Summary The establishment of pollen–pistil compatibility is strictly regulated by factors derived from both male and female reproductive structures. Highly diverse small cysteine‐rich proteins (CRPs) have been found to play multiple roles in plant reproduction, including the earliest stages of the pollen–stigma interaction. Secreted CRPs found in the pollen coat of members of the Brassicaceae, the pollen coat proteins (PCPs), are emerging as important signalling molecules that regulate the pollen–stigma interaction.Using a combination of protein characterization, expression and phylogenetic analyses we identified a novel class of Arabidopsis thaliana pollen‐borne CRPs, the PCP‐Bs (for pollen coat protein B‐class) that are related to embryo surrounding factor (ESF1) developmental regulators. Single and multiple PCP‐B mutant lines were utilized in bioassays to assess effects on pollen hydration, adhesion and pollen tube growth.Our results revealed that pollen hydration is severely impaired when multiple PCP‐Bs are lost from the pollen coat. The hydration defect also resulted in reduced pollen adhesion and delayed pollen tube growth in all mutants studied.These results demonstrate that At PCP‐Bs are key regulators of the hydration ‘checkpoint’ in establishment of pollen–stigma compatibility. In addition, we propose that interspecies diversity of PCP‐Bs may contribute to reproductive barriers in the Brassicaceae.

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients’ health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

Heart rate variability (HRV) is an effective predictor of congestive heart failure (CHF). However, important challenges exist regarding the effective temporal feature extraction and efficient classification using high-dimensional HRV representations. To solve these challenges, an ensemble method for CHF detection using short-term HRV data and deep neural networks was proposed. In this paper, five opensource databases, the BIDMC CHF database (BIDMC-CHF), CHF RR interval database (CHF-RR), MIT-BIH normal sinus rhythm (NSR) database, fantasia database (FD), and NSR RR interval database (NSR-RR), were used. Additionally, three RR segment length types (N = 500, 1000, and 2000) were used to evaluate the proposed method. First, we extracted the expert features of RR intervals (RRIs) and then built a long short-term memory-convolutional neural network-based network to extract deep-learning (DL) features automatically. Finally, an ensemble classifier was used for CHF detection using the above features. With blindfold validation (three CHF subjects and three normal subjects), the proposed method achieved 99.85%, 99.41%, and 99.17% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the BIDMC-CHF, NSR, and FD databases. With blindfold validation (six CHF subjects and six normal subjects), the proposed method achieved 83.84%, 87.54%, and 85.71% accuracy on N = 500, 1000, and 2000 length RRIs, respectively, using the NSR-RR and CHF-RR ndatabases. Based on feature ranking, the significant effectiveness provided by the DL features has been proven. The results have shown that the deep ensemble method can achieve reliable CHF detection using short-term heart rate signals and enable CHF detection through intelligent hardware. INDEX TERMS Electrocardiography, boosting, artificial intelligence.

Self-incompatibility (SI) involves specific interactions during pollination to reject incompatible ('self') pollen, preventing inbreeding in angiosperms. A key event observed in pollen undergoing the Papaver rhoeas SI response is the formation of punctate F-actin foci.Pollen tube growth is heavily energy-dependent, yet ATP levels in pollen tubes have not been directly measured during SI. Here we used transgenic Arabidopsis lines expressing the Papaver pollen S-determinant to investigate a possible link between ATP levels, cytosolic pH ([pH] cyt ) and alterations to the actin cytoskeleton.We identify for the first time that SI triggers a rapid and significant ATP depletion in pollen tubes. Artificial depletion of ATP triggered cytosolic acidification and formation of actin aggregates. We also identify in vivo, evidence for a threshold [pH] cyt of 5.8 for actin foci formation. Imaging revealed that SI stimulates acidic cytosolic patches adjacent to the plasma membrane.In conclusion, this study provides evidence that ATP depletion plays a pivotal role in SI upstream of programmed cell death and reveals a link between the cellular energy status, cytosolic acidification and alterations to the actin cytoskeleton in regulating Papaver SI in pollen tubes.