PubMed HealthSearch

Biomedical subjects

Shulong Li

Publications and source records attributed to Shulong Li.

2 recordsLinked to original sources

Pan-genome-based resequencing of 2,320 accessions reveals structural variations and accelerates breeding advances in cultivated peanut.

The cultivated peanut is a crucial global legume crop that is essential for food security and nutrition, particularly in developing regions. However, its limited genetic variation hampers breeding progress and yield improvement. Here we constructed a graph-based pan-genome for peanut, incorporating 14 genomes that represent all 6 peanut varieties. Using this pan-genome, we genotyped 2,320 accessions, covering 88.03% of ICRISAT and 59.21% of USDA core germplasm, enriching valuable resources for genomic studies and breeding. We cataloged genomic structural variations and investigated the role of homoeologous exchanges in population divergence. Through our pan-genome approach, we overcame the challenges of genotyping posed by homoeologous exchanges and identified key genes associated with flowering and dwarfism in peanut. By integrating superior haplotypes and germplasm resources guided by the pan-genome, we further developed high-yield dwarf lines. This work provides essential genomic resources to accelerate functional gene discovery and modern peanut breeding.

Journal Article

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans