Joutsenvaara, J. (Jari), Jungthawan, S. (Sirichok), Kalousis, L. (Leonidas), Kampmann, P. (Philipp), Kang, L. (Li), Karaparambil, R. (Rebin), Kazarian, N. (Narine), Khosonthongkee, K. (Khanchai), Korablev, D. (Denis), Kouzakov, K. (Konstantin), Krasnoperov, A. (Alexey), Kruth, A. (Andre), Kutovskiy, N. (Nikolay), Kuusiniemi, P. (Pasi), Lachenmaier, T. (Tobias), Landini, C. (Cecilia), Leblanc, S. (Sebastien), Lebrin, V. (Victor), Lefevre, F. (Frederic), Lei, R. (Ruiting), Leitner, R. (Rupert), Leung, J. (Jason), Li, D. (Demin), Li, F. (Fei), Li, F. (Fule), Li, H. (Haitao), Li, H. (Huiling), Li, J. (Jiaqi), Li, M. (Mengzhao), Li, M. (Min), Li, N. (Nan), Li, Q. (Qingjiang), Li, R. (Ruhui), Li, S. (Shanfeng), Li, T. (Tao), Li, W. (Weidong), Li, W. (Weiguo), Li, X. (Xiaomei), Li, X. (Xiaonan), Li, X. (Xinglong), Li, Y. (Yi), Li, Y. (Yufeng), Li, Z. (Zhaohan), Li, Z. (Zhibing), Li, Z. (Ziyuan), Liang, H. (Hao), Liebau, D. (Daniel), Limphirat, A. (Ayut), Limpijumnong, S. (Sukit), Lin, G.-L. (Guey-Lin), Lin, S. (Shengxin), Lin, T. (Tao), Ling, J. (Jiajie), Lippi, I. (Ivano), Liu, F. (Fang), Liu, H. (Haidong), Liu, H. (Hongbang), Liu, H. (Hongjuan), Liu, H. (Hongtao), and Liu, H. (Hui)

Abstract

We study damping signatures at the Jiangmen Underground Neutrino Observatory (JUNO), a medium-baseline reactor neutrino oscillation experiment. These damping signatures are motivated by various new physics models, including quantum decoherence, nu(3) decay, neutrino absorption, and wave packet decoherence. The phenomenological effects of these models can be characterized by exponential damping factors at the probability level. We assess how well JUNO can constrain these damping parameters and how to disentangle these different damping signatures at JUNO. Compared to current experimental limits, JUNO can significantly improve the limits on tau(3)/m(3) in the nu(3) decay model, the width of the neutrino wave packet sigma(x), and the intrinsic relative dispersion of neutrino momentum sigma(rel).

Published
2022

20. Deep reinforcement learning for dependency-aware microservice deployment in edge computing

Author

Wang, C. (Chenyang), Jia, B. (Bosen), Yu, H. (Hao), Li, X. (Xiuhua), Wang, X. (Xiaofei), Taleb, T. (Tarik), Wang, C. (Chenyang), Jia, B. (Bosen), Yu, H. (Hao), Li, X. (Xiuhua), Wang, X. (Xiaofei), and Taleb, T. (Tarik)

Abstract

Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm’s priorities in terms of average system cost and system reward.

Published
2023

21. Wind power forecasting based on WaveNet and multitask learning

Author

Wang, H. (Hao), Peng, C. (Chen), Liao, B. (Bolin), Cao, X. (Xinwei), Li, S. (Shuai), Wang, H. (Hao), Peng, C. (Chen), Liao, B. (Bolin), Cao, X. (Xinwei), and Li, S. (Shuai)

Abstract

Accurately predicting the power output of wind turbines is crucial for ensuring the reliable and efficient operation of large-scale power systems. To address the inherent limitations of physical models, statistical models, and machine learning algorithms, we propose a novel framework for wind turbine power prediction. This framework combines a special type of convolutional neural network, WaveNet, with a multigate mixture-of-experts (MMoE) architecture. The integration aims to overcome the inherent limitations by effectively capturing and utilizing complex patterns and trends in the time series data. First, the maximum information coefficient (MIC) method is applied to handle data features, and the wavelet transform technique is employed to remove noise from the data. Subsequently, WaveNet utilizes its scalable convolutional network to extract representations of wind power data and effectively capture long-range temporal information. These representations are then fed into the MMoE architecture, which treats multistep time series prediction as a set of independent yet interrelated tasks, allowing for information sharing among different tasks to prevent error accumulation and improve prediction accuracy. We conducted predictions for various forecasting horizons and compared the performance of the proposed model against several benchmark models. The experimental results confirm the strong predictive capability of the WaveNet–MMoE framework.

Published
2023

22. Toward 6G-based metaverse:supporting highly-dynamic deterministic multi-user extended reality services

Author

Yu, H. (Hao), Shokrnezhad, M. (Masoud), Taleb, T. (Tarik), Li, R. (Richard), Song, J. (JaeSeung), Yu, H. (Hao), Shokrnezhad, M. (Masoud), Taleb, T. (Tarik), Li, R. (Richard), and Song, J. (JaeSeung)

Abstract

Metaverse is the concept of a fully immersive and universal virtual space for multiuser interaction, collaboration, and socializing; forming the next evolution of the Internet. Metaverse depends on the convergence of multiple broad technologies that enable eXtended Reality (XR), which is an umbrella term for technologies that lie on the reality-virtuality continuum, namely Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Compared to streaming volumetric content to a single user, the XR applications that involve multiple users who simultaneously watch the same volumetric content (e.g., VR-based online training/ education and multi-user online gaming) are much more attractive. Multi-user XR/Metaverse puts additional demand on the underlying networks, making it more difficult to offer high-quality immersive material in real-time. To cope with this, in this article, we present a comprehensive system and component design for immersive and seamless multi-user XR experiences. To satisfy the Quality of Experience/Quality of Service (QoE/ QoS) requirements and especially stream synchronization requirement in XR collaboration scenarios, we propose an AI-powered deterministic multi-user extended reality resource orchestrator (PRECISENESS) that aims to solve the multi-user XR service provisioning problem. Finally, we demonstrate the performance of our proposed solution in a single-site multi-user XR use case. The obtained results demonstrate that our solution can deliver high-quality immersive XR services.

Published
2023

23. Towards versatile access networks

Author

Ghoraishi, M. (Mir), Alexiou, A. (Angeliki), Cogalan, T. (Tezcan), Conrat, J.-M. (Jean-Marc), De Guzman, M. F. (Mar Francis), Devoti, F. (Francesco), Eappen, G. (Geoffrey), Fang, C. (Chao), Frenger, P. (Pål), Girycki, A. (Adam), Guo, H. (Hao), Halbauer, H. (Hardy), Haliloglu, O. (Omer), Haneda, K. (Katsuyuki), Koffman, I. (Israel), Kyösti, P. (Pekka), Leinonen, M. (Marko), Li, Y. (Yinggang), Madapatha, C. (Charitha), Makki, B. (Behrooz), Navarro-Ortiz, J. (Jorge), Nguyen, L. H. (Le Hang), Nimr, A. (Ahmad), Pärssinen, A. (Aarno), Pollin, S. (Sofie), Pryor, S. (Simon), Puerta, R. (Rafael), Rahman, M. A. (Md Arifur), Ramos-Munoz, J. J. (Juan J.), Ranjbar, V. (Vida), Roth, K. (Kilian), Sarajlic, M. (Muris), Sciancalepore, V. (Vincenzo), Svensson, T. (Tommy), Tervo, N. (Nuutti), Wolfgang, A. (Andreas), Ghoraishi, M. (Mir), Alexiou, A. (Angeliki), Cogalan, T. (Tezcan), Conrat, J.-M. (Jean-Marc), De Guzman, M. F. (Mar Francis), Devoti, F. (Francesco), Eappen, G. (Geoffrey), Fang, C. (Chao), Frenger, P. (Pål), Girycki, A. (Adam), Guo, H. (Hao), Halbauer, H. (Hardy), Haliloglu, O. (Omer), Haneda, K. (Katsuyuki), Koffman, I. (Israel), Kyösti, P. (Pekka), Leinonen, M. (Marko), Li, Y. (Yinggang), Madapatha, C. (Charitha), Makki, B. (Behrooz), Navarro-Ortiz, J. (Jorge), Nguyen, L. H. (Le Hang), Nimr, A. (Ahmad), Pärssinen, A. (Aarno), Pollin, S. (Sofie), Pryor, S. (Simon), Puerta, R. (Rafael), Rahman, M. A. (Md Arifur), Ramos-Munoz, J. J. (Juan J.), Ranjbar, V. (Vida), Roth, K. (Kilian), Sarajlic, M. (Muris), Sciancalepore, V. (Vincenzo), Svensson, T. (Tommy), Tervo, N. (Nuutti), and Wolfgang, A. (Andreas)

Abstract

Compared to its previous generations, the 5th generation (5G) cellular network features an additional type of densification, i.e., a large number of active antennas per access point (AP) can be deployed. This technique is known as massive multipleinput multiple-output (mMIMO) [1]. Meanwhile, multiple-input multiple-output (MIMO) evolution, e.g., in channel state information (CSI) enhancement, and also on the study of a larger number of orthogonal demodulation reference signal (DMRS) ports for MU-MIMO, was one of the Release 18 of 3rd generation partnership project (3GPP Rel-18) work item [2]. This release (3GPP Rel-18) package approval, in the fourth quarter of 2021, marked the start of the 5G Advanced evolution in 3GPP [3]. The other items in 3GPP Rel-18 are to study and add functionality in the areas of network energy savings, coverage, mobility support, multicast broadcast services, and positioning [2].

