Ultrasensitive detection of plant hormone abscisic acid-based surface-enhanced Raman spectroscopy aptamer sensor.

Abscisic acid (ABA), as the most common plant hormone in the growth of wheat, can greatly affect the yield when its levels deviate from normal. Therefore, highly sensitive and selective detection of this hormone is greatly needed. In this work, we developed an aptamer sensor based on surface-enhanced Raman spectroscopy (SERS) and applied it for the high sensitivity detection of ABA. Biotin-modified ABA aptamer complement chains were modified on ferrosoferric oxide magnetic nanoparticles (Fe3O4MNPs) and acted as capture probes, and sulfhydryl aptamer (SH-Apt)-modified silver-coated gold nanospheres (Au@Ag NPs) were used as signal probes. Through the recognition of the ABA aptamer and its complementary chains, an aptamer sensor based on SERS was constructed. As SERS internal standard molecules of 4-mercaptobenzoic acid (4-MBA) were encapsulated between the gold core and silver shell of the signal probes; the constructed aptamer sensor generated a strong SERS signal of 4-MBA after magnetic separation. When there were ABA molecules in the detection system, with the preferential binding of ABA aptamer and ABA molecule, the signal probes were released from the capture probes, after magnetic separation, leading to a linear decrease in SERS intensity of 4-MBA. Thus, the detection response was linear over a logarithmic concentration range, with an ultra-low detection limit of 0.67 fM. In addition, the practical use of this assay method was demonstrated in ABA detection from fresh wheat leaves, with a relative error (RE) of 5.43-8.94% when compared with results from enzyme-linked immunosorbent assay (ELISA). The low RE value proves that the aptamer sensor will be a promising method for ABA detection.

Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment.

Ongoing climate variability and change is impacting pollen exposure dynamics among sensitive populations. However, pollen data that can provide beneficial information to allergy experts and patients alike remains elusive. The lack of high spatial resolution pollen data has resulted in a growing interest in using phenology information that is derived using satellite observations to infer key pollen events including start of pollen season (SPS), timing of peak pollen season (PPS), and length of pollen season (LPS). However, it remains unclear if the agreement between satellite-based phenology information (e.g. start of season: SOS) and the in-situ pollen dynamics vary based on the type of satellite product itself or the processing methods used. To address this, we investigated the relationship between vegetation phenology indicator (SOS) derived from two separate sensor/satellite observations (MODIS, Landsat), and two different processing methods (double logistic regression (DLM) vs hybrid piecewise logistic regression (HPLM)) with in-situ pollen season dynamics (SPS, PPS, LPS) for three dominant allergenic tree pollen species (birch, oak, and poplar) that dominate the springtime allergy season in North America. Our results showed that irrespective of the data processing method (i.e. DLM vs HPLM), the MODIS-based SOS to be more closely aligned with the in-situ SPS, and PPS while upscaled Landsat based SOS had a better precision. The data products obtained using DLM processing methods tended to perform better than the HPLM based methods. We further showed that MODIS based phenology information along with temperature and latitude can be used to infer in-situ pollen dynamic for tree pollen during spring time. Our findings suggest that satellite-based phenology information may be useful in the development of early warning systems for allergic diseases.