Plant Penetration Research Articles

PARylation of 14-3-3 proteins controls the virulence of Magnaporthe oryzae

Magnaporthe oryzae is a devastating fungal pathogen that causes the rice blast disease worldwide. The post-translational modification of ADP-ribosylation holds significant importance in various fundamental biological processes. However, the specific function of this modification in M. oryzae remains unknown. This study revealed that Poly(ADP-ribosyl)ation (PARylation) executes a critical function in M. oryzae. M. oryzae Poly(ADP-ribose) polymerase 1 (PARP1) exhibits robust PARylation activity. Disruption of PARylation by PARP1 knock-out or chemical inhibition reveals its involvement in M. oryzae virulence, particularly in appressorium formation. Furthermore, we identified two M. oryzae 14-3-3 proteins, GRF1 and GRF2, as substrates of PARP1. Deletion of GRF1 or GRF2 results in delayed and dysfunctional appressorium, diminished plant penetration, and reduced virulence of the fungus. Biochemical and genetic evidence suggest that PARylation of 14-3-3s is essential for its function in M. oryzae virulence. Moreover, PARylation regulates 14-3-3 dimerization and is required for the activation of the mitogen-activated protein kinases (MAPKs), Pmk1 and Mps1. GRF1 interacts with both Mst7 and Pmk1, and bridges their interaction in a PARylation-dependent manner. This study unveils a distinctive mechanism that PARylation of 14-3-3 proteins controls appressorium formation through MAPK activation, and could facilitate the development of new strategies of rice blast disease control.

Shifts in the altitudinal distribution of alien and native synanthropic plants along roadsides over a 40-year period

Disturbed areas alongside roads shape unique vegetation communities dominated by synanthropic native and alien plant species, facilitated by roads serving as corridors for dispersal, penetration, and integration of synanthropic plants into new areas. We collected presence/absence data for individual species along roads in the Orlické Mountains, Czech Republic, in three time periods during 1970–2010. The distribution of species was mapped using a 1-square-kilometer grid. Analyzing 107 plant species (44 native, 63 alien), we found archaeophyte species favor lower elevations (foothills), while neophytes thrive at higher elevations. Over 40 years, neophyte frequency significantly rose at lower elevations, contrasting the decline at higher elevations, with no marked change in archaeophyte frequency. Native species decline notably at 400–500 m elevations. Some thermophilous native synanthropic species and alien archaeophytes spread from foothills to higher elevations, while some psychrophilic higher-elevation species shift upwards, diminishing at lower elevations. We emphasize human disturbance and global warming as pivotal factors influencing the altitudinal distribution shift in both native and alien plant species.

Probabilistic load flow based voltage stability assessment of solar power integration into power grids

The interconnection of renewable energies increases the complexity of modern power systems. Hence, stability assessments should be made to ensure the system’s stability after penetration. Solar power is a type of renewable energy that has become a widespread energy source among renewable energy sources. About 1 MWp of the solar power plant has been prepared to be interconnected to the IEEE 8 bus of Manokwari grid, and this paper investigates the voltage profile, power losses, and stability of the solar power plant penetration using an adaptive kernel density estimator (AKDE) and compares it to a Monte Carlo simulation (MCS)-based probabilistic load flow (PLF). About 5000 samples have been used to test the grid after the connection. Results of simulations show that the solar penetration can reduce power losses from 0.4084 MW to 0.3080 MW and 0.3045 MW by the proposed method and MCS method, respectively, and further increase the bus voltage profile. The power network has the stability to be connected to solar power, as indicated by the small stability index values of each bus. The proposed method using the AKDE method has a more accurate result in stability indices indicated by small fast voltage stability index (FVSI), line stability index (Lmn), and line stability factor (LQP) indices.