Published
2023

24. Probabilistic-assured resource provisioning with customizable hybrid isolation for vertical industrial slicing

Author

Guo, Q. (Qize), Gu, R. (Rentao), Yu, H. (Hao), Taleb, T. (Tarik), Ji, Y. (Yuefeng), Guo, Q. (Qize), Gu, R. (Rentao), Yu, H. (Hao), Taleb, T. (Tarik), and Ji, Y. (Yuefeng)

Abstract

With the increasing demand of network slices in vertical industries, slice resource provisioning in transport networks has encountered two challenges, one is efficient slice resource provisioning in the presence of traffic uncertainty of slices, and another is flexible slice resource isolation for customizable isolation needs. In this paper, we propose an innovative flexible hybrid isolation model to support any customized resource isolation from complete isolation to full sharing, and solve the slice resource provisioning problem named Hybrid Slicing Minimum Bandwidth (HSMB) by considering traffic prediction error to mitigate the negative impact of traffic uncertainty in the proposed model. After analyzing the HSMB problem, 1) we first try to solve the problem in steps and decompose the HSMB problem into grouping sub-problem and adjusting sub-problem, 2) we then propose a low-complexity dynamic programming grouping algorithm and a fast iterative adjustment algorithm for the two sub-problems based on probabilistic feature-based analysis, 3) we combine the algorithms of the two sub-problems and further propose a linking algorithm for the potential insufficient resource dilemma and high computational complexity dilemma to improve the efficiency of the solution. The numerical results show that the proposed flexible hybrid isolation model with different factors can facilitate flexible slice isolation with customized isolation demands, while the proposed algorithm can realize efficient slice resource provisioning with a probabilistic guarantee. The comparison result shows the proposed algorithms outperform the other benchmark algorithms.

Published
2023

25. Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management

Author

Huang, Y. (author), Yu, S. Suihuai (author), Chu, J. Jianjie (author), Fan, H. Hao (author), Du, B. Bin (author), Huang, Y. (author), Yu, S. Suihuai (author), Chu, J. Jianjie (author), Fan, H. Hao (author), and Du, B. Bin (author)

Abstract

Cultural heritage management poses significant challenges for museums due to fragmented data, limited intelligent frameworks, and insufficient applications. In response, a digital cultural heritage management approach based on knowledge graphs and deep learning algorithms is proposed to address the above challenges. A joint entity-relation triple extraction model is proposed to automatically identify entities and relations from fragmented data for knowledge graph construction. Additionally, a knowledge completion model is presented to predict missing information and improve knowledge graph completeness. Comparative simulations have been conducted to demonstrate the effectiveness and accuracy of the proposed approach for both the knowledge extraction model and the knowledge completion model. The efficacy of the knowledge graph application is corroborated through a case study utilizing ceramic data from the Palace Museum in China. This method may benefit users since it provides automated, interconnected, visually appealing, and easily accessible information about cultural heritage., Design Conceptualization and Communication

Published
2023
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26. Enhancing ion extraction with an inverse sheath in negative hydrogen ion sources for NBI heating

Author

Sun, G. (Guangyu), Yang, W. (Wei), J. Chen, Sun, H. (Hao-Min), Guo, B. (Baohong), Zhang, S. (Shu), Wang, Y. (Ying-Han), Yang, X. (Xiong), Sun, A.B. (Anbang), Zhang, G.J. (Guanjun), Sun, G. (Guangyu), Yang, W. (Wei), J. Chen, Sun, H. (Hao-Min), Guo, B. (Baohong), Zhang, S. (Shu), Wang, Y. (Ying-Han), Yang, X. (Xiong), Sun, A.B. (Anbang), and Zhang, G.J. (Guanjun)

Abstract

Negative hydrogen ion (H−) sources employed in neutral beam injection (NBI) systems are subject to extraction efficiency issues due to the considerable volumetric losses of negative hydrogen ions. Here, we propose to improve the H− extraction by activating an alternative sheath mode, the electronegative inverse sheath, in front of the H− production surface, which features zero sheath acceleration for H− with a negative sheath potential opposite to the classic sheath. With the inverse sheath activated, the produced H− exhibits smaller gyration, a shorter transport path, less destructive collisions, and therefore higher extraction probability than the commonly believed space-charge-limited (SCL) sheath. Formation of the proposed electronegative inverse sheath and the SCL sheath near the H–-emitting surface is investigated by the continuum kinetic simulation. Dedicated theoretical analyses are also performed to characterize the electronegative inverse sheath properties, which qualitatively agree with the simulation results. We further propose that the transition between the two sheath modes can be realized by tuning the cold ion generation near the emissive boundary. The electronegative inverse sheath is always coupled with a plasma consisting of only hydrogen ions with approximately zero electron concentration, which is reminiscent of the ion–ion plasma reported in previous NBI experiments.

Published
2023
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27. “The only strictly correct method of philosophy”: logical analysis and anti-metaphysical dialectic

Author

Stokhof, Martin, Tang, Hao, Stokhof, M ( Martin ), Tang, H ( Hao ), Glock, Hans-Johann; https://orcid.org/0000-0001-6176-909X, Stokhof, Martin, Tang, Hao, Stokhof, M ( Martin ), Tang, H ( Hao ), and Glock, Hans-Johann; https://orcid.org/0000-0001-6176-909X

Abstract

The Tractatus revolves around the connection between two central topics – the preconditions of symbolic representation and the nature of logic-cum-philosophy. Proper philosophy is an activity, namely of revealing the hidden structures that allow language to represent reality by way of logical analysis. At the same time the main purpose of such logical analysis consists in revealing metaphysical statements to be nonsensical. In the subsequent development of analytic philosophy, these two ideas parted company. The positive aim of revealing the logical form of sentences resulted in a program closely associated with Davidson, namely a theory of meaning for natural languages that yields metaphysical corollaries. The negative aim of overcoming metaphysics ushered in the activity of dissolving conceptual confusions through conceptual rather than logical analysis, propelled by the later Wittgenstein. My presentation pursues these historical lines of influence. But the ultimate aim is a substantive one, namely to establish whether the two projects can be kept apart. With the later Wittgenstein, I criticize the claim that formal calculi constitute the hidden structure of natural languages. But I also contend that the hope of engaging in anti-metaphysical dialectic without relying on logico-conceptual analysis of some kind falls prey to a “myth of mere method”.

Published
2023

30. Molecular and morphological characterization of the root -lesion nematode, Pratylenchus neglectus, on corn from Henan Province of China

Author

Y. H. Xia, Y. K. Liu, P. H. Hao, H. X. Yuan, K. Wang, H. L. Li, and Y. Li

Subjects
Medicine (General), R5-920, corn (zea mays), pratylenchus neglectus, Agriculture (General), root-lesion nematodes, identification, Animal Science and Zoology, Parasitology, phylogeny, S1-972
Abstract

Summary Root-lesion nematodes, Pratylenchus spp., are economically important pathogens because of their detrimental and economic impact on a wide range of crops. In August 2018, two samples of both roots and rhizosphere soil were collected from a corn field in Liangyuanqu of Shangqiu city, Henan Province, China. Root-lesion nematodes were recovered from the roots and soil samples using the modified Baermann funnel extraction method. Both the morphological characters and molecular analysis of the internal transcribed spacer (ITS) and D2-D3 expansion region of 28S ribosomal RNA sequences confirmed that the root-lesion nematode population collected from corn in this study was P. neglectus. Phylogenetic analyses showed that this isolate formed a highly supported clade with other P. neglectus isolates. To the best of our knowledge, this is the first report of P. neglectus on corn in Henan Province of China. This study reports the first partial sequences of 28S D2-D3 region of P. neglectus on corn in China. Due to the great harmfulness of root-lesion nematodes to corn, care should be taken to prevent the spread of P. neglectus to other regions in China. At the same time, further study on the biological characteristics of P. neglectus is needed, which will be helpful to develop corresponding management and control strategies.

Published
2021

31. Increased Expression of Efflux Pump norA Drives the Rapid Evolutionary Trajectory from Tolerance to Resistance against Ciprofloxacin in Staphylococcus aureus

Author

X. H. Yu, Z. H. Hao, P. L. Liu, M. M. Liu, L. L. Zhao, and X. Zhao

Subjects
Pharmacology, Infectious Diseases, Pharmacology (medical)
Abstract

The intensively intermittent use of antibiotics promotes the rapid evolution of tolerance, which may lead to resistance acquisition in the following evolutionary trajectory. In addition to directly exporting antibiotics as an instant resistance strategy, efflux pumps are overexpressed in tolerant strains.

Published
2022
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32. [Research progress on microvascular injury after reperfused ST-segment elevation myocardial infarction assessed by CMR imaging]

Author

Y T, Cai, H R, Xing, M H, Hao, L, Yang, R, Xu, and X T, Song

Subjects
Percutaneous Coronary Intervention, Humans, ST Elevation Myocardial Infarction
Abstract

接受直接经皮冠状动脉介入治疗的急性ST段抬高型心肌梗死（STEMI）患者中约90%以上心外膜血流可恢复正常，然而这部分患者中却有高达50%以上通过影像学检查发现仍存在心肌微循环灌注不足，主要为缺血再灌注导致的微血管损伤（MVI）所致。非侵入性心脏磁共振成像可将微血管阻塞（MVO）和心肌内出血（IMH）可视化，研究表明MVO与IMH是心肌梗死不良预后的成像标记物，而IMH则预示着患者心肌梗死面积更广泛、预后更差。该文概述了再灌注治疗后STEMI患者发生MVI的可能机制，并重点阐述了其相关的心脏磁共振成像技术的新进展。.

Published
2022

33. [Atypical teratoid/rhabdoid tumors in adult patients: report of two cases]

Author

Y H, Hao, X B, Tang, and D Z, Wang

Subjects
Adult, Central Nervous System Neoplasms, Brain Neoplasms, Teratoma, Humans, Cerebellar Neoplasms, Rhabdoid Tumor, Medulloblastoma
Abstract

非典型畸胎样/横纹肌样肿瘤是一种好发于小儿的高度恶性中枢神经系统胚胎性肿瘤。其组织形态学表现多样，具有向神经上皮、上皮与间叶组织多向分化的特点，典型表现为出现数量不等的横纹肌样细胞。发生在成人和/或鞍区的病例罕见，对它的临床病理学与生物学行为等特点的认识可能存在不足。因此，本文分析了2例发生于成人的非典型畸胎样/横纹肌样肿瘤，并复习相关文献，以期总结其临床病理特征。.

Published
2022

34. [A multicenter epidemiological study of acute bacterial meningitis in children]

Author

C Y, Wang, H M, Xu, J, Tian, S Q, Hong, G, Liu, S X, Wang, F, Gao, J, Liu, F R, Liu, H, Yu, X, Wu, B Q, Chen, F F, Shen, G, Zheng, J, Yu, M, Shu, L, Liu, L J, Du, P, Li, Z W, Xu, M Q, Zhu, L S, Huang, H Y, Huang, H B, Li, Y Y, Huang, D, Wang, F, Wu, S T, Bai, J J, Tang, Q W, Shan, L C, Lan, C H, Zhu, Y, Xiong, J M, Tian, J H, Wu, J H, Hao, H Y, Zhao, A W, Lin, S S, Song, D J, Lin, Q H, Zhou, Y P, Guo, J Z, Wu, X Q, Yang, X H, Zhang, Y, Guo, Q, Cao, L J, Luo, Z B, Tao, W K, Yang, Y K, Zhou, Y, Chen, L J, Feng, G L, Zhu, Y H, Zhang, P, Xue, X Q, Li, Z Z, Tang, D H, Zhang, X W, Su, Z H, Qu, Y, Zhang, S Y, Zhao, Z Z, Qi, L, Pang, H L, Deng, X L, Liu, Y H, Chen, and Sainan, Shu

Subjects
Male, Adolescent, Infant, Newborn, Brain Abscess, Infant, Subdural Effusion, beta-Lactamases, Meningitis, Bacterial, Streptococcus agalactiae, Streptococcus pneumoniae, Child, Preschool, Escherichia coli, Humans, Female, Child, Hydrocephalus, Retrospective Studies
Published
2022

36. Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Author

Natarajan, P. (Pradeep), Pampana, A. (Akhil), Graham, S. E. (Sarah E.), Ruotsalainen, S. E. (Sanni E.), Perry, J. A. (James A.), de Vries, P. S. (Paul S.), Broome, J. G. (Jai G.), Pirruccello, J. P. (James P.), Honigbere, M. C. (Michael C.), Aragam, K. (Krishna), Wolford, B. (Brooke), Brody, J. A. (Jennifer A.), Antonacci-Fulton, L. (Lucinda), Arden, M. (Moscati), Aslibekyan, S. (Stella), Assimes, T. L. (Themistocles L.), Ballantyne, C. M. (Christie M.), Bielak, L. F. (Lawrence F.), Bisl, J. C. (Joshua C.), Cade, B. E. (Brian E.), Do, R. (Ron), Doddapaneni, H. (Harsha), Emery, L. S. (Leslie S.), Hung, Y.-J. (Yi-Jen), Irvin, M. R. (Marguerite R.), Khan, A. T. (Alyna T.), Lange, L. (Leslie), Lee, J. (Jiwon), Lemaitre, R. N. (Rozenn N.), Martin, L. W. (Lisa W.), Metcalf, G. (Ginger), Montasser, M. E. (May E.), Moon, J.-Y. (Jee-Young), Muzny, D. (Donna), Connell, J. R. (Jeffrey R. O.), Palmer, N. D. (Nicholette D.), Peralta, J. M. (Juan M.), Peyser, P. A. (Patricia A.), Stilp, A. M. (Adrienne M.), Tsai, M. (Michael), Wang, F. F. (Fei Fei), Weeks, D. E. (Daniel E.), Yanek, L. R. (Lisa R.), Wilson, J. G. (James G.), Abecasis, G. (Goncalo), Arnett, D. K. (Donna K.), Becker, L. C. (Lewis C.), Blangercy, J. (John), Boerwinkle, E. (Eric), Bowden, D. W. (Donald W.), Chang, Y.-C. (Yi-Cheng), Chen, Y. I. (Yii-Der, I), Choi, W. J. (Won Jung), Correa, A. (Adolfo), Curran, J. E. (Joanne E.), Daly, M. J. (Mark J.), DutcherE, S. K. (Susan K.), Ellinor, P. T. (Patrick T.), Fornage, M. (Myriam), Freedman, B. I. (Barry, I), Gabriel, S. (Stacey), Germer, S. (Soren), Gibbs, R. A. (Richard A.), He, J. (Jiang), Hveem, K. (Kristian), Jarvik, G. P. (Gail P.), Kaplan, R. C. (Robert C.), Kardia, S. L. (Sharon L. R.), Kennyn, E. (Eimear), Kim, R. W. (Ryan W.), Kooperberg, C. (Charles), Laurie, C. C. (Cathy C.), Lee, S. (Seonwook), Lloyd-Jones, D. M. (Don M.), Loos, R. J. (Ruth J. F.), Lubitz, S. A. (Steven A.), Mathias, R. A. (Rasika A.), Martinez, K. A. (Karine A. Viaud), McGarvey, S. T. (Stephen T.), Mitche, B. D. (Braxton D.), Nickerson, D. A. (Deborah A.), North, K. E. (Kari E.), Palotie, A. (Aarno), Park, C. J. (Cheol Joo), Psat, B. M. (Bruce M. Y.), Rao, D. C. (D. C.), Redline, S. (Susan), Reiner, A. P. (Alexander P.), Seo, D. (Daekwan), Seo, J.-S. (Jeong-Sun), Smith, A. V. (Albert, V), Tracy, R. P. (Russell P.), Kathiresan, S. (Sekar), Cupples, L. A. (L. Adrienne), Rotten, J. I. (Jerome, I), Morrison, A. C. (Alanna C.), Rich, S. S. (Stephen S.), Ripatti, S. (Samuli), Wilier, C. (Cristen), Peloso, G. M. (Gina M.), Vasan, R. S. (Ramachandran S.), Abe, N. (Namiko), Albert, C. (Christine), Almasy, L. (Laura), Alonso, A. (Alvaro), Ament, S. (Seth), Anderson, P. (Peter), Applebaum-Bowden, D. (Deborah), Arking, D. (Dan), Ashley-Koch, A. (Allison), Auer, P. (Paul), Avramopoulos, D. (Dimitrios), Barnard, J. (John), Barnes, K. (Kathleen), Barr, R. G. (R. Graham), Barron-Casella, E. (Emily), Beaty, T. (Terri), Becker, D. (Diane), Beer, R. (Rebecca), Begum, F. (Ferdouse), Beitelshees, A. (Amber), Benjamin, E. (Emelia), Bezerra, M. (Marcos), Bielak, L. (Larry), Blackwel, T. (Thomas), Bowler, R. (Russell), Broecke, U. (Ulrich), Bunting, K. (Karen), Burchard, E. (Esteban), Buth, E. (Erin), Cardwel, J. (Jonathan), Carty, C. (Cara), Casaburi, R. (Richard), Casella, J. (James), Chaffin, M. (Mark), Chang, C. (Christy), Chasman, D. (Daniel), Chavan, S. (Sameer), Chen, B.-J. (Bo-Juen), Chen, W.-M. (Wei-Min), Chol, M. (Michael), Choi, S. H. (Seung Hoan), Chuang, L.-M. (Lee-Ming), Chung, M. (Mina), Conomos, M. P. (Matthew P.), Cornell, E. (Elaine), Crapo, J. (James), Curtis, J. (Jeffrey), Custer, B. (Brian), Damcott, C. (Coleen), Darbar, D. (Dawood), Das, S. (Sayantan), David, S. (Sean), Davis, C. (Colleen), Daya, M. (Michelle), de Andrade, M. (Mariza), DeBaunuo, M. (Michael), Duan, Q. (Qing), Devine, R. D. (Ranjan Deka Dawn DeMeo Scott), Duggirala, Q. R. (Qing Ravi), Durda, J. P. (Jon Peter), Dutcher, S. (Susan), Eaton, C. (Charles), Ekunwe, L. (Lynette), Farber, C. (Charles), Farnaml, L. (Leanna), Fingerlin, T. (Tasha), Flickinger, M. (Matthew), Franceschini, N. (Nora), Fu, M. (Mao), Fullerton, S. M. (Stephanie M.), Fulton, L. (Lucinda), Gan, W. (Weiniu), Gao, Y. (Yan), Gass, M. (Margery), Ge, B. (Bruce), Geng, X. P. (Xiaoqi Priscilla), Gignoux, C. (Chris), Gladwin, M. (Mark), Glahn, D. (David), Gogarten, S. (Stephanie), Gong, D.-W. (Da-Wei), Goring, H. (Harald), Gu, C. C. (C. Charles), Guan, Y. (Yue), Guo, X. (Xiuqing), Haessler, J. (Jeff), Hall, M. (Michael), Harris, D. (Daniel), Hawle, N. Y. (Nicola Y.), Heavner, B. (Ben), Heckbert, S. (Susan), Hernandez, R. (Ryan), Herrington, D. (David), Hersh, C. (Craig), Hidalgo, B. (Bertha), Hixson, J. (James), Hokanson, J. (John), Hong, E. (Elliott), Hoth, K. (Karin), Hsiung, C. A. (Chao Agnes), Huston, H. (Haley), Hwu, C. M. (Chii Min), Jackson, R. (Rebecca), Jain, D. (Deepti), Jaquish, C. (Cashell), Jhun, M. A. (Min A.), Johnsen, J. (Jill), Johnson, A. (Andrew), Johnson, C. (Craig), Johnston, R. (Rich), Jones, K. (Kimberly), Kang, H. M. (Hyun Min), Kaufman, L. (Laura), Kell, S. Y. (Shannon Y.), Kessler, M. (Michael), Kinney, G. (Greg), Konkle, B. (Barbara), Kramer, H. (Holly), Krauter, S. (Stephanie), Lange, C. (Christoph), Lange, E. (Ethan), Laurie, C. (Cecelia), LeBoff, M. (Meryl), Lee, S. S. (Seunggeun Shawn), Lee, W.-J. (Wen-Jane), LeFaive, J. (Jonathon), Levine, D. (David), Levy, D. (Dan), Lewis, J. (Joshua), Li, Y. (Yun), Lin, H. (Honghuang), Lin, K. H. (Keng Han), Lin, X. (Xihong), Liu, S. (Simin), Liu, Y. (Yongmei), Lunetta, K. (Kathryn), Luo, J. (James), Mahaney, M. (Michael), Make, B. (Barry), Manichaikul, A. (Ani), Mansonl, J. (JoAnn), Margolin, L. (Lauren), Mathai, S. (Susan), McArdle, P. (Patrick), Mcdonald, M.-L. (Merry-Lynn), McFarland, S. (Sean), McHugh, C. (Caitlin), Mei, H. (Hao), Meyers, D. A. (Deborah A.), Mikulla, J. (Julie), Min, N. (Nancy), Minear, M. (Mollie), Minster, R. L. (Ryan L.), Musani, S. (Solomon), Mwasongwe, S. (Stanford), Mychaleckyj, J. C. (Josyf C.), Nadkarni, G. (Girish), Naik, R. (Rakhi), Naseri, T. (Take), Nekhai, S. (Sergei), Nelson, S. C. (Sarah C.), Nickerson, D. (Deborah), Connell, J. O. (Jeff O.), Connor, T. O. (Tim O.), Ochs-Balcom, H. (Heather), Pankow, J. (James), Papanicolaou, G. (George), Parkerl, M. (Margaret), Parsa, A. (Afshin), Penchey, S. (Sara), Perez, M. (Marco), Peters, U. (Ulrike), Phillips, L. S. (Lawrence S.), Phillips, S. (Sam), Pollin, T. (Toni), Post, W. (Wendy), Becker, J. P. (Julia Powers), Boorgula, M. P. (Meher Preethi), Preuss, M. (Michael), Prokopenko, D. (Dmitry), Qasba, P. (Pankaj), Qiao, D. (Dandi), Rafaels, N. (Nicholas), Raffield, L. (Laura), Rasmussen-Torvik, L. (Laura), Ratan, A. (Aakrosh), Reed, R. (Robert), Reganl, E. (Elizabeth), Reupena, M. S. (Muagututi Sefuiva), Rice, K. (Ken), Roden, D. (Dan), Roselli, C. (Carolina), Ruczinski, I. (Ingo), Russel, P. (Pamela), Ruuska, S. (Sarah), Ryan, K. (Kathleen), Sabino, E. C. (Ester Cerdeira), Sakornsakolpatl, P. (Phuwanat), Salzberg, S. (Steven), Sandow, K. (Kevin), Sankaran, V. G. (Vijay G.), Scheller, C. (Christopher), Schmidt, E. (Ellen), Schwander, K. (Karen), Schwartz, D. (David), Sciurba, F. (Frank), Seidman, C. (Christine), Seidman, J. (Jonathan), Sheehan, V. (Vivien), Shetty, A. (Amol), Shetty, A. (Aniket), Sheu, W. H. (Wayne Hui-Heng), Shoemaker, M. B. (M. Benjamin), Silver, B. (Brian), Silvermanl, E. (Edwin), Smith, J. (Jennifer), Smith, J. (Josh), Smith, N. (Nicholas), Smith, T. (Tanja), Smoller, S. (Sylvia), Snively, B. (Beverly), Soferlm, T. (Tamar), Streeten, E. (Elizabeth), Su, J. L. (Jessica Lasky), Sung, Y. J. (Yun Ju), Sylvia, J. (Jody), Sztalryd, C. (Carole), Taliun, D. (Daniel), Tang, H. (Hua), Taub, M. (Margaret), Taylor, K. D. (Kent D.), Taylor, S. (Simeon), Telen, M. (Marilyn), Thornton, T. A. (Timothy A.), Tinker, L. (Lesley), Tirschwel, D. (David), Tiwari, H. (Hemant), Vaidya, D. (Dhananjay), VandeHaar, P. (Peter), Vrieze, S. (Scott), Walker, T. (Tarik), Wallace, R. (Robert), Waits, A. (Avram), Wan, E. (Emily), Wang, H. (Heming), Watson, K. (Karol), Weir, B. (Bruce), Weiss, S. (Scott), Weng, L.-C. (Lu-Chen), Williams, K. (Kayleen), Williams, L. K. (L. Keoki), Wilson, C. (Carla), Wong, Q. (Quenna), Xu, H. (Huichun), Yang, I. (Ivana), Yang, R. (Rongze), Zaghlou, N. (Norann), Zekavat, M. (Maryam), Zhang, Y. (Yingze), Zhao, S. X. (Snow Xueyan), Zhao, W. (Wei), Zni, D. (Degui), Zhou, X. (Xiang), Zhu, X. (Xiaofeng), Zody, M. (Michael), Zoellner, S. (Sebastian), Daly, M. (Mark), Jacob, H. (Howard), Matakidou, A. (Athena), Runz, H. (Heiko), John, S. (Sally), Plenge, R. (Robert), McCarthy, M. (Mark), Hunkapiller, J. (Julie), Ehm, M. (Meg), Waterworth, D. (Dawn), Fox, C. (Caroline), Malarstig, A. (Anders), Klinger, K. (Kathy), Call, K. (Kathy), Mkel, T. (Tomi), Kaprio, J. (Jaakko), Virolainen, P. (Petri), Pulkki, K. (Kari), Kilpi, T. (Terhi), Perola, M. (Markus), Partanen, J. (Jukka), Pitkranta, A. (Anne), Kaarteenaho, R. (Riitta), Vainio, S. (Seppo), Savinainen, K. (Kimmo), Kosma, V.-M. (Veli-Matti), Kujala, U. (Urho), Tuovila, O. (Outi), Hendolin, M. (Minna), Pakkanen, R. (Raimo), Waring, J. (Jeff), Riley-Gillis, B. (Bridget), Liu, J. (Jimmy), Biswas, S. (Shameek), Diogo, D. (Dorothee), Marshall, C. (Catherine), Hu, X. (Xinli), Gossel, M. (Matthias), Schleutker, J. (Johanna), Arvas, M. (Mikko), Hinttala, R. (Reetta), Kettunen, J. (Johannes), Laaksonen, R. (Reijo), Mannermaa, A. (Arto), Paloneva, J. (Juha), Soininen, H. (Hilkka), Julkunen, V. (Valtteri), Remes, A. (Anne), Klviinen, R. (Reetta), Hiltunen, M. (Mikko), Peltola, J. (Jukka), Tienari, P. (Pentti), Rinne, J. (Juha), Ziemann, A. (Adam), Waring, J. (Jeffrey), Esmaeeli, S. (Sahar), Smaoui, N. (Nizar), Lehtonen, A. (Anne), Eaton, S. (Susan), Landenper, S. (Sanni), Michon, J. (John), Kerchner, G. (Geoff), Bowers, N. (Natalie), Teng, E. (Edmond), Eicher, J. (John), Mehta, V. (Vinay), Gormle, P. Y. (Padhraig Y.), Linden, K. (Kari), Whelan, C. (Christopher), Xu, F. (Fanli), Pulford, D. (David), Frkkil, M. (Martti), Pikkarainen, S. (Sampsa), Jussila, A. (Airi), Blomster, T. (Timo), Kiviniemi, M. (Mikko), Voutilainen, M. (Markku), Georgantas, B. (Bob), Heap, G. (Graham), Rahimov, F. (Fedik), Usiskin, K. (Keith), Maranville, J. (Joseph), Lu, T. (Tim), Oh, D. (Danny), Kalpala, K. (Kirsi), Miller, M. (Melissa), McCarthy, L. (Linda), Eklund, K. (Kari), Palomki, A. (Antti), Isomki, P. (Pia), Piri, L. (Laura), Kaipiainen-Seppnen, O. (Oili), Lertratanaku, A. (Apinya), Bing, D. C. (David Close Marla Hochfeld Nan), Gordillo, J. E. (Jorge Esparza), Mars, N. (Nina), Laitinen, T. (Tarja), Pelkonen, M. (Margit), Kauppi, P. (Paula), Kankaanranta, H. (Hannu), Harju, T. (Terttu), Greenberg, S. (Steven), Chen, H. (Hubert), Betts, J. (Jo), Ghosh, S. (Soumitra), Salomaa, V. (Veikko), Niiranen, T. (Teemu), Juonala, M. (Markus), Metsrinne, K. (Kaj), Khnen, M. (Mika), Junttila, J. (Juhani), Laakso, M. (Markku), Pihlajamki, J. (Jussi), Sinisalo, J. (Juha), Taskinen, M.-R. (Marja-Riitta), Tuomi, T. (Tiinamaija), Laukkanen, J. (Jari), Challis, B. (Ben), Peterson, A. (Andrew), Chu, A. (Audrey), Parkkinen, J. (Jaakko), Muslin, A. (Anthony), Joensuu, H. (Heikki), Meretoja, T. (Tuomo), Aaltonen, L. (Lauri), Auranen, A. (Annika), Karihtala, P. (Peeter), Kauppila, S. (Saila), Auvinen, P. (Pivi), Elenius, K. (Klaus), Popovic, R. (Relja), Schutzman, J. (Jennifer), Loboda, A. (Andrey), Chhibber, A. (Aparna), Lehtonen, H. (Heli), McDonough, S. (Stefan), Crohns, M. (Marika), Kulkarni, D. (Diptee), Kaarniranta, K. (Kai), Turunen, J. (Joni), Ollila, T. (Terhi), Seitsonen, S. (Sanna), Uusitalo, H. (Hannu), Aaltonen, V. (Vesa), Uusitalo-Jrvinen, H. (Hannele), Luodonp, M. (Marja), Hautala, N. (Nina), Strauss, E. (Erich), Chen, H. (Hao), Podgornaia, A. (Anna), Hoffman, J. (Joshua), Tasanen, K. (Kaisa), Huilaja, L. (Laura), Hannula-Jouppi, K. (Katariina), Salmi, T. (Teea), Peltonen, S. (Sirkku), Koulu, L. (Leena), Harvima, I. (Ilkka), Wu, Y. (Ying), Choy, D. (David), Jalanko, A. (Anu), Kajanne, R. (Risto), Lyhs, U. (Ulrike), Kaunisto, M. (Mari), Davis, J. W. (Justin Wade), Quarless, D. (Danjuma), Petrovski, S. (Slav), Chen, C.-Y. (Chia-Yen), Bronson, P. (Paola), Yang, R. (Robert), Chang, D. (Diana), Bhangale, T. (Tushar), Holzinger, E. (Emily), Wang, X. (Xulong), Chen, X. (Xing), Auro, K. (Kirsi), Wang, C. (Clarence), Xu, E. (Ethan), Auge, F. (Franck), Chatelain, C. (Clement), Kurki, M. (Mitja), Karjalainen, J. (Juha), Havulinna, A. (Aki), Palin, K. (Kimmo), Palta, P. (Priit), Parolo, P. D. (Pietro Della Briotta), Zhou, W. (Wei), Lemmel, S. (Susanna), Rivas, M. (Manuel), Harju, J. (Jarmo), Lehisto, A. (Arto), Ganna, A. (Andrea), Llorens, V. (Vincent), Karlsson, A. (Antti), Kristiansson, K. (Kati), Hyvrinen, K. (Kati), Ritari, J. (Jarmo), Wahlfors, T. (Tiina), Koskinen, M. (Miika), Pylkäs, K. (Katri), Kalaoja, M. (Marita), Karjalainen, M. (Minna), Mantere, T. (Tuomo), Kangasniemi, E. (Eeva), Heikkinen, S. (Sami), Laakkonen, E. (Eija), Kononen, J. (Juha), Loukola, A. (Anu), Laiho, P. (Pivi), Sistonen, T. (Tuuli), Kaiharju, E. (Essi), Laukkanen, M. (Markku), Jrvensivu, E. (Elina), Lhteenmki, S. (Sini), Mnnikk, L. (Lotta), Wong, R. (Regis), Mattsson, H. (Hannele), Hiekkalinna, T. (Tero), Jimnez, M. G. (Manuel Gonzlez), Donner, K. (Kati), Prn, K. (KaIle), Nunez-Fontarnau, J. (Javier), Kilpelinen, E. (Elina), Sipi, T. P. (Timo P.), Brein, G. (Georg), Dada, A. (Alexander), Awaisa, G. (Ghazal), Shcherban, A. (Anastasia), Sipil, T. (Tuomas), Laivuori, H. (Hannele), Kiiskinen, T. (Tuomo), Siirtola, H. (Harri), Tabuenca, J. G. (Javier Gracia), Kallio, L. (Lila), Soini, S. (Sirpa), Pitknen, K. (Kimmo), Kuopio, T. (Teijo), Natarajan, P. (Pradeep), Pampana, A. (Akhil), Graham, S. E. (Sarah E.), Ruotsalainen, S. E. (Sanni E.), Perry, J. A. (James A.), de Vries, P. S. (Paul S.), Broome, J. G. (Jai G.), Pirruccello, J. P. (James P.), Honigbere, M. C. (Michael C.), Aragam, K. (Krishna), Wolford, B. (Brooke), Brody, J. A. (Jennifer A.), Antonacci-Fulton, L. (Lucinda), Arden, M. (Moscati), Aslibekyan, S. (Stella), Assimes, T. L. (Themistocles L.), Ballantyne, C. M. (Christie M.), Bielak, L. F. (Lawrence F.), Bisl, J. C. (Joshua C.), Cade, B. E. (Brian E.), Do, R. (Ron), Doddapaneni, H. (Harsha), Emery, L. S. (Leslie S.), Hung, Y.