Search for: All records

The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate. 

Due to the increasing complexity of robot swarm algorithms, ana- lyzing their performance theoretically is often very difficult. Instead, simulators are often used to benchmark the performance of robot swarm algorithms. However, we are not aware of simulators that take advantage of the naturally highly parallel nature of distributed robot swarms. This paper presents ParSwarm, a parallel C++ frame- work for simulating robot swarms at scale on multicore machines. We demonstrate the power of ParSwarm by implementing two applications, task allocation and density estimation, and running simulations on large numbers of agents. 

Summary Herbivore‐induced plant volatiles act as danger signals to prime defense responses in neighboring plants, yet in many cases the mechanism behind this priming is not known. Volatile signals may be recognized directly by receptors and/or converted into other active compounds. Here we investigate the metabolic fate of volatile indole, a known priming signal in maize (Zea mays), to determine if its conversion to other compounds could play a role in its priming of defenses.We identified benzoxazinoids as major products from volatile indole using heavy isotope‐labeled volatile indole and Pathway of Origin Determination in Untargeted Metabolomics (PODIUM) analysis. We then used benzoxazinoid biosynthesis maize mutants to investigate their role in indole‐mediated priming.Labeled volatile indole was converted into DIMBOA‐glucoside in abx2(benzoxazinone synthesis2)‐dependent manner. Thebx2mutant plants showed elevated green leaf volatile (GLV) production in response to wounding andSpodoptera frugiperdaregurgitant irrespective of indole exposure.Thus, volatile indole is converted into benzoxazinoids, and part of its priming mechanism may be due to the enhanced production of these phytoanticipins. However, indole‐mediated enhanced GLV production does not rely on the conversion of indole to benzoxazinoids, so indole also has other signaling functions.