The rapid development of highly multiplexed microscopy systems has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging, and there is a need for more robust and generalizable tools for evaluating the cellular constituents and underlying stroma captured by high-plex imaging.


Adverse climatic events like heat stress, floods, unseasonal rainfall, and droughts frequently hinder crop productivity. Long-term crop yield data plays a crucial role in food security planning. This study presents historical wheat yield data at the satellite pixel level from 2001 to 2019 in Uttar Pradesh, India. We use various satellite indicators to develop wheat yield models, including the normalized difference vegetation index and gridded weather data, such as precipitation, temperature, and evapotranspiration.


One of the weak points of most of denoising algoritms (deep learning based ones) is the training data. Due to no or very limited amount of groundtruth data available, these algorithms are often evaluated using synthetic noise models such as Additive Zero-Mean Gaussian noise. The downside of this approach is that these simple model do not represent noise present in natural imagery.