-J. (Yi-Jen), Irvin, M. R. (Marguerite R.), Khan, A. T. (Alyna T.), Lange, L. (Leslie), Lee, J. (Jiwon), Lemaitre, R. N. (Rozenn N.), Martin, L. W. (Lisa W.), Metcalf, G. (Ginger), Montasser, M. E. (May E.), Moon, J.-Y. (Jee-Young), Muzny, D. (Donna), Connell, J. R. (Jeffrey R. O.), Palmer, N. D. (Nicholette D.), Peralta, J. M. (Juan M.), Peyser, P. A. (Patricia A.), Stilp, A. M. (Adrienne M.), Tsai, M. (Michael), Wang, F. F. (Fei Fei), Weeks, D. E. (Daniel E.), Yanek, L. R. (Lisa R.), Wilson, J. G. (James G.), Abecasis, G. (Goncalo), Arnett, D. K. (Donna K.), Becker, L. C. (Lewis C.), Blangercy, J. (John), Boerwinkle, E. (Eric), Bowden, D. W. (Donald W.), Chang, Y.-C. (Yi-Cheng), Chen, Y. I. (Yii-Der, I), Choi, W. J. (Won Jung), Correa, A. (Adolfo), Curran, J. E. (Joanne E.), Daly, M. J. (Mark J.), DutcherE, S. K. (Susan K.), Ellinor, P. T. (Patrick T.), Fornage, M. (Myriam), Freedman, B. I. (Barry, I), Gabriel, S. (Stacey), Germer, S. (Soren), Gibbs, R. A. (Richard A.), He, J. (Jiang), Hveem, K. (Kristian), Jarvik, G. P. (Gail P.), Kaplan, R. C. (Robert C.), Kardia, S. L. (Sharon L. R.), Kennyn, E. (Eimear), Kim, R. W. (Ryan W.), Kooperberg, C. (Charles), Laurie, C. C. (Cathy C.), Lee, S. (Seonwook), Lloyd-Jones, D. M. (Don M.), Loos, R. J. (Ruth J. F.), Lubitz, S. A. (Steven A.), Mathias, R. A. (Rasika A.), Martinez, K. A. (Karine A. Viaud), McGarvey, S. T. (Stephen T.), Mitche, B. D. (Braxton D.), Nickerson, D. A. (Deborah A.), North, K. E. (Kari E.), Palotie, A. (Aarno), Park, C. J. (Cheol Joo), Psat, B. M. (Bruce M. Y.), Rao, D. C. (D. C.), Redline, S. (Susan), Reiner, A. P. (Alexander P.), Seo, D. (Daekwan), Seo, J.-S. (Jeong-Sun), Smith, A. V. (Albert, V), Tracy, R. P. (Russell P.), Kathiresan, S. (Sekar), Cupples, L. A. (L. Adrienne), Rotten, J. I. (Jerome, I), Morrison, A. C. (Alanna C.), Rich, S. S. (Stephen S.), Ripatti, S. (Samuli), Wilier, C. (Cristen), Peloso, G. M. (Gina M.), Vasan, R. S. (Ramachandran S.), Abe, N. (Namiko), Albert, C. (Christine), Almasy, L. (Laura), Alonso, A. (Alvaro), Ament, S. (Seth), Anderson, P. (Peter), Applebaum-Bowden, D. (Deborah), Arking, D. (Dan), Ashley-Koch, A. (Allison), Auer, P. (Paul), Avramopoulos, D. (Dimitrios), Barnard, J. (John), Barnes, K. (Kathleen), Barr, R. G. (R. Graham), Barron-Casella, E. (Emily), Beaty, T. (Terri), Becker, D. (Diane), Beer, R. (Rebecca), Begum, F. (Ferdouse), Beitelshees, A. (Amber), Benjamin, E. (Emelia), Bezerra, M. (Marcos), Bielak, L. (Larry), Blackwel, T. (Thomas), Bowler, R. (Russell), Broecke, U. (Ulrich), Bunting, K. (Karen), Burchard, E. (Esteban), Buth, E. (Erin), Cardwel, J. (Jonathan), Carty, C. (Cara), Casaburi, R. (Richard), Casella, J. (James), Chaffin, M. (Mark), Chang, C. (Christy), Chasman, D. (Daniel), Chavan, S. (Sameer), Chen, B.-J. (Bo-Juen), Chen, W.-M. (Wei-Min), Chol, M. (Michael), Choi, S. H. (Seung Hoan), Chuang, L.-M. (Lee-Ming), Chung, M. (Mina), Conomos, M. P. (Matthew P.), Cornell, E. (Elaine), Crapo, J. (James), Curtis, J. (Jeffrey), Custer, B. (Brian), Damcott, C. (Coleen), Darbar, D. (Dawood), Das, S. (Sayantan), David, S. (Sean), Davis, C. (Colleen), Daya, M. (Michelle), de Andrade, M. (Mariza), DeBaunuo, M. (Michael), Duan, Q. (Qing), Devine, R. D. (Ranjan Deka Dawn DeMeo Scott), Duggirala, Q. R. (Qing Ravi), Durda, J. P. (Jon Peter), Dutcher, S. (Susan), Eaton, C. (Charles), Ekunwe, L. (Lynette), Farber, C. (Charles), Farnaml, L. (Leanna), Fingerlin, T. (Tasha), Flickinger, M. (Matthew), Franceschini, N. (Nora), Fu, M. (Mao), Fullerton, S. M. (Stephanie M.), Fulton, L. (Lucinda), Gan, W. (Weiniu), Gao, Y. (Yan), Gass, M. (Margery), Ge, B. (Bruce), Geng, X. P. (Xiaoqi Priscilla), Gignoux, C. (Chris), Gladwin, M. (Mark), Glahn, D. (David), Gogarten, S. (Stephanie), Gong, D.-W. (Da-Wei), Goring, H. (Harald), Gu, C. C. (C. Charles), Guan, Y. (Yue), Guo, X. (Xiuqing), Haessler, J. (Jeff), Hall, M. (Michael), Harris, D. (Daniel), Hawle, N. Y. (Nicola Y.), Heavner, B. (Ben), Heckbert, S. (Susan), Hernandez, R. (Ryan), Herrington, D. (David), Hersh, C. (Craig), Hidalgo, B. (Bertha), Hixson, J. (James), Hokanson, J. (John), Hong, E. (Elliott), Hoth, K. (Karin), Hsiung, C. A. (Chao Agnes), Huston, H. (Haley), Hwu, C. M. (Chii Min), Jackson, R. (Rebecca), Jain, D. (Deepti), Jaquish, C. (Cashell), Jhun, M. A. (Min A.), Johnsen, J. (Jill), Johnson, A. (Andrew), Johnson, C. (Craig), Johnston, R. (Rich), Jones, K. (Kimberly), Kang, H. M. (Hyun Min), Kaufman, L. (Laura), Kell, S. Y. (Shannon Y.), Kessler, M. (Michael), Kinney, G. (Greg), Konkle, B. (Barbara), Kramer, H. (Holly), Krauter, S. (Stephanie), Lange, C. (Christoph), Lange, E. (Ethan), Laurie, C. (Cecelia), LeBoff, M. (Meryl), Lee, S. S. (Seunggeun Shawn), Lee, W.-J. (Wen-Jane), LeFaive, J. (Jonathon), Levine, D. (David), Levy, D. (Dan), Lewis, J. (Joshua), Li, Y. (Yun), Lin, H. (Honghuang), Lin, K. H. (Keng Han), Lin, X. (Xihong), Liu, S. (Simin), Liu, Y. (Yongmei), Lunetta, K. (Kathryn), Luo, J. (James), Mahaney, M. (Michael), Make, B. (Barry), Manichaikul, A. (Ani), Mansonl, J. (JoAnn), Margolin, L. (Lauren), Mathai, S. (Susan), McArdle, P. (Patrick), Mcdonald, M.-L. (Merry-Lynn), McFarland, S. (Sean), McHugh, C. (Caitlin), Mei, H. (Hao), Meyers, D. A. (Deborah A.), Mikulla, J. (Julie), Min, N. (Nancy), Minear, M. (Mollie), Minster, R. L. (Ryan L.), Musani, S. (Solomon), Mwasongwe, S. (Stanford), Mychaleckyj, J. C. (Josyf C.), Nadkarni, G. (Girish), Naik, R. (Rakhi), Naseri, T. (Take), Nekhai, S. (Sergei), Nelson, S. C. (Sarah C.), Nickerson, D. (Deborah), Connell, J. O. (Jeff O.), Connor, T. O. (Tim O.), Ochs-Balcom, H. (Heather), Pankow, J. (James), Papanicolaou, G. (George), Parkerl, M. (Margaret), Parsa, A. (Afshin), Penchey, S. (Sara), Perez, M. (Marco), Peters, U. (Ulrike), Phillips, L. S. (Lawrence S.), Phillips, S. (Sam), Pollin, T. (Toni), Post, W. (Wendy), Becker, J. P. (Julia Powers), Boorgula, M. P. (Meher Preethi), Preuss, M. (Michael), Prokopenko, D. (Dmitry), Qasba, P. (Pankaj), Qiao, D. (Dandi), Rafaels, N. (Nicholas), Raffield, L. (Laura), Rasmussen-Torvik, L. (Laura), Ratan, A. (Aakrosh), Reed, R. (Robert), Reganl, E. (Elizabeth), Reupena, M. S. (Muagututi Sefuiva), Rice, K. (Ken), Roden, D. (Dan), Roselli, C. (Carolina), Ruczinski, I. (Ingo), Russel, P. (Pamela), Ruuska, S. (Sarah), Ryan, K. (Kathleen), Sabino, E. C. (Ester Cerdeira), Sakornsakolpatl, P. (Phuwanat), Salzberg, S. (Steven), Sandow, K. (Kevin), Sankaran, V. G. (Vijay G.), Scheller, C. (Christopher), Schmidt, E. (Ellen), Schwander, K. (Karen), Schwartz, D. (David), Sciurba, F. (Frank), Seidman, C. (Christine), Seidman, J. (Jonathan), Sheehan, V. (Vivien), Shetty, A. (Amol), Shetty, A. (Aniket), Sheu, W. H. (Wayne Hui-Heng), Shoemaker, M. B. (M. Benjamin), Silver, B. (Brian), Silvermanl, E. (Edwin), Smith, J. (Jennifer), Smith, J. (Josh), Smith, N. (Nicholas), Smith, T. (Tanja), Smoller, S. (Sylvia), Snively, B. (Beverly), Soferlm, T. (Tamar), Streeten, E. (Elizabeth), Su, J. L. (Jessica Lasky), Sung, Y. J. (Yun Ju), Sylvia, J. (Jody), Sztalryd, C. (Carole), Taliun, D. (Daniel), Tang, H. (Hua), Taub, M. (Margaret), Taylor, K. D. (Kent D.), Taylor, S. (Simeon), Telen, M. (Marilyn), Thornton, T. A. (Timothy A.), Tinker, L. (Lesley), Tirschwel, D. (David), Tiwari, H. (Hemant), Vaidya, D. (Dhananjay), VandeHaar, P. (Peter), Vrieze, S. (Scott), Walker, T. (Tarik), Wallace, R. (Robert), Waits, A. (Avram), Wan, E. (Emily), Wang, H. (Heming), Watson, K. (Karol), Weir, B. (Bruce), Weiss, S. (Scott), Weng, L.-C. (Lu-Chen), Williams, K. (Kayleen), Williams, L. K. (L. Keoki), Wilson, C. (Carla), Wong, Q. (Quenna), Xu, H. (Huichun), Yang, I. (Ivana), Yang, R. (Rongze), Zaghlou, N. (Norann), Zekavat, M. (Maryam), Zhang, Y. (Yingze), Zhao, S. X. (Snow Xueyan), Zhao, W. (Wei), Zni, D. (Degui), Zhou, X. (Xiang), Zhu, X. (Xiaofeng), Zody, M. (Michael), Zoellner, S. (Sebastian), Daly, M. (Mark), Jacob, H. (Howard), Matakidou, A. (Athena), Runz, H. (Heiko), John, S. (Sally), Plenge, R. (Robert), McCarthy, M. (Mark), Hunkapiller, J. (Julie), Ehm, M. (Meg), Waterworth, D. (Dawn), Fox, C. (Caroline), Malarstig, A. (Anders), Klinger, K. (Kathy), Call, K. (Kathy), Mkel, T. (Tomi), Kaprio, J. (Jaakko), Virolainen, P. (Petri), Pulkki, K. (Kari), Kilpi, T. (Terhi), Perola, M. (Markus), Partanen, J. (Jukka), Pitkranta, A. (Anne), Kaarteenaho, R. (Riitta), Vainio, S. (Seppo), Savinainen, K. (Kimmo), Kosma, V.-M. (Veli-Matti), Kujala, U. (Urho), Tuovila, O. (Outi), Hendolin, M. (Minna), Pakkanen, R. (Raimo), Waring, J. (Jeff), Riley-Gillis, B. (Bridget), Liu, J. (Jimmy), Biswas, S. (Shameek), Diogo, D. (Dorothee), Marshall, C. (Catherine), Hu, X. (Xinli), Gossel, M. (Matthias), Schleutker, J. (Johanna), Arvas, M. (Mikko), Hinttala, R. (Reetta), Kettunen, J. (Johannes), Laaksonen, R. (Reijo), Mannermaa, A. (Arto), Paloneva, J. (Juha), Soininen, H. (Hilkka), Julkunen, V. (Valtteri), Remes, A. (Anne), Klviinen, R. (Reetta), Hiltunen, M. (Mikko), Peltola, J. (Jukka), Tienari, P. (Pentti), Rinne, J. (Juha), Ziemann, A. (Adam), Waring, J. (Jeffrey), Esmaeeli, S. (Sahar), Smaoui, N. (Nizar), Lehtonen, A. (Anne), Eaton, S. (Susan), Landenper, S. (Sanni), Michon, J. (John), Kerchner, G. (Geoff), Bowers, N. (Natalie), Teng, E. (Edmond), Eicher, J. (John), Mehta, V. (Vinay), Gormle, P. Y. (Padhraig Y.), Linden, K. (Kari), Whelan, C. (Christopher), Xu, F. (Fanli), Pulford, D. (David), Frkkil, M. (Martti), Pikkarainen, S. (Sampsa), Jussila, A. (Airi), Blomster, T. (Timo), Kiviniemi, M. (Mikko), Voutilainen, M. (Markku), Georgantas, B. (Bob), Heap, G. (Graham), Rahimov, F. (Fedik), Usiskin, K. (Keith), Maranville, J. (Joseph), Lu, T. (Tim), Oh, D. (Danny), Kalpala, K. (Kirsi), Miller, M. (Melissa), McCarthy, L. (Linda), Eklund, K. (Kari), Palomki, A. (Antti), Isomki, P. (Pia), Piri, L. (Laura), Kaipiainen-Seppnen, O. (Oili), Lertratanaku, A. (Apinya), Bing, D. C. (David Close Marla Hochfeld Nan), Gordillo, J. E. (Jorge Esparza), Mars, N. (Nina), Laitinen, T. (Tarja), Pelkonen, M. (Margit), Kauppi, P. (Paula), Kankaanranta, H. (Hannu), Harju, T. (Terttu), Greenberg, S. (Steven), Chen, H. (Hubert), Betts, J. (Jo), Ghosh, S. (Soumitra), Salomaa, V. (Veikko), Niiranen, T. (Teemu), Juonala, M. (Markus), Metsrinne, K. (Kaj), Khnen, M. (Mika), Junttila, J. (Juhani), Laakso, M. (Markku), Pihlajamki, J. (Jussi), Sinisalo, J. (Juha), Taskinen, M.-R. (Marja-Riitta), Tuomi, T. (Tiinamaija), Laukkanen, J. (Jari), Challis, B. (Ben), Peterson, A. (Andrew), Chu, A. (Audrey), Parkkinen, J. (Jaakko), Muslin, A. (Anthony), Joensuu, H. (Heikki), Meretoja, T. (Tuomo), Aaltonen, L. (Lauri), Auranen, A. (Annika), Karihtala, P. (Peeter), Kauppila, S. (Saila), Auvinen, P. (Pivi), Elenius, K. (Klaus), Popovic, R. (Relja), Schutzman, J. (Jennifer), Loboda, A. (Andrey), Chhibber, A. (Aparna), Lehtonen, H. (Heli), McDonough, S. (Stefan), Crohns, M. (Marika), Kulkarni, D. (Diptee), Kaarniranta, K. (Kai), Turunen, J. (Joni), Ollila, T. (Terhi), Seitsonen, S. (Sanna), Uusitalo, H. (Hannu), Aaltonen, V. (Vesa), Uusitalo-Jrvinen, H. (Hannele), Luodonp, M. (Marja), Hautala, N. (Nina), Strauss, E. (Erich), Chen, H. (Hao), Podgornaia, A. (Anna), Hoffman, J. (Joshua), Tasanen, K. (Kaisa), Huilaja, L. (Laura), Hannula-Jouppi, K. (Katariina), Salmi, T. (Teea), Peltonen, S. (Sirkku), Koulu, L. (Leena), Harvima, I. (Ilkka), Wu, Y. (Ying), Choy, D. (David), Jalanko, A. (Anu), Kajanne, R. (Risto), Lyhs, U. (Ulrike), Kaunisto, M. (Mari), Davis, J. W. (Justin Wade), Quarless, D. (Danjuma), Petrovski, S. (Slav), Chen, C.-Y. (Chia-Yen), Bronson, P. (Paola), Yang, R. (Robert), Chang, D. (Diana), Bhangale, T. (Tushar), Holzinger, E. (Emily), Wang, X. (Xulong), Chen, X. (Xing), Auro, K. (Kirsi), Wang, C. (Clarence), Xu, E. (Ethan), Auge, F. (Franck), Chatelain, C. (Clement), Kurki, M. (Mitja), Karjalainen, J. (Juha), Havulinna, A. (Aki), Palin, K. (Kimmo), Palta, P. (Priit), Parolo, P. D. (Pietro Della Briotta), Zhou, W. (Wei), Lemmel, S. (Susanna), Rivas, M. (Manuel), Harju, J. (Jarmo), Lehisto, A. (Arto), Ganna, A. (Andrea), Llorens, V. (Vincent), Karlsson, A. (Antti), Kristiansson, K. (Kati), Hyvrinen, K. (Kati), Ritari, J. (Jarmo), Wahlfors, T. (Tiina), Koskinen, M. (Miika), Pylkäs, K. (Katri), Kalaoja, M. (Marita), Karjalainen, M. (Minna), Mantere, T. (Tuomo), Kangasniemi, E. (Eeva), Heikkinen, S. (Sami), Laakkonen, E. (Eija), Kononen, J. (Juha), Loukola, A. (Anu), Laiho, P. (Pivi), Sistonen, T. (Tuuli), Kaiharju, E. (Essi), Laukkanen, M. (Markku), Jrvensivu, E. (Elina), Lhteenmki, S. (Sini), Mnnikk, L. (Lotta), Wong, R. (Regis), Mattsson, H. (Hannele), Hiekkalinna, T. (Tero), Jimnez, M. G. (Manuel Gonzlez), Donner, K. (Kati), Prn, K. (KaIle), Nunez-Fontarnau, J. (Javier), Kilpelinen, E. (Elina), Sipi, T. P. (Timo P.), Brein, G. (Georg), Dada, A. (Alexander), Awaisa, G. (Ghazal), Shcherban, A. (Anastasia), Sipil, T. (Tuomas), Laivuori, H. (Hannele), Kiiskinen, T. (Tuomo), Siirtola, H. (Harri), Tabuenca, J. G. (Javier Gracia), Kallio, L. (Lila), Soini, S. (Sirpa), Pitknen, K. (Kimmo), and Kuopio, T. (Teijo)

