Phytohormones Trigger Drought Tolerance in Crop Plants: Outlook and Future Perspectives

In the past and present, human activities have been involved in triggering global warming, causing drought stresses that affect animals and plants. Plants are more defenseless against drought stress; and therefore, plant development and productive output are decreased. To decrease the effect of drought stress on plants, it is crucial to establish a plant feedback mechanism of resistance to drought. The drought reflex mechanisms include the physical stature physiology and biochemical, cellular, and molecular-based processes. Briefly, improving the root system, leaf structure, osmotic-balance, comparative water contents and stomatal adjustment are considered as most prominent features against drought resistance in crop plants. In addition, the signal transduction pathway and reactive clearance of oxygen are crucial mechanisms for coping with drought stress via calcium and phytohormones such as abscisic acid, salicylic acid, jasmonic acid, auxin, gibberellin, ethylene, brassinosteroids and peptide molecules. Furthermore, microorganisms, such as fungal and bacterial organisms, play a vital role in increasing resistance against drought stress in plants. The number of characteristic loci, transgenic methods and the application of exogenous substances [nitric oxide, (C28H48O6) 24-epibrassinolide, proline, and glycine betaine] are also equally important for enhancing the drought resistance of plants. In a nutshell, the current review will mainly focus on the role of phytohormones and related mechanisms involved in drought tolerance in various crop plants.

De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning

Motivation: The successful application of deep learning has promoted progress in protein model quality assessment. How to use model quality assessment to further improve the accuracy of protein structure prediction, especially not reliant on the existing templates, is helpful for unraveling the folding mechanism. Here, we investigate whether model quality assessment can be introduced into structure prediction to form a closed-loop feedback, and iteratively improve the accuracy of de novo protein structure prediction. Results: In this study, we propose a de novo protein structure prediction method called RocketX. In RocketX, a feedback mechanism is constructed through the geometric constraint prediction network GeomNet, the structural simulation module, and the model quality evaluation network EmaNet. In GeomNet, the co-evolutionary features extracted from MSA that search from the sequence databases are sent to an improved residual neural network to predict the inter-residue geometric constraints. The structure model is folded based on the predicted geometric constraints. In EmaNet, the 1D and 2D features are extracted from the folded model and sent to the deep residual neural network to estimate the inter-residue distance deviation and per-residue lDDT of the model, which will be fed back to GeomNet as dynamic features to correct the geometries prediction and progressively improve model accuracy. RocketX is tested on 483 benchmark proteins and 20 FM targets of CASP14. Experimental results show that the closed-loop feedback mechanism significantly contributes to the performance of RocketX, and the prediction accuracy of RocketX outperforms that of the state-of-the-art methods trRosetta (without templates) and RaptorX. In addition, the blind test results on CAMEO show that although no template is used, the prediction accuracy of RocketX on medium and hard targets is comparable to the advanced methods that integrate templates.

Observed slump of sea land breeze in Brisbane under the effect of aerosols from remote transport during 2019 Australian mega fire events

The 2019 Australian mega fires were unprecedented considering their intensity and consistency. There has been much research on the environmental and ecological effects of these mega fires, most of which focused on the effect of huge aerosol loadings and the ecological devastation. Sea land breeze (SLB) is a regional thermodynamic circulation closely related to coastal pollution dispersion, yet few have looked into how it is influenced by different types of aerosols transported from either nearby or remote areas. Mega fires provide an optimal scenario of large aerosol emissions. Near the coastal site of Brisbane Archerfield during January 2020, when mega fires were the strongest, reanalysis data from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) showed that mega fires did release huge amounts of aerosols, making aerosol optical depth (AOD) of total aerosols, black carbon (BC) and organic carbon (OC) approximately 240 %, 425 % and 630 % of the averages in other non-fire years. Using 20 years' wind observations of hourly time resolution from a global observation network managed by the National Oceanic and Atmospheric Administration (NOAA), we found that the SLB day number during that month was only 4, accounting for 33.3 % of the multi-years' average. The land wind (LW) speed and sea wind (SW) speed also decreased by 22.3 % and 14.8 % compared with their averages respectively. Surprisingly, fire spot and fire radiative power (FRP) analysis showed that heating effects and aerosol emission of the nearby fire spots were not the main causes of the local SLB anomaly, while the remote transport of aerosols from the fire centre was mainly responsible for the decrease of SW, which was partially offset by the heating effect of nearby fire spots and the warming effect of long-range transported BC and CO2. The large-scale cooling effect of aerosols on sea surface temperature (SST) and the burst of BC contributed to the slump of LW. The remote transport of total aerosols was mainly caused by free diffusion, while the large-scale wind field played a secondary role at 500 m. The large-scale wind field played a more important role in aerosol transport at 3 km than at 500 m, especially for the gathered smoke, but free diffusion remained the major contributor. The decrease of SLB speed boosted the local accumulation of aerosols, thus making SLB speed decrease further, forming a positive feedback mechanism.

