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Our understanding of broad-scale forest disturbances under climatic extremes remains incomplete. Drought, as a typical extreme event, is a key driver of forest mortality but there have been no reports on continental-scale quantification of its impact on forest mortality or how it compares to other natural or anthropogenic drivers. Thus, our ability to understand and predict broad-scale carbon cycling in response to changing climate and extreme events is limited. In this study, we applied an attribution approach based on different sources of data to quantify the area and potential carbon loss/transfer in continental U.S. (CONUS) from four types of disturbance: (1) anthropogenic (especially timber harvest); (2) fire; (3) drought-associated; and (4) other from 2000 to 2014. Our results showed that anthropogenic disturbances, fire, drought-associated disturbances, and other disturbances accounted for 54.3, 10.7, 12.7, and 22.3% of total canopy area loss, respectively. Anthropogenic disturbance was the most important driver contributing to 58.1% potential carbon loss/transfer in CONUS for 2000–2014. The potential carbon loss/transfer from natural disturbances (fire, drought, and other) for the same study period accounted for approximately 41.9% of the total loss/transfer from all agents, suggesting that natural disturbances also played a very important role in forest carbon turnover. Potential carbon loss/transfer associated with drought accounted for approximately 11.6% of the total loss/transfer in CONUS, which was of similar magnitude to potential carbon loss/transfer from fire (∼11.0%). The other natural disturbance accounted for 19.3% of potential carbon loss/transfer. Our results demonstrated the importance of the impacts of various disturbances on forest carbon stocks at the continental scale, and the drought-associated carbon loss/transfer data developed here could be used for evaluating the performance of predictive models of tree mortality under droughts.

Wetlands serve many important ecosystem services, yet the United States lacks up-to-date, high-resolution wetland inventories. New, automated techniques for developing wetland segmentation maps from high-resolution aerial imagery can improve our understanding of the location and amount of wetlands. We assembled training and testing data sets (patch sizes of 28 × 28 m2 and 56 × 56 m2) of high-resolution aerial imagery of wetlands using Illinois Natural History Survey wetland location data and National Agricultural Imagery Project data. Each patch was labeled as wetland or non-wetland. To augment these data sets with additional information, we incorporated digital surface and digital terrain models and topographic wetness index data in the same two patch sizes. Subsequently, we evaluated convolutional neural network (CNN) and Gaussian process-based machine learning methods to produce wetland segmentation maps. We developed the best performing method into a new CNN algorithm, WetSegNet. It exhibited an area under the curve of 98% when used with 56 × 56 m2 patch sizes. WetSegNet developed reliable wetland segmentation maps in test cases in which wetlands would have gone undetected using only the National Land Cover Database. The development of WetSegNet exemplifies the types of data sets and methods that are needed to accelerate the use of high-resolution aerial imagery towards an improved understanding of wetlands. This algorithm could be used by state and federal agencies or other groups to identify wetlands with higher accuracy and at a finer scale than previously possible.

Lipase-catalyzed synthesis offerulic acid oleyl alcohol ester in an ionic liquid (IL)/isooctane system was investigated. Considerable bioconversion and volumetric productivity were achieved in inexpensive 1-hexyl-3-methylimidazolium hexafluorophosphate ([Hmim][PF 6 ]) and 1-methyl-3-octylimidazolium hexafluorophosphate ([O mim ][PF 6 ]) mediated systems, and thus, the two types of ILs were selected for further optimization of variables. The results showed that, before reaching a maximum, the increase offerulic acid concentration, temperature, or enzyme dosage led to an increase in volumetric productivity. Variations of the ratios of IL/isooctane and concentrations of oleyl alcohol also profoundly affected the volumetric productivity. To a higher extent, [Hmim][PF 6 ]/isooctane and [Omim][PF 6 ]/isooctane show similar reaction behaviors. Under the optimized reaction conditions (60 °C, 150 mg of Novozym 435 and 100 mg of molecular sieves), up to 48.50 mg/mL productivity of oleyl feruleate could be achieved for the [Hmim][PF 6 ]/isooctane (0.5 mL/1.5 mL) system with a substrate concentration of ferulic acid of 0.08 mmol/mL and oleyl alcohol of 0.32 mmol; while an optimum volumetric productivity of 26.92 mg/mL was obtained for the [Omim] [PF 6 ]/ isooctane (0.5 mL/1.5 mL) system under a similar reaction condition other than the substrate concentrations of ferulic acid at 0.05 mmol/mL and oleyl alcohol at 0.20 mmol.

To develop a specific, rapid, and convenient immunochromatography assay (ICA) to detect melamine residues in dairy products and feed sample. Colloidal gold particles labeled with purified monoclonal antibody against anti-melamine were used as the detector reagent. The MEL-OVA (the conjugate of melamine and ovalbumin) and goat anti-mouse melamine IgG were blotted on the test and control regions of nitrocellulose membrane. The strip was then assembled with sample pad, absorbing pad, and dorsal shield. The limit of detection (LOD) is 50 microg/L. The test trip was applied to detect melamine in milk, milk powder, and animal feeds, with detection limits of 100 microg/L for milk, 100 ng/g for milk powder, 200 ng/g for feeds. Compared to LC-MS/MS, the ICA could be used to screen a large number of dairy products and feed samples for melamine residue.