The need for improved diagnostic methods in ophthalmology is acute, especially in the underdeveloped regions with limited access to specialists and advanced equipment. Therefore, we introduce VisionUnite, a novel vision-language foundation model for ophthalmology enhanced with clinical knowledge. VisionUnite has been pretrained on an extensive dataset comprising 1.24 million image-text pairs, and further refined using our proposed MMFundus dataset, which includes 296,379 high-quality fundus image-text pairs and 889,137 simulated doctor-patient dialogue instances. Our experiments indicate that VisionUnite outperforms existing generative foundation models such as GPT-4V and Gemini Pro. It also demonstrates diagnostic capabilities comparable to junior ophthalmologists. VisionUnite performs well in various clinical scenarios including open-ended multi-disease diagnosis, clinical explanation, and patient interaction, making it a highly versatile tool for initial ophthalmic disease screening. VisionUnite can also serve as an educational aid for junior ophthalmologists, accelerating their acquisition of knowledge regarding both common and underrepresented ophthalmic conditions. VisionUnite represents a significant advancement in ophthalmology, with broad implications for diagnostics, medical education, and understanding of disease mechanisms.

Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).

With the rapid development of distributed energy, traditional power consumers have been transformed into prosumers who both produce and consume power, and are able to conduct flexible peer-to-peer transactions. Blockchain is a distributed ledger technology suitable for peer-to-peer transactions. However, the single-chain blockchain cannot simultaneously support the network security check and decentralized power transactions. Therefore, a double-chain blockchain and the corresponding peer-to-peer transaction model are proposed with the reorganization of the transaction chain and power chain. The peer-to-peer independent decision-making transactions are achieved through the transaction chain (public chain) to maximize the prosumer's economic interests. The security check and adjusted transaction scheme are achieved through the power chain (alliance chain) with the power network constraint. Based on the Ethereum blockchain development platform, simulations of peer-to-peer transactions in the IEEE 14-node distribution network are implemented. The simulation results show that the designed double-chain blockchain has higher transaction processing efficiency than the single-chain blockchain. The secure and autonomous peer-to-peer transaction of prosumers can be well supported by the proposed method.