Search

A novel mechanism of organobase-mediated Brook rearrangement and C–C coupling in the copper-catalyzed reductive coupling of benzaldehyde and benzophenone is proposed. The results demonstrate that this reaction proceeds mainly through five sequential elementary steps: transmetalation, carbonyl addition, σ-bond metathesis, Brook rearrangement, and C–C coupling. The organobases played a significant role not only in forming the active catalyst but also in mediating the Brook rearrangement and chemoselectivity in homo- and cross-coupling. Brook rearrangement mediated by organobases is more favored than that without organobases. In the C–C coupling step, the cation bridge combines two O atoms with the same electronegativity to form a pre-reaction complex. Moreover, a significant charge difference is a major factor in the selectivity of carbonyl addition and C–C coupling.

Drainage water recycling (DWR) involves capture, storage, and reuse of surface and subsurface drainage water as irrigation to enhance crop production during critical times of the growing season. Our objectives were to synthesize 53 site‐years of data from 1996 to 2017 in the midwestern United States to determine the effect of DWR using primarily subirrigation on corn (Zea maysL.) grain yield and yield variability and to identify precipitation factors at key stages of corn development (V1–V8, V9–VT, R1–R2, R3–R4, and R5–R6) that correlated to an increase in yield with DWR. A generalized additive model was used to quantify and characterize the relationship between precipitation and corn grain yield during corn development stages and to determine if that relationship differed between DWR and free drainage (FD). Corn yield response to precipitation was generally similar between DWR and FD, except during the critical period of V9–R2, in which DWR was more resilient to precipitation extremes than FD. Drainage water recycling was generally more responsive than FD in years with low and normal precipitation (<181 mm). When precipitation was low (27–85 mm) from V9 to R2, DWR had higher yields (77% of the site‐years evaluated), with an average yield increase of 3.6 Mg ha−1(1.2–7.5 Mg ha−1). Overall, FD had 28% greater yield variability than DWR. Additional research is needed on DWR impacts on different soils and locations throughout this region to improve the stability of corn yields and to develop automated DWR systems for enhancing efficiency of water management with increasing climate variability.

Human‐induced landscape changes affect hydrologic responses (e.g., floods) that can be detected from a suite of satellite and model data sets. Tapping these vast data sets using machine learning algorithms can produce critically important and accurate insights. In the Red River of the North Basin in the United States, agricultural subsurface drainage (SD; so‐called tile drainage) systems have greatly increased since the late 1990s. Over this period, river flow in the Red River has markedly increased and 6 of 13 major floods during the past century have occurred in the past two decades. The impact of SD systems on river flow is elusive because there are surprisingly few SD records in the United States. In this study, Random Forest machine learning (RFML) classification method running on Google Earth Engine's cloud computing platform was able to capture SD within a field (30 m) and its expansion over time for a large watershed (>100,000 km2). The resulting RFML classifier drew from operational multiple satellites and model data sets (total 14 variables with 36 layers including vegetation, land cover, soil properties, and climate variables). The classifier identified soil properties and land surface temperature to be the strongest predictors of SD. The maps agreed well with SD permit records (overall accuracies of 76.9–87.0%) and corresponded with subwatershed‐level statistics (r= 0.77–0.96). It is expected that the maps produced with this data‐intensive machine learning approach will help water resource managers to assess the hydrological impact from SD expansion and improve flood predictions in SD‐dominated regions. Farmers install subsurface drainage pipes (so‐called tile drainage) to improve crop yields on poorly drained soils, which impacts hydrological response (e.g., floods). Consistent records of subsurface drainage expansion are needed to understand its impacts on water resources. In the Red River of the North Basin in the United States, subsurface drainage systems have increased since the late 1990s. Over this period, river flow in the Red River has markedly increased and 6 of 13 major floods during the past century have occurred in the past two decades. Because the current National Oceanic and Atmospheric Administration's National Weather Service flood forecasting model does not include subsurface drainage information, they sometimes overpredict or underpredict flood flows. We developed high‐resolution (30 m) subsurface drainage maps by combining multiple satellite “big” data and model products using a Random Forest machine learning classification via Google Earth Engine's cloud computing platform. The maps showed good agreement with available subsurface permit records. It is expected that the machine learning‐based subsurface drainage maps will help water resource managers and flood forecasters to improve flood prediction in agricultural dominated regions. High‐resolution subsurface drainage maps were developed using satellite big data and random forest machine learning via Google Earth EngineReliable subsurface drainage records are needed for sustainable water resource management, but such records are very limited in the United StatesWhile soil variables are important to identify potential drainage areas, land surface temperature distinguishes where drainage has occurred

