Xiaobin Guan

Normalized difference vegetation index (NDVI) derived from satellites has been ubiquitously utilized in the field of remote sensing. Nevertheless, there are multitudinous contaminations in NDVI time series because of the atmospheric disturbance, cloud cover, sensor failure, and so on. It is crucial to remove the noises prior to further applications. Numerous techniques have been proposed to alleviate this issue in the last few decades. To the best of our knowledge, there hasn’t been a systematical study to summarize and analyze the status of NDVI time series reconstruction techniques since 1980s. As a result, our goal is to recapitulate the current approaches for reconstructing high-quality NDVI time series, followed by an interpretation on the principle, merits and demerits of different kinds of methods. They were mainly classified into temporal-based methods, frequency-based methods and hybrid methods. The evaluation approaches on the quality of NDVI reconstruction were introduced, accompanied with the future development tendency.

-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.

Image mosaicking is often a necessary process to cover a large and full region of interest (ROI) for many remote sensing applications (e.g., geographical mapping, resource and environmental monitoring, and disaster monitoring).