Search

Your search keyword '"Ji Nan"' showing total 1,094 results
Start Over Author "Ji Nan" Search Limiters Full Text
1,094 results on '"Ji Nan"'

1. Electrolysis of Rare Earth Oxides(Gd2O3, Nd2O3, Sm2O3 and Dy2O3) in KCl-LiCl-Li2O Molten Salt

Author

JI Nan, PENG Hao, JIANG Feng, HUANG Wei, ZHU Tie-jian, SHE Chang-feng, and GONG Yu

Subjects
li2o, kcl-licl molten salt, electrochemical behavior, rare earth oxide, electrolytic reduction, Nuclear engineering. Atomic power, TK9001-9401, Chemical technology, TP1-1185
Abstract

The electrochemical behavior of Li2O in KCl-LiCl molten salt has been studied by cyclic voltammetry and chronopotentiometry. The diffusion coefficient D=0.5×10-5 cm2/s of O2- in KCl-LiCl molten salt at 923 K has been calculated by convolutional voltammetry. Gd2O3, Nd2O3, Sm2O3 and Dy2O3 were used as cathodes to conduct electrolysis in KCl-LiCl-Li2O(w=1%) molten salt(constant voltage 3.40 V, electrolysis temperature 923 K, electrolysis time 25 h). By XRD analysis, it was confirmed that the rare earth oxides are partially reduced to metals, and the possible reactions during the electrolysis process were analyzed. At the same time, the PRS model was used to analyze the electrolytic reduction models of these rare earth oxides, and the optimal porosity(Popt) of Gd2O3, Nd2O3, Sm2O3 and Dy2O3 are 18.7%, 24.2%, 30.6%,16.7%, respectively. The minimum electrolysis time of them are 133, 157, 143, and 119 h, respectively, which are compared with the results of the electrolytic experiment, it is found that the porosity and electrolysis time of the cathode do not meet the requirements for complete reduction of metal oxides, and the corresponding explanation is given.

Published
2022
Full Text
View/download PDF

2. Pig Sound Analysis: A Measure of Welfare

Author

JI Nan, YIN Yanling, SHEN Weizheng, KOU Shengli, DAI Baisheng, and WANG Guowei

Subjects
pig sound classification, animal welfare, sound analysis, feature extraction, precision livestock farming, sound monitering, Agriculture (General), S1-972, Technology (General), T1-995
Abstract

Pig welfare is closely related to the economical production of pig farms. With regard to pig welfare assessment, pig sounds are significant indicators, which can reflect the quality of the barn environment, the physical condition and the health of pigs. Therefore, pig sound analysis is of high priority and necessary. In this review, the relationship between pig sound and welfare was analyzed. Three kinds of pig sounds are closely related to pig welfare, including coughs, screams, and grunts. Subsequently, both wearable and non-contact sensors were briefly described in two aspects of advantages and disadvantages. Based on the advantages and feasibility of microphone sensors in contactless way, the existing techniques for processing pig sounds were elaborated and evaluated for further in-depth research from three aspects: sound recording and labeling, feature extraction, and sound classification. Finally, the challenges and opportunities of pig sound research were discussed for the ultimate purpose of precision livestock farming (PLF) in four ways: concerning sound monitoring technologies, individual pig welfare monitoring, commercial applications and pig farmers. In summary, it was found that most of the current researches on pig sound recognition tasks focused on the selection of classifiers and algorithm improvement, while fewer research was conducted on sound labeling and feature extraction. Meanwhile, pig sound recognition faces some challenging problems, involving the difficulty in obtaining the audio data from different pig growth stages and verifying the developed algorithms in a variety of pig farms. Overall, it is suggested that technologies involved in the automatic identification process should be explored in depth. In the future, strengthen cooperation among cross-disciplinary experts to promote the development and application of PLF is also nessary.

Published
2022
Full Text
View/download PDF

3. Fusion Vectorized Representation Learning of Multi-source Heterogeneous User-generated Contents

Author

JI Nan-xun, SUN Xiao-yan, LI Zhen-qi

Subjects
user generated contents, representation learning, multi-source heterogeneous, fusion, short text, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

With the development of mobile networks and APPs,user generated contents (UGC) containing multi-source heterogeneous data such as evaluations,markings,scoring,images and videos are greatly valuable information for improving the quality of personalized services.The representation learning of fusion and vectorization on the multi-source heterogeneous UGC is the most critical issue for the successful application.Motivated by this,we propose a representation learning method for effectively fusing and vectorizing the comments and image data.We utilize the Doc2vec and LDA models to sufficiently extract the features of the multi-source comments.The images correlated with the comments are represented with deep convolutional network.A hybrid vectorized representation learning for fusing comments and a convolution strategy for integrating images and comments are presented.The feasibility and effectiveness of the proposed method is demonstrated by applying it to typical Amazon public data sets with heterogeneous UGC,in which the vectorized multi-source heterogeneous UGC is taken as the representation of each product and the classification accuracy of the products are compared.

Published
2021
Full Text
View/download PDF

5. Research on adaptive RBF neural network prediction method for core thermal-hydraulic parameters of fast reactor

Author

JI Nan, YI Jinhao, ZHAO Pengcheng, and YU Tao

Subjects
rbf neural network algorithm, adaptive gradient descent method, fast reactor, thermal-hydraulic parameters prediction method, Nuclear engineering. Atomic power, TK9001-9401
Abstract

BackgroundAlterations in thermal parameters directly affect the safety of reactors. Accurately predicting the trends of key thermal parameters under various working conditions can greatly improve reactor safety, thereby effectively preventing the occurrence of nuclear power plant accidents. The thermal and hydraulic characteristic parameters in the reactor are affected by many factors.PurposeThis study aims to explore the prediction method of core thermal-hydraulic parameters of fast reactor and determine the feasibility of neural network prediction.MethodsThe China experimental fast reactor (CEFR) was selected as the research object, and the maximum temperature of the fuel cladding surface and mass flow rate were used as predictor variables. After data samples were generated through the Subchannel code (named Subchanflow), two widely used adaptive neural networks were employed to perform the thermal parameter forecast analysis of CEFR fuel assembly under steady-state conditions, and CEFR 1/2 core was taken as subject to carry out single-step and continuous predictive analysis of thermal parameters under transient conditions.ResultsThe results show that, compared with adaptive BP neural network, the adaptive RBF neural network exhibits a better fitting ability and higher forecasting accuracy, and its maximum error under steady-state conditions is 0.5%. Under transient conditions, some local points have poor forecasting accuracy, however, the adaptive RBF neural network is generally excellent at predicting temperature and mass flow. The average relative error of temperature does not exceed 1%, and the average relative error of flow does not exceed 6%.ConclusionsThe adaptive RBF neural network model can provide real-time forecasting in a short time under unstable flow conditions, and its forecasting results have certain reference value.

Published
2022
Full Text
View/download PDF

6. Microbial diversity in the rhizosphere soils of three Stipa species from the eastern Inner Mongolian grasslands

Author

Ji Nan, Lumeng Chao, Xiaodan Ma, Daolong Xu, Li Mo, Xiaodong Zhang, Xupeng Zhao, and Yuying Bao

Subjects
Stipa, Microbial community, Vegetation diversity, Soil physicochemical properties, Ecology, QH540-549.5
Abstract

Rhizosphere soil microorganisms affect the diversity and ecological distribution of aboveground plants and play an important role in the functions of terrestrial ecosystems. Using high-throughput sequencing, we have investigated the rhizosphere soil microbial communities of three species of Stipa from six plots, in the eastern steppe of Inner Mongolia, and analyzed the relationship between microbial diversity and vegetation diversity and soil physicochemical characteristics. The dominant bacteria phyla were: Actinobacteria, 31.73–44.73%; Proteobacteria, 15.79–23.85%; Acidobacteria, 9.42–15.14%; and Chloroflexi, 4.90–13.60%. While the dominant fungi phyla were: Ascomycota, 16.93–82.12%; Basidiomycota, 2.44–81.28%; and Zygomycota, 0.40–18.92%. Principal coordinates analysis (PCoA) indicated that the soil microbial community compositions in the Stipa baicalensis steppe in Hulunbuir and the Stipa krylovii steppe in the Xilingol plots, were significantly different from the others. The species characteristics of the plant communities were different in each plot for the vegetation diversity factors, the Shannon Weiner index, Simpson index, and the importance value indices of the Stipa and Leymus chinensis significantly affected the community structures of the bacteria and fungi. Among the physicochemical soil parameters, soil pH, ammonium nitrogen (AN), and sucrase (SC), significantly affected the community structures of the bacteria and fungi, and were inseparable from the interactions between the microorganisms and environmental factors. We found that microbial community diversity was complex in the rhizosphere soils of the Stipa, and that they were mainly affected by the vegetation diversity, soil pH, ammonium nitrogen, and soil sucrase activities. These results improve our understanding of the correlations between the Stipa rhizosphere soil microorganisms and the environmental factors, and provide guidance for future research on the distribution characteristics and growth of the Stipa.

Published
2020
Full Text
View/download PDF

7. Subpopulations of children with multiple chronic health outcomes in relation to chemical exposures in the ECHO-PATHWAYS consortium.

Author

Day, Drew, LeWinn, Kaja, Karr, Catherine, Loftus, Christine, Carroll, Kecia, Bush, Nicole, Zhao, Qi, Barrett, Emily, Swan, Shanna, Nguyen, Ruby, Trasande, Leonardo, Moore, Paul, Adams Ako, Ako, Ji, Nan, Liu, Chang, Szpiro, Adam, and Sathyanarayana, Sheela

Subjects
Asthma, Behavior, Clustering, Multi-morbidity, Multi-outcome, Phthalates, Female, Pregnancy, Humans, Child, Male, Child, Preschool, Prospective Studies, Phthalic Acids, Asthma, Rhinitis, Respiratory Sounds, Outcome Assessment, Health Care, Environmental Exposure, Environmental Pollutants
Abstract

A multimorbidity-focused approach may reflect common etiologic mechanisms and lead to better targeting of etiologic agents for broadly impactful public health interventions. Our aim was to identify clusters of chronic obesity-related, neurodevelopmental, and respiratory outcomes in children, and to examine associations between cluster membership and widely prevalent chemical exposures to demonstrate our epidemiologic approach. Early to middle childhood outcome data collected 2011-2022 for 1092 children were harmonized across the ECHO-PATHWAYS consortium of 3 prospective pregnancy cohorts in six U.S. cities. 15 outcomes included age 4-9 BMI, cognitive and behavioral assessment scores, speech problems, and learning disabilities, asthma, wheeze, and rhinitis. To form generalizable clusters across study sites, we performed k-means clustering on scaled residuals of each variable regressed on study site. Outcomes and demographic variables were summarized between resulting clusters. Logistic weighted quantile sum regressions with permutation test p-values associated odds of cluster membership with a mixture of 15 prenatal urinary phthalate metabolites in full-sample and sex-stratified models. Three clusters emerged, including a healthier Cluster 1 (n = 734) with low morbidity across outcomes; Cluster 2 (n = 192) with low IQ and higher levels of all outcomes, especially 0.4-1.8-standard deviation higher mean neurobehavioral outcomes; and Cluster 3 (n = 179) with the highest asthma (92 %), wheeze (53 %), and rhinitis (57 %) frequencies. We observed a significant positive, male-specific stratified association (odds ratio = 1.6; p = 0.01) between a phthalate mixture with high weights for MEP and MHPP and odds of membership in Cluster 3 versus Cluster 1. These results identified subpopulations of children with co-occurring elevated levels of BMI, neurodevelopmental, and respiratory outcomes that may reflect shared etiologic pathways. The observed association between phthalates and respiratory outcome cluster membership could inform policy efforts towards children with respiratory disease. Similar cluster-based epidemiology may identify environmental factors that impact multi-outcome prevalence and efficiently direct public policy efforts.

Published
2024

8. Do small effects matter more in vulnerable populations? an investigation using Environmental influences on Child Health Outcomes (ECHO) cohorts

Author

Peacock, Janet L, Coto, Susana Diaz, Rees, Judy R, Sauzet, Odile, Jensen, Elizabeth T, Fichorova, Raina, Dunlop, Anne L, Paneth, Nigel, Padula, Amy, Woodruff, Tracey, Morello-Frosch, Rachel, Trowbridge, Jessica, Goin, Dana, Maldonado, Luis E, Niu, Zhongzheng, Ghassabian, Akhgar, Transande, Leonardo, Ferrara, Assiamira, Croen, Lisa A, Alexeeff, Stacey, Breton, Carrie, Litonjua, Augusto, O’Connor, Thomas G, Lyall, Kristen, Volk, Heather, Alshawabkeh, Akram, Manjourides, Justin, Camargo, Carlos A, Dabelea, Dana, Hockett, Christine W, Bendixsen, Casper G, Hertz-Picciotto, Irva, Schmidt, Rebecca J, Hipwell, Alison E, Keenan, Kate, Karr, Catherine, LeWinn, Kaja Z, Lester, Barry, Camerota, Marie, Ganiban, Jody, McEvoy, Cynthia, Elliott, Michael R, Sathyanarayana, Sheela, Ji, Nan, Braun, Joseph M, and Karagas, Margaret R

Subjects
Public Health, Health Sciences, Women's Health, Pediatric, Prevention, 2.2 Factors relating to the physical environment, Generic health relevance, Humans, Vulnerable Populations, Female, Infant, Low Birth Weight, Infant, Newborn, Child Health, Birth Weight, Environmental Exposure, Cohort Studies, Pregnancy, Socioeconomic Factors, Male, Adult, Pregnancy outcomes, Child health outcome, Health disparities, Environmental exposure, Social determinants of health, Program Collaborators for Environmental influences on Child Health Outcomes, Public Health and Health Services
Abstract

BackgroundA major challenge in epidemiology is knowing when an exposure effect is large enough to be clinically important, in particular how to interpret a difference in mean outcome in unexposed/exposed groups. Where it can be calculated, the proportion/percentage beyond a suitable cut-point is useful in defining individuals at high risk to give a more meaningful outcome. In this simulation study we compute differences in outcome means and proportions that arise from hypothetical small effects in vulnerable sub-populations.MethodsData from over 28,000 mother/child pairs belonging to the Environmental influences on Child Health Outcomes Program were used to examine the impact of hypothetical environmental exposures on mean birthweight, and low birthweight (LBW) (birthweight

Published
2024

11. Decentralised sliding mode control for nonlinear interconnected systems with uncertainties

Author

Ji, Nan

Subjects
T Technology
Abstract

With the advances in science and technology, nonlinear large-scale interconnected systems have widely appeared in the real life. Traditional centralised control methods have inevitable disadvantages when they are used to deal with complex nonlinear interconnected systems with uncertainties. In connection with this, people desire to develop the novel control strategy which can be applied to complex interconnected systems. Therefore, decentralised sliding mode control (SMC) for interconnected systems has attracted great attention in related fields due to its advantages, for instance, simple structure, low cost of calculation, fast response, reduced-order sliding mode dynamics and insensitivity to matched variation of parameters and disturbances in systems. This thesis focuses on the development of decentralised SMC for nonlinear interconnected systems with uncertainties under certain assumptions. Several methods and different techniques have been considered in design of the controller to improve the robustness. The main contributions of this thesis include: • The state feedback decentralised SMC is developed for nonlinear interconnected systems with matched uncertainty and mismatched unknown interconnections. A state feedback decentralised SMC strategy, under the assumption that all system states are accessible, is proposed to attenuate the impact of the uncertainties by using bounds on uncertainties and interconnections. The bounds used in the design are fully nonlinear which provide higher applicability for different complex interconnected systems. Especially, for this fully nonlinear system, the proposed method does not need to use the technique of linearisation, which is widely used in existing work to deal with nonlinear interconnected systems with uncertainties. • The dynamic observer is applied to complex nonlinear interconnected systems with matched and mismatched uncertainties. This dynamic observer can estimate the system states which can not be achieved during the controller design. The proposed method has great identification ability with small estimated errors for the states of nonlinear interconnected systems with matched and mismatched uncertainties. It should be pointed out that the considered uncertainties of nonlinear interconnected systems have general forms, which means that the proposed method can be effectively used in more generalised nonlinear interconnected systems. • A variable structure observer-based decentralised SMC is proposed to control a class of nonlinear interconnected systems with matched and mismatched uncertainties. Based on the designed dynamic observer, a dynamic decentralised output feedback SMC using outputs and estimated states is presented to control the interconnected systems with matched and mismatched uncertainties. The nonlinear interconnections are employed in the control design to reduce the conservatism of the developed results. The bounds of the uncertainties are relaxed which are nonlinear and take more general forms. Moreover, the limitation for the interconnected system is reduced when compared with the existing results in which the proposed strategies adopt the full-order observer. Besides that, the presented method improves the robustness of nonlinear interconnected systems to be against the effects of uncertainties. This thesis also provides several numerical and practical simulations to demonstrate the effectiveness of the proposed decentralised SMC for nonlinear interconnected systems with matched uncertainty, mismatched uncertainty and nonlinear interconnections.

