IMPORTANCE Although almost equal numbers of male and female medical students enter into ophthalmology residency programs, whether they have similar surgical experiences during training is unclear.OBJECTIVE To determine differences for cataract surgery and total procedural volume between male and female residents during ophthalmology residency.

Hepatocellular carcinoma (HCC) accounts for approximately 85%-90% of primary liver cancers. Based on in silico analysis, differentially expressed long non-coding RNA (lncRNA) LINC01224 in HCC, the downstream microRNA (miRNA) miR-330-5p, and its target gene checkpoint kinase 1 (CHEK1) were selected as research subjects. Herein, this study was designed to evaluate their interaction effects on the malignant phenotypes of HCC cells. LINC01224 and CHEK1 were upregulated and miR-330-5p was downregulated in HCC cells. miR-330-5p shared negative correlations with LINC01224 and CHEK1, and LINC01224 shared a positive correlation with CHEK1. Notably, LINC01224 could specifically bind to miR-330-5p, and CHEK1 was identified as a target gene of miR-330-5p. When LINC01224 was silenced or miR-330-5p was elevated, the sphere and colony formation abilities and proliferative, migrative, and invasive potentials of HCC cells were diminished, while cell cycle arrest and apoptosis were enhanced. Moreover, LINC01224 induced HCC progression in vitro and accelerated tumor formation in nude mice by increasing CHEK1 expression.The key findings of the present study demonstrated that silencing LINC01224 could downregulate the expression of CHEK1 by competitively binding to miR-330-5p, thus inhibiting HCC progression. This result highlights the LINC01224/miR-330-5p/CHEK1 axis as a novel molecular mechanism involved in the pathology of HCC.

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Precis: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the “best case” consensus between the ophthalmologists. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Furthermore, the high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. Purpose: The purpose of this study was to evaluate the performance of a deep learning system for the identification of glaucomatous optic neuropathy. Materials and Methods: Six ophthalmologists and the deep learning system, Pegasus, graded 110 color fundus photographs in this retrospective single-center study. Patient images were randomly sampled from the Singapore Malay Eye Study. Ophthalmologists and Pegasus were compared with each other and to the original clinical diagnosis given by the Singapore Malay Eye Study, which was defined as the gold standard. Pegasus’ performance was compared with the “best case” consensus scenario, which was the combination of ophthalmologists whose consensus opinion most closely matched the gold standard. The performance of the ophthalmologists and Pegasus, at the binary classification of nonglaucoma versus glaucoma from fundus photographs, was assessed in terms of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC), and the intraobserver and interobserver agreements were determined. Results: Pegasus achieved an AUROC of 92.6% compared with ophthalmologist AUROCs that ranged from 69.6% to 84.9% and the “best case” consensus scenario AUROC of 89.1%. Pegasus had a sensitivity of 83.7% and a specificity of 88.2%, whereas the ophthalmologists’ sensitivity ranged from 61.3% to 81.6% and specificity ranged from 80.0% to 94.1%. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Intraobserver agreement ranged from 0.62 to 0.97 for ophthalmologists and was perfect (1.00) for Pegasus. The deep learning system took ∼10% of the time of the ophthalmologists in determining classification. Conclusions: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the “best case” consensus between the ophthalmologists. The high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. Future work will extend this study to a larger sample of patients.

Rice bean (Vigna umbellata) is an underexploited domesticated legume crop consumed for dietary protein in Asia, yet little is known about the genetic diversity of this species. Here, we present a high-quality reference genome for a rice bean landrace (FF25) built using PacBio long-read data and a Hi-C chromatin interaction map, and assess the phylogenetic position and speciation time of rice bean within the Vigna genus. We sequence 440 landraces (two core collections), and GWAS based on data for growth sites at three widely divergent latitudes reveal loci associated with flowering and yield. Loci harboring orthologs of FUL (FRUITFULL), FT (FLOWERING LOCUS T), and PRR3 (PSEUDO-RESPONSE REGULATOR 3) contribute to the adaptation of rice bean from its low latitude center of origin towards higher latitudes, and the landraces which pyramid early-flowering alleles for these loci display maximally short flowering times. We also demonstrate that copy-number-variation for VumCYP78A6 can regulate seed-yield traits. Intriguingly, 32 landraces collected from a mountainous region in South-Central China harbor a recently acquired InDel in TFL1 (TERMINAL FLOWER1) affecting stem determinacy; these materials also have exceptionally high values for multiple human-desired traits and could therefore substantially advance breeding efforts to improve rice bean.