Synthetic biology-inspired cell engineering in diagnosis,treatment,and drug development

The fast-developing synthetic biology(SB)has provided many genetic tools to reprogram and engineer cells for improved performance,novel functions,and diverse applications.Such cell engineering resources can play a critical role in the research and development of novel therapeutics.However,there are certain limitations and challenges in applying genetically engineered cells in clinical practice.This literature review updates the recent advances in biomedical applications,including diagnosis,treatment,and drug development,of SB-inspired cell engineering.It describes technologies and relevant examples in a clinical and experimental setup that may significantly impact the biomedicine field.At last,this review concludes the results with future directions to optimize the performances of synthetic gene circuits to regulate the therapeutic activities of cell-based tools in specific diseases.

Land cover classification from remote sensing images based on multi-scale fully convolutional network

Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.