The Multiple Promoting Effects of Suaeda glauca Root Exudates on the Growth of Alfalfa under NaCl Stress.

Under abiotic stress, plant root exudates can improve plant growth performance. However, studies on the effect of root exudates on the stress resistance of another plant are insufficient. In this study, root exudates (REs) were extracted from Suaeda glauca to explore their effect on alfalfa seedlings under salt stress. The results showed that the plant height and fresh weight of alfalfa significantly increased by 47.72% and 53.39% after 7 days of RE treatment at a 0.4% NaCl concentration. Under 1.2% salt stress, REs reduced the Malondialdehyde content in alfalfa by 30.14% and increased the activity of its antioxidant enzymes (peroxidase and catalase) and the content of its osmotic regulators (soluble sugar and proline) by 60.68%, 52%, 45.67%, and 38.67%, respectively. Soil enzyme activity and the abundance of soil-beneficial bacteria were increased by REs. Spearman analysis showed that urease and neutral phosphatase were related to the richness of beneficial bacteria. Redundancy analysis confirmed that urease affected the composition of the soil bacterial community. The partial least squares structural equation model (PLS-SEM) revealed that REs had a direct positive effect on alfalfa growth under salt stress by regulating the plant's injury and antioxidant systems, and the soil bacterial community had an indirect positive effect on alfalfa growth through soil enzyme activity.

Mining of long non-coding RNAs with target genes in response to rust based on full-length transcriptome in Kentucky bluegrass.

Kentucky bluegrass (Poa pratensis L.) is an eminent turfgrass species with a complex genome, but it is sensitive to rust (Puccinia striiformis). The molecular mechanisms of Kentucky bluegrass in response to rust still remain unclear. This study aimed to elucidate differentially expressed lncRNAs (DELs) and genes (DEGs) for rust resistance based on the full-length transcriptome. First, we used single-molecule real-time sequencing technology to generate the full-length transcriptome of Kentucky bluegrass. A total of 33,541 unigenes with an average read length of 2,233 bp were obtained, which contained 220 lncRNAs and 1,604 transcription factors. Then, the comparative transcriptome between the mock-inoculated leaves and rust-infected leaves was analyzed using the full-length transcriptome as a reference genome. A total of 105 DELs were identified in response to rust infection. A total of 15,711 DEGs were detected (8,278 upregulated genes, 7,433 downregulated genes) and were enriched in plant hormone signal transduction and plant-pathogen interaction pathways. Additionally, through co-location and expression analysis, it was found that lncRNA56517, lncRNA53468, and lncRNA40596 were highly expressed in infected plants and upregulated the expression of target genes AUX/IAA, RPM1, and RPS2, respectively; meanwhile, lncRNA25980 decreased the expression level of target gene EIN3 after infection. The results suggest that these DEGs and DELs are important candidates for potentially breeding the rust-resistant Kentucky bluegrass.

Automated multidimensional deep learning platform for referable diabetic retinopathy detection: a multicentre, retrospective study.

OBJECTIVE: To develop and validate a real-world screening, guideline-based deep learning (DL) system for referable diabetic retinopathy (DR) detection. DESIGN: This is a multicentre platform development study based on retrospective, cross-sectional data sets. Images were labelled by two-level certificated graders as the ground truth. According to the UK DR screening guideline, a DL model based on colour retinal images with five-dimensional classifiers, namely image quality, retinopathy, maculopathy gradability, maculopathy and photocoagulation, was developed. Referable decisions were generated by integrating the output of all classifiers and reported at the image, eye and patient level. The performance of the DL was compared with DR experts. SETTING: DR screening programmes from three hospitals and the Lifeline Express Diabetic Retinopathy Screening Program in China. PARTICIPANTS: 83 465 images of 39 836 eyes from 21 716 patients were annotated, of which 53 211 images were used as the development set and 30 254 images were used as the external validation set, split based on centre and period. MAIN OUTCOMES: Accuracy, F1 score, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), Cohen's unweighted κ and Gwet's AC1 were calculated to evaluate the performance of the DL algorithm. RESULTS: In the external validation set, the five classifiers achieved an accuracy of 0.915-0.980, F1 score of 0.682-0.966, sensitivity of 0.917-0.978, specificity of 0.907-0.981, AUROC of 0.9639-0.9944 and AUPRC of 0.7504-0.9949. Referable DR at three levels was detected with an accuracy of 0.918-0.967, F1 score of 0.822-0.918, sensitivity of 0.970-0.971, specificity of 0.905-0.967, AUROC of 0.9848-0.9931 and AUPRC of 0.9527-0.9760. With reference to the ground truth, the DL system showed comparable performance (Cohen's κ: 0.86-0.93; Gwet's AC1: 0.89-0.94) with three DR experts (Cohen's κ: 0.89-0.96; Gwet's AC1: 0.91-0.97) in detecting referable lesions. CONCLUSIONS: The automatic DL system for detection of referable DR based on the UK guideline could achieve high accuracy in multidimensional classifications. It is suitable for large-scale, real-world DR screening.

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.

Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

Differential Metabolomic Responses of Kentucky Bluegrass Cultivars to Low Nitrogen Stress.

Kentucky bluegrass (Poa pratensis L.) is a cool-season turfgrass species that responds strongly to nitrogen (N), but the metabolomic responses of this grass species to N supply is unknown. The N-tolerant cultivar Bluemoon and N-sensitive cultivar Balin were exposed to normal N (15 mM) and low N (0.5 mM) for 21 days for identification of differentially expressed metabolites (DEMs) between normal N and low N treatments. Balin had more reductions of chlorophyll and total soluble protein concentrations and a higher accumulation of superoxide radicals under low N stress. A total of 99 known DEMs were identified in either cultivar or both including 22 amino acids and derivatives, 16 carbohydrates, 29 organic acids, and 32 other metabolites. In Bluemoon, ß-alanine metabolism was most enriched, followed by alanine, aspartate, and glutamate metabolism, biosynthesis of valine, leucine, and isoleucine biosynthesis, and glycine, serine, and threonine metabolism. In Balin, alanine, aspartate, and glutamate metabolism were most enriched, followed by the tricarboxylic acid (TCA), glyoxylate and decarbohydrate metabolism, and carbon fixation. Bluemoon generally maintained higher TCA cycle capacity and had more downregulated amino acids, while changes in more organic acids occurred in Balin under low N stress. Some metabolite changes by low-N stress were cultivar-specific. The results suggested that regulation of metabolites related to energy production or energy saving could contribute to low N tolerance in Kentucky bluegrass.

Congenital cataract: prevalence and surgery age at Zhongshan Ophthalmic Center (ZOC).

Congenital cataract (CC) is the primary cause of treatable childhood blindness. Population-based assessments of prevalence and surgery age of CC, which are critical for improving management strategies, have been unavailable in China until now. We conducted a hospital-based, cross-sectional study of the hospital charts of CC patients younger than 18 years old from January 2005 to December 2010 at Zhongshan Ophthalmic Center (ZOC) in Guangzhou, China. Residence, gender, age at surgery, hospitalization time, and the presence of other ocular abnormalities were extracted and statistically analyzed in different subgroups. The search identified 1314 patients diagnosed with CC from a total of 136154 hospitalizations, which accounted for 2.39% of all the cataract in-patients and 1.06% of the total in-patients over the six-year study period. Of the identified CC patients, 9.2% had ≥ 2 hospitalizations due to the necessity of additional surgeries, with a total ratio of boys to girls of 1.75 ∶ 1. Based on a subgroup analysis according to age, patients 2-6 years old constituted the highest proportion (29.22%) of all hospitalized CC patients, and those 13-18 years old constituted the lowest proportion (13.47%) of the total number. The average age at surgery was 27.62 ± 23.36 months, but CC patients ≤ 6 years old (especially ≤ 6 months old) became increasingly prevalent throughout the 6-year study period. A total of 276 cases (20.93%) of CC were associated with one or more other ocular abnormalities, the highest incidence rates were observed for exotropia (6.24%), nystagmus (6.16%), and refractive error (3.65%). In conclusion, CC patients accounted for 2.39% of all cataract in-patients in a review of 6 years of hospitalization charts from ZOC. The age at the time of surgery decreased over the 6-year study period, which probably reflects the continuing improvement of public awareness of children's eye care in China.

An improved phase-locked loop method for automatic resonance frequency tracing based on static capacitance broadband compensation for a high-power ultrasonic transducer.

The phase-locked loop (PLL) method is widely used for automatic resonance frequency tracing (ARFT) of high-power ultrasonic transducers, which are usually vibrating systems with high mechanical quality factor (Qm). However, a heavily-loaded transducer usually has a low Qm because the load has a large mechanical loss. In this paper, a series of theoretical analyses is carried out to detail why the traditional PLL method could cause serious frequency tracing problems, including loss of lock, antiresonance frequency tracing, and large tracing errors. The authors propose an improved ARFT method based on static capacitance broadband compensation (SCBC), which is able to address these problems. Experiments using a generator based on the novel method were carried out using crude oil as the transducer load. The results obtained have demonstrated the effectiveness of the novel method, compared with the conventional PLL method, in terms of improved tracing accuracy (±9 Hz) and immunity to antiresonance frequency tracing and loss of lock.