Browsing by Author "Li, G."

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Open Access
Comparative analysis of the domestic cat genome reveals genetic signatures underlying feline biology and domestication
(Proceedings of the National Academy of Sciences, 2014-12-02) Montague, M. J.; Li, G.; Gandolfi, B.; Khan, R.; Aken, B. L.; Marques Bonet, T.; Alkan C.; Thomas, G. W. C.; Warren, W. C.; Searle, S. M. J.; Minx, M.; Hilliera, LaDeana W.; Koboldt, D. C.; Davis, B. W.; Driscoll, C. A.; Barr, C. S.; Blackistone, K.; Quilez, J.; Lorente-Galdos, B.; Marques Bonet, T.; Hahnj, M. W.; Menotti-Raymond, M.; O’Brien, S. J.; Wilson, R. K.; Lyons, L. A.; Murphy, W. J.
Little is known about the genetic changes that distinguish domestic cat populations from their wild progenitors. Here we describe a high-quality domestic cat reference genome assembly and comparative inferences made with other cat breeds, wildcats, and other mammals. Based upon these comparisons, we identified positively selected genes enriched for genes involved in lipid metabolism that underpin adaptations to a hypercarnivorous diet. We also found positive selection signals within genes underlying sensory processes, especially those affecting vision and hearing in the carnivore lineage. We observed an evolutionary tradeoff between functional olfactory and vomeronasal receptor gene repertoires in the cat and dog genomes, with an expansion of the feline chemosensory system for detecting pheromones at the expense of odorant detection. Genomic regions harboring signatures of natural selection that distinguish domestic cats from their wild congeners are enriched in neural crest-related genes associated with behavior and reward in mouse models, as predicted by the domestication syndrome hypothesis. Our description of a previously unidentified allele for the gloving pigmentation pattern found in the Birman breed supports the hypothesis that cat breeds experienced strong selection on specific mutations drawn from random bred populations. Collectively, these findings provide insight into how the process of domestication altered the ancestral wildcat genome and build a resource for future disease mapping and phylogenomic studies across all members of the Felidae.
Open Access
Facial feedback for reinforcement learning: A case study and ofine analysis using the TAMER framework
(Springer, 2020-02) Li, G.; Dibeklioğlu, Hamdi; Whiteson, S.; Hung, H.
Interactive reinforcement learning provides a way for agents to learn to solve tasks from evaluative feedback provided by a human user. Previous research showed that humans give copious feedback early in training but very sparsely thereafter. In this article, we investigate the potential of agent learning from trainers’ facial expressions via interpreting them as evaluative feedback. To do so, we implemented TAMER which is a popular interactive reinforcement learning method in a reinforcement-learning benchmark problem—Infinite Mario, and conducted the first large-scale study of TAMER involving 561 participants. With designed CNN–RNN model, our analysis shows that telling trainers to use facial expressions and competition can improve the accuracies for estimating positive and negative feedback using facial expressions. In addition, our results with a simulation experiment show that learning solely from predicted feedback based on facial expressions is possible and using strong/effective prediction models or a regression method, facial responses would significantly improve the performance of agents. Furthermore, our experiment supports previous studies demonstrating the importance of bi-directional feedback and competitive elements in the training interface.
Open Access
A global reference for human genetic variation
(Nature Publishing Group, 2015) Auton, A.; Abecasis, G. R.; Altshuler, D. M.; Durbin, R. M.; Bentley, D. R.; Chakravarti, A.; Clark, A. G.; Donnelly, P.; Eichler, E. E.; Flicek, P.; Gabriel, S. B.; Gibbs, R. A.; Green, E. D.; Hurles, M. E.; Knoppers, B. M.; Korbel, J. O.; Lander, E. S.; Lee, C.; Lehrach, H.; Mardis, E. R.; Marth, G. T.; McVean, G. A.; Nickerson, D. A.; Schmidt, J. P.; Sherry, S. T.; Wang, J.; Wilson, R. K.; Boerwinkle, E.; Doddapaneni, H.; Han, Y.; Korchina, V.; Kovar, C.; Lee, S.; Muzny, D.; Reid, J. G.; Zhu, Y.; Chang, Y.; Feng, Q.; Fang, X.; Guo, X.; Jian, M.; Jiang, H.; Jin, X.; Lan, T.; Li, G.; Li, J.; Li, Y.; Liu, S.; Liu, X.; Lu, Y.; Ma, X.; Tang, M.; Wang, B.; Wang, G.; Wu, H.; Wu, R.; Xu, X.; Yin, Y.; Zhang, D.; Zhang, W.; Zhao, J.; Zhao, M.; Zheng, X.; Gupta, N.; Gharani, N.; Toji, L. H.; Gerry, N. P.; Resch, A. M.; Barker, J.; Clarke, L.; Gil, L.; Hunt, S. E.; Kelman, G.; Kulesha, E.; Leinonen, R.; McLaren, W. M.; Radhakrishnan, R.; Roa, A.; Smirnov, D.; Smith, R. E.; Streeter, I.; Thormann, A.; Toneva, I.; Vaughan, B.; Zheng-Bradley, X.; Grocock, R.; Humphray, S.; James, T.; Kingsbury, Z.; Sudbrak, R.; Albrecht, M. W.; Amstislavskiy, V. S.; Borodina, T. A.; Lienhard, M.; Mertes, F.; Sultan, M.; Timmermann, B.; Yaspo, Marie-Laure; Fulton, L.; Ananiev, V.; Belaia, Z.; Beloslyudtsev, D.; Bouk, N.; Chen, C.; Church, D.; Cohen, R.; Cook, C.; Garner, J.; Hefferon, T.; Kimelman, M.; Liu, C.; Lopez, J.; Meric, P.; O'Sullivan, C.; Ostapchuk, Y.; Phan, L.; Ponomarov, S.; Schneider, V.; Shekhtman, E.; Sirotkin, K.; Slotta, D.; Zhang, H.; Balasubramaniam, S.; Burton, J.; Danecek, P.; Keane, T. M.; Kolb-Kokocinski, A.; McCarthy, S.; Stalker, J.; Quail, M.; Davies, C. J.; Gollub, J.; Webster, T.; Wong, B.; Zhan, Y.; Campbell, C. L.; Kong, Y.; Marcketta, A.; Yu, F.; Antunes, L.; Bainbridge, M.; Sabo, A.; Huang, Z.; Coin, L. J. M.; Fang, L.; Li, Q.; Li, Z.; Lin, H.; Liu, B.; Luo, R.; Shao, H.; Xie, Y.; Ye, C.; Yu, C.; Zhang, F.; Zheng, H.; Zhu, H.; Alkan, C.; Dal, E.; Kahveci, F.; Garrison, E. P.; Kural, D.; Lee, W. P.; Leong, W. F.; Stromberg, M.; Ward, A. N.; Wu, J.; Zhang, M.; Daly, M. J.; DePristo, M. A.; Handsaker, R. E.; Banks, E.; Bhatia, G.; Del Angel, G.; Genovese, G.; Li, H.; Kashin, S.; McCarroll, S. A.; Nemesh, J. C.; Poplin, R. E.; Yoon, S. C.; Lihm, J.; Makarov, V.; Gottipati, S.; Keinan, A.; Rodriguez-Flores, J. L.; Rausch, T.; Fritz, M. H.; Stütz, A. M.; Beal, K.; Datta, A.; Herrero, J.; Ritchie, G. R. S.; Zerbino, D.; Sabeti, P. C.; Shlyakhter, I.; Schaffner, S. F.; Vitti, J.; Cooper, D. N.; Ball, E. V.; Stenson, P. D.; Barnes, B.; Bauer, M.; Cheetham, R. K.; Cox, A.; Eberle, M.; Kahn, S.; Murray, L.; Peden, J.; Shaw, R.; Kenny, E. E.; Batzer, M. A.; Konkel, M. K.; Walker, J. A.; MacArthur, D. G.; Lek, M.; Herwig, R.; Ding, L.; Koboldt, D. C.; Larson, D.; Ye, K.; Gravel, S.; Swaroop, A.; Chew, E.; Lappalainen, T.; Erlich, Y.; Gymrek, M.; Willems, T. F.; Simpson, J. T.; Shriver, M. D.; Rosenfeld, J. A.; Bustamante, C. D.; Montgomery, S. B.; De La Vega, F. M.; Byrnes, J. K.; Carroll, A. W.; DeGorter, M. K.; Lacroute, P.; Maples, B. K.; Martin, A. R.; Moreno-Estrada, A.; Shringarpure, S. S.; Zakharia, F.; Halperin, E.; Baran, Y.; Cerveira, E.; Hwang, J.; Malhotra, A.; Plewczynski, D.; Radew, K.; Romanovitch, M.; Zhang, C.; Hyland, F. C. L.; Craig, D. W.; Christoforides, A.; Homer, N.; Izatt, T.; Kurdoglu, A. A.; Sinari, S. A.; Squire, K.; Xiao, C.; Sebat, J.; Antaki, D.; Gujral, M.; Noor, A.; Ye, K.; Burchard, E. G.; Hernandez, R. D.; Gignoux, C. R.; Haussler, D.; Katzman, S. J.; Kent, W. J.; Howie, B.; Ruiz-Linares, A.; Dermitzakis, E. T.; Devine, S. E.; Kang, H. M.; Kidd, J. M.; Blackwell, T.; Caron, S.; Chen, W.; Emery, S.; Fritsche, L.; Fuchsberger, C.; Jun, G.; Li, B.; Lyons, R.; Scheller, C.; Sidore, C.; Song, S.; Sliwerska, E.; Taliun, D.; Tan, A.; Welch, R.; Wing, M. K.; Zhan, X.; Awadalla, P.; Hodgkinson, A.; Li, Y.; Shi, X.; Quitadamo, A.; Lunter, G.; Marchini, J. L.; Myers, S.; Churchhouse, C.; Delaneau, O.; Gupta-Hinch, A.; Kretzschmar, W.; Iqbal, Z.; Mathieson, I.; Menelaou, A.; Rimmer, A.; Xifara, D. K.; Oleksyk, T. K.; Fu, Y.; Liu, X.; Xiong, M.; Jorde, L.; Witherspoon, D.; Xing, J.; Browning, B. L.; Browning, S. R.; Hormozdiari, F.; Sudmant, P. H.; Khurana, E.; Tyler-Smith, C.; Albers, C. A.; Ayub, Q.; Chen, Y.; Colonna, V.; Jostins, L.; Walter, K.; Xue, Y.; Gerstein, M. B.; Abyzov, A.; Balasubramanian, S.; Chen, J.; Clarke, D.; Fu, Y.; Harmanci, A. O.; Jin, M.; Lee, D.; Liu, J.; Mu, X. J.; Zhang, J.; Zhang, Y.; Hartl, C.; Shakir, K.; Degenhardt, J.; Meiers, S.; Raeder, B.; Casale, F. P.; Stegle, O.; Lameijer, E. W.; Hall, I.; Bafna, V.; Michaelson, J.; Gardner, E. J.; Mills, R. E.; Dayama, G.; Chen, K.; Fan, X.; Chong, Z.; Chen, T.; Chaisson, M. J.; Huddleston, J.; Malig, M.; Nelson, B. J.; Parrish, N. F.; Blackburne, B.; Lindsay, S. J.; Ning, Z.; Zhang, Y.; Lam, H.; Sisu, C.; Challis, D.; Evani, U. S.; Lu, J.; Nagaswamy, U.; Yu, J.; Li, W.; Habegger, L.; Yu, H.; Cunningham, F.; Dunham, I.; Lage, K.; Jespersen, J. B.; Horn, H.; Kim, D.; Desalle, R.; Narechania, A.; Sayres, M. A. W.; Mendez, F. L.; Poznik, G. D.; Underhill, P. A.; Mittelman, D.; Banerjee, R.; Cerezo, M.; Fitzgerald, T. W.; Louzada, S.; Massaia, A.; Yang, F.; Kalra, D.; Hale, W.; Dan, X.; Barnes, K. C.; Beiswanger, C.; Cai, H.; Cao, H.; Henn, B.; Jones, D.; Kaye, J. S.; Kent, A.; Kerasidou, A.; Mathias, R.; Ossorio, P. N.; Parker, M.; Rotimi, C. N.; Royal, C. D.; Sandoval, K.; Su, Y.; Tian, Z.; Tishkoff, S.; Via, M.; Wang, Y.; Yang, H.; Yang, L.; Zhu, J.; Bodmer, W.; Bedoya, G.; Cai, Z.; Gao, Y.; Chu, J.; Peltonen, L.; Garcia-Montero, A.; Orfao, A.; Dutil, J.; Martinez-Cruzado, J. C.; Mathias, R. A.; Hennis, A.; Watson, H.; McKenzie, C.; Qadri, F.; LaRocque, R.; Deng, X.; Asogun, D.; Folarin, O.; Happi, C.; Omoniwa, O.; Stremlau, M.; Tariyal, R.; Jallow, M.; Joof, F. S.; Corrah, T.; Rockett, K.; Kwiatkowski, D.; Kooner, J.; Hien, T. T.; Dunstan, S. J.; ThuyHang, N.; Fonnie, R.; Garry, R.; Kanneh, L.; Moses, L.; Schieffelin, J.; Grant, D. S.; Gallo, C.; Poletti, G.; Saleheen, D.; Rasheed, A.; Brooks, L. D.; Felsenfeld, A. L.; McEwen, J. E.; Vaydylevich, Y.; Duncanson, A.; Dunn, M.; Schloss, J. A.
The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies. © 2015 Macmillan Publishers Limited. All rights reserved.
Open Access
Vertically aligned gold nanorod monolayer on arbitrary substrates: self-assembly and femtomolar detection of food contaminants
(American Chemical Society, 2013) Peng, B.; Li, G.; Li, D.; Dodson, S.; Zhang, Q.; Zhang, J.; Lee, Y. H.; Demir, Hilmi Volkan; Ling, X. Y.; Xiong, Q.
Public attention to the food scandals raises an urgent need to develop effective and reliable methods to detect food contaminants. The current prevailing detections are primarily based upon liquid chromatography, mass spectroscopy, or colorimetric methods, which usually require sophisticated and time-consuming steps or sample preparation. Herein, we develop a facile strategy to assemble the vertically aligned monolayer of Au nanorods with a nominal 0.8 nm gap distance and demonstrate their applications in the rapid detection of plasticizers and melamine contamination at femtomolar level by surface-enhanced Raman scattering spectroscopy (SERS). The SERS signals of plasticizers are sensitive down to 0.9 fM concentrations in orange juices. It is the lowest detection limit reported to date, which is 7 orders of magnitude lower than the standard of United States (6 ppb). The highly organized vertical arrays generate the reproducible "SERS-active sites" and can be achieved on arbitrary substrates, ranging from silicon, gallium nitride, glass to flexible poly(ethylene naphthalate) substrates.