Enhancement of electrokinetic-phytoremediation by Ophiopogon japonicus: stimulation of electrokinetic on root system and improvement of polycyclic aromatic hydrocarbon degradation.

Root systems are sensitive to voltage and tend to improve the degradation of organic pollutants by promoting the root exudates and increasing microbial enzyme activity in the rhizosphere under the effect of electrokinetic. In this study, electrokinetic-assisted phytoremediation (EKPR) was applied for the remediation of soil containing phenanthrene (PHE) and pyrene (PYR). Direct current (DC) voltage (1 V cm-1) was applied across the soils for 30 days following 3 treatment schedules (0 h, 4 h, and 12 h per day), referred to as treatments EK0, EK4, and EK12. Electrokinetic assistance improved phytoremediation. Compared to EK0, the removal of PHE and PYR increased by 51.79% and 45.07% for EK4 and by 43.18% and 38.75% for EK12. The applied voltage promoted root growth, stimulated the root exudate release, and increased accumulation of PHE and PYR by plants, and the effect was most pronounced in treatment EK4. Catalase and urease activities in rhizosphere soil also increased, by respective increments of 44.51% and 40.86% for EK4 and by 28.53% and 21.24% for EK12. In this study, we demonstrated that a low voltage applied for an appropriate duration (4 h per day) improves removal of PAHs by stimulating root growth, promoting the root exudate release and enhancing enzyme activity in the microbiome of rhizosphere soil.

OsLPR5 Encoding Ferroxidase Positively Regulates the Tolerance to Salt Stress in Rice.

Salinity is a major abiotic stress that harms rice growth and productivity. Low phosphate roots (LPRs) play a central role in Pi deficiency-mediated inhibition of primary root growth and have ferroxidase activity. However, the function of LPRs in salt stress response and tolerance in plants remains largely unknown. Here, we reported that the OsLPR5 was induced by NaCl stress and positively regulates the tolerance to salt stress in rice. Under NaCl stress, overexpression of OsLPR5 led to increased ferroxidase activity, more green leaves, higher levels of chlorophyll and lower MDA contents compared with the WT. In addition, OsLPR5 could promote the accumulation of cell osmotic adjustment substances and promote ROS-scavenging enzyme activities. Conversely, the mutant lpr5 had a lower ferroxidase activity and suffered severe damage under salt stress. Moreover, knock out of OsLPR5 caused excessive Na+ levels and Na+/K+ ratios. Taken together, our results exemplify a new molecular link between ferroxidase and salt stress tolerance in rice.

Robust Brain Age Estimation Based on sMRI via Nonlinear Age-Adaptive Ensemble Learning.

Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients' brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.

Acoustic micro-Doppler radar for human gait imaging.

A portable acoustic micro-Doppler radar system for the acquisition of human gait signatures in indoor and outdoor environments is reported. Signals from an accelerometer attached to the leg support the identification of the components in the measured micro-Doppler signature. The acoustic micro-Doppler system described in this paper is simpler and offers advantages over the widely used electromagnetic wave micro-Doppler radars.