Diverse nature of the seasonally coastal eutrophication dominated by oceanic nutrients: An eco-system based analysis characterized by salmon migration and aquaculture.

Previously, studies of coastal eutrophication have usually focused on the nutrients input from adjacent land sectors, such as rivers, submarine-ground discharges, and atmospheric depositions. Here we report two examples of well-managed seasonal eutrophication phenomena in coastal marine environments, where nutrients come predominantly from offshore: one by humans and the other by nature (higher trophic animals). In the Sanggou Bay of North China, the total amount of incoming nutrients from the open Yellow Sea is taken up by seaweeds. Seaweed, in turn, supports bivalves culture activities and absorbs nutrients emitted by finfish. In the Academy Bay of Russian Far East, a relatively high plankton primary production sustains throughout the salmon-returning season when nutrients are released from the massive carcasses of dead fish after return from the ocean to their natal streams to spawn and die. This high plankton productivity, in turn, fuels higher trophic ecosystem constituents, including whale populations of global importance. In the future, dominance of nutrients from marine sources needs to be seriously considered in studies of coastal eutrophication.

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge.

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.