successive experiment
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Weed Science ◽  
2020 ◽  
pp. 1-23
Author(s):  
Tao Li ◽  
Jiequn Fan ◽  
Zhenguan Qian ◽  
Guohui Yuan ◽  
Dandan Meng ◽  
...  

Abstract The use of a corn-earthworm coculture (CE) system is an eco-agricultural technology that has been gradually extended due to its high economic output and diverse ecological benefits for urban agriculture in China. However, the effect of CE on weed occurrence has received little attention. A five-year successive experiment (2015 to 2019) was conducted to compare weed occurrence in CE and a corn (Zea mays L.) monoculture (CM). The results show that CE significantly decreased weed diversity, the dominance index, total weed density and biomass, but increased the weed evenness index. The five-year mean number of weed species per plot was 8.4 in CE and 10.7 in CM. Compared to those in CM, the five-year mean density and biomass of total weeds in CE decreased by 59.2% and 66.6%, respectively. The effect of CE on weed occurrence was species specific. The mean density of large crabgrass [Digitaria sanguinalis (L.) Scop.], green foxtail [Setaria viridis (L.) Beauv.], goosegrass [Eleusine indica (L.) Gaertn.], and common purslane (Portulaca oleracea L.) in CE decreased by 94.5, 78.1, 75.0, and 45.8%, whereas the mean biomass decreased by 96.2, 80.8, 76.9, and 41.4%, respectively. Our study suggests that the use of CE could suppress weed occurrence and reduce herbicide inputs in agriculture.


2019 ◽  
Author(s):  
Jacob Schreiber ◽  
Jeffrey Bilmes ◽  
William Stafford Noble

AbstractSuccessful science often involves not only performing experiments well, but also choosing well among many possible experiments. In a hypothesis generation setting, choosing an experiment well means choosing an experiment whose results are interesting or novel. In this work, we formalize this selection procedure in the context of genomics and epigenomics data generation. Specifically, we consider the task faced by a scientific consortium such as the National Institutes of Health ENCODE Consortium, whose goal is to characterize all of the functional elements in the human genome. Given a list of possible cell types or tissue types (“biosamples”) and a list of possible high throughput sequencing assays, we ask “Which experiments should ENCODE perform next?” We demonstrate how to represent this task as an optimization problem, where the goal is to maximize the information gained in each successive experiment. Compared with previous work that has addressed a similar problem, our approach has the advantage that it can use imputed data to tailor the selected list of experiments based on data collected previously by the consortium. We demonstrate the utility of our proposed method in simulations, and we provide a general software framework, named Kiwano, for selecting genomic and epigenomic experiments.


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