Published
2023
Full Text
View/download PDF

13. Adversarial Jamming for a More Effective Constellation Attack

Author

Xie, Haidong, Xu, Yizhou, Chen, Yuanqing, Ji, Nan, Yuan, Shuai, Liu, Naijin, and Xiang, Xueshuang

Subjects
Computer Science - Cryptography and Security, Electrical Engineering and Systems Science - Signal Processing
Abstract

The common jamming mode in wireless communication is band barrage jamming, which is controllable and difficult to resist. Although this method is simple to implement, it is obviously not the best jamming waveform. Therefore, based on the idea of adversarial examples, we propose the adversarial jamming waveform, which can independently optimize and find the best jamming waveform. We attack QAM with adversarial jamming and find that the optimal jamming waveform is equivalent to the amplitude and phase between the nearest constellation points. Furthermore, by verifying the jamming performance on a hardware platform, it is shown that our method significantly improves the bit error rate compared to other methods., Comment: 3 pages, 2 figures, published in The 13th International Symposium on Antennas, Propagation and EM Theory (ISAPE 2021)

Published
2022
Full Text
View/download PDF

14. Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples

Author

Xie, Haidong, Tan, Jia, Zhang, Xiaoying, Ji, Nan, Liao, Haihua, Yu, Zuguo, Xiang, Xueshuang, and Liu, Naijin

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Deep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the image domain without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose a low-intercept waveform~(LIW) generation method that can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Our LIW exhibits significant low-interception performance even in the physical hardware experiment, decreasing the accuracy of the state of the art model to approximately $15\%$ with small perturbations., Comment: 4 pages, 4 figures, published in 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science, URSI GASS 2021

Published
2022
Full Text
View/download PDF

21. Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches

Author

Ji, Nan, Feng, YanFei, Xie, Haidong, Xiang, Xueshuang, and Liu, Naijin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The security of object detection systems has attracted increasing attention, especially when facing adversarial patch attacks. Since patch attacks change the pixels in a restricted area on objects, they are easy to implement in the physical world, especially for attacking human detection systems. The existing defenses against patch attacks are mostly applied for image classification problems and have difficulty resisting human detection attacks. Towards this critical issue, we propose an efficient and effective plug-in defense component on the YOLO detection system, which we name Ad-YOLO. The main idea is to add a patch class on the YOLO architecture, which has a negligible inference increment. Thus, Ad-YOLO is expected to directly detect both the objects of interest and adversarial patches. To the best of our knowledge, our approach is the first defense strategy against human detection attacks. We investigate Ad-YOLO's performance on the YOLOv2 baseline. To improve the ability of Ad-YOLO to detect variety patches, we first use an adversarial training process to develop a patch dataset based on the Inria dataset, which we name Inria-Patch. Then, we train Ad-YOLO by a combination of Pascal VOC, Inria, and Inria-Patch datasets. With a slight drop of $0.70\%$ mAP on VOC 2007 test set, Ad-YOLO achieves $80.31\%$ AP of persons, which highly outperforms $33.93\%$ AP for YOLOv2 when facing white-box patch attacks. Furthermore, compared with YOLOv2, the results facing a physical-world attack are also included to demonstrate Ad-YOLO's excellent generalization ability., Comment: 9 pages, 7 figures

Published
2021

33. National Brain Tumour Registry of China (NBTRC) statistical report of primary brain tumours diagnosed in China in years 2019–2020

Author

Xiao, Dan, Yan, Changxiang, Li, Deling, Xi, Tianshu, Liu, Xianzhi, Zhu, Dan, Huang, Guodong, Xu, Jianguo, He, Zhengwen, Wu, Anhua, Ma, Chiyuan, Long, Jiang, Shu, Kai, Ji, Hongming, Wang, Ning, Chen, Gang, Yang, Jiankai, Ma, Hui, Li, Zhiyong, Sun, Xiaochuan, Qu, Yan, Liu, Zhixiong, Jiang, Xiaofan, Tian, Chunlei, Ni, Shilei, Zhan, Renya, Chen, Liwen, Ge, Ming, Wang, Maode, Jiang, Xiaobing, Guo, Geng, Han, Zhenmin, Zhang, Chuan, Zhang, Tingrong, Dou, Changwu, Chu, Liangzhao, Wang, Pengcheng, Shao, Junfei, Wu, Xiyue, Yu, Ju, Wang, Yu, Wu, Nan, Zhang, Rui, Zhang, Mingming, Hong, Yong, Gao, Jianzhong, Li, Yunqian, Pan, Yawen, Zhao, Bing, Ji, Nan, Shan, Guangliang, Patel, Chirag B., Jia, Wang, and Zhang, Liwei

Published
2023
Full Text
View/download PDF

47. US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States (vol 69, pg s7, 2017)

Author

Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavallee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula K, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zeng, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, and Shahinian, Vahakn

Subjects
Urology & Nephrology, Clinical Sciences, Public Health and Health Services
Published
2017

48. Proteogenomic and metabolomic characterization of human glioblastoma

Author

Agarwal, Anupriya, Amin, Mitual, An, Eunkyung, Anderson, Matthew L., Andrews, David W., Bauer, Thomas, Birger, Chet, Birrer, Michael J., Blumenberg, Lili, Bocik, William E., Borate, Uma, Borucki, Melissa, Burke, Meghan C., Cai, Shuang, Calinawan, Anna P., Carr, Steven A., Cerda, Sandra, Chan, Daniel W., Charamut, Alyssa, Chen, Lin S., Chesla, David, Chinnaiyan, Arul M., Chowdhury, Shrabanti, Cieślik, Marcin P., Clark, David J., Culpepper, Houston, Czernicki, Tomasz, D'Angelo, Fulvio, Day, Jacob, De Young, Stephanie, Demir, Emek, Dhanasekaran, Saravana Mohan, Dhir, Rajiv, Domagalski, Marcin J., Druker, Brian, Duffy, Elizabeth, Dyer, Maureen, Edwards, Nathan J., Edwards, Robert, Elburn, Kimberly, Ellis, Matthew J., Eschbacher, Jennifer, Francis, Alicia, Gabriel, Stacey, Gabrovski, Nikolay, Garofano, Luciano, Getz, Gad, Gillette, Michael A., Godwin, Andrew K., Golbin, Denis, Hanhan, Ziad, Hannick, Linda I., Hariharan, Pushpa, Hindenach, Barbara, Hoadley, Katherine A., Hostetter, Galen, Huang, Chen, Jaehnig, Eric, Jewell, Scott D., Ji, Nan, Jones, Corbin D., Karz, Alcida, Kaspera, Wojciech, Kim, Lyndon, Kothadia, Ramani B., Kumar-Sinha, Chandan, Lei, Jonathan, Leprevost, Felipe D., Li, Kai, Liao, Yuxing, Lilly, Jena, Liu, Hongwei, Lubínski, Jan, Madan, Rashna, Maggio, William, Malc, Ewa, Malovannaya, Anna, Mareedu, Sailaja, Markey, Sanford P., Marrero-Oliveras, Annette, Martinez, Nina, Maunganidze, Nicollette, McDermott, Jason E., McGarvey, Peter B., McGee, John, Mieczkowski, Piotr, Migliozzi, Simona, Modugno, Francesmary, Montgomery, Rebecca, Newton, Chelsea J., Omenn, Gilbert S., Ozbek, Umut, Paklina, Oxana V., Paulovich, Amanda G., Perou, Amy M., Pico, Alexander R., Piehowski, Paul D., Placantonakis, Dimitris G., Polonskaya, Larisa, Potapova, Olga, Pruetz, Barbara, Qi, Liqun, Ramkissoon, Shakti, Resnick, Adam, Richey, Shannon, Riggins, Gregory, Robinson, Karna, Roche, Nancy, Rohrer, Daniel C., Rood, Brian R., Rossell, Larissa, Savage, Sara R., Schadt, Eric E., Shi, Yan, Shi, Zhiao, Shutack, Yvonne, Singh, Shilpi, Skelly, Tara, Sokoll, Lori J., Stawicki, Jakub, Stein, Stephen E., Suh, James, Szopa, Wojciech, Tabor, Dave, Tan, Donghui, Tansil, Darlene, Thangudu, Ratna R., Tognon, Cristina, Traer, Elie, Tsang, Shirley, Tyner, Jeffrey, Um, Ki Sung, Valley, Dana R., Vasaikar, Suhas, Vatanian, Negin, Velvulou, Uma, Vernon, Michael, Wan, Weiqing, Wang, Junmei, Webster, Alex, Wen, Bo, Whiteaker, Jeffrey R., Wilson, George D., Zakhartsev, Yuriy, Zelt, Robert, Zhang, Hui, Zhang, Liwei, Zhang, Zhen, Zhao, Grace, Zhu, Jun, Wang, Liang-Bo, Karpova, Alla, Gritsenko, Marina A., Kyle, Jennifer E., Cao, Song, Li, Yize, Rykunov, Dmitry, Colaprico, Antonio, Rothstein, Joseph H., Hong, Runyu, Stathias, Vasileios, Cornwell, MacIntosh, Petralia, Francesca, Wu, Yige, Reva, Boris, Krug, Karsten, Pugliese, Pietro, Kawaler, Emily, Olsen, Lindsey K., Liang, Wen-Wei, Song, Xiaoyu, Dou, Yongchao, Wendl, Michael C., Caravan, Wagma, Liu, Wenke, Cui Zhou, Daniel, Ji, Jiayi, Tsai, Chia-Feng, Petyuk, Vladislav A., Moon, Jamie, Ma, Weiping, Chu, Rosalie K., Weitz, Karl K., Moore, Ronald J., Monroe, Matthew E., Zhao, Rui, Yang, Xiaolu, Yoo, Seungyeul, Krek, Azra, Demopoulos, Alexis, Zhu, Houxiang, Wyczalkowski, Matthew A., McMichael, Joshua F., Henderson, Brittany L., Lindgren, Caleb M., Boekweg, Hannah, Lu, Shuangjia, Baral, Jessika, Yao, Lijun, Stratton, Kelly G., Bramer, Lisa M., Zink, Erika, Couvillion, Sneha P., Bloodsworth, Kent J., Satpathy, Shankha, Sieh, Weiva, Boca, Simina M., Schürer, Stephan, Chen, Feng, Wiznerowicz, Maciej, Ketchum, Karen A., Boja, Emily S., Kinsinger, Christopher R., Robles, Ana I., Hiltke, Tara, Thiagarajan, Mathangi, Nesvizhskii, Alexey I., Zhang, Bing, Mani, D.R., Ceccarelli, Michele, Chen, Xi S., Cottingham, Sandra L., Li, Qing Kay, Kim, Albert H., Fenyö, David, Ruggles, Kelly V., Rodriguez, Henry, Mesri, Mehdi, Payne, Samuel H., Resnick, Adam C., Wang, Pei, Smith, Richard D., Iavarone, Antonio, Chheda, Milan G., Barnholtz-Sloan, Jill S., Rodland, Karin D., Liu, Tao, and Ding, Li

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results