Abstract

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

Published
2021

37. [Public health and clinical care integration to improve immunization of children with special health care needs]

Author

L, Wang, Y H, Hao, and Yunhua, Bai

Subjects
Vaccination, Humans, Immunization, Health Facilities, Public Health, Child, Delivery of Health Care
Abstract

Immunization of children with special health care needs has always been one of the difficulties in community-level public health. In recent years the relevant consensus opinions on vaccination have been issued in China, but for a long time there is a lack of effective communication channels between disease prevention and clinical medical systems in China. Pediatricians play an unoccupied role in immunization, and community-level vaccinators face the difficulties including dilemma of disease identification, evidence-based evaluation and upward referral. These lead to the vaccination hesitancy, thus the multi-disciplinary management and three-level referral model of children with special health care needs should be improved urgently. It should strengthen the integration of public health and clinical care, with the active participation of pediatricians, to promote the immunization of children with special health care needs effectively.特殊健康状态儿童的疫苗接种一直是基层公共卫生工作的难点之一。尽管近年来各地出台了相关接种共识，但长期以来我国疾病预防和临床医疗两大系统之间缺乏相对有效的沟通渠道和交流机制，儿科医生在免疫接种工作中角色缺位，而基层接种人员面临疾病鉴别、科学评估和向上转诊等困境，导致疫苗接种犹豫问题凸显，特殊健康状态儿童的多学科管理和三级转诊模式亟待完善。需进一步加强医防融合，儿科医生积极参与，有效促进特殊健康状态儿童的疫苗接种。.

Published
2022

39. DeepDefrag:spatio-temporal defragmentation of time-varying virtual networks in computing power network based on model-assisted reinforcement learning

Author

Ma, H. (Huangxu), Zhang, J. (Jiawei), Gu, Z. (Zhiqun), Yu, H. (Hao), Taleb, T. (Tarik), Ji, Y. (Yuefeng), Ma, H. (Huangxu), Zhang, J. (Jiawei), Gu, Z. (Zhiqun), Yu, H. (Hao), Taleb, T. (Tarik), and Ji, Y. (Yuefeng)

Abstract

We propose DeepDefrag, a model-assisted reinforcement learning for spatio-temporal defragmentation of time-varying virtual networks in a cross-layer optical network testbed, which realizes the efficient utilization of computing nodes and lightpaths by co-optimizing scheduling and embedding with fragment matching, reduces >13.5% cost of computing power network.

Published
2022

40. Time-aware deterministic bandwidth allocation scheme for industrial TDM-PO

Author

Su, C. (Chen), Zhang, J. (Jiawei), Yu, H. (Hao), Taleb, T. (Tarik), Ji, Y. (Yuefeng), Su, C. (Chen), Zhang, J. (Jiawei), Yu, H. (Hao), Taleb, T. (Tarik), and Ji, Y. (Yuefeng)

Abstract

For Industrial Internet with TDM-PON, we propose a time-aware deterministic bandwidth allocation (TA-DBA) scheme that allocates proper transmission windows based on flow arrival time and cycle. Simulation results show that TA-DBA can achieve deterministic transmission, and the average bandwidth efficiency is 20.4% higher than FBA.

Published
2022

41. Suspect fault screening assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks

Author

Wang, R. (Ruikun), Zhang, J. (Jiawei), Yan, S. (Shuangyi), Zeng, C. (Chuidian), Yu, H. (Hao), Gu, Z. (Zhiqun), Zhang, B. (Bojun), Taleb, T. (Tarik), Ji, Y. (Yuefeng), Wang, R. (Ruikun), Zhang, J. (Jiawei), Yan, S. (Shuangyi), Zeng, C. (Chuidian), Yu, H. (Hao), Gu, Z. (Zhiqun), Zhang, B. (Bojun), Taleb, T. (Tarik), and Ji, Y. (Yuefeng)

Abstract

We propose a suspect fault screening assisted graph aggregation network for intra-/inter-node failure localization in ROADM-based optical networks, which is validated in both simulated topology and testbed. Results show that it achieves satisfactory accuracy under different percentage of OPMs and the number of service requests.

Published
2022

42. Deterministic latency/jitter-aware service function chaining over beyond 5G edge fabric

Author

Yu, H. (Hao), Taleb, T. (Tarik), Zhang, J. (Jiawei), Yu, H. (Hao), Taleb, T. (Tarik), and Zhang, J. (Jiawei)

Abstract

Deterministic Networking (DetNet) has recently attracted much attention. It aims at studying the deterministic bounded latency and low latency variation for time-sensitive applications (e.g., industrial automation). To improve the quality of service (QoS) guarantee and make the network management efficient, it is desirable for Internet Service Provider (ISP) to obtain an optimal service function chain (SFC) provision strategy while providing deterministic service performance for the time-sensitive applications. In this paper, we will study the deterministic SFC lifetime management problem in beyond 5G edge fabric with the objective of maximizing the overall profits and ensuring the deterministic latency and jitter of SFC requests. We first formulate this problem as a mathematical model with the maximal profits for ISP. Then, the novel Deterministic SFC Deployment algorithm (Det-SFCD) and SFC Adjustment algorithm (Det-SFCA) due to traffic load variation are proposed to efficiently solve the SFC lifetime management problem. Extensive simulation results show that our proposed algorithms can achieve better performance in terms of SFC request acceptance rates, overall profits and latency variation compared with the benchmark algorithm.

Published
2022

43. Attention-aided federated learning for dependency-aware collaborative task allocation in edge-assisted smart grid scenarios

Author

Wang, C. (Chenyang), Jia, B. (Bosen), Yu, H. (Hao), Chen, L. (Liandong), Cheng, K. (Kai), Wang, X. (Xiaofei), Wang, C. (Chenyang), Jia, B. (Bosen), Yu, H. (Hao), Chen, L. (Liandong), Cheng, K. (Kai), and Wang, X. (Xiaofei)

Abstract

With the significant improvement of the intelligent capabilities of smart devices accompanied by the increasingly high requirements. Edge computing is regarded as an effective solution to achieve rapid response by deploying applications and tasks close to users. However, many studies only consider complete offloading, or offload tasks to edge servers in any proportion when designing the allocation strategies, ignoring the dependencies between subtasks. To deal with the dynamic environment, some learning-based task allocation methods generally adopt a centralized training way, which leads to the excessive network transmission resource consumption, especially in the smart grid scenario. To tackle the aforementioned challenges, we investigate the collaborative task allocation (CTA) problem by jointly considering the difference between the benefit of the tasks execution under a certain allocation strategy and when all tasks are executed locally. In this paper, the objective is to maximize the system gain, and we propose an attention-aided federated learning algorithm to deal with the CTA problem, named AteFL, by learning a shared model and extracting the system context for better representing the network information. The simulation results also show the superiority of the proposed AteFL algorithm.

Published
2022

44. Deep reinforcement learning-based deterministic routing and scheduling for mixed-criticality flows

Author

Yu, H. (Hao), Taleb, T. (Tarik), Zhang, J. (Jiawei), Yu, H. (Hao), Taleb, T. (Tarik), and Zhang, J. (Jiawei)

Abstract

Deterministic networking has recently drawn much attention by investigating deterministic flow scheduling. Combined with artificial intelligent (AI) technologies, it can be leveraged as a promising network technology for facilitating automated network configuration in the Industrial Internet of Things (IIoT). However, the stricter requirements of the IIoT have posed significant challenges, that is, deterministic and bounded latency for time-critical applications. This article incorporates deep reinforcement learning (DRL) in cycle specified queuing and forwarding and proposes a DRL-based deterministic flow scheduler (Deep-DFS) to solve the deterministic flow routing and scheduling problem. Novel delay aware network representations, action masking and criticality aware reward function design are proposed to make deep-DFS more scalable and efficient. Simulation experiments are conducted to evaluate the performances of deep-DFS, and the results show that deep-DFS can schedule more flows than the other benchmark methods (heuristic- and AI-based methods).

Published
2022

46. cisRED: a database system for genome-scale computational discovery of regulatory elements.

Author

Gordon Robertson, Misha Bilenky, Keven Lin, An He, W. Yuen, M. Dagpinar, Richard Varhol, Kevin Teague, Obi L. Griffith, X. Zhang, Y. Pan, Maik Hassel, Monica C. Sleumer, W. Pan, Erin Pleasance, M. Chuang, H. Hao, Yvonne Y. Li, Neil Robertson 0004, C. Fjell, Bernard Li, Stephen B. Montgomery, Tamara Astakhova, Jianjun Zhou, Jörg Sander 0001, Asim S. Siddiqui, and Steven J. M. Jones

Published
2006
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50. Lessons learned from public participation in hydrologic engineering projects

Author

Alex K. Manda, J. R. Etheridge, H. Hao, Cynthia A. Grace-McCaskey, and Thomas R. Allen

Subjects
Sea level rise, Political science, Public participation, 0208 environmental biotechnology, Citizen science, 02 engineering and technology, Environmental planning, 020801 environmental engineering, Water Science and Technology
Abstract

Public participation in engineering projects has been minimal to date, whereas it is growing in other fields. This paper assesses the lessons learned from public participation in two hydrologic eng...

Published
2019
Full Text
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