Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km 3 yr 21 ) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ;50 W m 22 in latent heat flux averaged over June-August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ;450 km 3 yr 21 , which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ;40% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology.

This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21–33% and 17–22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions.

The UDP-2,3-diacylglucosamine pyrophosphate hydrolase LpxH is an essential lipid A biosynthetic enzyme that is conserved in the majority of gram-negative bacteria. It has emerged as an attractive novel antibiotic target due to the recent discovery of an LpxH-targeting sulfonyl piperazine compound (referred to as AZ1) by AstraZeneca. However, the molecular details of AZ1 inhibition have remained unresolved, stymieing further development of this class of antibiotics. Here we report the crystal structure of Klebsiella pneumoniae LpxH in complex with AZ1. We show that AZ1 fits snugly into the L-shaped acyl chain-binding chamber of LpxH with its indoline ring situating adjacent to the active site, its sulfonyl group adopting a sharp kink, and its N-CF3–phenyl substituted piperazine group reaching out to the far side of the LpxH acyl chain-binding chamber. Intriguingly, despite the observation of a single AZ1 conformation in the crystal structure, our solution NMR investigation has revealed the presence of a second ligand conformation invisible in the crystalline state. Together, these distinct ligand conformations delineate a cryptic inhibitor envelope that expands the observed footprint of AZ1 in the LpxH-bound crystal structure and enables the design of AZ1 analogs with enhanced potency in enzymatic assays. These designed compounds display striking improvement in antibiotic activity over AZ1 against wild-type K. pneumoniae, and coadministration with outer membrane permeability enhancers profoundly sensitizes Escherichia coli to designed LpxH inhibitors. Remarkably, none of the sulfonyl piperazine compounds occupies the active site of LpxH, foretelling a straightforward path for rapid optimization of this class of antibiotics.

Most patients with relapsed or refractory ENKTL had poor prognosis with short survival. Further studies are warranted to determine the optimal treatment of patients with relapsed or refractory ENKTL.