作者:
Yong LeiHui ZhaoXiaogang GuoYun LiuYang CaoYi DengXiongfeng ChenHon Ki Christopher ChengIgor B.DawidYonglong ChenShenzhen Research Institute
The Chinese University of Hong Kong Shenzhen 518057 P.R.China Key Laboratory of Regenerative Biology Guangzhou Institutes of Biomedicine and HealthChinese Academy of Sciences Guangzhou 510530 P.R.China Department of Biology South University of Science and Technology of China Shenzhen 518055 P.R.China Advanced Biomedical Computing Center National Cancer Institute National Institutes of Health Frederick MD 21702 USA Program in Genomics of Differentiation The Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD 20892 USA School of Biomedical Sciences
Faculty of Medicine The Chinese University of Hong Kong Shatin New TerritoriesHong Kong P.R.China School of Biomedical Sciences Faculty of Medicine The Chinese University of Hong Kong Shatin New TerritoriesHong Kong P.R.China
Transcription activator-like effector nucleases (TALENs) are a novel approach for directed gene disruption and have been proved to be effective in various animal *** we report that TALENs can induce somatic mutations ...
Transcription activator-like effector nucleases (TALENs) are a novel approach for directed gene disruption and have been proved to be effective in various animal *** we report that TALENs can induce somatic mutations in Xenopus embryos with reliably high efficiency and that such mutations are heritable through germline *** modified the Golden Gate method for TALEN assembly to make the product suitable for RNA transcription and microinjection into Xenopus embryos.
Microbial evolution is complex and is influenced by many sources of variation. Experimental evolution is no exception, although it is more controlled, easily replicated, and typically devoid of interactions between sp...
Microbial evolution is complex and is influenced by many sources of variation. Experimental evolution is no exception, although it is more controlled, easily replicated, and typically devoid of interactions between species. Mathematical modeling of the evolutionary process can help in understanding the underlying mechanisms that drive outcome of such experiments. These models can be complex and parameter rich, limiting their feasibility for statistical inference. In this paper, we introduce the use of Approximate Bayesian Computation (ABC) as a tool for statistical inference in the study of experimental evolution. ABC is a fast and simple method for fitting complex models to data. We utilize this method, coupled with a mechanistic model of experimental evolution, to study the evolution process of bacteriophage ϕ X174 under benign selection pressure. Our results highlight three mutation-selection scenarios that could explain this process: high mutation/low selection pressure, low mutation/high selection pressure, and low mutation/low selection pressure, with posterior support of 19%, 9.5%, and 71.5% for each of these scenarios, respectively. Sequence data support the first candidate. Though surprising, this scenario was not improbable based on our analysis.
This Nano Focus article highlights recent advances in RNA nanotechnology as presented at the First International Conference of RNA Nanotechnology and Therapeutics, which took place in Cleveland, OH, USA (October 23-25...
详细信息
Rapid advances in telehealth development and adoption are increasing the spectrum of information and communication technologies that can be applied not only to individual patient care but more broadly to population he...
详细信息
暂无评论