Multilayer Extreme Learning Convolutional Feature Neural Network Model for the Weak Feature Classification and Status Identification of Planetary Bearing

A convolutional neural network has the characteristics of sharing information between layers, which can realize high-dimensional data processing. In general, the convolutional neural network uses a feedback mechanism to realize parameter self-regulation, which solves the disadvantages of manual parameter adjustment. However, it is unable to determine the iteration number with the best calculation accuracy. Calculation efficiency cannot be guaranteed while achieving the best accuracy. In this paper, a multilayer extreme learning convolutional neural network model is proposed for feature recognition and classification. Firstly, two-dimensional spatial characteristics of planetary bearing status data were enhanced. Then, extreme learning machine is embedded in a convolution layer to solve convex optimization problems. Finally, the parameters obtained from the training model were nested into a network to initialize the model parameters to separate each status feature. Planetary bearing experimental cases show the effectiveness and superiority of the proposed model in the recognition and classification of weak signals.

Evolution of the repression mechanisms in circadian clocks

Background Circadian (daily) timekeeping is essential to the survival of many organisms. An integral part of all circadian timekeeping systems is negative feedback between an activator and repressor. However, the role of this feedback varies widely between lower and higher organisms. Results Here, we study repression mechanisms in the cyanobacterial and eukaryotic clocks through mathematical modeling and systems analysis. We find a common mathematical model that describes the mechanism by which organisms generate rhythms; however, transcription’s role in this has diverged. In cyanobacteria, protein sequestration and phosphorylation generate and regulate rhythms while transcription regulation keeps proteins in proper stoichiometric balance. Based on recent experimental work, we propose a repressor phospholock mechanism that models the negative feedback through transcription in clocks of higher organisms. Interestingly, this model, when coupled with activator phosphorylation, allows for oscillations over a wide range of protein stoichiometries, thereby reconciling the negative feedback mechanism in Neurospora with that in mammals and cyanobacteria. Conclusions Taken together, these results paint a picture of how circadian timekeeping may have evolved.

An anomalous atmospheric river linked to the late June 2021 western North America heatwave

Atmospheric rivers (ARs) are long and narrow bands of enhanced water vapour flux concentrated in the lower troposphere. Many studies have documented the important role of cold-season ARs in producing heavy precipitation and triggering extreme flooding in many parts of the world. However, relatively little research has been conducted on the warm-season ARs and their impacts on extreme heatwave development. Here we show an anomalous warm-season AR moving across the North Pacific and its interaction with the western North American heatwave in late June 2021. We call it an “oriental express’’ to highlight its capability to transport tropical moisture to the west coast of North America from sources in Southeast Asia. Its landfall over the Alaska Panhandle lasted for more than two days and resulted in significant spillover of moisture into western Canada. We provide evidence that the injected water vapour was trapped under the heat dome and may have formed a positive feedback mechanism to regulate the heatwave development in western North America.

Coordinative Optimization Control of Microgrid Based on Model Predictive Control

This article describes how basing on the future behavior of microgrid system, forecasting renewable energy power generation, load and real-time electricity price, a model predictive control (MPC) strategy is proposed in this article to optimize microgrid operations, while meeting the time-varying requirements and operation constraints. Considering the problems of unit commitment, energy storage, economic dispatching, sale-purchase of electricity and load reduction schedule, the authors first model a microgrid system with a large number of constraints and variables to model the power generation technology and physical characteristics. Meanwhile the authors use a mixed logic dynamical framework to guarantee a reasonable behavior for grid interaction and storage and consider the influences of battery life and recession. Then for forecasting uncertainties in the microgrid, a feedback mechanism is introduced in MPC to solve the problem by using a receding horizon control. The objective of minimizing the operation costs is achieved by an MPC strategy for scheduling the behaviors of components in the microgrid. Finally, a comparative analysis has been carried out between the MPC and some traditional control methods. The MPC leads to a significant improvement in operating costs and on the computational burden. The economy and efficiency of the MPC are shown by the simulations.