[Effects of shading on photosynthetic physiology and energy metabolism of Asarum forbesii].

Light is the main source for plants to obtain energy.Asarum forbesii is a typical shade medicinal plant, which generally grows in the shady and wet place under the bushes or beside the ditches.It can grow and develop without too much light intensity.This experiment explores the effects of shading on the growth, physiological characteristics and energy metabolism of A.forbesii, which can provide reference and guidance for its artificial planting.In this experiment, A.forbesii was planted under 80%, 60%, 40%, 20% and no shade.During the vigorous growth period, the photosynthetic physiological characteristics such as fluorescence parameters, photosynthetic parameters, photosynthetic pigment content and ultrastructure, as well as the content of mitochondrial electron transport chain(ETC) synthase and nutrients were measured.The results showed that the photosynthetic pigment content, chlorophyll fluorescence parameters and net photosynthesis rate(P_n) decreased with the decrease of shading.Under 20%-40% shading treatment, the plants had damaged ultrastructure, expanded and disintegrated chloroplast, disordered stroma lamella and grana lamella, and increased osmiophi-lic granules and starch granules.The activities of nicotinamide adenine dinucleotide dehydrogenase(NADH), succinate dehydrogenase(SDH), cytochrome C oxidoreductase(CCO) and adenosine triphosphate(ATP) synthasewere positively related to light intensity.With the reduction of shading, the content of total sugar and protein in nutrients increased first and then decreased, and the content was the highest under 60% shade.In conclusion, under 60%-80% shading treatment, the chloroplast and mitochondria had more complete structure, faster energy metabolism, higher light energy-conversion efficiency, better absorption and utilization of light energy and more nutrient synthesis, which was more suitable for the growth and development of A.forbesii.

Context-guided entropy minimization for semi-supervised domain adaptation.

Semi-Supervised Domain Adaptation has been widely studied with various approaches to address domain shift with labeled source-domain data combined with scarcely labeled target-domain data. Model adaptation is becoming promising with a paradigm of source pre-training and target fine-tuning, which eliminates the simultaneous availability of data from both domains and makes for data privacy. Among the model adaptation methods, Entropy Minimization (EM) is popularly incorporated to encourage a low-density separation on target samples. However, EM tends to brutally force models to make over-confident predictions, which could make the models collapse with deteriorated performance. In this paper, we first study the over-confidence of EM with a quantitative analysis, which shows the importance of capturing the dependency among labels. To address this issue, we propose to guide EM via longitudinal self-distillation. Specifically, we produce a dynamic "teacher" label distribution during training by constructing a graph on target data and perform pseudo-label propagation to encourage the "teacher" distribution to capture context category dependency based on a global data structure. Then EM is guided longitudinally by distilling the learned label distribution to combat the brute-force over-confidence. Extensive experiments demonstrate the effectiveness of our methods.

AI-Model for Identifying Pathologic Myopia Based on Deep Learning Algorithms of Myopic Maculopathy Classification and "Plus" Lesion Detection in Fundus Images.

Background: Pathologic myopia (PM) associated with myopic maculopathy (MM) and "Plus" lesions is a major cause of irreversible visual impairment worldwide. Therefore, we aimed to develop a series of deep learning algorithms and artificial intelligence (AI)-models for automatic PM identification, MM classification, and "Plus" lesion detection based on retinal fundus images. Materials and Methods: Consecutive 37,659 retinal fundus images from 32,419 patients were collected. After excluding 5,649 ungradable images, a total dataset of 32,010 color retinal fundus images was manually graded for training and cross-validation according to the META-PM classification. We also retrospectively recruited 1,000 images from 732 patients from the three other hospitals in Zhejiang Province, serving as the external validation dataset. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and quadratic-weighted kappa score were calculated to evaluate the classification algorithms. The precision, recall, and F1-score were calculated to evaluate the object detection algorithms. The performance of all the algorithms was compared with the experts' performance. To better understand the algorithms and clarify the direction of optimization, misclassification and visualization heatmap analyses were performed. Results: In five-fold cross-validation, algorithm I achieved robust performance, with accuracy = 97.36% (95% CI: 0.9697, 0.9775), AUC = 0.995 (95% CI: 0.9933, 0.9967), sensitivity = 93.92% (95% CI: 0.9333, 0.9451), and specificity = 98.19% (95% CI: 0.9787, 0.9852). The macro-AUC, accuracy, and quadratic-weighted kappa were 0.979, 96.74% (95% CI: 0.963, 0.9718), and 0.988 (95% CI: 0.986, 0.990) for algorithm II. Algorithm III achieved an accuracy of 0.9703 to 0.9941 for classifying the "Plus" lesions and an F1-score of 0.6855 to 0.8890 for detecting and localizing lesions. The performance metrics in external validation dataset were comparable to those of the experts and were slightly inferior to those of cross-validation. Conclusion: Our algorithms and AI-models were confirmed to achieve robust performance in real-world conditions. The application of our algorithms and AI-models has promise for facilitating clinical diagnosis and healthcare screening for PM on a large scale.

Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.

Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale.