Frequency shift surface-enhanced Raman scattering (SERS) achieves multiplex microRNA sensing for early serological diagnosis of, and discrimination between, primary liver cancers in a patient cohort for whom only biopsy is effective clinically. Raman reporters microprinted on plasmonic substrates shift their vibrational frequencies upon biomarker binding with a dynamic range allowing direct, multiplex assay of serum microRNAs and the current best protein biomarker, α-fetoprotein. Benchmarking against current gold-standard polymerase chain reaction and chemiluminescence methods validates the assay. The work further establishes the frequency shift approach, sensing shifts in an intense SERS band, as a viable alternative to conventional SERS sensing which involves the more difficult task of resolving a peak above noise at ultralow analyte concentrations.

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Soil moisture is a key parameter in hydrological research and drought management. The inversion of soil moisture based on land surface temperature (LST) and NDVI triangular feature spaces has been widely used in various studies. Remote sensing provides regional LST data with coarse spatial resolutions which are insufficient for field scale (tens of meters). In this study, we bridged the data gap by adopting a Data Mining Sharpener algorithm to downscale MODIS thermal data with Vis-NIR imagery from Sentinel-2. To evaluate the downscaling algorithm, an unmanned aerial system (UAS) equipped with a thermal sensor was used to capture the ultra-fine resolution LST at three sites in the Tang River Basin in China. The obtained fine-resolution LST data were then used to calculate the Temperature Vegetation Dryness Index (TVDI) for soil moisture monitoring. Results indicated that downscaled LST data from satellites showed spatial patterns similar to UAS-measured LST, although discrepancies still existed. Based on the fine-resolution LST data, a 10-m resolution TVDI map was generated. Significant negative correlations were observed between the TVDI and in-situ soil moisture measurements (Pearson’s r of −0.67 and −0.71). Overall, the fine-resolution TVDI derived from the downscaled LST has a high potential for capturing spatial soil moisture variation.