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4. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J Castro, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, and van de Walle, Axel

Subjects
Bioengineering, Good Health and Well Being, COVID-19, Data Accuracy, Forecasting, Humans, Pandemics, Probability, Public Health, United States, forecasting, ensemble forecast, model evaluation
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022

8. Targeted PERK inhibition with biomimetic nanoclusters confers preventative and interventional benefits to elastase-induced abdominal aortic aneurysms

Author

Yodsanit, Nisakorn, Shirasu, Takuro, Huang, Yitao, Yin, Li, Islam, Zain Husain, Gregg, Alexander Christopher, Riccio, Alessandra Marie, Tang, Runze, Kent, Eric William, Wang, Yuyuan, Xie, Ruosen, Zhao, Yi, Ye, Mingzhou, Zhu, Jingcheng, Huang, Yi, Hoyt, Nicholas, Zhang, Mengxue, Hossack, John A., Salmon, Morgan, Kent, K. Craig, Guo, Lian-Wang, Gong, Shaoqin, and Wang, Bowen

Published
2023
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13. The Influence of Rock and Natural Weak Plane Properties on the Vertical Propagation of Hydraulic Fractures.

Author

Yang, Xuefeng, Chang, Cheng, Cheng, Qiuyang, Xie, Weiyang, Hu, Haoran, Li, Yongming, Huang, Yitao, and Peng, Yu

Abstract

China has abundant shale gas resources with good exploration value and development potential, making it a recent hotspot for exploration and development. It is widely agreed that large-scale hydraulic fracturing is essential for reservoir enhancement in shale formations. However, the evolution of fractures during hydraulic fracturing is highly complex, necessitating research on the influence of various factors on the vertical propagation of hydraulic fractures. Based on geological and engineering parameters from the Luzhou block in southern Sichuan, this study employed the finite element method (FEM) and the cohesive element method to establish a coupled fluid-solid model for the vertical propagation of hydraulic fractures. Numerical simulations were conducted to investigate the interaction between hydraulic fractures and natural weak planes, clarifying the mechanisms involved. This study elucidates how different rock and natural weak plane properties affect the vertical propagation of hydraulic fractures and draws diagrams illustrating these interactions. The research indicated three fracture distribution patterns after the intersection of hydraulic fractures with natural weak planes: passive fractures, 'I'-shaped fractures, and crossing fractures. The main fractures in these patterns exhibit initial damage and damage evolution characterized by tensile failure. Specifically, in passive fractures, the initial damage and damage evolution of natural weak planes manifest as shear failure. In 'I'-shaped fractures, the initial damage in natural weak planes is characterized by shear failure, with damage evolution showing tensile failure. Crossing fractures show minimal damage in the weak planes. Under conditions of high natural weak plane cohesive strength, high Young's modulus, low interlayer rock cohesive strength, high vertical stress difference, low interlayer stress difference, and high intersection angles, crossing fractures tend to form. Conversely, conditions of low natural weak plane cohesive strength, low Young's modulus, high interlayer rock cohesive strength, low vertical stress difference, high interlayer stress difference, and low intersection angles favor the formation of 'I'-shaped fractures. Passive fractures form under conditions of low natural weak plane cohesive strength and high vertical stress difference. This study found that Poisson's ratio has a minimal effect on the vertical expansion of hydraulic fractures under the studied conditions, with natural weak plane strength being the primary control factor for fracture patterns. These findings enhance the theoretical foundation for the vertical propagation of hydraulic fractures in deep shale formations, facilitating the development and implementation of strategies for enhancing production in shale reservoirs with natural weak planes and better optimizing production in different types of shale reservoirs. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Numerical simulation study on evolution law of three-dimensional fracture network in unconventional reservoirs.

Author

Chen, Xiyu, Huang, Yitao, Li, Yongming, Shen, Cheng, Zhou, Zaile, and Zhou, Dawei

Subjects
HYDRAULIC fracturing, SHALE gas reservoirs, FINITE volume method, BOUNDARY element methods, LEGAL education, GAS condensate reservoirs, SHALE gas, GAS reservoirs
Abstract

