Genetic diversity and differentiation in indica and japonica groups of the cultivated rice (Oryza sativa L.) were studied by assaying DNA restriction fragment length polymorphisms of 12 indica and 14 japonica rice lines digested with three restriction endonucleases. A total of 49 probes were selected to represent the entire RFLP map at intervals of 20-30 cM. It was shown that 95 of the 145 possible probe/enzyme combinations, involving 43 probes and all three enzymes, detected restriction fragment length variation, and the degree of polymorphism varied greatly from one probe/enzyme combination to another. These results demonstrate that indica rice is genetically more diverse than japonica type. Significant differentiation between the two rice groups was detected by 33 probes representing 11 of the 12 rice chromosomes. It was deduced that the processes leading to differentiation involved a combination of molecular events that include base substitutions and insertion/deletions.
Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.
National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO 2 , PM 2.5 , O 3 , CO, PM 10 , SO 2 ) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10fold spatial cross-validation (CV) R 2 increased 0.02−0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R 2 = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.