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Plant pathogens manipulate host development, facilitating colonization and proliferation. Ralstonia solanacearum is a soil-borne bacterial pathogen that penetrates roots and colonizes plants through the vascular system, causing wilting and death. Here, we find that RipAC, an effector protein from R. solanacearum, alters root development in Arabidopsis, promoting the formation of lateral roots and root hairs. RipAC interacts with CELLULOSE SYNTHASE (CESA)-INTERACTIVE PROTEIN 1 (CSI1), which regulates the activity of CESA complexes at the plasma membrane. RipAC disrupts CESA-CSI1 interaction, leading to a reduction in cellulose content, root developmental alterations, and a promotion of bacterial pathogenicity. We find that CSI1 also associates with the receptor kinase FERONIA, forming a complex that negatively regulates immunity in roots; this interaction, however, is not affected by RipAC. Our work reveals a bacterial virulence strategy that selectively affects the activities of a host target, promoting anatomical alterations that facilitate infection without causing activation of immunity.

The smart control informed by IoT sensors and enabled by remotely controlled devices can optimize the building operation to minimize unnecessary energy consumption and improve indoor thermal comfort. This paper quantifies the potential for electricity savings in small office buildings from smart thermostat control and occupancy-informed smart plug control. This is done by simulating the effect of adaptive setpoint temperature, occupancy-based HVAC control, and night-purge free cooling on small office buildings across all major climate zones in the United States. Adopting these smart control measures can achieve 8.9% to 20.4% of savings in total electricity consumption of small office buildings, or equivalent to annual reductions between 12.2 kWh/m2 and 30.4 kWh/m2 in electricity usage intensity. Among all climate zones, the hot and dry climates benefit the most from proposed smart controls and achieve the highest percentages of electricity savings

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method and achieve superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.

Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operational conditions in power systems, existing methods have outstanding issues in terms of either speed, adaptiveness, or scalability. Deep reinforcement learning (DRL) was regarded and adopted as a promising approach for fast and adaptive grid stability control in recent years. However, existing DRL algorithms show two outstanding issues when being applied to power system control problems: 1) computational inefficiency that requires extensive training and tuning time; and 2) poor scalability making it difficult to scale to high dimensional control problems. To overcome these issues, an accelerated DRL algorithm named PARS was developed and tailored for power system voltage stability control via load shedding. PARS features high scalability and is easy to tune with only five main hyperparameters. The method was tested on both the IEEE 39-bus and IEEE 300-bus systems, and the latter is by far the largest scale for such a study. Test results show that, compared to other methods including model-predictive control (MPC) and proximal policy optimization(PPO) methods, PARS shows better computational efficiency (faster convergence), more robustness in learning, excellent scalability and generalization capability.

type:text, [ABSTRACT] Eliminating the excess energetic driving force in organic solar cells leads to a smaller energy loss and higher device performance; hence, it is vital to understand the relation between the interfacial energetics and the photoelectric conversion efficiency. In this study, we systematically investigate 16 combinations of four donor polymers and four acceptors in planar heterojunction. The charge generation efficiency and its electric field dependence correlate with the energy difference between the singlet excited state and the interfacial charge transfer state. The threshold energy difference is 0.2 to 0.3 eV, below which the efficiency starts dropping and the charge generation becomes electric field-dependent. In contrast, the charge generation efficiency does not correlate with the energy difference between the charge transfer and the charge-separated states, indicating that the binding of the charge pairs in the charge transfer state is not the determining factor for the charge generation.

Wenlin Bai,1,2,* Yujiao Chen,1,2,* Pengling Sun,1,2 Ai Gao1,2 1Department of Occupational Health and Environmental Health, School of Public Health, 2Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, People’s Republic of China *These authors contributed equally to this work Abstract: Titanium dioxide (TiO2) nanoparticles (TNPs) are manufactured worldwide for a wide range of applications and the toxic effect of TNPs on biological systems is gaining attention. Autophagy is recognized as an emerging toxicity mechanism triggered by nanomaterials. MicroRNA 34a (miR34a) acts as a tumor suppressor gene by targeting many oncogenes, but how it affects autophagy induced by TNPs is not completely understood. Here, we observed the activation of TNP-induced autophagy through monodansylcadaverine staining and LC3-I/LC3-II conversion. Meanwhile, the transmission electron microscope ultrastructural analysis showed typical morphological characteristics in autophagy process. We detected the expression of miR34a and B-cell lymphoma/leukemia-2 (Bcl-2). In addition, the underlying mechanism of TNP-induced autophagy was performed using overexpression of miR34a by lentivirus vector transfection. Results showed that TNPs induced autophagy generation evidently. Typical morphological changes in the process of autophagy were observed by the transmission electron microscope ultrastructural analysis and LC3-I/LC3-II conversion increased significantly in TNP-treated cells. Meanwhile, TNPs induced the downregulation of miR34a and increased the expression of Bcl-2. Furthermore, overexpressed miR34a decreased the expression of Bcl-2 both in messenger RNA and protein level, following which the level of autophagy and cell death rate increased after the transfected cells were incubated with TNPs for 24 hours. These findings provide the first evidence that overexpressed miR34a enhanced TNP-induced autophagy and cell death through targeted downreg