Plant triterpenoid saponins function as susceptibility factors to promote the pathogenicity of Botrytis cinerea

The gray mold fungus Botrytis cinerea is a necrotrophic pathogen that causes diseases in hundreds of plant species, including high-value crops. Its polyxenous nature and pathogenic success are due to its ability to perceive host signals in its favor. In this study, we found that laticifer cells of Euphorbia lathyris are a source of susceptibility factors required by B. cinerea to cause disease. Consequently, poor-in-latex (pil) mutants, which lack laticifer cells, show full resistance to this pathogen, whereas lot-of-latex mutants, which produce more laticifer cells, are hypersusceptible. These S factors are triterpenoid saponins, which are widely distributed natural products of vast structural diversity. The downregulation of laticifer-specific oxydosqualene cyclase genes, which encode the first committed step enzymes for triterpene and, therefore, saponin biosynthesis, conferred disease resistance to B. cinerea. Likewise, the Medicago truncatula lha-1 mutant, compromised in triterpenoid saponin biosynthesis, showed enhanced resistance. Interestingly, the application of different purified triterpenoid saponins pharmacologically complemented the disease-resistant phenotype of pil and hla-1 mutants and enhanced disease susceptibility in different plant species. We found that triterpenoid saponins function as plant cues that signal transcriptional reprogramming in B. cinerea, leading to a change in its growth habit and infection strategy, culminating in the abundant formation of infection cushions, the multicellular appressoria apparatus dedicated to plant penetration and biomass destruction in B. cinerea. Taken together, these results provide an explanation for how plant triterpenoid saponins function as disease susceptibility factors to promote B. cinerea pathogenicity.

Row1, a member of a new family of conserved fungal proteins involved in infection, is required for appressoria functionality in Ustilago maydis.

The appressorium of phytopathogenic fungi is a specific structure with a crucial role in plant cuticle penetration. Pathogens with melanized appressoria break the cuticle through cell wall melanization and intracellular turgor pressure. However, in fungi with nonmelanized appressorium, the mechanisms governing cuticle penetration are poorly understood. Here we characterize Row1, a previously uncharacterized appressoria-specific protein of Ustilago maydis that localizes to membrane and secretory vesicles. Deletion of row1 decreases appressoria formation and plant penetration, thereby reducing virulence. Specifically, the Δrow1 mutant has a thicker cell wall that is more resistant to glucanase degradation. We also observed that the Δrow1 mutant has secretion defects. We show that Row1 is functionally conserved at least among Ustilaginaceae and belongs to the Row family, which consists of five other proteins that are highly conserved among Basidiomycota fungi and are involved in U. maydis virulence. We observed similarities in localization between Row1 and Row2, which is also involved in cell wall remodelling and secretion, suggesting similar molecular functions for members of this protein family. Our data suggest that Row1 could modify the chitin-glucan matrix of the fungal cell wall and may be involved in unconventional protein secretion, thereby promoting both appressoria maturation and penetration.

Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.

The disordered C-terminal tail of fungal LPMOs from phytopathogens mediates protein dimerization and impacts plant penetration

Lytic polysaccharide monooxygenases (LPMOs) are monocopper enzymes that oxidatively degrade various polysaccharides, such as cellulose. Despite extensive research on this class of enzymes, the role played by their C-terminal regions predicted to be intrinsically disordered (dCTR) has been overlooked. Here, we investigated the function of the dCTR of an LPMO, called CoAA9A, up-regulated during plant infection by Colletotrichum orbiculare, the causative agent of anthracnose. After recombinant production of the full-length protein, we found that the dCTR mediates CoAA9A dimerization in vitro, via a disulfide bridge, a hitherto-never-reported property that positively affects both binding and activity on cellulose. Using SAXS experiments, we show that the homodimer is in an extended conformation. In vivo, we demonstrate that gene deletion impairs formation of the infection-specialized cell called appressorium and delays penetration of the plant. Using immunochemistry, we show that the protein is a dimer not only in vitro but also in vivo when secreted by the appressorium. As these peculiar LPMOs are also found in other plant pathogens, our findings open up broad avenues for crop protection.

Detecting surface defects of heritage buildings based on deep learning

Abstract The present study examined the usage of deep convolutional neural networks (DCNNs) for the classification, segmentation, and detection of the images of surface defects in heritage buildings. A survey was conducted on the building surface defects in Gulang Island (a UNESCO World Cultural Heritage Site), which were subsequently classified into six categories according to relevant standards. A Swin Transformer- and YOLOv5-based model was built for the automated detection of surface defects. Experimental results suggested that the proposed model was 99.2% accurate at classifying plant penetration and achieved a mean intersection-over-union (mIoU) of over 92% in relation to moss, cracking, alkalization, staining, and deterioration, outperforming CNN-based semantic segmentation networks such as FCN, PSPNet, and DeepLabv3plus. The Swin Transformer-based approach for the segmentation of building surface defect images achieved the highest accuracy regardless of the evaluation metric (with an mIoU of 90.96% and an mAcc of 95.78%), when contrasted to mainstream DCNNs such as SegFormer, PSPNet, and DANet.

An advanced secondary voltage control strategy for future power systems

The network voltage control together with the regulation of reactive power, has always been recognized as vital feature of power systems. It is aimed at containing the bus voltage within acceptable limits for guaranteeing system security, first and then assessing power quality. The voltage control is a key field in which several Transmission System Operators are currently investing resources to be well prepared in managing power systems with a high share of renewable energy sources, overcoming the drawbacks of the existing architectures. As is well known the voltage control is realized through a hierarchical structure, which is based on classical primary control, secondary control, and tertiary optimization. The secondary control is concerned with the regional level, implying proper coordination at both the geographical and temporal level. Due to its central role in power system operation, the secondary voltage control cannot be excluded from the effects of the dramatic changes that power systems are experiencing worldwide, even though regulators work by implementing well consolidated methodologies and strategies which show excellent performances. The motivation for this study in fact relies on the observation of some oscillatory phenomena in some areas of the Italian power system characterized by a high number of regulating resources highly coupled each other on the basis of the grid topology: despite these oscillatory phenomena are not so frequent, with the increasing of regulating resources enslaved by secondary regulation (such as renewable-based power plants) could potentially be worsened in the near future. Thus, in this paper, an optimal control strategy is proposed by tailoring a proper linear quadratic integral control which allows guaranteeing the desired profile for pilot buses voltage, ensuring a time response of aperiodic type without any oscillations. The aim of the proposed approach is to preserve the strengths of the existing secondary voltage regulation while enhancing the overall stability so enabling the progressive large penetration of renewable energy sources-based power plants and minimize the occurrence of oscillatory phenomena especially in highly electrically coupled areas. The robustness of the proposed regulation is verified first with respect to the WSCC 9-bus test system and successively on an actual regional area of Italian transmission network.

Economic Dispatch between Distribution Grids and Virtual Power Plants under Voltage Security Constraints

Due to the high penetration of virtual power plants (VPPs), the bi-directional power flow between VPPs and active distribution grids makes the grid operation complex. Without congestion management, the operation schedule only considers the economic benefits, and power flow constraints might be violated. Hence, it is necessary to conduct power interaction within the operation constraints. This paper proposes a coordinated economic dispatch method under voltage security constraints. The linear expressions were derived by simplifying the AC power flow equations to reduce the computation complicity. Then, optimal economic dispatch models with voltage security constraints were established for the active distribution grid and VPPs, respectively. Meanwhile, the transacted power and clearing price were set as the communication variables, and a coordinated strategy was proposed for the overall optimal goal. The modified IEEE 33-node and PG&E-node distribution grids were utilized for the simulations, and the results affirmed the validity of the proposed method.

A key sphingolipid pathway gene, MoDES1, regulates conidiation, virulence and plasma membrane tension in Magnaporthe oryzae

Rice blast, caused by the plant pathogenic fungus Magnaporthe oryzae, is a destructive disaster all over the earth that causes enormous losses in crop production. Sphingolipid, an important biological cell membrane lipid, is an essential structural component in the plasma membrane (PM) and has several biological functions, including cell mitosis, apoptosis, stress resistance, and signal transduction. Previous studies have suggested that sphingolipid and its derivatives play essential roles in the virulence of plant pathogenic fungi. However, the functions of sphingolipid biosynthesis-related proteins are not fully understood. In this article, we identified a key sphingolipid synthesis enzyme, MoDes1, and found that it is engaged in cell development and pathogenicity in M. oryzae. Deletion of MoDES1 gave rise to pleiotropic defects in vegetative growth, conidiation, plant penetration, and pathogenicity. MoDes1 is also required for lipid homeostasis and participates in the cell wall integrity (CWI) and Osm1-MAPK pathways. Notably, our results showed that there is negative feedback in the TORC2 signaling pathway to compensate for the decreased sphingolipid level due to the knockout of MoDES1 by regulating the phosphorylated Ypk1 level and PM tension. Furthermore, protein structure building has shown that MoDes1 is a potential drug target. These findings further refine the function of Des1 and deepen our understanding of the sphingolipid biosynthesis pathway in M. oryzae, laying a foundation for developing novel and specific drugs for rice blast control.

Analysis of voltage and frequency stability of electric power system network with photovoltaic-based generation penetration

The operation of Distributed Generation (DG) with renewable energy sources integrated with distribution networks through microgrids poses challenges in terms of operation and control. If this is left unchecked, it can have a negative impact on system security and reliability in terms of voltage and frequency stability that will be disrupted because of frequent variations in power production and loading levels. This study investigates voltage and frequency stability in microgrids because of the penetration of DG with photovoltaic (PV) renewable energy sources in the power system using the Virtual Synchronous Generator (VSG) control technique. The VSG is a control alteration that enhances the capabilities of the power system so that voltage and frequency stability can be preserved and improved. The VSG control method with additional damping controllers that increase inertia with additional virtual inertia is used to simulate the speed of restoration of voltage and frequency stability of the power system due to the penetration of PV-based power plants. The simulation results show that at the time of penetration of PV-based power plants in the power system, there is a momentary instability in voltage and frequency, but it is immediately dampened by VSG control and can be quickly restored so that the stability of voltage and frequency is maintained.

Deficiency of ChPks and ChThr1 Inhibited DHN-Melanin Biosynthesis, Disrupted Cell Wall Integrity and Attenuated Pathogenicity in Colletotrichum higginsianum.

Colletotrichum higginsianum is a major pathogen causing anthracnose in Chinese flowering cabbage (Brassica parachinensis), posing a significant threat to the Chinese flowering cabbage industry. The conidia of C. higginsianum germinate and form melanized infection structures called appressoria, which enable penetration of the host plant's epidermal cells. However, the molecular mechanism underlying melanin biosynthesis in C. higginsianum remains poorly understood. In this study, we identified two enzymes related to DHN-melanin biosynthesis in C. higginsianum: ChPks and ChThr1. Our results demonstrate that the expression levels of genes ChPKS and ChTHR1 were significantly up-regulated during hyphal and appressorial melanization processes. Furthermore, knockout of the gene ChPKS resulted in a blocked DHN-melanin biosynthetic pathway in hyphae and appressoria, leading to increased sensitivity of the ChpksΔ mutant to cell-wall-interfering agents as well as decreased turgor pressure and pathogenicity. It should be noted that although the Chthr1Δ mutant still exhibited melanin accumulation in colonies and appressoria, its sensitivity to cell-wall-interfering agents and turgor pressure decreased compared to wild-type strains; however, complete loss of pathogenicity was not observed. In conclusion, our results indicate that DHN-melanin plays an essential role in both pathogenicity and cell wall integrity in C. higginsianum. Specifically, ChPks is crucial for DHN-melanin biosynthesis while deficiency of ChThr1 does not completely blocked melanin production.

Wind Power Deviation Charge Reduction using Machine Learning

High penetration of wind power plants in power systems resulted in various challenges such as frequent system imbalances due to highly uncertain and variable wind generation. Additional spinning reserves and specific balancing products such as flexible ramp products are used to handle such frequent imbalances. Incorporation of these ancillary services leads to increased total operational costs. Increased operational costs should be transferred to wind power producers as it is caused by wind power plants. This leads to penalizing the wind power producers for the deviation of power generation from forecasts, called deviation charges. These deviation charges can be reduced by improving the forecasting accuracy. Existing forecasting models show performance in terms of error matrices. Such error matrices do not indicate the financial loss associated with it. This can be overcome by expressing forecasting performance in terms of deviation charge and it will directly encourage wind power producers to improve forecasting accuracy or arrange reserves to accommodate the error. This paper proposes a backpropagation-based artificial neural network model for reducing deviation charges in this context. An analysis is conducted on the data collected from the Bonneville Power Administration (BPA) Balancing Area. Seasonal analysis (Spring, Summer, Fall, and Winter) is conducted to show the performance of the proposed model throughout the year. The proposed model performance is compared with linear regression and ARIMA models. The comparison shows that the proposed ANN model gives the least deviation charges in the Spring, Summer, and Winter seasons and deviation charges in the Fall season are higher than the ARIMA model.

Optimal configuration of concentrating solar power generation in power system with high share of renewable energy resources

Under the worldwide carbon neutralization targets, concentrating solar power (CSP) is arousing great attention. With the thermal energy storage (TES), CSP is friendly to the power system operation by supplying controllable renewable energy. The capacities of its solar field and TES are essential parameters for maximizing the profit of a CSP plant. This paper formulates an explicit expression of the CSP plant's profit instead of using a simulation-based method. Then, an unconstrained optimization model is proposed to calculate its optimal configuration directly. This model provides insights into the optimal configuration of CSP with different penetrations of wind power in the case study. The results show that to obtain a better profit for the CSP plant, large solar multiple (more than 3.0) and TES (more than 13 h) are preferred to collaborate with high penetration of wind and photovoltaic plants. The effectiveness of the proposed method is verified compared to the enumeration searching method. The economy and feasibility of installing an electric heater (EH) in CSP are also demonstrated. Generally, the optimal investment in EH is linearly correlated to the penetration of variable energy resources.

A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants

This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software.

Optimal placement of battery energy storage systems with energy time shift strategy in power networks with high penetration of photovoltaic plants

This paper introduces a novel approach for the optimal placement of battery energy storage systems (BESS) in power networks with high penetration of photovoltaic (PV) plants. Initially, a fit-for-purpose steady-state, power flow BESS model with energy time shift strategy is formulated following fundamental operation principles. The optimal BESS placement methodology is subsequently developed in the realm of incremental modelling of power system losses, which permits to identify the best candidate node for the BESS connection that reduces the total power losses. And for practical results, daily varying patterns of nodal demand, generation dispatch and solar irradiance are considered, all this accompanied by the BESS operating strategy, i.e., electric energy time shift, which is one of the most sounding strategic applications in current BESS facilities. In addition to providing a suitable validation proof using the standard IEEE 5-bus test system, two practical test power network models with 24 and 118 nodes are used to showcase the usefulness of the incremental modelling approach for optimal BESS placement in power grids with high penetration of PV plants.

Models for MATLAB Simulation of a University Campus Micro-Grid

This work presents a library of microgrid (MG) component models integrated in a complete university campus MG model in the Simulink/MATLAB environment. The model allows simulations on widely varying time scales and evaluation of the electrical, economic, and environmental performance of the MG. The models include photovoltaic (PV) generation (with MPPT control), battery storage (including charge/discharge control), loads (campus buildings and outdoor lighting), electric vehicle charging stations, and interconnections. The campus MG model is shown to be useful for (i) week-long simulations throughout the year, to analyze seasonal differences; (ii) year-long simulations for the estimation of overall economic and environmental impacts; (iii) MG islanded-mode simulations. The model is an agile tool for planning the deployment and penetration of photovoltaic plants and battery storage, with consideration of economic and environmental (carbon footprint) aspects.

Scrutiny of power grids by penetrating PV energy in wind farms: a case study of the wind corridor of Jhampir, Pakistan

This study examines the problems caused by intermittent renewable energy sources, especially wind farms, and suggests a different solar energy penetration strategy to improve their loading capacity. The study uses real-time data from a wind farm in Jhampir, Pakistan, to analyse and assess various aspects of grid stations connected to wind farms. Electrical Transient Analyzer Program is used to validate the results by linking these with actual grid system. The article focuses on creating a model for a grid connected to a wind farm and the simulation of outcomes following capacity expansion, with the installation of an autotransformer. The original capacity of the wind farm was 750 MW, which was increased to 1,250 MW, i.e., 1.66 times the actual capability. Furthermore, this capacity was further enhanced to 1,540 MW, which becomes 1.23 times the previous capacity by the penetration of a photovoltaic power plant.

Steady-State Load Flow Model of DFIG Wind Turbine Based on Generator Power Loss Calculation

Penetration of wind power plants (WPPs) in the electric power system will complicate the system load flow analysis. Consequently, the traditional load flow algorithm can no longer be used to find the solution to the load flow problem of such a system. This paper proposes a doubly fed induction generator (DFIG)-based WPP model for a load flow analysis of the electric power system. The proposed model is derived based on the power formulations of the WPP—namely, DFIG power, DFIG power loss, and WPP power output formulas. The model can be applied to various DFIG power factor operating modes. In the present paper, applications of the proposed methods in two representative electric power systems (i.e., IEEE 14-bus and 30-bus systems) have been investigated. The investigation results verify the proposed method’s capability to solve the load flow problem of the system embedded with DFIG-based variable-speed WPPs.