Evaluation of the Arctic Sea-Ice Simulation on SODA3 Datasets

This study evaluates the Arctic sea-ice simulation of the SODA3 dataset driven by different atmospheric forcing fields and explores the errors of the Arctic sea-ice simulation caused by the forcing field.We find that the SODA3 data driven by different forcing fields represent a significant systematical error in the simulation of Arctic sea-ice concentration,showing a low concentration of thick ice and a high concentration of thin ice.In terms of sea-ice extent,the SODA3 data from different versions well characterize the interannual variability and declining trend in the observed data,but they overestimate the overall Arctic sea-ice extent,which is related to excessive simulation of ice in the sea-ice margin.Compared to observations,all the chosen SODA3 reanalysis versions driven by different atmospheric forcing generally tend to underestimate the Arctic sea-ice thickness,especially for thick ice in the multi-year sea-ice regions.Inaccurate simulations of Arctic sea-ice transport may partly explain the error in SODA3 sea-ice thickness in multi-year sea-ice areas.The results of different SDOA3 versions differ greatly in the Beaufort Sea,the Fram Strait,and the Central Arctic Sea.The difference in sea-ice thickness among different SODA3 versions is primarily due to the thermodynamic contribution,which may come from the diversity of atmospheric forcing fields.Our work provides a reference for using SODA3 data to study Arctic sea ice.

Multiomics metabolic and epigenetics regulatory network in cancer:A systems biology perspective

Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.