Wenlin Bai,1,2,* Yujiao Chen,1,2,* Pengling Sun,1,2 Ai Gao1,2 1Department of Occupational Health and Environmental Health, School of Public Health, 2Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, People’s Republic of China *These authors contributed equally to this work Abstract: Titanium dioxide (TiO2) nanoparticles (TNPs) are manufactured worldwide for a wide range of applications and the toxic effect of TNPs on biological systems is gaining attention. Autophagy is recognized as an emerging toxicity mechanism triggered by nanomaterials. MicroRNA 34a (miR34a) acts as a tumor suppressor gene by targeting many oncogenes, but how it affects autophagy induced by TNPs is not completely understood. Here, we observed the activation of TNP-induced autophagy through monodansylcadaverine staining and LC3-I/LC3-II conversion. Meanwhile, the transmission electron microscope ultrastructural analysis showed typical morphological characteristics in autophagy process. We detected the expression of miR34a and B-cell lymphoma/leukemia-2 (Bcl-2). In addition, the underlying mechanism of TNP-induced autophagy was performed using overexpression of miR34a by lentivirus vector transfection. Results showed that TNPs induced autophagy generation evidently. Typical morphological changes in the process of autophagy were observed by the transmission electron microscope ultrastructural analysis and LC3-I/LC3-II conversion increased significantly in TNP-treated cells. Meanwhile, TNPs induced the downregulation of miR34a and increased the expression of Bcl-2. Furthermore, overexpressed miR34a decreased the expression of Bcl-2 both in messenger RNA and protein level, following which the level of autophagy and cell death rate increased after the transfected cells were incubated with TNPs for 24 hours. These findings provide the first evidence that overexpressed miR34a enhanced TNP-induced autophagy and cell death through targeted downreg

Wenlin Bai,1,2 Yujiao Chen,1,2 Ai Gao1,2 1Department of Occupational Health and Environmental Health, School of Public Health, 2Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, People’s Republic of China Abstract: Given the tremendous growth in the application of titanium dioxide nanoparticles (TNPs), concerns about the potential health hazards of TNPs to humans have been raised. Poly(ADP-ribose) polymerase 1 (PARP-1), a highly conserved DNA-binding protein, is involved in many molecular and cellular processes. Limited data demonstrated that certain nanomaterials induced the aberrant hypermethylation of PARP-1. However, the mechanism involved in TNP-induced PARP-1 abnormal methylation has not been studied. A549 cells were incubated with anatase TNPs (22.1 nm) for 24 hours pretreatment with or without methyltransferase inhibitor 5-aza-2'-deoxycytidine and the reactive oxygen species (ROS) scavenger α-lipoic acid to assess the possible role of methylation and ROS in the toxic effect of TNPs. After TNPs characterization, a battery of assays was performed to evaluate the toxic effect of TNPs, PARP-1 methylation status, and oxidative damage. Results showed that TNPs decreased the cell viability in a dose-dependent manner, in accordance with the increase of lactate dehydrogenase activity, which indicated membrane damage of cells. Similar to the high level of PARP-1 methylation, the generation of ROS was significantly increased after exposure to TNPs for 24 hours. Furthermore, α-lipoic acid decreased TNP-induced ROS generation and then attenuated TNP-triggered PARP-1 hypermethylation. Meanwhile, 5-aza-2'-deoxycytidine simultaneously decreased the ROS generation induced by TNPs, resulting in the decline of PARP-1 methylation. In summary, TNPs triggered the aberrant hypermethylation of the PARP-1 promoter and there was a cross talk between oxidative stress and PAR

Wenlin Bai,1,2 Yujiao Chen,1,2 Ai Gao1,2 1Department of Occupational Health and Environmental Health, School of Public Health, 2Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, People’s Republic of China Abstract: Given the tremendous growth in the application of titanium dioxide nanoparticles (TNPs), concerns about the potential health hazards of TNPs to humans have been raised. Poly(ADP-ribose) polymerase 1 (PARP-1), a highly conserved DNA-binding protein, is involved in many molecular and cellular processes. Limited data demonstrated that certain nanomaterials induced the aberrant hypermethylation of PARP-1. However, the mechanism involved in TNP-induced PARP-1 abnormal methylation has not been studied. A549 cells were incubated with anatase TNPs (22.1 nm) for 24 hours pretreatment with or without methyltransferase inhibitor 5-aza-2'-deoxycytidine and the reactive oxygen species (ROS) scavenger α-lipoic acid to assess the possible role of methylation and ROS in the toxic effect of TNPs. After TNPs characterization, a battery of assays was performed to evaluate the toxic effect of TNPs, PARP-1 methylation status, and oxidative damage. Results showed that TNPs decreased the cell viability in a dose-dependent manner, in accordance with the increase of lactate dehydrogenase activity, which indicated membrane damage of cells. Similar to the high level of PARP-1 methylation, the generation of ROS was significantly increased after exposure to TNPs for 24 hours. Furthermore, α-lipoic acid decreased TNP-induced ROS generation and then attenuated TNP-triggered PARP-1 hypermethylation. Meanwhile, 5-aza-2'-deoxycytidine simultaneously decreased the ROS generation induced by TNPs, resulting in the decline of PARP-1 methylation. In summary, TNPs triggered the aberrant hypermethylation of the PARP-1 promoter and there was a cross talk between oxidative stress and PAR