A Sharding Scheme Based on Graph Partitioning Algorithm for Public Blockchain

Blockchain technology,with its attributes of decentralization,immutability,and traceability,has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes.However,transaction performance and scalability has become the main challenges hindering the widespread adoption of blockchain.Due to its inability to meet the demands of high-frequency trading,blockchain cannot be adopted in many scenarios.To improve the transaction capacity,researchers have proposed some on-chain scaling technologies,including lightning networks,directed acyclic graph technology,state channels,and shardingmechanisms,inwhich sharding emerges as a potential scaling technology.Nevertheless,excessive cross-shard transactions and uneven shard workloads prevent the sharding mechanism from achieving the expected aim.This paper proposes a graphbased sharding scheme for public blockchain to efficiently balance the transaction distribution.Bymitigating crossshard transactions and evening-out workloads among shards,the scheme reduces transaction confirmation latency and enhances the transaction capacity of the blockchain.Therefore,the scheme can achieve a high-frequency transaction as well as a better blockchain scalability.Experiments results show that the scheme effectively reduces the cross-shard transaction ratio to a range of 35%-56%and significantly decreases the transaction confirmation latency to 6 s in a blockchain with no more than 25 shards.

High-throughput“read-on-ski”automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids

Organoid models are used to study kidney physiology,such as the assessment of nephrotoxicity and underlying disease processes.Personalized human pluripotent stem cell-derived kidney organoids are ideal models for compound toxicity studies,but there is a need to accelerate basic and translational research in the field.Here,we developed an automated continuous imaging setup with the“read-on-ski”law of control to maximize temporal resolution with minimum culture plate vibration.High-accuracy performance was achieved:organoid screening and imaging were performed at a spatial resolution of 1.1µm for the entire multi-well plate under 3 min.We used the in-house developed multi-well spinning device and cisplatin-induced nephrotoxicity model to evaluate the toxicity in kidney organoids using this system.The acquired images were processed via machine learning-based classification and segmentation algorithms,and the toxicity in kidney organoids was determined with 95%accuracy.The results obtained by the automated“read-on-ski”imaging device,combined with label-free and non-invasive algorithms for detection,were verified using conventional biological procedures.Taking advantage of the close-to-in vivo-kidney organoid model,this new development opens the door for further application of scaled-up screening using organoids in basic research and drug discovery.