It has become a consensus that large-scale hydraulic fracturing is adopted to achieve the stimulation of unconventional oil and gas reservoir. The complex fracture network formed by fracturing is closely related to the effect of reservoir stimulation, which has extremely complicated evolution process. Therefore, it is necessary to study the evolution law of fracture network in large-scale hydraulic fracturing of unconventional reservoirs. In this article, the geological engineering parameters of horizontal well H in shale gas reservoir in southern Sichuan are taken as an example, a three-dimensional fracture network expansion model is established based on the boundary element method and finite volume method, and the simulation of the complex fracture network in a whole well section is carried out to analyze the evolution law of reservoir fracture network under different geological and engineering parameters. The results show that the horizontal stress field distribution has a significant effect on fracture geometric form. Hydraulic fractures in reservoirs with larger horizontal stress difference have stronger directivity, while the horizontal wellbore tends to obtain better reservoir stimulation results when it is parallel to the minimum horizontal principal stress setting. The conjugated natural fracture developed in the reservoir inhibits the hydraulic expansion fractures in both directions. Although it increases the complexity of the fractures, it is not necessarily conducive to improving the reservoir stimulation effectiveness. The lower the strength of natural fracture is, the more complex the fracture geometric form becomes, and the smaller the stimulated reservoir volume is. Correspondingly, the higher the strength of natural fracture is, the simpler the fracture geometric form becomes, and the larger the stimulated reservoir volume is. Suitable fracturing construction displacement can not only contribute to form a more complex fracture distribution, but also help to obtain a larger stimulated reservoir volume. The optimal construction displacement ranges from 10 to 14 m3/min. Low viscosity fracturing fluids are suitable for the formation of long-narrow fractures and able to connect with the remote reservoir and form complex fracture networks. Lower viscosity fluids can be used to achieve better reservoir stimulation effectiveness when sand-carrying capacity is met. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Abstract 631: Neointima Abating Yet Endothelium Preserving-An Adventitia-localized Nanoformulation To Inhibit The Epigenetic Writer Dot1L

Author

Li, Jing, primary, Shirasu, Takuro, additional, Yodsanit, Nisakorn, additional, Huang, Yitao, additional, Xie, Xiujie, additional, Tang, Runze, additional, Wang, Qingwei, additional, Zhang, Mengxue, additional, Urabe, Go, additional, Webb, Amy, additional, Wang, Yuyuan, additional, Wang, Xiuxiu, additional, Xie, Ruosen, additional, Wang, Bowen, additional, Kent, K. Craig, additional, Gong, Shaoqin, additional, and Guo, Lian-wang, additional

Published
2023
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25. Abstract 605: Mechanistic And Therapeutic Implications Of A Non-canonical Amino Acid Starvation Response In Restenosis Post Angioplasty

Author

Yin, Li, primary, Shirasu, Takuro, additional, Islam, Zain, additional, Kent, Eric W, additional, Hoyt, Nicholas, additional, Le, Quang, additional, Huang, Yitao, additional, Zhang, Mengxue, additional, Patel, Paranjay, additional, Ahn, JungEun, additional, Lian-Wang, Guo, additional, and Wang, Bowen, additional

Published
2023
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30. Study on the development options of tight sandstone oil reservoirs and their influencing factors

Author

Li, Yongming, Huang, Yitao, Ma, Hanwei, Chang, Cheng, and Xie, Weiyang

Subjects
Economics and Econometrics, Fuel Technology, Renewable Energy, Sustainability and the Environment, Energy Engineering and Power Technology
Abstract

The research area of tight sandstone oil reservoirs was selected, a numerical model of the oil reservoir was developed, and a study of the development options and influencing factors was carried out to analyze the influence of different development methods, physical and engineering parameters on the development dynamics. Study shows that the two main factors limiting the efficient development of tight sandstone reservoirs are reservoir properties and formation energy. Fractured horizontal well injection huff and puff development can effectively improve reservoir physical properties and timely replenish formation energy, which is suitable for the development of such oil reservoirs. In dense sandstone reservoirs, its impact on production capacity is also relatively small when the permeability ratio is small. Due to both gravity and reservoir physical properties, the permeability ratio increases, the cumulative oil production of positive rhythm reservoirs decreases and that of reverse rhythm reservoirs increases, and the location of high-quality reservoirs in the upper part of producing wells is conducive to increasing the final recovery rate. A lower oil to water viscosity ratio can significantly increase the swept volume and improve development effect. Hydrophilic reservoirs can reduce the injection pressure and increase the spread range, effectively improving the problem of inability to inject, and improving reservoir hydrophilicity through surface activators can increase reservoir recovery. The water injection rate determines the recovery rate of formation energy. Generally, the faster the rate, the higher the cumulative oil production. Therefore, the rate of water injection should be increased as much as possible, taking into account construction conditions and economic evaluation. Additionally, the effect of water injection on the development effect is different at different stages, so the appropriate timing of water injection is very important to the water injection huff and puff development effect, and the use of early water injection in this research area is not conducive. Soaking can promote pressure and fluid redistribution and improve water injection huff and puff development effect, but soaking for a long time can lead to reservoir contamination and reduce crude oil production, so the preferred time for a soaking is about 20 days.

Published
2022
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31. The United States COVID-19 Forecast Hub dataset

Author

US COVID-19 Forecast Hub Consortium, Cramer, Estee Y., Huang, Yuxin, Wang, Yijin, Ray, Evan L., Cornell, Matthew, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, House, Katie, Jayawardena, Dasuni, Kanji, Abdul Hannan, Khandelwal, Ayush, Le, Khoa, Mody, Vidhi, Mody, Vrushti, Niemi, Jarad, Stark, Ariane, Shah, Apurv, Wattanchit, Nutcha, Zorn, Martha W., Reich, Nicholas G., Gneiting, Tilmann, Mühlemann, Anja, Gu, Youyang, Chen, Yixian, Chintanippu, Krishna, Jivane, Viresh, Khurana, Ankita, Kumar, Ajay, Lakhani, Anshul, Mehrotra, Prakhar, Pasumarty, Sujitha, Shrivastav, Monika, You, Jialu, Bannur, Nayana, Deva, Ayush, Jain, Sansiddh, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Adiga, Aniruddha, Hurt, Benjamin, Lewis, Bryan, Marathe, Madhav, Peddireddy, Akhil Sai, Porebski, Przemyslaw, Venkatramanan, Srinivasan, Wang, Lijing, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G., Tec, Mauricio, Woody, Spencer, Srivastava, Ajitesh, Xu, Tianjian, Cegan, Jeffrey C., Dettwiller, Ian D., England, William P., Farthing, Matthew W., George, Glover E., Hunter, Robert H., Lafferty, Brandon, Linkov, Igor, Mayo, Michael L., Parno, Matthew D., Rowland, Michael A., Trump, Benjamin D., Chen, Samuel, Faraone, Stephen V., Hess, Jonathan, Morley, Christopher P., Salekin, Asif, Wang, Dongliang, Zhang-James, Yanli, Baer, Thomas M., Corsetti, Sabrina M., Eisenberg, Marisa C., Falb, Karl, Huang, Yitao, Martin, Emily T., McCauley, Ella, Myers, Robert L., Schwarz, Tom, Gibson, Graham Casey, Sheldon, Daniel, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yu, Rose, Jin, Xiaoyong, Wang, Yu-Xiang, Yan, Xifeng, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Chen, Jinghui, Gu, Quanquan, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Chattopadhyay, Ishanu, Huang, Yi, Lu, Guoqing, Pfeiffer, Ruth, Sumner, Timothy, Wang, Dongdong, Wang, Liqiang, Zhang, Shunpu, Zou, Zihang, Biegel, Hannah, Lega, Joceline, Hussain, Fazle, Khan, Zeina, Van Bussel, Frank, McConnell, Steve, Guertin, Stephanie L., Hulme-Lowe, Christopher, Nagraj, V. P., Turner, Stephen D., Bejar, Benjamín, Choirat, Christine, Flahault, Antoine, Krymova, Ekaterina, Lee, Gavin, Manetti, Elisa, Namigai, Kristen, Obozinski, Guillaume, Sun, Tao, Thanou, Dorina, Ban, Xuegang, Shi, Yunfeng, Walraven, Robert, Hong, Qi-Jun, Van De Walle, Axel, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Turtle, James, Cao, Duy, Galasso, Joseph, Cho, Jae H., Jo, Areum, DesRoches, David, Forli, Pedro, Hamory, Bruce, Koyluoglu, Ugur, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Ivy, Julie S., Mayorga, Maria E., Mele, Jessica, Rosenstrom, Erik T., Swann, Julie L., Kraus, Andrea, Kraus, David, Bian, Jiang, Cao, Wei, Gao, Zhifeng, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Chinazzi, Matteo, Vespignani, Alessandro, Xiong, Xinyue, Davis, Jessica T., Mu, Kunpeng, Piontti, Ana Pastore Y, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Zheng, Andrew, Lami, Omar Skali, Bennouna, Amine, Ndong, David Nze, Perakis, Georgia, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Weisberg, Shane, Jadbabaie, Ali, Sarker, Arnab, Shah, Devavrat, Celi, Leo A., Penna, Nicolas D., Sundar, Saketh, Berlin, Abraham, Gandhi, Parth D., McAndrew, Thomas, Piriya, Matthew, Chen, Ye, Hlavacek, William, Lin, Yen Ting, Mallela, Abhishek, Miller, Ely, Neumann, Jacob, Posner, Richard, Wolfinger, Russ, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Osthus, Dave, Wolffram, Daniel, Karlen, Dean, Panaggio, Mark J., Kinsey, Matt, Mullany, Luke C., Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Brenner, Michael, Coram, Marc, Edwards, Jessie K., Joshi, Keya, Klein, Ellen, Hulse, Juan Dent, Grantz, Kyra H., Hill, Alison L., Kaminsky, Kathryn, Kaminsky, Joshua, Keegan, Lindsay T., Lauer, Stephen A., Lee, Elizabeth C., Lemaitre, Joseph C., Lessler, Justin, Meredith, Hannah R., Perez-Saez, Javier, Shah, Sam, Smith, Claire P., Truelove, Shaun A., Wills, Josh, Gardner, Lauren, Marshall, Maximilian, Nixon, Kristen, Burant, John C., Budzinski, Jozef, Chiang, Wen-Hao, Mohler, George, Gao, Junyi, Glass, Lucas, Qian, Cheng, Romberg, Justin, Sharma, Rakshith, Spaeder, Jeffrey, Sun, Jimeng, Xiao, Cao, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Yueying, Wang, Guannan, Wang, Lily, Yu, Shan, Jain, Chaman, Bhatia, Sangeeta, Nouvellet, Pierre, Barber, Ryan, Gaikedu, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris, Pigott, David, Reiner, Robert C., Baccam, Prasith, Gurung, Heidi L., Stage, Steven A., Suchoski, Bradley T., Fong, Chung-Yan, Yeung, Dit-Yan, Adhikari, Bijaya, Cui, Jiaming, Prakash, B. Aditya, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Asplund, John, Baxter, Arden, Keskinocak, Pinar, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan O., Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long T., Li, Chun-Liang, Pfister, Tomas, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Wilson, Daniel, Belov, Artur A., Chow, Carson C., Gerkin, Richard C., Yogurtcu, Osman N., Ibrahim, Mark, Lacroix, Timothee, Le, Matthew, Liao, Jason, Nickel, Maximilian, Sagun, Levent, Abbott, Sam, Bosse, Nikos I., Funk, Sebastian, Hellewell, Joel, Meakin, Sophie R., Sherratt, Katharine, Kalantari, Rahi, Zhou, Mingyuan, Karimzadeh, Morteza, Lucas, Benjamin, Ngo, Thoai, Zoraghein, Hamidreza, Vahedi, Behzad, Wang, Zhongying, Pei, Sen, Shaman, Jeffrey, Yamana, Teresa K., Bertsimas, Dimitris, Li, Michael L., Soni, Saksham, Bouardi, Hamza Tazi, Adee, Madeline, Ayer, Turgay, Chhatwal, Jagpreet, Dalgic, Ozden O., Ladd, Mary A., Linas, Benjamin P., Mueller, Peter, Xiao, Jade, Bosch, Jurgen, Wilson, Austin, Zimmerman, Peter, Wang, Qinxia, Wang, Yuanjia, Xie, Shanghong, Zeng, Donglin, Bien, Jacob, Brooks, Logan, Green, Alden, Hu, Addison J., Jahja, Maria, McDonald, Daniel, Narasimhan, Balasubramanian, Politsch, Collin, Rajanala, Samyak, Rumack, Aaron, Simon, Noah, Tibshirani, Ryan J., Tibshirani, Rob, Ventura, Valerie, Wasserman, Larry, Drake, John M., O’Dea, Eamon B., Abu-Mostafa, Yaser, Bathwal, Rahil, Chang, Nicholas A., Chitta, Pavan, Erickson, Anne, Goel, Sumit, Gowda, Jethin, Jin, Qixuan, Jo, HyeongChan, Kim, Juhyun, Kulkarni, Pranav, Lushtak, Samuel M., Mann, Ethan, Popken, Max, Soohoo, Connor, Tirumala, Kushal, Tseng, Albert, Varadarajan, Vignesh, Vytheeswaran, Jagath, Wang, Christopher, Yeluri, Akshay, Yurk, Dominic, Zhang, Michael, Zlokapa, Alexander, Pagano, Robert, Jain, Chandini, Tomar, Vishal, Ho, Lam, Huynh, Huong, Tran, Quoc, Lopez, Velma K., Walker, Jo W., Slayton, Rachel B., Johansson, Michael A., and Biggerstaff, Matthew

Subjects
ddc:510, Mathematics
Abstract

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Published
2022

34. Inverse Design of Digital Materials Using Corrected Generative Deep Neural Network and Generative Deep Convolutional Neural Network.

Author

Wan, Zhi, Chang, Ze, Xu, Yading, Huang, Yitao, and Šavija, Branko

Subjects
CONVOLUTIONAL neural networks, TENSILE strength, MACHINE learning, ELASTIC modulus, STRUCTURAL design
Abstract

Generative networks are effective tools for digital materials (DM) inverse design. However, the optimization performance of generative networks is restricted by the increasing discrepancy between the optimized input and the prescribed input domain as the design loop increases. Herein, a correction technique is incorporated into generative deep neural network (GDNN) and generative deep convolutional neural network (GDCNN). The correction is performed by pulling the machine learning (ML)‐optimized inputs back to the prescribed domain at certain interval during the optimization process instead of only postprocessing at the end. A DM system with two phases, i.e., the matrix phase and the pore phase, is used for the structural design. The datasets are produced using a numerical model and describe the relationship between the material structure and the elastic modulus and tensile strength. The results show that the optimization effectiveness of corrected GDNN/GDCNN significantly improves given the fact that more structures converge to the best structures and fewer nonrepetitive structures are left after optimization, which helps to search for the best structures and decreases the computational burden when verifying the ML‐recommended structures. The corrected GDNN and GDCNN also manage to find structures with higher tensile strength in the new inverse design. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Experiments on nitrogen assisted gravity drainage in fractured-vuggy reservoirs

Author

Jing Wang, Zemin Ji, Yulong Pu, Huang Yitao, Yishuang Wang, and Huiqing Liu

Subjects
Gravity (chemistry), Horizontal wells, Petroleum engineering, 0211 other engineering and technologies, Oil distribution, Energy Engineering and Power Technology, chemistry.chemical_element, Geology, 02 engineering and technology, 010502 geochemistry & geophysics, Geotechnical Engineering and Engineering Geology, 01 natural sciences, Nitrogen, Bottom water, Gravity drainage, chemistry, Geochemistry and Petrology, lcsh:TP690-692.5, Fracture (geology), Economic Geology, 021108 energy, Displacement (fluid), lcsh:Petroleum refining. Petroleum products, 0105 earth and related environmental sciences
Abstract

Visual models of fractured-vuggy reservoirs were designed and manufactured to conduct experiments of nitrogen assisted gravity drainage (NAGD). The impacts of flooding pattern, gas injection rate, well type, and displacement direction (vertical or horizontal) on development performances and remaining oil distribution were studied. The results show that during NAGD, the sweep scope is decided by the connections between producer and reservoir, and the local sweep efficiency is decided by fracture-vuggy configuration. The homogenous fractured reservoir has higher oil recovery, and the bigger the aperture of fracture is, the higher the recovery. The main regions of remaining oil due to poor connectivity and gas-oil gravity difference include blind fractures and vugs below the connected fractures, the bottom of vugs, and the narrow and low-angle fractures. The accumulation of remaining oil in the bottom of reservoir is easily formed and controlled by the connections between producers and reservoir. The higher the gas injection rate and the stronger the fracture heterogeneity, the earlier the gas channeling and the lower the oil recovery of the producer will be. Horizontal wells have the best development effect, so horizontal well can be applied in fractured-vuggy reservoirs without bottom water. Producers should be preferentially drilled at low structural position. Gas channeling firstly occurs in the producer at high structural position, and it should be shut in timely to improve the utilization of injected gas. Key Words: fractured-vuggy reservoir, NAGD, injection-production pattern, development effect, remaining oil distribution

Published
2019

39. Experiments on the influences of well pattern on water flooding characteristics of dissolution vug-cave reservoir

Author

Jing Wang, Jing Zhang, Huiqing Liu, Songqing Zheng, Wei Zhao, Zhijiang Kang, and Huang Yitao

Subjects
020209 energy, Energy Engineering and Power Technology, Injection rate, Soil science, 02 engineering and technology, 010502 geochemistry & geophysics, 01 natural sciences, Cave, Geochemistry and Petrology, 0202 electrical engineering, electronic engineering, information engineering, lcsh:Petroleum refining. Petroleum products, Dissolution, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, Geology, Water flooding, Geotechnical Engineering and Engineering Geology, stomatognathic diseases, Similarity criterion, lcsh:TP690-692.5, Oil production, Fracture (geology), Economic Geology, Stage (hydrology)
Abstract

Based on the similarity criterion, volcanic rock samples were taken from outcrops to make experimental models. Water flooding experiments of five-spot well pattern, nine-spot well pattern, five-spot to nine-spot well pattern, the relationship between relative well and fracture positions, and injection rate in dissolution vug-cave reservoirs with/without fractures were carried out to analyze variation regularities of development indexes, find out development characteristics of water flooding with different well patterns and sort out the optimal water flooding development mode. For dissolution vug-cave reservoirs without fractures, five-spot well pattern waterflooding has very small sweeping area, serious water channeling and low oil recovery. When the well pattern was adjusted from five-spot to nine-spot well pattern, oil recovery could be largely improved, but the corner well far from the injector is little affected. In dissolution vug-cave reservoirs with fractures, when the injector and producer are not connected by fractures, the fractures could effectively connect the poorly linked vugs to improve the development effect of water flooding. Whether there are fractures or not in dissolution vug-cave reservoirs, the development effect of nine-spot well-pattern is much better than that of five-spot well pattern and five-spot to nine-spot well pattern, this is more evident when there are fractures, and the edge well has better development indexes than corner well. At the high-water cut stage of water flooding with nine-spot well pattern, the oil recovery can be further improved with staggered line-drive pattern by converting the corner well into injection well. It is helpful to increase the oil production of corner well of nine-spot well pattern by increasing the injection rate, and improve ultimate oil recovery, but the water-free production period would be greatly shortened and water-free recovery would decrease. Key words: similarity criterion, dissolution vug-cave reservoir, water flooding, injection-production well pattern, well placement, injection rate, development index, oil recovery

Published
2018
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40. The BD2 domain of BRD4 is a determinant in EndoMT and vein graft neointima formation: Supplemental figures and tables

Author

Zhang, Mengxue, Wang, Bowen, Urabe, Go, Huang, Yitao, Plutzky, Jorge, Kent, K. Craig, and Guo, Lian-Wang

Abstract

Background: Vein-graft bypass is commonly performed to overcome atherosclerosis but is limited by high failure rates, principally due to neointimal wall thickening. Recent studies reveal that endothelial-mesenchymal transition (EndoMT) is critical for vein-graft neointima formation. BETs are a family of Bromo/ExtraTerminal domains-containing epigenetic reader proteins (BRD2, BRD3, BRD4). They bind acetylated histones through their unique tandem bromodomains (BD1, BD2), facilitating transcriptional complex formation and cell-state transitions. The role for BETs, including individual BRDs and their unique BDs, is not well understood in EndoMT and neointimal formation. Methods and Results: Repression of BRD4 expression abrogated TGFbeta1-induced EndoMT, with greater effects than BRD2 or BRD3 knockdown. An inhibitor selective for BD2 in all BETs, but not that for BD1, blocked EndoMT. Moreover, expression of a dominant-negative BRD4-specific BD2 fully abolished EndoMT. Concordantly, BRD4 knockdown repressed TGFbeta1-stimulated increase of ZEB1 protein, a transcription factor integral in EndoMT. In vivo, lentiviral gene transfer of either BRD4 shRNA or dominant negative BRD4-specific BD2 mitigated neointimal development in rat jugular veins grafted to carotid arteries. Conclusions: Our data reveal the BD2 domain of BRD4 as a determinant driving EndoMT in vitro and neointimal formation in vivo. These findings provide new insight into BET biology, while offering prospects of specific BET domain targeting as an approach to limiting neointima and extending vein graft patency.

Published
2019
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44. Application status and research progress of CO2fracturing fluid in petroleum engineering: A brief review

Author

Zhao, Jinzhou, Wu, Tong, Pu, Wanfen, Daijun, Du, Chen, Qingyuan, Chen, Bowen, Li, Jintao, and Huang, Yitao

Abstract

This paper comprehensively reviews the application and research progress of CO2fracturing fluids in China, highlights the existing issues and puts forward suggestions for future development. Three types of fracturing fluid systems containing CO2, namely, CO2dry fracturing fluid, CO2energized fracturing fluid, and CO2foam fracturing fluid, are categorized based on the mass ratio and process difference between CO2, water, and treatment agents. Field applications in China reveal several problem areas to be resolved: (1) The application scope of CO2fracturing fluids is restricted to depleted reservoirs, re-fracturing of old wells, and medium-deep reservoirs with low formation pressure coefficients. (2) Different types of CO2fracturing fluids require different processes and ground supporting equipment. (3) Optimization of CO2compatibility, functionality, temperature and salt tolerance, as well as the cost of treatment agents is necessitated. (4) Existing CO2fracturing fluid system fail to perform well with low friction, low filtration, and high sand-carrying capacity. (5) There lacks a targeted industry standard for evaluation of performance of CO2fracturing fluid system and treatment agents. Therefore, to meet the goals of CCUS-EOR, CCUS-EGR, or integration of fracturing, displacement and burial by CO2, efforts should be made in the aspects that followed, including, in-depth investigation of the mechanism of CO2fracturing fluids, the adaptability and compatibility between existing equipment, different CO2fracturing fluid systems and processes, and construction of treatment agents, low-density proppants and high-performance systems of recyclability and industrial-grade. In addition, optimization of CO2fracturing fluid system based fracturing design is also crucial taking such related factors as overall reservoir geological conditions, petrophysical properties, CO2transportation, and well site layout into consideration.

Published
2023
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45. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H. House, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F. Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, Glover E. George, Jeffrey C. Cegan, Ian D. Dettwiller, William P. England, Matthew W. Farthing, Robert H. Hunter, Brandon Lafferty, Igor Linkov, Michael L. Mayo, Matthew D. Parno, Michael A. Rowland, Benjamin D. Trump, Yanli Zhang-James, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Sabrina M. Corsetti, Thomas M. Baer, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, V. P. Nagraj, Stephanie L. Guertin, Christopher Hulme-Lowe, Stephen D. Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A. Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A. Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C. Lee, Juan Dent, Kyra H. Grantz, Alison L. Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T. Keegan, Stephen A. Lauer, Joseph C. Lemaitre, Justin Lessler, Hannah R. Meredith, Javier Perez-Saez, Sam Shah, Claire P. Smith, Shaun A. Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C. Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris Murray, David Pigott, Heidi L. Gurung, Prasith Baccam, Steven A. Stage, Bradley T. Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O. Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T. Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I. Bosse, Sebastian Funk, Joel Hellewell, Sophie R. Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K. Yamana, Sen Pei, Jeffrey Shaman, Michael L. Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J. Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J. Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B. O’Dea, John M. Drake, Robert Pagano, Quoc T. Tran, Lam Si Tung Ho, Huong Huynh, Jo W. Walker, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff, Nicholas G. Reich, Cramer, Estee Y [0000-0003-1373-3177], Ray, Evan L [0000-0003-4035-0243], Lopez, Velma K [0000-0003-2926-4010], Bracher, Johannes [0000-0002-3777-1410], Gneiting, Tilmann [0000-0001-9397-3271], Niemi, Jarad [0000-0002-5079-158X], White, Jerome [0000-0003-4148-8834], Woody, Spencer [0000-0002-2882-3450], Fox, Spencer [0000-0003-1969-3778], Gaither, Kelly [0000-0002-4272-175X], Meyers, Lauren Ancel [0000-0002-5828-8874], Tec, Mauricio [0000-0002-1853-5842], George, Glover E [0000-0003-4779-8702], Cegan, Jeffrey C [0000-0002-3065-3403], Hunter, Robert H [0000-0002-2382-7938], Lafferty, Brandon [0000-0002-2618-3787], Mayo, Michael L [0000-0001-9014-1859], Rowland, Michael A [0000-0002-6759-8225], Chen, Samuel [0000-0002-1070-9801], Salekin, Asif [0000-0002-0807-8967], Corsetti, Sabrina M [0000-0003-2216-2492], Falb, Karl [0000-0002-3465-3988], Huang, Yitao [0000-0001-7846-2174], Sheldon, Daniel [0000-0002-4257-2432], Guo, Lihong [0000-0003-4804-4005], Gu, Quanquan [0000-0001-9830-793X], Xu, Pan [0000-0002-2559-8622], Lega, Joceline [0000-0003-2064-229X], McConnell, Steve [0000-0002-0294-3737], Turner, Stephen D [0000-0001-9140-9028], Shi, Yunfeng [0000-0003-1700-6049], Walraven, Robert [0000-0002-5755-4325], van de Walle, Axel [0000-0002-3415-1494], Turtle, James A [0000-0003-0735-7769], Ben-Nun, Michal [0000-0002-9164-0008], Riley, Steven [0000-0001-7904-4804], Koyluoglu, Ugur [0000-0002-6286-351X], Cavany, Sean [0000-0002-2559-797X], España, Guido [0000-0002-9915-8056], Moore, Sean [0000-0001-9062-6100], Oidtman, Rachel [0000-0003-1773-9533], Perkins, Alex [0000-0002-7518-4014], Kraus, David [0000-0003-4376-3932], Cao, Wei [0000-0001-5640-0917], Lavista Ferres, Juan [0000-0002-9654-3178], Vespignani, Alessandro [0000-0003-3419-4205], Sinha, Deeksha [0000-0002-9788-728X], Perakis, Georgia [0000-0002-0888-9030], Bennouna, Mohammed Amine [0000-0002-9123-8588], Spantidakis, Ioannis [0000-0002-5149-6320], Tsiourvas, Asterios [0000-0002-2979-6300], Sarker, Arnab [0000-0003-1680-9421], Jadbabaie, Ali [0000-0003-1122-3069], Shah, Devavrat [0000-0003-0737-3259], Celi, Leo A [0000-0001-6712-6626], Osthus, Dave [0000-0002-4681-091X], Fairchild, Geoffrey [0000-0001-5500-8120], Mullany, Luke C [0000-0003-4668-9803], Rainwater-Lovett, Kaitlin [0000-0002-8707-7339], Lee, Elizabeth C [0000-0002-4156-9637], Dent, Juan [0000-0003-3154-0731], Hill, Alison L [0000-0002-6583-3623], Keegan, Lindsay T [0000-0002-8526-3007], Lemaitre, Joseph C [0000-0002-2677-6574], Truelove, Shaun A [0000-0003-0538-0607], Wills, Josh [0000-0001-7285-9349], Gao, Lei [0000-0002-4707-0933], Gu, Zhiling [0000-0002-8052-7608], Yu, Shan [0000-0002-0271-5726], Hay, Simon I [0000-0002-0611-7272], Murray, Chris [0000-0002-4930-9450], Stage, Steven A [0000-0001-5361-6464], Prakash, B Aditya [0000-0002-3252-455X], Rodríguez, Alexander [0000-0002-4313-9913], Xie, Jiajia [0000-0001-6530-2489], Keskinocak, Pinar [0000-0003-2686-546X], Baxter, Arden [0000-0002-6345-2229], Oruc, Buse Eylul [0000-0003-2431-3864], Sinha, Rajarishi [0000-0001-9157-674X], Yoder, Nate [0000-0003-4153-4722], Zhang, Leyou [0000-0002-2454-0082], Funk, Sebastian [0000-0002-2842-3406], Meakin, Sophie R [0000-0002-6385-2652], Sherratt, Katharine [0000-0003-2049-3423], Yamana, Teresa K [0000-0001-8349-3151], Pei, Sen [0000-0002-7072-2995], Shaman, Jeffrey [0000-0002-7216-7809], Li, Michael L [0000-0002-2456-4834], Bertsimas, Dimitris [0000-0002-1985-1003], Skali Lami, Omar [0000-0002-8208-3035], Soni, Saksham [0000-0002-8898-5726], Tazi Bouardi, Hamza [0000-0002-7871-325X], Wang, Yuanjia [0000-0002-1510-3315], McDonald, Daniel [0000-0002-0443-4282], Politsch, Collin [0000-0003-3727-9167], Rajanala, Samyak [0000-0002-5791-3789], Rumack, Aaron [0000-0002-9181-1794], Tibshirani, Ryan J [0000-0002-2158-8304], Drake, John M [0000-0003-4646-1235], Ho, Lam Si Tung [0000-0002-0453-8444], Slayton, Rachel B [0000-0003-4699-8040], Johansson, Michael A [0000-0002-5090-7722], Biggerstaff, Matthew [0000-0001-5108-8311], Reich, Nicholas G [0000-0003-3503-9899], and Apollo - University of Cambridge Repository

Subjects
model evaluation, Multidisciplinary, COVID-19, prediction, United States, Data Accuracy, 510 Mathematics, 360 Social problems & social services, weather, Humans, Public Health, ddc:510, ensemble forecast, Pandemics, Mathematics, Forecasting, Probability
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022
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46. A hierarchical and collaborative BRD4/CEBPD partnership governs vascular smooth muscle cell inflammation.

Author

Wang Q, Ozer HG, Wang B, Zhang M, Urabe G, Huang Y, Kent KC, and Guo LW

Abstract

Bromodomain protein BRD4 reads histone acetylation (H3K27ac), an epigenomic mark of transcription enhancers. CCAAT enhancer binding protein delta (CEBPD) is a transcription factor typically studied in metabolism. While both are potent effectors and potential therapeutic targets, their relationship was previously unknown. Here we investigated their interplay in vascular smooth muscle cell (SMC) inflammation. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) revealed H3K27ac/BRD4 enrichment at Cebpd in injured rat carotid arteries. While genomic deletion of BRD4-associated enhancer in SMCs in vitro decreased Cebpd transcripts, BRD4 gene silencing also diminished Cebpd mRNA and protein, indicative of a BRD4 control over CEBPD expression. Bromodomain-1, but not bromodomain-2, accounted for this BRD4 function. Moreover, endogenous BRD4 protein co-immunoprecipitated with CEBPD, and both proteins co-immunoprecipitated the Cebpd promoter and enhancer DNA fragments. These co-immunoprecipitations (coIPs) were all abolished by the BRD4-bromodomain blocker JQ1, suggesting a BRD4/CEBPD /promoter/enhancer complex. While BRD4 and CEBPD were both upregulated upon tumor necrosis factor alpha (TNF-α) stimulation of SMC inflammation (increased interleukin [IL]-1b, IL-6, and MCP-1), they mediated this stimulation via preferentially elevated expression of platelet-derived growth factor receptor alpha (PDGFRα, versus PDGFRβ), as indicated by loss- and gain-of-function experiments. Taken together, our study unravels a hierarchical yet collaborative BRD4/CEBPD relationship, a previously unrecognized mechanism that prompts SMC inflammation and may underlie other pathophysiological processes as well., Competing Interests: The authors declare that no competing interests., (© 2021 The Author(s).)

Published
2021
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47. Sunitinib for patients with locally advanced or distantly metastatic dermatofibrosarcoma protuberans but resistant to imatinib.

Author

Fu Y, Kang H, Zhao H, Hu J, Zhang H, Li X, Du N, and Huang Y

Abstract

Purpose: This study evaluated the efficacy and adverse effects of Imatinib therapy to advanced Dermatofibrosarcoma protuberan (DFSP) and Sunitinib therapy to advanced Dermatofibrosarcoma protuberan (DFSP) after Imatinib resistance., Methods: We analyzed the efficacy, adverse effects and survival of 95 patients with locally advanced or metastatic DFSP treated by Imatinib between January 2003 and December 2009, and also analyzed the efficacy and adverse effects of 30 patients after Imatinib failure between January 2008 and December 2011., Results: In all 95 patients treated by Imatinib, 16 had complete response (CR, 16.8%), 44 had partial response (PR, 46.3%), 23 had stable disease (SD, 24.2%) and 12 had progressive disease (PD, 12.6%). The DCR (CR+PR+SD) was 87.4%. The median PFS was 23 months and the OS was 40 months. For 30 patients had Sunitinib treatment after Imatinib failure, 2 had CR (6.7%), 10 had PR (33.3%), 12 had SD (40%) and 6 had PD (20%). The disease control rate (DCR=CR+PR+SD) was 73.3%. The progression free survival (PFS) of CR and PR patients were 22 months and 20 months respectively. The PFS of 12 SD was 18 months, and overall survival (OS) was 28 months. And the median PFS and OS of all 30 patients were 19 and 27 months respectively after Sunitinib treatment. Most of the Imatinib-induced adverse effects are of grade 1-2, including nausea, water retention/edema, fatigue, etc., Conclusion: Imatinib has been proven to be effective and well-tolerated in the treatment of locally advanced or inoperable patients with DFSP. After Imatinib failure, Sunitinib therapy showed good clinical efficacy and tolerated adverse effects as a new treatment option for such patients.

Published
2015

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