Activatable NIR Fluorescence Probe for Epinephrine Detection and Bioimaging Based on Anionic Heptamethine Cyanine.

Epinephrine (EP) is an essential catecholamine in the human body. Currently, most EP detection methods are not suitable for in vivo detection due to material limitations. An organic small molecule fluorescent probe based on a chemical cascade reaction for the detection of EP was designed. Anionic heptamethine cyanine dye was selected as a fluorescent dye because of its NIR fluorescence emission with excellent biocompatibility. The secondary amine of EP nucleophilically attacks the carbonate of the probe with its stronger nucleophilicity and further undergoes intramolecular nucleophilic cyclization to release the fluorophore. Other substances containing only primary amines or no ß-OH lack reaction competitiveness due to their weaker nucleophilicity or inability to undergo further cyclization. The fluorescence recovery of the probe was linearly related to the EP concentration of 2-75 µmol/L. The detection limit was 0.4 µmol/L. The recovery rate was 94.78-111.32%. Finally, we successfully achieved bioimaging of EP in living cells and EP analogue in nematodes.

Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

Background and Aims: Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). Methods: The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Key Results: Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. Conclusions: We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels.

Using machine learning to understand the implications of meteorological conditions for fish kills.

Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet (Mugil cephalus) using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2-3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems.