Comprehensive assessment of drought resistance and recovery in kiwifruit genotypes using multivariate analysis.

As global climate change persists, ongoing warming exposes plants, including kiwifruit, to repeated cycles of drought stress and rewatering, necessitating the identification of drought-resistant genotypes for breeding purposes. To better understand the physiological mechanisms underlying drought resistance and recovery in kiwifruit, moderate (40-45% field capacity) and severe (25-30% field capacity) drought stresses were applied, followed by rewatering (80-85% field capacity) to eight kiwifruit rootstocks in this study. We then conducted a multivariate analysis of 20 indices for the assessment of drought resistance and recovery capabilities. Additionally, we identified four principal components, each playing a vital role in coping with diverse water conditions. Three optimal indicator groups were pinpointed, enhancing precision in kiwifruit drought resistance and recovery assessment and simplifying the evaluation system. Finally, MX-1 and HW were identified as representative rootstocks for future research on kiwifruit's responses to moderate and severe drought stresses. This study not only enhances our understanding of the response mechanisms of kiwifruit rootstocks to progressive drought stress and recovery but also provides theoretical guidance for reliable screening of drought-adaptive kiwifruit genotypes.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.

AIM: To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). METHODS: Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. RESULTS: The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. CONCLUSION: The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.