Golgi-localized APYRASE 1 is critical for Arabidopsis growth by affecting cell wall integrity under boron deficiency.

Many nucleoside triphosphate-diphosphohydrolases (NTPDases/APYRASEs, APYs) play a key role in modulating extracellular nucleotide levels. However, the Golgi-localized APYs, which help control glycosylation, have rarely been studied. Here, we identified AtAPY1, a gene encoding an NTPDase in the Golgi apparatus, which is required for cell wall integrity and plant growth under boron (B) limited availability. Loss of function in AtAPY1 hindered cell elongation and division in root tips while increasing the number of cortical cell layers, leading to swelling of the root tip and abundant root hairs under low B stress. Further, expression pattern analysis revealed that B deficiency significantly induced AtAPY1, especially in the root meristem and stele. Fluorescent-labeled AtAPY1-GFP localized to the Golgi stack. Biochemical analysis showed that AtAPY1 exhibited a preference of UDP and GDP hydrolysis activities. Consequently, the loss of function in AtAPY1 might disturb the homoeostasis of NMP-driven NDP-sugar transport, which was closely related to the synthesis of cell wall polysaccharides. Further, cell wall-composition analysis showed that pectin content increased and borate-dimerized RG-II decreased in apy1 mutants, along with a decrease in cellulose content. Eventually, altered polysaccharide characteristics presumably cause growth defects in apy1 mutants under B deficiency. Altogether, these data strongly support a novel role for AtAPY1 in mediating responses to low B availability by regulating cell wall integrity.

Sublingual Edaravone Dexborneol for the Treatment of Acute Ischemic Stroke: The TASTE-SL Randomized Clinical Trial.

Importance: Sublingual edaravone dexborneol, which can rapidly diffuse and be absorbed through the oral mucosa after sublingual exposure, is a multitarget brain cytoprotection composed of antioxidant and anti-inflammatory ingredients edaravone and dexborneol. Objective: To investigate the efficacy and safety of sublingual edaravone dexborneol on 90-day functional outcome in patients with acute ischemic stroke (AIS). Design, Setting, and Participants: This was a double-blind, placebo-controlled, multicenter, parallel-group, phase 3 randomized clinical trial conducted from June 28, 2021, to August 10, 2022, with 90-day follow-up. Participants were recruited from 33 centers in China. Patients randomly assigned to treatment groups were aged 18 to 80 years and had a National Institutes of Health Stroke Scale score between 6 and 20, a total motor deficit score of the upper and lower limbs of 2 or greater, a clinically diagnosed AIS symptom within 48 hours, and a modified Rankin Scale (mRS) score of 1 or less before stroke. Patients who did not meet the eligibility criteria or declined to participate were excluded. Intervention: Patients were assigned, in a 1:1 ratio, to receive sublingual edaravone dexborneol (edaravone, 30 mg; dexborneol, 6 mg) or placebo (edaravone, 0 mg; dexborneol, 60 µg) twice daily for 14 days and were followed up until 90 days. Main Outcomes and Measures: The primary efficacy outcome was the proportion of patients with mRS score of 1 or less on day 90 after randomization. Results: Of 956 patients, 42 were excluded. A total of 914 patients (median [IQR] age, 64.0 [56.0-70.0] years; 608 male [66.5%]) were randomly allocated to the edaravone dexborneol group (450 [49.2%]) or placebo group (464 [50.8%]). The edaravone dexborneol group showed a significantly higher proportion of patients experiencing good functional outcomes on day 90 after randomization compared with the placebo group (290 [64.4%] vs 254 [54.7%]; risk difference, 9.70%; 95% CI, 3.37%-16.03%; odds ratio, 1.50; 95% CI, 1.15-1.95, P = .003). The rate of adverse events was similar between the 2 groups (89.8% [405 of 450] vs 90.1% [418 of 464]). Conclusion and Relevance: Among patients with AIS within 48 hours, sublingual edaravone dexborneol could improve the proportion of those achieving a favorable functional outcome at 90 days compared with placebo. Trial Registration: ClinicalTrials.gov Identifier: NCT04950920.

A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station.

As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

The transcription factor BnaA9.WRKY47 coordinates leaf senescence and nitrogen remobilization in Brassica napus.

Nitrogen (N) is an essential macronutrient for plants, and its remobilization is key for adaptation to deficiency stress. However, there is limited understanding of the regulatory mechanisms of N remobilization in the important crop species Brassica napus (oilseed rape). Here, we report the identification of a transcription factor, BnaA9.WRKY47, that is induced by N starvation in a canola variety. At the seedling stage, BnaA9.WRKY47-overexpressing (OE) lines displayed earlier senescence of older leaves and preferential growth of juvenile leaves compared to the wild type under N starvation. At the field scale, the seed yield was significantly increased in the BnaA9.WRKY47-OE lines compared with the wild type when grown under N deficiency conditions and, conversely, it was reduced in BnaA9.WRKY47-knockout mutants. Biochemical analyses demonstrated that BnaA9.WRKY47 directly activates BnaC7.SGR1 to accelerate senescence of older leaves. In line with leaf senescence, the concentration of amino acids in the older leaves of the OE lines was elevated, and the proportion of plant N that they contained was reduced. This was associated with BnaA9.WRKY47 activating the amino acid permease BnaA9.AAP1 and the nitrate transporter BnaA2.NRT1.7. Thus, the expression of BnaA9.WRKY47 efficiently facilitated N remobilization from older to younger leaves or to seeds. Taken together, our results demonstrate that BnaA9.WRKY47 up-regulates the expression of BnaC7.SGR1, BnaA2.NRT1.7, and BnaA9AAP1, thus promoting the remobilization of N in B. napus under starvation conditions.

Calibration of Near Infrared Spectroscopy of Apples with Different Fruit Sizes to Improve Soluble Solids Content Model Performance.

The transmission spectrum of apples is affected by the fruit's size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with various apple diameters were collected by applying NIR spectroscopy detection equipment to compare the spectra differences among various apple diameter groups. The NIR spectra of apples were corrected by studying the extinction rates within different apples. The corrected spectra were used to develop a partial least squares prediction model for their soluble solids content. Compared with the prediction model of the soluble solids content of apples without size correction, the Rp of PLSR improved from 0.769 to 0.869 and RMSEP declined from 0.990 to 0.721 in the small fruit diameter group; the Rp of PLSR improved from 0.787 to 0.932 and RMSEP declined from 0.878 to 0.531 in the large fruit diameter group. The proposed apple spectra correction method is effective and can be used to reduce the influence of sample diameter on NIR spectra.

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia.

Arrhythmia is a cardiovascular disease that seriously affects human health. The identification and diagnosis of arrhythmia is an effective means of preventing most heart diseases. In this paper, a BiLSTM-Treg algorithm that integrates rhythm information is proposed to realize the automatic classification of arrhythmia. Firstly, the discrete wavelet transform is used to denoise the ECG signal, based on which we performed heartbeat segmentation and preserved the timing relationship between heartbeats. Then, different heartbeat segment lengths and the BiLSTM network model are used to conduct multiple experiments to select the optimal heartbeat segment length. Finally, the tree regularization method is used to optimize the BiLSTM network model to improve classification accuracy. And the interpretability of the neural network model is analyzed by analyzing the simulated decision tree generated in the tree regularization method. This method divides the heartbeat into five categories (nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fused heartbeats (F), and unknown heartbeats (Q)) and is validated on the MIT-BIH arrhythmia database. The results show that the overall classification accuracy of the algorithm is 99.32%. Compared with other methods of classifying heartbeat, the BiLSTM-Treg network model algorithm proposed in this paper not only improves the classification accuracy and obtains higher sensitivity and positive predictive value but also has higher interpretability.

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features.

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm.

Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification. The analysis results are output in the form of reports. In this system, by comparing and analyzing the classification accuracy of different feature sets and classifiers, the optimal classification algorithm is obtained and applied to the system. The algorithm accuracy of the system is tested based on the MIT-BIH data set. The result shows that AdaBoost + Random Forest achieved 99.11% accuracy with optimal feature sets. The intelligent heartbeat classification system based on this algorithm has also achieved good results on clinical data.