Core IdeasSoil water release curve is important but difficult to measure.HYPROP and WP4 are used to develop the soil water release curves for three soils.HYPROP and WP4 provided a good and simple way to measure soil water release curves. A soil water release curve (SWRC) describes the critical and soil‐specific relationship between soil water content and matric potential. In this study, soil moisture and corresponding matric potentials were measured using (1) a new method by HYPROP and WP4 dewpoint potentiometer, and, (2) the traditional method by hanging water column, Tempe cell, and pressure plate. The SWRCs were developed for Fargo silty clay, Glyndon silty loam, and Hecla sandy loam soils by using the van Genuchten model. The goodness of fit between the fitted SWRC and the measured data agreed well with R2between 0.91 and 0.98. The comparison for the fitted SWRCs showed that the SWRCs for Hecla sandy loam soil provided the best agreement while Glyndon silty loam soil had the best match in terms of slope and shape. The SWRCs for Fargo silty clay soil did not provide a good match between the two methods. The difference in water content between the two fitted SWRCs was less than 2% for Glyndon silty loam and Hecla sandy loam soils. However, Fargo silty clay had a 4.5 to 5% difference for 66% of the measurements, possibly due to the different bulk densities caused by shrinkage and swelling nature of the clay soil. Since the best fitted van Genuchten parameters were within the reference range that was acceptable for the same type of soils, the HYPROP and WP4 can be used to develop SWRCs that are comparable to the traditional laboratory methods for the three soils in the Red River Valley.

Soil surface topography affects fundamental hydrologic processes, such as infiltration and soil water percolation. Topographic variations potentially alter both the magnitude and directions of unsaturated flow. The objective of this study is to evaluate the effects of surface topography on wetting front moving patterns under different rainfall and soil conditions through combined experimental and numerical modelling studies. Specifically, laboratory-scale infiltration and unsaturated flow experiments and HYDRUS-2D modelling were conducted for different topographic surfaces, rainfall intensities, and soil types. The simulated and observed wetting front distributions were compared and evaluated. Two different stages were observed: topography-dominated two-dimensional flow and uniform one-dimensional flow. A uniformly distributed wetting front was eventually achieved although soil surfaces had dissimilar topographic characteristics. However, the timing or duration to reach such a uniform flat wetting front varied, mainly depending on surface topography, rainfall characteristics, and soil hydraulic properties. The findings from this study are important to better understand the mechanism of topography-controlled unsaturated flow, wetting front movement, and overland flow generation, and to further improve modelling of soil water flow and transport processes under such complex conditions across different scales.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease that leads to progressive dyspnea and dry cough, with extracellular matrix deposition as the main pathological feature. Yifei Tongluo granules (YTG) are a traditional Chinese medicine formula that could nourish Qi-Yin, clear phlegm, and invigorate blood circulation. In this research, network pharmacology and molecular docking were used to elucidate the potential mechanism of YTG for treating IPF. A total of 278 biologically active compounds were included in YTG, and 16 compounds were selected for pharmacological analysis and molecular docking through “drugs-compounds-intersecting targets of YTG and IPF” network construction. Protein-protein interaction network was constructed using 330 YTG-IPF intersecting targets. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed. A total of 10 core targets were screened by protein-protein interaction, and molecular docking was used to further validate the binding ability of the compounds to the core targets. The network pharmacology and molecular docking results showed that Danshenol A, isorhamnetin, Ginsenoside-Rh4, quercetin, and kaempferol might be the main active compounds in the treatment of IPF by YTG, whereas MAPK1, MAPK3, EGFR, and SRC are the core targets while PI3K/AKT pathway and MAPK pathway are the main signaling pathways through which YTG regulates relevant biological processes to intervene in IPF. This study shows that YTG can treat IPF by inhibiting the epithelial-mesenchymal transit process, fibroblast proliferation, fibroblast-to-myofibroblast conversion, myofibroblast anti-apoptosis, collagen expression, and other mechanisms.YTG can be widely used as an adjuvant therapy for IPF in clinical practice, and this study provides the basis for subsequent experimental studies.

Due to poor drainage and flat terrain, a prolonged wet cycle since the early 1990s in the Red River Valley (RRV) of the North has caused frequent flooding in most farmland, delaying or even preventing spring planting. To combat high soil water content, farmers have adopted tile drainage practices. To schedule crop planting or to evaluate the efficiency of tile drainage and its potential impacts on water balance and quality in the watershed, soil water content and its changes need to be monitored. Compared to in situ measurements, the use of remote sensing for soil water content monitoring is affordable and can be applied over a large area repetitively. In this study multispectral reflectance of the soil sample from RRV at the bands of Landsat 5 TM sensor were evaluated for various soil water contents in the laboratory experiments. Empirically, the soil water contents at 5 and 15 cm were found to be best predicted using an exponential model based on the difference of bands 1 and 5. While the 5 cm model better represents remotely sensed soil water content, 15 cm model better represents root zone condition and therefore is more relevant for supporting field management decision. Because it was challenging to measure water content accurately at 5‐cm depth in the fields, only 15 cm model was validated. Validation using a total of 70 observations over nine different fields in the RRV showed that the model compared well with the field measurements (r= 0.94) with an average difference less than the model uncertainty of 0.02 cm3/cm3. The 15 cm model has an application range for soil water content between 0.20 and 0.40 cm3/cm3.